From 0622ce3f2c44c7602bbafba707cee4aee4df328b Mon Sep 17 00:00:00 2001 From: jquinter Date: Fri, 23 Jan 2026 00:34:29 -0300 Subject: [PATCH 01/62] Fix/nova grounding (#19598) * added support for nova grounding for amazon nova model * added citations support * added integration tests * removing test file * refactor: Use web_search_options for Nova grounding instead of system_tool --------- Co-authored-by: Juhie Co-authored-by: Juhie <75068056+juhiechandra@users.noreply.github.com> --- .../prompt_templates/factory.py | 3 +- .../bedrock/chat/converse_transformation.py | 71 ++- litellm/llms/bedrock/chat/invoke_handler.py | 5 + litellm/types/llms/bedrock.py | 85 +++ poetry.lock | 592 ++++++++++++++++-- .../test_bedrock_completion.py | 285 +++++++++ ...llm_core_utils_prompt_templates_factory.py | 83 ++- 7 files changed, 1057 insertions(+), 67 deletions(-) diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index 30263543fc..1d1e38c09d 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -4408,7 +4408,7 @@ def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]: ] """ """ - Bedrock toolConfig looks like: + Bedrock toolConfig looks like: "tools": [ { "toolSpec": { @@ -4436,6 +4436,7 @@ def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]: tool_block_list: List[BedrockToolBlock] = [] for tool in tools: + # Handle regular function tools parameters = tool.get("function", {}).get( "parameters", {"type": "object", "properties": {}} ) diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index cb26a22edf..2ec27a7af0 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -298,6 +298,39 @@ class AmazonConverseConfig(BaseConfig): # Check if the model is specifically Nova Lite 2 return "nova-2-lite" in model_without_region + def _map_web_search_options( + self, + web_search_options: dict, + model: str + ) -> Optional[BedrockToolBlock]: + """ + Map web_search_options to Nova grounding systemTool. + + Nova grounding (web search) is only supported on Amazon Nova models. + Returns None for non-Nova models. + + Args: + web_search_options: The web_search_options dict from the request + model: The model identifier string + + Returns: + BedrockToolBlock with systemTool for Nova models, None otherwise + + Reference: https://docs.aws.amazon.com/nova/latest/userguide/grounding.html + """ + # Only Nova models support nova_grounding + # Model strings can be like: "amazon.nova-pro-v1:0", "us.amazon.nova-pro-v1:0", etc. + if "nova" not in model.lower(): + verbose_logger.debug( + f"web_search_options passed but model {model} is not a Nova model. " + "Nova grounding is only supported on Amazon Nova models." + ) + return None + + # Nova doesn't support search_context_size or user_location params + # (unlike Anthropic), so we just enable grounding with no options + return BedrockToolBlock(systemTool={"name": "nova_grounding"}) + def _transform_reasoning_effort_to_reasoning_config( self, reasoning_effort: str ) -> dict: @@ -438,6 +471,10 @@ class AmazonConverseConfig(BaseConfig): ): supported_params.append("tools") + # Nova models support web_search_options (mapped to nova_grounding systemTool) + if base_model.startswith("amazon.nova"): + supported_params.append("web_search_options") + if litellm.utils.supports_tool_choice( model=model, custom_llm_provider=self.custom_llm_provider ) or litellm.utils.supports_tool_choice( @@ -730,6 +767,13 @@ class AmazonConverseConfig(BaseConfig): if bedrock_tier in ("default", "flex", "priority"): optional_params["serviceTier"] = {"type": bedrock_tier} + if param == "web_search_options" and value and isinstance(value, dict): + grounding_tool = self._map_web_search_options(value, model) + if grounding_tool is not None: + optional_params = self._add_tools_to_optional_params( + optional_params=optional_params, tools=[grounding_tool] + ) + # Only update thinking tokens for non-GPT-OSS models and non-Nova-Lite-2 models # Nova Lite 2 handles token budgeting differently through reasoningConfig if "gpt-oss" not in model and not self._is_nova_lite_2_model(model): @@ -1388,20 +1432,23 @@ class AmazonConverseConfig(BaseConfig): str, List[ChatCompletionToolCallChunk], Optional[List[BedrockConverseReasoningContentBlock]], + Optional[List[CitationsContentBlock]], ]: """ - Translate the message content to a string and a list of tool calls and reasoning content blocks + Translate the message content to a string and a list of tool calls, reasoning content blocks, and citations. Returns: content_str: str tools: List[ChatCompletionToolCallChunk] reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] + citationsContentBlocks: Optional[List[CitationsContentBlock]] - Citations from Nova grounding """ content_str = "" tools: List[ChatCompletionToolCallChunk] = [] reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( None ) + citationsContentBlocks: Optional[List[CitationsContentBlock]] = None for idx, content in enumerate(content_blocks): """ - Content is either a tool response or text @@ -1446,8 +1493,13 @@ class AmazonConverseConfig(BaseConfig): if reasoningContentBlocks is None: reasoningContentBlocks = [] reasoningContentBlocks.append(content["reasoningContent"]) + # Handle Nova grounding citations content + if "citationsContent" in content: + if citationsContentBlocks is None: + citationsContentBlocks = [] + citationsContentBlocks.append(content["citationsContent"]) - return content_str, tools, reasoningContentBlocks + return content_str, tools, reasoningContentBlocks, citationsContentBlocks def _transform_response( self, @@ -1525,18 +1577,27 @@ class AmazonConverseConfig(BaseConfig): reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( None ) + citationsContentBlocks: Optional[List[CitationsContentBlock]] = None if message is not None: ( content_str, tools, reasoningContentBlocks, + citationsContentBlocks, ) = self._translate_message_content(message["content"]) + # Initialize provider_specific_fields if we have any special content blocks + provider_specific_fields: dict = {} + if reasoningContentBlocks is not None: + provider_specific_fields["reasoningContentBlocks"] = reasoningContentBlocks + if citationsContentBlocks is not None: + provider_specific_fields["citationsContent"] = citationsContentBlocks + + if provider_specific_fields: + chat_completion_message["provider_specific_fields"] = provider_specific_fields + if reasoningContentBlocks is not None: - chat_completion_message["provider_specific_fields"] = { - "reasoningContentBlocks": reasoningContentBlocks, - } chat_completion_message["reasoning_content"] = ( self._transform_reasoning_content(reasoningContentBlocks) ) diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py index dfa1f02a15..77d2a3c0c2 100644 --- a/litellm/llms/bedrock/chat/invoke_handler.py +++ b/litellm/llms/bedrock/chat/invoke_handler.py @@ -1476,6 +1476,11 @@ class AWSEventStreamDecoder: reasoning_content = ( "" # set to non-empty string to ensure consistency with Anthropic ) + elif "citationsContent" in delta_obj: + # Handle Nova grounding citations in streaming responses + provider_specific_fields = { + "citationsContent": delta_obj["citationsContent"], + } return ( text, tool_use, diff --git a/litellm/types/llms/bedrock.py b/litellm/types/llms/bedrock.py index ef2f1ba4d5..a85aaafe23 100644 --- a/litellm/types/llms/bedrock.py +++ b/litellm/types/llms/bedrock.py @@ -93,6 +93,67 @@ class GuardrailConverseContentBlock(TypedDict, total=False): text: GuardrailConverseTextBlock +class CitationWebLocationBlock(TypedDict, total=False): + """ + Web location block for Nova grounding citations. + Contains the URL and domain from web search results. + + Reference: https://docs.aws.amazon.com/nova/latest/userguide/grounding.html + """ + + url: str + domain: str + + +class CitationLocationBlock(TypedDict, total=False): + """ + Location block containing the web location for a citation. + """ + + web: CitationWebLocationBlock + + +class CitationReferenceBlock(TypedDict, total=False): + """ + Citation reference block containing a single citation with its location. + + Each citation contains: + - location.web.url: The URL of the source + - location.web.domain: The domain of the source + """ + + location: CitationLocationBlock + + +class CitationsContentBlock(TypedDict, total=False): + """ + Citations content block returned by Nova grounding (web search) tool. + + When Nova grounding is enabled via systemTool, the model may return + citationsContent blocks containing web search citation references. + + Reference: https://docs.aws.amazon.com/nova/latest/userguide/grounding.html + + Example response structure: + { + "citationsContent": { + "citations": [ + { + "location": { + "web": { + "url": "https://example.com/article", + "domain": "example.com" + } + } + } + ] + } + } + """ + + citations: List[CitationReferenceBlock] + + class ContentBlock(TypedDict, total=False): text: str image: ImageBlock @@ -103,6 +164,7 @@ class ContentBlock(TypedDict, total=False): cachePoint: CachePointBlock reasoningContent: BedrockConverseReasoningContentBlock guardContent: GuardrailConverseContentBlock + citationsContent: CitationsContentBlock class MessageBlock(TypedDict): @@ -159,8 +221,24 @@ class ToolSpecBlock(TypedDict, total=False): description: str +class SystemToolBlock(TypedDict, total=False): + """ + System tool block for Nova grounding and other built-in tools. + + Example: + { + "systemTool": { + "name": "nova_grounding" + } + } + """ + + name: Required[str] + + class ToolBlock(TypedDict, total=False): toolSpec: Optional[ToolSpecBlock] + systemTool: Optional[SystemToolBlock] cachePoint: Optional[CachePointBlock] @@ -210,11 +288,13 @@ class ContentBlockStartEvent(TypedDict, total=False): class ContentBlockDeltaEvent(TypedDict, total=False): """ Either 'text' or 'toolUse' will be specified for Converse API streaming response. + May also include 'citationsContent' when Nova grounding is enabled. """ text: str toolUse: ToolBlockDeltaEvent reasoningContent: BedrockConverseReasoningContentBlockDelta + citationsContent: CitationsContentBlock class PerformanceConfigBlock(TypedDict): @@ -879,3 +959,8 @@ class BedrockGetBatchResponse(TypedDict, total=False): outputDataConfig: BedrockOutputDataConfig timeoutDurationInHours: Optional[int] clientRequestToken: Optional[str] + +class BedrockToolBlock(TypedDict, total=False): + toolSpec: Optional[ToolSpecBlock] + systemTool: Optional[SystemToolBlock] # For Nova grounding + cachePoint: Optional[CachePointBlock] diff --git a/poetry.lock b/poetry.lock index c5f5a87894..e5ef1750da 100644 --- a/poetry.lock +++ b/poetry.lock @@ -275,7 +275,7 @@ files = [ {file = "annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320"}, {file = "annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4"}, ] -markers = {main = "python_version >= \"3.10\" and (extra == \"mlflow\" or extra == \"proxy\") or extra == \"proxy\""} +markers = {main = "(extra == \"mlflow\" or extra == \"proxy\") and python_version >= \"3.10\" or extra == \"proxy\""} [[package]] name = "annotated-types" @@ -343,7 +343,7 @@ zookeeper = ["kazoo"] name = "async-timeout" version = "5.0.1" description = "Timeout context manager for asyncio programs" -optional = true +optional = false python-versions = ">=3.8" groups = ["main"] markers = "python_full_version < \"3.11.3\" and (extra == \"extra-proxy\" or extra == \"proxy\" or python_version < \"3.11\")" @@ -557,48 +557,48 @@ files = [ [[package]] name = "boto3" -version = "1.36.0" +version = "1.40.76" description = "The AWS SDK for Python" optional = true -python-versions = ">=3.8" +python-versions = ">=3.9" groups = ["main"] markers = "extra == \"proxy\"" files = [ - {file = "boto3-1.36.0-py3-none-any.whl", hash = "sha256:d0ca7a58ce25701a52232cc8df9d87854824f1f2964b929305722ebc7959d5a9"}, - {file = "boto3-1.36.0.tar.gz", hash = "sha256:159898f51c2997a12541c0e02d6e5a8fe2993ddb307b9478fd9a339f98b57e00"}, + {file = "boto3-1.40.76-py3-none-any.whl", hash = "sha256:8df6df755727be40ad9e309cfda07f9a12c147e17b639430c55d4e4feee8a167"}, + {file = "boto3-1.40.76.tar.gz", hash = "sha256:16f4cf97f8dd8e0aae015f4dc66219bd7716a91a40d1e2daa0dafa241a4761c5"}, ] [package.dependencies] -botocore = ">=1.36.0,<1.37.0" +botocore = ">=1.40.76,<1.41.0" jmespath = ">=0.7.1,<2.0.0" -s3transfer = ">=0.11.0,<0.12.0" +s3transfer = ">=0.14.0,<0.15.0" [package.extras] crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.36.26" +version = "1.40.76" description = "Low-level, data-driven core of boto 3." optional = true -python-versions = ">=3.8" +python-versions = ">=3.9" groups = ["main"] markers = "extra == \"proxy\"" files = [ - {file = "botocore-1.36.26-py3-none-any.whl", hash = "sha256:4e3f19913887a58502e71ef8d696fe7eaa54de7813ff73390cd5883f837dfa6e"}, - {file = "botocore-1.36.26.tar.gz", hash = "sha256:4a63bcef7ecf6146fd3a61dc4f9b33b7473b49bdaf1770e9aaca6eee0c9eab62"}, + {file = "botocore-1.40.76-py3-none-any.whl", hash = "sha256:fe425d386e48ac64c81cbb4a7181688d813df2e2b4c78b95ebe833c9e868c6f4"}, + {file = "botocore-1.40.76.tar.gz", hash = "sha256:2b16024d68b29b973005adfb5039adfe9099ebe772d40a90ca89f2e165c495dc"}, ] [package.dependencies] jmespath = ">=0.7.1,<2.0.0" python-dateutil = ">=2.1,<3.0.0" urllib3 = [ - {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""}, {version = ">=1.25.4,<1.27", markers = "python_version < \"3.10\""}, + {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""}, ] [package.extras] -crt = ["awscrt (==0.23.8)"] +crt = ["awscrt (==0.28.4)"] [[package]] name = "cachetools" @@ -607,7 +607,7 @@ description = "Extensible memoizing collections and decorators" optional = true python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.10\" and (extra == \"mlflow\" or extra == \"extra-proxy\") or extra == \"extra-proxy\"" +markers = "(extra == \"extra-proxy\" or extra == \"google\" or extra == \"mlflow\") and python_version >= \"3.10\" or extra == \"google\" or extra == \"extra-proxy\"" files = [ {file = "cachetools-6.2.2-py3-none-any.whl", hash = "sha256:6c09c98183bf58560c97b2abfcedcbaf6a896a490f534b031b661d3723b45ace"}, {file = "cachetools-6.2.2.tar.gz", hash = "sha256:8e6d266b25e539df852251cfd6f990b4bc3a141db73b939058d809ebd2590fc6"}, @@ -1393,6 +1393,24 @@ docs = ["myst-parser (==0.18.0)", "sphinx (==5.1.1)"] ssh = ["paramiko (>=2.4.3)"] websockets = ["websocket-client (>=1.3.0)"] +[[package]] +name = "docstring-parser" +version = "0.17.0" +description = "Parse Python docstrings in reST, Google and Numpydoc format" +optional = true +python-versions = ">=3.8" +groups = ["main"] +markers = "extra == \"google\"" +files = [ + {file = "docstring_parser-0.17.0-py3-none-any.whl", hash = "sha256:cf2569abd23dce8099b300f9b4fa8191e9582dda731fd533daf54c4551658708"}, + {file = "docstring_parser-0.17.0.tar.gz", hash = "sha256:583de4a309722b3315439bb31d64ba3eebada841f2e2cee23b99df001434c912"}, +] + +[package.extras] +dev = ["pre-commit (>=2.16.0) ; python_version >= \"3.9\"", "pydoctor (>=25.4.0)", "pytest"] +docs = ["pydoctor (>=25.4.0)"] +test = ["pytest"] + [[package]] name = "docutils" version = "0.21.2" @@ -1453,7 +1471,7 @@ files = [ {file = "fastapi-0.121.3-py3-none-any.whl", hash = "sha256:0c78fc87587fcd910ca1bbf5bc8ba37b80e119b388a7206b39f0ecc95ebf53e9"}, {file = "fastapi-0.121.3.tar.gz", hash = "sha256:0055bc24fe53e56a40e9e0ad1ae2baa81622c406e548e501e717634e2dfbc40b"}, ] -markers = {main = "python_version >= \"3.10\" and (extra == \"mlflow\" or extra == \"proxy\") or extra == \"proxy\""} +markers = {main = "(extra == \"mlflow\" or extra == \"proxy\") and python_version >= \"3.10\" or extra == \"proxy\""} [package.dependencies] annotated-doc = ">=0.0.2" @@ -1982,7 +2000,7 @@ description = "Google API client core library" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "python_version >= \"3.14\" and extra == \"extra-proxy\"" +markers = "python_version >= \"3.14\" and (extra == \"extra-proxy\" or extra == \"google\")" files = [ {file = "google_api_core-2.25.2-py3-none-any.whl", hash = "sha256:e9a8f62d363dc8424a8497f4c2a47d6bcda6c16514c935629c257ab5d10210e7"}, {file = "google_api_core-2.25.2.tar.gz", hash = "sha256:1c63aa6af0d0d5e37966f157a77f9396d820fba59f9e43e9415bc3dc5baff300"}, @@ -2010,7 +2028,7 @@ description = "Google API client core library" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "extra == \"extra-proxy\" and python_version < \"3.14\"" +markers = "python_version == \"3.9\" and (extra == \"google\" or extra == \"extra-proxy\") or python_version < \"3.14\" and (extra == \"extra-proxy\" or extra == \"google\")" files = [ {file = "google_api_core-2.28.1-py3-none-any.whl", hash = "sha256:4021b0f8ceb77a6fb4de6fde4502cecab45062e66ff4f2895169e0b35bc9466c"}, {file = "google_api_core-2.28.1.tar.gz", hash = "sha256:2b405df02d68e68ce0fbc138559e6036559e685159d148ae5861013dc201baf8"}, @@ -2020,12 +2038,12 @@ files = [ google-auth = ">=2.14.1,<3.0.0" googleapis-common-protos = ">=1.56.2,<2.0.0" grpcio = [ + {version = ">=1.33.2,<2.0.0", optional = true, markers = "extra == \"grpc\""}, {version = ">=1.49.1,<2.0.0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}, - {version = ">=1.33.2,<2.0.0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""}, ] grpcio-status = [ - {version = ">=1.49.1,<2.0.0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}, {version = ">=1.33.2,<2.0.0", optional = true, markers = "extra == \"grpc\""}, + {version = ">=1.49.1,<2.0.0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}, ] proto-plus = [ {version = ">=1.22.3,<2.0.0"}, @@ -2047,7 +2065,7 @@ description = "Google Authentication Library" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "python_version >= \"3.10\" and (extra == \"mlflow\" or extra == \"extra-proxy\") or extra == \"extra-proxy\"" +markers = "(extra == \"extra-proxy\" or extra == \"google\" or extra == \"mlflow\") and python_version >= \"3.10\" or extra == \"google\" or extra == \"extra-proxy\"" files = [ {file = "google_auth-2.43.0-py2.py3-none-any.whl", hash = "sha256:af628ba6fa493f75c7e9dbe9373d148ca9f4399b5ea29976519e0a3848eddd16"}, {file = "google_auth-2.43.0.tar.gz", hash = "sha256:88228eee5fc21b62a1b5fe773ca15e67778cb07dc8363adcb4a8827b52d81483"}, @@ -2056,6 +2074,7 @@ files = [ [package.dependencies] cachetools = ">=2.0.0,<7.0" pyasn1-modules = ">=0.2.1" +requests = {version = ">=2.20.0,<3.0.0", optional = true, markers = "extra == \"requests\""} rsa = ">=3.1.4,<5" [package.extras] @@ -2068,6 +2087,120 @@ requests = ["requests (>=2.20.0,<3.0.0)"] testing = ["aiohttp (<3.10.0)", "aiohttp (>=3.6.2,<4.0.0)", "aioresponses", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (>=38.0.3)", "cryptography (>=38.0.3)", "flask", "freezegun", "grpcio", "mock", "oauth2client", "packaging", "pyjwt (>=2.0)", "pyopenssl (<24.3.0)", "pyopenssl (>=20.0.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-localserver", "pyu2f (>=0.1.5)", "requests (>=2.20.0,<3.0.0)", "responses", "urllib3"] urllib3 = ["packaging", "urllib3"] +[[package]] +name = "google-cloud-aiplatform" +version = "1.130.0" +description = "Vertex AI API client library" +optional = true +python-versions = ">=3.9" +groups = ["main"] +markers = "extra == \"google\"" +files = [ + {file = "google_cloud_aiplatform-1.130.0-py2.py3-none-any.whl", hash = "sha256:f578ccee55655dd9e2300cfcafb178e47c3dfdcf746ad465234b875d3e955929"}, + {file = "google_cloud_aiplatform-1.130.0.tar.gz", hash = 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"mcp", "orjson", "polars", "pynacl", "python-multipart", "pyyaml", "rich", "rq", "soundfile", "uvicorn", "uvloop", "websockets"] semantic-router = ["semantic-router"] @@ -8005,4 +8491,4 @@ utils = ["numpydoc"] [metadata] lock-version = "2.1" python-versions = ">=3.9,<4.0" -content-hash = "f6a98e687d478db6e30274a4cf70391960775cbf648da0783558444da3a662ea" +content-hash = "76f5b5fb10667c2dcf428976292672e7a1f3f2eb5f5bf78998e2192899f5037e" diff --git a/tests/llm_translation/test_bedrock_completion.py b/tests/llm_translation/test_bedrock_completion.py index 7c0db41d13..d48dd1bfd9 100644 --- a/tests/llm_translation/test_bedrock_completion.py +++ b/tests/llm_translation/test_bedrock_completion.py @@ -3954,3 +3954,288 @@ def test_bedrock_openai_error_handling(): assert exc_info.value.status_code == 422 print("✓ Error handling works correctly") + +# ============================================================================ +# Nova Grounding (web_search_options) Unit Tests (Mocked) +# ============================================================================ + +def test_bedrock_nova_grounding_web_search_options_non_streaming(): + """ + Unit test for Nova grounding using web_search_options parameter (non-streaming). + + This test mocks the HTTP call to verify: + 1. web_search_options is correctly mapped to systemTool for Nova models + 2. The request structure is correct + + Related: https://docs.aws.amazon.com/nova/latest/userguide/grounding.html + """ + from unittest.mock import patch, MagicMock + from litellm.llms.custom_httpx.http_handler import HTTPHandler + + client = HTTPHandler() + + messages = [ + { + "role": "user", + "content": "What is the current population of Tokyo, Japan?", + } + ] + + with patch.object(client, "post") as mock_post: + try: + completion( + model="us.amazon.nova-pro-v1:0", # No bedrock/ prefix when using api_base + messages=messages, + web_search_options={}, # Enables Nova grounding + max_tokens=500, + client=client, + api_base="https://bedrock-runtime.us-east-1.amazonaws.com", + ) + except Exception: + pass # Expected - we're just checking the request structure + + # Verify the request was made correctly + if mock_post.called: + request_body = json.loads(mock_post.call_args.kwargs.get("data", "{}")) + print(f"Request body: {json.dumps(request_body, indent=2)}") + + # Verify toolConfig is present with systemTool + assert "toolConfig" in request_body, "toolConfig should be in request" + tool_config = request_body["toolConfig"] + assert "tools" in tool_config, "tools should be in toolConfig" + + # Find the systemTool for nova_grounding + system_tool_found = False + for tool in tool_config["tools"]: + if "systemTool" in tool: + assert tool["systemTool"]["name"] == "nova_grounding" + system_tool_found = True + break + + assert system_tool_found, "systemTool with nova_grounding should be present" + print(f"✓ web_search_options correctly transformed to systemTool (non-streaming)") + + +def test_bedrock_nova_grounding_with_function_tools(): + """ + Unit test for Nova grounding combined with regular function tools. + + This tests the scenario where users want both web grounding AND + custom function calling capabilities. + """ + from unittest.mock import patch + from litellm.llms.custom_httpx.http_handler import HTTPHandler + + client = HTTPHandler() + + # Regular function tool + tools = [ + { + "type": "function", + "function": { + "name": "get_stock_price", + "description": "Get the current stock price for a given ticker symbol", + "parameters": { + "type": "object", + "properties": { + "ticker": { + "type": "string", + "description": "The stock ticker symbol, e.g. AAPL, GOOGL", + } + }, + "required": ["ticker"], + }, + }, + } + ] + + messages = [ + { + "role": "user", + "content": "What is the current market cap of Apple Inc?", + } + ] + + with patch.object(client, "post") as mock_post: + try: + completion( + model="us.amazon.nova-pro-v1:0", # No bedrock/ prefix when using api_base + messages=messages, + tools=tools, + web_search_options={}, # Also enable web grounding + max_tokens=500, + client=client, + api_base="https://bedrock-runtime.us-east-1.amazonaws.com", + ) + except Exception: + pass # Expected - we're just checking the request structure + + # Verify the request was made correctly + if mock_post.called: + request_body = json.loads(mock_post.call_args.kwargs.get("data", "{}")) + print(f"Request body: {json.dumps(request_body, indent=2)}") + + # Verify toolConfig has both function tool and systemTool + assert "toolConfig" in request_body, "toolConfig should be in request" + tool_config = request_body["toolConfig"] + assert "tools" in tool_config, "tools should be in toolConfig" + + tools_in_request = tool_config["tools"] + + # Should have both the function tool and the systemTool + function_tool_found = False + system_tool_found = False + + for tool in tools_in_request: + if "toolSpec" in tool: + assert tool["toolSpec"]["name"] == "get_stock_price" + function_tool_found = True + if "systemTool" in tool: + assert tool["systemTool"]["name"] == "nova_grounding" + system_tool_found = True + + assert function_tool_found, "Function tool (get_stock_price) should be present" + assert system_tool_found, "systemTool (nova_grounding) should be present" + print(f"✓ Both function tools and web_search_options correctly combined") + + +@pytest.mark.asyncio +async def test_bedrock_nova_grounding_async(): + """ + Async unit test for Nova grounding via web_search_options. + + This test verifies the request transformation for async calls. + """ + from unittest.mock import patch, AsyncMock + from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler + + client = AsyncHTTPHandler() + + messages = [ + { + "role": "user", + "content": "What is the weather forecast for New York City today?", + } + ] + + with patch.object(client, "post", new=AsyncMock()) as mock_post: + try: + await litellm.acompletion( + model="us.amazon.nova-pro-v1:0", # No bedrock/ prefix when using api_base + messages=messages, + web_search_options={}, + max_tokens=500, + client=client, + api_base="https://bedrock-runtime.us-east-1.amazonaws.com", + ) + except Exception: + pass # Expected - we're just checking the request structure + + # Verify the request was made correctly + if mock_post.called: + request_body = json.loads(mock_post.call_args.kwargs.get("data", "{}")) + print(f"Request body: {json.dumps(request_body, indent=2)}") + + # Verify toolConfig is present with systemTool + assert "toolConfig" in request_body, "toolConfig should be in request" + tool_config = request_body["toolConfig"] + assert "tools" in tool_config, "tools should be in toolConfig" + + # Find the systemTool for nova_grounding + system_tool_found = False + for tool in tool_config["tools"]: + if "systemTool" in tool: + assert tool["systemTool"]["name"] == "nova_grounding" + system_tool_found = True + break + + assert system_tool_found, "systemTool with nova_grounding should be present" + print(f"✓ Async web_search_options correctly transformed to systemTool") + + +def test_bedrock_nova_web_search_options_ignored_for_non_nova(): + """ + Test that web_search_options is ignored for non-Nova Bedrock models. + + Nova grounding is only supported on Nova models. For other models, + the parameter should be silently ignored. + """ + from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig + + config = AmazonConverseConfig() + + # Should return None for non-Nova models + result = config._map_web_search_options({}, "anthropic.claude-3-sonnet-v1") + assert result is None + + result = config._map_web_search_options({}, "amazon.titan-text-express-v1") + assert result is None + + # Should return systemTool for Nova models + result = config._map_web_search_options({}, "amazon.nova-pro-v1:0") + assert result is not None + system_tool = result.get("systemTool") + assert system_tool is not None + assert system_tool["name"] == "nova_grounding" + + result2 = config._map_web_search_options({}, "us.amazon.nova-premier-v1:0") + assert result2 is not None + system_tool2 = result2.get("systemTool") + assert system_tool2 is not None + assert system_tool2["name"] == "nova_grounding" + + +def test_bedrock_nova_grounding_request_transformation(): + """ + Unit test to verify that web_search_options transforms to systemTool in the request. + """ + from unittest.mock import patch, MagicMock + from litellm.llms.custom_httpx.http_handler import HTTPHandler + + client = HTTPHandler() + + messages = [{"role": "user", "content": "What is the population of Tokyo?"}] + + with patch.object(client, "post") as mock_post: + mock_post.return_value = MagicMock( + status_code=200, + json=lambda: { + "output": {"message": {"role": "assistant", "content": [{"text": "Test"}]}}, + "stopReason": "end_turn", + "usage": {"inputTokens": 10, "outputTokens": 5} + } + ) + + try: + response = completion( + model="bedrock/us.amazon.nova-pro-v1:0", + messages=messages, + web_search_options={}, + max_tokens=100, + client=client, + ) + except Exception: + pass # Expected - we're just checking the request + + if mock_post.called: + request_body = json.loads(mock_post.call_args.kwargs.get("data", "{}")) + print(f"Request body: {json.dumps(request_body, indent=2)}") + + # Verify toolConfig is present with systemTool + assert "toolConfig" in request_body, "toolConfig should be in request" + + tool_config = request_body["toolConfig"] + assert "tools" in tool_config, "tools should be in toolConfig" + + tools_in_request = tool_config["tools"] + + # Find the systemTool + system_tool_found = False + for tool in tools_in_request: + if "systemTool" in tool: + assert tool["systemTool"]["name"] == "nova_grounding" + system_tool_found = True + break + + assert system_tool_found, "systemTool with nova_grounding should be present" + print("✓ web_search_options correctly transformed to systemTool") diff --git a/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py b/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py index a22fe13798..e87233a52a 100644 --- a/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py +++ b/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py @@ -1138,6 +1138,73 @@ def test_bedrock_create_bedrock_block_different_document_formats(): assert block["document"]["name"].endswith(f"_{format_type}") assert block["document"]["format"] == format_type +def test_bedrock_nova_web_search_options_mapping(): + """ + Test that web_search_options is correctly mapped to Nova grounding. + + This follows the LiteLLM pattern for web search where: + - Vertex AI maps web_search_options to {"googleSearch": {}} + - Anthropic maps web_search_options to {"type": "web_search_20250305", ...} + - Nova should map web_search_options to {"systemTool": {"name": "nova_grounding"}} + """ + from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig + + config = AmazonConverseConfig() + + # Test basic mapping for Nova model + result = config._map_web_search_options({}, "amazon.nova-pro-v1:0") + + assert result is not None + system_tool = result.get("systemTool") + assert system_tool is not None + assert system_tool["name"] == "nova_grounding" + + # Test with search_context_size (should be ignored for Nova) + result2 = config._map_web_search_options( + {"search_context_size": "high"}, + "us.amazon.nova-premier-v1:0" + ) + + assert result2 is not None + system_tool2 = result2.get("systemTool") + assert system_tool2 is not None + assert system_tool2["name"] == "nova_grounding" + # Nova doesn't support search_context_size, so it's just ignored + +def test_bedrock_tools_pt_does_not_handle_system_tool(): + """ + Verify that _bedrock_tools_pt does NOT handle system_tool format. + + System tools (nova_grounding) should be added via web_search_options, + not via the tools parameter directly. + """ + + from litellm.litellm_core_utils.prompt_templates.factory import _bedrock_tools_pt + + # Regular function tools should still work + tools = [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the current weather", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string"} + }, + "required": ["location"] + } + } + } + ] + + result = _bedrock_tools_pt(tools=tools) + + assert len(result) == 1 + tool_spec = result[0].get("toolSpec") + assert tool_spec is not None + assert tool_spec["name"] == "get_weather" def test_convert_to_anthropic_tool_result_image_with_cache_control(): """ @@ -1305,12 +1372,12 @@ def test_convert_to_anthropic_tool_result_image_url_as_http(): assert result["content"][0]["cache_control"]["type"] == "ephemeral" def test_anthropic_messages_pt_server_tool_use_passthrough(): """ - Test that anthropic_messages_pt passes through server_tool_use and + Test that anthropic_messages_pt passes through server_tool_use and tool_search_tool_result blocks in assistant message content. - + These are Anthropic-native content types used for tool search functionality that need to be preserved when reconstructing multi-turn conversations. - + Fixes: https://github.com/BerriAI/litellm/issues/XXXXX """ from litellm.litellm_core_utils.prompt_templates.factory import anthropic_messages_pt @@ -1359,15 +1426,15 @@ def test_anthropic_messages_pt_server_tool_use_passthrough(): # Verify we have 3 messages (user, assistant, user) assert len(result) == 3 - + # Verify the assistant message content assistant_msg = result[1] assert assistant_msg["role"] == "assistant" assert isinstance(assistant_msg["content"], list) - + # Find the different content block types content_types = [block.get("type") for block in assistant_msg["content"]] - + # Verify server_tool_use block is preserved assert "server_tool_use" in content_types server_tool_use_block = next( @@ -1376,7 +1443,7 @@ def test_anthropic_messages_pt_server_tool_use_passthrough(): assert server_tool_use_block["id"] == "srvtoolu_01ABC123" assert server_tool_use_block["name"] == "tool_search_tool_regex" assert server_tool_use_block["input"] == {"query": ".*time.*"} - + # Verify tool_search_tool_result block is preserved assert "tool_search_tool_result" in content_types tool_result_block = next( @@ -1385,7 +1452,7 @@ def test_anthropic_messages_pt_server_tool_use_passthrough(): assert tool_result_block["tool_use_id"] == "srvtoolu_01ABC123" assert tool_result_block["content"]["type"] == "tool_search_tool_search_result" assert tool_result_block["content"]["tool_references"][0]["tool_name"] == "get_time" - + # Verify text block is also preserved assert "text" in content_types text_block = next( From 324f1f4682c82cc0572398ecb774b862a8804231 Mon Sep 17 00:00:00 2001 From: ruanjiefeng Date: Fri, 23 Jan 2026 11:38:14 +0800 Subject: [PATCH 02/62] add Vertex_AI llm credentials sensitive keywords "vertex_credentials" (#19551) * add Vertex_AI llm credentials sensitive keywords "vertex_credentials" * Update test_litellm_logging.py add test case --- litellm/litellm_core_utils/litellm_logging.py | 1 + tests/test_litellm/litellm_core_utils/test_litellm_logging.py | 2 ++ 2 files changed, 3 insertions(+) diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index d3bcfe8200..d512893465 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -3296,6 +3296,7 @@ def _get_masked_values( "token", "key", "secret", + "vertex_credentials", ] return { k: ( diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py index e035e193fe..1f3f558a49 100644 --- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py +++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py @@ -787,11 +787,13 @@ def test_get_masked_values(): "presidio_ad_hoc_recognizers": None, "aws_bedrock_runtime_endpoint": None, "presidio_anonymizer_api_base": None, + "vertex_credentials": "{sensitive_api_key}", } masked_values = _get_masked_values( sensitive_object, unmasked_length=4, number_of_asterisks=4 ) assert masked_values["presidio_anonymizer_api_base"] is None + assert masked_values["vertex_credentials"] == "{s****y}" @pytest.mark.asyncio From 65e943dc2b99b68e5d2b0a23653c8670c282fa48 Mon Sep 17 00:00:00 2001 From: moh-dev-stack Date: Fri, 23 Jan 2026 03:39:58 +0000 Subject: [PATCH 03/62] Bugfix/19481 num retries env var type (#19507) * Enhance error handling for num_retries in Router class to support string values. Add test case to verify conversion from string to int for deployment num_retries. * Refactor Router class for improved readability by formatting long lines and enhancing exception handling tests for num_retries. Ensure consistent style in test cases for better maintainability. * Update exception handling for num_retries in Router class to suppress mypy warnings. Add type ignore comment for clarity in type conversion from string to int. --- litellm/router.py | 19 +++++-- .../test_router_per_deployment_num_retries.py | 57 +++++++++++++++---- 2 files changed, 59 insertions(+), 17 deletions(-) diff --git a/litellm/router.py b/litellm/router.py index 62070d2375..3234366cfb 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -1696,8 +1696,11 @@ class Router: litellm_params = deployment.get("litellm_params", {}) dep_num_retries = litellm_params.get("num_retries") - if dep_num_retries is not None and isinstance(dep_num_retries, int): - exception.num_retries = dep_num_retries # type: ignore + if dep_num_retries is not None: + try: + exception.num_retries = int(dep_num_retries) # type: ignore # Handle both int and str + except (ValueError, TypeError): + pass # Skip if value can't be converted to int def _update_kwargs_with_default_litellm_params( self, kwargs: dict, metadata_variable_name: Optional[str] = "metadata" @@ -4692,9 +4695,12 @@ class Router: # get num_retries from retry policy # Use the model_group captured at the start of the function, or get it from metadata # kwargs.get("model") at this point is the deployment model, not the model_group - _model_group_for_retry_policy = model_group or _metadata.get("model_group") or kwargs.get("model") + _model_group_for_retry_policy = ( + model_group or _metadata.get("model_group") or kwargs.get("model") + ) _retry_policy_retries = self.get_num_retries_from_retry_policy( - exception=original_exception, model_group=_model_group_for_retry_policy + exception=original_exception, + model_group=_model_group_for_retry_policy, ) if _retry_policy_retries is not None: num_retries = _retry_policy_retries @@ -5879,7 +5885,10 @@ class Router: ) # done reading model["litellm_params"] # Check if provider is supported: either in enum or JSON-configured - if custom_llm_provider not in litellm.provider_list and not JSONProviderRegistry.exists(custom_llm_provider): + if ( + custom_llm_provider not in litellm.provider_list + and not JSONProviderRegistry.exists(custom_llm_provider) + ): raise Exception(f"Unsupported provider - {custom_llm_provider}") #### DEPLOYMENT NAMES INIT ######## diff --git a/tests/test_litellm/test_router_per_deployment_num_retries.py b/tests/test_litellm/test_router_per_deployment_num_retries.py index 4021ca2807..154ba579e4 100644 --- a/tests/test_litellm/test_router_per_deployment_num_retries.py +++ b/tests/test_litellm/test_router_per_deployment_num_retries.py @@ -32,17 +32,17 @@ class TestPerDeploymentNumRetries: ) deployment = router.model_list[0] - + # Create a mock exception without num_retries class MockException(Exception): pass - + exc = MockException("test error") assert not hasattr(exc, "num_retries") or exc.num_retries is None - + # Call the helper router._set_deployment_num_retries_on_exception(exc, deployment) - + # Verify num_retries was set from deployment assert exc.num_retries == 5 @@ -66,16 +66,16 @@ class TestPerDeploymentNumRetries: ) deployment = router.model_list[0] - + # Create an exception that already has num_retries class MockException(Exception): num_retries = 10 # Already set - + exc = MockException("test error") - + # Call the helper router._set_deployment_num_retries_on_exception(exc, deployment) - + # Verify num_retries was NOT overridden assert exc.num_retries == 10 @@ -99,15 +99,15 @@ class TestPerDeploymentNumRetries: ) deployment = router.model_list[0] - + class MockException(Exception): pass - + exc = MockException("test error") - + # Call the helper router._set_deployment_num_retries_on_exception(exc, deployment) - + # Verify num_retries was not set (deployment has no num_retries) assert not hasattr(exc, "num_retries") or exc.num_retries is None @@ -155,3 +155,36 @@ class TestPerDeploymentNumRetries: kwargs = {} router._update_kwargs_before_fallbacks(model="test-model", kwargs=kwargs) assert kwargs["num_retries"] == 7 # Uses global + + def test_set_deployment_num_retries_with_string_value(self): + """ + Test that _set_deployment_num_retries_on_exception handles string values + from environment variables correctly. + GitHub Issue: #19481 + """ + router = Router( + model_list=[ + { + "model_name": "test-model", + "litellm_params": { + "model": "openai/gpt-4", + "api_key": "test-key", + "num_retries": "6", # String value (as from env var) + }, + }, + ], + num_retries=0, # Global setting + ) + + deployment = router.model_list[0] + + class MockException(Exception): + pass + + exc = MockException("test error") + + # Call the helper + router._set_deployment_num_retries_on_exception(exc, deployment) + + # Verify num_retries was converted from string to int + assert exc.num_retries == 6 From 6cf7bd7c0fc7962bd63b9d6b87439b82cdee1d0a Mon Sep 17 00:00:00 2001 From: Cesar Garcia <128240629+Chesars@users.noreply.github.com> Date: Fri, 23 Jan 2026 00:42:15 -0300 Subject: [PATCH 04/62] Fix gpt-image-1.5 cost calculation not including output image tokens (#19515) Fixes #19508 The cost calculation for gpt-image-1.5 was not including image tokens from output_tokens_details, causing costs to be underreported (e.g., $0.046 instead of $0.14). Root cause: The OpenAI image generation API uses Responses API naming (input_tokens, output_tokens, output_tokens_details) but the cost calculator expected Chat Completions API naming (prompt_tokens, completion_tokens, completion_tokens_details). Changes: - convert_dict_to_response.py: Map Responses API fields to Chat Completions API fields and convert dicts to wrapper objects - cost_calculator.py: Use usage directly if already transformed, avoiding double transformation that lost the wrapper objects - Added test for gpt-image-1.5 output image token cost calculation --- .../convert_dict_to_response.py | 18 +++++ .../image_generation/cost_calculator.py | 20 ++++-- .../test_gpt_image_cost_calculator.py | 68 +++++++++++++++++++ 3 files changed, 99 insertions(+), 7 deletions(-) diff --git a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py index bbe28e3ec2..25ad0a570c 100644 --- a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py +++ b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py @@ -21,11 +21,13 @@ from litellm.types.utils import ( ChatCompletionMessageToolCall, ChatCompletionRedactedThinkingBlock, Choices, + CompletionTokensDetailsWrapper, Delta, EmbeddingResponse, Function, HiddenParams, ImageResponse, + PromptTokensDetailsWrapper, ) from litellm.types.utils import Logprobs as TextCompletionLogprobs from litellm.types.utils import ( @@ -304,6 +306,22 @@ class LiteLLMResponseObjectHandler: "text_tokens": 0, } + # Map Responses API naming to Chat Completions API naming for cost calculator + if usage.get("prompt_tokens") is None: + usage["prompt_tokens"] = usage.get("input_tokens", 0) + if usage.get("completion_tokens") is None: + usage["completion_tokens"] = usage.get("output_tokens", 0) + + # Convert dicts to wrapper objects so getattr() works in cost calculation + if isinstance(usage.get("input_tokens_details"), dict): + usage["prompt_tokens_details"] = PromptTokensDetailsWrapper( + **usage["input_tokens_details"] + ) + if isinstance(usage.get("output_tokens_details"), dict): + usage["completion_tokens_details"] = CompletionTokensDetailsWrapper( + **usage["output_tokens_details"] + ) + if model_response_object is None: model_response_object = ImageResponse(**response_object) return model_response_object diff --git a/litellm/llms/openai/image_generation/cost_calculator.py b/litellm/llms/openai/image_generation/cost_calculator.py index 35caaf6e9b..988d562613 100644 --- a/litellm/llms/openai/image_generation/cost_calculator.py +++ b/litellm/llms/openai/image_generation/cost_calculator.py @@ -8,8 +8,7 @@ from typing import Optional from litellm import verbose_logger from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token -from litellm.responses.utils import ResponseAPILoggingUtils -from litellm.types.utils import ImageResponse +from litellm.types.utils import ImageResponse, Usage def cost_calculator( @@ -39,11 +38,18 @@ def cost_calculator( ) return 0.0 - # Transform ImageUsage to Usage using the existing helper - # ImageUsage has the same format as ResponseAPIUsage - chat_usage = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( - usage - ) + # If usage is already a Usage object with completion_tokens_details set, + # use it directly (it was already transformed in convert_to_image_response) + if isinstance(usage, Usage) and usage.completion_tokens_details is not None: + chat_usage = usage + else: + # Transform ImageUsage to Usage using the existing helper + # ImageUsage has the same format as ResponseAPIUsage + from litellm.responses.utils import ResponseAPILoggingUtils + + chat_usage = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( + usage + ) # Use generic_cost_per_token for cost calculation prompt_cost, completion_cost = generic_cost_per_token( diff --git a/tests/test_litellm/test_gpt_image_cost_calculator.py b/tests/test_litellm/test_gpt_image_cost_calculator.py index 0a2a62b6c9..620c073498 100644 --- a/tests/test_litellm/test_gpt_image_cost_calculator.py +++ b/tests/test_litellm/test_gpt_image_cost_calculator.py @@ -19,10 +19,13 @@ import pytest import litellm from litellm.types.utils import ( + CompletionTokensDetailsWrapper, ImageResponse, ImageObject, ImageUsage, ImageUsageInputTokensDetails, + PromptTokensDetailsWrapper, + Usage, ) @@ -202,6 +205,71 @@ class TestGPTImageCostRouting: assert cost >= 0 +class TestGPTImage15OutputImageTokens: + """ + Test for GitHub issue #19508: + Image usage calculation does not include image tokens in gpt-image-1.5 + + gpt-image-1.5 returns output_tokens_details with separate image_tokens and text_tokens, + and these must be correctly included in cost calculation. + """ + + def test_gpt_image_15_output_image_tokens_cost(self): + """ + Test that output image tokens are correctly included in cost calculation. + + This tests the fix for issue #19508 where output_tokens_details.image_tokens + were not being included in the cost calculation, causing costs to be + underreported (e.g., $0.046 instead of $0.14). + """ + # Simulate gpt-image-1.5 response with output_tokens_details + # This is what the API returns and what convert_to_image_response transforms + usage = Usage( + prompt_tokens=169, + completion_tokens=4599, + total_tokens=4768, + prompt_tokens_details=PromptTokensDetailsWrapper( + text_tokens=169, + image_tokens=0, + ), + completion_tokens_details=CompletionTokensDetailsWrapper( + text_tokens=439, + image_tokens=4160, + ), + ) + + image_response = ImageResponse( + created=1234567890, + data=[ImageObject(b64_json="test")], + ) + image_response.usage = usage + image_response._hidden_params = {"custom_llm_provider": "openai"} + + cost = litellm.completion_cost( + completion_response=image_response, + model="gpt-image-1.5", + call_type="image_generation", + custom_llm_provider="openai", + ) + + # gpt-image-1.5 pricing: + # - input_cost_per_token: 5e-06 ($5/1M for text input) + # - output_cost_per_token: 1e-05 ($10/1M for text output) + # - output_cost_per_image_token: 3.2e-05 ($32/1M for image output) + # + # Expected cost: + # Input text: 169 * $5/1M = $0.000845 + # Output text: 439 * $10/1M = $0.00439 + # Output image: 4160 * $32/1M = $0.13312 + # Total: $0.138355 + expected_cost = 169 * 5e-06 + 439 * 1e-05 + 4160 * 3.2e-05 + + assert abs(cost - expected_cost) < 1e-6, ( + f"Expected {expected_cost}, got {cost}. " + f"Image tokens may not be included in cost calculation." + ) + + class TestCompletionCostIntegration: """Test the full completion_cost integration for gpt-image-1""" From 3372430d40cc94223ec4d7f12101f38e4e606455 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=8B=90=E7=88=B7=26=26=E8=80=81=E6=8B=90=E7=98=A6?= Date: Fri, 23 Jan 2026 11:47:21 +0800 Subject: [PATCH 05/62] Add pricing for volcengine models (deepseek-v3-2, glm-4-7, kimi-k2-thinking) (#19335) Co-authored-by: Claude Opus 4.5 --- model_prices_and_context_window.json | 42 ++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 6d87e0b599..3d67bc25b5 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -10219,6 +10219,48 @@ "mode": "completion", "output_cost_per_token": 5e-07 }, + "deepseek-v3-2-251201": { + "input_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "max_input_tokens": 98304, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 0.0, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "glm-4-7-251222": { + "input_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "max_input_tokens": 204800, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 0.0, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "kimi-k2-thinking-251104": { + "input_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "max_input_tokens": 229376, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 0.0, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "doubao-embedding": { "input_cost_per_token": 0.0, "litellm_provider": "volcengine", From 1d04414f305497a14d3005be23717d514619b5aa Mon Sep 17 00:00:00 2001 From: Harshit Jain <48647625+Harshit28j@users.noreply.github.com> Date: Fri, 23 Jan 2026 09:19:22 +0530 Subject: [PATCH 06/62] feat(datadog): add agent support for LLM Observability (#19574) --- docs/my-website/docs/observability/datadog.md | 7 +- litellm/integrations/datadog/datadog.py | 17 ++--- .../integrations/datadog/datadog_handler.py | 8 +++ .../integrations/datadog/datadog_llm_obs.py | 64 ++++++++++++++----- .../datadog/test_datadog_llm_obs_agent.py | 62 ++++++++++++++++++ 5 files changed, 127 insertions(+), 31 deletions(-) create mode 100644 tests/test_litellm/integrations/datadog/test_datadog_llm_obs_agent.py diff --git a/docs/my-website/docs/observability/datadog.md b/docs/my-website/docs/observability/datadog.md index 7cf91ced34..0f50cbd5c9 100644 --- a/docs/my-website/docs/observability/datadog.md +++ b/docs/my-website/docs/observability/datadog.md @@ -73,7 +73,7 @@ Send logs through a local DataDog agent (useful for containerized environments): ```shell LITELLM_DD_AGENT_HOST="localhost" # hostname or IP of DataDog agent LITELLM_DD_AGENT_PORT="10518" # [OPTIONAL] port of DataDog agent (default: 10518) -DD_API_KEY="5f2d0f310***********" # [OPTIONAL] your datadog API Key (agent handles auth) +DD_API_KEY="5f2d0f310***********" # [OPTIONAL] your datadog API Key (Agent handles auth for Logs. REQUIRED for LLM Observability) DD_SOURCE="litellm_dev" # [OPTIONAL] your datadog source ``` @@ -84,6 +84,9 @@ When `LITELLM_DD_AGENT_HOST` is set, logs are sent to the agent instead of direc **Note:** We use `LITELLM_DD_AGENT_HOST` instead of `DD_AGENT_HOST` to avoid conflicts with `ddtrace` which automatically sets `DD_AGENT_HOST` for APM tracing. +> [!IMPORTANT] +> **Datadog LLM Observability**: `DD_API_KEY` is **REQUIRED** even when using the Datadog Agent (`LITELLM_DD_AGENT_HOST`). The agent acts as a proxy but the API key header is mandatory for the LLM Observability endpoint. + **Step 3**: Start the proxy, make a test request Start proxy @@ -203,5 +206,5 @@ LiteLLM supports customizing the following Datadog environment variables | `POD_NAME` | Pod name tag (useful for Kubernetes deployments) | "unknown" | ❌ No | \* **Required when using Direct API** (default): `DD_API_KEY` and `DD_SITE` are required -\* **Optional when using DataDog Agent**: Set `LITELLM_DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required +\* **Optional when using DataDog Agent**: Set `LITELLM_DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required for **Datadog Logs**. (**Note: `DD_API_KEY` IS REQUIRED for Datadog LLM Observability**) diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py index 503e8d8c87..735d1005d2 100644 --- a/litellm/integrations/datadog/datadog.py +++ b/litellm/integrations/datadog/datadog.py @@ -32,6 +32,7 @@ from litellm.integrations.datadog.datadog_handler import ( get_datadog_service, get_datadog_source, get_datadog_tags, + get_datadog_base_url_from_env, ) from litellm.litellm_core_utils.dd_tracing import tracer from litellm.llms.custom_httpx.http_handler import ( @@ -100,7 +101,9 @@ class DataDogLogger( self._configure_dd_direct_api() # Optional override for testing - self._apply_dd_base_url_override() + dd_base_url = get_datadog_base_url_from_env() + if dd_base_url: + self.intake_url = f"{dd_base_url}/api/v2/logs" self.sync_client = _get_httpx_client() asyncio.create_task(self.periodic_flush()) self.flush_lock = asyncio.Lock() @@ -159,18 +162,6 @@ class DataDogLogger( self.DD_API_KEY = os.getenv("DD_API_KEY") self.intake_url = f"https://http-intake.logs.{os.getenv('DD_SITE')}/api/v2/logs" - def _apply_dd_base_url_override(self) -> None: - """ - Apply base URL override for testing purposes - """ - dd_base_url: Optional[str] = ( - os.getenv("_DATADOG_BASE_URL") - or os.getenv("DATADOG_BASE_URL") - or os.getenv("DD_BASE_URL") - ) - if dd_base_url is not None: - self.intake_url = f"{dd_base_url}/api/v2/logs" - async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): """ Async Log success events to Datadog diff --git a/litellm/integrations/datadog/datadog_handler.py b/litellm/integrations/datadog/datadog_handler.py index 26fab77759..e2f30f2f61 100644 --- a/litellm/integrations/datadog/datadog_handler.py +++ b/litellm/integrations/datadog/datadog_handler.py @@ -20,6 +20,14 @@ def get_datadog_hostname() -> str: return os.getenv("HOSTNAME", "") +def get_datadog_base_url_from_env() -> Optional[str]: + """ + Get base URL override from common DD_BASE_URL env var. + This is useful for testing or custom endpoints. + """ + return os.getenv("DD_BASE_URL") + + def get_datadog_env() -> str: return os.getenv("DD_ENV", "unknown") diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py index 6ffdbc0a00..4f6a5b339a 100644 --- a/litellm/integrations/datadog/datadog_llm_obs.py +++ b/litellm/integrations/datadog/datadog_llm_obs.py @@ -21,6 +21,7 @@ from litellm.integrations.custom_batch_logger import CustomBatchLogger from litellm.integrations.datadog.datadog_handler import ( get_datadog_service, get_datadog_tags, + get_datadog_base_url_from_env, ) from litellm.litellm_core_utils.dd_tracing import tracer from litellm.litellm_core_utils.prompt_templates.common_utils import ( @@ -43,24 +44,22 @@ class DataDogLLMObsLogger(CustomBatchLogger): def __init__(self, **kwargs): try: verbose_logger.debug("DataDogLLMObs: Initializing logger") - if os.getenv("DD_API_KEY", None) is None: - raise Exception("DD_API_KEY is not set, set 'DD_API_KEY=<>'") - if os.getenv("DD_SITE", None) is None: - raise Exception( - "DD_SITE is not set, set 'DD_SITE=<>', example sit = `us5.datadoghq.com`" - ) + # Configure DataDog endpoint (Agent or Direct API) + # Use LITELLM_DD_AGENT_HOST to avoid conflicts with ddtrace's DD_AGENT_HOST + dd_agent_host = os.getenv("LITELLM_DD_AGENT_HOST") self.async_client = get_async_httpx_client( llm_provider=httpxSpecialProvider.LoggingCallback ) self.DD_API_KEY = os.getenv("DD_API_KEY") - self.DD_SITE = os.getenv("DD_SITE") - self.intake_url = ( - f"https://api.{self.DD_SITE}/api/intake/llm-obs/v1/trace/spans" - ) - # testing base url - dd_base_url = os.getenv("DD_BASE_URL") + if dd_agent_host: + self._configure_dd_agent(dd_agent_host=dd_agent_host) + else: + self._configure_dd_direct_api() + + # Optional override for testing + dd_base_url = get_datadog_base_url_from_env() if dd_base_url: self.intake_url = f"{dd_base_url}/api/intake/llm-obs/v1/trace/spans" @@ -78,6 +77,38 @@ class DataDogLLMObsLogger(CustomBatchLogger): verbose_logger.exception(f"DataDogLLMObs: Error initializing - {str(e)}") raise e + def _configure_dd_agent(self, dd_agent_host: str): + """ + Configure the Datadog logger to send traces to the Agent. + """ + # When using the Agent, LLM Observability Intake does NOT require the API Key + # Reference: https://docs.datadoghq.com/llm_observability/setup/sdk/#agent-setup + + # Use specific port for LLM Obs (Trace Agent) to avoid conflict with Logs Agent (10518) + agent_port = os.getenv("LITELLM_DD_LLM_OBS_PORT", "8126") + self.DD_SITE = "localhost" # Not used for URL construction in agent mode + self.intake_url = ( + f"http://{dd_agent_host}:{agent_port}/api/intake/llm-obs/v1/trace/spans" + ) + verbose_logger.debug(f"DataDogLLMObs: Using DD Agent at {self.intake_url}") + + def _configure_dd_direct_api(self): + """ + Configure the Datadog logger to send traces directly to the Datadog API. + """ + if not self.DD_API_KEY: + raise Exception("DD_API_KEY is not set, set 'DD_API_KEY=<>'") + + self.DD_SITE = os.getenv("DD_SITE") + if not self.DD_SITE: + raise Exception( + "DD_SITE is not set, set 'DD_SITE=<>', example site = `us5.datadoghq.com`" + ) + + self.intake_url = ( + f"https://api.{self.DD_SITE}/api/intake/llm-obs/v1/trace/spans" + ) + def _get_datadog_llm_obs_params(self) -> Dict: """ Get the datadog_llm_observability_params from litellm.datadog_llm_observability_params @@ -164,13 +195,14 @@ class DataDogLLMObsLogger(CustomBatchLogger): json_payload = safe_dumps(payload) + headers = {"Content-Type": "application/json"} + if self.DD_API_KEY: + headers["DD-API-KEY"] = self.DD_API_KEY + response = await self.async_client.post( url=self.intake_url, content=json_payload, - headers={ - "DD-API-KEY": self.DD_API_KEY, - "Content-Type": "application/json", - }, + headers=headers, ) if response.status_code != 202: diff --git a/tests/test_litellm/integrations/datadog/test_datadog_llm_obs_agent.py b/tests/test_litellm/integrations/datadog/test_datadog_llm_obs_agent.py new file mode 100644 index 0000000000..2bb51e1e1b --- /dev/null +++ b/tests/test_litellm/integrations/datadog/test_datadog_llm_obs_agent.py @@ -0,0 +1,62 @@ +import os +from unittest.mock import patch +from litellm.integrations.datadog.datadog_llm_obs import DataDogLLMObsLogger + + +def test_datadog_llm_obs_agent_configuration(): + """ + Test that DataDog LLM Obs logger correctly configures agent endpoint. + """ + test_env = { + "LITELLM_DD_AGENT_HOST": "localhost", + "LITELLM_DD_LLM_OBS_PORT": "10518", + "DD_API_KEY": "test-api-key", # Optional, but checking if it's preserved + } + + # Ensure DD_SITE is NOT set to verify we don't need it in agent mode + + with patch.dict(os.environ, test_env, clear=True): + with patch("asyncio.create_task"): # Prevent periodic flush task from running + dd_logger = DataDogLLMObsLogger() + + expected_url = "http://localhost:10518/api/intake/llm-obs/v1/trace/spans" + assert dd_logger.intake_url == expected_url + assert dd_logger.DD_API_KEY == "test-api-key" + + +def test_datadog_llm_obs_agent_no_api_key_ok(): + """ + Test that agent mode works WITHOUT DD_API_KEY (agent handles auth). + """ + test_env = { + "LITELLM_DD_AGENT_HOST": "localhost", + # No DD_API_KEY + } + + with patch.dict(os.environ, test_env, clear=True): + with patch("asyncio.create_task"): + # Should NOT raise exception anymore + dd_logger = DataDogLLMObsLogger() + + assert dd_logger.DD_API_KEY is None + # Default port is 8126 if not set + expected_url = "http://localhost:8126/api/intake/llm-obs/v1/trace/spans" + assert dd_logger.intake_url == expected_url + + +def test_datadog_llm_obs_direct_api_configuration(): + """ + Test that direct API configuration still works as expected. + """ + test_env = { + "DD_API_KEY": "direct-api-key", + "DD_SITE": "us5.datadoghq.com", + } + + with patch.dict(os.environ, test_env, clear=True): + with patch("asyncio.create_task"): + dd_logger = DataDogLLMObsLogger() + + expected_url = "https://api.us5.datadoghq.com/api/intake/llm-obs/v1/trace/spans" + assert dd_logger.intake_url == expected_url + assert dd_logger.DD_API_KEY == "direct-api-key" From 06a749708d5b9d0a2851ce85400aee107dd469bb Mon Sep 17 00:00:00 2001 From: Harshit Jain <48647625+Harshit28j@users.noreply.github.com> Date: Fri, 23 Jan 2026 09:22:14 +0530 Subject: [PATCH 07/62] feat: add datadog cost management support and fix startup callback issue (#19584) --- docs/my-website/docs/observability/datadog.md | 45 ++++ litellm/integrations/callback_configs.json | 29 ++- .../datadog/datadog_cost_management.py | 202 ++++++++++++++++++ litellm/proxy/common_utils/callback_utils.py | 45 ++-- .../integrations/datadog_cost_management.py | 27 +++ .../datadog/test_datadog_cost_management.py | 169 +++++++++++++++ 6 files changed, 498 insertions(+), 19 deletions(-) create mode 100644 litellm/integrations/datadog/datadog_cost_management.py create mode 100644 litellm/types/integrations/datadog_cost_management.py create mode 100644 tests/test_litellm/integrations/datadog/test_datadog_cost_management.py diff --git a/docs/my-website/docs/observability/datadog.md b/docs/my-website/docs/observability/datadog.md index 0f50cbd5c9..6f785be101 100644 --- a/docs/my-website/docs/observability/datadog.md +++ b/docs/my-website/docs/observability/datadog.md @@ -7,6 +7,7 @@ import TabItem from '@theme/TabItem'; LiteLLM Supports logging to the following Datdog Integrations: - `datadog` [Datadog Logs](https://docs.datadoghq.com/logs/) - `datadog_llm_observability` [Datadog LLM Observability](https://www.datadoghq.com/product/llm-observability/) +- `datadog_cost_management` [Datadog Cloud Cost Management](#datadog-cloud-cost-management) - `ddtrace-run` [Datadog Tracing](#datadog-tracing) ## Datadog Logs @@ -164,6 +165,50 @@ On the Datadog LLM Observability page, you should see that both input messages a + + + +## Datadog Cloud Cost Management + +| Feature | Details | +|---------|---------| +| **What is logged** | Aggregated LLM Costs (FOCUS format) | +| **Events** | Periodic Uploads of Aggregated Cost Data | +| **Product Link** | [Datadog Cloud Cost Management](https://docs.datadoghq.com/cost_management/) | + +We will use the `--config` to set `litellm.callbacks = ["datadog_cost_management"]`. This will periodically upload aggregated LLM cost data to Datadog. + +**Step 1**: Create a `config.yaml` file and set `litellm_settings`: `success_callback` + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo +litellm_settings: + callbacks: ["datadog_cost_management"] +``` + +**Step 2**: Set Required env variables + +```shell +DD_API_KEY="your-api-key" +DD_APP_KEY="your-app-key" # REQUIRED for Cost Management +DD_SITE="us5.datadoghq.com" +``` + +**Step 3**: Start the proxy + +```shell +litellm --config config.yaml +``` + +**How it works** +* LiteLLM aggregates costs in-memory by Provider, Model, Date, and Tags. +* Requires `DD_APP_KEY` for the Custom Costs API. +* Costs are uploaded periodically (flushed). + + ### Datadog Tracing Use `ddtrace-run` to enable [Datadog Tracing](https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html) on litellm proxy diff --git a/litellm/integrations/callback_configs.json b/litellm/integrations/callback_configs.json index 6b30b6b736..6a003b8c49 100644 --- a/litellm/integrations/callback_configs.json +++ b/litellm/integrations/callback_configs.json @@ -83,6 +83,33 @@ }, "description": "Datadog Logging Integration" }, + { + "id": "datadog_cost_management", + "displayName": "Datadog Cost Management", + "logo": "datadog.png", + "supports_key_team_logging": false, + "dynamic_params": { + "dd_api_key": { + "type": "password", + "ui_name": "API Key", + "description": "Datadog API key for authentication", + "required": true + }, + "dd_app_key": { + "type": "password", + "ui_name": "App Key", + "description": "Datadog Application Key for Cloud Cost Management", + "required": true + }, + "dd_site": { + "type": "text", + "ui_name": "Site", + "description": "Datadog site URL (e.g., us5.datadoghq.com)", + "required": true + } + }, + "description": "Datadog Cloud Cost Management Integration" + }, { "id": "lago", "displayName": "Lago", @@ -407,4 +434,4 @@ }, "description": "SQS Queue (AWS) Logging Integration" } -] +] \ No newline at end of file diff --git a/litellm/integrations/datadog/datadog_cost_management.py b/litellm/integrations/datadog/datadog_cost_management.py new file mode 100644 index 0000000000..9bfb2aba71 --- /dev/null +++ b/litellm/integrations/datadog/datadog_cost_management.py @@ -0,0 +1,202 @@ +import asyncio +import os +import time +from datetime import datetime +from typing import Dict, List, Optional + +from litellm._logging import verbose_logger +from litellm.integrations.custom_batch_logger import CustomBatchLogger +from litellm.llms.custom_httpx.http_handler import ( + get_async_httpx_client, + httpxSpecialProvider, +) +from litellm.types.integrations.datadog_cost_management import ( + DatadogFOCUSCostEntry, +) +from litellm.types.utils import StandardLoggingPayload + + +class DatadogCostManagementLogger(CustomBatchLogger): + def __init__(self, **kwargs): + self.dd_api_key = os.getenv("DD_API_KEY") + self.dd_app_key = os.getenv("DD_APP_KEY") + self.dd_site = os.getenv("DD_SITE", "datadoghq.com") + + if not self.dd_api_key or not self.dd_app_key: + verbose_logger.warning( + "Datadog Cost Management: DD_API_KEY and DD_APP_KEY are required. Integration will not work." + ) + + self.upload_url = f"https://api.{self.dd_site}/api/v2/cost/custom_costs" + + self.async_client = get_async_httpx_client( + llm_provider=httpxSpecialProvider.LoggingCallback + ) + + # Initialize lock and start periodic flush task + self.flush_lock = asyncio.Lock() + asyncio.create_task(self.periodic_flush()) + + # Check if flush_lock is already in kwargs to avoid double passing (unlikely but safe) + if "flush_lock" not in kwargs: + kwargs["flush_lock"] = self.flush_lock + + super().__init__(**kwargs) + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get( + "standard_logging_object", None + ) + + if standard_logging_object is None: + return + + # Only log if there is a cost associated + if standard_logging_object.get("response_cost", 0) > 0: + self.log_queue.append(standard_logging_object) + + if len(self.log_queue) >= self.batch_size: + await self.async_send_batch() + + except Exception as e: + verbose_logger.exception( + f"Datadog Cost Management: Error in async_log_success_event: {str(e)}" + ) + + async def async_send_batch(self): + if not self.log_queue: + return + + try: + # Aggregate costs from the batch + aggregated_entries = self._aggregate_costs(self.log_queue) + + if not aggregated_entries: + return + + # Send to Datadog + await self._upload_to_datadog(aggregated_entries) + + # Clear queue only on success (or if we decide to drop on failure) + # CustomBatchLogger clears queue in flush_queue, so we just process here + + except Exception as e: + verbose_logger.exception( + f"Datadog Cost Management: Error in async_send_batch: {str(e)}" + ) + + def _aggregate_costs( + self, logs: List[StandardLoggingPayload] + ) -> List[DatadogFOCUSCostEntry]: + """ + Aggregates costs by Provider, Model, and Date. + Returns a list of DatadogFOCUSCostEntry. + """ + aggregator: Dict[str, DatadogFOCUSCostEntry] = {} + + for log in logs: + try: + # Extract keys for aggregation + provider = log.get("custom_llm_provider") or "unknown" + model = log.get("model") or "unknown" + cost = log.get("response_cost", 0) + + if cost == 0: + continue + + # Get date strings (FOCUS format requires specific keys, but for aggregation we group by Day) + # UTC date + # We interpret "ChargePeriod" as the day of the request. + ts = log.get("startTime") or time.time() + dt = datetime.fromtimestamp(ts) + date_str = dt.strftime("%Y-%m-%d") + + # ChargePeriodStart and End + # If we want daily granularity, end date is usually same day or next day? + # Datadog Custom Costs usually expects periods. + # "ChargePeriodStart": "2023-01-01", "ChargePeriodEnd": "2023-12-31" in example. + # If we send daily, we can say Start=Date, End=Date. + + # Grouping Key: Provider + Model + Date + Tags? + # For simplicity, let's aggregate by Provider + Model + Date first. + # If we handle tags, we need to include them in the key. + + tags = self._extract_tags(log) + tags_key = tuple(sorted(tags.items())) if tags else () + + key = (provider, model, date_str, tags_key) + + if key not in aggregator: + aggregator[key] = { + "ProviderName": provider, + "ChargeDescription": f"LLM Usage for {model}", + "ChargePeriodStart": date_str, + "ChargePeriodEnd": date_str, + "BilledCost": 0.0, + "BillingCurrency": "USD", + "Tags": tags if tags else None, + } + + aggregator[key]["BilledCost"] += cost + + except Exception as e: + verbose_logger.warning( + f"Error processing log for cost aggregation: {e}" + ) + continue + + return list(aggregator.values()) + + def _extract_tags(self, log: StandardLoggingPayload) -> Dict[str, str]: + from litellm.integrations.datadog.datadog_handler import ( + get_datadog_env, + get_datadog_hostname, + get_datadog_pod_name, + get_datadog_service, + ) + + tags = { + "env": get_datadog_env(), + "service": get_datadog_service(), + "host": get_datadog_hostname(), + "pod_name": get_datadog_pod_name(), + } + + # Add metadata as tags + metadata = log.get("metadata", {}) + if metadata: + # Add user info + if "user_api_key_alias" in metadata: + tags["user"] = str(metadata["user_api_key_alias"]) + if "user_api_key_team_alias" in metadata: + tags["team"] = str(metadata["user_api_key_team_alias"]) + if "model_group" in metadata: + tags["model_group"] = str(metadata["model_group"]) + + return tags + + async def _upload_to_datadog(self, payload: List[Dict]): + if not self.dd_api_key or not self.dd_app_key: + return + + headers = { + "Content-Type": "application/json", + "DD-API-KEY": self.dd_api_key, + "DD-APPLICATION-KEY": self.dd_app_key, + } + + # The API endpoint expects a list of objects directly in the body (file content behavior) + from litellm.litellm_core_utils.safe_json_dumps import safe_dumps + + data_json = safe_dumps(payload) + + response = await self.async_client.put( + self.upload_url, content=data_json, headers=headers + ) + + response.raise_for_status() + + verbose_logger.debug( + f"Datadog Cost Management: Uploaded {len(payload)} cost entries. Status: {response.status_code}" + ) diff --git a/litellm/proxy/common_utils/callback_utils.py b/litellm/proxy/common_utils/callback_utils.py index cb434da55b..a9e9bd237b 100644 --- a/litellm/proxy/common_utils/callback_utils.py +++ b/litellm/proxy/common_utils/callback_utils.py @@ -274,11 +274,20 @@ def initialize_callbacks_on_proxy( # noqa: PLR0915 WebSearchInterceptionLogger, ) - websearch_interception_obj = WebSearchInterceptionLogger.initialize_from_proxy_config( - litellm_settings=litellm_settings, - callback_specific_params=callback_specific_params, + websearch_interception_obj = ( + WebSearchInterceptionLogger.initialize_from_proxy_config( + litellm_settings=litellm_settings, + callback_specific_params=callback_specific_params, + ) ) imported_list.append(websearch_interception_obj) + elif isinstance(callback, str) and callback == "datadog_cost_management": + from litellm.integrations.datadog.datadog_cost_management import ( + DatadogCostManagementLogger, + ) + + datadog_cost_management_obj = DatadogCostManagementLogger() + imported_list.append(datadog_cost_management_obj) elif isinstance(callback, CustomLogger): imported_list.append(callback) else: @@ -353,17 +362,17 @@ def get_remaining_tokens_and_requests_from_request_data(data: Dict) -> Dict[str, remaining_requests_variable_name = f"litellm-key-remaining-requests-{model_group}" remaining_requests = _metadata.get(remaining_requests_variable_name, None) if remaining_requests: - headers[f"x-litellm-key-remaining-requests-{h11_model_group_name}"] = ( - remaining_requests - ) + headers[ + f"x-litellm-key-remaining-requests-{h11_model_group_name}" + ] = remaining_requests # Remaining Tokens remaining_tokens_variable_name = f"litellm-key-remaining-tokens-{model_group}" remaining_tokens = _metadata.get(remaining_tokens_variable_name, None) if remaining_tokens: - headers[f"x-litellm-key-remaining-tokens-{h11_model_group_name}"] = ( - remaining_tokens - ) + headers[ + f"x-litellm-key-remaining-tokens-{h11_model_group_name}" + ] = remaining_tokens return headers @@ -412,9 +421,9 @@ def add_guardrail_response_to_standard_logging_object( ): if litellm_logging_obj is None: return - standard_logging_object: Optional[StandardLoggingPayload] = ( - litellm_logging_obj.model_call_details.get("standard_logging_object") - ) + standard_logging_object: Optional[ + StandardLoggingPayload + ] = litellm_logging_obj.model_call_details.get("standard_logging_object") if standard_logging_object is None: return guardrail_information = standard_logging_object.get("guardrail_information", []) @@ -443,7 +452,9 @@ def get_metadata_variable_name_from_kwargs( return "litellm_metadata" if "litellm_metadata" in kwargs else "metadata" -def process_callback(_callback: str, callback_type: str, environment_variables: dict) -> dict: +def process_callback( + _callback: str, callback_type: str, environment_variables: dict +) -> dict: """Process a single callback and return its data with environment variables""" env_vars = CustomLogger.get_callback_env_vars(_callback) @@ -455,11 +466,9 @@ def process_callback(_callback: str, callback_type: str, environment_variables: else: env_vars_dict[_var] = env_variable - return { - "name": _callback, - "variables": env_vars_dict, - "type": callback_type - } + return {"name": _callback, "variables": env_vars_dict, "type": callback_type} + + def normalize_callback_names(callbacks: Iterable[Any]) -> List[Any]: if callbacks is None: return [] diff --git a/litellm/types/integrations/datadog_cost_management.py b/litellm/types/integrations/datadog_cost_management.py new file mode 100644 index 0000000000..fe04f43ea0 --- /dev/null +++ b/litellm/types/integrations/datadog_cost_management.py @@ -0,0 +1,27 @@ +from typing import Dict, Optional, TypedDict + + +from litellm.types.integrations.custom_logger import StandardCustomLoggerInitParams + + +class DatadogCostManagementInitParams(StandardCustomLoggerInitParams): + """ + Init params for Datadog Cost Management + """ + + datadog_cost_management_params: Optional[Dict] = None + + +class DatadogFOCUSCostEntry(TypedDict): + """ + Represents a single cost line item in the FOCUS format. + Ref: https://focus.finops.org/#specification + """ + + ProviderName: str + ChargeDescription: str + ChargePeriodStart: str + ChargePeriodEnd: str + BilledCost: float + BillingCurrency: str + Tags: Optional[Dict[str, str]] diff --git a/tests/test_litellm/integrations/datadog/test_datadog_cost_management.py b/tests/test_litellm/integrations/datadog/test_datadog_cost_management.py new file mode 100644 index 0000000000..be2084969a --- /dev/null +++ b/tests/test_litellm/integrations/datadog/test_datadog_cost_management.py @@ -0,0 +1,169 @@ +import os +import time +from unittest.mock import AsyncMock + +import pytest +from httpx import Response + +from litellm.integrations.datadog.datadog_cost_management import ( + DatadogCostManagementLogger, +) +from litellm.types.utils import StandardLoggingPayload + + +@pytest.fixture +def clean_env(): + # Save original env + original_api_key = os.environ.get("DD_API_KEY") + original_app_key = os.environ.get("DD_APP_KEY") + original_site = os.environ.get("DD_SITE") + + # Set test env + os.environ["DD_API_KEY"] = "test_api_key" + os.environ["DD_APP_KEY"] = "test_app_key" + os.environ["DD_SITE"] = "test.datadoghq.com" + + yield + + # Restore original env + if original_api_key: + os.environ["DD_API_KEY"] = original_api_key + else: + del os.environ["DD_API_KEY"] + + if original_app_key: + os.environ["DD_APP_KEY"] = original_app_key + else: + del os.environ["DD_APP_KEY"] + + if original_site: + os.environ["DD_SITE"] = original_site + else: + del os.environ["DD_SITE"] + + +@pytest.mark.asyncio +async def test_init(clean_env): + """ + Test initialization sets up clients and url correctly + """ + logger = DatadogCostManagementLogger() + assert logger.dd_api_key == "test_api_key" + assert logger.dd_app_key == "test_app_key" + assert ( + logger.upload_url == "https://api.test.datadoghq.com/api/v2/cost/custom_costs" + ) + + +@pytest.mark.asyncio +async def test_aggregate_costs(clean_env): + """ + Test that costs are correctly aggregated by provider, model, and date + """ + logger = DatadogCostManagementLogger() + + # Mock some log payloads + now = time.time() + day_str = time.strftime("%Y-%m-%d", time.localtime(now)) + + logs = [ + StandardLoggingPayload( + custom_llm_provider="openai", + model="gpt-4", + response_cost=0.01, + startTime=now, + metadata={"user_api_key_team_alias": "team-a"}, + ), + StandardLoggingPayload( + custom_llm_provider="openai", + model="gpt-4", + response_cost=0.02, + startTime=now, + metadata={"user_api_key_team_alias": "team-a"}, + ), + StandardLoggingPayload( + custom_llm_provider="anthropic", + model="claude-3", + response_cost=0.05, + startTime=now, + ), + ] + + aggregated = logger._aggregate_costs(logs) + + assert len(aggregated) == 2 + + # Check OpenAI entry + openai_entry = next(e for e in aggregated if e["ProviderName"] == "openai") + assert openai_entry["BilledCost"] == 0.03 + assert openai_entry["ChargeDescription"] == "LLM Usage for gpt-4" + assert openai_entry["ChargePeriodStart"] == day_str + assert openai_entry["Tags"]["team"] == "team-a" + assert "env" in openai_entry["Tags"] + assert "service" in openai_entry["Tags"] + + # Check Anthropic entry + anthropic_entry = next(e for e in aggregated if e["ProviderName"] == "anthropic") + assert anthropic_entry["BilledCost"] == 0.05 + + +@pytest.mark.asyncio +async def test_async_log_success_event(clean_env): + """ + Test that logs are added to queue + """ + logger = DatadogCostManagementLogger(batch_size=10) + + await logger.async_log_success_event( + kwargs={"standard_logging_object": {"response_cost": 0.01}}, + response_obj={}, + start_time=time.time(), + end_time=time.time(), + ) + + assert len(logger.log_queue) == 1 + assert logger.log_queue[0]["response_cost"] == 0.01 + + # Test zero cost ignored + await logger.async_log_success_event( + kwargs={"standard_logging_object": {"response_cost": 0.0}}, + response_obj={}, + start_time=time.time(), + end_time=time.time(), + ) + + assert len(logger.log_queue) == 1 + + +@pytest.mark.asyncio +async def test_async_send_batch(clean_env): + """ + Test that batch is aggregated and uploaded + """ + logger = DatadogCostManagementLogger() + logger.async_client = AsyncMock() + logger.async_client.put.return_value = Response(202, json={"status": "ok"}) + + # Add logs directly to queue + logger.log_queue = [ + StandardLoggingPayload( + custom_llm_provider="openai", + model="gpt-4", + response_cost=0.01, + startTime=time.time(), + ) + ] + + await logger.async_send_batch() + + # Verify API called + assert logger.async_client.put.called + call_args = logger.async_client.put.call_args + assert call_args[0][0] == "https://api.test.datadoghq.com/api/v2/cost/custom_costs" + + import json + + # Use call_args.kwargs['content'] + content = json.loads(call_args[1]["content"]) + assert content[0]["ProviderName"] == "openai" + assert content[0]["BilledCost"] == 0.01 From 89ecdc405dd95ac7739d450cd2718153ec270964 Mon Sep 17 00:00:00 2001 From: Harshit Jain <48647625+Harshit28j@users.noreply.github.com> Date: Fri, 23 Jan 2026 09:26:41 +0530 Subject: [PATCH 08/62] fix: recursive pydantic issue (#19531) --- litellm/integrations/custom_guardrail.py | 16 +++- litellm/integrations/langfuse/langfuse.py | 14 ++-- .../test_custom_guardrail_recursion.py | 73 +++++++++++++++++++ 3 files changed, 94 insertions(+), 9 deletions(-) create mode 100644 tests/test_litellm/integrations/test_custom_guardrail_recursion.py diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index 6a76b57e7f..a5bb530fc5 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -516,7 +516,9 @@ class CustomGuardrail(CustomLogger): from litellm.types.utils import GuardrailMode # Use event_type if provided, otherwise fall back to self.event_hook - guardrail_mode: Union[GuardrailEventHooks, GuardrailMode, List[GuardrailEventHooks]] + guardrail_mode: Union[ + GuardrailEventHooks, GuardrailMode, List[GuardrailEventHooks] + ] if event_type is not None: guardrail_mode = event_type elif isinstance(self.event_hook, Mode): @@ -524,11 +526,21 @@ class CustomGuardrail(CustomLogger): else: guardrail_mode = self.event_hook # type: ignore[assignment] + from litellm.litellm_core_utils.core_helpers import ( + filter_exceptions_from_params, + ) + + # Sanitize the response to ensure it's JSON serializable and free of circular refs + # This prevents RecursionErrors in downstream loggers (Langfuse, Datadog, etc.) + clean_guardrail_response = filter_exceptions_from_params( + guardrail_json_response + ) + slg = StandardLoggingGuardrailInformation( guardrail_name=self.guardrail_name, guardrail_provider=guardrail_provider, guardrail_mode=guardrail_mode, - guardrail_response=guardrail_json_response, + guardrail_response=clean_guardrail_response, guardrail_status=guardrail_status, start_time=start_time, end_time=end_time, diff --git a/litellm/integrations/langfuse/langfuse.py b/litellm/integrations/langfuse/langfuse.py index 8087c17caf..b64af66ce9 100644 --- a/litellm/integrations/langfuse/langfuse.py +++ b/litellm/integrations/langfuse/langfuse.py @@ -23,6 +23,7 @@ from litellm.constants import MAX_LANGFUSE_INITIALIZED_CLIENTS from litellm.litellm_core_utils.core_helpers import ( safe_deep_copy, reconstruct_model_name, + filter_exceptions_from_params, ) from litellm.litellm_core_utils.redact_messages import redact_user_api_key_info from litellm.llms.custom_httpx.http_handler import _get_httpx_client @@ -71,9 +72,8 @@ def _extract_cache_read_input_tokens(usage_obj) -> int: # Check prompt_tokens_details.cached_tokens (used by Gemini and other providers) if hasattr(usage_obj, "prompt_tokens_details"): prompt_tokens_details = getattr(usage_obj, "prompt_tokens_details", None) - if ( - prompt_tokens_details is not None - and hasattr(prompt_tokens_details, "cached_tokens") + if prompt_tokens_details is not None and hasattr( + prompt_tokens_details, "cached_tokens" ): cached_tokens = getattr(prompt_tokens_details, "cached_tokens", None) if ( @@ -526,7 +526,6 @@ class LangFuseLogger: verbose_logger.debug("Langfuse Layer Logging - logging to langfuse v2") try: - metadata = metadata or {} standard_logging_object: Optional[StandardLoggingPayload] = cast( Optional[StandardLoggingPayload], kwargs.get("standard_logging_object", None), @@ -692,9 +691,10 @@ class LangFuseLogger: clean_metadata["litellm_response_cost"] = cost if standard_logging_object is not None: - clean_metadata["hidden_params"] = standard_logging_object[ - "hidden_params" - ] + hidden_params = standard_logging_object.get("hidden_params", {}) + clean_metadata["hidden_params"] = filter_exceptions_from_params( + hidden_params + ) if ( litellm.langfuse_default_tags is not None diff --git a/tests/test_litellm/integrations/test_custom_guardrail_recursion.py b/tests/test_litellm/integrations/test_custom_guardrail_recursion.py new file mode 100644 index 0000000000..f05b5848bd --- /dev/null +++ b/tests/test_litellm/integrations/test_custom_guardrail_recursion.py @@ -0,0 +1,73 @@ +import pytest +from litellm.integrations.custom_guardrail import CustomGuardrail +from litellm.types.guardrails import GuardrailEventHooks +import json + + +class TestCustomGuardrailRecursion: + """ + Specific tests for the circular reference / RecursionError fix in logging. + """ + + def test_log_guardrail_information_handles_circular_references(self): + """ + Test that add_standard_logging method sanitizes input data containing circular references + instead of crashing. + + This reproduces the Langfuse crash scenario: + Request -> Metadata -> GuardrailResponse -> DebugContext -> Request + """ + guardrail = CustomGuardrail( + guardrail_name="recursion_test_guardrail", + event_hook=GuardrailEventHooks.pre_call, + ) + + # 1. Setup Circular Data + request_data = {"user_id": "test_recursive_user"} + metadata = {"session_id": "123"} + request_data["metadata"] = metadata + + # Create the danger: Guardrail Response holding a reference back to request_data + dirty_response = { + "flagged": False, + "debug_context": request_data, # <--- ACCESS TO ROOT (Circular Ref) + } + + # 2. Invoke the logging method + # If the fix is working, this will NOT raise RecursionError + try: + guardrail.add_standard_logging_guardrail_information_to_request_data( + guardrail_json_response=dirty_response, + request_data=request_data, + guardrail_status="success", + start_time=1.0, + end_time=2.0, + duration=1.0, + masked_entity_count={}, + event_type=GuardrailEventHooks.pre_call, + ) + except RecursionError: + pytest.fail( + "RecursionError raised! The cyclic reference sanitization failed." + ) + + # 3. Verify the data stored is safe + stored_info = request_data["metadata"][ + "standard_logging_guardrail_information" + ][0] + stored_response = stored_info["guardrail_response"] + + # Check that we can dump it to JSON without crashing (Ultimate proof) + try: + json.dumps(stored_response) + except Exception as e: + pytest.fail(f"Stored data is not JSON serializable: {e}") + + # Check content - keys should be preserved but recursion broken + assert "debug_context" in stored_response + debug_context = stored_response["debug_context"] + + # In a sanitized copy, the nested metadata should be a copy, not the original live dict + assert debug_context["user_id"] == "test_recursive_user" + # The 'metadata' inside 'debug_context' would be where recursion stops or is filtered + assert "metadata" in debug_context From 188c2315adaaa401669072cdf04d9b0b80678f58 Mon Sep 17 00:00:00 2001 From: Chesars Date: Fri, 23 Jan 2026 15:11:37 -0300 Subject: [PATCH 09/62] feat(vercel_ai_gateway): add embeddings support Add support for /embeddings endpoint via Vercel AI Gateway. Closes #19658 Changes: - Add VercelAIGatewayEmbeddingConfig in litellm/llms/vercel_ai_gateway/embedding/ - Register provider in utils.py and main.py - Add unit tests for embedding transformation - Update documentation with embeddings examples Usage: ```python from litellm import embedding response = embedding( model="vercel_ai_gateway/openai/text-embedding-3-small", input="Hello world", api_key="your-api-key" ) ``` --- .../docs/providers/vercel_ai_gateway.md | 36 ++- .../vercel_ai_gateway/embedding/__init__.py | 0 .../embedding/transformation.py | 176 ++++++++++++++ litellm/main.py | 30 +++ litellm/utils.py | 6 + .../vercel_ai_gateway/embedding/__init__.py | 0 .../test_vercel_ai_gateway_embedding.py | 218 ++++++++++++++++++ 7 files changed, 464 insertions(+), 2 deletions(-) create mode 100644 litellm/llms/vercel_ai_gateway/embedding/__init__.py create mode 100644 litellm/llms/vercel_ai_gateway/embedding/transformation.py create mode 100644 tests/test_litellm/llms/vercel_ai_gateway/embedding/__init__.py create mode 100644 tests/test_litellm/llms/vercel_ai_gateway/embedding/test_vercel_ai_gateway_embedding.py diff --git a/docs/my-website/docs/providers/vercel_ai_gateway.md b/docs/my-website/docs/providers/vercel_ai_gateway.md index 91f0a18ea1..3ff007171e 100644 --- a/docs/my-website/docs/providers/vercel_ai_gateway.md +++ b/docs/my-website/docs/providers/vercel_ai_gateway.md @@ -11,7 +11,7 @@ import TabItem from '@theme/TabItem'; | Provider Route on LiteLLM | `vercel_ai_gateway/` | | Link to Provider Doc | [Vercel AI Gateway Documentation ↗](https://vercel.com/docs/ai-gateway) | | Base URL | `https://ai-gateway.vercel.sh/v1` | -| Supported Operations | `/chat/completions`, `/models` | +| Supported Operations | `/chat/completions`, `/embeddings`, `/models` |

@@ -73,7 +73,7 @@ messages = [{"content": "Hello, how are you?", "role": "user"}] # Vercel AI Gateway call with streaming response = completion( - model="vercel_ai_gateway/openai/gpt-4o", + model="vercel_ai_gateway/openai/gpt-4o", messages=messages, stream=True ) @@ -82,6 +82,33 @@ for chunk in response: print(chunk) ``` +### Embeddings + +```python showLineNumbers title="Vercel AI Gateway Embeddings" +import os +from litellm import embedding + +os.environ["VERCEL_AI_GATEWAY_API_KEY"] = "your-api-key" + +# Vercel AI Gateway embedding call +response = embedding( + model="vercel_ai_gateway/openai/text-embedding-3-small", + input="Hello world" +) + +print(response.data[0]["embedding"][:5]) # Print first 5 dimensions +``` + +You can also specify the `dimensions` parameter: + +```python showLineNumbers title="Vercel AI Gateway Embeddings with Dimensions" +response = embedding( + model="vercel_ai_gateway/openai/text-embedding-3-small", + input=["Hello world", "Goodbye world"], + dimensions=768 +) +``` + ## Usage - LiteLLM Proxy Add the following to your LiteLLM Proxy configuration file: @@ -97,6 +124,11 @@ model_list: litellm_params: model: vercel_ai_gateway/anthropic/claude-4-sonnet api_key: os.environ/VERCEL_AI_GATEWAY_API_KEY + + - model_name: text-embedding-3-small-gateway + litellm_params: + model: vercel_ai_gateway/openai/text-embedding-3-small + api_key: os.environ/VERCEL_AI_GATEWAY_API_KEY ``` Start your LiteLLM Proxy server: diff --git a/litellm/llms/vercel_ai_gateway/embedding/__init__.py b/litellm/llms/vercel_ai_gateway/embedding/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/litellm/llms/vercel_ai_gateway/embedding/transformation.py b/litellm/llms/vercel_ai_gateway/embedding/transformation.py new file mode 100644 index 0000000000..7238b05f10 --- /dev/null +++ b/litellm/llms/vercel_ai_gateway/embedding/transformation.py @@ -0,0 +1,176 @@ +""" +Vercel AI Gateway Embedding API Configuration. + +This module provides the configuration for Vercel AI Gateway's Embedding API. +Vercel AI Gateway is OpenAI-compatible and supports embeddings via the /v1/embeddings endpoint. + +Docs: https://vercel.com/docs/ai-gateway/openai-compat/embeddings +""" + +from typing import TYPE_CHECKING, Any, Optional + +import httpx + +from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllEmbeddingInputValues +from litellm.types.utils import EmbeddingResponse +from litellm.utils import convert_to_model_response_object + +from ..common_utils import VercelAIGatewayException + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class VercelAIGatewayEmbeddingConfig(BaseEmbeddingConfig): + """ + Configuration for Vercel AI Gateway's Embedding API. + + Reference: https://vercel.com/docs/ai-gateway/openai-compat/embeddings + """ + + def validate_environment( + self, + headers: dict, + model: str, + messages: list, + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """ + Validate environment and set up headers for Vercel AI Gateway API. + + Vercel AI Gateway requires: + - Authorization header with Bearer token (API key or OIDC token) + """ + vercel_headers = { + "Content-Type": "application/json", + } + + # Add Authorization header if api_key is provided + if api_key: + vercel_headers["Authorization"] = f"Bearer {api_key}" + + # Merge with existing headers (user's extra_headers take priority) + merged_headers = {**vercel_headers, **headers} + + return merged_headers + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + Get the complete URL for Vercel AI Gateway Embedding API endpoint. + """ + if api_base: + api_base = api_base.rstrip("/") + else: + api_base = ( + get_secret_str("VERCEL_AI_GATEWAY_API_BASE") + or "https://ai-gateway.vercel.sh/v1" + ) + + return f"{api_base}/embeddings" + + def transform_embedding_request( + self, + model: str, + input: AllEmbeddingInputValues, + optional_params: dict, + headers: dict, + ) -> dict: + """ + Transform embedding request to Vercel AI Gateway format (OpenAI-compatible). + """ + # Ensure input is a list + if isinstance(input, str): + input = [input] + + # Strip 'vercel_ai_gateway/' prefix if present + if model.startswith("vercel_ai_gateway/"): + model = model.replace("vercel_ai_gateway/", "", 1) + + return { + "model": model, + "input": input, + **optional_params, + } + + def transform_embedding_response( + self, + model: str, + raw_response: httpx.Response, + model_response: EmbeddingResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str], + request_data: dict, + optional_params: dict, + litellm_params: dict, + ) -> EmbeddingResponse: + """ + Transform embedding response from Vercel AI Gateway format (OpenAI-compatible). + """ + logging_obj.post_call(original_response=raw_response.text) + + # Vercel AI Gateway returns standard OpenAI-compatible embedding response + response_json = raw_response.json() + + return convert_to_model_response_object( + response_object=response_json, + model_response_object=model_response, + response_type="embedding", + ) + + def get_supported_openai_params(self, model: str) -> list: + """ + Get list of supported OpenAI parameters for Vercel AI Gateway embeddings. + + Vercel AI Gateway supports the standard OpenAI embeddings parameters + and auto-maps 'dimensions' to each provider's expected field. + """ + return [ + "timeout", + "dimensions", + "encoding_format", + "user", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + Map OpenAI parameters to Vercel AI Gateway format. + """ + for param, value in non_default_params.items(): + if param in self.get_supported_openai_params(model): + optional_params[param] = value + return optional_params + + def get_error_class( + self, error_message: str, status_code: int, headers: Any + ) -> Any: + """ + Get the error class for Vercel AI Gateway errors. + """ + return VercelAIGatewayException( + message=error_message, + status_code=status_code, + headers=headers, + ) diff --git a/litellm/main.py b/litellm/main.py index ea41919e19..24aac661db 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -4863,6 +4863,36 @@ def embedding( # noqa: PLR0915 headers = openrouter_headers + response = base_llm_http_handler.embedding( + model=model, + input=input, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + api_key=api_key, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding, + litellm_params=litellm_params_dict, + headers=headers, + ) + elif custom_llm_provider == "vercel_ai_gateway": + api_base = ( + api_base + or litellm.api_base + or get_secret_str("VERCEL_AI_GATEWAY_API_BASE") + or "https://ai-gateway.vercel.sh/v1" + ) + + api_key = ( + api_key + or litellm.api_key + or get_secret_str("VERCEL_AI_GATEWAY_API_KEY") + or get_secret_str("VERCEL_OIDC_TOKEN") + ) + response = base_llm_http_handler.embedding( model=model, input=input, diff --git a/litellm/utils.py b/litellm/utils.py index 06eceefe0c..21d096b97c 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -8014,6 +8014,12 @@ class ProviderConfigManager: ) return OpenrouterEmbeddingConfig() + elif litellm.LlmProviders.VERCEL_AI_GATEWAY == provider: + from litellm.llms.vercel_ai_gateway.embedding.transformation import ( + VercelAIGatewayEmbeddingConfig, + ) + + return VercelAIGatewayEmbeddingConfig() elif litellm.LlmProviders.GIGACHAT == provider: return litellm.GigaChatEmbeddingConfig() elif litellm.LlmProviders.SAGEMAKER == provider: diff --git a/tests/test_litellm/llms/vercel_ai_gateway/embedding/__init__.py b/tests/test_litellm/llms/vercel_ai_gateway/embedding/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/test_litellm/llms/vercel_ai_gateway/embedding/test_vercel_ai_gateway_embedding.py b/tests/test_litellm/llms/vercel_ai_gateway/embedding/test_vercel_ai_gateway_embedding.py new file mode 100644 index 0000000000..af1e1df92f --- /dev/null +++ b/tests/test_litellm/llms/vercel_ai_gateway/embedding/test_vercel_ai_gateway_embedding.py @@ -0,0 +1,218 @@ +import os +import sys +from unittest.mock import MagicMock, patch + +import httpx +import pytest + +sys.path.insert( + 0, os.path.abspath("../../../../..") +) # Adds the parent directory to the system path + +from litellm.llms.vercel_ai_gateway.embedding.transformation import ( + VercelAIGatewayEmbeddingConfig, +) +from litellm.llms.vercel_ai_gateway.common_utils import VercelAIGatewayException +from litellm.types.utils import EmbeddingResponse + + +def test_vercel_ai_gateway_embedding_get_complete_url(): + """Test URL generation for embeddings endpoint""" + config = VercelAIGatewayEmbeddingConfig() + + # Test with default API base + url = config.get_complete_url( + api_base=None, + api_key=None, + model="openai/text-embedding-3-small", + optional_params={}, + litellm_params={}, + ) + assert url == "https://ai-gateway.vercel.sh/v1/embeddings" + + # Test with custom API base + url = config.get_complete_url( + api_base="https://custom.vercel.sh/v1", + api_key=None, + model="openai/text-embedding-3-small", + optional_params={}, + litellm_params={}, + ) + assert url == "https://custom.vercel.sh/v1/embeddings" + + # Test with trailing slash + url = config.get_complete_url( + api_base="https://custom.vercel.sh/v1/", + api_key=None, + model="openai/text-embedding-3-small", + optional_params={}, + litellm_params={}, + ) + assert url == "https://custom.vercel.sh/v1/embeddings" + + +def test_vercel_ai_gateway_embedding_transform_request(): + """Test request transformation for embeddings""" + config = VercelAIGatewayEmbeddingConfig() + + # Test with string input + request = config.transform_embedding_request( + model="openai/text-embedding-3-small", + input="Hello world", + optional_params={}, + headers={}, + ) + assert request["model"] == "openai/text-embedding-3-small" + assert request["input"] == ["Hello world"] + + # Test with list input + request = config.transform_embedding_request( + model="openai/text-embedding-3-small", + input=["Hello", "World"], + optional_params={}, + headers={}, + ) + assert request["model"] == "openai/text-embedding-3-small" + assert request["input"] == ["Hello", "World"] + + # Test stripping vercel_ai_gateway/ prefix + request = config.transform_embedding_request( + model="vercel_ai_gateway/openai/text-embedding-3-small", + input="Hello", + optional_params={}, + headers={}, + ) + assert request["model"] == "openai/text-embedding-3-small" + + +def test_vercel_ai_gateway_embedding_transform_request_with_dimensions(): + """Test request transformation with dimensions parameter""" + config = VercelAIGatewayEmbeddingConfig() + + request = config.transform_embedding_request( + model="openai/text-embedding-3-small", + input="Hello world", + optional_params={"dimensions": 768}, + headers={}, + ) + assert request["model"] == "openai/text-embedding-3-small" + assert request["input"] == ["Hello world"] + assert request["dimensions"] == 768 + + +def test_vercel_ai_gateway_embedding_validate_environment(): + """Test header validation and setup""" + config = VercelAIGatewayEmbeddingConfig() + + headers = config.validate_environment( + headers={}, + model="openai/text-embedding-3-small", + messages=[], + optional_params={}, + litellm_params={}, + api_key="test_key", + ) + assert headers["Content-Type"] == "application/json" + assert headers["Authorization"] == "Bearer test_key" + + # Test with existing headers (should merge) + headers = config.validate_environment( + headers={"X-Custom": "value"}, + model="openai/text-embedding-3-small", + messages=[], + optional_params={}, + litellm_params={}, + api_key="test_key", + ) + assert headers["X-Custom"] == "value" + assert headers["Authorization"] == "Bearer test_key" + + +def test_vercel_ai_gateway_embedding_get_supported_params(): + """Test supported OpenAI parameters""" + config = VercelAIGatewayEmbeddingConfig() + supported = config.get_supported_openai_params("openai/text-embedding-3-small") + + assert "dimensions" in supported + assert "encoding_format" in supported + assert "timeout" in supported + assert "user" in supported + + +def test_vercel_ai_gateway_embedding_map_openai_params(): + """Test OpenAI parameter mapping""" + config = VercelAIGatewayEmbeddingConfig() + + optional_params = config.map_openai_params( + non_default_params={"dimensions": 768, "encoding_format": "float"}, + optional_params={}, + model="openai/text-embedding-3-small", + drop_params=False, + ) + assert optional_params["dimensions"] == 768 + assert optional_params["encoding_format"] == "float" + + +def test_vercel_ai_gateway_embedding_error_class(): + """Test error class creation""" + config = VercelAIGatewayEmbeddingConfig() + + error = config.get_error_class( + error_message="Test error", + status_code=400, + headers={"Content-Type": "application/json"}, + ) + + assert isinstance(error, VercelAIGatewayException) + assert error.message == "Test error" + assert error.status_code == 400 + + +def test_vercel_ai_gateway_embedding_transform_response(): + """Test response transformation""" + config = VercelAIGatewayEmbeddingConfig() + + mock_response = MagicMock(spec=httpx.Response) + mock_response.text = '{"object":"list","data":[{"object":"embedding","index":0,"embedding":[0.1,0.2,0.3]}],"model":"openai/text-embedding-3-small","usage":{"prompt_tokens":2,"total_tokens":2}}' + mock_response.json.return_value = { + "object": "list", + "data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}], + "model": "openai/text-embedding-3-small", + "usage": {"prompt_tokens": 2, "total_tokens": 2}, + } + + mock_logging = MagicMock() + + response = config.transform_embedding_response( + model="openai/text-embedding-3-small", + raw_response=mock_response, + model_response=EmbeddingResponse(), + logging_obj=mock_logging, + api_key="test_key", + request_data={}, + optional_params={}, + litellm_params={}, + ) + + assert response is not None + mock_logging.post_call.assert_called_once() + + +def test_vercel_ai_gateway_embedding_env_vars(): + """Test environment variable handling""" + config = VercelAIGatewayEmbeddingConfig() + + with patch.dict( + os.environ, + { + "VERCEL_AI_GATEWAY_API_BASE": "https://env.vercel.sh/v1", + }, + ): + url = config.get_complete_url( + api_base=None, + api_key=None, + model="openai/text-embedding-3-small", + optional_params={}, + litellm_params={}, + ) + assert url == "https://env.vercel.sh/v1/embeddings" From de538456e3447db5bd27f440a9b9974a38bac80a Mon Sep 17 00:00:00 2001 From: Misha <72940774+formorter@users.noreply.github.com> Date: Sat, 24 Jan 2026 06:54:23 +0300 Subject: [PATCH 10/62] feat: support role_mappings from environment variables (#19498) * feat: support role_mappings from environment variables * fix linter --- litellm/proxy/management_endpoints/ui_sso.py | 96 +++++++++++---- .../test_proxy_setting_endpoints.py | 110 ++++++++++++++++++ 2 files changed, 183 insertions(+), 23 deletions(-) diff --git a/litellm/proxy/management_endpoints/ui_sso.py b/litellm/proxy/management_endpoints/ui_sso.py index 5adf54c162..1756fe9c6f 100644 --- a/litellm/proxy/management_endpoints/ui_sso.py +++ b/litellm/proxy/management_endpoints/ui_sso.py @@ -99,24 +99,24 @@ def determine_role_from_groups( ) -> Optional[LitellmUserRoles]: """ Determine the highest privilege role for a user based on their groups. - + Role hierarchy (highest to lowest): - proxy_admin - proxy_admin_viewer - internal_user - internal_user_viewer - + Args: user_groups: List of group names from the SSO token role_mappings: RoleMappings configuration object - + Returns: The highest privilege role found, or default_role if no matches, or None """ if not role_mappings.roles: # No role mappings configured, return default_role return role_mappings.default_role - + # Role hierarchy (highest to lowest) role_hierarchy = [ LitellmUserRoles.PROXY_ADMIN, @@ -124,20 +124,22 @@ def determine_role_from_groups( LitellmUserRoles.INTERNAL_USER, LitellmUserRoles.INTERNAL_USER_VIEW_ONLY, ] - + # Convert user_groups to a set for efficient lookup user_groups_set = set(user_groups) if isinstance(user_groups, list) else set() - + # Find the highest privilege role the user belongs to for role in role_hierarchy: if role in role_mappings.roles: role_groups = role_mappings.roles[role] - if isinstance(role_groups, list) and user_groups_set.intersection(set(role_groups)): + if isinstance(role_groups, list) and user_groups_set.intersection( + set(role_groups) + ): verbose_proxy_logger.debug( f"User groups {user_groups} matched role '{role.value}' via groups: {role_groups}" ) return role - + # No matching groups found, return default_role verbose_proxy_logger.debug( f"User groups {user_groups} did not match any role mappings, using default_role: {role_mappings.default_role}" @@ -326,9 +328,7 @@ def generic_response_convertor( "GENERIC_USER_PROVIDER_ATTRIBUTE", "provider" ) - generic_user_role_attribute_name = os.getenv( - "GENERIC_USER_ROLE_ATTRIBUTE", "role" - ) + generic_user_role_attribute_name = os.getenv("GENERIC_USER_ROLE_ATTRIBUTE", "role") verbose_proxy_logger.debug( f" generic_user_id_attribute_name: {generic_user_id_attribute_name}\n generic_user_email_attribute_name: {generic_user_email_attribute_name}" @@ -345,12 +345,15 @@ def generic_response_convertor( # Determine user role based on role_mappings if available # Only apply role_mappings for GENERIC SSO provider user_role: Optional[LitellmUserRoles] = None - - if role_mappings is not None and role_mappings.provider.lower() in ["generic", "okta"]: + + if role_mappings is not None and role_mappings.provider.lower() in [ + "generic", + "okta", + ]: # Use role_mappings to determine role from groups group_claim = role_mappings.group_claim user_groups_raw: Any = get_nested_value(response, group_claim) - + # Handle different formats: could be a list, string (comma-separated), or single value user_groups: List[str] = [] if isinstance(user_groups_raw, list): @@ -361,7 +364,7 @@ def generic_response_convertor( elif user_groups_raw is not None: # Single value user_groups = [str(user_groups_raw)] - + if user_groups: user_role = determine_role_from_groups(user_groups, role_mappings) verbose_proxy_logger.debug( @@ -373,10 +376,12 @@ def generic_response_convertor( verbose_proxy_logger.debug( f"No groups found in '{group_claim}', using default_role: {role_mappings.default_role}" ) - + # Fallback to existing logic if role_mappings not used if user_role is None: - user_role_from_sso = get_nested_value(response, generic_user_role_attribute_name) + user_role_from_sso = get_nested_value( + response, generic_user_role_attribute_name + ) if user_role_from_sso is not None: role = get_litellm_user_role(user_role_from_sso) if role is not None: @@ -399,7 +404,9 @@ def generic_response_convertor( ) -def _setup_generic_sso_env_vars(generic_client_id: str, redirect_url: str) -> Tuple[str, List[str], str, str, str, bool]: +def _setup_generic_sso_env_vars( + generic_client_id: str, redirect_url: str +) -> Tuple[str, List[str], str, str, str, bool]: """Setup and validate Generic SSO environment variables.""" generic_client_secret = os.getenv("GENERIC_CLIENT_SECRET", None) generic_scope = os.getenv("GENERIC_SCOPE", "openid email profile").split(" ") @@ -492,7 +499,43 @@ async def _setup_role_mappings() -> Optional["RoleMappings"]: verbose_proxy_logger.debug( f"Could not load role_mappings from database: {e}. Continuing with existing role logic." ) + + generic_role_mappings = os.getenv("GENERIC_ROLE_MAPPINGS_ROLES", None) + generic_role_mappings_group_claim = os.getenv( + "GENERIC_ROLE_MAPPINGS_GROUP_CLAIM", None + ) + generic_role_mappoings_default_role = os.getenv( + "GENERIC_ROLE_MAPPINGS_DEFAULT_ROLE", None + ) + if generic_role_mappings is not None: + verbose_proxy_logger.debug( + "Found role_mappings for generic provider in environment variables" + ) + import ast + try: + generic_user_role_mappings_data: Dict[ + LitellmUserRoles, List[str] + ] = ast.literal_eval(generic_role_mappings) + if isinstance(generic_user_role_mappings_data, dict): + from litellm.types.proxy.management_endpoints.ui_sso import ( + RoleMappings, + ) + + role_mappings_data = { + "provider": "generic", + "group_claim": generic_role_mappings_group_claim, + "default_role": generic_role_mappoings_default_role, + "roles": generic_user_role_mappings_data, + } + + role_mappings = RoleMappings(**role_mappings_data) + verbose_proxy_logger.debug( + f"Loaded role_mappings from environments for provider '{role_mappings.provider}'." + ) + return role_mappings + except TypeError as e: + verbose_proxy_logger.warning(f"Error decoding role mappings from environment variables: {e}. Continuing with existing role logic.") return role_mappings @@ -529,7 +572,7 @@ async def get_generic_sso_response( # Get role_mappings from SSO settings if available role_mappings = await _setup_role_mappings() - + def response_convertor(response, client): nonlocal received_response # return for user debugging received_response = response @@ -1217,20 +1260,24 @@ async def insert_sso_user( role_mappings_configured = False try: from litellm.proxy.utils import get_prisma_client_or_throw - + prisma_client = get_prisma_client_or_throw( "Prisma client is None, connect a database to your proxy" ) - + # Get SSO config from dedicated table sso_db_record = await prisma_client.db.litellm_ssoconfig.find_unique( where={"id": "sso_config"} ) - + if sso_db_record and sso_db_record.sso_settings: sso_settings_dict = dict(sso_db_record.sso_settings) role_mappings_data = sso_settings_dict.get("role_mappings") role_mappings_configured = role_mappings_data is not None + generic_user_role_mappings = os.getenv("GENERIC_USER_ROLE_MAPPINGS", None) + if generic_user_role_mappings is not None: + role_mappings_configured = True + except Exception as e: # If we can't check role_mappings, continue with existing logic verbose_proxy_logger.debug( @@ -1240,7 +1287,10 @@ async def insert_sso_user( # Apply default_internal_user_params if litellm.default_internal_user_params: # If role_mappings is configured and user_role is already set from SSO, preserve it - if role_mappings_configured and user_defined_values.get("user_role") is not None: + if ( + role_mappings_configured + and user_defined_values.get("user_role") is not None + ): # Preserve the SSO-extracted role, but apply other defaults preserved_role = user_defined_values.get("user_role") user_defined_values.update(litellm.default_internal_user_params) # type: ignore diff --git a/tests/test_litellm/proxy/ui_crud_endpoints/test_proxy_setting_endpoints.py b/tests/test_litellm/proxy/ui_crud_endpoints/test_proxy_setting_endpoints.py index ad4f53dac4..60bdb7d12c 100644 --- a/tests/test_litellm/proxy/ui_crud_endpoints/test_proxy_setting_endpoints.py +++ b/tests/test_litellm/proxy/ui_crud_endpoints/test_proxy_setting_endpoints.py @@ -1227,3 +1227,113 @@ class TestProxySettingEndpoints: assert retrieved_role_mappings["provider"] == "google" assert retrieved_role_mappings["group_claim"] == "groups" assert retrieved_role_mappings["default_role"] == LitellmUserRoles.INTERNAL_USER + + def test_setup_role_mappings_custom_logic_with_env_vars(self, monkeypatch): + """Test the _setup_role_mappings function directly with custom role mapping logic from environment variables""" + import asyncio + import os + from litellm.proxy.management_endpoints.ui_sso import _setup_role_mappings + from litellm.proxy._types import LitellmUserRoles + + # Set up environment variables for custom role mappings using valid Python dict format + monkeypatch.setenv("GENERIC_ROLE_MAPPINGS_ROLES", "{'proxy_admin': ['custom-admin-group'], 'internal_user': ['custom-user-group'], 'proxy_admin_viewer': ['custom-viewer-group']}") + monkeypatch.setenv("GENERIC_ROLE_MAPPINGS_GROUP_CLAIM", "custom-groups") + monkeypatch.setenv("GENERIC_ROLE_MAPPINGS_DEFAULT_ROLE", "internal_user_viewer") + + # Debug: Print environment variables + print("GENERIC_ROLE_MAPPINGS_ROLES:", os.getenv("GENERIC_ROLE_MAPPINGS_ROLES")) + print("GENERIC_ROLE_MAPPINGS_GROUP_CLAIM:", os.getenv("GENERIC_ROLE_MAPPINGS_GROUP_CLAIM")) + print("GENERIC_ROLE_MAPPINGS_DEFAULT_ROLE:", os.getenv("GENERIC_ROLE_MAPPINGS_DEFAULT_ROLE")) + + # Run the async function + role_mappings = asyncio.run(_setup_role_mappings()) + + # Debug: Print result + print("role_mappings result:", role_mappings) + + # Verify role_mappings is returned correctly from environment variables + assert role_mappings is not None + assert role_mappings.provider == "generic" + assert role_mappings.group_claim == "custom-groups" + assert role_mappings.default_role == LitellmUserRoles.INTERNAL_USER_VIEW_ONLY + assert role_mappings.roles[LitellmUserRoles.PROXY_ADMIN] == ["custom-admin-group"] + assert role_mappings.roles[LitellmUserRoles.INTERNAL_USER] == ["custom-user-group"] + assert role_mappings.roles[LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY] == ["custom-viewer-group"] + + def test_setup_role_mappings_custom_logic_with_no_config(self, monkeypatch): + """Test the _setup_role_mappings function returns None when no configuration is available""" + import asyncio + from unittest.mock import AsyncMock, MagicMock + from litellm.proxy.management_endpoints.ui_sso import _setup_role_mappings + + # Ensure environment variables are not set + monkeypatch.delenv("GENERIC_ROLE_MAPPINGS_ROLES", raising=False) + monkeypatch.delenv("GENERIC_ROLE_MAPPINGS_GROUP_CLAIM", raising=False) + monkeypatch.delenv("GENERIC_ROLE_MAPPINGS_DEFAULT_ROLE", raising=False) + + # Mock the prisma client to return None (no database record) + mock_prisma = MagicMock() + mock_prisma.db.litellm_ssoconfig.find_unique = AsyncMock(return_value=None) + # Run the async function + role_mappings = asyncio.run(_setup_role_mappings()) + + # Should return None when no configuration is available + assert role_mappings is None + + def test_get_sso_settings_with_env_role_mappings(self, mock_proxy_config, mock_auth, monkeypatch): + import json + from unittest.mock import AsyncMock, MagicMock + from litellm.proxy._types import LitellmUserRoles + + monkeypatch.setenv("GENERIC_ROLE_MAPPINGS_ROLES", '{"proxy_admin": ["custom-admin-group"], "internal_user": ["custom-user-group"], "proxy_admin_viewer": ["custom-viewer-group"]}') + monkeypatch.setenv("GENERIC_ROLE_MAPPINGS_GROUP_CLAIM", "custom-groups") + monkeypatch.setenv("GENERIC_ROLE_MAPPINGS_DEFAULT_ROLE", "internal_user_viewer") + + mock_prisma = MagicMock() + mock_db_record = MagicMock() + mock_db_record.sso_settings = { + "google_client_id": "test_google_client_id", + "role_mappings": { + "provider": "google", + "group_claim": "db-groups", + "default_role": "proxy_admin", + "roles": { + "proxy_admin": ["db-admin-group"], + }, + }, + } + mock_prisma.db.litellm_ssoconfig.find_unique = AsyncMock(return_value=mock_db_record) + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma) + + from litellm.proxy.proxy_server import proxy_config + monkeypatch.setattr( + proxy_config, "_decrypt_and_set_db_env_variables", lambda environment_variables: environment_variables + ) + + response = client.get("/get/sso_settings") + + assert response.status_code == 200 + data = response.json() + + values = data["values"] + assert "role_mappings" in values + assert values["role_mappings"] is not None + + # The database values shoeld override the environment variables + assert values["role_mappings"]["provider"] == "google" + assert values["role_mappings"]["group_claim"] == "db-groups" + assert values["role_mappings"]["default_role"] == LitellmUserRoles.PROXY_ADMIN + assert values["role_mappings"]["roles"][LitellmUserRoles.PROXY_ADMIN] == ["db-admin-group"] + + # Verify that the database was checked but environment variables took priority + mock_prisma.db.litellm_ssoconfig.find_unique.assert_called_once_with( + where={"id": "sso_config"} + ) + + # Verify other SSO settings are still correctly returned + assert values["google_client_id"] == "test_google_client_id" + + # Verify field_schema is still present + assert "field_schema" in data + assert "properties" in data["field_schema"] + assert "role_mappings" in data["field_schema"]["properties"] From 7ffc1a718ade0e52c681869420f260d8ab8a3e49 Mon Sep 17 00:00:00 2001 From: houdataali <84786211+houdataali@users.noreply.github.com> Date: Sat, 24 Jan 2026 04:56:04 +0100 Subject: [PATCH 11/62] a2a agent Header-Based Context Propagation (#19504) * a2a agent Header-Based Context Propagation * docs: add guide for A2A context header forwarding --- docs/my-website/docs/a2a.md | 114 ++++++++++++++++++ docs/my-website/img/a2a_agent_spend.png | Bin 0 -> 188367 bytes docs/my-website/img/a2a_trace_grouping.png | Bin 0 -> 396967 bytes litellm/a2a_protocol/main.py | 13 +- litellm/proxy/_types.py | 2 + litellm/proxy/litellm_pre_call_utils.py | 10 ++ .../spend_tracking/spend_tracking_utils.py | 2 +- 7 files changed, 138 insertions(+), 3 deletions(-) create mode 100644 docs/my-website/img/a2a_agent_spend.png create mode 100644 docs/my-website/img/a2a_trace_grouping.png diff --git a/docs/my-website/docs/a2a.md b/docs/my-website/docs/a2a.md index d7145e4b83..7b863f185d 100644 --- a/docs/my-website/docs/a2a.md +++ b/docs/my-website/docs/a2a.md @@ -193,6 +193,120 @@ The logs show: style={{width: '100%', display: 'block', margin: '2rem auto'}} /> + +## Forwarding LiteLLM Context Headers + +When LiteLLM invokes your A2A agent, it sends special headers that enable: +- **Trace Grouping**: All LLM calls from the same agent execution appear under one trace +- **Agent Spend Tracking**: Costs are attributed to the specific agent + +| Header | Purpose | +|--------|---------| +| `X-LiteLLM-Trace-Id` | Links all LLM calls to the same execution flow | +| `X-LiteLLM-Agent-Id` | Attributes spend to the correct agent | + + +To enable these features, your A2A server must **forward these headers** to any LLM calls it makes back to LiteLLM. + +### Implementation Steps + +**Step 1: Extract headers from incoming A2A request** +```python def get_litellm_headers(request) -> dict: + """Extract X-LiteLLM-* headers from incoming A2A request.""" + all_headers = request.call_context.state.get('headers', {}) + return { + k: v for k, v in all_headers.items() + if k.lower().startswith('x-litellm-') + } +``` + +**Step 2: Forward headers to your LLM calls** +Pass the extracted headers when making calls back to LiteLLM: + + + +```python from openai import OpenAI + +headers = get_litellm_headers(request) + +client = OpenAI( + api_key="sk-your-litellm-key", + base_url="http://localhost:4000", + default_headers=headers, # Forward headers +) + +response = client.chat.completions.create( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}] +) +``` + + + + +```python +from langchain_openai import ChatOpenAI + +headers = get_litellm_headers(request) + +llm = ChatOpenAI( + model="gpt-4o", + openai_api_key="sk-your-litellm-key", + base_url="http://localhost:4000", + default_headers=headers, # Forward headers +) +``` + + + +```python +import litellm + +headers = get_litellm_headers(request) + +response = litellm.completion( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}], + api_base="http://localhost:4000", + extra_headers=headers, # Forward headers +) +``` + + + +```python +import httpx + +headers = get_litellm_headers(request) +headers["Authorization"] = "Bearer sk-your-litellm-key" + +response = httpx.post( + "http://localhost:4000/v1/chat/completions", + headers=headers, + json={"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}]} +) +``` + + + +### Result + +With header forwarding enabled, you'll see: + +**Trace Grouping in Langfuse:** + + + +**Agent Spend Attribution:** + + + ## API Reference ### Endpoint diff --git a/docs/my-website/img/a2a_agent_spend.png b/docs/my-website/img/a2a_agent_spend.png new file mode 100644 index 0000000000000000000000000000000000000000..15ec769392a8f68c346b245819cc07c584d1e106 GIT binary patch literal 188367 zcmd?RWmuH$+BQsg2}nt)fFRvniXb8&-67pWHw+;l($bA0AksDT(A^*m-CaWuIeg=K z*4mzXz2Cj*{r-M`%r*m@*L9t9ocq!HLHIi*IeZ){93&(peEHWhsz^wfKqMs8MJx=& zZ}2!qWf5=4E~;`bkV=QCcMw0wm}|>hC@La7M|{RYLJqS=Li@D@@uEV!5SQj4BcUSR zk$?Rz2jxG1#RTS{{^v95;;)6}C89b=NK#1hGScdv$omWE0qPp$m`C^S4;o!Po_-8) zO~ztY#bM4tdH%`&6HW@n$AC{?zcYXRn(*P<_YVQ%3k#X)c0ImZ^Y24v%yB_0z!reV zVw1;`AMDOT=lyVoOQupb*ckZ{I&zjes^IIKw;oV6=Aj3Zb+f z64BCEYweTE|Cg7eqM4YQmYdx?p>FiVN)8$}y)dd;0bcQ#n3(V^+faZ0!^t5;5y1L@ zSt{`C;m4?muB@t?a^t(~IwDuHE#({26>#ddXCDfmb&&7SZUGcsnCj2RD-)2~a?70~ zC4&1QG;FnX<)9b36`69REfXU5xW6X|^~Qa5JkMpvm!ClH&&Vo53U0ZTN8Ajjvsa{L 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zIEMfQng-_xZW*=y^!gz3unCokC5gtiue{$mh{8rKHIRWFbmKmAr@Gf^lziejbyLh< zGBIl^IlQ1%t;zDcQ?qN`Q&rVCICQG#4|bHsr4tMdG{O)`;{l~J?$y{;Q`Uj9oo3Xx zCPJb7xuYW(VFNPN zgiff`3AIdA?>4#03oJ51yiJS2cDW6E! z*_8&EAPMyE->(lH>DorLc5CA%h3FMrYpT8B>kQ>o;_d$-RZTy}_}|#0FZONEHDMBW%)5W@m%pF Date: Sat, 24 Jan 2026 09:28:49 +0530 Subject: [PATCH 12/62] fix(ui): Change default key type from 'Default' to 'LLM API' for improved security (#19516) - Changed initial keyType state to 'llm_api' instead of 'default' - Updated reset functions (handleOk, handleCancel) to use 'llm_api' - Updated form initialValue and Select defaultValue to 'llm_api' Addresses #19492 --- .../src/components/organisms/create_key_button.tsx | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx b/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx index 90ed9a62cc..7664393a9e 100644 --- a/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx +++ b/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx @@ -159,7 +159,7 @@ const CreateKey: React.FC = ({ team, teams, data, addKey }) => { const [userSearchLoading, setUserSearchLoading] = useState(false); const [mcpAccessGroups, setMcpAccessGroups] = useState([]); const [disabledCallbacks, setDisabledCallbacks] = useState([]); - const [keyType, setKeyType] = useState("default"); + const [keyType, setKeyType] = useState("llm_api"); const [modelAliases, setModelAliases] = useState<{ [key: string]: string }>({}); const [autoRotationEnabled, setAutoRotationEnabled] = useState(false); const [rotationInterval, setRotationInterval] = useState("30d"); @@ -168,7 +168,7 @@ const CreateKey: React.FC = ({ team, teams, data, addKey }) => { form.resetFields(); setLoggingSettings([]); setDisabledCallbacks([]); - setKeyType("default"); + setKeyType("llm_api"); setModelAliases({}); setAutoRotationEnabled(false); setRotationInterval("30d"); @@ -181,7 +181,7 @@ const CreateKey: React.FC = ({ team, teams, data, addKey }) => { form.resetFields(); setLoggingSettings([]); setDisabledCallbacks([]); - setKeyType("default"); + setKeyType("llm_api"); setModelAliases({}); setAutoRotationEnabled(false); setRotationInterval("30d"); @@ -676,11 +676,11 @@ const CreateKey: React.FC = ({ team, teams, data, addKey }) => { } name="key_type" - initialValue="default" + initialValue="llm_api" className="mt-4" > onChange(e.target.value)} + data-placeholder={placeholder} + > + {children} + + ); + Select.Option = Option; + return { + Select, + Tooltip: ({ children, title }: any) => ( +
      + {children} +
      + ), + Switch: ({ checked, onChange }: any) => ( + onChange(e.target.checked)} + /> + ), + Divider: () =>
      , + }; +}); + +vi.mock("@ant-design/icons", () => ({ + InfoCircleOutlined: () => ℹ, +})); + +vi.mock("@tremor/react", () => ({ + TextInput: ({ value, onValueChange, onChange, placeholder, name, className }: any) => { + const handleChange = (e: React.ChangeEvent) => { + if (onChange) { + onChange(e); + } + if (onValueChange) { + onValueChange(e.target.value); + } + }; + return ( + + ); + }, +})); + +describe("KeyLifecycleSettings", () => { + const mockForm = { + getFieldValue: vi.fn(), + setFieldValue: vi.fn(), + setFieldsValue: vi.fn(), + }; + + const defaultProps = { + form: mockForm, + autoRotationEnabled: false, + onAutoRotationChange: vi.fn(), + rotationInterval: "", + onRotationIntervalChange: vi.fn(), + isCreateMode: false, + }; + + beforeEach(() => { + vi.clearAllMocks(); + mockForm.getFieldValue.mockReturnValue(""); + }); + + it("should render without crashing", () => { + renderWithProviders(); + + expect(screen.getByText("Key Expiry Settings")).toBeInTheDocument(); + expect(screen.getByText("Auto-Rotation Settings")).toBeInTheDocument(); + }); + + describe("Key Expiry Settings", () => { + it("should render expiry input field", () => { + renderWithProviders(); + + expect(screen.getByText("Expire Key")).toBeInTheDocument(); + expect(screen.getByTestId("duration-input")).toBeInTheDocument(); + }); + + it("should show correct placeholder in create mode", () => { + renderWithProviders(); + + const input = screen.getByTestId("duration-input"); + expect(input).toHaveAttribute( + "placeholder", + "e.g., 30d or leave empty to never expire" + ); + }); + + it("should show correct placeholder in edit mode", () => { + renderWithProviders(); + + const input = screen.getByTestId("duration-input"); + expect(input).toHaveAttribute("placeholder", "e.g., 30d or -1 to never expire"); + }); + + it("should show correct tooltip in create mode", () => { + renderWithProviders(); + + const tooltips = screen.getAllByTestId("tooltip"); + const expiryTooltip = tooltips.find((tooltip) => + tooltip.getAttribute("title")?.includes("Leave empty to never expire") + ); + expect(expiryTooltip).toBeInTheDocument(); + expect(expiryTooltip).toHaveAttribute( + "title", + "Set when this key should expire. Format: 30s (seconds), 30m (minutes), 30h (hours), 30d (days). Leave empty to never expire." + ); + }); + + it("should show correct tooltip in edit mode", () => { + renderWithProviders(); + + const tooltips = screen.getAllByTestId("tooltip"); + const expiryTooltip = tooltips.find((tooltip) => + tooltip.getAttribute("title")?.includes("Use -1 to never expire") + ); + expect(expiryTooltip).toBeInTheDocument(); + expect(expiryTooltip).toHaveAttribute( + "title", + "Set when this key should expire. Format: 30s (seconds), 30m (minutes), 30h (hours), 30d (days). Use -1 to never expire." + ); + }); + + it("should initialize with form value if present", () => { + mockForm.getFieldValue.mockReturnValue("30d"); + renderWithProviders(); + + const input = screen.getByTestId("duration-input") as HTMLInputElement; + expect(input.value).toBe("30d"); + }); + + it("should update form using setFieldValue when duration changes", async () => { + const user = userEvent.setup(); + renderWithProviders(); + + const input = screen.getByTestId("duration-input"); + await user.type(input, "60d"); + + expect(mockForm.setFieldValue).toHaveBeenCalledWith("duration", "60d"); + }); + + it("should update form using setFieldsValue when setFieldValue is not available", async () => { + const user = userEvent.setup(); + const formWithoutSetFieldValue = { + getFieldValue: vi.fn().mockReturnValue(""), + setFieldsValue: vi.fn(), + }; + renderWithProviders( + + ); + + const input = screen.getByTestId("duration-input"); + await user.type(input, "90d"); + + expect(formWithoutSetFieldValue.setFieldsValue).toHaveBeenCalledWith({ duration: "90d" }); + }); + }); + + describe("Auto-Rotation Settings", () => { + it("should render auto-rotation switch", () => { + renderWithProviders(); + + expect(screen.getByText("Enable Auto-Rotation")).toBeInTheDocument(); + expect(screen.getByTestId("switch")).toBeInTheDocument(); + }); + + it("should show switch as unchecked when autoRotationEnabled is false", () => { + renderWithProviders(); + + const switchElement = screen.getByTestId("switch") as HTMLInputElement; + expect(switchElement.checked).toBe(false); + }); + + it("should show switch as checked when autoRotationEnabled is true", () => { + renderWithProviders(); + + const switchElement = screen.getByTestId("switch") as HTMLInputElement; + expect(switchElement.checked).toBe(true); + }); + + it("should call onAutoRotationChange when switch is toggled", async () => { + const user = userEvent.setup(); + const onAutoRotationChange = vi.fn(); + renderWithProviders( + + ); + + const switchElement = screen.getByTestId("switch"); + await user.click(switchElement); + + expect(onAutoRotationChange).toHaveBeenCalledWith(true); + }); + + it("should not show rotation interval section when auto-rotation is disabled", () => { + renderWithProviders(); + + expect(screen.queryByText("Rotation Interval")).not.toBeInTheDocument(); + expect(screen.queryByTestId("select")).not.toBeInTheDocument(); + }); + + it("should show rotation interval section when auto-rotation is enabled", () => { + renderWithProviders( + + ); + + expect(screen.getByText("Rotation Interval")).toBeInTheDocument(); + expect(screen.getByTestId("select")).toBeInTheDocument(); + }); + + it("should show all predefined interval options", () => { + renderWithProviders( + + ); + + expect(screen.getByText("7 days")).toBeInTheDocument(); + expect(screen.getByText("30 days")).toBeInTheDocument(); + expect(screen.getByText("90 days")).toBeInTheDocument(); + expect(screen.getByText("180 days")).toBeInTheDocument(); + expect(screen.getByText("365 days")).toBeInTheDocument(); + expect(screen.getByText("Custom interval")).toBeInTheDocument(); + }); + + it("should display current rotation interval in select", () => { + renderWithProviders( + + ); + + const select = screen.getByTestId("select") as HTMLSelectElement; + expect(select.value).toBe("90d"); + }); + + it("should call onRotationIntervalChange when predefined interval is selected", async () => { + const user = userEvent.setup(); + const onRotationIntervalChange = vi.fn(); + renderWithProviders( + + ); + + const select = screen.getByTestId("select"); + await user.selectOptions(select, "30d"); + + expect(onRotationIntervalChange).toHaveBeenCalledWith("30d"); + }); + + it("should show custom input when custom option is selected", async () => { + const user = userEvent.setup(); + renderWithProviders( + + ); + + const select = screen.getByTestId("select"); + await user.selectOptions(select, "custom"); + + expect(screen.getByTestId("custom-interval-input")).toBeInTheDocument(); + expect(screen.getByText("Supported formats: seconds (s), minutes (m), hours (h), days (d)")).toBeInTheDocument(); + }); + + it("should hide custom input when predefined interval is selected after custom", async () => { + const user = userEvent.setup(); + const onRotationIntervalChange = vi.fn(); + renderWithProviders( + + ); + + const select = screen.getByTestId("select"); + await user.selectOptions(select, "7d"); + + expect(screen.queryByTestId("custom-interval-input")).not.toBeInTheDocument(); + expect(onRotationIntervalChange).toHaveBeenCalledWith("7d"); + }); + + it("should call onRotationIntervalChange when custom interval is entered", async () => { + const user = userEvent.setup(); + const onRotationIntervalChange = vi.fn(); + renderWithProviders( + + ); + + const select = screen.getByTestId("select"); + await user.selectOptions(select, "custom"); + + const customInput = screen.getByTestId("custom-interval-input"); + await user.type(customInput, "14d"); + + expect(onRotationIntervalChange).toHaveBeenCalledWith("14d"); + }); + + it("should show info message when auto-rotation is enabled", () => { + renderWithProviders(); + + expect( + screen.getByText( + "When rotation occurs, you'll receive a notification with the new key. The old key will be deactivated after a brief grace period." + ) + ).toBeInTheDocument(); + }); + + it("should not show info message when auto-rotation is disabled", () => { + renderWithProviders(); + + expect( + screen.queryByText( + "When rotation occurs, you'll receive a notification with the new key. The old key will be deactivated after a brief grace period." + ) + ).not.toBeInTheDocument(); + }); + + it("should initialize with custom interval input visible when custom interval is provided", () => { + renderWithProviders( + + ); + + expect(screen.getByTestId("custom-interval-input")).toBeInTheDocument(); + const customInput = screen.getByTestId("custom-interval-input") as HTMLInputElement; + expect(customInput.value).toBe("14d"); + }); + + it("should show custom option selected when custom interval is provided", () => { + renderWithProviders( + + ); + + const select = screen.getByTestId("select") as HTMLSelectElement; + expect(select.value).toBe("custom"); + }); + + it("should not call onRotationIntervalChange when selecting custom option", async () => { + const user = userEvent.setup(); + const onRotationIntervalChange = vi.fn(); + renderWithProviders( + + ); + + const select = screen.getByTestId("select"); + await user.selectOptions(select, "custom"); + + expect(onRotationIntervalChange).not.toHaveBeenCalled(); + }); + }); +}); diff --git a/ui/litellm-dashboard/src/components/common_components/KeyLifecycleSettings.tsx b/ui/litellm-dashboard/src/components/common_components/KeyLifecycleSettings.tsx index 81d22b5634..0f29a47d1d 100644 --- a/ui/litellm-dashboard/src/components/common_components/KeyLifecycleSettings.tsx +++ b/ui/litellm-dashboard/src/components/common_components/KeyLifecycleSettings.tsx @@ -11,6 +11,7 @@ interface KeyLifecycleSettingsProps { onAutoRotationChange: (enabled: boolean) => void; rotationInterval: string; onRotationIntervalChange: (interval: string) => void; + isCreateMode?: boolean; // If true, shows "leave empty to never expire" instead of "-1 to never expire" } const KeyLifecycleSettings: React.FC = ({ @@ -19,6 +20,7 @@ const KeyLifecycleSettings: React.FC = ({ onAutoRotationChange, rotationInterval, onRotationIntervalChange, + isCreateMode = false, }) => { // Predefined intervals const predefinedIntervals = ["7d", "30d", "90d", "180d", "365d"]; @@ -64,13 +66,19 @@ const KeyLifecycleSettings: React.FC = ({
      = ({ team, teams, data, addKey }) => { formValues.rotation_interval = rotationInterval; } - // Handle duration field for key expiry - if (formValues.duration) { - formValues.duration = formValues.duration; + // Handle duration field for key expiry - convert empty string to null + if (!formValues.duration || formValues.duration.trim() === "") { + formValues.duration = null; } // Update the formValues with the final metadata @@ -1270,6 +1270,7 @@ const CreateKey: React.FC = ({ team, teams, data, addKey }) => { onAutoRotationChange={setAutoRotationEnabled} rotationInterval={rotationInterval} onRotationIntervalChange={setRotationInterval} + isCreateMode={true} />
      From f879b8b1cbf9e81be5896135ea7cad391d02f6c2 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Mon, 26 Jan 2026 14:35:35 -0800 Subject: [PATCH 30/62] fixing team member add --- .../management_endpoints/team_endpoints.py | 123 ++++++++++++++++++ .../test_team_endpoints.py | 120 +++++++++++++++++ 2 files changed, 243 insertions(+) diff --git a/litellm/proxy/management_endpoints/team_endpoints.py b/litellm/proxy/management_endpoints/team_endpoints.py index d0afb62e8b..2c31b59b79 100644 --- a/litellm/proxy/management_endpoints/team_endpoints.py +++ b/litellm/proxy/management_endpoints/team_endpoints.py @@ -1771,6 +1771,116 @@ async def _add_team_members_to_team( return updated_team, updated_users, updated_team_memberships +async def _validate_and_populate_member_user_info( + member: Member, + prisma_client: PrismaClient, +) -> Member: + """ + Validate and populate user_email/user_id for a member. + + Logic: + 1. If both user_email and user_id are provided, verify they belong to the same user (use user_email as source of truth) + 2. If only user_email is provided, populate user_id from DB + 3. If only user_id is provided, populate user_email from DB + 4. If only user_id is provided and doesn't exist, throw error + 5. If user_email and user_id mismatch, throw error + + Returns a Member with both user_email and user_id populated. + """ + if member.user_email is None and member.user_id is None: + raise HTTPException( + status_code=400, + detail={"error": "Either user_id or user_email must be provided"}, + ) + + # Case 1: Both user_email and user_id provided - verify they match + if member.user_email is not None and member.user_id is not None: + # Use user_email as source of truth + # Check for multiple users with same email first + users_by_email = await prisma_client.get_data( + key_val={"user_email": member.user_email}, + table_name="user", + query_type="find_all", + ) + + if users_by_email is None or ( + isinstance(users_by_email, list) and len(users_by_email) == 0 + ): + # User doesn't exist yet - this is fine, will be created later + return member + + if isinstance(users_by_email, list) and len(users_by_email) > 1: + raise HTTPException( + status_code=400, + detail={ + "error": f"Multiple users found with email '{member.user_email}'. Please use 'user_id' instead." + }, + ) + + # Get the single user + user_by_email = users_by_email[0] + + # Verify the user_id matches + if user_by_email.user_id != member.user_id: + raise HTTPException( + status_code=400, + detail={ + "error": f"user_email '{member.user_email}' and user_id '{member.user_id}' do not belong to the same user." + }, + ) + + # Both match, return as is + return member + + # Case 2: Only user_email provided - populate user_id from DB + if member.user_email is not None and member.user_id is None: + user_by_email = await prisma_client.db.litellm_usertable.find_first( + where={"user_email": {"equals": member.user_email, "mode": "insensitive"}} + ) + + if user_by_email is None: + # User doesn't exist yet - this is fine, will be created later + return member + + # Check for multiple users with same email + users_by_email = await prisma_client.get_data( + key_val={"user_email": member.user_email}, + table_name="user", + query_type="find_all", + ) + + if users_by_email and isinstance(users_by_email, list) and len(users_by_email) > 1: + raise HTTPException( + status_code=400, + detail={ + "error": f"Multiple users found with email '{member.user_email}'. Please use 'user_id' instead." + }, + ) + + # Populate user_id + member.user_id = user_by_email.user_id + return member + + # Case 3: Only user_id provided - populate user_email from DB + if member.user_id is not None and member.user_email is None: + user_by_id = await prisma_client.db.litellm_usertable.find_unique( + where={"user_id": member.user_id} + ) + + if user_by_id is None: + raise HTTPException( + status_code=404, + detail={ + "error": f"User with user_id '{member.user_id}' not found in database" + }, + ) + + # Populate user_email + member.user_email = user_by_id.user_email + return member + + return member + @router.post( "/team/member_add", tags=["team management"], @@ -1846,6 +1956,19 @@ async def team_member_add( complete_team_data=complete_team_data, ) + # Validate and populate user_email/user_id for members before processing + if isinstance(data.member, Member): + await _validate_and_populate_member_user_info( + member=data.member, + prisma_client=prisma_client, + ) + elif isinstance(data.member, List): + for m in data.member: + await _validate_and_populate_member_user_info( + member=m, + prisma_client=prisma_client, + ) + updated_team, updated_users, updated_team_memberships = ( await _add_team_members_to_team( data=data, diff --git a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py index a97ed93a85..a586457690 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py @@ -36,6 +36,7 @@ from litellm.proxy.management_endpoints.team_endpoints import ( _persist_deleted_team_records, _save_deleted_team_records, _transform_teams_to_deleted_records, + _validate_and_populate_member_user_info, delete_team, router, team_member_add_duplication_check, @@ -5466,3 +5467,122 @@ async def test_get_team_daily_activity_team_admin_sees_all_spend(mock_db_client) ) and mock_db_client.db.litellm_verificationtoken.find_many.called: # If it was called, that's unexpected for admin users assert False, "API keys should not be fetched for team admin users" + + +@pytest.mark.asyncio +async def test_validate_and_populate_member_user_info_both_provided_match(): + """ + Test _validate_and_populate_member_user_info when both user_email and user_id + are provided and they match the same user in the database. + """ + # Create member with both user_email and user_id + member = Member(user_email="test@example.com", user_id="user-123", role="user") + + # Mock prisma client + mock_prisma_client = MagicMock() + + # Mock user object that matches both email and user_id + mock_user = MagicMock() + mock_user.user_id = "user-123" + mock_user.user_email = "test@example.com" + + # Mock get_data to return single user matching email + mock_prisma_client.get_data = AsyncMock(return_value=[mock_user]) + + # Call the function + result = await _validate_and_populate_member_user_info( + member=member, + prisma_client=mock_prisma_client, + ) + + # Verify result matches input (both already provided and match) + assert result.user_email == "test@example.com" + assert result.user_id == "user-123" + + # Verify get_data was called with correct parameters + mock_prisma_client.get_data.assert_called_once_with( + key_val={"user_email": "test@example.com"}, + table_name="user", + query_type="find_all", + ) + + +@pytest.mark.asyncio +async def test_validate_and_populate_member_user_info_only_email_provided(): + """ + Test _validate_and_populate_member_user_info when only user_email is provided. + Should populate user_id from database. + """ + # Create member with only user_email + member = Member(user_email="test@example.com", user_id=None, role="user") + + # Mock prisma client + mock_prisma_client = MagicMock() + + # Mock user object from find_first + mock_user_find_first = MagicMock() + mock_user_find_first.user_id = "user-456" + mock_user_find_first.user_email = "test@example.com" + + # Mock find_first to return the user + mock_prisma_client.db.litellm_usertable.find_first = AsyncMock( + return_value=mock_user_find_first + ) + + # Mock get_data to return single user (no duplicates) + mock_prisma_client.get_data = AsyncMock(return_value=[mock_user_find_first]) + + # Call the function + result = await _validate_and_populate_member_user_info( + member=member, + prisma_client=mock_prisma_client, + ) + + # Verify user_id was populated + assert result.user_email == "test@example.com" + assert result.user_id == "user-456" + + # Verify find_first was called with correct parameters + mock_prisma_client.db.litellm_usertable.find_first.assert_called_once_with( + where={"user_email": {"equals": "test@example.com", "mode": "insensitive"}} + ) + + # Verify get_data was called to check for duplicates + mock_prisma_client.get_data.assert_called_once_with( + key_val={"user_email": "test@example.com"}, + table_name="user", + query_type="find_all", + ) + + +@pytest.mark.asyncio +async def test_validate_and_populate_member_user_info_only_user_id_not_found(): + """ + Test _validate_and_populate_member_user_info when only user_id is provided + but the user doesn't exist in the database. Should raise HTTPException. + """ + # Create member with only user_id + member = Member(user_email=None, user_id="nonexistent-user", role="user") + + # Mock prisma client + mock_prisma_client = MagicMock() + + # Mock find_unique to return None (user not found) + mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=None) + + # Call the function and expect HTTPException + with pytest.raises(HTTPException) as exc_info: + await _validate_and_populate_member_user_info( + member=member, + prisma_client=mock_prisma_client, + ) + + # Verify the exception details + assert exc_info.value.status_code == 404 + assert "not found" in exc_info.value.detail["error"].lower() + assert "nonexistent-user" in exc_info.value.detail["error"] + + # Verify find_unique was called with correct parameters + mock_prisma_client.db.litellm_usertable.find_unique.assert_called_once_with( + where={"user_id": "nonexistent-user"} + ) From 9ff0daa24defccc9a6aa049e8a4c6f4a98b0f846 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Mon, 26 Jan 2026 16:58:38 -0800 Subject: [PATCH 31/62] Add dont ask me again option in nudges --- .../hooks/useDisableShowPrompts.ts | 35 +++++++++ .../src/components/navbar.tsx | 24 +++++- .../src/components/survey/NudgePrompt.tsx | 73 +++++++++++++++++-- 3 files changed, 123 insertions(+), 9 deletions(-) create mode 100644 ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableShowPrompts.ts diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableShowPrompts.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableShowPrompts.ts new file mode 100644 index 0000000000..801fbdbb99 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableShowPrompts.ts @@ -0,0 +1,35 @@ +// hooks/useDisableShowPrompts.ts +import { useSyncExternalStore } from "react"; +import { getLocalStorageItem } from "@/utils/localStorageUtils"; +import { LOCAL_STORAGE_EVENT } from "@/utils/localStorageUtils"; + +function subscribe(callback: () => void) { + const onStorage = (e: StorageEvent) => { + if (e.key === "disableShowPrompts") { + callback(); + } + }; + + const onCustom = (e: Event) => { + const { key } = (e as CustomEvent).detail; + if (key === "disableShowPrompts") { + callback(); + } + }; + + window.addEventListener("storage", onStorage); + window.addEventListener(LOCAL_STORAGE_EVENT, onCustom); + + return () => { + window.removeEventListener("storage", onStorage); + window.removeEventListener(LOCAL_STORAGE_EVENT, onCustom); + }; +} + +function getSnapshot() { + return getLocalStorageItem("disableShowPrompts") === "true"; +} + +export function useDisableShowPrompts() { + return useSyncExternalStore(subscribe, getSnapshot); +} diff --git a/ui/litellm-dashboard/src/components/navbar.tsx b/ui/litellm-dashboard/src/components/navbar.tsx index 6dac073b3a..8179bb8a53 100644 --- a/ui/litellm-dashboard/src/components/navbar.tsx +++ b/ui/litellm-dashboard/src/components/navbar.tsx @@ -1,4 +1,5 @@ import { useHealthReadiness } from "@/app/(dashboard)/hooks/healthReadiness/useHealthReadiness"; +import { useDisableShowPrompts } from "@/app/(dashboard)/hooks/useDisableShowPrompts"; import { getProxyBaseUrl } from "@/components/networking"; import { useTheme } from "@/contexts/ThemeContext"; import { clearTokenCookies } from "@/utils/cookieUtils"; @@ -51,9 +52,9 @@ const Navbar: React.FC = ({ onToggleSidebar, }) => { const baseUrl = getProxyBaseUrl(); - console.log("baseUrl", baseUrl); const [logoutUrl, setLogoutUrl] = useState(""); const [disableShowNewBadge, setDisableShowNewBadge] = useState(false); + const disableShowPrompts = useDisableShowPrompts(); const { logoUrl } = useTheme(); const { data: healthData } = useHealthReadiness(); const version = healthData?.litellm_version; @@ -152,6 +153,27 @@ const Navbar: React.FC = ({ aria-label="Toggle hide new feature indicators" /> +
      e.stopPropagation()} + > + Hide All Prompts + { + if (checked) { + setLocalStorageItem("disableShowPrompts", "true"); + emitLocalStorageChange("disableShowPrompts"); + } else { + removeLocalStorageItem("disableShowPrompts"); + emitLocalStorageChange("disableShowPrompts"); + } + }} + aria-label="Toggle hide all prompts" + /> +
      ), diff --git a/ui/litellm-dashboard/src/components/survey/NudgePrompt.tsx b/ui/litellm-dashboard/src/components/survey/NudgePrompt.tsx index 9095c6c21c..ef72144b89 100644 --- a/ui/litellm-dashboard/src/components/survey/NudgePrompt.tsx +++ b/ui/litellm-dashboard/src/components/survey/NudgePrompt.tsx @@ -1,6 +1,8 @@ import React, { useEffect, useState } from "react"; -import { X, LucideIcon } from "lucide-react"; +import { X, LucideIcon, Check } from "lucide-react"; import { Button } from "antd"; +import { useDisableShowPrompts } from "@/app/(dashboard)/hooks/useDisableShowPrompts"; +import { setLocalStorageItem, emitLocalStorageChange } from "@/utils/localStorageUtils"; interface NudgePromptProps { onOpen: () => void; @@ -15,6 +17,7 @@ interface NudgePromptProps { } const DISMISS_DURATION = 15000; // 15 seconds +const CONFIRMATION_DURATION = 5000; // 5 seconds export function NudgePrompt({ onOpen, @@ -27,11 +30,14 @@ export function NudgePrompt({ accentColor, buttonStyle, }: NudgePromptProps) { + const disableShowPrompts = useDisableShowPrompts(); const [progress, setProgress] = useState(100); + const [showConfirmation, setShowConfirmation] = useState(false); useEffect(() => { if (!isVisible) { setProgress(100); + setShowConfirmation(false); return; } @@ -49,13 +55,53 @@ export function NudgePrompt({ return () => clearInterval(interval); }, [isVisible]); - if (!isVisible) return null; + useEffect(() => { + if (showConfirmation) { + const timer = setTimeout(() => { + setShowConfirmation(false); + onDismiss(); + }, CONFIRMATION_DURATION); + + return () => clearTimeout(timer); + } + }, [showConfirmation, onDismiss]); + + const handleDontAskAgain = () => { + setLocalStorageItem("disableShowPrompts", "true"); + emitLocalStorageChange("disableShowPrompts"); + setShowConfirmation(true); + }; + + // Show confirmation even if disableShowPrompts is true (since we just set it) + if (showConfirmation) { + return ( +
      +
      +
      +
      + +
      +
      +

      + Got it, we will not ask again. Reactivate this at any time in the User Menu. +

      +
      +
      +
      +
      + ); + } + + // Don't show the prompt if disabled (unless we're showing confirmation) + if (!isVisible || disableShowPrompts) return null; return (
      {/* Progress bar at top showing time remaining */}
      @@ -81,9 +127,20 @@ export function NudgePrompt({

      {description}

      - +
      + + +
      ); From aa4b3ba87addedf3f7869909e13a63e8d9687f82 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Mon, 26 Jan 2026 17:53:01 -0800 Subject: [PATCH 32/62] Adding tests: --- .../src/components/navbar.test.tsx | 6 ++ .../components/survey/NudgePrompt.test.tsx | 101 ++++++++++++++++++ .../src/components/survey/NudgePrompt.tsx | 2 +- 3 files changed, 108 insertions(+), 1 deletion(-) create mode 100644 ui/litellm-dashboard/src/components/survey/NudgePrompt.test.tsx diff --git a/ui/litellm-dashboard/src/components/navbar.test.tsx b/ui/litellm-dashboard/src/components/navbar.test.tsx index 5d3c7254ff..4db814d897 100644 --- a/ui/litellm-dashboard/src/components/navbar.test.tsx +++ b/ui/litellm-dashboard/src/components/navbar.test.tsx @@ -16,6 +16,7 @@ vi.mock("@/utils/proxyUtils", () => ({ let mockUseThemeImpl = () => ({ logoUrl: null as string | null }); let mockUseHealthReadinessImpl = () => ({ data: null as any }); let mockGetLocalStorageItemImpl = () => null as string | null; +let mockUseDisableShowPromptsImpl = () => false; vi.mock("@/contexts/ThemeContext", () => ({ useTheme: () => mockUseThemeImpl(), @@ -25,7 +26,12 @@ vi.mock("@/app/(dashboard)/hooks/healthReadiness/useHealthReadiness", () => ({ useHealthReadiness: () => mockUseHealthReadinessImpl(), })); +vi.mock("@/app/(dashboard)/hooks/useDisableShowPrompts", () => ({ + useDisableShowPrompts: () => mockUseDisableShowPromptsImpl(), +})); + vi.mock("@/utils/localStorageUtils", () => ({ + LOCAL_STORAGE_EVENT: "local-storage-change", getLocalStorageItem: () => mockGetLocalStorageItemImpl(), setLocalStorageItem: vi.fn(), removeLocalStorageItem: vi.fn(), diff --git a/ui/litellm-dashboard/src/components/survey/NudgePrompt.test.tsx b/ui/litellm-dashboard/src/components/survey/NudgePrompt.test.tsx new file mode 100644 index 0000000000..26db8a680c --- /dev/null +++ b/ui/litellm-dashboard/src/components/survey/NudgePrompt.test.tsx @@ -0,0 +1,101 @@ +import { render, screen } from "@testing-library/react"; +import { MessageSquare } from "lucide-react"; +import { afterEach, beforeEach, describe, expect, it, vi } from "vitest"; +import { NudgePrompt } from "./NudgePrompt"; + +vi.mock("@/app/(dashboard)/hooks/useDisableShowPrompts", () => ({ + useDisableShowPrompts: vi.fn(), +})); + +vi.mock("@/utils/localStorageUtils", () => ({ + setLocalStorageItem: vi.fn(), + emitLocalStorageChange: vi.fn(), + LOCAL_STORAGE_EVENT: "local-storage-change", +})); + +import { useDisableShowPrompts } from "@/app/(dashboard)/hooks/useDisableShowPrompts"; +import { emitLocalStorageChange, setLocalStorageItem } from "@/utils/localStorageUtils"; + +const mockUseDisableShowPrompts = vi.mocked(useDisableShowPrompts); +const mockSetLocalStorageItem = vi.mocked(setLocalStorageItem); +const mockEmitLocalStorageChange = vi.mocked(emitLocalStorageChange); + +const defaultProps = { + onOpen: vi.fn(), + onDismiss: vi.fn(), + isVisible: true, + title: "Test Title", + description: "Test Description", + buttonText: "Open Modal", + icon: MessageSquare, + accentColor: "#3b82f6", +}; + +describe("NudgePrompt", () => { + beforeEach(() => { + vi.clearAllMocks(); + mockUseDisableShowPrompts.mockReturnValue(false); + vi.useFakeTimers(); + }); + + afterEach(() => { + vi.useRealTimers(); + }); + + it("should render", () => { + render(); + + expect(screen.getByText("Test Title")).toBeInTheDocument(); + }); + + it("should render with all provided props", () => { + const { container } = render(); + + expect(screen.getByText("Test Title")).toBeInTheDocument(); + expect(screen.getByText("Test Description")).toBeInTheDocument(); + expect(screen.getByRole("button", { name: "Open Modal" })).toBeInTheDocument(); + expect(container.querySelector("svg")).toBeInTheDocument(); + }); + + it("should not render when isVisible is false", () => { + render(); + + expect(screen.queryByText("Test Title")).not.toBeInTheDocument(); + }); + + it("should not render when disableShowPrompts is true", () => { + mockUseDisableShowPrompts.mockReturnValue(true); + + render(); + + expect(screen.queryByText("Test Title")).not.toBeInTheDocument(); + }); + + it("should display progress bar with correct accent color", () => { + const { container } = render(); + + const progressBar = container.querySelector("div[style*='width']"); + expect(progressBar).toHaveStyle({ backgroundColor: "#ff0000" }); + }); + + it("should reset progress when isVisible becomes false", () => { + const { rerender, container } = render(); + + vi.advanceTimersByTime(5000); + + rerender(); + + rerender(); + + const progressBar = container.querySelector("div[style*='width']"); + expect(progressBar?.getAttribute("style")).toContain("width: 100%"); + }); + + it("should apply custom button style when provided", () => { + const buttonStyle = { backgroundColor: "#custom-color" }; + render(); + + const openButton = screen.getByRole("button", { name: "Open Modal" }); + expect(openButton).toHaveStyle(buttonStyle); + }); +}); diff --git a/ui/litellm-dashboard/src/components/survey/NudgePrompt.tsx b/ui/litellm-dashboard/src/components/survey/NudgePrompt.tsx index ef72144b89..3c7f10af74 100644 --- a/ui/litellm-dashboard/src/components/survey/NudgePrompt.tsx +++ b/ui/litellm-dashboard/src/components/survey/NudgePrompt.tsx @@ -138,7 +138,7 @@ export function NudgePrompt({ onClick={handleDontAskAgain} className="text-xs" > - Don't ask me again + Don't ask me again From b10f71d5833e5af3c972caafec799cee4a62c906 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Mon, 26 Jan 2026 18:10:51 -0800 Subject: [PATCH 33/62] fixing breaking change: just user_id provided should upsert still --- .../management_endpoints/team_endpoints.py | 17 ++++++-------- .../test_team_endpoints.py | 22 +++++++++---------- 2 files changed, 18 insertions(+), 21 deletions(-) diff --git a/litellm/proxy/management_endpoints/team_endpoints.py b/litellm/proxy/management_endpoints/team_endpoints.py index 2c31b59b79..63db2d72fe 100644 --- a/litellm/proxy/management_endpoints/team_endpoints.py +++ b/litellm/proxy/management_endpoints/team_endpoints.py @@ -1781,11 +1781,11 @@ async def _validate_and_populate_member_user_info( Logic: 1. If both user_email and user_id are provided, verify they belong to the same user (use user_email as source of truth) 2. If only user_email is provided, populate user_id from DB - 3. If only user_id is provided, populate user_email from DB - 4. If only user_id is provided and doesn't exist, throw error + 3. If only user_id is provided, populate user_email from DB (if user exists) + 4. If only user_id is provided and doesn't exist, allow it to pass with user_email as None (will be upserted later) 5. If user_email and user_id mismatch, throw error - Returns a Member with both user_email and user_id populated. + Returns a Member with user_email and user_id populated (user_email may be None if only user_id provided and user doesn't exist). """ if member.user_email is None and member.user_id is None: raise HTTPException( @@ -1861,19 +1861,16 @@ async def _validate_and_populate_member_user_info( member.user_id = user_by_email.user_id return member - # Case 3: Only user_id provided - populate user_email from DB + # Case 3: Only user_id provided - populate user_email from DB if user exists if member.user_id is not None and member.user_email is None: user_by_id = await prisma_client.db.litellm_usertable.find_unique( where={"user_id": member.user_id} ) if user_by_id is None: - raise HTTPException( - status_code=404, - detail={ - "error": f"User with user_id '{member.user_id}' not found in database" - }, - ) + # User doesn't exist yet - allow it to pass with user_email as None + # Will be upserted later with just user_id and null email + return member # Populate user_email member.user_email = user_by_id.user_email diff --git a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py index a586457690..467ee3661d 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py @@ -5559,7 +5559,8 @@ async def test_validate_and_populate_member_user_info_only_email_provided(): async def test_validate_and_populate_member_user_info_only_user_id_not_found(): """ Test _validate_and_populate_member_user_info when only user_id is provided - but the user doesn't exist in the database. Should raise HTTPException. + but the user doesn't exist in the database. Should allow it to pass with + user_email as None (will be upserted later). """ # Create member with only user_id member = Member(user_email=None, user_id="nonexistent-user", role="user") @@ -5570,17 +5571,16 @@ async def test_validate_and_populate_member_user_info_only_user_id_not_found(): # Mock find_unique to return None (user not found) mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=None) - # Call the function and expect HTTPException - with pytest.raises(HTTPException) as exc_info: - await _validate_and_populate_member_user_info( - member=member, - prisma_client=mock_prisma_client, - ) + # Call the function - should NOT raise an exception + result = await _validate_and_populate_member_user_info( + member=member, + prisma_client=mock_prisma_client, + ) - # Verify the exception details - assert exc_info.value.status_code == 404 - assert "not found" in exc_info.value.detail["error"].lower() - assert "nonexistent-user" in exc_info.value.detail["error"] + # Verify the result - should return member with user_id set and user_email as None + assert result.user_id == "nonexistent-user" + assert result.user_email is None + assert result.role == "user" # Verify find_unique was called with correct parameters mock_prisma_client.db.litellm_usertable.find_unique.assert_called_once_with( From c70d04d85f7a6dbc1170b492cbd80640c5dbe5a8 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 27 Jan 2026 08:07:36 +0530 Subject: [PATCH 34/62] Fix: A2A Python SDK URL --- docs/my-website/docs/a2a.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/my-website/docs/a2a.md b/docs/my-website/docs/a2a.md index d7145e4b83..2b4a710c7d 100644 --- a/docs/my-website/docs/a2a.md +++ b/docs/my-website/docs/a2a.md @@ -68,7 +68,7 @@ Follow [this guide, to add your pydantic ai agent to LiteLLM Agent Gateway](./pr ## Invoking your Agents -Use the [A2A Python SDK](https://pypi.org/project/a2a/) to invoke agents through LiteLLM. +Use the [A2A Python SDK](https://pypi.org/project/a2a-sdk) to invoke agents through LiteLLM. This example shows how to: 1. **List available agents** - Query `/v1/agents` to see which agents your key can access From 52d73c2c16a25c477fad33260f968b4bc4eae5e8 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Mon, 26 Jan 2026 19:23:43 -0800 Subject: [PATCH 35/62] [Feat] Add UI for /rag/ingest API - upload docs, pdfs etc to create vector stores (#19822) * feat: _save_vector_store_to_db_from_rag_ingest * UI features for RAG ingest * fix: Endpoints * ragIngestCall * _save_vector_store_to_db_from_rag_ingest * fix: rag_ingest Code QA CHECK * UI fixes unit tests --- litellm/proxy/rag_endpoints/endpoints.py | 198 ++++++++++ .../management_endpoints.py | 185 +++++++--- .../src/components/networking.tsx | 56 +++ .../CreateVectorStore.test.tsx | 166 +++++++++ .../CreateVectorStore.tsx | 340 ++++++++++++++++++ .../DocumentsTable.test.tsx | 102 ++++++ .../DocumentsTable.tsx | 98 +++++ .../TestVectorStoreTab.test.tsx | 90 +++++ .../TestVectorStoreTab.tsx | 75 ++++ .../VectorStoreTable.test.tsx | 12 +- .../VectorStoreTable.tsx | 26 ++ .../vector_store_management/index.tsx | 56 ++- .../vector_store_management/types.tsx | 45 ++- 13 files changed, 1374 insertions(+), 75 deletions(-) create mode 100644 ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.test.tsx create mode 100644 ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.tsx create mode 100644 ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.test.tsx create mode 100644 ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.tsx create mode 100644 ui/litellm-dashboard/src/components/vector_store_management/TestVectorStoreTab.test.tsx create mode 100644 ui/litellm-dashboard/src/components/vector_store_management/TestVectorStoreTab.tsx diff --git a/litellm/proxy/rag_endpoints/endpoints.py b/litellm/proxy/rag_endpoints/endpoints.py index 79b4fd6873..79f182817f 100644 --- a/litellm/proxy/rag_endpoints/endpoints.py +++ b/litellm/proxy/rag_endpoints/endpoints.py @@ -26,6 +26,184 @@ from litellm.proxy.common_utils.http_parsing_utils import ( router = APIRouter() +def _build_file_metadata_entry( + response: Any, + file_data: Optional[Tuple[str, bytes, str]] = None, + file_url: Optional[str] = None, +) -> Dict[str, Any]: + """ + Build a file metadata entry for storing in vector_store_metadata. + + Args: + response: The response from litellm.aingest containing file_id + file_data: Optional tuple of (filename, content, content_type) + file_url: Optional URL if file was ingested from URL + + Returns: + Dictionary with file metadata (file_id, filename, file_url, ingested_at, etc.) + """ + from datetime import datetime, timezone + + # Extract file_id from response + file_id = None + if hasattr(response, "get"): + file_id = response.get("file_id") + elif hasattr(response, "file_id"): + file_id = response.file_id + + # Extract file information from file_data tuple + filename = None + file_size = None + content_type = None + + if file_data: + filename = file_data[0] + file_size = len(file_data[1]) if len(file_data) > 1 else None + content_type = file_data[2] if len(file_data) > 2 else None + + # Build file metadata entry + file_entry = { + "file_id": file_id, + "filename": filename, + "file_url": file_url, + "ingested_at": datetime.now(timezone.utc).isoformat(), + } + + # Add optional fields if available + if file_size is not None: + file_entry["file_size"] = file_size + if content_type is not None: + file_entry["content_type"] = content_type + + return file_entry + + +async def _save_vector_store_to_db_from_rag_ingest( + response: Any, + ingest_options: Dict[str, Any], + prisma_client, + user_api_key_dict: UserAPIKeyAuth, + file_data: Optional[Tuple[str, bytes, str]] = None, + file_url: Optional[str] = None, +) -> None: + """ + Helper function to save a newly created vector store from RAG ingest to the database. + + This function: + - Extracts vector store ID and config from the ingest response + - Checks if the vector store already exists in the database + - Creates a new database entry if it doesn't exist + - Adds the vector store to the registry + + Args: + response: The response from litellm.aingest() + ingest_options: The ingest options containing vector store config + prisma_client: The Prisma database client + user_api_key_dict: User API key authentication info + """ + from litellm.proxy.vector_store_endpoints.management_endpoints import ( + create_vector_store_in_db, + ) + + # Handle both dict and object responses + if hasattr(response, "get"): + vector_store_id = response.get("vector_store_id") + elif hasattr(response, "vector_store_id"): + vector_store_id = response.vector_store_id + else: + verbose_proxy_logger.warning( + f"Unable to extract vector_store_id from response type: {type(response)}" + ) + return + + if vector_store_id is None or not isinstance(vector_store_id, str): + verbose_proxy_logger.warning( + "Vector store ID is None or not a string, skipping database save" + ) + return + + vector_store_config = ingest_options.get("vector_store", {}) + custom_llm_provider = vector_store_config.get("custom_llm_provider") + + # Extract litellm_vector_store_params for custom name and description + litellm_vector_store_params = ingest_options.get("litellm_vector_store_params", {}) + custom_vector_store_name = litellm_vector_store_params.get("vector_store_name") + custom_vector_store_description = litellm_vector_store_params.get("vector_store_description") + + # Build file metadata entry using helper + file_entry = _build_file_metadata_entry( + response=response, + file_data=file_data, + file_url=file_url, + ) + + try: + # Check if vector store already exists in database + existing_vector_store = ( + await prisma_client.db.litellm_managedvectorstorestable.find_unique( + where={"vector_store_id": vector_store_id} + ) + ) + + # Only create if it doesn't exist + if existing_vector_store is None: + verbose_proxy_logger.info( + f"Saving newly created vector store {vector_store_id} to database" + ) + + # Initialize metadata with first file + initial_metadata = { + "ingested_files": [file_entry] + } + + # Use custom name if provided, otherwise default + vector_store_name = custom_vector_store_name or f"RAG Vector Store - {vector_store_id[:8]}" + vector_store_description = custom_vector_store_description or "Created via RAG ingest endpoint" + + await create_vector_store_in_db( + vector_store_id=vector_store_id, + custom_llm_provider=custom_llm_provider or "openai", + prisma_client=prisma_client, + vector_store_name=vector_store_name, + vector_store_description=vector_store_description, + vector_store_metadata=initial_metadata, + ) + + verbose_proxy_logger.info( + f"Vector store {vector_store_id} saved to database successfully" + ) + else: + verbose_proxy_logger.info( + f"Vector store {vector_store_id} already exists, appending file to metadata" + ) + + # Update existing vector store with new file + existing_metadata = existing_vector_store.vector_store_metadata or {} + if isinstance(existing_metadata, str): + import json + existing_metadata = json.loads(existing_metadata) + + ingested_files = existing_metadata.get("ingested_files", []) + ingested_files.append(file_entry) + existing_metadata["ingested_files"] = ingested_files + + # Update the vector store + from litellm.proxy.utils import safe_dumps + await prisma_client.db.litellm_managedvectorstorestable.update( + where={"vector_store_id": vector_store_id}, + data={"vector_store_metadata": safe_dumps(existing_metadata)} + ) + + verbose_proxy_logger.info( + f"Added file {file_entry.get('filename') or file_entry.get('file_url', 'Unknown')} to vector store {vector_store_id} metadata" + ) + except Exception as db_error: + # Log the error but don't fail the request since ingestion succeeded + verbose_proxy_logger.exception( + f"Failed to save vector store {vector_store_id} to database: {db_error}" + ) + + async def parse_rag_ingest_request( request: Request, ) -> Tuple[Dict[str, Any], Optional[Tuple[str, bytes, str]], Optional[str], Optional[str]]: @@ -158,6 +336,7 @@ async def rag_ingest( add_litellm_data_to_request, general_settings, llm_router, + prisma_client, proxy_config, version, ) @@ -189,6 +368,25 @@ async def rag_ingest( **request_data, ) + # Save vector store to database if it was newly created and prisma_client is available + verbose_proxy_logger.debug( + f"RAG Ingest - Checking database save conditions: prisma_client={prisma_client is not None}, response={response is not None}, response_type={type(response)}" + ) + + if prisma_client is not None and response is not None: + await _save_vector_store_to_db_from_rag_ingest( + response=response, + ingest_options=ingest_options, + prisma_client=prisma_client, + user_api_key_dict=user_api_key_dict, + file_data=file_data, + file_url=file_url, + ) + else: + verbose_proxy_logger.warning( + f"Skipping database save: prisma_client={prisma_client is not None}, response={response is not None}" + ) + return response except HTTPException: diff --git a/litellm/proxy/vector_store_endpoints/management_endpoints.py b/litellm/proxy/vector_store_endpoints/management_endpoints.py index bc61a60fe5..f3787e62f4 100644 --- a/litellm/proxy/vector_store_endpoints/management_endpoints.py +++ b/litellm/proxy/vector_store_endpoints/management_endpoints.py @@ -133,6 +133,112 @@ async def _resolve_embedding_config_from_db( return None +######################################################## +# Helper Functions +######################################################## +async def create_vector_store_in_db( + vector_store_id: str, + custom_llm_provider: str, + prisma_client, + vector_store_name: Optional[str] = None, + vector_store_description: Optional[str] = None, + vector_store_metadata: Optional[Dict] = None, + litellm_params: Optional[Dict] = None, + litellm_credential_name: Optional[str] = None, +) -> LiteLLM_ManagedVectorStore: + """ + Helper function to create a vector store in the database. + + This function handles: + - Checking if vector store already exists + - Creating the vector store in the database + - Adding it to the vector store registry + + Returns: + LiteLLM_ManagedVectorStore: The created vector store object + + Raises: + HTTPException: If vector store already exists or database error occurs + """ + from litellm.types.router import GenericLiteLLMParams + + if prisma_client is None: + raise HTTPException(status_code=500, detail="Database not connected") + + # Check if vector store already exists + existing_vector_store = ( + await prisma_client.db.litellm_managedvectorstorestable.find_unique( + where={"vector_store_id": vector_store_id} + ) + ) + if existing_vector_store is not None: + raise HTTPException( + status_code=400, + detail=f"Vector store with ID {vector_store_id} already exists", + ) + + # Prepare data for database + data_to_create: Dict[str, Any] = { + "vector_store_id": vector_store_id, + "custom_llm_provider": custom_llm_provider, + } + + if vector_store_name is not None: + data_to_create["vector_store_name"] = vector_store_name + if vector_store_description is not None: + data_to_create["vector_store_description"] = vector_store_description + if vector_store_metadata is not None: + data_to_create["vector_store_metadata"] = safe_dumps(vector_store_metadata) + if litellm_credential_name is not None: + data_to_create["litellm_credential_name"] = litellm_credential_name + + # Handle litellm_params - always provide at least an empty dict + if litellm_params: + # Auto-resolve embedding config if embedding model is provided but config is not + embedding_model = litellm_params.get("litellm_embedding_model") + if embedding_model and not litellm_params.get("litellm_embedding_config"): + resolved_config = await _resolve_embedding_config_from_db( + embedding_model=embedding_model, + prisma_client=prisma_client + ) + if resolved_config: + litellm_params["litellm_embedding_config"] = resolved_config + verbose_proxy_logger.info( + f"Auto-resolved embedding config for model {embedding_model}" + ) + + litellm_params_dict = GenericLiteLLMParams( + **litellm_params + ).model_dump(exclude_none=True) + data_to_create["litellm_params"] = safe_dumps(litellm_params_dict) + else: + # Provide empty dict if no litellm_params provided + data_to_create["litellm_params"] = safe_dumps({}) + + # Create in database + _new_vector_store = ( + await prisma_client.db.litellm_managedvectorstorestable.create( + data=data_to_create + ) + ) + + new_vector_store: LiteLLM_ManagedVectorStore = LiteLLM_ManagedVectorStore( + **_new_vector_store.model_dump() + ) + + # Add vector store to registry + if litellm.vector_store_registry is not None: + litellm.vector_store_registry.add_vector_store_to_registry( + vector_store=new_vector_store + ) + + verbose_proxy_logger.info( + f"Vector store {vector_store_id} created in database successfully" + ) + + return new_vector_store + + ######################################################## # Management Endpoints ######################################################## @@ -156,71 +262,34 @@ async def new_vector_store( - vector_store_metadata: Optional[Dict] - Additional metadata for the vector store """ from litellm.proxy.proxy_server import prisma_client - from litellm.types.router import GenericLiteLLMParams - - if prisma_client is None: - raise HTTPException(status_code=500, detail="Database not connected") try: - # Check if vector store already exists - existing_vector_store = ( - await prisma_client.db.litellm_managedvectorstorestable.find_unique( - where={"vector_store_id": vector_store.get("vector_store_id")} - ) - ) - if existing_vector_store is not None: + vector_store_id = vector_store.get("vector_store_id") + custom_llm_provider = vector_store.get("custom_llm_provider") + + if not vector_store_id or not custom_llm_provider: raise HTTPException( status_code=400, - detail=f"Vector store with ID {vector_store.get('vector_store_id')} already exists", - ) - - if vector_store.get("vector_store_metadata") is not None: - vector_store["vector_store_metadata"] = safe_dumps( - vector_store.get("vector_store_metadata") - ) - - # Safely handle JSON serialization of litellm_params - litellm_params_json: Optional[str] = None - _input_litellm_params: dict = vector_store.get("litellm_params", {}) or {} - if _input_litellm_params is not None: - # Auto-resolve embedding config if embedding model is provided but config is not - embedding_model = _input_litellm_params.get("litellm_embedding_model") - if embedding_model and not _input_litellm_params.get("litellm_embedding_config"): - resolved_config = await _resolve_embedding_config_from_db( - embedding_model=embedding_model, - prisma_client=prisma_client - ) - if resolved_config: - _input_litellm_params["litellm_embedding_config"] = resolved_config - verbose_proxy_logger.info( - f"Auto-resolved embedding config for model {embedding_model}" - ) - - litellm_params_dict = GenericLiteLLMParams( - **_input_litellm_params - ).model_dump(exclude_none=True) - litellm_params_json = safe_dumps(litellm_params_dict) - del vector_store["litellm_params"] - - _new_vector_store = ( - await prisma_client.db.litellm_managedvectorstorestable.create( - data={ - **vector_store, - "litellm_params": litellm_params_json, - } + detail="vector_store_id and custom_llm_provider are required" ) + + # Extract and validate metadata + metadata = vector_store.get("vector_store_metadata") + validated_metadata: Optional[Dict] = None + if metadata is not None and isinstance(metadata, dict): + validated_metadata = metadata + + new_vector_store = await create_vector_store_in_db( + vector_store_id=vector_store_id, + custom_llm_provider=custom_llm_provider, + prisma_client=prisma_client, + vector_store_name=vector_store.get("vector_store_name"), + vector_store_description=vector_store.get("vector_store_description"), + vector_store_metadata=validated_metadata, + litellm_params=vector_store.get("litellm_params"), + litellm_credential_name=vector_store.get("litellm_credential_name"), ) - new_vector_store: LiteLLM_ManagedVectorStore = LiteLLM_ManagedVectorStore( - **_new_vector_store.model_dump() - ) - - # Add vector store to registry - if litellm.vector_store_registry is not None: - litellm.vector_store_registry.add_vector_store_to_registry( - vector_store=new_vector_store - ) - return { "status": "success", "message": f"Vector store {vector_store.get('vector_store_id')} created successfully", diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx index 6c8fb6f834..a1625b6ffb 100644 --- a/ui/litellm-dashboard/src/components/networking.tsx +++ b/ui/litellm-dashboard/src/components/networking.tsx @@ -6948,6 +6948,62 @@ export const vectorStoreUpdateCall = async (accessToken: string, formValues: Rec } }; +export const ragIngestCall = async ( + accessToken: string, + file: File, + customLlmProvider: string, + vectorStoreId?: string, + vectorStoreName?: string, + vectorStoreDescription?: string +): Promise => { + try { + let url = proxyBaseUrl ? `${proxyBaseUrl}/rag/ingest` : `/rag/ingest`; + + const formData = new FormData(); + formData.append("file", file); + + const ingestOptions: any = { + ingest_options: { + vector_store: { + custom_llm_provider: customLlmProvider, + ...(vectorStoreId && { vector_store_id: vectorStoreId }), + }, + }, + }; + + // Add litellm_vector_store_params if name or description provided + if (vectorStoreName || vectorStoreDescription) { + ingestOptions.ingest_options.litellm_vector_store_params = {}; + if (vectorStoreName) { + ingestOptions.ingest_options.litellm_vector_store_params.vector_store_name = vectorStoreName; + } + if (vectorStoreDescription) { + ingestOptions.ingest_options.litellm_vector_store_params.vector_store_description = vectorStoreDescription; + } + } + + formData.append("request", JSON.stringify(ingestOptions)); + + const response = await fetch(url, { + method: "POST", + headers: { + [globalLitellmHeaderName]: `Bearer ${accessToken}`, + }, + body: formData, + }); + + if (!response.ok) { + const error = await response.json(); + throw new Error(error.error?.message || error.detail || "Failed to ingest document"); + } + + return await response.json(); + } catch (error) { + console.error("Error ingesting document:", error); + throw error; + } +}; + export const getEmailEventSettings = async (accessToken: string): Promise => { try { const url = proxyBaseUrl ? `${proxyBaseUrl}/email/event_settings` : `/email/event_settings`; diff --git a/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.test.tsx b/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.test.tsx new file mode 100644 index 0000000000..db2975781b --- /dev/null +++ b/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.test.tsx @@ -0,0 +1,166 @@ +import { render, screen, fireEvent, waitFor, act } from "@testing-library/react"; +import { describe, it, expect, vi, beforeEach } from "vitest"; +import CreateVectorStore from "./CreateVectorStore"; +import * as networking from "../networking"; + +// Mock the networking module +vi.mock("../networking", () => ({ + ragIngestCall: vi.fn(), +})); + +// Mock NotificationsManager +vi.mock("../molecules/notifications_manager", () => ({ + default: { + success: vi.fn(), + fromBackend: vi.fn(), + }, +})); + +// Mock vector_store_providers +vi.mock("../vector_store_providers", () => ({ + VectorStoreProviders: { + BEDROCK: "Amazon Bedrock", + OPENAI: "OpenAI", + AZURE_OPENAI: "Azure OpenAI", + }, + vectorStoreProviderMap: { + BEDROCK: "bedrock", + OPENAI: "openai", + AZURE_OPENAI: "azure_openai", + }, + vectorStoreProviderLogoMap: { + "Amazon Bedrock": "https://example.com/bedrock.png", + "OpenAI": "https://example.com/openai.png", + "Azure OpenAI": "https://example.com/azure.png", + }, +})); + +describe("CreateVectorStore", () => { + beforeEach(() => { + vi.clearAllMocks(); + }); + + it("should render the component successfully", () => { + render(); + + expect(screen.getByText("Create Vector Store")).toBeInTheDocument(); + expect(screen.getByText("Step 1: Upload Documents")).toBeInTheDocument(); + expect(screen.getByText("Step 2: Select Provider")).toBeInTheDocument(); + }); + + it("should display upload area with correct text", () => { + render(); + + expect(screen.getByText("Click or drag files to this area to upload")).toBeInTheDocument(); + expect(screen.getByText(/Support for single or bulk upload/)).toBeInTheDocument(); + }); + + it("should have provider selection dropdown", () => { + render(); + + expect(screen.getByText("Provider")).toBeInTheDocument(); + }); + + it("should have create button disabled initially when no documents", () => { + render(); + + const createButton = screen.getByRole("button", { name: /Create Vector Store/i }); + expect(createButton).toBeDisabled(); + }); + + it("should show uploaded documents table when files are added", async () => { + render(); + + // Create a mock file + const file = new File(["test content"], "test.pdf", { type: "application/pdf" }); + + // Find the upload input (it's hidden but accessible) + const uploadInput = document.querySelector('input[type="file"]') as HTMLInputElement; + + await act(async () => { + if (uploadInput) { + fireEvent.change(uploadInput, { target: { files: [file] } }); + } + }); + + await waitFor(() => { + expect(screen.getByText("Uploaded Documents (1)")).toBeInTheDocument(); + }); + }); + + it("should call ragIngestCall when create button is clicked", async () => { + const mockRagIngestCall = vi.spyOn(networking, "ragIngestCall"); + mockRagIngestCall.mockResolvedValue({ + id: "test-id", + status: "completed", + vector_store_id: "vs_123", + file_id: "file_123", + }); + + const onSuccess = vi.fn(); + render(); + + // Create a mock file + const file = new File(["test content"], "test.pdf", { type: "application/pdf" }); + const uploadInput = document.querySelector('input[type="file"]') as HTMLInputElement; + + await act(async () => { + if (uploadInput) { + fireEvent.change(uploadInput, { target: { files: [file] } }); + } + }); + + // Wait for file to be added + await waitFor(() => { + expect(screen.getByText("Uploaded Documents (1)")).toBeInTheDocument(); + }); + + // Click create button + const createButton = screen.getByRole("button", { name: /Create Vector Store/i }); + + await act(async () => { + fireEvent.click(createButton); + }); + + await waitFor(() => { + expect(mockRagIngestCall).toHaveBeenCalledWith("test-token", expect.any(File), "bedrock", undefined); + }); + }); + + it("should display success message after successful creation", async () => { + const mockRagIngestCall = vi.spyOn(networking, "ragIngestCall"); + mockRagIngestCall.mockResolvedValue({ + id: "test-id", + status: "completed", + vector_store_id: "vs_123", + file_id: "file_123", + }); + + render(); + + // Create and upload a mock file + const file = new File(["test content"], "test.pdf", { type: "application/pdf" }); + const uploadInput = document.querySelector('input[type="file"]') as HTMLInputElement; + + await act(async () => { + if (uploadInput) { + fireEvent.change(uploadInput, { target: { files: [file] } }); + } + }); + + await waitFor(() => { + expect(screen.getByText("Uploaded Documents (1)")).toBeInTheDocument(); + }); + + // Click create button + const createButton = screen.getByRole("button", { name: /Create Vector Store/i }); + + await act(async () => { + fireEvent.click(createButton); + }); + + await waitFor(() => { + expect(screen.getByText("Vector Store Created Successfully")).toBeInTheDocument(); + }); + }); +}); diff --git a/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.tsx b/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.tsx new file mode 100644 index 0000000000..685e37ac73 --- /dev/null +++ b/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.tsx @@ -0,0 +1,340 @@ +import React, { useState } from "react"; +import { Card, Title, Text } from "@tremor/react"; +import { Upload, Button, Select, Form, message, Alert, Tooltip, Input } from "antd"; +import { InboxOutlined, InfoCircleOutlined } from "@ant-design/icons"; +import type { UploadProps } from "antd"; +import { ragIngestCall } from "../networking"; +import { DocumentUpload, RAGIngestResponse } from "./types"; +import DocumentsTable from "./DocumentsTable"; +import { + VectorStoreProviders, + vectorStoreProviderLogoMap, + vectorStoreProviderMap, +} from "../vector_store_providers"; +import NotificationsManager from "../molecules/notifications_manager"; + +const { Dragger } = Upload; + +interface CreateVectorStoreProps { + accessToken: string | null; + onSuccess?: (vectorStoreId: string) => void; +} + +const CreateVectorStore: React.FC = ({ accessToken, onSuccess }) => { + const [form] = Form.useForm(); + const [documents, setDocuments] = useState([]); + const [isCreating, setIsCreating] = useState(false); + const [selectedProvider, setSelectedProvider] = useState("bedrock"); + const [vectorStoreName, setVectorStoreName] = useState(""); + const [vectorStoreDescription, setVectorStoreDescription] = useState(""); + const [ingestResults, setIngestResults] = useState([]); + + const uploadProps: UploadProps = { + name: "file", + multiple: true, + accept: ".pdf,.txt,.docx,.md,.doc", + beforeUpload: (file) => { + const isValidType = [ + "application/pdf", + "text/plain", + "application/vnd.openxmlformats-officedocument.wordprocessingml.document", + "application/msword", + "text/markdown", + ].includes(file.type); + + if (!isValidType) { + message.error(`${file.name} is not a supported file type. Please upload PDF, TXT, DOCX, or MD files.`); + return Upload.LIST_IGNORE; + } + + const isLt50M = file.size / 1024 / 1024 < 50; + if (!isLt50M) { + message.error(`${file.name} must be smaller than 50MB!`); + return Upload.LIST_IGNORE; + } + + const newDoc: DocumentUpload = { + uid: file.uid, + name: file.name, + status: "done", + size: file.size, + type: file.type, + originFileObj: file, + }; + + setDocuments((prev) => [...prev, newDoc]); + return false; // Prevent auto upload + }, + onRemove: (file) => { + setDocuments((prev) => prev.filter((doc) => doc.uid !== file.uid)); + }, + fileList: documents.map((doc) => ({ + uid: doc.uid, + name: doc.name, + status: doc.status, + size: doc.size, + })), + showUploadList: false, // We'll use our custom table + }; + + const handleRemoveDocument = (uid: string) => { + setDocuments((prev) => prev.filter((doc) => doc.uid !== uid)); + }; + + const handleCreateVectorStore = async () => { + if (documents.length === 0) { + message.warning("Please upload at least one document"); + return; + } + + if (!selectedProvider) { + message.warning("Please select a provider"); + return; + } + + if (!accessToken) { + message.error("No access token available"); + return; + } + + setIsCreating(true); + const results: RAGIngestResponse[] = []; + let vectorStoreId: string | undefined; + + try { + // Ingest each document + for (const doc of documents) { + if (!doc.originFileObj) continue; + + // Update document status to uploading + setDocuments((prev) => + prev.map((d) => (d.uid === doc.uid ? { ...d, status: "uploading" as const } : d)) + ); + + try { + const result = await ragIngestCall( + accessToken, + doc.originFileObj, + selectedProvider, + vectorStoreId, // Use the same vector store ID for subsequent uploads + vectorStoreName || undefined, + vectorStoreDescription || undefined + ); + + // Store the vector store ID from the first successful ingest + if (!vectorStoreId && result.vector_store_id) { + vectorStoreId = result.vector_store_id; + } + + results.push(result); + + // Update document status to done + setDocuments((prev) => + prev.map((d) => (d.uid === doc.uid ? { ...d, status: "done" as const } : d)) + ); + } catch (error) { + console.error(`Error ingesting ${doc.name}:`, error); + // Update document status to error + setDocuments((prev) => + prev.map((d) => (d.uid === doc.uid ? { ...d, status: "error" as const } : d)) + ); + throw error; // Stop processing on first error + } + } + + setIngestResults(results); + NotificationsManager.success( + `Successfully created vector store with ${results.length} document(s). Vector Store ID: ${vectorStoreId}` + ); + + if (onSuccess && vectorStoreId) { + onSuccess(vectorStoreId); + } + + // Clear documents after successful creation + setTimeout(() => { + setDocuments([]); + setIngestResults([]); + }, 3000); + } catch (error) { + console.error("Error creating vector store:", error); + NotificationsManager.fromBackend(`Failed to create vector store: ${error}`); + } finally { + setIsCreating(false); + } + }; + + return ( +
      +
      + Create Vector Store + + Upload documents and select a provider to create a new vector store with embedded content. + +
      + + {/* Upload Area */} + +
      + Step 1: Upload Documents + + Upload one or more documents (PDF, TXT, DOCX, MD). Maximum file size: 50MB per file. + +
      + +

      + +

      +

      Click or drag files to this area to upload

      +

      + Support for single or bulk upload. Supported formats: PDF, TXT, DOCX, MD +

      +       +       + + {/* Documents Table */} + {documents.length > 0 && ( + +
      + Uploaded Documents ({documents.length}) +
      + +       + )} + + {/* Provider Selection and Vector Store Details */} + +
      +
      + Step 2: Configure Vector Store + + Choose the provider and optionally provide a name and description for your vector store. + +
      + +
      + + Vector Store Name{" "} + + + + + } + > + setVectorStoreName(e.target.value)} + placeholder="e.g., Product Documentation, Customer Support KB" + size="large" + className="rounded-md" + /> + + + + Description{" "} + + + + + } + > + setVectorStoreDescription(e.target.value)} + placeholder="e.g., Contains all product documentation and user guides" + rows={2} + size="large" + className="rounded-md" + /> + + + + Provider{" "} + + + + + } + required + > + + +
      + +
      + +
      +
      +       + + {/* Success Message */} + {ingestResults.length > 0 && ( + +

      + Vector Store ID: {ingestResults[0]?.vector_store_id} +

      +

      + Documents Ingested: {ingestResults.length} +

      +
      + } + type="success" + showIcon + closable + /> + )} + + ); +}; + +export default CreateVectorStore; diff --git a/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.test.tsx b/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.test.tsx new file mode 100644 index 0000000000..761bc2b7df --- /dev/null +++ b/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.test.tsx @@ -0,0 +1,102 @@ +import { render, screen, fireEvent, act } from "@testing-library/react"; +import { describe, it, expect, vi } from "vitest"; +import DocumentsTable from "./DocumentsTable"; +import { DocumentUpload } from "./types"; + +// Mock antd message +vi.mock("antd", async () => { + const actual = await vi.importActual("antd"); + return { + ...actual, + message: { + success: vi.fn(), + }, + }; +}); + +describe("DocumentsTable", () => { + const mockDocuments: DocumentUpload[] = [ + { + uid: "1", + name: "test1.pdf", + status: "done", + size: 1024000, + type: "application/pdf", + }, + { + uid: "2", + name: "test2.txt", + status: "uploading", + size: 2048000, + type: "text/plain", + }, + { + uid: "3", + name: "test3.docx", + status: "error", + size: 512000, + type: "application/vnd.openxmlformats-officedocument.wordprocessingml.document", + }, + ]; + + it("should render the table successfully", () => { + const onRemove = vi.fn(); + render(); + + expect(screen.getByText("test1.pdf")).toBeInTheDocument(); + expect(screen.getByText("test2.txt")).toBeInTheDocument(); + expect(screen.getByText("test3.docx")).toBeInTheDocument(); + }); + + it("should display correct status badges", () => { + const onRemove = vi.fn(); + render(); + + expect(screen.getByText("Ready")).toBeInTheDocument(); + expect(screen.getByText("Uploading")).toBeInTheDocument(); + expect(screen.getByText("Error")).toBeInTheDocument(); + }); + + it("should display file sizes", () => { + const onRemove = vi.fn(); + render(); + + expect(screen.getByText(/1000.00 KB/)).toBeInTheDocument(); + expect(screen.getByText(/2.00 MB/)).toBeInTheDocument(); + expect(screen.getByText(/500.00 KB/)).toBeInTheDocument(); + }); + + it("should call onRemove when delete button is clicked", () => { + const onRemove = vi.fn(); + render(); + + const deleteButtons = screen.getAllByLabelText(/delete/i); + + act(() => { + fireEvent.click(deleteButtons[0]); + }); + + expect(onRemove).toHaveBeenCalledWith("1"); + }); + + it("should show empty state when no documents", () => { + const onRemove = vi.fn(); + render(); + + expect(screen.getByText(/No documents uploaded yet/)).toBeInTheDocument(); + }); + + it("should have action buttons for each document", () => { + const onRemove = vi.fn(); + render(); + + // Each document should have 3 action buttons (view, copy, delete) + const viewButtons = screen.getAllByLabelText(/eye/i); + const copyButtons = screen.getAllByLabelText(/copy/i); + const deleteButtons = screen.getAllByLabelText(/delete/i); + + expect(viewButtons).toHaveLength(3); + expect(copyButtons).toHaveLength(3); + expect(deleteButtons).toHaveLength(3); + }); +}); diff --git a/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.tsx b/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.tsx new file mode 100644 index 0000000000..aeb4240d36 --- /dev/null +++ b/ui/litellm-dashboard/src/components/vector_store_management/DocumentsTable.tsx @@ -0,0 +1,98 @@ +import React from "react"; +import { Table, Badge, Tooltip, message } from "antd"; +import { EyeOutlined, CopyOutlined, DeleteOutlined } from "@ant-design/icons"; +import { DocumentUpload } from "./types"; + +interface DocumentsTableProps { + documents: DocumentUpload[]; + onRemove: (uid: string) => void; +} + +const DocumentsTable: React.FC = ({ documents, onRemove }) => { + const handleCopyId = (uid: string) => { + navigator.clipboard.writeText(uid); + message.success("Document ID copied to clipboard"); + }; + + const getStatusBadge = (status: DocumentUpload["status"]) => { + const statusConfig = { + uploading: { color: "blue", text: "Uploading" }, + done: { color: "green", text: "Ready" }, + error: { color: "red", text: "Error" }, + removed: { color: "default", text: "Removed" }, + }; + + const config = statusConfig[status]; + return ; + }; + + const formatFileSize = (bytes?: number) => { + if (!bytes) return "-"; + const kb = bytes / 1024; + if (kb < 1024) return `${kb.toFixed(2)} KB`; + return `${(kb / 1024).toFixed(2)} MB`; + }; + + const columns = [ + { + title: "Name", + dataIndex: "name", + key: "name", + render: (name: string, record: DocumentUpload) => ( +
      + {name} + {record.size && ({formatFileSize(record.size)})} +
      + ), + }, + { + title: "Status", + dataIndex: "status", + key: "status", + width: 150, + render: (status: DocumentUpload["status"]) => getStatusBadge(status), + }, + { + title: "Actions", + key: "actions", + width: 120, + render: (_: any, record: DocumentUpload) => ( +
      + + console.log("View", record)} + /> + + + handleCopyId(record.uid)} + /> + + + onRemove(record.uid)} + /> + +
      + ), + }, + ]; + + return ( + + ); +}; + +export default DocumentsTable; diff --git a/ui/litellm-dashboard/src/components/vector_store_management/TestVectorStoreTab.test.tsx b/ui/litellm-dashboard/src/components/vector_store_management/TestVectorStoreTab.test.tsx new file mode 100644 index 0000000000..ad6ef02e87 --- /dev/null +++ b/ui/litellm-dashboard/src/components/vector_store_management/TestVectorStoreTab.test.tsx @@ -0,0 +1,90 @@ +import { render, screen, fireEvent } from "@testing-library/react"; +import { describe, it, expect, vi } from "vitest"; +import TestVectorStoreTab from "./TestVectorStoreTab"; +import { VectorStore } from "./types"; + +// Mock VectorStoreTester component +vi.mock("./VectorStoreTester", () => ({ + VectorStoreTester: ({ vectorStoreId, accessToken }: { vectorStoreId: string; accessToken: string }) => ( +
      +
      {vectorStoreId}
      +
      {accessToken}
      +
      + ), +})); + +const mockVectorStores: VectorStore[] = [ + { + vector_store_id: "vs_123", + custom_llm_provider: "openai", + vector_store_name: "Test Store 1", + vector_store_description: "Description 1", + created_at: "2024-01-01T00:00:00Z", + updated_at: "2024-01-01T00:00:00Z", + }, + { + vector_store_id: "vs_456", + custom_llm_provider: "bedrock", + vector_store_name: "Test Store 2", + vector_store_description: "Description 2", + created_at: "2024-01-02T00:00:00Z", + updated_at: "2024-01-02T00:00:00Z", + }, +]; + +describe("TestVectorStoreTab", () => { + it("should render the component successfully", () => { + render(); + + expect(screen.getByText("Select Vector Store")).toBeInTheDocument(); + expect(screen.getByText("Choose a vector store to test search queries against")).toBeInTheDocument(); + }); + + it("should show message when no access token", () => { + render(); + + expect(screen.getByText("Access token is required to test vector stores.")).toBeInTheDocument(); + }); + + it("should show message when no vector stores available", () => { + render(); + + expect(screen.getByText("No vector stores available. Create one first to test it.")).toBeInTheDocument(); + }); + + it("should render VectorStoreTester with first vector store by default", () => { + render(); + + expect(screen.getByTestId("vector-store-tester")).toBeInTheDocument(); + expect(screen.getByTestId("tester-vector-store-id")).toHaveTextContent("vs_123"); + expect(screen.getByTestId("tester-access-token")).toHaveTextContent("test-token"); + }); + + it("should update VectorStoreTester when selecting different vector store", () => { + render(); + + // Find the select component + const selectElement = screen.getByRole("combobox"); + + // Change selection + fireEvent.mouseDown(selectElement); + + // Wait for options to appear and click the second one + const option2 = screen.getByText("Test Store 2"); + fireEvent.click(option2); + + // Verify the tester component updated + expect(screen.getByTestId("tester-vector-store-id")).toHaveTextContent("vs_456"); + }); + + it("should display vector store names in select options", () => { + render(); + + const selectElement = screen.getByRole("combobox"); + fireEvent.mouseDown(selectElement); + + // Use getAllByText since the selected value also shows the name + expect(screen.getAllByText("Test Store 1").length).toBeGreaterThan(0); + expect(screen.getByText("Test Store 2")).toBeInTheDocument(); + }); +}); diff --git a/ui/litellm-dashboard/src/components/vector_store_management/TestVectorStoreTab.tsx b/ui/litellm-dashboard/src/components/vector_store_management/TestVectorStoreTab.tsx new file mode 100644 index 0000000000..da005491ca --- /dev/null +++ b/ui/litellm-dashboard/src/components/vector_store_management/TestVectorStoreTab.tsx @@ -0,0 +1,75 @@ +import React, { useState } from "react"; +import { Card, Select, Typography } from "antd"; +import { VectorStoreTester } from "./VectorStoreTester"; +import { VectorStore } from "./types"; + +const { Text, Title } = Typography; + +interface TestVectorStoreTabProps { + accessToken: string | null; + vectorStores: VectorStore[]; +} + +const TestVectorStoreTab: React.FC = ({ accessToken, vectorStores }) => { + const [selectedVectorStoreId, setSelectedVectorStoreId] = useState( + vectorStores.length > 0 ? vectorStores[0].vector_store_id : undefined + ); + + if (!accessToken) { + return ( + + Access token is required to test vector stores. + + ); + } + + if (vectorStores.length === 0) { + return ( + +
      + No vector stores available. Create one first to test it. +
      +       + ); + } + + return ( +
      + +
      +
      + Select Vector Store + Choose a vector store to test search queries against +
      + + +
      +       + + {selectedVectorStoreId && ( + + )} +
      + ); +}; + +export default TestVectorStoreTab; diff --git a/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.test.tsx b/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.test.tsx index 65d15260c4..0e2be7f62d 100644 --- a/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.test.tsx +++ b/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.test.tsx @@ -130,9 +130,9 @@ describe("VectorStoreTable", () => { expect(screen.getByText("Provider")).toBeInTheDocument(); expect(screen.getByText("Created At")).toBeInTheDocument(); expect(screen.getByText("Updated At")).toBeInTheDocument(); - // Check that we have the expected number of header cells (6 data + 1 actions) + // Check that we have the expected number of header cells (7 data + 1 actions) const headers = screen.getAllByRole("columnheader"); - expect(headers).toHaveLength(7); + expect(headers).toHaveLength(8); }); it("should render all vector store rows", () => { @@ -183,7 +183,7 @@ describe("VectorStoreTable", () => { it("should render fallback for missing name", () => { renderComponent(); const fallbackElements = screen.getAllByText("-"); - expect(fallbackElements.length).toBe(2); // One for missing name, one for missing description + expect(fallbackElements.length).toBe(3); // One for missing name, one for missing description, one for missing files }); it("should wrap name in tooltip", () => { @@ -203,7 +203,7 @@ describe("VectorStoreTable", () => { it("should render fallback for missing description", () => { renderComponent(); const fallbackElements = screen.getAllByText("-"); - expect(fallbackElements.length).toBe(2); // One for missing name, one for missing description + expect(fallbackElements.length).toBe(3); // One for missing name, one for missing description, one for missing files }); it("should wrap description in tooltip", () => { @@ -386,7 +386,7 @@ describe("VectorStoreTable", () => { it("should span all columns in empty state", () => { renderComponent({ data: [] }); const emptyCell = screen.getByText("No vector stores found").closest("td"); - expect(emptyCell).toHaveAttribute("colSpan", "7"); // 6 data columns + 1 actions column + expect(emptyCell).toHaveAttribute("colSpan", "8"); // 7 data columns + 1 actions column }); }); @@ -403,7 +403,7 @@ describe("VectorStoreTable", () => { renderComponent({ data: minimalData }); expect(screen.getByText("minimal")).toBeInTheDocument(); - expect(screen.getAllByText("-")).toHaveLength(2); // Name and description fallbacks + expect(screen.getAllByText("-")).toHaveLength(3); // Name, description, and files fallbacks }); it("should handle single vector store", () => { diff --git a/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.tsx b/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.tsx index 52462c02e9..41e2b63112 100644 --- a/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.tsx +++ b/ui/litellm-dashboard/src/components/vector_store_management/VectorStoreTable.tsx @@ -66,6 +66,32 @@ const VectorStoreTable: React.FC = ({ data, onView, onEdi ); }, }, + { + header: "Files", + accessorKey: "vector_store_metadata", + cell: ({ row }) => { + const vectorStore = row.original; + const ingestedFiles = vectorStore.vector_store_metadata?.ingested_files || []; + + if (ingestedFiles.length === 0) { + return -; + } + + const filenames = ingestedFiles + .map((file) => file.filename || file.file_url || "Unknown") + .join(", "); + + const displayText = ingestedFiles.length === 1 + ? ingestedFiles[0].filename || ingestedFiles[0].file_url || "1 file" + : `${ingestedFiles.length} files`; + + return ( + + {displayText} + + ); + }, + }, { header: "Provider", accessorKey: "custom_llm_provider", diff --git a/ui/litellm-dashboard/src/components/vector_store_management/index.tsx b/ui/litellm-dashboard/src/components/vector_store_management/index.tsx index 6d21e861d4..9cb57b8b9f 100644 --- a/ui/litellm-dashboard/src/components/vector_store_management/index.tsx +++ b/ui/litellm-dashboard/src/components/vector_store_management/index.tsx @@ -1,5 +1,5 @@ import React, { useState, useEffect } from "react"; -import { Icon, Button as TremorButton, Col, Text, Grid } from "@tremor/react"; +import { Icon, Button as TremorButton, Col, Text, Grid, TabGroup, TabList, Tab, TabPanels, TabPanel } from "@tremor/react"; import { RefreshIcon } from "@heroicons/react/outline"; import { vectorStoreListCall, vectorStoreDeleteCall, credentialListCall, CredentialItem } from "../networking"; import { VectorStore } from "./types"; @@ -7,6 +7,8 @@ import VectorStoreTable from "./VectorStoreTable"; import VectorStoreForm from "./VectorStoreForm"; import DeleteResourceModal from "../common_components/DeleteResourceModal"; import VectorStoreInfoView from "./vector_store_info"; +import CreateVectorStore from "./CreateVectorStore"; +import TestVectorStoreTab from "./TestVectorStoreTab"; import { isAdminRole } from "@/utils/roles"; import NotificationsManager from "../molecules/notifications_manager"; @@ -101,6 +103,12 @@ const VectorStoreManagement: React.FC = ({ accessToken, userID fetchVectorStores(); }; + const handleVectorStoreCreated = (vectorStoreId: string) => { + console.log("Vector store created:", vectorStoreId); + fetchVectorStores(); + // Optionally switch to the manage tab + }; + useEffect(() => { fetchVectorStores(); fetchCredentials(); @@ -134,18 +142,46 @@ const VectorStoreManagement: React.FC = ({ accessToken, userID -

      You can use vector stores to store and retrieve LLM embeddings..

      +

      You can use vector stores to store and retrieve LLM embeddings.

       - setIsCreateModalVisible(true)}> - + Add Vector Store - + + + Create Vector Store + Manage Vector Stores + Test Vector Store + - -       - - - + + {/* Tab 1: Create Vector Store */} + + + + + {/* Tab 2: Manage Vector Stores */} + + setIsCreateModalVisible(true)}> + + Add Vector Store + + + + + + + + + + {/* Tab 3: Test Vector Store */} + + + + + {/* Create Vector Store Modal */} ; + vector_store_metadata?: VectorStoreMetadata; created_at: string; updated_at: string; created_by?: string; @@ -41,3 +55,32 @@ export interface VectorStoreListResponse { current_page: number; total_pages: number; } + +// Document ingestion types +export interface DocumentUpload { + uid: string; + name: string; + status: "uploading" | "done" | "error" | "removed"; + size?: number; + type?: string; + originFileObj?: File; +} + +export interface RAGIngestRequest { + file_url?: string; + file_id?: string; + ingest_options: { + vector_store: { + custom_llm_provider: string; + vector_store_id?: string; + }; + }; +} + +export interface RAGIngestResponse { + id: string; + status: "completed" | "processing" | "failed"; + vector_store_id: string; + file_id: string; + error?: string; +} From 014f783cc973f7dcdd0675bec53d50602e116fc6 Mon Sep 17 00:00:00 2001 From: Cesar Garcia <128240629+Chesars@users.noreply.github.com> Date: Tue, 27 Jan 2026 00:24:46 -0300 Subject: [PATCH 36/62] docs(readme): add OpenAI Agents SDK to OSS Adopters (#19820) * docs(readme): add OpenAI Agents SDK to OSS Adopters * docs(readme): add OpenAI Agents SDK logo --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 914fda384b..77adddf897 100644 --- a/README.md +++ b/README.md @@ -267,6 +267,7 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature +
      Greptile OpenHands

      Netflix

      		OpenAI Agents SDK
      From ef7261d0eb4d214c43f47a1f3d095073efc85623 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Mon, 26 Jan 2026 20:07:30 -0800 Subject: [PATCH 37/62] Fixing tests --- tests/proxy_unit_tests/test_proxy_server.py | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/tests/proxy_unit_tests/test_proxy_server.py b/tests/proxy_unit_tests/test_proxy_server.py index 839d09b52d..d864a0d986 100644 --- a/tests/proxy_unit_tests/test_proxy_server.py +++ b/tests/proxy_unit_tests/test_proxy_server.py @@ -1117,7 +1117,10 @@ async def test_create_team_member_add(prisma_client, new_member_method): ) as mock_litellm_usertable, patch( "litellm.proxy.auth.auth_checks._get_team_object_from_user_api_key_cache", new=AsyncMock(return_value=team_obj), - ) as mock_team_obj: + ) as mock_team_obj, patch( + "litellm.proxy.proxy_server.prisma_client.get_data", + new=AsyncMock(return_value=[]), + ) as mock_get_data: mock_client = AsyncMock( return_value=LiteLLM_UserTable( @@ -1126,6 +1129,10 @@ async def test_create_team_member_add(prisma_client, new_member_method): ) mock_litellm_usertable.upsert = mock_client mock_litellm_usertable.find_many = AsyncMock(return_value=None) + # Mock find_first for user_email validation (returns None for new users) + mock_litellm_usertable.find_first = AsyncMock(return_value=None) + # Mock find_unique for user_id validation (returns None for new users) + mock_litellm_usertable.find_unique = AsyncMock(return_value=None) team_mock_client = AsyncMock() original_val = getattr( litellm.proxy.proxy_server.prisma_client.db, "litellm_teamtable" @@ -1299,7 +1306,10 @@ async def test_create_team_member_add_team_admin( ) as mock_litellm_usertable, patch( "litellm.proxy.auth.auth_checks._get_team_object_from_user_api_key_cache", new=AsyncMock(return_value=team_obj), - ) as mock_team_obj: + ) as mock_team_obj, patch( + "litellm.proxy.proxy_server.prisma_client.get_data", + new=AsyncMock(return_value=[]), + ) as mock_get_data: mock_client = AsyncMock( return_value=LiteLLM_UserTable( user_id="1234", max_budget=100, user_email="1234" @@ -1307,6 +1317,10 @@ async def test_create_team_member_add_team_admin( ) mock_litellm_usertable.upsert = mock_client mock_litellm_usertable.find_many = AsyncMock(return_value=None) + # Mock find_first for user_email validation (returns None for new users) + mock_litellm_usertable.find_first = AsyncMock(return_value=None) + # Mock find_unique for user_id validation (returns None for new users) + mock_litellm_usertable.find_unique = AsyncMock(return_value=None) team_mock_client = AsyncMock() original_val = getattr( From 664715b68efe539fd4dc38c3d1452f56cb134802 Mon Sep 17 00:00:00 2001 From: Krish Dholakia Date: Mon, 26 Jan 2026 20:11:56 -0800 Subject: [PATCH 38/62] Litellm release notes 01 26 2026 (#19836) * docs: document new models/endpoints * docs: cleanup * feat: update model table --- .../release_notes/v1.81.3-stable/index.md | 429 ++++++++++++++++++ 1 file changed, 429 insertions(+) create mode 100644 docs/my-website/release_notes/v1.81.3-stable/index.md diff --git a/docs/my-website/release_notes/v1.81.3-stable/index.md b/docs/my-website/release_notes/v1.81.3-stable/index.md new file mode 100644 index 0000000000..accacd0abd --- /dev/null +++ b/docs/my-website/release_notes/v1.81.3-stable/index.md @@ -0,0 +1,429 @@ +--- +title: "v1.81.3-stable - Performance - 25% CPU Usage Reduction" +slug: "v1-81-3" +date: 2026-01-26T10:00:00 +authors: + - name: Krrish Dholakia + title: CEO, LiteLLM + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaff + title: CTO, LiteLLM + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg +hide_table_of_contents: false +--- + +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +## Deploy this version + + + + +``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +docker.litellm.ai/berriai/litellm:v1.81.3.rc.2 +``` + + + + + +``` showLineNumbers title="pip install litellm" +pip install litellm==1.81.3.rc.2 +``` + + + + +--- + +## Key Highlights +[TODO] +--- + +[TODO] + +## New Models / Updated Models + +### New Model Support + +| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Deprecation Date | +| -------- | ----- | -------------- | ------------------- | -------------------- | ---------------- | +| OpenAI | `gpt-audio`, `gpt-audio-2025-08-28` | 128K | $32/1M audio tokens, $2.5/1M text tokens | $64/1M audio tokens, $10/1M text tokens | - | +| OpenAI | `gpt-audio-mini`, `gpt-audio-mini-2025-08-28` | 128K | $10/1M audio tokens, $0.6/1M text tokens | $20/1M audio tokens, $2.4/1M text tokens | - | +| Deepinfra, Vertex AI, Google AI Studio, OpenRouter, Vercel AI Gateway | `gemini-2.0-flash-001`, `gemini-2.0-flash` | - | - | - | 2026-03-31 | +| Groq | `openai/gpt-oss-120b` | 131K | 0.075/1M cache read | 0.6/1M output tokens | - | +| Groq | `groq/openai/gpt-oss-20b` | 131K | 0.0375/1M cache read, $0.075/1M text tokens | 0.3/1M output tokens | - | +| Vertex AI | `gemini-2.5-computer-use-preview-10-2025` | 128K | $1.25 | $10 | - | +| Azure AI | `claude-haiku-4-5` | $1.25/1M cache read, $2/1M cache read above 1 hr, $0.1/1M text tokens | $5/1M output tokens | - | +| Azure AI | `claude-sonnet-4-5` | $3.75/1M cache read, $6/1M cache read above 1 hr, $3/1M text tokens | $15/1M output tokens | - | +| Azure AI | `claude-opus-4-5` | $6.25/1M cache read, $10/1M cache read above 1 hr, $0.5/1M text tokens | $25/1M output tokens | - | +| Azure AI | `claude-opus-4-1` | $18.75/1M cache read, $30/1M cache read above 1 hr, $1.5/1M text tokens | $75/1M output tokens | - | + +### Features + +- **[OpenAI](../../docs/providers/openai)** + - Add gpt-audio and gpt-audio-mini models to pricing - [PR #19509](https://github.com/BerriAI/litellm/pull/19509) + - correct audio token costs for gpt-4o-audio-preview models - [PR #19500](https://github.com/BerriAI/litellm/pull/19500) + - Limit stop sequence as per openai spec (ensures JetBrains IDE compatibility) - [PR #19562](https://github.com/BerriAI/litellm/pull/19562) + +- **[VertexAI](../../docs/providers/vertex)** + - Docs - Google Workload Identity Federation (WIF) support - [PR #19320](https://github.com/BerriAI/litellm/pull/19320) + +- **[Agentcore](../../docs/providers/bedrock_agentcore)** + - Fixes streaming issues with AWS Bedrock AgentCore where responses would stop after the first chunk, particularly affecting OAuth-enabled agents - [PR #17141](https://github.com/BerriAI/litellm/pull/17141) + +- **[Chatgpt](../../docs/providers/chatgpt)** + - Adds support for calling chatgpt subscription via LiteLLM - [PR #19030](https://github.com/BerriAI/litellm/pull/19030) + - Adds responses API bridge support for chatgpt subscription provider - [PR #19030](https://github.com/BerriAI/litellm/pull/19030) + +- **[Bedrock](../../docs/providers/bedrock)** + - support for output format for bedrock invoke via v1/messages - [PR #19560](https://github.com/BerriAI/litellm/pull/19560) + +- **[Azure](../../docs/providers/azure/azure)** + - Add support for Azure OpenAI v1 API - [PR #19313](https://github.com/BerriAI/litellm/pull/19313) + - preserve content_policy_violation details for images (#19328) - [PR #19372](https://github.com/BerriAI/litellm/pull/19372) + - Support OpenAI-format nested tool definitions for Responses API - [PR #19526](https://github.com/BerriAI/litellm/pull/19526) + +- **Gemini([Vertex AI](../../docs/providers/vertex), [Google AI Studio](../../docs/providers/gemini))** + - use responseJsonSchema for Gemini 2.0+ models - [PR #19314](https://github.com/BerriAI/litellm/pull/19314) + +- **[Volcengine](../../docs/providers/volcano)** + - Support Volcengine responses api - [PR #18508](https://github.com/BerriAI/litellm/pull/18508) + +- **[Anthropic](../../docs/providers/anthropic)** + - Add Support for calling Claude Code Max subscriptions via LiteLLM - [PR #19453](https://github.com/BerriAI/litellm/pull/19453) + - Add Structured output for /v1/messages with Anthropic API, Azure Anthropic API, Bedrock Converse - [PR #19545](https://github.com/BerriAI/litellm/pull/19545) + +- **[Brave Search](../../docs/search/brave)** + - New Search provider - [PR #19433](https://github.com/BerriAI/litellm/pull/19433) + +- **Sarvam ai** [TODO: MISSING DOCS] + - Add support for new sarvam models - [PR #19479](https://github.com/BerriAI/litellm/pull/19479) + +- **[GMI](../../docs/providers/gmi)** + - add GMI Cloud provider support - [PR #19376](https://github.com/BerriAI/litellm/pull/19376) + + +### Bug Fixes + +- **[Anthropic](../../docs/providers/anthropic)** + - Fix anthropic-beta sent client side being overridden instead of appended to - [PR #19343](https://github.com/BerriAI/litellm/pull/19343) + - Filter out unsupported fields from JSON schema for Anthropic's output_format API - [PR #19482](https://github.com/BerriAI/litellm/pull/19482) + +- **[Bedrock](../../docs/providers/bedrock)** + - Expose stability models via /image_edits endpoint and ensure proper request transformation - [PR #19323](https://github.com/BerriAI/litellm/pull/19323) + - Claude Code x Bedrock Invoke fails with advanced-tool-use-2025-11-20 - [PR #19373](https://github.com/BerriAI/litellm/pull/19373) + - deduplicate tool calls in assistant history - [PR #19324](https://github.com/BerriAI/litellm/pull/19324) + - fix: correct us.anthropic.claude-opus-4-5 In-region pricing - [PR #19310](https://github.com/BerriAI/litellm/pull/19310) + - Fix request validation errors when using Claude 4 via bedrock invoke - [PR #19381](https://github.com/BerriAI/litellm/pull/19381) + - Handle thinking with tool calls for Claude 4 models - [PR #19506](https://github.com/BerriAI/litellm/pull/19506) + - correct streaming choice index for tool calls - [PR #19506](https://github.com/BerriAI/litellm/pull/19506) + +- **[Ollama](../../docs/providers/ollama)** + - Fix tool call errors due with improved message extraction - [PR #19369](https://github.com/BerriAI/litellm/pull/19369) + +- **[VertexAI](../../docs/providers/vertex)** + - Removed optional vertex_count_tokens_location param before request is sent to vertex - [PR #19359](https://github.com/BerriAI/litellm/pull/19359) + +- **Gemini([Vertex AI](../../docs/providers/vertex), [Google AI Studio](../../docs/providers/gemini))** + - Supports setting media_resolution and fps parameters on each video file, when using Gemini video understanding - [PR #19273](https://github.com/BerriAI/litellm/pull/19273) + - handle reasoning_effort as dict from OpenAI Agents SDK - [PR #19419](https://github.com/BerriAI/litellm/pull/19419) + - add file content support in tool results - [PR #19416](https://github.com/BerriAI/litellm/pull/19416) + +- **[Azure](../../docs/providers/azure_ai)** + - Fix Azure AI costs for Anthropic models - [PR #19530](https://github.com/BerriAI/litellm/pull/19530) + +- **[Giga Chat](../../docs/providers/gigachat)** + - Add tool choice mapping - [PR #19645](https://github.com/BerriAI/litellm/pull/19645) +--- + +## AI API Endpoints (LLMs, MCP, Agents) + +### Features + +- **[Files API](../../docs/files_endpoints)** + - Add managed files support when load_balancing is True - [PR #19338](https://github.com/BerriAI/litellm/pull/19338) + +- **[Claude Plugin Marketplace](../../docs/tutorials/claude_code_plugin_marketplace)** + - Add self hosted Claude Code Plugin Marketplace - [PR #19378](https://github.com/BerriAI/litellm/pull/19378) + +- **[MCP](../../docs/mcp)** + - Add MCP Protocol version 2025-11-25 support - [PR #19379](https://github.com/BerriAI/litellm/pull/19379) + - Log MCP tool calls and list tools in the LiteLLM Spend Logs table for easier debugging - [PR #19469](https://github.com/BerriAI/litellm/pull/19469) + +- **[Vertex AI](../../docs/providers/vertex)** + - Ensure only anthropic betas are forwarded down to LLM API (by default) - [PR #19542](https://github.com/BerriAI/litellm/pull/19542) + - Allow overriding to support forwarding incoming headers are forwarded down to target - [PR #19524](https://github.com/BerriAI/litellm/pull/19524) + +- **[Chat/Completions](../../docs/completion/input)** + - Add MCP tools response to chat completions - [PR #19552](https://github.com/BerriAI/litellm/pull/19552) + - Add custom vertex ai finish reasons to the output - [PR #19558](https://github.com/BerriAI/litellm/pull/19558) + - Return MCP execution in /chat/completions before model output during streaming - [PR #19623](https://github.com/BerriAI/litellm/pull/19623) + +### Bugs + +- **[Responses API](../../docs/response_api)** + - Fix duplicate messages during MCP streaming tool execution - [PR #19317](https://github.com/BerriAI/litellm/pull/19317) + - Fix pickle error when using OpenAI's Responses API with stream=True and tool_choice of type allowed_tools (an OpenAI-native parameter) - [PR #17205](https://github.com/BerriAI/litellm/pull/17205) + - stream tool call events for non-openai models - [PR #19368](https://github.com/BerriAI/litellm/pull/19368) + - preserve tool output ordering for gemini in responses bridge - [PR #19360](https://github.com/BerriAI/litellm/pull/19360) + - Add ID caching to prevent ID mismatch text-start and text-delta - [PR #19390](https://github.com/BerriAI/litellm/pull/19390) + - Include output_item, reasoning_summary_Text_done and reasoning_summary_part_done events for non-openai models - [PR #19472](https://github.com/BerriAI/litellm/pull/19472) + +- **[Chat/Completions](../../docs/completion/input)** + - fix: drop_params not dropping prompt_cache_key for non-OpenAI providers - [PR #19346](https://github.com/BerriAI/litellm/pull/19346) + +- **[Realtime API](../../docs/realtime)** + - disable SSL for ws:// WebSocket connections - [PR #19345](https://github.com/BerriAI/litellm/pull/19345) + +- **[Generate Content](../../docs/generateContent)** + - Log actual user input when google genai/vertex endpoints are called client-side - [PR #19156](https://github.com/BerriAI/litellm/pull/19156) + +- **[/messages/count_tokens Anthropic Token Counting](../../docs/anthropic_count_tokens)** + - ensure it works for Anthropic, Azure AI Anthropic on AI Gateway - [PR #19432](https://github.com/BerriAI/litellm/pull/19432) + +- **[MCP](../../docs/mcp)** + - forward static_headers to MCP servers - [PR #19366](https://github.com/BerriAI/litellm/pull/19366) + +- **[Batch API](../../docs/batches)** + - Fix: generation config empty for batch - [PR #19556](https://github.com/BerriAI/litellm/pull/19556) + +- **[Pass Through Endpoints](../../docs/proxy/pass_through)** + - Always reupdate registry - [PR #19420](https://github.com/BerriAI/litellm/pull/19420) +--- + +## Management Endpoints / UI + +### Features + +- **Cost Estimator** + - Fix model dropdown - [PR #19529](https://github.com/BerriAI/litellm/pull/19529) + +- **Claude Code Plugins** + - Allow Adding Claude Code Plugins via UI - [PR #19387](https://github.com/BerriAI/litellm/pull/19387) + +- **Guardrails** + - New Policy management UI - [PR #19668](https://github.com/BerriAI/litellm/pull/19668) + - Allow adding policies on Keys/Teams + Viewing on Info panels - [PR #19688](https://github.com/BerriAI/litellm/pull/19688) + +- **General** + - respects custom authentication header override - [PR #19276](https://github.com/BerriAI/litellm/pull/19276) + +- **Playground** + - Button to Fill Custom API Base - [PR #19440](https://github.com/BerriAI/litellm/pull/19440) + - display mcp output on the play ground - [PR #19553](https://github.com/BerriAI/litellm/pull/19553) + +- **Models** + - Paginate /v2/models/info - [PR #19521](https://github.com/BerriAI/litellm/pull/19521) + - All Model Tab Pagination - [PR #19525](https://github.com/BerriAI/litellm/pull/19525) + - Adding Optional scope Param to /models - [PR #19539](https://github.com/BerriAI/litellm/pull/19539) + - Model Search - [PR #19622](https://github.com/BerriAI/litellm/pull/19622) + - Filter by Model ID and Team ID - [PR #19713](https://github.com/BerriAI/litellm/pull/19713) + +- **MCP Servers** + - MCP Tools Tab Resetting to Overview - [PR #19468](https://github.com/BerriAI/litellm/pull/19468) + +- **Organizations** + - Prevent org admin from creating a new user with proxy_admin permissions - [PR #19296](https://github.com/BerriAI/litellm/pull/19296) + - Edit Page: Reusable Model Select - [PR #19601](https://github.com/BerriAI/litellm/pull/19601) + +- **Teams** + - Reusable Model Select - [PR #19543](https://github.com/BerriAI/litellm/pull/19543) + - [Fix] Team Update with Organization having All Proxy Models - [PR #19604](https://github.com/BerriAI/litellm/pull/19604) + +- **Logs** + - Include tool arguments in spend logs table - [PR #19640](https://github.com/BerriAI/litellm/pull/19640) + +- **Fallbacks / Loadbalancing** + - New fallbacks modal - [PR #19673](https://github.com/BerriAI/litellm/pull/19673) + - Set fallbacks/loadbalancing by team/key - [PR #19686](https://github.com/BerriAI/litellm/pull/19686) + +### Bugs + +- **Playground** + - increase model selector width in playground Compare view - [PR #19423](https://github.com/BerriAI/litellm/pull/19423) + +- **Virtual Keys** + - Sorting Shows Incorrect Entries - [PR #19534](https://github.com/BerriAI/litellm/pull/19534) + +- **General** + - UI 404 error when SERVER_ROOT_PATH is set - [PR #19467](https://github.com/BerriAI/litellm/pull/19467) + - Redirect to ui/login on expired JWT - [PR #19687](https://github.com/BerriAI/litellm/pull/19687) + +- **SSO** + - Fix SSO user roles not updating for existing users - [PR #19621](https://github.com/BerriAI/litellm/pull/19621) + +- **Guardrails** + - ensure guardrail patterns persist on edit and mode toggle - [PR #19265](https://github.com/BerriAI/litellm/pull/19265) +--- + +## AI Integrations + +### Logging + +- **General Logging** + - prevent printing duplicate StandardLoggingPayload logs - [PR #19325](https://github.com/BerriAI/litellm/pull/19325) + - Fix: log duplication when json_logs is enabled - [PR #19705](https://github.com/BerriAI/litellm/pull/19705) +- **Langfuse OTEL** + - ignore service logs and fix callback shadowing - [PR #19298](https://github.com/BerriAI/litellm/pull/19298) +- **Langfuse** + - Send litellm_trace_id - [PR #19528](https://github.com/BerriAI/litellm/pull/19528) + - Add Langfuse mock mode for testing without API calls - [PR #19676](https://github.com/BerriAI/litellm/pull/19676) +- **GCS Bucket** + - prevent unbounded queue growth due to slow API calls - [PR #19297](https://github.com/BerriAI/litellm/pull/19297) + - Add GCS mock mode for testing without API calls - [PR #19683](https://github.com/BerriAI/litellm/pull/19683) +- **Responses API Logging** + - Fix pydantic serialization error - [PR #19486](https://github.com/BerriAI/litellm/pull/19486) +- **Arize Phoenix** + - add openinference span kinds to arize phoenix - [PR #19267](https://github.com/BerriAI/litellm/pull/19267) +- **Prometheus** + - Added new prometheus metrics for user count and team count - [PR #19520](https://github.com/BerriAI/litellm/pull/19520) + +### Guardrails + +- **Bedrock Guardrails** + - Ensure post_call guardrail checks input+output - [PR #19151](https://github.com/BerriAI/litellm/pull/19151) +- **Prompt Security** + - fixing prompt-security's guardrail implementation - [PR #19374](https://github.com/BerriAI/litellm/pull/19374) +- **Presidio** + - Fixes crash in Presidio Guardrail when running in background threads (logging_hook) - [PR #19714](https://github.com/BerriAI/litellm/pull/19714) +- **Pillar Security** + - Migrate Pillar Security to Generic Guardrail API - [PR #19364](https://github.com/BerriAI/litellm/pull/19364) +- **Policy Engine** + - New LiteLLM Policy engine - create policies to manage guardrails, conditions - permissions per Key, Team - [PR #19612](https://github.com/BerriAI/litellm/pull/19612) +- **General** + - add case-insensitive support for guardrail mode and actions - [PR #19480](https://github.com/BerriAI/litellm/pull/19480) + +### Prompt Management + +- **General** + - fix prompt info lookup and delete using correct IDs - [PR #19358](https://github.com/BerriAI/litellm/pull/19358) + +### Secret Manager + +- **AWS Secret Manager** + - ensure auto-rotation updates existing AWS secret instead of creating new one - [PR #19455](https://github.com/BerriAI/litellm/pull/19455) +- **Hashicorp Vault** + - Ensure key rotations work with Vault - [PR #19634](https://github.com/BerriAI/litellm/pull/19634) + +--- + +## Spend Tracking, Budgets and Rate Limiting + +- **Pricing Updates** + - Add openai/dall-e base pricing entries - [PR #19133](https://github.com/BerriAI/litellm/pull/19133) + - Add `input_cost_per_video_per_second` in ModelInfoBase - [PR #19398](https://github.com/BerriAI/litellm/pull/19398) + +--- + +## Performance / Loadbalancing / Reliability improvements + + +- **General** + - Fix date overflow/division by zero in proxy utils - [PR #19527](https://github.com/BerriAI/litellm/pull/19527) + - Fix in-flight request termination on SIGTERM when health-check runs in a separate process - [PR #19427](https://github.com/BerriAI/litellm/pull/19427) + - Fix Pass through routes to work with server root path - [PR #19383](https://github.com/BerriAI/litellm/pull/19383) + - Fix logging error for stop iteration - [PR #19649](https://github.com/BerriAI/litellm/pull/19649) + - prevent retrying 4xx client errors - [PR #19275](https://github.com/BerriAI/litellm/pull/19275) + - add better error handling for misconfig on health check - [PR #19441](https://github.com/BerriAI/litellm/pull/19441) + +- **Router** + - Fix Azure RPM calculation formula - [PR #19513](https://github.com/BerriAI/litellm/pull/19513) + - Persist scheduler request queue to redis - [PR #19304](https://github.com/BerriAI/litellm/pull/19304) + - pass search_tools to Router during DB-triggered initialization - [PR #19388](https://github.com/BerriAI/litellm/pull/19388) + - Fixed PromptCachingCache to correctly handle messages where cache_control is a sibling key of string content - [PR #19266](https://github.com/BerriAI/litellm/pull/19266) + +- **Memory Leaks/OOM** + - prevent OOM with nested $defs in tool schemas - [PR #19112](https://github.com/BerriAI/litellm/pull/19112) + - fix: HTTP client memory leaks in Presidio, OpenAI, and Gemini - [PR #19190](https://github.com/BerriAI/litellm/pull/19190) + +- **Non root** + - fix logfile and pidfile of supervisor for non root environment - [PR #17267](https://github.com/BerriAI/litellm/pull/17267) + - resolve Read-only file system error in non-root images - [PR #19449](https://github.com/BerriAI/litellm/pull/19449) + +- **Dockerfile** + - Redis Semantic Caching - add missing redisvl dependency to requirements.txt - [PR #19417](https://github.com/BerriAI/litellm/pull/19417) + - Bump OTEL versions to support a2a dependency - resolves modulenotfounderror for Microsoft Agents by @Harshit28j in #18991 + +- **DB** + - Handle PostgreSQL cached plan errors during rolling deployments - [PR #19424](https://github.com/BerriAI/litellm/pull/19424) + +- **Timeouts** + - Fix: total timeout is not respected - [PR #19389](https://github.com/BerriAI/litellm/pull/19389) + +- **SDK** + - Field-Existence Checks to Type Classes to Prevent Attribute Errors - [PR #18321](https://github.com/BerriAI/litellm/pull/18321) + - add google-cloud-aiplatform as optional dependency with clear error message - [PR #19437](https://github.com/BerriAI/litellm/pull/19437) + - Make grpc dependency optional - [PR #19447](https://github.com/BerriAI/litellm/pull/19447) + - Add support for retry policies - [PR #19645](https://github.com/BerriAI/litellm/pull/19645) + +- **Performance** + - Cut chat_completion latency by ~21% by reducing pre-call processing time - [PR #19535](https://github.com/BerriAI/litellm/pull/19535) + - Optimize strip_trailing_slash with O(1) index check - [PR #19679](https://github.com/BerriAI/litellm/pull/19679) + - Optimize use_custom_pricing_for_model with set intersection - [PR #19677](https://github.com/BerriAI/litellm/pull/19677) + - perf: skip pattern_router.route() for non-wildcard models - [PR #19664](https://github.com/BerriAI/litellm/pull/19664) + - perf: Add LRU caching to get_model_info for faster cost lookups - [PR #19606](https://github.com/BerriAI/litellm/pull/19606) + +--- + +## General Proxy Improvements + +### Doc Improvements + - new tutorial for adding MCPs to Cursor via LiteLLM - [PR #19317](https://github.com/BerriAI/litellm/pull/19317) + - fix vertex_region to vertex_location in Vertex AI pass-through docs - [PR #19380](https://github.com/BerriAI/litellm/pull/19380) + - clarify Gemini and Vertex AI model prefix in json file - [PR #19443](https://github.com/BerriAI/litellm/pull/19443) + - update Claude Code integration guides - [PR #19415](https://github.com/BerriAI/litellm/pull/19415) + - adjust opencode tutorial - [PR #19605](https://github.com/BerriAI/litellm/pull/19605) + - add spend-queue-troubleshooting docs - [PR #19659](https://github.com/BerriAI/litellm/pull/19659) + - docs: add litellm-enterprise requirement for managed files - [PR #19689](https://github.com/BerriAI/litellm/pull/19689) + +### Helm + - Add support for keda in helm chart - [PR #19337](https://github.com/BerriAI/litellm/pull/19337) + - sync Helm chart version with LiteLLM release version - [PR #19438](https://github.com/BerriAI/litellm/pull/19438) + - Enable PreStop hook configuration in values.yaml - [PR #19613](https://github.com/BerriAI/litellm/pull/19613) + +### General + - Add health check scripts and parallel execution support - [PR #19295](https://github.com/BerriAI/litellm/pull/19295) + + +--- + +## New Contributors + + +* @dushyantzz made their first contribution in [PR #19158](https://github.com/BerriAI/litellm/pull/19158) +* @obod-mpw made their first contribution in [PR #19133](https://github.com/BerriAI/litellm/pull/19133) +* @msexxeta made their first contribution in [PR #19030](https://github.com/BerriAI/litellm/pull/19030) +* @rsicart made their first contribution in [PR #19337](https://github.com/BerriAI/litellm/pull/19337) +* @cluebbehusen made their first contribution in [PR #19311](https://github.com/BerriAI/litellm/pull/19311) +* @Lucky-Lodhi2004 made their first contribution in [PR #19315](https://github.com/BerriAI/litellm/pull/19315) +* @binbandit made their first contribution in [PR #19324](https://github.com/BerriAI/litellm/pull/19324) +* @flex-myeonghyeon made their first contribution in [PR #19381](https://github.com/BerriAI/litellm/pull/19381) +* @Lrakotoson made their first contribution in [PR #18321](https://github.com/BerriAI/litellm/pull/18321) +* @bensi94 made their first contribution in [PR #18787](https://github.com/BerriAI/litellm/pull/18787) +* @victorigualada made their first contribution in [PR #19368](https://github.com/BerriAI/litellm/pull/19368) +* @VedantMadane made their first contribution in #19266 +* @stiyyagura0901 made their first contribution in #19276 +* @kamilio made their first contribution in [PR #19447](https://github.com/BerriAI/litellm/pull/19447) +* @jonathansampson made their first contribution in [PR #19433](https://github.com/BerriAI/litellm/pull/19433) +* @rynecarbone made their first contribution in [PR #19416](https://github.com/BerriAI/litellm/pull/19416) +* @jayy-77 made their first contribution in #19366 +* @davida-ps made their first contribution in [PR #19374](https://github.com/BerriAI/litellm/pull/19374) +* @joaodinissf made their first contribution in [PR #19506](https://github.com/BerriAI/litellm/pull/19506) +* @ecao310 made their first contribution in [PR #19520](https://github.com/BerriAI/litellm/pull/19520) +* @mpcusack-altos made their first contribution in [PR #19577](https://github.com/BerriAI/litellm/pull/19577) +* @milan-berri made their first contribution in [PR #19602](https://github.com/BerriAI/litellm/pull/19602) +* @xqe2011 made their first contribution in #19621 + +--- + +## Full Changelog + +**[View complete changelog on GitHub](https://github.com/BerriAI/litellm/releases/tag/v1.81.3.rc)** From 7307992cea72d9e3c338bd2a160f9c040f8d40f2 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Mon, 26 Jan 2026 20:17:53 -0800 Subject: [PATCH 39/62] fixing tests --- tests/proxy_unit_tests/test_auth_checks.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/proxy_unit_tests/test_auth_checks.py b/tests/proxy_unit_tests/test_auth_checks.py index 24c005443a..05c6e4984a 100644 --- a/tests/proxy_unit_tests/test_auth_checks.py +++ b/tests/proxy_unit_tests/test_auth_checks.py @@ -584,7 +584,7 @@ async def test_get_fuzzy_user_object(): ) assert result == test_user mock_prisma.db.litellm_usertable.find_first.assert_called_with( - where={"user_email": "test@example.com"}, + where={"user_email": {"equals": "test@example.com", "mode": "insensitive"}}, include={"organization_memberships": True}, ) @@ -612,7 +612,7 @@ async def test_get_fuzzy_user_object(): ) assert result == test_user mock_prisma.db.litellm_usertable.find_first.assert_called_with( - where={"user_email": "test@example.com"}, + where={"user_email": {"equals": "test@example.com", "mode": "insensitive"}}, include={"organization_memberships": True}, ) From 7ba0235a502349d8069e0146d23377bcd41b7210 Mon Sep 17 00:00:00 2001 From: Krish Dholakia Date: Mon, 26 Jan 2026 20:20:20 -0800 Subject: [PATCH 40/62] Litellm release notes 01 26 2026 (#19838) * docs: document new models/endpoints * docs: cleanup * feat: update model table * fix: cleanup --- docs/my-website/release_notes/v1.81.3-stable/index.md | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/docs/my-website/release_notes/v1.81.3-stable/index.md b/docs/my-website/release_notes/v1.81.3-stable/index.md index accacd0abd..22b6f43dee 100644 --- a/docs/my-website/release_notes/v1.81.3-stable/index.md +++ b/docs/my-website/release_notes/v1.81.3-stable/index.md @@ -43,12 +43,6 @@ pip install litellm==1.81.3.rc.2 --- -## Key Highlights -[TODO] ---- - -[TODO] - ## New Models / Updated Models ### New Model Support @@ -104,7 +98,7 @@ pip install litellm==1.81.3.rc.2 - **[Brave Search](../../docs/search/brave)** - New Search provider - [PR #19433](https://github.com/BerriAI/litellm/pull/19433) -- **Sarvam ai** [TODO: MISSING DOCS] +- **Sarvam ai** - Add support for new sarvam models - [PR #19479](https://github.com/BerriAI/litellm/pull/19479) - **[GMI](../../docs/providers/gmi)** From 1bf33c4e41f829f24316d614c2eec98fb9643b9d Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 27 Jan 2026 11:19:31 +0530 Subject: [PATCH 41/62] Fix:Support both JSON array format and comma-separated values from user headers --- litellm/llms/bedrock/common_utils.py | 23 ++++++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 89b42f5e94..65d237bdbd 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -797,7 +797,7 @@ class BedrockEventStreamDecoderBase: def get_anthropic_beta_from_headers(headers: dict) -> List[str]: """ Extract anthropic-beta header values and convert them to a list. - Supports comma-separated values from user headers. + Supports both JSON array format and comma-separated values from user headers. Used by both converse and invoke transformations for consistent handling of anthropic-beta headers that should be passed to AWS Bedrock. @@ -812,8 +812,25 @@ def get_anthropic_beta_from_headers(headers: dict) -> List[str]: if not anthropic_beta_header: return [] - # Split comma-separated values and strip whitespace - return [beta.strip() for beta in anthropic_beta_header.split(",")] + # If it's already a list, return it + if isinstance(anthropic_beta_header, list): + return anthropic_beta_header + + # Try to parse as JSON array first (e.g., '["interleaved-thinking-2025-05-14", "claude-code-20250219"]') + if isinstance(anthropic_beta_header, str): + anthropic_beta_header = anthropic_beta_header.strip() + if anthropic_beta_header.startswith("[") and anthropic_beta_header.endswith("]"): + try: + parsed = json.loads(anthropic_beta_header) + if isinstance(parsed, list): + return [str(beta).strip() for beta in parsed] + except json.JSONDecodeError: + pass # Fall through to comma-separated parsing + + # Fall back to comma-separated values + return [beta.strip() for beta in anthropic_beta_header.split(",")] + + return [] class CommonBatchFilesUtils: From 3f32562587f0945cd73d09ebe7820d147b465512 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 27 Jan 2026 11:20:16 +0530 Subject: [PATCH 42/62] Translate advanced-tool-use to Bedrock-specific headers for Claude Opus 4.5 --- .../anthropic_claude3_transformation.py | 38 +++- ...ations_anthropic_claude3_transformation.py | 185 ++++++++++++++++++ .../bedrock/test_anthropic_beta_support.py | 63 ++++++ 3 files changed, 282 insertions(+), 4 deletions(-) diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py index a7065caece..67ca9d5024 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -162,6 +162,22 @@ class AmazonAnthropicClaudeMessagesConfig( return any(pattern in model_lower for pattern in supported_patterns) + def _is_claude_opus_4_5(self, model: str) -> bool: + """ + Check if the model is Claude Opus 4.5. + + Args: + model: The model name + + Returns: + True if the model is Claude Opus 4.5 + """ + model_lower = model.lower() + opus_4_5_patterns = [ + "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5", + ] + return any(pattern in model_lower for pattern in opus_4_5_patterns) + def _filter_unsupported_beta_headers_for_bedrock( self, model: str, beta_set: set ) -> None: @@ -169,25 +185,32 @@ class AmazonAnthropicClaudeMessagesConfig( Remove beta headers that are not supported on Bedrock for the given model. Extended thinking beta headers are only supported on specific Claude 4+ models. - Advanced tool use headers are not supported on Bedrock Invoke API. + Advanced tool use headers are not supported on Bedrock Invoke API, but need to be + translated to Bedrock-specific headers for Claude Opus 4.5. This prevents 400 "invalid beta flag" errors on Bedrock. Note: Bedrock Invoke API fails with a 400 error when unsupported beta headers are sent, returning: {"message":"invalid beta flag"} + Translation for Claude Opus 4.5: + - advanced-tool-use-2025-11-20 -> tool-search-tool-2025-10-19 + tool-examples-2025-10-29 + Args: model: The model name beta_set: The set of beta headers to filter in-place """ beta_headers_to_remove = set() + has_advanced_tool_use = False - # 1. Filter out beta headers that are universally unsupported on Bedrock Invoke + # 1. Filter out beta headers that are universally unsupported on Bedrock Invoke and track if advanced-tool-use header is present for beta in beta_set: for unsupported_pattern in self.UNSUPPORTED_BEDROCK_INVOKE_BETA_PATTERNS: if unsupported_pattern in beta.lower(): beta_headers_to_remove.add(beta) + has_advanced_tool_use = True break - + + # 2. Filter out extended thinking headers for models that don't support them extended_thinking_patterns = [ "extended-thinking", @@ -203,6 +226,13 @@ class AmazonAnthropicClaudeMessagesConfig( # Remove all filtered headers for beta in beta_headers_to_remove: beta_set.discard(beta) + + # 3. Translate advanced-tool-use to Bedrock-specific headers for Claude Opus 4.5 + # Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html + if has_advanced_tool_use and self._is_claude_opus_4_5(model): + beta_set.add("tool-search-tool-2025-10-19") + beta_set.add("tool-examples-2025-10-29") + def _get_tool_search_beta_header_for_bedrock( self, @@ -256,7 +286,7 @@ class AmazonAnthropicClaudeMessagesConfig( Ref: https://aws.amazon.com/blogs/machine-learning/structured-data-response-with-amazon-bedrock-prompt-engineering-and-tool-use/ """ import json - + # Extract schema from output_format schema = output_format.get("schema") if not schema: diff --git a/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py b/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py index 1cb84d32c1..98b392a353 100644 --- a/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py +++ b/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py @@ -279,3 +279,188 @@ def test_output_format_with_no_schema(): # Content should remain as string (not converted to list) assert isinstance(last_user_message["content"], str) assert last_user_message["content"] == "Hello" + + +def test_advanced_tool_use_header_translation_for_opus_4_5(): + """ + Test that advanced-tool-use-2025-11-20 header is translated to Bedrock-specific headers + for Claude Opus 4.5. + + Regression test for: Claude Code sends advanced-tool-use header which needs to be + translated to tool-search-tool-2025-10-19 and tool-examples-2025-10-29 for Bedrock + Invoke API on Claude Opus 4.5. + + Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html + """ + from litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import ( + AmazonAnthropicClaudeMessagesConfig, + ) + + config = AmazonAnthropicClaudeMessagesConfig() + + messages = [ + {"role": "user", "content": "What's the weather like?"} + ] + + anthropic_messages_optional_request_params = { + "max_tokens": 100, + } + + # Simulate advanced-tool-use header from Claude Code + headers = { + "anthropic-beta": "advanced-tool-use-2025-11-20" + } + + # Test with Claude Opus 4.5 + result = config.transform_anthropic_messages_request( + model="anthropic.claude-opus-4-5-20250514-v1:0", + messages=messages, + anthropic_messages_optional_request_params=anthropic_messages_optional_request_params, + litellm_params={}, + headers=headers, + ) + + # Verify advanced-tool-use header was removed + assert "anthropic_beta" in result + beta_headers = result["anthropic_beta"] + assert "advanced-tool-use-2025-11-20" not in beta_headers, \ + "advanced-tool-use header should be removed for Bedrock" + + # Verify Bedrock-specific headers were added + assert "tool-search-tool-2025-10-19" in beta_headers, \ + "tool-search-tool-2025-10-19 should be added for Opus 4.5" + assert "tool-examples-2025-10-29" in beta_headers, \ + "tool-examples-2025-10-29 should be added for Opus 4.5" + + +def test_advanced_tool_use_header_filtered_for_non_opus_4_5(): + """ + Test that advanced-tool-use-2025-11-20 header is filtered out for non-Opus 4.5 models + without adding Bedrock-specific headers. + + The translation to tool-search-tool-2025-10-19 and tool-examples-2025-10-29 should + only happen for Claude Opus 4.5. + """ + from litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import ( + AmazonAnthropicClaudeMessagesConfig, + ) + + config = AmazonAnthropicClaudeMessagesConfig() + + messages = [ + {"role": "user", "content": "What's the weather like?"} + ] + + anthropic_messages_optional_request_params = { + "max_tokens": 100, + } + + # Simulate advanced-tool-use header from Claude Code + headers = { + "anthropic-beta": "advanced-tool-use-2025-11-20" + } + + # Test with Claude Sonnet 4.5 (not Opus 4.5) + result = config.transform_anthropic_messages_request( + model="anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=messages, + anthropic_messages_optional_request_params=anthropic_messages_optional_request_params, + litellm_params={}, + headers=headers, + ) + + # Verify advanced-tool-use header was removed + beta_headers = result.get("anthropic_beta", []) + assert "advanced-tool-use-2025-11-20" not in beta_headers, \ + "advanced-tool-use header should be removed for Bedrock" + + # Verify Bedrock-specific headers were NOT added (only for Opus 4.5) + assert "tool-search-tool-2025-10-19" not in beta_headers, \ + "tool-search-tool should not be added for non-Opus 4.5 models" + assert "tool-examples-2025-10-29" not in beta_headers, \ + "tool-examples should not be added for non-Opus 4.5 models" + + +def test_advanced_tool_use_header_translation_with_multiple_beta_headers(): + """ + Test that advanced-tool-use header translation works correctly when multiple + beta headers are present. + """ + from litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import ( + AmazonAnthropicClaudeMessagesConfig, + ) + + config = AmazonAnthropicClaudeMessagesConfig() + + messages = [ + {"role": "user", "content": "What's the weather like?"} + ] + + anthropic_messages_optional_request_params = { + "max_tokens": 100, + } + + # Multiple beta headers including advanced-tool-use + headers = { + "anthropic-beta": "claude-code-20250219,advanced-tool-use-2025-11-20,interleaved-thinking-2025-05-14" + } + + # Test with Claude Opus 4.5 + result = config.transform_anthropic_messages_request( + model="anthropic.claude-opus-4-5-20250514-v1:0", + messages=messages, + anthropic_messages_optional_request_params=anthropic_messages_optional_request_params, + litellm_params={}, + headers=headers, + ) + + beta_headers = result.get("anthropic_beta", []) + + # Verify advanced-tool-use was removed + assert "advanced-tool-use-2025-11-20" not in beta_headers + + # Verify Bedrock-specific headers were added + assert "tool-search-tool-2025-10-19" in beta_headers + assert "tool-examples-2025-10-29" in beta_headers + + # Verify other beta headers are preserved + assert "claude-code-20250219" in beta_headers + assert "interleaved-thinking-2025-05-14" in beta_headers + + +def test_opus_4_5_model_detection(): + """ + Test that the _is_claude_opus_4_5 method correctly identifies Opus 4.5 models + with various naming conventions. + """ + from litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import ( + AmazonAnthropicClaudeMessagesConfig, + ) + + config = AmazonAnthropicClaudeMessagesConfig() + + # Test various Opus 4.5 naming patterns + opus_4_5_models = [ + "anthropic.claude-opus-4-5-20250514-v1:0", + "anthropic.claude-opus-4.5-20250514-v1:0", + "anthropic.claude-opus_4_5-20250514-v1:0", + "anthropic.claude-opus_4.5-20250514-v1:0", + "us.anthropic.claude-opus-4-5-20250514-v1:0", + "ANTHROPIC.CLAUDE-OPUS-4-5-20250514-V1:0", # Case insensitive + ] + + for model in opus_4_5_models: + assert config._is_claude_opus_4_5(model), \ + f"Should detect {model} as Opus 4.5" + + # Test non-Opus 4.5 models + non_opus_4_5_models = [ + "anthropic.claude-sonnet-4-5-20250929-v1:0", + "anthropic.claude-opus-4-20250514-v1:0", # Opus 4, not 4.5 + "anthropic.claude-opus-4-1-20250514-v1:0", # Opus 4.1, not 4.5 + "anthropic.claude-haiku-4-5-20251001-v1:0", + ] + + for model in non_opus_4_5_models: + assert not config._is_claude_opus_4_5(model), \ + f"Should not detect {model} as Opus 4.5" diff --git a/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py b/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py index b9324e4966..0955636de6 100644 --- a/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py +++ b/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py @@ -390,3 +390,66 @@ class TestAnthropicBetaHeaderSupport: "anthropic_beta SHOULD be added for Anthropic models with cross-region prefix." ) assert "context-1m-2025-08-07" in additional_fields["anthropic_beta"] + + def test_messages_advanced_tool_use_translation_opus_4_5(self): + """Test that advanced-tool-use header is translated to Bedrock-specific headers for Opus 4.5. + + Regression test for: Claude Code sends advanced-tool-use-2025-11-20 header which needs + to be translated to tool-search-tool-2025-10-19 and tool-examples-2025-10-29 for + Bedrock Invoke API on Claude Opus 4.5. + + Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html + """ + config = AmazonAnthropicClaudeMessagesConfig() + headers = {"anthropic-beta": "advanced-tool-use-2025-11-20"} + + result = config.transform_anthropic_messages_request( + model="us.anthropic.claude-opus-4-5-20250514-v1:0", + messages=[{"role": "user", "content": "Test"}], + anthropic_messages_optional_request_params={"max_tokens": 100}, + litellm_params={}, + headers=headers + ) + + assert "anthropic_beta" in result + beta_headers = result["anthropic_beta"] + + # advanced-tool-use should be removed + assert "advanced-tool-use-2025-11-20" not in beta_headers, ( + "advanced-tool-use-2025-11-20 should be removed for Bedrock Invoke API" + ) + + # Bedrock-specific headers should be added for Opus 4.5 + assert "tool-search-tool-2025-10-19" in beta_headers, ( + "tool-search-tool-2025-10-19 should be added for Opus 4.5" + ) + assert "tool-examples-2025-10-29" in beta_headers, ( + "tool-examples-2025-10-29 should be added for Opus 4.5" + ) + + def test_messages_advanced_tool_use_filtered_non_opus_4_5(self): + """Test that advanced-tool-use header is filtered out for non-Opus 4.5 models. + + The translation to Bedrock-specific headers should only happen for Opus 4.5. + For other models, the advanced-tool-use header should just be removed. + """ + config = AmazonAnthropicClaudeMessagesConfig() + headers = {"anthropic-beta": "advanced-tool-use-2025-11-20"} + + # Test with Sonnet 4.5 (not Opus 4.5) + result = config.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": "Test"}], + anthropic_messages_optional_request_params={"max_tokens": 100}, + litellm_params={}, + headers=headers + ) + + beta_headers = result.get("anthropic_beta", []) + + # advanced-tool-use should be removed + assert "advanced-tool-use-2025-11-20" not in beta_headers + + # Bedrock-specific headers should NOT be added for non-Opus 4.5 + assert "tool-search-tool-2025-10-19" not in beta_headers + assert "tool-examples-2025-10-29" not in beta_headers From faf9c9ba768ac446352d4f7329b762dd48c630ae Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 27 Jan 2026 13:11:29 +0530 Subject: [PATCH 43/62] Add sarvam doc --- docs/my-website/docs/providers/sarvam.md | 89 ++++++++++++++++++++++++ docs/my-website/sidebars.js | 1 + 2 files changed, 90 insertions(+) create mode 100644 docs/my-website/docs/providers/sarvam.md diff --git a/docs/my-website/docs/providers/sarvam.md b/docs/my-website/docs/providers/sarvam.md new file mode 100644 index 0000000000..d77e9c0c75 --- /dev/null +++ b/docs/my-website/docs/providers/sarvam.md @@ -0,0 +1,89 @@ +# Sarvam.ai + +LiteLLM supports all the text models from [Sarvam ai](https://docs.sarvam.ai/api-reference-docs/chat/chat-completions) + +## Usage + +```python +import os +from litellm import completion + +# Set your Sarvam API key +os.environ["SARVAM_API_KEY"] = "" + +messages = [{"role": "user", "content": "Hello"}] + +response = completion( + model="sarvam/sarvam-m", + messages=messages, +) +print(response) +``` + +## Usage with LiteLLM Proxy Server + +Here's how to call a Sarvam.ai model with the LiteLLM Proxy Server + +1. **Modify the `config.yaml`:** + + ```yaml + model_list: + - model_name: my-model + litellm_params: + model: sarvam/ # add sarvam/ prefix to route as Sarvam provider + api_key: api-key # api key to send your model + ``` + +2. **Start the proxy:** + + ```bash + $ litellm --config /path/to/config.yaml + ``` + +3. **Send a request to LiteLLM Proxy Server:** + + + + + + ```python + import openai + + client = openai.OpenAI( + api_key="sk-1234", # pass litellm proxy key, if you're using virtual keys + base_url="http://0.0.0.0:4000" # litellm-proxy-base url + ) + + response = client.chat.completions.create( + model="my-model", + messages=[ + { + "role": "user", + "content": "what llm are you" + } + ], + ) + + print(response) + ``` + + + + + ```shell + curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "my-model", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + }' + ``` + + + diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 85573599e4..41854a4177 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -775,6 +775,7 @@ const sidebars = { "providers/oci", "providers/ollama", "providers/openrouter", + "providers/sarvam", "providers/ovhcloud", "providers/perplexity", "providers/petals", From cf012a2f657dab12f8fdda9d10de827517975f09 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 27 Jan 2026 13:50:47 +0530 Subject: [PATCH 44/62] Add gemini-robotics-er-1.5-preview model in model map --- ...odel_prices_and_context_window_backup.json | 37 +++++++++++++++++++ model_prices_and_context_window.json | 37 +++++++++++++++++++ 2 files changed, 74 insertions(+) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index d958ea4503..7f33e96dbb 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -13480,6 +13480,43 @@ "supports_vision": true, "supports_web_search": true }, + "gemini-robotics-er-1.5-preview": { + "cache_read_input_token_cost": 0, + "input_cost_per_token": 3e-07, + "input_cost_per_audio_token": 1e-06, + "litellm_provider": "gemini", + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_tokens": 65535, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "output_cost_per_reasoning_token": 2.5e-06, + "source": "https://ai.google.dev/gemini-api/docs/models#gemini-robotics-er-1-5-preview", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions" + ], + "supported_modalities": [ + "text", + "image", + "video", + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": false, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true, + "supports_web_search": true + }, "gemini-2.5-computer-use-preview-10-2025": { "input_cost_per_token": 1.25e-06, "input_cost_per_token_above_200k_tokens": 2.5e-06, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index d958ea4503..7f33e96dbb 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -13480,6 +13480,43 @@ "supports_vision": true, "supports_web_search": true }, + "gemini-robotics-er-1.5-preview": { + "cache_read_input_token_cost": 0, + "input_cost_per_token": 3e-07, + "input_cost_per_audio_token": 1e-06, + "litellm_provider": "gemini", + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_tokens": 65535, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "output_cost_per_reasoning_token": 2.5e-06, + "source": "https://ai.google.dev/gemini-api/docs/models#gemini-robotics-er-1-5-preview", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions" + ], + "supported_modalities": [ + "text", + "image", + "video", + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": false, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true, + "supports_web_search": true + }, "gemini-2.5-computer-use-preview-10-2025": { "input_cost_per_token": 1.25e-06, "input_cost_per_token_above_200k_tokens": 2.5e-06, From e273c858487b4c1b59deb92e918974c845544fcd Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 27 Jan 2026 13:56:05 +0530 Subject: [PATCH 45/62] Add gemini-robotics-er-1.5-preview model documentation --- docs/my-website/docs/providers/gemini.md | 51 ++++++++++++++++++++++++ 1 file changed, 51 insertions(+) diff --git a/docs/my-website/docs/providers/gemini.md b/docs/my-website/docs/providers/gemini.md index 23a02f7365..b9ad7820dd 100644 --- a/docs/my-website/docs/providers/gemini.md +++ b/docs/my-website/docs/providers/gemini.md @@ -1840,6 +1840,57 @@ content = response.get('choices', [{}])[0].get('message', {}).get('content') print(content) ``` +## gemini-robotics-er-1.5-preview Usage + +```python +from litellm import api_base +from openai import OpenAI +import os +import base64 + +client = OpenAI(base_url="http://0.0.0.0:4000", api_key="sk-12345") +base64_image = base64.b64encode(open("closeup-object-on-table-many-260nw-1216144471.webp", "rb").read()).decode() + +import json +import re +tools = [{"codeExecution": {}}] +response = client.chat.completions.create( + model="gemini/gemini-robotics-er-1.5-preview", + messages=[ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "Point to no more than 10 items in the image. The label returned should be an identifying name for the object detected. The answer should follow the json format: [{\"point\": [y, x], \"label\": }, ...]. The points are in [y, x] format normalized to 0-1000." + }, + { + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"} + } + ] + } + ], + tools=tools +) + +# Extract JSON from markdown code block if present +content = response.choices[0].message.content +# Look for triple-backtick JSON block +match = re.search(r'```json\s*(.*?)\s*```', content, re.DOTALL) +if match: + json_str = match.group(1) +else: + json_str = content + +try: + data = json.loads(json_str) + print(json.dumps(data, indent=2)) +except Exception as e: + print("Error parsing response as JSON:", e) + print("Response content:", content) +``` + ## Usage - PDF / Videos / etc. Files ### Inline Data (e.g. audio stream) From 988dd2a911aaae4ef67f401eb5613a5ab960cac8 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 27 Jan 2026 14:35:54 +0530 Subject: [PATCH 46/62] Fix: Stream the download in chunks --- .../prompt_templates/image_handling.py | 14 ++-- .../litellm_core_utils/test_image_handling.py | 79 +++++++++++++++++++ 2 files changed, 88 insertions(+), 5 deletions(-) diff --git a/litellm/litellm_core_utils/prompt_templates/image_handling.py b/litellm/litellm_core_utils/prompt_templates/image_handling.py index 5d0bedb776..7137a4e422 100644 --- a/litellm/litellm_core_utils/prompt_templates/image_handling.py +++ b/litellm/litellm_core_utils/prompt_templates/image_handling.py @@ -31,15 +31,19 @@ def _process_image_response(response: Response, url: str) -> str: f"Error: Image size ({size_mb:.2f}MB) exceeds maximum allowed size ({MAX_IMAGE_URL_DOWNLOAD_SIZE_MB}MB). url={url}" ) - image_bytes = response.content + # Stream download with size checking to prevent downloading huge files + max_bytes = int(MAX_IMAGE_URL_DOWNLOAD_SIZE_MB * 1024 * 1024) + image_bytes = bytearray() + bytes_downloaded = 0 - # Check actual size after download if Content-Length was not available - if content_length is None: - size_mb = len(image_bytes) / (1024 * 1024) - if size_mb > MAX_IMAGE_URL_DOWNLOAD_SIZE_MB: + for chunk in response.iter_bytes(chunk_size=8192): + bytes_downloaded += len(chunk) + if bytes_downloaded > max_bytes: + size_mb = bytes_downloaded / (1024 * 1024) raise litellm.ImageFetchError( f"Error: Image size ({size_mb:.2f}MB) exceeds maximum allowed size ({MAX_IMAGE_URL_DOWNLOAD_SIZE_MB}MB). url={url}" ) + image_bytes.extend(chunk) base64_image = base64.b64encode(image_bytes).decode("utf-8") diff --git a/tests/test_litellm/litellm_core_utils/test_image_handling.py b/tests/test_litellm/litellm_core_utils/test_image_handling.py index b15d75a414..9c2939b2da 100644 --- a/tests/test_litellm/litellm_core_utils/test_image_handling.py +++ b/tests/test_litellm/litellm_core_utils/test_image_handling.py @@ -66,6 +66,41 @@ class LargeImageClient: ) +class StreamingLargeImageClient: + """ + Client that streams a large image to test streaming download protection. + This simulates a huge file without actually creating it all in memory. + """ + + def __init__(self, size_mb=100, include_content_length=False): + self.size_mb = size_mb + self.include_content_length = include_content_length + + def get(self, url, follow_redirects=True): + size_bytes = int(self.size_mb * 1024 * 1024) + headers = {"Content-Type": "image/jpeg"} + if self.include_content_length: + headers["Content-Length"] = str(size_bytes) + + # Create a generator that yields chunks without creating the whole file in memory + def generate_chunks(total_size, chunk_size=8192): + bytes_sent = 0 + while bytes_sent < total_size: + chunk = b"x" * min(chunk_size, total_size - bytes_sent) + bytes_sent += len(chunk) + yield chunk + + # Create response with streaming content + response = Response( + status_code=200, + headers=headers, + request=Request("GET", url), + ) + # Mock the iter_bytes method to return our generator + response.iter_bytes = lambda chunk_size=8192: generate_chunks(size_bytes, chunk_size) + return response + + def test_image_exceeds_size_limit_with_content_length(monkeypatch): """ Test that images exceeding MAX_IMAGE_URL_DOWNLOAD_SIZE_MB are rejected when Content-Length header is present. @@ -83,6 +118,7 @@ def test_image_exceeds_size_limit_with_content_length(monkeypatch): def test_image_exceeds_size_limit_without_content_length(monkeypatch): """ Test that images exceeding MAX_IMAGE_URL_DOWNLOAD_SIZE_MB are rejected even without Content-Length header. + This uses the old non-streaming mock for backward compatibility. """ monkeypatch.setattr( litellm, "module_level_client", LargeImageClient(size_mb=100, include_content_length=False) @@ -94,6 +130,29 @@ def test_image_exceeds_size_limit_without_content_length(monkeypatch): assert "exceeds maximum allowed size" in str(excinfo.value) +def test_streaming_download_protects_against_huge_files(monkeypatch): + """ + Test that streaming download aborts early when file exceeds size limit, + preventing memory exhaustion from huge files (e.g., petabyte-sized files). + + This test verifies that the streaming implementation doesn't download the entire + file into memory before checking size. Instead, it should abort as soon as the + limit is exceeded during streaming. + """ + # Simulate a 1GB file - far larger than the 50MB default limit + client = StreamingLargeImageClient(size_mb=1024, include_content_length=False) + monkeypatch.setattr(litellm, "module_level_client", client) + + with pytest.raises(litellm.ImageFetchError) as excinfo: + convert_url_to_base64("https://example.com/huge-image.jpg") + + # Verify the error message shows it was caught during streaming + assert "exceeds maximum allowed size" in str(excinfo.value) + + # The error should be raised after downloading just slightly more than the limit + # not after downloading the full 1GB + + class SmallImageClient: """ Client that returns a small valid image. @@ -124,6 +183,26 @@ def test_image_within_size_limit(monkeypatch): assert result.startswith("data:image/jpeg;base64,") +def test_streaming_download_handles_petabyte_file(monkeypatch): + """ + Test that streaming download can handle extremely large file URLs (e.g., petabyte-sized) + without attempting to download the entire file or causing memory exhaustion. + + This simulates what happens if a malicious actor or misconfiguration provides + a URL to an extremely large file. + """ + # Simulate a 1 petabyte file (1,000,000 GB) + # Without streaming protection, this would cause OOM or hang indefinitely + client = StreamingLargeImageClient(size_mb=1_000_000_000, include_content_length=False) + monkeypatch.setattr(litellm, "module_level_client", client) + + with pytest.raises(litellm.ImageFetchError) as excinfo: + convert_url_to_base64("https://example.com/petabyte-file.jpg") + + # Should fail fast without downloading anywhere near 1 petabyte + assert "exceeds maximum allowed size" in str(excinfo.value) + + def test_image_size_limit_disabled(monkeypatch): """ Test that setting MAX_IMAGE_URL_DOWNLOAD_SIZE_MB to 0 disables all image URL downloads. From e695cb53674c62d433bb8b3c5a5d335ce833e326 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 27 Jan 2026 16:34:57 +0530 Subject: [PATCH 47/62] Add grok reasoning content --- litellm/llms/groq/chat/transformation.py | 8 ++ tests/llm_translation/test_groq.py | 126 ++++++++++++++++++++++- 2 files changed, 133 insertions(+), 1 deletion(-) diff --git a/litellm/llms/groq/chat/transformation.py b/litellm/llms/groq/chat/transformation.py index a75ecd8cc7..34ea7b03dd 100644 --- a/litellm/llms/groq/chat/transformation.py +++ b/litellm/llms/groq/chat/transformation.py @@ -323,4 +323,12 @@ class GroqChatCompletionStreamingHandler(OpenAIChatCompletionStreamingHandler): status_code=error.get("code"), message=error.get("message"), body=error ) + # Map Groq's 'reasoning' field to LiteLLM's 'reasoning_content' field + # Groq returns delta.reasoning, but LiteLLM expects delta.reasoning_content + choices = chunk.get("choices", []) + for choice in choices: + delta = choice.get("delta", {}) + if "reasoning" in delta: + delta["reasoning_content"] = delta.pop("reasoning") + return super().chunk_parser(chunk) diff --git a/tests/llm_translation/test_groq.py b/tests/llm_translation/test_groq.py index 33c1a42587..b82cffd418 100644 --- a/tests/llm_translation/test_groq.py +++ b/tests/llm_translation/test_groq.py @@ -11,7 +11,10 @@ import pytest import litellm from base_llm_unit_tests import BaseLLMChatTest -from litellm.llms.groq.chat.transformation import GroqChatConfig +from litellm.llms.groq.chat.transformation import ( + GroqChatConfig, + GroqChatCompletionStreamingHandler, +) class TestGroq(BaseLLMChatTest): def get_base_completion_call_args(self) -> dict: @@ -164,3 +167,124 @@ class TestGroqStructuredOutputs: if "tools" in result: tool_names = [t.get("function", {}).get("name") for t in result["tools"]] assert "json_tool_call" not in tool_names + + +class TestGroqReasoning: + """ + Tests for Groq reasoning field mapping. + + Groq returns 'reasoning' field in delta, but LiteLLM expects 'reasoning_content'. + """ + + def test_reasoning_field_mapping_in_streaming_chunks(self): + """ + Test that Groq's 'reasoning' field in streaming chunks is properly mapped + to LiteLLM's 'reasoning_content' field. + """ + handler = GroqChatCompletionStreamingHandler( + streaming_response=None, sync_stream=True + ) + + # Simulate a chunk with reasoning field as returned by Groq + groq_chunk = { + "id": "chatcmpl-test", + "object": "chat.completion.chunk", + "created": 1769511767, + "model": "qwen/qwen3-32b", + "choices": [ + { + "delta": { + "reasoning": "This is reasoning content", + "role": None, + }, + "finish_reason": None, + "index": 0, + } + ], + } + + # Parse the chunk + parsed_chunk = handler.chunk_parser(groq_chunk) + + # Verify that reasoning was mapped to reasoning_content + assert parsed_chunk.choices[0].delta.reasoning_content == "This is reasoning content" + # Verify that the original 'reasoning' field was removed + assert not hasattr(parsed_chunk.choices[0].delta, "reasoning") + + def test_reasoning_field_not_present(self): + """ + Test that chunks without reasoning field still work correctly. + """ + handler = GroqChatCompletionStreamingHandler( + streaming_response=None, sync_stream=True + ) + + # Simulate a chunk without reasoning field + groq_chunk = { + "id": "chatcmpl-test", + "object": "chat.completion.chunk", + "created": 1769511767, + "model": "qwen/qwen3-32b", + "choices": [ + { + "delta": { + "content": "Regular content", + "role": "assistant", + }, + "finish_reason": None, + "index": 0, + } + ], + } + + # Parse the chunk + parsed_chunk = handler.chunk_parser(groq_chunk) + + # Verify that content is present + assert parsed_chunk.choices[0].delta.content == "Regular content" + assert parsed_chunk.choices[0].delta.role == "assistant" + # Verify that reasoning_content is not set (it should be deleted by Delta.__init__) + assert not hasattr(parsed_chunk.choices[0].delta, "reasoning_content") + + def test_reasoning_with_tool_calls(self): + """ + Test that reasoning field is properly mapped even when tool_calls are present. + """ + handler = GroqChatCompletionStreamingHandler( + streaming_response=None, sync_stream=True + ) + + # Simulate a chunk with both reasoning and tool_calls + groq_chunk = { + "id": "chatcmpl-test", + "object": "chat.completion.chunk", + "created": 1769511767, + "model": "qwen/qwen3-32b", + "choices": [ + { + "delta": { + "reasoning": "Reasoning before tool call", + "tool_calls": [ + { + "index": 0, + "id": "call_123", + "function": {"name": "test_function", "arguments": "{}"}, + "type": "function", + } + ], + }, + "finish_reason": None, + "index": 0, + } + ], + } + + # Parse the chunk + parsed_chunk = handler.chunk_parser(groq_chunk) + + # Verify that reasoning was mapped to reasoning_content + assert parsed_chunk.choices[0].delta.reasoning_content == "Reasoning before tool call" + # Verify tool_calls are still present + assert parsed_chunk.choices[0].delta.tool_calls is not None + assert len(parsed_chunk.choices[0].delta.tool_calls) == 1 + assert parsed_chunk.choices[0].delta.tool_calls[0]["function"]["name"] == "test_function" From 154ad179af74f03c756be6e0c5bc262f4c9bbb5c Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 27 Jan 2026 16:39:56 +0530 Subject: [PATCH 48/62] Revert poetry lock --- poetry.lock | 67 +++++++++++++++++++---------------------------------- 1 file changed, 24 insertions(+), 43 deletions(-) diff --git a/poetry.lock b/poetry.lock index e5ef1750da..462a56fe42 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.2.0 and should not be changed by hand. [[package]] name = "a2a-sdk" @@ -343,7 +343,7 @@ zookeeper = ["kazoo"] name = "async-timeout" version = "5.0.1" description = "Timeout context manager for asyncio programs" -optional = false +optional = true python-versions = ">=3.8" groups = ["main"] markers = "python_full_version < \"3.11.3\" and (extra == \"extra-proxy\" or extra == \"proxy\" or python_version < \"3.11\")" @@ -593,8 +593,8 @@ files = [ jmespath = ">=0.7.1,<2.0.0" python-dateutil = ">=2.1,<3.0.0" urllib3 = [ - {version = ">=1.25.4,<1.27", markers = "python_version < \"3.10\""}, {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""}, + {version = ">=1.25.4,<1.27", markers = "python_version < \"3.10\""}, ] [package.extras] @@ -902,7 +902,7 @@ files = [ {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, ] -markers = {main = "platform_system == \"Windows\" or sys_platform == \"win32\" and python_version < \"3.14\" and (extra == \"utils\" or extra == \"semantic-router\") or sys_platform == \"win32\" and extra == \"utils\" or python_version < \"3.14\" and extra == \"semantic-router\"", dev = "platform_system == \"Windows\" or sys_platform == \"win32\"", proxy-dev = "platform_system == \"Windows\""} +markers = {main = "platform_system == \"Windows\" or sys_platform == \"win32\" and python_version < \"3.14\" and (extra == \"utils\" or extra == \"semantic-router\") or sys_platform == \"win32\" and extra == \"utils\" or python_version <= \"3.13\" and extra == \"semantic-router\"", dev = "platform_system == \"Windows\" or sys_platform == \"win32\"", proxy-dev = "platform_system == \"Windows\""} [[package]] name = "coloredlogs" @@ -2028,7 +2028,7 @@ description = "Google API client core library" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "python_version == \"3.9\" and (extra == \"google\" or extra == \"extra-proxy\") or python_version < \"3.14\" and (extra == \"extra-proxy\" or extra == \"google\")" +markers = "(extra == \"extra-proxy\" or extra == \"google\") and python_version < \"3.14\"" files = [ {file = "google_api_core-2.28.1-py3-none-any.whl", hash = "sha256:4021b0f8ceb77a6fb4de6fde4502cecab45062e66ff4f2895169e0b35bc9466c"}, {file = "google_api_core-2.28.1.tar.gz", hash = "sha256:2b405df02d68e68ce0fbc138559e6036559e685159d148ae5861013dc201baf8"}, @@ -2038,12 +2038,12 @@ files = [ google-auth = ">=2.14.1,<3.0.0" googleapis-common-protos = ">=1.56.2,<2.0.0" grpcio = [ - {version = ">=1.33.2,<2.0.0", optional = true, markers = "extra == \"grpc\""}, {version = ">=1.49.1,<2.0.0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}, + {version = ">=1.33.2,<2.0.0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""}, ] grpcio-status = [ - {version = ">=1.33.2,<2.0.0", optional = true, markers = "extra == \"grpc\""}, {version = ">=1.49.1,<2.0.0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}, + {version = ">=1.33.2,<2.0.0", optional = true, markers = "extra == \"grpc\""}, ] proto-plus = [ {version = ">=1.22.3,<2.0.0"}, @@ -2457,7 +2457,7 @@ files = [ {file = "googleapis_common_protos-1.72.0-py3-none-any.whl", hash = "sha256:4299c5a82d5ae1a9702ada957347726b167f9f8d1fc352477702a1e851ff4038"}, {file = "googleapis_common_protos-1.72.0.tar.gz", hash = "sha256:e55a601c1b32b52d7a3e65f43563e2aa61bcd737998ee672ac9b951cd49319f5"}, ] -markers = {main = "extra == \"extra-proxy\" or extra == \"google\" or python_version == \"3.9\" and (extra == \"google\" or extra == \"extra-proxy\")"} +markers = {main = "extra == \"extra-proxy\" or extra == \"google\""} [package.dependencies] grpcio = {version = ">=1.44.0,<2.0.0", optional = true, markers = "extra == \"grpc\""} @@ -2687,7 +2687,7 @@ files = [ {file = "grpcio-1.76.0-cp39-cp39-win_amd64.whl", hash = "sha256:acab0277c40eff7143c2323190ea57b9ee5fd353d8190ee9652369fae735668a"}, {file = "grpcio-1.76.0.tar.gz", hash = "sha256:7be78388d6da1a25c0d5ec506523db58b18be22d9c37d8d3a32c08be4987bd73"}, ] -markers = {main = "extra == \"extra-proxy\" or extra == \"grpc\""} +markers = {main = "extra == \"extra-proxy\" or extra == \"google\" or extra == \"grpc\""} [package.dependencies] typing-extensions = ">=4.12,<5.0" @@ -2720,7 +2720,7 @@ description = "WSGI HTTP Server for UNIX" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "(python_version < \"3.14\" or extra == \"mlflow\" or extra == \"proxy\") and (platform_system != \"Windows\" or extra == \"proxy\") and (python_version >= \"3.10\" or extra == \"proxy\") and (extra == \"proxy\" or extra == \"mlflow\")" +markers = "extra == \"proxy\" or python_version >= \"3.10\" and (extra == \"proxy\" or extra == \"mlflow\") and platform_system != \"Windows\"" files = [ {file = "gunicorn-23.0.0-py3-none-any.whl", hash = "sha256:ec400d38950de4dfd418cff8328b2c8faed0edb0d517d3394e457c317908ca4d"}, {file = "gunicorn-23.0.0.tar.gz", hash = "sha256:f014447a0101dc57e294f6c18ca6b40227a4c90e9bdb586042628030cba004ec"}, @@ -3426,15 +3426,15 @@ files = [ [[package]] name = "litellm-proxy-extras" -version = "0.4.25" +version = "0.4.27" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." optional = true python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8" groups = ["main"] markers = "extra == \"proxy\"" files = [ - {file = "litellm_proxy_extras-0.4.25-py3-none-any.whl", hash = "sha256:da79e1a7a999020a82ec33c45d8fd35eb390ff3d0bc3d7686542b3529aff2cda"}, - {file = "litellm_proxy_extras-0.4.25.tar.gz", hash = "sha256:a03790e574ec6b8098c74d49836313651c0a0e72354a716c76c50ed16b087815"}, + {file = "litellm_proxy_extras-0.4.27-py3-none-any.whl", hash = "sha256:752c1faabc86ce3d2b1fa451495d34de82323798e37b9cb5c0fea93deae1c5c8"}, + {file = "litellm_proxy_extras-0.4.27.tar.gz", hash = "sha256:81059120016cfc03c82aa9664424912bdcffad103f66a5f925fef6b26f2cc151"}, ] [[package]] @@ -3777,10 +3777,10 @@ files = [ [package.dependencies] numpy = [ - {version = ">1.20"}, {version = ">=1.23.3", markers = "python_version >= \"3.11\""}, - {version = ">=1.21.2", markers = "python_version >= \"3.10\""}, {version = ">=1.26.0", markers = "python_version >= \"3.12\""}, + {version = ">1.20"}, + {version = ">=1.21.2", markers = "python_version >= \"3.10\""}, ] [package.extras] @@ -4191,7 +4191,7 @@ description = "Fundamental package for array computing in Python" optional = true python-versions = ">=3.9" groups = ["main"] -markers = "python_version < \"3.12\" and (extra == \"extra-proxy\" or extra == \"semantic-router\" or extra == \"google\" or python_version >= \"3.10\") and (extra == \"extra-proxy\" or extra == \"semantic-router\" or extra == \"google\" or extra == \"mlflow\")" +markers = "(python_version >= \"3.10\" or extra == \"extra-proxy\" or extra == \"semantic-router\" or extra == \"google\") and python_version < \"3.12\" and (extra == \"extra-proxy\" or extra == \"semantic-router\" or extra == \"google\" or extra == \"mlflow\")" files = [ {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"}, {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"}, @@ -4708,8 +4708,8 @@ files = [ [package.dependencies] numpy = [ {version = ">=1.23.2", markers = "python_version == \"3.11\""}, - {version = ">=1.22.4", markers = "python_version < \"3.11\""}, {version = ">=1.26.0", markers = "python_version >= \"3.12\""}, + {version = ">=1.22.4", markers = "python_version < \"3.11\""}, ] python-dateutil = ">=2.8.2" pytz = ">=2020.1" @@ -5169,7 +5169,7 @@ description = "Beautiful, Pythonic protocol buffers" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "extra == \"google\" or extra == \"extra-proxy\"" +markers = "extra == \"extra-proxy\" or extra == \"google\"" files = [ {file = "proto_plus-1.26.1-py3-none-any.whl", hash = "sha256:13285478c2dcf2abb829db158e1047e2f1e8d63a077d94263c2b88b043c75a66"}, {file = "proto_plus-1.26.1.tar.gz", hash = "sha256:21a515a4c4c0088a773899e23c7bbade3d18f9c66c73edd4c7ee3816bc96a012"}, @@ -5316,7 +5316,7 @@ files = [ {file = "pycparser-2.23-py3-none-any.whl", hash = "sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934"}, {file = "pycparser-2.23.tar.gz", hash = "sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2"}, ] -markers = {main = "implementation_name != \"PyPy\" and (platform_python_implementation != \"PyPy\" or extra == \"proxy\")", dev = "platform_python_implementation != \"PyPy\" and implementation_name != \"PyPy\"", proxy-dev = "platform_python_implementation != \"PyPy\" and implementation_name != \"PyPy\""} +markers = {main = "implementation_name != \"PyPy\" and (platform_python_implementation != \"PyPy\" or extra == \"proxy\")", dev = "implementation_name != \"PyPy\" and platform_python_implementation != \"PyPy\"", proxy-dev = "implementation_name != \"PyPy\" and platform_python_implementation != \"PyPy\""} [[package]] name = "pydantic" @@ -5617,7 +5617,7 @@ description = "A python implementation of GNU readline." optional = true python-versions = ">=3.8" groups = ["main"] -markers = "extra == \"extra-proxy\" and sys_platform == \"win32\" and python_version < \"3.14\"" +markers = "sys_platform == \"win32\" and extra == \"extra-proxy\" and python_version < \"3.14\"" files = [ {file = "pyreadline3-3.5.4-py3-none-any.whl", hash = "sha256:eaf8e6cc3c49bcccf145fc6067ba8643d1df34d604a1ec0eccbf7a18e6d3fae6"}, {file = "pyreadline3-3.5.4.tar.gz", hash = "sha256:8d57d53039a1c75adba8e50dd3d992b28143480816187ea5efbd5c78e6c885b7"}, @@ -7450,26 +7450,6 @@ examples = ["aiosqlite (>=0.21.0)", "fastapi (>=0.115.12)", "sqlalchemy[asyncio] granian = ["granian (>=2.3.1)"] uvicorn = ["uvicorn (>=0.34.0)"] -[[package]] -name = "starlette" -version = "0.49.3" -description = "The little ASGI library that shines." -optional = false -python-versions = ">=3.9" -groups = ["main", "dev"] -files = [ - {file = "starlette-0.49.3-py3-none-any.whl", hash = "sha256:b579b99715fdc2980cf88c8ec96d3bf1ce16f5a8051a7c2b84ef9b1cdecaea2f"}, - {file = "starlette-0.49.3.tar.gz", hash = "sha256:1c14546f299b5901a1ea0e34410575bc33bbd741377a10484a54445588d00284"}, -] -markers = {main = "python_version == \"3.9\" and extra == \"proxy\"", dev = "python_version == \"3.9\""} - -[package.dependencies] -anyio = ">=3.6.2,<5" -typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""} - -[package.extras] -full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"] - [[package]] name = "starlette" version = "0.50.0" @@ -7481,7 +7461,7 @@ files = [ {file = "starlette-0.50.0-py3-none-any.whl", hash = "sha256:9e5391843ec9b6e472eed1365a78c8098cfceb7a74bfd4d6b1c0c0095efb3bca"}, {file = "starlette-0.50.0.tar.gz", hash = "sha256:a2a17b22203254bcbc2e1f926d2d55f3f9497f769416b3190768befe598fa3ca"}, ] -markers = {main = "python_version >= \"3.10\" and (extra == \"mlflow\" or extra == \"proxy\")", dev = "python_version >= \"3.10\""} +markers = {main = "(extra == \"mlflow\" or extra == \"proxy\") and python_version >= \"3.10\" or extra == \"proxy\""} [package.dependencies] anyio = ">=3.6.2,<5" @@ -7530,7 +7510,7 @@ description = "Retry code until it succeeds" optional = true python-versions = ">=3.9" groups = ["main"] -markers = "(extra == \"extra-proxy\" or extra == \"google\") and (python_version < \"3.14\" or extra == \"google\")" +markers = "(extra == \"extra-proxy\" or extra == \"google\") and python_version < \"3.14\" or extra == \"google\"" files = [ {file = "tenacity-9.1.2-py3-none-any.whl", hash = "sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138"}, {file = "tenacity-9.1.2.tar.gz", hash = "sha256:1169d376c297e7de388d18b4481760d478b0e99a777cad3a9c86e556f4b697cb"}, @@ -8483,6 +8463,7 @@ type = ["pytest-mypy"] caching = ["diskcache"] extra-proxy = ["a2a-sdk", "azure-identity", "azure-keyvault-secrets", "google-cloud-iam", "google-cloud-kms", "prisma", "redisvl", "resend"] google = ["google-cloud-aiplatform"] +grpc = ["grpcio", "grpcio"] mlflow = ["mlflow"] proxy = ["PyJWT", "apscheduler", "azure-identity", "azure-storage-blob", "backoff", "boto3", "cryptography", "fastapi", "fastapi-sso", "gunicorn", "litellm-enterprise", "litellm-proxy-extras", "mcp", "orjson", "polars", "pynacl", "python-multipart", "pyyaml", "rich", "rq", "soundfile", "uvicorn", "uvloop", "websockets"] semantic-router = ["semantic-router"] @@ -8491,4 +8472,4 @@ utils = ["numpydoc"] [metadata] lock-version = "2.1" python-versions = ">=3.9,<4.0" -content-hash = "76f5b5fb10667c2dcf428976292672e7a1f3f2eb5f5bf78998e2192899f5037e" +content-hash = "73b5e1ab0badbee6c564d5e056e4f9c320c2f722bf176ce97d42e77110e37d60" From f30742fe6e8e0ae3a753f37631f1c20fd0124f0f Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 27 Jan 2026 16:49:39 +0530 Subject: [PATCH 49/62] Fix mypy and code quality issues --- .../integrations/datadog/datadog_cost_management.py | 10 ++++++---- litellm/llms/bedrock/chat/converse_transformation.py | 2 +- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/litellm/integrations/datadog/datadog_cost_management.py b/litellm/integrations/datadog/datadog_cost_management.py index 9bfb2aba71..2eb94b59dd 100644 --- a/litellm/integrations/datadog/datadog_cost_management.py +++ b/litellm/integrations/datadog/datadog_cost_management.py @@ -2,7 +2,7 @@ import asyncio import os import time from datetime import datetime -from typing import Dict, List, Optional +from typing import Dict, List, Optional, Tuple from litellm._logging import verbose_logger from litellm.integrations.custom_batch_logger import CustomBatchLogger @@ -93,7 +93,7 @@ class DatadogCostManagementLogger(CustomBatchLogger): Aggregates costs by Provider, Model, and Date. Returns a list of DatadogFOCUSCostEntry. """ - aggregator: Dict[str, DatadogFOCUSCostEntry] = {} + aggregator: Dict[Tuple[str, str, str, Tuple[Tuple[str, str], ...]], DatadogFOCUSCostEntry] = {} for log in logs: try: @@ -171,8 +171,10 @@ class DatadogCostManagementLogger(CustomBatchLogger): tags["user"] = str(metadata["user_api_key_alias"]) if "user_api_key_team_alias" in metadata: tags["team"] = str(metadata["user_api_key_team_alias"]) - if "model_group" in metadata: - tags["model_group"] = str(metadata["model_group"]) + # model_group is not in StandardLoggingMetadata TypedDict, so we need to access it via dict.get() + model_group = metadata.get("model_group") # type: ignore[misc] + if model_group: + tags["model_group"] = str(model_group) return tags diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index 2ec27a7af0..ec66514207 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -1501,7 +1501,7 @@ class AmazonConverseConfig(BaseConfig): return content_str, tools, reasoningContentBlocks, citationsContentBlocks - def _transform_response( + def _transform_response( # noqa: PLR0915 self, model: str, response: httpx.Response, From 0f0b71e6d9755f74b94520977b55f066ee27891f Mon Sep 17 00:00:00 2001 From: Harshit Jain <48647625+Harshit28j@users.noreply.github.com> Date: Tue, 27 Jan 2026 22:46:53 +0530 Subject: [PATCH 50/62] feat: add feature to make silent calls (#19544) * feat: add feature to make silent calls * add test or silent feat * add docs for silent feat * fix lint issues and UI logs * add docs of ab testing and deep copy --- docs/my-website/docs/routing.md | 7 +- docs/my-website/docs/traffic_mirroring.md | 83 +++++++++ docs/my-website/sidebars.js | 1 + litellm/router.py | 138 +++++++++++++-- .../test_router_silent_experiment.py | 157 ++++++++++++++++++ 5 files changed, 373 insertions(+), 13 deletions(-) create mode 100644 docs/my-website/docs/traffic_mirroring.md create mode 100644 tests/test_litellm/test_router_silent_experiment.py diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md index 47967775e1..b5ece7237a 100644 --- a/docs/my-website/docs/routing.md +++ b/docs/my-website/docs/routing.md @@ -828,7 +828,12 @@ asyncio.run(router_acompletion()) ``` - + +## Traffic Mirroring / Silent Experiments + +Traffic mirroring allows you to "mimic" production traffic to a secondary (silent) model for evaluation purposes. The silent model's response is gathered in the background and does not affect the latency or result of the primary request. + +[**See detailed guide on A/B Testing - Traffic Mirroring here**](./traffic_mirroring.md) ## Basic Reliability diff --git a/docs/my-website/docs/traffic_mirroring.md b/docs/my-website/docs/traffic_mirroring.md new file mode 100644 index 0000000000..3bdcb0f161 --- /dev/null +++ b/docs/my-website/docs/traffic_mirroring.md @@ -0,0 +1,83 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# A/B Testing - Traffic Mirroring + +Traffic mirroring allows you to "mimic" production traffic to a secondary (silent) model for evaluation purposes. The silent model's response is gathered in the background and does not affect the latency or result of the primary request. + +This is useful for: +- Testing a new model's performance on production prompts before switching. +- Comparing costs and latency between different providers. +- Debugging issues by mirroring traffic to a more verbose model. + +## Quick Start + +To enable traffic mirroring, add `silent_model` to the `litellm_params` of a deployment. + + + + +```python +from litellm import Router + +model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": "...", + "silent_model": "gpt-4" # 👈 Mirror traffic to gpt-4 + }, + }, + { + "model_name": "gpt-4", + "litellm_params": { + "model": "openai/gpt-4", + "api_key": "..." + }, + } +] + +router = Router(model_list=model_list) + +# The request to "gpt-3.5-turbo" will trigger a background call to "gpt-4" +response = await router.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "How does traffic mirroring work?"}] +) +``` + + + + +Add `silent_model` to your `config.yaml`: + +```yaml +model_list: + - model_name: primary-model + litellm_params: + model: azure/gpt-35-turbo + api_key: os.environ/AZURE_API_KEY + silent_model: evaluation-model # 👈 Mirror traffic here + - model_name: evaluation-model + litellm_params: + model: openai/gpt-4o + api_key: os.environ/OPENAI_API_KEY +``` + + + + +## How it works +1. **Request Received**: A request is made to a model group (e.g. `primary-model`). +2. **Deployment Picked**: LiteLLM picks a deployment from the group. +3. **Primary Call**: LiteLLM makes the call to the primary deployment. +4. **Mirroring**: If `silent_model` is present, LiteLLM triggers a background call to that model. + - For **Sync** calls: Uses a shared thread pool. + - For **Async** calls: Uses `asyncio.create_task`. +5. **Isolation**: The background call uses a `deepcopy` of the original request parameters and sets `metadata["is_silent_experiment"] = True`. It also strips out logging IDs to prevent collisions in usage tracking. + +## Key Features +- **Latency Isolation**: The primary request returns as soon as it's ready. The background (silent) call does not block. +- **Unified Logging**: Background calls are processed via the Router, meaning they are automatically logged to your configured observability tools (Langfuse, S3, etc.). +- **Evaluation**: Use the `is_silent_experiment: True` flag in your logs to filter and compare results between the primary and mirrored calls. diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 41854a4177..ef7573e9ce 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -364,6 +364,7 @@ const sidebars = { label: "Load Balancing, Routing, Fallbacks", href: "https://docs.litellm.ai/docs/routing-load-balancing", }, + "traffic_mirroring", { type: "category", label: "Logging, Alerting, Metrics", diff --git a/litellm/router.py b/litellm/router.py index 9fe37efa3a..09d71b6b49 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -58,6 +58,7 @@ from litellm.litellm_core_utils.core_helpers import ( _get_parent_otel_span_from_kwargs, get_metadata_variable_name_from_kwargs, ) +from litellm.litellm_core_utils.thread_pool_executor import executor from litellm.litellm_core_utils.coroutine_checker import coroutine_checker from litellm.litellm_core_utils.credential_accessor import CredentialAccessor from litellm.litellm_core_utils.dd_tracing import tracer @@ -1250,10 +1251,25 @@ class Router: specific_deployment=kwargs.pop("specific_deployment", None), request_kwargs=kwargs, ) - self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) + # Check for silent model experiment + # Make a local copy of litellm_params to avoid mutating the Router's state + litellm_params = deployment["litellm_params"].copy() + silent_model = litellm_params.pop("silent_model", None) + if silent_model is not None: + # Mirroring traffic to a secondary model + # Use shared thread pool for background calls + executor.submit( + self._silent_experiment_completion, + silent_model, + messages, + **kwargs, + ) + + self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) + kwargs.pop("silent_model", None) # Ensure it's not in kwargs either # No copy needed - data is only read and spread into new dict below - data = deployment["litellm_params"] + data = litellm_params.copy() # Use the local copy of litellm_params model_name = data["model"] potential_model_client = self._get_client( deployment=deployment, kwargs=kwargs @@ -1274,15 +1290,14 @@ class Router: if not self.has_model_id(model): self.routing_strategy_pre_call_checks(deployment=deployment) - response = litellm.completion( - **{ - **data, - "messages": messages, - "caching": self.cache_responses, - "client": model_client, - **kwargs, - } - ) + input_kwargs = { + **data, + "messages": messages, + "caching": self.cache_responses, + "client": model_client, + **kwargs, + } + response = litellm.completion(**input_kwargs) verbose_router_logger.info( f"litellm.completion(model={model_name})\033[32m 200 OK\033[0m" ) @@ -1309,6 +1324,56 @@ class Router: self._set_deployment_num_retries_on_exception(e, deployment) raise e + def _get_silent_experiment_kwargs(self, **kwargs) -> dict: + """ + Prepare kwargs for a silent experiment by ensuring isolation from the primary call. + """ + # Copy kwargs to ensure isolation + silent_kwargs = copy.deepcopy(kwargs) + if "metadata" not in silent_kwargs: + silent_kwargs["metadata"] = {} + + silent_kwargs["metadata"]["is_silent_experiment"] = True + + # Pop logging objects and call IDs to ensure a fresh logging context + # This prevents collisions in the Proxy's database (spend_logs) + silent_kwargs.pop("litellm_call_id", None) + silent_kwargs.pop("litellm_logging_obj", None) + silent_kwargs.pop("standard_logging_object", None) + silent_kwargs.pop("proxy_server_request", None) + + return silent_kwargs + + def _silent_experiment_completion( + self, silent_model: str, messages: List[Any], **kwargs + ): + """ + Run a silent experiment in the background (thread). + """ + try: + # Prevent infinite recursion if silent model also has a silent model + if kwargs.get("metadata", {}).get("is_silent_experiment", False): + return + + messages = copy.deepcopy(messages) + + verbose_router_logger.info( + f"Starting silent experiment for model {silent_model}" + ) + + silent_kwargs = self._get_silent_experiment_kwargs(**kwargs) + + # Trigger the silent request + self.completion( + model=silent_model, + messages=cast(List[Dict[str, str]], messages), + **silent_kwargs, + ) + except Exception as e: + verbose_router_logger.error( + f"Silent experiment failed for model {silent_model}: {str(e)}" + ) + # fmt: off @overload @@ -1517,6 +1582,36 @@ class Router: return FallbackStreamWrapper(stream_with_fallbacks()) + async def _silent_experiment_acompletion( + self, silent_model: str, messages: List[Any], **kwargs + ): + """ + Run a silent experiment in the background. + """ + try: + # Prevent infinite recursion if silent model also has a silent model + if kwargs.get("metadata", {}).get("is_silent_experiment", False): + return + + messages = copy.deepcopy(messages) + + verbose_router_logger.info( + f"Starting silent experiment for model {silent_model}" + ) + + silent_kwargs = self._get_silent_experiment_kwargs(**kwargs) + + # Trigger the silent request + await self.acompletion( + model=silent_model, + messages=cast(List[AllMessageValues], messages), + **silent_kwargs, + ) + except Exception as e: + verbose_router_logger.error( + f"Silent experiment failed for model {silent_model}: {str(e)}" + ) + async def _acompletion( # noqa: PLR0915 self, model: str, messages: List[Dict[str, str]], **kwargs ) -> Union[ModelResponse, CustomStreamWrapper,]: @@ -1563,9 +1658,27 @@ class Router: self._track_deployment_metrics( deployment=deployment, parent_otel_span=parent_otel_span ) + + # Check for silent model experiment + # Make a local copy of litellm_params to avoid mutating the Router's state + litellm_params = deployment["litellm_params"].copy() + silent_model = litellm_params.pop("silent_model", None) + + if silent_model is not None: + # Mirroring traffic to a secondary model + # This is a silent experiment, so we don't want to block the primary request + asyncio.create_task( + self._silent_experiment_acompletion( + silent_model=silent_model, + messages=messages, # Use messages instead of *args + **kwargs, + ) + ) + self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) + kwargs.pop("silent_model", None) # Ensure it's not in kwargs either # No copy needed - data is only read and spread into new dict below - data = deployment["litellm_params"] + data = litellm_params.copy() # Use the local copy of litellm_params model_name = data["model"] @@ -1582,6 +1695,7 @@ class Router: "client": model_client, **kwargs, } + input_kwargs.pop("silent_model", None) _response = litellm.acompletion(**input_kwargs) diff --git a/tests/test_litellm/test_router_silent_experiment.py b/tests/test_litellm/test_router_silent_experiment.py new file mode 100644 index 0000000000..9b82cde13c --- /dev/null +++ b/tests/test_litellm/test_router_silent_experiment.py @@ -0,0 +1,157 @@ +import asyncio +from unittest.mock import MagicMock, patch + +import pytest + +import litellm +from litellm.router import Router + + +@pytest.mark.asyncio +async def test_router_silent_experiment_acompletion(): + """ + Test that silent_model triggers a background acompletion call + and that the silent_model parameter is stripped from both calls. + """ + model_list = [ + { + "model_name": "primary-model", + "litellm_params": { + "model": "openai/gpt-3.5-turbo", + "api_key": "fake-key", + "silent_model": "silent-model", + }, + }, + { + "model_name": "silent-model", + "litellm_params": { + "model": "openai/gpt-4", + "api_key": "fake-key", + }, + }, + ] + + router = Router(model_list=model_list) + + # Mock litellm.acompletion + mock_acompletion = MagicMock() + # Create a future that resolves to a ModelResponse + mock_response = litellm.ModelResponse(choices=[{"message": {"content": "hello"}}]) + future = asyncio.Future() + future.set_result(mock_response) + mock_acompletion.return_value = future + + with patch("litellm.acompletion", mock_acompletion): + response = await router.acompletion( + model="primary-model", + messages=[{"role": "user", "content": "hi"}], + ) + + assert response.choices[0].message.content == "hello" + + # Give the background task a moment to trigger (it's an asyncio task) + await asyncio.sleep(0.1) + + # Should have 2 calls: one for primary, one for silent + assert mock_acompletion.call_count == 2 + + # Check call arguments + call_args_list = mock_acompletion.call_args_list + + # Verify no silent_model in any call to litellm.acompletion + for call in call_args_list: + args, kwargs = call + assert "silent_model" not in kwargs + if "metadata" in kwargs: + # One call should have is_silent_experiment=True + pass + + # Find the silent call + silent_call = next( + ( + c + for c in call_args_list + if c[1].get("metadata", {}).get("is_silent_experiment") is True + ), + None, + ) + assert silent_call is not None + assert silent_call[1]["model"] == "openai/gpt-4" + + # Find the primary call + primary_call = next( + ( + c + for c in call_args_list + if not c[1].get("metadata", {}).get("is_silent_experiment") + ), + None, + ) + assert primary_call is not None + assert primary_call[1]["model"] == "openai/gpt-3.5-turbo" + + +def test_router_silent_experiment_completion(): + """ + Test that silent_model triggers a background completion call (sync) + and that the silent_model parameter is stripped. + """ + model_list = [ + { + "model_name": "primary-model", + "litellm_params": { + "model": "openai/gpt-3.5-turbo", + "api_key": "fake-key", + "silent_model": "silent-model", + }, + }, + { + "model_name": "silent-model", + "litellm_params": { + "model": "openai/gpt-4", + "api_key": "fake-key", + }, + }, + ] + + router = Router(model_list=model_list) + + # Mock litellm.completion + mock_completion = MagicMock() + mock_response = litellm.ModelResponse(choices=[{"message": {"content": "hello"}}]) + mock_completion.return_value = mock_response + + with patch("litellm.completion", mock_completion): + response = router.completion( + model="primary-model", + messages=[{"role": "user", "content": "hi"}], + ) + + assert response.choices[0].message.content == "hello" + + # The sync background call uses a thread pool. We might need to wait a bit. + import time + + time.sleep(0.5) + + # Should have 2 calls + assert mock_completion.call_count == 2 + + call_args_list = mock_completion.call_args_list + + # Verify no silent_model in any call + for call in call_args_list: + args, kwargs = call + assert "silent_model" not in kwargs + + # Find the silent call + silent_call = next( + ( + c + for c in call_args_list + if c[1].get("metadata", {}).get("is_silent_experiment") is True + ), + None, + ) + assert silent_call is not None + assert silent_call[1]["model"] == "openai/gpt-4" From fc7a9b4cb076e91b42e427df54fcac6513faac65 Mon Sep 17 00:00:00 2001 From: michelligabriele Date: Tue, 27 Jan 2026 18:34:30 +0100 Subject: [PATCH 51/62] fix(enterprise): correct error message for DISABLE_ADMIN_ENDPOINTS (#19861) The error message for DISABLE_ADMIN_ENDPOINTS incorrectly said "DISABLING LLM API ENDPOINTS is an Enterprise feature" instead of "DISABLING ADMIN ENDPOINTS is an Enterprise feature". This was a copy-paste bug from the is_llm_api_route_disabled() function. Added regression tests to verify both error messages are correct. --- .../proxy/auth/route_checks.py | 2 +- .../proxy/auth/test_route_checks.py | 65 +++++++++++++++++++ 2 files changed, 66 insertions(+), 1 deletion(-) diff --git a/enterprise/litellm_enterprise/proxy/auth/route_checks.py b/enterprise/litellm_enterprise/proxy/auth/route_checks.py index 6cce781faf..6f7cf9143f 100644 --- a/enterprise/litellm_enterprise/proxy/auth/route_checks.py +++ b/enterprise/litellm_enterprise/proxy/auth/route_checks.py @@ -36,7 +36,7 @@ class EnterpriseRouteChecks: if not premium_user: raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail=f"🚨🚨🚨 DISABLING LLM API ENDPOINTS is an Enterprise feature\n🚨 {CommonProxyErrors.not_premium_user.value}", + detail=f"🚨🚨🚨 DISABLING ADMIN ENDPOINTS is an Enterprise feature\n🚨 {CommonProxyErrors.not_premium_user.value}", ) return get_secret_bool("DISABLE_ADMIN_ENDPOINTS") is True diff --git a/tests/enterprise/litellm_enterprise/proxy/auth/test_route_checks.py b/tests/enterprise/litellm_enterprise/proxy/auth/test_route_checks.py index ce77ff69e0..706e3b7187 100644 --- a/tests/enterprise/litellm_enterprise/proxy/auth/test_route_checks.py +++ b/tests/enterprise/litellm_enterprise/proxy/auth/test_route_checks.py @@ -180,3 +180,68 @@ class TestEnterpriseRouteChecks: # Should not raise exception since management routes are enabled EnterpriseRouteChecks.should_call_route("/config/update") + + +class TestEnterpriseRouteChecksErrorMessages: + """Test that error messages correctly identify which feature requires Enterprise license""" + + @patch("litellm.secret_managers.main.get_secret_bool") + @patch("litellm.proxy.proxy_server.premium_user", False) + def test_disable_llm_api_endpoints_error_message(self, mock_get_secret_bool): + """ + Test that when DISABLE_LLM_API_ENDPOINTS is set without Enterprise license, + the error message correctly mentions 'LLM API ENDPOINTS' + """ + with patch.dict(os.environ, {"DISABLE_LLM_API_ENDPOINTS": "true"}): + with pytest.raises(HTTPException) as exc_info: + EnterpriseRouteChecks.is_llm_api_route_disabled() + + assert exc_info.value.status_code == 500 + assert "DISABLING LLM API ENDPOINTS is an Enterprise feature" in str( + exc_info.value.detail + ) + + @patch("litellm.secret_managers.main.get_secret_bool") + @patch("litellm.proxy.proxy_server.premium_user", False) + def test_disable_admin_endpoints_error_message(self, mock_get_secret_bool): + """ + Test that when DISABLE_ADMIN_ENDPOINTS is set without Enterprise license, + the error message correctly mentions 'ADMIN ENDPOINTS' (not 'LLM API ENDPOINTS') + + This is a regression test for a bug where the error message incorrectly said + 'DISABLING LLM API ENDPOINTS' when the actual issue was DISABLE_ADMIN_ENDPOINTS. + """ + with patch.dict(os.environ, {"DISABLE_ADMIN_ENDPOINTS": "true"}): + with pytest.raises(HTTPException) as exc_info: + EnterpriseRouteChecks.is_management_routes_disabled() + + assert exc_info.value.status_code == 500 + assert "DISABLING ADMIN ENDPOINTS is an Enterprise feature" in str( + exc_info.value.detail + ) + # Ensure it does NOT mention LLM API ENDPOINTS (the old buggy message) + assert "LLM API ENDPOINTS" not in str(exc_info.value.detail) + + @patch("litellm.secret_managers.main.get_secret_bool") + @patch("litellm.proxy.proxy_server.premium_user", True) + def test_disable_llm_api_endpoints_with_premium_user(self, mock_get_secret_bool): + """ + Test that premium users can use DISABLE_LLM_API_ENDPOINTS without error + """ + mock_get_secret_bool.return_value = True + with patch.dict(os.environ, {"DISABLE_LLM_API_ENDPOINTS": "true"}): + # Should not raise exception for premium users + result = EnterpriseRouteChecks.is_llm_api_route_disabled() + assert result is True + + @patch("litellm.secret_managers.main.get_secret_bool") + @patch("litellm.proxy.proxy_server.premium_user", True) + def test_disable_admin_endpoints_with_premium_user(self, mock_get_secret_bool): + """ + Test that premium users can use DISABLE_ADMIN_ENDPOINTS without error + """ + mock_get_secret_bool.return_value = True + with patch.dict(os.environ, {"DISABLE_ADMIN_ENDPOINTS": "true"}): + # Should not raise exception for premium users + result = EnterpriseRouteChecks.is_management_routes_disabled() + assert result is True From 388b4c90b6df3e7bacda5bb3097e2f713a82658f Mon Sep 17 00:00:00 2001 From: michelligabriele Date: Tue, 27 Jan 2026 18:34:58 +0100 Subject: [PATCH 52/62] fix(proxy): handle agent parameter in /interactions endpoint (#19866) --- litellm/proxy/google_endpoints/endpoints.py | 2 +- litellm/proxy/route_llm_request.py | 3 +- .../test_interactions_agent_param.py | 75 +++++++++++++++++++ 3 files changed, 78 insertions(+), 2 deletions(-) create mode 100644 tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py diff --git a/litellm/proxy/google_endpoints/endpoints.py b/litellm/proxy/google_endpoints/endpoints.py index bba5b87024..5c41b371ca 100644 --- a/litellm/proxy/google_endpoints/endpoints.py +++ b/litellm/proxy/google_endpoints/endpoints.py @@ -264,7 +264,7 @@ async def create_interaction( general_settings=general_settings, proxy_config=proxy_config, select_data_generator=select_data_generator, - model=data.get("model"), + model=data.get("model") or data.get("agent"), user_model=user_model, user_temperature=user_temperature, user_request_timeout=user_request_timeout, diff --git a/litellm/proxy/route_llm_request.py b/litellm/proxy/route_llm_request.py index 236f58bb82..af441ee43e 100644 --- a/litellm/proxy/route_llm_request.py +++ b/litellm/proxy/route_llm_request.py @@ -221,8 +221,9 @@ async def route_request( "aretrieve_container_file_content", ]: return getattr(llm_router, f"{route_type}")(**data) - # Interactions API: get/delete/cancel don't need model routing + # Interactions API: create with agent, get/delete/cancel don't need model routing if route_type in [ + "acreate_interaction", "aget_interaction", "adelete_interaction", "acancel_interaction", diff --git a/tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py b/tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py new file mode 100644 index 0000000000..1063f59afb --- /dev/null +++ b/tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py @@ -0,0 +1,75 @@ +""" +Test for interactions endpoint agent parameter handling. + +Tests that the /v1beta/interactions endpoint correctly extracts +the `agent` parameter as a fallback when `model` is not provided. +""" + +import pytest + + +class TestInteractionsAgentParameter: + """Test agent parameter handling in interactions endpoint.""" + + def test_agent_parameter_fallback_logic(self): + """ + Test the core logic: model or agent extraction. + + This tests the fix in endpoints.py line ~267: + model=data.get("model") or data.get("agent") + """ + # Case 1: Only agent provided (Deep Research use case) + data = { + "agent": "deep-research-pro-preview-12-2025", + "input": "Research quantum computing", + "background": True, + } + model = data.get("model") or data.get("agent") + assert model == "deep-research-pro-preview-12-2025" + + # Case 2: Only model provided (normal use case) + data = { + "model": "gemini-2.5-flash", + "input": "Hello world", + } + model = data.get("model") or data.get("agent") + assert model == "gemini-2.5-flash" + + # Case 3: Both provided (model takes precedence) + data = { + "model": "gemini-2.5-flash", + "agent": "deep-research-pro-preview-12-2025", + "input": "Test", + } + model = data.get("model") or data.get("agent") + assert model == "gemini-2.5-flash" + + # Case 4: Neither provided + data = { + "input": "Test", + } + model = data.get("model") or data.get("agent") + assert model is None + + def test_route_type_in_skip_model_routing_list(self): + """ + Test that acreate_interaction is in the list of routes + that skip model-based routing. + + This tests the fix in route_llm_request.py. + """ + # The list of routes that skip model routing for interactions + skip_model_routing_routes = [ + "acreate_interaction", + "aget_interaction", + "adelete_interaction", + "acancel_interaction", + ] + + # acreate_interaction should be in the list (this is the fix) + assert "acreate_interaction" in skip_model_routing_routes + + # All interaction routes should be covered + assert "aget_interaction" in skip_model_routing_routes + assert "adelete_interaction" in skip_model_routing_routes + assert "acancel_interaction" in skip_model_routing_routes From 7d5439addac7a291d5e37a5e261a6557d37bed11 Mon Sep 17 00:00:00 2001 From: michelligabriele Date: Tue, 27 Jan 2026 21:17:09 +0100 Subject: [PATCH 53/62] fix(bedrock): support tool search header translation for Sonnet 4.5 (#19871) Extend advanced-tool-use header translation to include Claude Sonnet 4.5 in addition to Opus 4.5 on Bedrock Invoke API. When Claude Code sends the advanced-tool-use-2025-11-20 header, it now gets correctly translated to Bedrock-specific headers for both: - Claude Opus 4.5 - Claude Sonnet 4.5 Headers translated: - tool-search-tool-2025-10-19 - tool-examples-2025-10-29 Fixes defer_loading validation error on Bedrock with Sonnet 4.5. Ref: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool --- .../anthropic_claude3_transformation.py | 39 ++++++++++-- .../bedrock/test_anthropic_beta_support.py | 59 +++++++++++++++---- 2 files changed, 82 insertions(+), 16 deletions(-) diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py index 67ca9d5024..fc4220c054 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -178,6 +178,33 @@ class AmazonAnthropicClaudeMessagesConfig( ] return any(pattern in model_lower for pattern in opus_4_5_patterns) + def _supports_tool_search_on_bedrock(self, model: str) -> bool: + """ + Check if the model supports tool search on Bedrock. + + On Amazon Bedrock, server-side tool search is supported on Claude Opus 4.5 + and Claude Sonnet 4.5 with the tool-search-tool-2025-10-19 beta header. + + Ref: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool + + Args: + model: The model name + + Returns: + True if the model supports tool search on Bedrock + """ + model_lower = model.lower() + + # Supported models for tool search on Bedrock + supported_patterns = [ + # Opus 4.5 + "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5", + # Sonnet 4.5 + "sonnet-4.5", "sonnet_4.5", "sonnet-4-5", "sonnet_4_5", + ] + + return any(pattern in model_lower for pattern in supported_patterns) + def _filter_unsupported_beta_headers_for_bedrock( self, model: str, beta_set: set ) -> None: @@ -186,13 +213,14 @@ class AmazonAnthropicClaudeMessagesConfig( Extended thinking beta headers are only supported on specific Claude 4+ models. Advanced tool use headers are not supported on Bedrock Invoke API, but need to be - translated to Bedrock-specific headers for Claude Opus 4.5. + translated to Bedrock-specific headers for models that support tool search + (Claude Opus 4.5, Sonnet 4.5). This prevents 400 "invalid beta flag" errors on Bedrock. Note: Bedrock Invoke API fails with a 400 error when unsupported beta headers are sent, returning: {"message":"invalid beta flag"} - Translation for Claude Opus 4.5: + Translation for models supporting tool search (Opus 4.5, Sonnet 4.5): - advanced-tool-use-2025-11-20 -> tool-search-tool-2025-10-19 + tool-examples-2025-10-29 Args: @@ -226,10 +254,11 @@ class AmazonAnthropicClaudeMessagesConfig( # Remove all filtered headers for beta in beta_headers_to_remove: beta_set.discard(beta) - - # 3. Translate advanced-tool-use to Bedrock-specific headers for Claude Opus 4.5 + + # 3. Translate advanced-tool-use to Bedrock-specific headers for models that support tool search # Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html - if has_advanced_tool_use and self._is_claude_opus_4_5(model): + # Ref: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool + if has_advanced_tool_use and self._supports_tool_search_on_bedrock(model): beta_set.add("tool-search-tool-2025-10-19") beta_set.add("tool-examples-2025-10-29") diff --git a/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py b/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py index 0955636de6..e7b6de29b6 100644 --- a/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py +++ b/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py @@ -427,16 +427,18 @@ class TestAnthropicBetaHeaderSupport: "tool-examples-2025-10-29 should be added for Opus 4.5" ) - def test_messages_advanced_tool_use_filtered_non_opus_4_5(self): - """Test that advanced-tool-use header is filtered out for non-Opus 4.5 models. - - The translation to Bedrock-specific headers should only happen for Opus 4.5. - For other models, the advanced-tool-use header should just be removed. + def test_messages_advanced_tool_use_translation_sonnet_4_5(self): + """Test that advanced-tool-use header is translated to Bedrock-specific headers for Sonnet 4.5. + + Regression test for: Claude Code sends advanced-tool-use-2025-11-20 header which needs + to be translated to tool-search-tool-2025-10-19 and tool-examples-2025-10-29 for + Bedrock Invoke API on Claude Sonnet 4.5. + + Ref: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool """ config = AmazonAnthropicClaudeMessagesConfig() headers = {"anthropic-beta": "advanced-tool-use-2025-11-20"} - - # Test with Sonnet 4.5 (not Opus 4.5) + result = config.transform_anthropic_messages_request( model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", messages=[{"role": "user", "content": "Test"}], @@ -444,12 +446,47 @@ class TestAnthropicBetaHeaderSupport: litellm_params={}, headers=headers ) - + + assert "anthropic_beta" in result + beta_headers = result["anthropic_beta"] + + # advanced-tool-use should be removed + assert "advanced-tool-use-2025-11-20" not in beta_headers, ( + "advanced-tool-use-2025-11-20 should be removed for Bedrock Invoke API" + ) + + # Bedrock-specific headers should be added for Sonnet 4.5 + assert "tool-search-tool-2025-10-19" in beta_headers, ( + "tool-search-tool-2025-10-19 should be added for Sonnet 4.5" + ) + assert "tool-examples-2025-10-29" in beta_headers, ( + "tool-examples-2025-10-29 should be added for Sonnet 4.5" + ) + + def test_messages_advanced_tool_use_filtered_unsupported_model(self): + """Test that advanced-tool-use header is filtered out for models that don't support tool search. + + The translation to Bedrock-specific headers should only happen for models that + support tool search on Bedrock (Opus 4.5, Sonnet 4.5). + For other models, the advanced-tool-use header should just be removed. + """ + config = AmazonAnthropicClaudeMessagesConfig() + headers = {"anthropic-beta": "advanced-tool-use-2025-11-20"} + + # Test with Claude 3.5 Sonnet (does NOT support tool search on Bedrock) + result = config.transform_anthropic_messages_request( + model="us.anthropic.claude-3-5-sonnet-20241022-v2:0", + messages=[{"role": "user", "content": "Test"}], + anthropic_messages_optional_request_params={"max_tokens": 100}, + litellm_params={}, + headers=headers + ) + beta_headers = result.get("anthropic_beta", []) - + # advanced-tool-use should be removed assert "advanced-tool-use-2025-11-20" not in beta_headers - - # Bedrock-specific headers should NOT be added for non-Opus 4.5 + + # Bedrock-specific headers should NOT be added for unsupported models assert "tool-search-tool-2025-10-19" not in beta_headers assert "tool-examples-2025-10-29" not in beta_headers From fe444f3ed5cae334d013450f96befdad113a09a2 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 27 Jan 2026 14:45:26 -0800 Subject: [PATCH 54/62] [Feat] RAG API - Add support for using s3 Vectors as Vector Store Provider for /rag/ingest (#19888) * init S3VectorsRAGIngestion as a supported ingestion provider for RAG API * test: TestRAGS3Vectors * init S3VectorsVectorStoreOptions * init s3 vectors * code clean up + QA * fix: get_credentials * S3VectorsRAGIngestion * TestRAGS3Vectors * docs: AWS S3 Vectors * add asyncio QA checks * fix: S3_VECTORS_DEFAULT_DIMENSION --- docs/my-website/docs/rag_ingest.md | 78 ++- litellm/constants.py | 7 + ...odel_prices_and_context_window_backup.json | 42 ++ litellm/rag/ingestion/s3_vectors_ingestion.py | 546 ++++++++++++++++++ litellm/rag/main.py | 2 + litellm/types/rag.py | 46 +- .../rag/test_rag_s3_vectors.py | 107 ++++ 7 files changed, 826 insertions(+), 2 deletions(-) create mode 100644 litellm/rag/ingestion/s3_vectors_ingestion.py create mode 100644 tests/vector_store_tests/rag/test_rag_s3_vectors.py diff --git a/docs/my-website/docs/rag_ingest.md b/docs/my-website/docs/rag_ingest.md index 1133b85f20..7adc2d70b5 100644 --- a/docs/my-website/docs/rag_ingest.md +++ b/docs/my-website/docs/rag_ingest.md @@ -5,7 +5,7 @@ All-in-one document ingestion pipeline: **Upload → Chunk → Embed → Vector | Feature | Supported | |---------|-----------| | Logging | Yes | -| Supported Providers | `openai`, `bedrock`, `vertex_ai`, `gemini` | +| Supported Providers | `openai`, `bedrock`, `vertex_ai`, `gemini`, `s3_vectors` | :::tip After ingesting documents, use [/rag/query](./rag_query.md) to search and generate responses with your ingested content. @@ -75,6 +75,31 @@ curl -X POST "http://localhost:4000/v1/rag/ingest" \ }" ``` +### AWS S3 Vectors + +```bash showLineNumbers title="Ingest to S3 Vectors" +curl -X POST "http://localhost:4000/v1/rag/ingest" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d "{ + \"file\": { + \"filename\": \"document.txt\", + \"content\": \"$(base64 -i document.txt)\", + \"content_type\": \"text/plain\" + }, + \"ingest_options\": { + \"embedding\": { + \"model\": \"text-embedding-3-small\" + }, + \"vector_store\": { + \"custom_llm_provider\": \"s3_vectors\", + \"vector_bucket_name\": \"my-embeddings\", + \"aws_region_name\": \"us-west-2\" + } + } + }" +``` + ## Response ```json @@ -265,6 +290,57 @@ When `vector_store_id` is omitted, LiteLLM automatically creates: 4. Install: `pip install 'google-cloud-aiplatform>=1.60.0'` ::: +### vector_store (AWS S3 Vectors) + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `custom_llm_provider` | string | - | `"s3_vectors"` | +| `vector_bucket_name` | string | **required** | S3 vector bucket name | +| `index_name` | string | auto-create | Vector index name | +| `dimension` | integer | auto-detect | Vector dimension (auto-detected from embedding model) | +| `distance_metric` | string | `cosine` | Distance metric: `cosine` or `euclidean` | +| `non_filterable_metadata_keys` | array | `["source_text"]` | Metadata keys excluded from filtering | +| `aws_region_name` | string | `us-west-2` | AWS region | +| `aws_access_key_id` | string | env | AWS access key | +| `aws_secret_access_key` | string | env | AWS secret key | + +:::info S3 Vectors Auto-Creation +When `index_name` is omitted, LiteLLM automatically creates: +- S3 vector bucket (if it doesn't exist) +- Vector index with auto-detected dimensions from your embedding model + +**Dimension Auto-Detection**: The vector dimension is automatically detected by making a test embedding request to your specified model. No need to manually specify dimensions! + +**Supported Embedding Models**: Works with any LiteLLM-supported embedding model (OpenAI, Cohere, Bedrock, Azure, etc.) +::: + +**Example with auto-detection:** +```json +{ + "embedding": { + "model": "text-embedding-3-small" // Dimension auto-detected as 1536 + }, + "vector_store": { + "custom_llm_provider": "s3_vectors", + "vector_bucket_name": "my-embeddings" + } +} +``` + +**Example with custom embedding provider:** +```json +{ + "embedding": { + "model": "cohere/embed-english-v3.0" // Dimension auto-detected as 1024 + }, + "vector_store": { + "custom_llm_provider": "s3_vectors", + "vector_bucket_name": "my-embeddings", + "distance_metric": "cosine" + } +} +``` + ## Input Examples ### File (Base64) diff --git a/litellm/constants.py b/litellm/constants.py index 0525bf3843..0c9d1d941b 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -1327,6 +1327,13 @@ COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY = int( DEFAULT_CHUNK_SIZE = int(os.getenv("DEFAULT_CHUNK_SIZE", 1000)) DEFAULT_CHUNK_OVERLAP = int(os.getenv("DEFAULT_CHUNK_OVERLAP", 200)) +########################### S3 Vectors RAG Constants ########################### +S3_VECTORS_DEFAULT_DIMENSION = int(os.getenv("S3_VECTORS_DEFAULT_DIMENSION", 1024)) +S3_VECTORS_DEFAULT_DISTANCE_METRIC = str( + os.getenv("S3_VECTORS_DEFAULT_DISTANCE_METRIC", "cosine") +) +S3_VECTORS_DEFAULT_NON_FILTERABLE_METADATA_KEYS = ["source_text"] + ########################### Microsoft SSO Constants ########################### MICROSOFT_USER_EMAIL_ATTRIBUTE = str( os.getenv("MICROSOFT_USER_EMAIL_ATTRIBUTE", "userPrincipalName") diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 7f33e96dbb..4ac4159558 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -10232,6 +10232,48 @@ "mode": "completion", "output_cost_per_token": 5e-07 }, + "deepseek-v3-2-251201": { + "input_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "max_input_tokens": 98304, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 0.0, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "glm-4-7-251222": { + "input_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "max_input_tokens": 204800, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 0.0, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "kimi-k2-thinking-251104": { + "input_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "max_input_tokens": 229376, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 0.0, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "doubao-embedding": { "input_cost_per_token": 0.0, "litellm_provider": "volcengine", diff --git a/litellm/rag/ingestion/s3_vectors_ingestion.py b/litellm/rag/ingestion/s3_vectors_ingestion.py new file mode 100644 index 0000000000..13964cd89a --- /dev/null +++ b/litellm/rag/ingestion/s3_vectors_ingestion.py @@ -0,0 +1,546 @@ +""" +S3 Vectors-specific RAG Ingestion implementation. + +S3 Vectors is AWS's native vector storage service that provides: +- Purpose-built vector buckets for storing and querying vectors +- Vector indexes with configurable dimensions and distance metrics +- Metadata filtering for semantic search + +This implementation: +1. Auto-creates vector buckets and indexes if not provided +2. Uses LiteLLM's embedding API (supports any provider) +3. Uses httpx + AWS SigV4 signing (no boto3 dependency for S3 Vectors APIs) +4. Stores vectors with metadata using PutVectors API +""" + +from __future__ import annotations + +import hashlib +import uuid +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple + +import litellm +from litellm._logging import verbose_logger +from litellm.constants import ( + S3_VECTORS_DEFAULT_DIMENSION, + S3_VECTORS_DEFAULT_DISTANCE_METRIC, + S3_VECTORS_DEFAULT_NON_FILTERABLE_METADATA_KEYS, +) +from litellm.litellm_core_utils.safe_json_dumps import safe_dumps +from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM +from litellm.llms.custom_httpx.http_handler import ( + get_async_httpx_client, + httpxSpecialProvider, +) +from litellm.rag.ingestion.base_ingestion import BaseRAGIngestion + +if TYPE_CHECKING: + from litellm import Router + from litellm.types.rag import RAGIngestOptions + + +class S3VectorsRAGIngestion(BaseRAGIngestion, BaseAWSLLM): + """ + S3 Vectors RAG ingestion using httpx + AWS SigV4 signing. + + Workflow: + 1. Auto-create vector bucket if needed (CreateVectorBucket API) + 2. Auto-create vector index if needed (CreateVectorIndex API) + 3. Generate embeddings using LiteLLM (supports any provider) + 4. Store vectors with PutVectors API + + Configuration: + - vector_bucket_name: S3 vector bucket name (required) + - index_name: Vector index name (auto-creates if not provided) + - dimension: Vector dimension (default: S3_VECTORS_DEFAULT_DIMENSION) + - distance_metric: "cosine" or "euclidean" (default: S3_VECTORS_DEFAULT_DISTANCE_METRIC) + - non_filterable_metadata_keys: List of metadata keys to exclude from filtering + """ + + def __init__( + self, + ingest_options: "RAGIngestOptions", + router: Optional["Router"] = None, + ): + BaseRAGIngestion.__init__(self, ingest_options=ingest_options, router=router) + BaseAWSLLM.__init__(self) + + # Extract config + self.vector_bucket_name = self.vector_store_config["vector_bucket_name"] + self.index_name = self.vector_store_config.get("index_name") + self.distance_metric = self.vector_store_config.get( + "distance_metric", S3_VECTORS_DEFAULT_DISTANCE_METRIC + ) + self.non_filterable_metadata_keys = self.vector_store_config.get( + "non_filterable_metadata_keys", + S3_VECTORS_DEFAULT_NON_FILTERABLE_METADATA_KEYS, + ) + + # Get dimension from config (will be auto-detected on first use if not provided) + self.dimension = self._get_dimension_from_config() + + # Get AWS region using BaseAWSLLM method + _aws_region = self.vector_store_config.get("aws_region_name") + self.aws_region_name = self.get_aws_region_name_for_non_llm_api_calls( + aws_region_name=str(_aws_region) if _aws_region else None + ) + + # Create httpx client (similar to s3_v2.py) + ssl_verify = self._get_ssl_verify( + ssl_verify=self.vector_store_config.get("ssl_verify") + ) + self.async_httpx_client = get_async_httpx_client( + llm_provider=httpxSpecialProvider.RAG, + params={"ssl_verify": ssl_verify} if ssl_verify is not None else None, + ) + + # Track if infrastructure is initialized + self._config_initialized = False + + async def _get_dimension_from_embedding_request(self) -> int: + """ + Auto-detect dimension by making a test embedding request. + + Makes a single embedding request with a test string to determine + the output dimension of the embedding model. + """ + if not self.embedding_config or "model" not in self.embedding_config: + return S3_VECTORS_DEFAULT_DIMENSION + + try: + model_name = self.embedding_config["model"] + verbose_logger.debug( + f"Auto-detecting dimension by making test embedding request to {model_name}" + ) + + # Make a test embedding request + test_input = "test" + if self.router: + response = await self.router.aembedding(model=model_name, input=[test_input]) + else: + response = await litellm.aembedding(model=model_name, input=[test_input]) + + # Get dimension from the response + if response.data and len(response.data) > 0: + dimension = len(response.data[0]["embedding"]) + verbose_logger.debug( + f"Auto-detected dimension {dimension} for embedding model {model_name}" + ) + return dimension + except Exception as e: + verbose_logger.warning( + f"Could not auto-detect dimension from embedding model: {e}. " + f"Using default dimension of {S3_VECTORS_DEFAULT_DIMENSION}." + ) + + return S3_VECTORS_DEFAULT_DIMENSION + + def _get_dimension_from_config(self) -> Optional[int]: + """ + Get vector dimension from config if explicitly provided. + + Returns None if dimension should be auto-detected. + """ + if "dimension" in self.vector_store_config: + return int(self.vector_store_config["dimension"]) + return None + + async def _ensure_config_initialized(self): + """Lazily initialize S3 Vectors infrastructure.""" + if self._config_initialized: + return + + # Auto-detect dimension if not provided + if self.dimension is None: + self.dimension = await self._get_dimension_from_embedding_request() + + # Ensure vector bucket exists + await self._ensure_vector_bucket_exists() + + # Ensure vector index exists + if not self.index_name: + # Auto-generate index name + unique_id = uuid.uuid4().hex[:8] + self.index_name = f"litellm-index-{unique_id}" + + await self._ensure_vector_index_exists() + + self._config_initialized = True + + async def _sign_and_execute_request( + self, + method: str, + url: str, + data: Optional[str] = None, + headers: Optional[Dict[str, str]] = None, + ) -> Any: + """ + Helper to sign and execute AWS API requests using httpx + SigV4. + + Pattern from litellm/integrations/s3_v2.py + """ + try: + import requests + from botocore.auth import SigV4Auth + from botocore.awsrequest import AWSRequest + except ImportError: + raise ImportError( + "Missing botocore to call S3 Vectors. Run 'pip install boto3'." + ) + + # Get AWS credentials using BaseAWSLLM's get_credentials method + credentials = self.get_credentials( + aws_access_key_id=self.vector_store_config.get("aws_access_key_id"), + aws_secret_access_key=self.vector_store_config.get("aws_secret_access_key"), + aws_session_token=self.vector_store_config.get("aws_session_token"), + aws_region_name=self.aws_region_name, + aws_session_name=self.vector_store_config.get("aws_session_name"), + aws_profile_name=self.vector_store_config.get("aws_profile_name"), + aws_role_name=self.vector_store_config.get("aws_role_name"), + aws_web_identity_token=self.vector_store_config.get("aws_web_identity_token"), + aws_sts_endpoint=self.vector_store_config.get("aws_sts_endpoint"), + aws_external_id=self.vector_store_config.get("aws_external_id"), + ) + + # Prepare headers + if headers is None: + headers = {} + + if data: + headers["Content-Type"] = "application/json" + # Calculate SHA256 hash of the content + content_hash = hashlib.sha256(data.encode("utf-8")).hexdigest() + headers["x-amz-content-sha256"] = content_hash + else: + # For requests without body, use hash of empty string + headers["x-amz-content-sha256"] = hashlib.sha256(b"").hexdigest() + + # Prepare the request + req = requests.Request(method, url, data=data, headers=headers) + prepped = req.prepare() + + # Sign the request + aws_request = AWSRequest( + method=prepped.method, + url=prepped.url, + data=prepped.body, + headers=prepped.headers, + ) + SigV4Auth(credentials, "s3vectors", self.aws_region_name).add_auth(aws_request) + + # Prepare the signed headers + signed_headers = dict(aws_request.headers.items()) + + # Make the request using specific method (pattern from s3_v2.py) + method_upper = method.upper() + if method_upper == "PUT": + response = await self.async_httpx_client.put( + url, data=data, headers=signed_headers + ) + elif method_upper == "POST": + response = await self.async_httpx_client.post( + url, data=data, headers=signed_headers + ) + elif method_upper == "GET": + response = await self.async_httpx_client.get(url, headers=signed_headers) + else: + raise ValueError(f"Unsupported HTTP method: {method}") + + return response + + async def _ensure_vector_bucket_exists(self): + """Create vector bucket if it doesn't exist using GetVectorBucket and CreateVectorBucket APIs.""" + verbose_logger.debug( + f"Ensuring S3 vector bucket exists: {self.vector_bucket_name}" + ) + + # Try to get bucket info using GetVectorBucket API + get_url = f"https://s3vectors.{self.aws_region_name}.api.aws/GetVectorBucket" + get_body = safe_dumps({"vectorBucketName": self.vector_bucket_name}) + + try: + response = await self._sign_and_execute_request("POST", get_url, data=get_body) + if response.status_code == 200: + verbose_logger.debug(f"Vector bucket {self.vector_bucket_name} exists") + return + except Exception as e: + verbose_logger.debug( + f"Bucket check failed (may not exist): {e}, attempting to create" + ) + + # Create vector bucket using CreateVectorBucket API + try: + verbose_logger.debug(f"Creating vector bucket: {self.vector_bucket_name}") + create_url = f"https://s3vectors.{self.aws_region_name}.api.aws/CreateVectorBucket" + create_body = safe_dumps({ + "vectorBucketName": self.vector_bucket_name + }) + + response = await self._sign_and_execute_request("POST", create_url, data=create_body) + + if response.status_code in (200, 201): + verbose_logger.info(f"Created vector bucket: {self.vector_bucket_name}") + elif response.status_code == 409: + # Bucket already exists (ConflictException) + verbose_logger.debug( + f"Vector bucket {self.vector_bucket_name} already exists" + ) + else: + verbose_logger.error(f"CreateVectorBucket failed: {response.status_code} - {response.text}") + response.raise_for_status() + except Exception as e: + verbose_logger.exception(f"Error creating vector bucket: {e}") + raise + + async def _ensure_vector_index_exists(self): + """Create vector index if it doesn't exist using GetIndex and CreateIndex APIs.""" + verbose_logger.debug( + f"Ensuring vector index exists: {self.vector_bucket_name}/{self.index_name}" + ) + + # Try to get index info using GetIndex API + get_url = f"https://s3vectors.{self.aws_region_name}.api.aws/GetIndex" + get_body = safe_dumps({ + "vectorBucketName": self.vector_bucket_name, + "indexName": self.index_name + }) + + try: + response = await self._sign_and_execute_request("POST", get_url, data=get_body) + if response.status_code == 200: + verbose_logger.debug(f"Vector index {self.index_name} exists") + return + except Exception as e: + verbose_logger.debug( + f"Index check failed (may not exist): {e}, attempting to create" + ) + + # Create vector index using CreateIndex API + try: + verbose_logger.debug( + f"Creating vector index: {self.index_name} with dimension={self.dimension}, metric={self.distance_metric}" + ) + + # Prepare index configuration per AWS API docs + index_config = { + "vectorBucketName": self.vector_bucket_name, + "indexName": self.index_name, + "dataType": "float32", + "dimension": self.dimension, + "distanceMetric": self.distance_metric, + } + + if self.non_filterable_metadata_keys: + index_config["metadataConfiguration"] = { + "nonFilterableMetadataKeys": self.non_filterable_metadata_keys + } + + create_url = f"https://s3vectors.{self.aws_region_name}.api.aws/CreateIndex" + response = await self._sign_and_execute_request( + "POST", create_url, data=safe_dumps(index_config) + ) + + if response.status_code in (200, 201): + verbose_logger.info(f"Created vector index: {self.index_name}") + elif response.status_code == 409: + verbose_logger.debug(f"Vector index {self.index_name} already exists") + else: + verbose_logger.error(f"CreateIndex failed: {response.status_code} - {response.text}") + response.raise_for_status() + except Exception as e: + verbose_logger.exception(f"Error creating vector index: {e}") + raise + + async def _put_vectors(self, vectors: List[Dict[str, Any]]): + """ + Call PutVectors API to store vectors in S3 Vectors. + + Args: + vectors: List of vector objects with keys: "key", "data", "metadata" + """ + verbose_logger.debug( + f"Storing {len(vectors)} vectors in {self.vector_bucket_name}/{self.index_name}" + ) + + url = f"https://s3vectors.{self.aws_region_name}.api.aws/PutVectors" + + # Prepare request body per AWS API docs + request_body = { + "vectorBucketName": self.vector_bucket_name, + "indexName": self.index_name, + "vectors": vectors + } + + try: + response = await self._sign_and_execute_request( + "POST", url, data=safe_dumps(request_body) + ) + + if response.status_code in (200, 201): + verbose_logger.info( + f"Successfully stored {len(vectors)} vectors in index {self.index_name}" + ) + else: + verbose_logger.error( + f"PutVectors failed with status {response.status_code}: {response.text}" + ) + response.raise_for_status() + except Exception as e: + verbose_logger.exception(f"Error storing vectors: {e}") + raise + + async def embed( + self, + chunks: List[str], + ) -> Optional[List[List[float]]]: + """ + Generate embeddings using LiteLLM's embedding API. + + Supports any embedding provider (OpenAI, Bedrock, Cohere, etc.) + """ + if not chunks: + return None + + # Use embedding config from ingest_options or default + if not self.embedding_config: + verbose_logger.warning( + "No embedding config provided, using default text-embedding-3-small" + ) + self.embedding_config = {"model": "text-embedding-3-small"} + + embedding_model = self.embedding_config.get("model", "text-embedding-3-small") + + verbose_logger.debug( + f"Generating embeddings for {len(chunks)} chunks using {embedding_model}" + ) + + # Convert to list to ensure type compatibility + input_chunks: List[str] = list(chunks) + + if self.router: + response = await self.router.aembedding(model=embedding_model, input=input_chunks) + else: + response = await litellm.aembedding(model=embedding_model, input=input_chunks) + + return [item["embedding"] for item in response.data] + + async def store( + self, + file_content: Optional[bytes], + filename: Optional[str], + content_type: Optional[str], + chunks: List[str], + embeddings: Optional[List[List[float]]], + ) -> Tuple[Optional[str], Optional[str]]: + """ + Store vectors in S3 Vectors using PutVectors API. + + Steps: + 1. Ensure vector bucket exists (auto-create if needed) + 2. Ensure vector index exists (auto-create if needed) + 3. Prepare vector data with metadata + 4. Call PutVectors API with httpx + SigV4 signing + + Args: + file_content: Raw file bytes (not used for S3 Vectors) + filename: Name of the file + content_type: MIME type (not used for S3 Vectors) + chunks: Text chunks + embeddings: Vector embeddings + + Returns: + Tuple of (index_name, filename) + """ + # Ensure infrastructure exists + await self._ensure_config_initialized() + + if not embeddings or not chunks: + verbose_logger.warning("No embeddings or chunks to store") + return self.index_name, None + + # Prepare vectors for PutVectors API + vectors = [] + for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)): + vector_obj = { + "key": f"{filename}_{i}" if filename else f"chunk_{i}", + "data": {"float32": embedding}, + "metadata": { + "source_text": chunk, # Non-filterable (for reference) + "chunk_index": str(i), # Filterable + }, + } + + if filename: + vector_obj["metadata"]["filename"] = filename # Filterable + + vectors.append(vector_obj) + + # Call PutVectors API + await self._put_vectors(vectors) + + return self.index_name, filename + + async def query_vector_store( + self, vector_store_id: str, query: str, top_k: int = 5 + ) -> Optional[Dict[str, Any]]: + """ + Query S3 Vectors using QueryVectors API. + + Args: + vector_store_id: Index name + query: Query text + top_k: Number of results to return + + Returns: + Query results with vectors and metadata + """ + verbose_logger.debug(f"Querying index {vector_store_id} with query: {query}") + + # Generate query embedding + if not self.embedding_config: + self.embedding_config = {"model": "text-embedding-3-small"} + + embedding_model = self.embedding_config.get("model", "text-embedding-3-small") + + response = await litellm.aembedding(model=embedding_model, input=[query]) + query_embedding = response.data[0]["embedding"] + + # Call QueryVectors API + url = f"https://s3vectors.{self.aws_region_name}.api.aws/QueryVectors" + + request_body = { + "vectorBucketName": self.vector_bucket_name, + "indexName": vector_store_id, + "queryVector": {"float32": query_embedding}, + "topK": top_k, + "returnDistance": True, + "returnMetadata": True, + } + + try: + response = await self._sign_and_execute_request( + "POST", url, data=safe_dumps(request_body) + ) + + if response.status_code == 200: + results = response.json() + verbose_logger.debug(f"Query returned {len(results.get('vectors', []))} results") + + # Check if query terms appear in results + if results.get("vectors"): + for result in results["vectors"]: + metadata = result.get("metadata", {}) + source_text = metadata.get("source_text", "") + if query.lower() in source_text.lower(): + return results + + # Return results even if exact match not found + return results + else: + verbose_logger.error( + f"QueryVectors failed with status {response.status_code}: {response.text}" + ) + return None + except Exception as e: + verbose_logger.exception(f"Error querying vectors: {e}") + return None diff --git a/litellm/rag/main.py b/litellm/rag/main.py index 620097f83d..571f78f2f7 100644 --- a/litellm/rag/main.py +++ b/litellm/rag/main.py @@ -31,6 +31,7 @@ from litellm.rag.ingestion.base_ingestion import BaseRAGIngestion from litellm.rag.ingestion.bedrock_ingestion import BedrockRAGIngestion from litellm.rag.ingestion.gemini_ingestion import GeminiRAGIngestion from litellm.rag.ingestion.openai_ingestion import OpenAIRAGIngestion +from litellm.rag.ingestion.s3_vectors_ingestion import S3VectorsRAGIngestion from litellm.rag.rag_query import RAGQuery from litellm.types.rag import ( RAGIngestOptions, @@ -48,6 +49,7 @@ INGESTION_REGISTRY: Dict[str, Type[BaseRAGIngestion]] = { "openai": OpenAIRAGIngestion, "bedrock": BedrockRAGIngestion, "gemini": GeminiRAGIngestion, + "s3_vectors": S3VectorsRAGIngestion, } diff --git a/litellm/types/rag.py b/litellm/types/rag.py index fe237a1343..cae0770868 100644 --- a/litellm/types/rag.py +++ b/litellm/types/rag.py @@ -129,9 +129,53 @@ class VertexAIVectorStoreOptions(TypedDict, total=False): import_timeout: Optional[int] # Timeout in seconds (default: 600) +class S3VectorsVectorStoreOptions(TypedDict, total=False): + """ + AWS S3 Vectors configuration. + + Example (auto-create): + {"custom_llm_provider": "s3_vectors", "vector_bucket_name": "my-embeddings"} + + Example (use existing): + {"custom_llm_provider": "s3_vectors", "vector_bucket_name": "my-embeddings", + "index_name": "my-index"} + + Example (with credentials): + {"custom_llm_provider": "s3_vectors", "vector_bucket_name": "my-embeddings", + "litellm_credential_name": "my-aws-creds"} + + Auto-creation creates: S3 vector bucket and vector index (if not provided). + Embeddings are generated using LiteLLM's embedding API (supports any provider). + """ + + custom_llm_provider: Literal["s3_vectors"] + vector_bucket_name: str # Required - S3 vector bucket name + index_name: Optional[str] # Vector index name (auto-creates if not provided) + + # Index configuration (for auto-creation) + dimension: Optional[int] # Vector dimension (auto-detected from embedding model, or default: 1024) + distance_metric: Optional[Literal["cosine", "euclidean"]] # Default: cosine + non_filterable_metadata_keys: Optional[List[str]] # Keys excluded from filtering (e.g., ["source_text"]) + + # Credentials (loaded from litellm.credential_list if litellm_credential_name is provided) + litellm_credential_name: Optional[str] # Credential name to load from litellm.credential_list + + # AWS auth (uses BaseAWSLLM) + aws_access_key_id: Optional[str] + aws_secret_access_key: Optional[str] + aws_session_token: Optional[str] + aws_region_name: Optional[str] # default: us-west-2 + aws_role_name: Optional[str] + aws_session_name: Optional[str] + aws_profile_name: Optional[str] + aws_web_identity_token: Optional[str] + aws_sts_endpoint: Optional[str] + aws_external_id: Optional[str] + + # Union type for vector store options RAGIngestVectorStoreOptions = Union[ - OpenAIVectorStoreOptions, BedrockVectorStoreOptions, VertexAIVectorStoreOptions + OpenAIVectorStoreOptions, BedrockVectorStoreOptions, VertexAIVectorStoreOptions, S3VectorsVectorStoreOptions ] diff --git a/tests/vector_store_tests/rag/test_rag_s3_vectors.py b/tests/vector_store_tests/rag/test_rag_s3_vectors.py new file mode 100644 index 0000000000..cd8a362a7b --- /dev/null +++ b/tests/vector_store_tests/rag/test_rag_s3_vectors.py @@ -0,0 +1,107 @@ +""" +S3 Vectors RAG ingestion tests. + +Requires environment variables: +- AWS_ACCESS_KEY_ID +- AWS_SECRET_ACCESS_KEY +- AWS_REGION_NAME (optional, defaults to us-west-2) + +Optional: +- S3_VECTOR_BUCKET_NAME (optional, auto-generates if not set) +""" + +import os +import sys +from typing import Any, Dict, Optional + +import pytest + +sys.path.insert(0, os.path.abspath("../../..")) + +import litellm +from litellm.types.rag import RAGIngestOptions +from tests.vector_store_tests.rag.base_rag_tests import BaseRAGTest + + +class TestRAGS3Vectors(BaseRAGTest): + """Test RAG Ingest with AWS S3 Vectors.""" + + @pytest.fixture(autouse=True) + def check_env_vars(self): + """Check required environment variables before each test.""" + aws_key = os.environ.get("AWS_ACCESS_KEY_ID") + aws_secret = os.environ.get("AWS_SECRET_ACCESS_KEY") + + if not aws_key or not aws_secret: + pytest.skip("Skipping S3 Vectors test: AWS credentials required") + + def get_base_ingest_options(self) -> RAGIngestOptions: + """ + Return S3 Vectors-specific ingest options. + + Chunking is configured via chunking_strategy (unified interface). + Embeddings are generated using LiteLLM's embedding API. + """ + vector_bucket_name = os.environ.get( + "S3_VECTOR_BUCKET_NAME", "test-litellm-vectors" + ) + aws_region = os.environ.get("AWS_REGION_NAME", "us-west-2") + + return { + "chunking_strategy": { + "chunk_size": 512, + "chunk_overlap": 100, + }, + "embedding": { + "model": "text-embedding-3-small" # Can use any LiteLLM-supported model + }, + "vector_store": { + "custom_llm_provider": "s3_vectors", + "vector_bucket_name": vector_bucket_name, + "index_name": "test-index", + # dimension is auto-detected from embedding model (text-embedding-3-small = 1536) + "distance_metric": "cosine", + "non_filterable_metadata_keys": ["source_text"], + "aws_region_name": aws_region, + }, + } + + async def query_vector_store( + self, + vector_store_id: str, + query: str, + ) -> Optional[Dict[str, Any]]: + """Query S3 Vectors index.""" + try: + # Import the ingestion class to use its query method + from litellm.rag.ingestion.s3_vectors_ingestion import ( + S3VectorsRAGIngestion, + ) + except ImportError: + pytest.skip("S3 Vectors ingestion not available") + + vector_bucket_name = os.environ.get( + "S3_VECTOR_BUCKET_NAME", "test-litellm-vectors" + ) + aws_region = os.environ.get("AWS_REGION_NAME", "us-west-2") + + # Create ingestion instance to use query method + ingest_options = { + "embedding": {"model": "text-embedding-3-small"}, + "vector_store": { + "custom_llm_provider": "s3_vectors", + "vector_bucket_name": vector_bucket_name, + "aws_region_name": aws_region, + }, + } + + ingestion = S3VectorsRAGIngestion(ingest_options=ingest_options) + + # Query the index + results = await ingestion.query_vector_store( + vector_store_id=vector_store_id, + query=query, + top_k=5, + ) + + return results From 51339f5ef189e1b3f79d9b04e1f61859e2373675 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 27 Jan 2026 16:30:59 -0800 Subject: [PATCH 55/62] [Feat] RAG API - Add s3_vectors as provider on /vector_store/search API + UI for creating + PDF support for /rag/ingest (#19895) * init S3VectorsRAGIngestion as a supported ingestion provider for RAG API * test: TestRAGS3Vectors * init S3VectorsVectorStoreOptions * init s3 vectors * code clean up + QA * fix: get_credentials * S3VectorsRAGIngestion * TestRAGS3Vectors * docs: AWS S3 Vectors * add asyncio QA checks * fix: S3_VECTORS_DEFAULT_DIMENSION * init ui for bedrock s3 vectors * fix add /search support for s3_vectors * init atransform_search_vector_store_request * feat: S3VectorsVectorStoreConfig * TestS3VectorsVectorStoreConfig * atransform_search_vector_store_request * fix: S3VectorsVectorStoreConfig * add validation for bucket name etd * fix UI validation for s3 vector store * init extract_text_from_pdf * add pypdf * fix code QA checks * fix navbar * init s3_vector.png * fix QA code --- .../base_llm/vector_store/transformation.py | 26 +- litellm/llms/bedrock/base_aws_llm.py | 2 +- litellm/llms/custom_httpx/llm_http_handler.py | 36 ++- litellm/llms/s3_vectors/__init__.py | 1 + .../llms/s3_vectors/vector_stores/__init__.py | 1 + .../vector_stores/transformation.py | 254 ++++++++++++++++++ .../out/assets/logos/s3_vector.png | Bin 0 -> 191076 bytes litellm/proxy/rag_endpoints/endpoints.py | 9 + litellm/rag/ingestion/base_ingestion.py | 17 +- .../rag/ingestion/file_parsers/__init__.py | 9 + .../rag/ingestion/file_parsers/pdf_parser.py | 70 +++++ litellm/rag/ingestion/s3_vectors_ingestion.py | 47 +++- litellm/types/router.py | 4 + litellm/types/utils.py | 11 +- litellm/utils.py | 8 +- requirements.txt | 1 + .../test_litellm/llms/s3_vectors/__init__.py | 1 + .../llms/s3_vectors/vector_stores/__init__.py | 1 + .../test_s3_vectors_transformation.py | 115 ++++++++ .../test_s3_vectors_vector_store.py | 42 +++ .../public/assets/logos/s3_vector.png | Bin 0 -> 191076 bytes .../src/components/navbar.tsx | 2 +- .../src/components/networking.tsx | 4 +- .../CreateVectorStore.test.tsx | 116 +++++++- .../CreateVectorStore.tsx | 96 ++++++- .../S3VectorsConfig.test.tsx | 203 ++++++++++++++ .../S3VectorsConfig.tsx | 192 +++++++++++++ .../src/components/vector_store_providers.tsx | 37 +++ 28 files changed, 1269 insertions(+), 36 deletions(-) create mode 100644 litellm/llms/s3_vectors/__init__.py create mode 100644 litellm/llms/s3_vectors/vector_stores/__init__.py create mode 100644 litellm/llms/s3_vectors/vector_stores/transformation.py create mode 100644 litellm/proxy/_experimental/out/assets/logos/s3_vector.png create mode 100644 litellm/rag/ingestion/file_parsers/__init__.py create mode 100644 litellm/rag/ingestion/file_parsers/pdf_parser.py create mode 100644 tests/test_litellm/llms/s3_vectors/__init__.py create mode 100644 tests/test_litellm/llms/s3_vectors/vector_stores/__init__.py create mode 100644 tests/test_litellm/llms/s3_vectors/vector_stores/test_s3_vectors_transformation.py create mode 100644 tests/vector_store_tests/test_s3_vectors_vector_store.py create mode 100644 ui/litellm-dashboard/public/assets/logos/s3_vector.png create mode 100644 ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.test.tsx create mode 100644 ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.tsx diff --git a/litellm/llms/base_llm/vector_store/transformation.py b/litellm/llms/base_llm/vector_store/transformation.py index 89f2094d5d..935fd53c19 100644 --- a/litellm/llms/base_llm/vector_store/transformation.py +++ b/litellm/llms/base_llm/vector_store/transformation.py @@ -5,8 +5,8 @@ import httpx from litellm.types.router import GenericLiteLLMParams from litellm.types.vector_stores import ( - BaseVectorStoreAuthCredentials, VECTOR_STORE_OPENAI_PARAMS, + BaseVectorStoreAuthCredentials, VectorStoreCreateOptionalRequestParams, VectorStoreCreateResponse, VectorStoreIndexEndpoints, @@ -64,6 +64,30 @@ class BaseVectorStoreConfig: pass + async def atransform_search_vector_store_request( + self, + vector_store_id: str, + query: Union[str, List[str]], + vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams, + api_base: str, + litellm_logging_obj: LiteLLMLoggingObj, + litellm_params: dict, + ) -> Tuple[str, Dict]: + """ + Optional async version of transform_search_vector_store_request. + If not implemented, the handler will fall back to the sync version. + Providers that need to make async calls (e.g., generating embeddings) should override this. + """ + # Default implementation: call the sync version + return self.transform_search_vector_store_request( + vector_store_id=vector_store_id, + query=query, + vector_store_search_optional_params=vector_store_search_optional_params, + api_base=api_base, + litellm_logging_obj=litellm_logging_obj, + litellm_params=litellm_params, + ) + @abstractmethod def transform_search_vector_store_response( self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index 642d15fe3e..1de1c40c43 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -1163,7 +1163,7 @@ class BaseAWSLLM: def _sign_request( self, - service_name: Literal["bedrock", "sagemaker", "bedrock-agentcore"], + service_name: Literal["bedrock", "sagemaker", "bedrock-agentcore", "s3vectors"], headers: dict, optional_params: dict, request_data: dict, diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 6a87967c3a..d2ea7e872a 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -7033,17 +7033,31 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - ( - url, - request_body, - ) = vector_store_provider_config.transform_search_vector_store_request( - vector_store_id=vector_store_id, - query=query, - vector_store_search_optional_params=vector_store_search_optional_params, - api_base=api_base, - litellm_logging_obj=logging_obj, - litellm_params=dict(litellm_params), - ) + # Check if provider has async transform method + if hasattr(vector_store_provider_config, "atransform_search_vector_store_request"): + ( + url, + request_body, + ) = await vector_store_provider_config.atransform_search_vector_store_request( + vector_store_id=vector_store_id, + query=query, + vector_store_search_optional_params=vector_store_search_optional_params, + api_base=api_base, + litellm_logging_obj=logging_obj, + litellm_params=dict(litellm_params), + ) + else: + ( + url, + request_body, + ) = vector_store_provider_config.transform_search_vector_store_request( + vector_store_id=vector_store_id, + query=query, + vector_store_search_optional_params=vector_store_search_optional_params, + api_base=api_base, + litellm_logging_obj=logging_obj, + litellm_params=dict(litellm_params), + ) all_optional_params: Dict[str, Any] = dict(litellm_params) all_optional_params.update(vector_store_search_optional_params or {}) headers, signed_json_body = vector_store_provider_config.sign_request( diff --git a/litellm/llms/s3_vectors/__init__.py b/litellm/llms/s3_vectors/__init__.py new file mode 100644 index 0000000000..e8367949c3 --- /dev/null +++ b/litellm/llms/s3_vectors/__init__.py @@ -0,0 +1 @@ +# S3 Vectors LLM integration diff --git a/litellm/llms/s3_vectors/vector_stores/__init__.py b/litellm/llms/s3_vectors/vector_stores/__init__.py new file mode 100644 index 0000000000..ac24b4a38d --- /dev/null +++ b/litellm/llms/s3_vectors/vector_stores/__init__.py @@ -0,0 +1 @@ +# S3 Vectors vector store integration diff --git a/litellm/llms/s3_vectors/vector_stores/transformation.py b/litellm/llms/s3_vectors/vector_stores/transformation.py new file mode 100644 index 0000000000..df81a78289 --- /dev/null +++ b/litellm/llms/s3_vectors/vector_stores/transformation.py @@ -0,0 +1,254 @@ +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union + +import httpx + +from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig +from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM +from litellm.types.router import GenericLiteLLMParams +from litellm.types.vector_stores import ( + VECTOR_STORE_OPENAI_PARAMS, + BaseVectorStoreAuthCredentials, + VectorStoreIndexEndpoints, + VectorStoreResultContent, + VectorStoreSearchOptionalRequestParams, + VectorStoreSearchResponse, + VectorStoreSearchResult, +) + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): + """Vector store configuration for AWS S3 Vectors.""" + + def __init__(self) -> None: + BaseVectorStoreConfig.__init__(self) + BaseAWSLLM.__init__(self) + + def get_auth_credentials( + self, litellm_params: dict + ) -> BaseVectorStoreAuthCredentials: + return {} + + def get_vector_store_endpoints_by_type(self) -> VectorStoreIndexEndpoints: + return { + "read": [("POST", "/QueryVectors")], + "write": [], + } + + def get_supported_openai_params( + self, model: str + ) -> List[VECTOR_STORE_OPENAI_PARAMS]: + return ["max_num_results"] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + drop_params: bool, + ) -> dict: + for param, value in non_default_params.items(): + if param == "max_num_results": + optional_params["maxResults"] = value + return optional_params + + def validate_environment( + self, headers: dict, litellm_params: Optional[GenericLiteLLMParams] + ) -> dict: + headers = headers or {} + headers.setdefault("Content-Type", "application/json") + return headers + + def get_complete_url(self, api_base: Optional[str], litellm_params: dict) -> str: + aws_region_name = litellm_params.get("aws_region_name") + if not aws_region_name: + raise ValueError("aws_region_name is required for S3 Vectors") + return f"https://s3vectors.{aws_region_name}.api.aws" + + def transform_search_vector_store_request( + self, + vector_store_id: str, + query: Union[str, List[str]], + vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams, + api_base: str, + litellm_logging_obj: LiteLLMLoggingObj, + litellm_params: dict, + ) -> Tuple[str, Dict]: + """Sync version - generates embedding synchronously.""" + # For S3 Vectors, vector_store_id should be in format: bucket_name:index_name + # If not in that format, try to construct it from litellm_params + bucket_name: str + index_name: str + + if ":" in vector_store_id: + bucket_name, index_name = vector_store_id.split(":", 1) + else: + # Try to get bucket_name from litellm_params + bucket_name_from_params = litellm_params.get("vector_bucket_name") + if not bucket_name_from_params or not isinstance(bucket_name_from_params, str): + raise ValueError( + "vector_store_id must be in format 'bucket_name:index_name' for S3 Vectors, " + "or vector_bucket_name must be provided in litellm_params" + ) + bucket_name = bucket_name_from_params + index_name = vector_store_id + + if isinstance(query, list): + query = " ".join(query) + + # Generate embedding for the query + embedding_model = litellm_params.get("embedding_model", "text-embedding-3-small") + + import litellm as litellm_module + embedding_response = litellm_module.embedding(model=embedding_model, input=[query]) + query_embedding = embedding_response.data[0]["embedding"] + + url = f"{api_base}/QueryVectors" + + request_body: Dict[str, Any] = { + "vectorBucketName": bucket_name, + "indexName": index_name, + "queryVector": {"float32": query_embedding}, + "topK": vector_store_search_optional_params.get("max_num_results", 5), # Default to 5 + "returnDistance": True, + "returnMetadata": True, + } + + litellm_logging_obj.model_call_details["query"] = query + return url, request_body + + async def atransform_search_vector_store_request( + self, + vector_store_id: str, + query: Union[str, List[str]], + vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams, + api_base: str, + litellm_logging_obj: LiteLLMLoggingObj, + litellm_params: dict, + ) -> Tuple[str, Dict]: + """Async version - generates embedding asynchronously.""" + # For S3 Vectors, vector_store_id should be in format: bucket_name:index_name + # If not in that format, try to construct it from litellm_params + bucket_name: str + index_name: str + + if ":" in vector_store_id: + bucket_name, index_name = vector_store_id.split(":", 1) + else: + # Try to get bucket_name from litellm_params + bucket_name_from_params = litellm_params.get("vector_bucket_name") + if not bucket_name_from_params or not isinstance(bucket_name_from_params, str): + raise ValueError( + "vector_store_id must be in format 'bucket_name:index_name' for S3 Vectors, " + "or vector_bucket_name must be provided in litellm_params" + ) + bucket_name = bucket_name_from_params + index_name = vector_store_id + + if isinstance(query, list): + query = " ".join(query) + + # Generate embedding for the query asynchronously + embedding_model = litellm_params.get("embedding_model", "text-embedding-3-small") + + import litellm as litellm_module + embedding_response = await litellm_module.aembedding(model=embedding_model, input=[query]) + query_embedding = embedding_response.data[0]["embedding"] + + url = f"{api_base}/QueryVectors" + + request_body: Dict[str, Any] = { + "vectorBucketName": bucket_name, + "indexName": index_name, + "queryVector": {"float32": query_embedding}, + "topK": vector_store_search_optional_params.get("max_num_results", 5), # Default to 5 + "returnDistance": True, + "returnMetadata": True, + } + + litellm_logging_obj.model_call_details["query"] = query + return url, request_body + + def sign_request( + self, + headers: dict, + optional_params: Dict, + request_data: Dict, + api_base: str, + api_key: Optional[str] = None, + ) -> Tuple[dict, Optional[bytes]]: + return self._sign_request( + service_name="s3vectors", + headers=headers, + optional_params=optional_params, + request_data=request_data, + api_base=api_base, + api_key=api_key, + ) + + def transform_search_vector_store_response( + self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj + ) -> VectorStoreSearchResponse: + try: + response_data = response.json() + results: List[VectorStoreSearchResult] = [] + + for item in response_data.get("vectors", []) or []: + metadata = item.get("metadata", {}) or {} + source_text = metadata.get("source_text", "") + + if not source_text: + continue + + # Extract file information from metadata + chunk_index = metadata.get("chunk_index", "0") + file_id = f"s3-vectors-chunk-{chunk_index}" + filename = metadata.get("filename", f"document-{chunk_index}") + + # S3 Vectors returns distance, convert to similarity score (0-1) + # Lower distance = higher similarity + # We'll normalize using 1 / (1 + distance) to get a 0-1 score + distance = item.get("distance") + score = None + if distance is not None: + # Convert distance to similarity score between 0 and 1 + # For cosine distance: similarity = 1 - distance + # For euclidean: use 1 / (1 + distance) + # Assuming cosine distance here + score = max(0.0, min(1.0, 1.0 - float(distance))) + + results.append( + VectorStoreSearchResult( + score=score, + content=[VectorStoreResultContent(text=source_text, type="text")], + file_id=file_id, + filename=filename, + attributes=metadata, + ) + ) + + return VectorStoreSearchResponse( + object="vector_store.search_results.page", + search_query=litellm_logging_obj.model_call_details.get("query", ""), + data=results, + ) + except Exception as e: + raise self.get_error_class( + error_message=str(e), + status_code=response.status_code, + headers=response.headers, + ) + + # Vector store creation is not yet implemented + def transform_create_vector_store_request( + self, + vector_store_create_optional_params, + api_base: str, + ) -> Tuple[str, Dict]: + raise NotImplementedError + + def transform_create_vector_store_response(self, response: httpx.Response): + raise NotImplementedError diff --git a/litellm/proxy/_experimental/out/assets/logos/s3_vector.png b/litellm/proxy/_experimental/out/assets/logos/s3_vector.png new file mode 100644 index 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zaA0R=H{bOO-nny|-923MZ)I=XVgy<|%X6N4j!K?d1`qzaowC&_eXLbaoAlC|2#aN0 zUo~d-YWsd7z@`vcEOD0c+=>lzSRyUlA%MX4?L;gduBw4pn(>&Z@@wB*Aj(jbO$Rs@ zc3NYxw(gNE4PD$>>GlZC#I+V#0N@lhX$usG!z#IyVR)f~8a})c8T$fkMULc&16Q4s6bxKE1cQdG7q?(80sIx;u!@Yc--PBrTU`L>vR9%Bx05b)J_X8}9Vz zbPnn6)(7Jb|0LnZQxr^5&FOIMPyx>wc*GOc;<+!zc+da>9=|p=oBs#rLfLz910lu$ O0000 = ({ style={{ animationDuration: "2s" }} title="Happy Holidays!" > - 🎄 + ❄️ diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx index a1625b6ffb..e0dccbe6e8 100644 --- a/ui/litellm-dashboard/src/components/networking.tsx +++ b/ui/litellm-dashboard/src/components/networking.tsx @@ -6954,7 +6954,8 @@ export const ragIngestCall = async ( customLlmProvider: string, vectorStoreId?: string, vectorStoreName?: string, - vectorStoreDescription?: string + vectorStoreDescription?: string, + providerSpecificParams?: Record ): Promise => { try { let url = proxyBaseUrl ? `${proxyBaseUrl}/rag/ingest` : `/rag/ingest`; @@ -6967,6 +6968,7 @@ export const ragIngestCall = async ( vector_store: { custom_llm_provider: customLlmProvider, ...(vectorStoreId && { vector_store_id: vectorStoreId }), + ...(providerSpecificParams && providerSpecificParams), }, }, }; diff --git a/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.test.tsx b/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.test.tsx index db2975781b..a8ba55b91f 100644 --- a/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.test.tsx +++ b/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.test.tsx @@ -22,17 +22,51 @@ vi.mock("../vector_store_providers", () => ({ BEDROCK: "Amazon Bedrock", OPENAI: "OpenAI", AZURE_OPENAI: "Azure OpenAI", + S3Vectors: "AWS S3 Vectors", }, vectorStoreProviderMap: { BEDROCK: "bedrock", OPENAI: "openai", AZURE_OPENAI: "azure_openai", + S3Vectors: "s3_vectors", }, vectorStoreProviderLogoMap: { "Amazon Bedrock": "https://example.com/bedrock.png", "OpenAI": "https://example.com/openai.png", "Azure OpenAI": "https://example.com/azure.png", + "AWS S3 Vectors": "https://example.com/aws.png", }, + getProviderSpecificFields: vi.fn((provider: string) => { + if (provider === "s3_vectors") { + return [ + { + name: "vector_bucket_name", + label: "Vector Bucket Name", + tooltip: "S3 bucket name for vector storage", + placeholder: "my-vector-bucket", + required: true, + type: "text", + }, + { + name: "aws_region_name", + label: "AWS Region", + tooltip: "AWS region", + placeholder: "us-west-2", + required: true, + type: "text", + }, + { + name: "embedding_model", + label: "Embedding Model", + tooltip: "Embedding model to use", + placeholder: "text-embedding-3-small", + required: true, + type: "select", + }, + ]; + } + return []; + }), })); describe("CreateVectorStore", () => { @@ -43,9 +77,9 @@ describe("CreateVectorStore", () => { it("should render the component successfully", () => { render(); - expect(screen.getByText("Create Vector Store")).toBeInTheDocument(); + expect(screen.getAllByText("Create Vector Store").length).toBeGreaterThan(0); expect(screen.getByText("Step 1: Upload Documents")).toBeInTheDocument(); - expect(screen.getByText("Step 2: Select Provider")).toBeInTheDocument(); + expect(screen.getByText("Step 2: Configure Vector Store")).toBeInTheDocument(); }); it("should display upload area with correct text", () => { @@ -123,7 +157,15 @@ describe("CreateVectorStore", () => { }); await waitFor(() => { - expect(mockRagIngestCall).toHaveBeenCalledWith("test-token", expect.any(File), "bedrock", undefined); + expect(mockRagIngestCall).toHaveBeenCalledWith( + "test-token", + expect.any(File), + "bedrock", + undefined, + undefined, + undefined, + {} + ); }); }); @@ -163,4 +205,72 @@ describe("CreateVectorStore", () => { expect(screen.getByText("Vector Store Created Successfully")).toBeInTheDocument(); }); }); + + it("should display S3 Vectors provider-specific fields when selected", async () => { + render(); + + // Find and click the provider dropdown + const providerSelect = screen.getByRole("combobox"); + + await act(async () => { + fireEvent.mouseDown(providerSelect); + }); + + // Wait for dropdown options to appear + await waitFor(() => { + const s3Option = screen.queryByText("AWS S3 Vectors"); + if (s3Option) { + fireEvent.click(s3Option); + } + }); + + // Check if S3-specific fields are displayed + await waitFor(() => { + expect(screen.queryByText("Vector Bucket Name")).toBeInTheDocument(); + expect(screen.queryByText("AWS Region")).toBeInTheDocument(); + expect(screen.queryByText("Embedding Model")).toBeInTheDocument(); + }); + }); + + it("should validate S3 Vectors required fields before submission", async () => { + render(); + + // Upload a file first + const file = new File(["test content"], "test.pdf", { type: "application/pdf" }); + const uploadInput = document.querySelector('input[type="file"]') as HTMLInputElement; + + await act(async () => { + if (uploadInput) { + fireEvent.change(uploadInput, { target: { files: [file] } }); + } + }); + + await waitFor(() => { + expect(screen.getByText("Uploaded Documents (1)")).toBeInTheDocument(); + }); + + // Select S3 Vectors provider + const providerSelect = screen.getByRole("combobox"); + + await act(async () => { + fireEvent.mouseDown(providerSelect); + }); + + await waitFor(() => { + const s3Option = screen.queryByText("AWS S3 Vectors"); + if (s3Option) { + fireEvent.click(s3Option); + } + }); + + // Try to create without filling required fields + const createButton = screen.getByRole("button", { name: /Create Vector Store/i }); + + await act(async () => { + fireEvent.click(createButton); + }); + + // Should show validation warning (mocked message.warning would be called) + // The actual validation happens in the component + }); }); diff --git a/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.tsx b/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.tsx index 685e37ac73..97162aa132 100644 --- a/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.tsx +++ b/ui/litellm-dashboard/src/components/vector_store_management/CreateVectorStore.tsx @@ -10,8 +10,11 @@ import { VectorStoreProviders, vectorStoreProviderLogoMap, vectorStoreProviderMap, + getProviderSpecificFields, + VectorStoreFieldConfig, } from "../vector_store_providers"; import NotificationsManager from "../molecules/notifications_manager"; +import S3VectorsConfig from "./S3VectorsConfig"; const { Dragger } = Upload; @@ -28,6 +31,7 @@ const CreateVectorStore: React.FC = ({ accessToken, onSu const [vectorStoreName, setVectorStoreName] = useState(""); const [vectorStoreDescription, setVectorStoreDescription] = useState(""); const [ingestResults, setIngestResults] = useState([]); + const [providerParams, setProviderParams] = useState>({}); const uploadProps: UploadProps = { name: "file", @@ -92,6 +96,27 @@ const CreateVectorStore: React.FC = ({ accessToken, onSu return; } + // Validate provider-specific required fields + const requiredFields = getProviderSpecificFields(selectedProvider).filter((field) => field.required); + for (const field of requiredFields) { + if (!providerParams[field.name]) { + message.warning(`Please provide ${field.label}`); + return; + } + } + + // S3 Vectors specific validation + if (selectedProvider === "s3_vectors") { + if (providerParams.vector_bucket_name && providerParams.vector_bucket_name.length < 3) { + message.warning("Vector bucket name must be at least 3 characters"); + return; + } + if (providerParams.index_name && providerParams.index_name.length > 0 && providerParams.index_name.length < 3) { + message.warning("Index name must be at least 3 characters if provided"); + return; + } + } + if (!accessToken) { message.error("No access token available"); return; @@ -118,7 +143,8 @@ const CreateVectorStore: React.FC = ({ accessToken, onSu selectedProvider, vectorStoreId, // Use the same vector store ID for subsequent uploads vectorStoreName || undefined, - vectorStoreDescription || undefined + vectorStoreDescription || undefined, + providerParams ); // Store the vector store ID from the first successful ingest @@ -298,6 +324,74 @@ const CreateVectorStore: React.FC = ({ accessToken, onSu })} + + {/* S3 Vectors Configuration */} + {selectedProvider === "s3_vectors" && ( + + )} + + {/* Other Provider-specific fields */} + {selectedProvider !== "s3_vectors" && + getProviderSpecificFields(selectedProvider).map((field: VectorStoreFieldConfig) => { + if (field.type === "select") { + // For embedding model selection, we'd need to fetch available models + // For now, provide a text input as fallback + return ( + + {field.label}{" "} + + + + + } + required={field.required} + > + + setProviderParams((prev) => ({ ...prev, [field.name]: e.target.value })) + } + placeholder={field.placeholder} + size="large" + className="rounded-md" + /> + + ); + } + + return ( + + {field.label}{" "} + + + + + } + required={field.required} + > + + setProviderParams((prev) => ({ ...prev, [field.name]: e.target.value })) + } + placeholder={field.placeholder} + size="large" + className="rounded-md" + /> + + ); + })}

      diff --git a/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.test.tsx b/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.test.tsx new file mode 100644 index 0000000000..beb13b5441 --- /dev/null +++ b/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.test.tsx @@ -0,0 +1,203 @@ +import { render, screen, fireEvent, waitFor, act } from "@testing-library/react"; +import { describe, it, expect, vi, beforeEach } from "vitest"; +import S3VectorsConfig from "./S3VectorsConfig"; +import * as fetchModels from "../playground/llm_calls/fetch_models"; + +// Mock fetchAvailableModels +vi.mock("../playground/llm_calls/fetch_models", () => ({ + fetchAvailableModels: vi.fn(), +})); + +describe("S3VectorsConfig", () => { + const mockOnParamsChange = vi.fn(); + const defaultProps = { + accessToken: "test-token", + providerParams: {}, + onParamsChange: mockOnParamsChange, + }; + + beforeEach(() => { + vi.clearAllMocks(); + }); + + it("should render the component successfully", () => { + vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue([]); + + render(); + + expect(screen.getByText("AWS S3 Vectors Setup")).toBeInTheDocument(); + expect(screen.getByText("Vector Bucket Name")).toBeInTheDocument(); + expect(screen.getByText("Index Name")).toBeInTheDocument(); + expect(screen.getByText("AWS Region")).toBeInTheDocument(); + expect(screen.getByText("Embedding Model")).toBeInTheDocument(); + }); + + it("should display setup instructions", () => { + vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue([]); + + render(); + + expect( + screen.getByText(/AWS S3 Vectors allows you to store and query vector embeddings directly in S3/) + ).toBeInTheDocument(); + expect(screen.getByText(/Vector buckets and indexes will be automatically created/)).toBeInTheDocument(); + expect(screen.getByText(/Vector dimensions are auto-detected/)).toBeInTheDocument(); + }); + + it("should fetch embedding models on mount", async () => { + const mockModels = [ + { model_group: "text-embedding-3-small", mode: "embedding" }, + { model_group: "text-embedding-3-large", mode: "embedding" }, + { model_group: "gpt-4", mode: "chat" }, + ]; + + const fetchSpy = vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue(mockModels); + + render(); + + await waitFor(() => { + expect(fetchSpy).toHaveBeenCalledWith("test-token"); + }); + }); + + it("should filter and display only embedding models", async () => { + const mockModels = [ + { model_group: "text-embedding-3-small", mode: "embedding" }, + { model_group: "text-embedding-3-large", mode: "embedding" }, + { model_group: "gpt-4", mode: "chat" }, + { model_group: "gpt-3.5-turbo", mode: "chat" }, + ]; + + vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue(mockModels); + + render(); + + // Wait for models to load + await waitFor(() => { + expect(fetchModels.fetchAvailableModels).toHaveBeenCalled(); + }); + + // The component should filter to only embedding models internally + // We can verify this by checking the component loaded successfully + expect(screen.getByText("Embedding Model")).toBeInTheDocument(); + }); + + it("should call onParamsChange when vector bucket name changes", async () => { + vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue([]); + + render(); + + const bucketInput = screen.getByPlaceholderText("my-vector-bucket"); + + await act(async () => { + fireEvent.change(bucketInput, { target: { value: "test-bucket" } }); + }); + + expect(mockOnParamsChange).toHaveBeenCalledWith({ + vector_bucket_name: "test-bucket", + }); + }); + + it("should call onParamsChange when AWS region changes", async () => { + vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue([]); + + render(); + + const regionInput = screen.getByPlaceholderText("us-west-2"); + + await act(async () => { + fireEvent.change(regionInput, { target: { value: "us-east-1" } }); + }); + + expect(mockOnParamsChange).toHaveBeenCalledWith({ + aws_region_name: "us-east-1", + }); + }); + + it("should call onParamsChange when embedding model is selected", async () => { + const mockModels = [ + { model_group: "text-embedding-3-small", mode: "embedding" }, + { model_group: "text-embedding-3-large", mode: "embedding" }, + ]; + + vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue(mockModels); + + render(); + + await waitFor(() => { + expect(fetchModels.fetchAvailableModels).toHaveBeenCalled(); + }); + + // Find the Select component and trigger change directly + const selectElement = screen.getByRole("combobox"); + + await act(async () => { + // Simulate selecting a value by firing the change event + fireEvent.change(selectElement, { target: { value: "text-embedding-3-small" } }); + }); + + // The component should handle the selection + expect(screen.getByText("Embedding Model")).toBeInTheDocument(); + }); + + it("should preserve existing params when updating a field", async () => { + vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue([]); + + const existingParams = { + vector_bucket_name: "existing-bucket", + aws_region_name: "us-west-2", + }; + + render(); + + const indexInput = screen.getByPlaceholderText("my-vector-index"); + + await act(async () => { + fireEvent.change(indexInput, { target: { value: "my-index" } }); + }); + + expect(mockOnParamsChange).toHaveBeenCalledWith({ + vector_bucket_name: "existing-bucket", + aws_region_name: "us-west-2", + index_name: "my-index", + }); + }); + + it("should display existing param values", () => { + vi.spyOn(fetchModels, "fetchAvailableModels").mockResolvedValue([]); + + const existingParams = { + vector_bucket_name: "my-bucket", + index_name: "my-index", + aws_region_name: "eu-west-1", + embedding_model: "text-embedding-3-small", + }; + + render(); + + expect(screen.getByDisplayValue("my-bucket")).toBeInTheDocument(); + expect(screen.getByDisplayValue("my-index")).toBeInTheDocument(); + expect(screen.getByDisplayValue("eu-west-1")).toBeInTheDocument(); + }); + + it("should handle model fetch error gracefully", async () => { + const consoleErrorSpy = vi.spyOn(console, "error").mockImplementation(() => {}); + vi.spyOn(fetchModels, "fetchAvailableModels").mockRejectedValue(new Error("Failed to fetch models")); + + render(); + + await waitFor(() => { + expect(consoleErrorSpy).toHaveBeenCalledWith("Error fetching embedding models:", expect.any(Error)); + }); + + consoleErrorSpy.mockRestore(); + }); + + it("should not fetch models if accessToken is null", () => { + const fetchSpy = vi.spyOn(fetchModels, "fetchAvailableModels"); + + render(); + + expect(fetchSpy).not.toHaveBeenCalled(); + }); +}); diff --git a/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.tsx b/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.tsx new file mode 100644 index 0000000000..e54d3a163c --- /dev/null +++ b/ui/litellm-dashboard/src/components/vector_store_management/S3VectorsConfig.tsx @@ -0,0 +1,192 @@ +import React, { useState, useEffect } from "react"; +import { Alert, Form, Input, Select, Tooltip } from "antd"; +import { InfoCircleOutlined } from "@ant-design/icons"; +import { fetchAvailableModels, ModelGroup } from "../playground/llm_calls/fetch_models"; + +interface S3VectorsConfigProps { + accessToken: string | null; + providerParams: Record; + onParamsChange: (params: Record) => void; +} + +const S3VectorsConfig: React.FC = ({ + accessToken, + providerParams, + onParamsChange, +}) => { + const [embeddingModels, setEmbeddingModels] = useState([]); + const [isLoadingModels, setIsLoadingModels] = useState(false); + + useEffect(() => { + if (!accessToken) return; + + const loadModels = async () => { + setIsLoadingModels(true); + try { + const models = await fetchAvailableModels(accessToken); + // Filter for embedding models only + const embeddingOnly = models.filter((model) => model.mode === "embedding"); + setEmbeddingModels(embeddingOnly); + } catch (error) { + console.error("Error fetching embedding models:", error); + } finally { + setIsLoadingModels(false); + } + }; + + loadModels(); + }, [accessToken]); + + const handleFieldChange = (fieldName: string, value: string) => { + onParamsChange({ + ...providerParams, + [fieldName]: value, + }); + }; + + return ( + <> + {/* S3 Vectors Setup Instructions */} + +

      AWS S3 Vectors allows you to store and query vector embeddings directly in S3:

      +       +
      + } + type="info" + showIcon + style={{ marginBottom: "16px" }} + /> + + {/* Vector Bucket Name */} + + Vector Bucket Name{" "} + + + + + } + required + validateStatus={ + providerParams.vector_bucket_name && providerParams.vector_bucket_name.length < 3 + ? "error" + : undefined + } + help={ + providerParams.vector_bucket_name && providerParams.vector_bucket_name.length < 3 + ? "Bucket name must be at least 3 characters" + : undefined + } + > + handleFieldChange("vector_bucket_name", e.target.value)} + placeholder="my-vector-bucket (min 3 chars)" + size="large" + className="rounded-md" + /> + + + {/* Index Name (Optional) */} + + Index Name{" "} + + + + + } + validateStatus={ + providerParams.index_name && providerParams.index_name.length > 0 && providerParams.index_name.length < 3 + ? "error" + : undefined + } + help={ + providerParams.index_name && providerParams.index_name.length > 0 && providerParams.index_name.length < 3 + ? "Index name must be at least 3 characters if provided" + : undefined + } + > + handleFieldChange("index_name", e.target.value)} + placeholder="my-vector-index (optional, min 3 chars)" + size="large" + className="rounded-md" + /> + + + {/* AWS Region */} + + AWS Region{" "} + + + + + } + required + > + handleFieldChange("aws_region_name", e.target.value)} + placeholder="us-west-2" + size="large" + className="rounded-md" + /> + + + {/* Embedding Model */} + + Embedding Model{" "} + + + + + } + required + > +

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zaA0R=H{bOO-nny|-923MZ)I=XVgy<|%X6N4j!K?d1`qzaowC&_eXLbaoAlC|2#aN0 zUo~d-YWsd7z@`vcEOD0c+=>lzSRyUlA%MX4?L;gduBw4pn(>&Z@@wB*Aj(jbO$Rs@ zc3NYxw(gNE4PD$>>GlZC#I+V#0N@lhX$usG!z#IyVR)f~8a})c8T$fkMULc&16Q4s6bxKE1cQdG7q?(80sIx;u!@Yc--PBrTU`L>vR9%Bx05b)J_X8}9Vz zbPnn6)(7Jb|0LnZQxr^5&FOIMPyx>wc*GOc;<+!zc+da>9=|p=oBs#rLfLz910lu$ O0000 Optional[str]: + """ + Extract text from PDF using pypdf if available. + + Args: + file_content: Raw PDF bytes + + Returns: + Extracted text or None if extraction fails + """ + try: + from io import BytesIO + + # Try pypdf first (most common) + try: + from pypdf import PdfReader as PypdfReader + + pdf_file = BytesIO(file_content) + reader = PypdfReader(pdf_file) + + text_parts = [] + for page in reader.pages: + text = page.extract_text() + if text: + text_parts.append(text) + + if text_parts: + extracted_text = "\n\n".join(text_parts) + verbose_logger.debug(f"Extracted {len(extracted_text)} characters from PDF using pypdf") + return extracted_text + + except ImportError: + verbose_logger.debug("pypdf not available, trying PyPDF2") + + # Fallback to PyPDF2 + try: + from PyPDF2 import PdfReader as PyPDF2Reader + + pdf_file = BytesIO(file_content) + reader = PyPDF2Reader(pdf_file) + + text_parts = [] + for page in reader.pages: + text = page.extract_text() + if text: + text_parts.append(text) + + if text_parts: + extracted_text = "\n\n".join(text_parts) + verbose_logger.debug(f"Extracted {len(extracted_text)} characters from PDF using PyPDF2") + return extracted_text + + except ImportError: + verbose_logger.debug("PyPDF2 not available, PDF extraction requires OCR or pypdf/PyPDF2 library") + + except Exception as e: + verbose_logger.debug(f"PDF text extraction failed: {e}") + + return None diff --git a/litellm/rag/ingestion/s3_vectors_ingestion.py b/litellm/rag/ingestion/s3_vectors_ingestion.py index 13964cd89a..e6c166a101 100644 --- a/litellm/rag/ingestion/s3_vectors_ingestion.py +++ b/litellm/rag/ingestion/s3_vectors_ingestion.py @@ -253,6 +253,20 @@ class S3VectorsRAGIngestion(BaseRAGIngestion, BaseAWSLLM): verbose_logger.debug( f"Ensuring S3 vector bucket exists: {self.vector_bucket_name}" ) + + # Validate bucket name (AWS S3 naming rules) + if len(self.vector_bucket_name) < 3: + raise ValueError( + f"Invalid vector_bucket_name '{self.vector_bucket_name}': " + f"AWS S3 bucket names must be at least 3 characters long. " + f"Please provide a valid bucket name (e.g., 'my-vector-bucket')." + ) + if not self.vector_bucket_name.replace("-", "").replace(".", "").isalnum(): + raise ValueError( + f"Invalid vector_bucket_name '{self.vector_bucket_name}': " + f"AWS S3 bucket names can only contain lowercase letters, numbers, hyphens, and periods. " + f"Please provide a valid bucket name (e.g., 'my-vector-bucket')." + ) # Try to get bucket info using GetVectorBucket API get_url = f"https://s3vectors.{self.aws_region_name}.api.aws/GetVectorBucket" @@ -455,30 +469,43 @@ class S3VectorsRAGIngestion(BaseRAGIngestion, BaseAWSLLM): await self._ensure_config_initialized() if not embeddings or not chunks: - verbose_logger.warning("No embeddings or chunks to store") - return self.index_name, None + error_msg = ( + "No text content could be extracted from the file for embedding. " + "Possible causes:\n" + " 1. PDF files require OCR - add 'ocr' config with a vision model (e.g., 'anthropic/claude-3-5-sonnet-20241022')\n" + " 2. Binary files cannot be processed - convert to text first\n" + " 3. File is empty or contains no extractable text\n" + "For PDFs, either enable OCR or use a PDF extraction library to convert to text before ingestion." + ) + verbose_logger.error(error_msg) + raise ValueError(error_msg) # Prepare vectors for PutVectors API vectors = [] for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)): + # Build metadata dict + metadata: Dict[str, str] = { + "source_text": chunk, # Non-filterable (for reference) + "chunk_index": str(i), # Filterable + } + + if filename: + metadata["filename"] = filename # Filterable + vector_obj = { "key": f"{filename}_{i}" if filename else f"chunk_{i}", "data": {"float32": embedding}, - "metadata": { - "source_text": chunk, # Non-filterable (for reference) - "chunk_index": str(i), # Filterable - }, + "metadata": metadata, } - if filename: - vector_obj["metadata"]["filename"] = filename # Filterable - vectors.append(vector_obj) # Call PutVectors API await self._put_vectors(vectors) - return self.index_name, filename + # Return vector_store_id in format bucket_name:index_name for S3 Vectors search compatibility + vector_store_id = f"{self.vector_bucket_name}:{self.index_name}" + return vector_store_id, filename async def query_vector_store( self, vector_store_id: str, query: str, top_k: int = 5 diff --git a/litellm/types/router.py b/litellm/types/router.py index 8ea7a20753..43943d9e07 100644 --- a/litellm/types/router.py +++ b/litellm/types/router.py @@ -404,6 +404,10 @@ class LiteLLMParamsTypedDict(TypedDict, total=False): aws_access_key_id: Optional[str] aws_secret_access_key: Optional[str] aws_region_name: Optional[str] + ## AWS S3 VECTORS ## + vector_bucket_name: Optional[str] + index_name: Optional[str] + embedding_model: Optional[str] ## IBM WATSONX ## watsonx_region_name: Optional[str] ## CUSTOM PRICING ## diff --git a/litellm/types/utils.py b/litellm/types/utils.py index 2d67e13e92..49c903502d 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -4,20 +4,22 @@ from enum import Enum from typing import TYPE_CHECKING, Any, Dict, List, Literal, Mapping, Optional, Union from openai._models import BaseModel as OpenAIObject -from openai.types.audio.transcription_create_params import FileTypes as FileTypes # type: ignore +from openai.types.audio.transcription_create_params import ( + FileTypes as FileTypes, # type: ignore +) from openai.types.chat.chat_completion import ChatCompletion as ChatCompletion from openai.types.completion_usage import ( CompletionTokensDetails, CompletionUsage, PromptTokensDetails, ) +from openai.types.moderation import Categories as Categories from openai.types.moderation import ( - Categories as Categories, CategoryAppliedInputTypes as CategoryAppliedInputTypes, - CategoryScores as CategoryScores, ) +from openai.types.moderation import CategoryScores as CategoryScores +from openai.types.moderation_create_response import Moderation as Moderation from openai.types.moderation_create_response import ( - Moderation as Moderation, ModerationCreateResponse as ModerationCreateResponse, ) from pydantic import BaseModel, ConfigDict, Field, PrivateAttr, model_validator @@ -3075,6 +3077,7 @@ class LlmProviders(str, Enum): LLAMA = "meta_llama" NSCALE = "nscale" PG_VECTOR = "pg_vector" + S3_VECTORS = "s3_vectors" HELICONE = "helicone" HYPERBOLIC = "hyperbolic" RECRAFT = "recraft" diff --git a/litellm/utils.py b/litellm/utils.py index bb95be05b5..d7fb4855a4 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -8452,6 +8452,12 @@ class ProviderConfigManager: ) return RAGFlowVectorStoreConfig() + elif litellm.LlmProviders.S3_VECTORS == provider: + from litellm.llms.s3_vectors.vector_stores.transformation import ( + S3VectorsVectorStoreConfig, + ) + + return S3VectorsVectorStoreConfig() return None @staticmethod @@ -8699,9 +8705,9 @@ class ProviderConfigManager: """ Get Search configuration for a given provider. """ + from litellm.llms.brave.search.transformation import BraveSearchConfig from litellm.llms.dataforseo.search.transformation import DataForSEOSearchConfig from litellm.llms.exa_ai.search.transformation import ExaAISearchConfig - from litellm.llms.brave.search.transformation import BraveSearchConfig from litellm.llms.firecrawl.search.transformation import FirecrawlSearchConfig from litellm.llms.google_pse.search.transformation import GooglePSESearchConfig from litellm.llms.linkup.search.transformation import LinkupSearchConfig diff --git a/requirements.txt b/requirements.txt index 36328a081a..f250fb213e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -68,6 +68,7 @@ jsonschema>=4.23.0,<5.0.0 # validating json schema - aligned with openapi-core + websockets==15.0.1 # for realtime API soundfile==0.12.1 # for audio file processing openapi-core==0.21.0 # for OpenAPI compliance tests +pypdf>=6.6.2 # for PDF text extraction in RAG ingestion ######################## # LITELLM ENTERPRISE DEPENDENCIES diff --git a/tests/test_litellm/llms/s3_vectors/__init__.py b/tests/test_litellm/llms/s3_vectors/__init__.py new file mode 100644 index 0000000000..d4b0c4d855 --- /dev/null +++ b/tests/test_litellm/llms/s3_vectors/__init__.py @@ -0,0 +1 @@ +# S3 Vectors tests diff --git a/tests/test_litellm/llms/s3_vectors/vector_stores/__init__.py b/tests/test_litellm/llms/s3_vectors/vector_stores/__init__.py new file mode 100644 index 0000000000..231735c1de --- /dev/null +++ b/tests/test_litellm/llms/s3_vectors/vector_stores/__init__.py @@ -0,0 +1 @@ +# S3 Vectors vector store tests diff --git a/tests/test_litellm/llms/s3_vectors/vector_stores/test_s3_vectors_transformation.py b/tests/test_litellm/llms/s3_vectors/vector_stores/test_s3_vectors_transformation.py new file mode 100644 index 0000000000..3a84da3154 --- /dev/null +++ b/tests/test_litellm/llms/s3_vectors/vector_stores/test_s3_vectors_transformation.py @@ -0,0 +1,115 @@ +from unittest.mock import MagicMock, Mock + +import httpx +import pytest + +from litellm.llms.s3_vectors.vector_stores.transformation import ( + S3VectorsVectorStoreConfig, +) +from litellm.types.vector_stores import VectorStoreSearchResponse + + +class TestS3VectorsVectorStoreConfig: + def test_init(self): + """Test that S3VectorsVectorStoreConfig initializes correctly""" + config = S3VectorsVectorStoreConfig() + assert config is not None + + def test_get_supported_openai_params(self): + """Test that supported OpenAI params are returned""" + config = S3VectorsVectorStoreConfig() + params = config.get_supported_openai_params("test-model") + assert "max_num_results" in params + + def test_get_complete_url(self): + """Test URL generation for S3 Vectors""" + config = S3VectorsVectorStoreConfig() + litellm_params = {"aws_region_name": "us-west-2"} + url = config.get_complete_url(None, litellm_params) + assert url == "https://s3vectors.us-west-2.api.aws" + + def test_get_complete_url_missing_region(self): + """Test that missing region raises error""" + config = S3VectorsVectorStoreConfig() + litellm_params = {} + with pytest.raises(ValueError, match="aws_region_name is required"): + config.get_complete_url(None, litellm_params) + + @pytest.mark.skip(reason="Requires embedding API call, tested in integration tests") + def test_transform_search_request(self): + """Test search request transformation""" + # This test requires making an actual embedding API call + # It's better tested in integration tests + pass + + def test_transform_search_request_invalid_vector_store_id(self): + """Test that invalid vector_store_id format raises error""" + config = S3VectorsVectorStoreConfig() + mock_logging_obj = Mock() + mock_logging_obj.model_call_details = {} + + with pytest.raises( + ValueError, match="vector_store_id must be in format 'bucket_name:index_name'" + ): + config.transform_search_vector_store_request( + vector_store_id="invalid-format", + query="test query", + vector_store_search_optional_params={}, + api_base="https://s3vectors.us-west-2.api.aws", + litellm_logging_obj=mock_logging_obj, + litellm_params={}, + ) + + def test_transform_search_response(self): + """Test search response transformation""" + config = S3VectorsVectorStoreConfig() + mock_logging_obj = Mock() + mock_logging_obj.model_call_details = {"query": "test query"} + + mock_response = Mock(spec=httpx.Response) + mock_response.json.return_value = { + "vectors": [ + { + "distance": 0.05, # S3 Vectors returns distance, not score + "metadata": { + "source_text": "This is test content", + "chunk_index": "0", + "filename": "test.pdf", + }, + }, + { + "distance": 0.15, + "metadata": { + "source_text": "More test content", + "chunk_index": "1", + }, + }, + ] + } + mock_response.status_code = 200 + mock_response.headers = {} + + result = config.transform_search_vector_store_response( + mock_response, mock_logging_obj + ) + + # VectorStoreSearchResponse is a TypedDict, so check structure instead of isinstance + assert result["object"] == "vector_store.search_results.page" + assert result["search_query"] == "test query" + assert len(result["data"]) == 2 + # Score should be 1 - distance (cosine similarity) + assert result["data"][0]["score"] == 0.95 # 1 - 0.05 + assert result["data"][0]["content"][0]["text"] == "This is test content" + assert result["data"][0]["filename"] == "test.pdf" + assert result["data"][1]["score"] == 0.85 # 1 - 0.15 + assert result["data"][1]["content"][0]["text"] == "More test content" + + def test_map_openai_params(self): + """Test OpenAI parameter mapping""" + config = S3VectorsVectorStoreConfig() + non_default_params = {"max_num_results": 5} + optional_params = {} + + result = config.map_openai_params(non_default_params, optional_params, False) + + assert result["maxResults"] == 5 diff --git a/tests/vector_store_tests/test_s3_vectors_vector_store.py b/tests/vector_store_tests/test_s3_vectors_vector_store.py new file mode 100644 index 0000000000..a7a1568c1c --- /dev/null +++ b/tests/vector_store_tests/test_s3_vectors_vector_store.py @@ -0,0 +1,42 @@ +from base_vector_store_test import BaseVectorStoreTest +import os +import pytest + + +class TestS3VectorsVectorStore(BaseVectorStoreTest): + @pytest.fixture(autouse=True) + def check_env_vars(self): + """Check if required environment variables are set""" + required_vars = ["AWS_ACCESS_KEY_ID", "AWS_SECRET_ACCESS_KEY"] + missing_vars = [var for var in required_vars if not os.getenv(var)] + if missing_vars: + pytest.skip(f"Missing required environment variables: {', '.join(missing_vars)}") + + def get_base_request_args(self) -> dict: + """ + Must return the base request args for searching. + For S3 Vectors, vector_store_id should be in format: bucket_name:index_name + """ + return { + "custom_llm_provider": "s3_vectors", + "vector_store_id": os.getenv( + "S3_VECTORS_VECTOR_STORE_ID", "test-litellm-vectors:test-index" + ), + "query": "What is machine learning?", + "aws_region_name": os.getenv("AWS_REGION_NAME", "us-west-2"), + "aws_access_key_id": os.getenv("AWS_ACCESS_KEY_ID"), + "aws_secret_access_key": os.getenv("AWS_SECRET_ACCESS_KEY"), + } + + def get_base_create_vector_store_args(self) -> dict: + """ + Vector store creation is not yet implemented for S3 Vectors. + This test will be skipped. + """ + return {} + + @pytest.mark.parametrize("sync_mode", [True, False]) + @pytest.mark.asyncio + async def test_basic_create_vector_store(self, sync_mode): + """S3 Vectors doesn't support vector store creation via this API yet""" + pytest.skip("Vector store creation not yet implemented for S3 Vectors") diff --git a/ui/litellm-dashboard/public/assets/logos/s3_vector.png b/ui/litellm-dashboard/public/assets/logos/s3_vector.png new file mode 100644 index 0000000000000000000000000000000000000000..15a1a456e129f9e2544d82399c6db3f2b9d8d204 GIT binary patch literal 191076 zcmagF1$10HvMy|9W{P8GW@dKG*k)#CjIqtk%*@O&Gcz+|?8J7=_VbxJcV_PV?_Y2C z(o(6azEV|bOG~X*hbziUBEsRpfq;M@N=u0;gMfglf0hCmuul%_wCBcW1?sFUDFRY6 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