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Fix/nova grounding (#19598)

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* 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 <juhiechandra@gmail.com>
Co-authored-by: Juhie <75068056+juhiechandra@users.noreply.github.com>
This commit is contained in:
jquinter
2026-01-23 00:34:29 -03:00
committed by GitHub
parent c9516d68d4
commit 0622ce3f2c
7 changed files with 1057 additions and 67 deletions
Download Patch File Download Diff File Expand all files Collapse all files
litellm/litellm_core_utils/prompt_templates/factory.py
+2 -1
View File
@@ -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": {}}
)
litellm/llms/bedrock/chat/converse_transformation.py
+66 -5
View File
@@ -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)
)
litellm/llms/bedrock/chat/invoke_handler.py
+5
View File
@@ -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,
litellm/types/llms/bedrock.py
+85
View File
@@ -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]
poetry.lock
Generated
+539 -53
View File
@@ -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 = "sha256:f66aeb23f0a6848fc2d5bbdf1b5777c3cf8e06056f73ef815317abf89d5a0262"},
]
[package.dependencies]
docstring_parser = "<1"
google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.8.dev0,<3.0.0", extras = ["grpc"]}
google-auth = ">=2.14.1,<3.0.0"
google-cloud-bigquery = ">=1.15.0,<3.20.0 || >3.20.0,<4.0.0"
google-cloud-resource-manager = ">=1.3.3,<3.0.0"
google-cloud-storage = [
{version = ">=1.32.0,<4.0.0", markers = "python_version < \"3.13\""},
{version = ">=2.10.0,<4.0.0", markers = "python_version >= \"3.13\""},
]
google-genai = ">=1.37.0,<2.0.0"
packaging = ">=14.3"
proto-plus = ">=1.22.3,<2.0.0"
protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0"
pydantic = "<3"
shapely = "<3.0.0"
typing_extensions = "*"
[package.extras]
adk = ["google-adk (>=1.0.0,<2.0.0)", "opentelemetry-instrumentation-google-genai (>=0.3b0,<1.0.0)"]
ag2 = ["ag2[gemini]", "openinference-instrumentation-autogen (>=0.1.6,<0.2)"]
ag2-testing = ["absl-py", "ag2[gemini]", "cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "openinference-instrumentation-autogen (>=0.1.6,<0.2)", "opentelemetry-exporter-gcp-logging (>=1.11.0a0,<2.0.0)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-exporter-otlp-proto-http (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.11.1,<3)", "pytest-xdist", "typing_extensions"]
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{file = "shapely-2.1.2-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:9a522f460d28e2bf4e12396240a5fc1518788b2fcd73535166d748399ef0c223"},
{file = "shapely-2.1.2-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:1ff629e00818033b8d71139565527ced7d776c269a49bd78c9df84e8f852190c"},
{file = "shapely-2.1.2-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f67b34271dedc3c653eba4e3d7111aa421d5be9b4c4c7d38d30907f796cb30df"},
{file = "shapely-2.1.2-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:21952dc00df38a2c28375659b07a3979d22641aeb104751e769c3ee825aadecf"},
{file = "shapely-2.1.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:1f2f33f486777456586948e333a56ae21f35ae273be99255a191f5c1fa302eb4"},
{file = "shapely-2.1.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:cf831a13e0d5a7eb519e96f58ec26e049b1fad411fc6fc23b162a7ce04d9cffc"},
{file = "shapely-2.1.2-cp314-cp314-win32.whl", hash = "sha256:61edcd8d0d17dd99075d320a1dd39c0cb9616f7572f10ef91b4b5b00c4aeb566"},
{file = "shapely-2.1.2-cp314-cp314-win_amd64.whl", hash = "sha256:a444e7afccdb0999e203b976adb37ea633725333e5b119ad40b1ca291ecf311c"},
{file = "shapely-2.1.2-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:5ebe3f84c6112ad3d4632b1fd2290665aa75d4cef5f6c5d77c4c95b324527c6a"},
{file = "shapely-2.1.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:5860eb9f00a1d49ebb14e881f5caf6c2cf472c7fd38bd7f253bbd34f934eb076"},
{file = "shapely-2.1.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b705c99c76695702656327b819c9660768ec33f5ce01fa32b2af62b56ba400a1"},
{file = "shapely-2.1.2-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a1fd0ea855b2cf7c9cddaf25543e914dd75af9de08785f20ca3085f2c9ca60b0"},
{file = "shapely-2.1.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:df90e2db118c3671a0754f38e36802db75fe0920d211a27481daf50a711fdf26"},
{file = "shapely-2.1.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:361b6d45030b4ac64ddd0a26046906c8202eb60d0f9f53085f5179f1d23021a0"},
{file = "shapely-2.1.2-cp314-cp314t-win32.whl", hash = "sha256:b54df60f1fbdecc8ebc2c5b11870461a6417b3d617f555e5033f1505d36e5735"},
{file = "shapely-2.1.2-cp314-cp314t-win_amd64.whl", hash = "sha256:0036ac886e0923417932c2e6369b6c52e38e0ff5d9120b90eef5cd9a5fc5cae9"},
{file = "shapely-2.1.2.tar.gz", hash = "sha256:2ed4ecb28320a433db18a5bf029986aa8afcfd740745e78847e330d5d94922a9"},
]
[package.dependencies]
numpy = ">=1.21"
[package.extras]
docs = ["matplotlib", "numpydoc (==1.1.*)", "sphinx", "sphinx-book-theme", "sphinx-remove-toctrees"]
test = ["pytest", "pytest-cov", "scipy-doctest"]
[[package]]
name = "shellingham"
version = "1.5.4"
@@ -6561,7 +7027,7 @@ description = "Python 2 and 3 compatibility utilities"
optional = true
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
groups = ["main"]
markers = "python_version >= \"3.10\" and (extra == \"mlflow\" or extra == \"proxy\") or extra == \"proxy\""
markers = "(extra == \"mlflow\" or extra == \"proxy\" or extra == \"google\") and python_version >= \"3.10\" or extra == \"proxy\" or extra == \"google\""
files = [
{file = "six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274"},
{file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"},
@@ -6984,6 +7450,26 @@ 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"
@@ -6995,7 +7481,7 @@ 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\") or extra == \"proxy\""}
markers = {main = "python_version >= \"3.10\" and (extra == \"mlflow\" or extra == \"proxy\")", dev = "python_version >= \"3.10\""}
[package.dependencies]
anyio = ">=3.6.2,<5"
@@ -7044,7 +7530,7 @@ description = "Retry code until it succeeds"
optional = true
python-versions = ">=3.9"
groups = ["main"]
markers = "extra == \"extra-proxy\" and python_version < \"3.14\""
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"},
@@ -7522,7 +8008,7 @@ description = "The lightning-fast ASGI server."
optional = true
python-versions = ">=3.8"
groups = ["main"]
markers = "python_version >= \"3.10\" and (extra == \"mlflow\" or extra == \"proxy\") or extra == \"proxy\""
markers = "(extra == \"mlflow\" or extra == \"proxy\") and python_version >= \"3.10\" or extra == \"proxy\""
files = [
{file = "uvicorn-0.31.1-py3-none-any.whl", hash = "sha256:adc42d9cac80cf3e51af97c1851648066841e7cfb6993a4ca8de29ac1548ed41"},
{file = "uvicorn-0.31.1.tar.gz", hash = "sha256:f5167919867b161b7bcaf32646c6a94cdbd4c3aa2eb5c17d36bb9aa5cfd8c493"},
@@ -7543,7 +8029,7 @@ description = "Fast implementation of asyncio event loop on top of libuv"
optional = true
python-versions = ">=3.8.0"
groups = ["main"]
markers = "sys_platform != \"win32\" and extra == \"proxy\""
markers = "extra == \"proxy\" and sys_platform != \"win32\""
files = [
{file = "uvloop-0.21.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:ec7e6b09a6fdded42403182ab6b832b71f4edaf7f37a9a0e371a01db5f0cb45f"},
{file = "uvloop-0.21.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:196274f2adb9689a289ad7d65700d37df0c0930fd8e4e743fa4834e850d7719d"},
@@ -7613,7 +8099,7 @@ description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)"
optional = true
python-versions = ">=3.9"
groups = ["main"]
markers = "extra == \"proxy\""
markers = "extra == \"google\" or extra == \"proxy\""
files = [
{file = "websockets-15.0.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d63efaa0cd96cf0c5fe4d581521d9fa87744540d4bc999ae6e08595a1014b45b"},
{file = "websockets-15.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ac60e3b188ec7574cb761b08d50fcedf9d77f1530352db4eef1707fe9dee7205"},
@@ -7996,7 +8482,7 @@ type = ["pytest-mypy"]
[extras]
caching = ["diskcache"]
extra-proxy = ["a2a-sdk", "azure-identity", "azure-keyvault-secrets", "google-cloud-iam", "google-cloud-kms", "prisma", "redisvl", "resend"]
grpc = ["grpcio", "grpcio"]
google = ["google-cloud-aiplatform"]
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"]
@@ -8005,4 +8491,4 @@ utils = ["numpydoc"]
[metadata]
lock-version = "2.1"
python-versions = ">=3.9,<4.0"
content-hash = "f6a98e687d478db6e30274a4cf70391960775cbf648da0783558444da3a662ea"
content-hash = "76f5b5fb10667c2dcf428976292672e7a1f3f2eb5f5bf78998e2192899f5037e"
tests/llm_translation/test_bedrock_completion.py
+285
View File
@@ -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")
tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
+75 -8
View File
@@ -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(