mirror of
https://github.com/tiennm99/litellm.git
synced 2026-07-14 07:05:10 +00:00
Revert "Support passing prompt_label to langfuse (#11018)"
This reverts commit 2b50b43ae2.
This commit is contained in:
@@ -28,7 +28,6 @@ class AnthropicCacheControlHook(CustomPromptManagement):
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prompt_id: Optional[str],
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prompt_variables: Optional[dict],
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dynamic_callback_params: StandardCallbackDynamicParams,
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prompt_label: Optional[str] = None,
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) -> Tuple[str, List[AllMessageValues], dict]:
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"""
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Apply cache control directives based on specified injection points.
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@@ -80,10 +79,10 @@ class AnthropicCacheControlHook(CustomPromptManagement):
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# Case 1: Target by specific index
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if targetted_index is not None:
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if 0 <= targetted_index < len(messages):
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messages[
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targetted_index
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] = AnthropicCacheControlHook._safe_insert_cache_control_in_message(
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messages[targetted_index], control
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messages[targetted_index] = (
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AnthropicCacheControlHook._safe_insert_cache_control_in_message(
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messages[targetted_index], control
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)
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)
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# Case 2: Target by role
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elif targetted_role is not None:
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@@ -87,7 +87,6 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
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dynamic_callback_params: StandardCallbackDynamicParams,
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litellm_logging_obj: LiteLLMLoggingObj,
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tools: Optional[List[Dict]] = None,
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prompt_label: Optional[str] = None,
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) -> Tuple[str, List[AllMessageValues], dict]:
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"""
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Returns:
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@@ -105,7 +104,6 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
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prompt_id: Optional[str],
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prompt_variables: Optional[dict],
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dynamic_callback_params: StandardCallbackDynamicParams,
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prompt_label: Optional[str] = None,
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) -> Tuple[str, List[AllMessageValues], dict]:
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"""
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Returns:
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@@ -18,7 +18,6 @@ class CustomPromptManagement(CustomLogger, PromptManagementBase):
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prompt_id: Optional[str],
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prompt_variables: Optional[dict],
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dynamic_callback_params: StandardCallbackDynamicParams,
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prompt_label: Optional[str] = None,
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) -> Tuple[str, List[AllMessageValues], dict]:
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"""
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Returns:
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@@ -44,7 +43,6 @@ class CustomPromptManagement(CustomLogger, PromptManagementBase):
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prompt_id: str,
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prompt_variables: Optional[dict],
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dynamic_callback_params: StandardCallbackDynamicParams,
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prompt_label: Optional[str] = None,
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) -> PromptManagementClient:
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raise NotImplementedError(
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"Custom prompt management does not support compile prompt helper"
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@@ -155,8 +155,11 @@ class HumanloopLogger(CustomLogger):
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prompt_id: Optional[str],
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prompt_variables: Optional[dict],
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dynamic_callback_params: StandardCallbackDynamicParams,
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prompt_label: Optional[str] = None,
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) -> Tuple[str, List[AllMessageValues], dict,]:
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) -> Tuple[
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str,
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List[AllMessageValues],
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dict,
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]:
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humanloop_api_key = dynamic_callback_params.get(
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"humanloop_api_key"
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) or get_secret_str("HUMANLOOP_API_KEY")
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@@ -130,12 +130,9 @@ class LangfusePromptManagement(LangFuseLogger, PromptManagementBase, CustomLogge
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return "langfuse"
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def _get_prompt_from_id(
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self,
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langfuse_prompt_id: str,
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langfuse_client: LangfuseClass,
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prompt_label: Optional[str] = None,
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self, langfuse_prompt_id: str, langfuse_client: LangfuseClass
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) -> PROMPT_CLIENT:
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return langfuse_client.get_prompt(langfuse_prompt_id, label=prompt_label)
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return langfuse_client.get_prompt(langfuse_prompt_id)
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def _compile_prompt(
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self,
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@@ -179,8 +176,11 @@ class LangfusePromptManagement(LangFuseLogger, PromptManagementBase, CustomLogge
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dynamic_callback_params: StandardCallbackDynamicParams,
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litellm_logging_obj: LiteLLMLoggingObj,
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tools: Optional[List[Dict]] = None,
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prompt_label: Optional[str] = None,
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) -> Tuple[str, List[AllMessageValues], dict,]:
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) -> Tuple[
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str,
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List[AllMessageValues],
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dict,
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]:
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return self.get_chat_completion_prompt(
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model,
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messages,
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@@ -188,7 +188,6 @@ class LangfusePromptManagement(LangFuseLogger, PromptManagementBase, CustomLogge
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prompt_id,
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prompt_variables,
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dynamic_callback_params,
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prompt_label=prompt_label,
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)
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def should_run_prompt_management(
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@@ -212,7 +211,6 @@ class LangfusePromptManagement(LangFuseLogger, PromptManagementBase, CustomLogge
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prompt_id: str,
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prompt_variables: Optional[dict],
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dynamic_callback_params: StandardCallbackDynamicParams,
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prompt_label: Optional[str] = None,
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) -> PromptManagementClient:
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langfuse_client = langfuse_client_init(
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langfuse_public_key=dynamic_callback_params.get("langfuse_public_key"),
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@@ -221,9 +219,7 @@ class LangfusePromptManagement(LangFuseLogger, PromptManagementBase, CustomLogge
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langfuse_host=dynamic_callback_params.get("langfuse_host"),
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)
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langfuse_prompt_client = self._get_prompt_from_id(
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langfuse_prompt_id=prompt_id,
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langfuse_client=langfuse_client,
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prompt_label=prompt_label,
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langfuse_prompt_id=prompt_id, langfuse_client=langfuse_client
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)
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## SET PROMPT
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@@ -33,7 +33,6 @@ class PromptManagementBase(ABC):
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prompt_id: str,
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prompt_variables: Optional[dict],
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dynamic_callback_params: StandardCallbackDynamicParams,
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prompt_label: Optional[str] = None,
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) -> PromptManagementClient:
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pass
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@@ -50,13 +49,11 @@ class PromptManagementBase(ABC):
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prompt_variables: Optional[dict],
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client_messages: List[AllMessageValues],
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dynamic_callback_params: StandardCallbackDynamicParams,
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prompt_label: Optional[str] = None,
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) -> PromptManagementClient:
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compiled_prompt_client = self._compile_prompt_helper(
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prompt_id=prompt_id,
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prompt_variables=prompt_variables,
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dynamic_callback_params=dynamic_callback_params,
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prompt_label=prompt_label,
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)
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try:
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@@ -85,7 +82,6 @@ class PromptManagementBase(ABC):
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prompt_id: Optional[str],
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prompt_variables: Optional[dict],
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dynamic_callback_params: StandardCallbackDynamicParams,
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prompt_label: Optional[str] = None,
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) -> Tuple[str, List[AllMessageValues], dict]:
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if prompt_id is None:
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raise ValueError("prompt_id is required for Prompt Management Base class")
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@@ -99,7 +95,6 @@ class PromptManagementBase(ABC):
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prompt_variables=prompt_variables,
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client_messages=messages,
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dynamic_callback_params=dynamic_callback_params,
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prompt_label=prompt_label,
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)
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completed_messages = prompt_template["completed_messages"] or messages
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@@ -75,7 +75,6 @@ class BedrockVectorStore(BaseVectorStore, BaseAWSLLM):
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dynamic_callback_params: StandardCallbackDynamicParams,
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litellm_logging_obj: LiteLLMLoggingObj,
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tools: Optional[List[Dict]] = None,
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prompt_label: Optional[str] = None,
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) -> Tuple[str, List[AllMessageValues], dict]:
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"""
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Retrieves the context from the Bedrock Knowledge Base and appends it to the messages.
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@@ -100,11 +99,10 @@ class BedrockVectorStore(BaseVectorStore, BaseAWSLLM):
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f"Bedrock Knowledge Base Response: {bedrock_kb_response}"
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)
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(
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context_message,
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context_string,
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) = self.get_chat_completion_message_from_bedrock_kb_response(
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bedrock_kb_response
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context_message, context_string = (
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self.get_chat_completion_message_from_bedrock_kb_response(
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bedrock_kb_response
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)
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)
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if context_message is not None:
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messages.append(context_message)
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@@ -128,9 +126,9 @@ class BedrockVectorStore(BaseVectorStore, BaseAWSLLM):
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)
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)
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litellm_logging_obj.model_call_details[
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"vector_store_request_metadata"
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] = vector_store_request_metadata
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litellm_logging_obj.model_call_details["vector_store_request_metadata"] = (
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vector_store_request_metadata
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)
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return model, messages, non_default_params
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@@ -142,9 +140,9 @@ class BedrockVectorStore(BaseVectorStore, BaseAWSLLM):
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"""
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Transform a BedrockKBResponse to a VectorStoreSearchResponse
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"""
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retrieval_results: Optional[
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List[BedrockKBRetrievalResult]
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] = bedrock_kb_response.get("retrievalResults", None)
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retrieval_results: Optional[List[BedrockKBRetrievalResult]] = (
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bedrock_kb_response.get("retrievalResults", None)
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)
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vector_store_search_response: VectorStoreSearchResponse = (
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VectorStoreSearchResponse(search_query=query, data=[])
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)
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@@ -539,7 +539,6 @@ class Logging(LiteLLMLoggingBaseClass):
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prompt_id: Optional[str],
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prompt_variables: Optional[dict],
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prompt_management_logger: Optional[CustomLogger] = None,
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prompt_label: Optional[str] = None,
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) -> Tuple[str, List[AllMessageValues], dict]:
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custom_logger = (
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prompt_management_logger
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@@ -560,7 +559,6 @@ class Logging(LiteLLMLoggingBaseClass):
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prompt_id=prompt_id,
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prompt_variables=prompt_variables,
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dynamic_callback_params=self.standard_callback_dynamic_params,
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prompt_label=prompt_label,
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)
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self.messages = messages
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return model, messages, non_default_params
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@@ -574,7 +572,6 @@ class Logging(LiteLLMLoggingBaseClass):
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prompt_variables: Optional[dict],
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prompt_management_logger: Optional[CustomLogger] = None,
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tools: Optional[List[Dict]] = None,
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prompt_label: Optional[str] = None,
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) -> Tuple[str, List[AllMessageValues], dict]:
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custom_logger = (
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prompt_management_logger
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@@ -597,7 +594,6 @@ class Logging(LiteLLMLoggingBaseClass):
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dynamic_callback_params=self.standard_callback_dynamic_params,
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litellm_logging_obj=self,
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tools=tools,
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prompt_label=prompt_label,
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)
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self.messages = messages
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return model, messages, non_default_params
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+2
-9
@@ -97,7 +97,6 @@ from litellm.utils import (
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get_optional_params_image_gen,
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get_optional_params_transcription,
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get_secret,
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get_standard_openai_params,
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mock_completion_streaming_obj,
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read_config_args,
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supports_httpx_timeout,
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@@ -429,7 +428,6 @@ async def acompletion(
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prompt_id=kwargs.get("prompt_id", None),
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prompt_variables=kwargs.get("prompt_variables", None),
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tools=tools,
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prompt_label=kwargs.get("prompt_label", None),
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)
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#########################################################
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@@ -985,7 +983,6 @@ def completion( # type: ignore # noqa: PLR0915
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assistant_continue_message=assistant_continue_message,
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)
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######## end of unpacking kwargs ###########
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standard_openai_params = get_standard_openai_params(params=args)
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non_default_params = get_non_default_completion_params(kwargs=kwargs)
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litellm_params = {} # used to prevent unbound var errors
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## PROMPT MANAGEMENT HOOKS ##
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@@ -1004,7 +1001,6 @@ def completion( # type: ignore # noqa: PLR0915
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non_default_params=non_default_params,
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prompt_id=prompt_id,
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prompt_variables=prompt_variables,
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prompt_label=kwargs.get("prompt_label", None),
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)
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try:
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@@ -1238,13 +1234,10 @@ def completion( # type: ignore # noqa: PLR0915
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max_retries=max_retries,
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timeout=timeout,
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)
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cast(LiteLLMLoggingObj, logging).update_environment_variables(
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logging.update_environment_variables(
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model=model,
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user=user,
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optional_params={
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**standard_openai_params,
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**non_default_params,
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}, # [IMPORTANT] - using standard_openai_params ensures consistent params logged to langfuse for finetuning / eval datasets.
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optional_params=optional_params,
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litellm_params=litellm_params,
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custom_llm_provider=custom_llm_provider,
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)
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@@ -1,8 +1,8 @@
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model_list:
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- model_name: "gemini-2.0-flash-gemini"
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- model_name: "gemini-2.0-flash"
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litellm_params:
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model: gemini/gemini-2.0-flash
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- model_name: "gpt-4o-mini-openai"
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model: gemini/gemini-2.0-flash-live-001
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- model_name: "gpt-4.1-openai"
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litellm_params:
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model: gpt-4.1-mini-2025-04-14
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api_key: os.environ/OPENAI_API_KEY
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@@ -71,16 +71,6 @@ model_list:
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model: mistral/*
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api_key: os.environ/MISTRAL_API_KEY
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access_groups: ["beta-models"]
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- model_name: my-langfuse-model
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litellm_params:
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model: langfuse/gpt-3.5-turbo
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prompt_id: "jokes"
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prompt_label: "latest"
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api_key: os.environ/OPENAI_API_KEY
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litellm_settings:
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callbacks: ["langfuse"]
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general_settings:
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store_model_in_db: true
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store_prompts_in_spend_logs: true
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cache: true
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@@ -670,20 +670,15 @@ class UserObjectCache:
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- update user object in cache
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"""
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if isinstance(user_object, LiteLLM_UserTable):
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user_object_dict = user_object.model_dump()
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else:
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user_object_dict = user_object
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for k, v in user_object_dict.items():
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if isinstance(v, datetime):
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user_object_dict[k] = v.isoformat()
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await self.user_api_key_cache.async_set_cache(
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key=user_id, value=user_object_dict
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)
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user_object = user_object.model_dump()
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for k, v in user_object.items():
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if isinstance(v, datetime):
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user_object[k] = v.isoformat()
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await self.user_api_key_cache.async_set_cache(key=user_id, value=user_object)
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if self.internal_usage_cache is not None:
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await self.internal_usage_cache.async_set_cache(
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key=user_id,
|
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value=user_object_dict,
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value=user_object,
|
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litellm_parent_otel_span=litellm_parent_otel_span,
|
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)
|
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|
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|
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@@ -15,7 +15,6 @@ class X42PromptManagement(CustomPromptManagement):
|
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prompt_id: Optional[str],
|
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prompt_variables: Optional[dict],
|
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dynamic_callback_params: StandardCallbackDynamicParams,
|
||||
prompt_label: Optional[str] = None,
|
||||
) -> Tuple[str, List[AllMessageValues], dict]:
|
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"""
|
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Returns:
|
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|
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+3
-12
@@ -1700,13 +1700,9 @@ class Router:
|
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specific_deployment=kwargs.pop("specific_deployment", None),
|
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)
|
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|
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self._update_kwargs_with_deployment(
|
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deployment=prompt_management_deployment, kwargs=kwargs
|
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litellm_model = prompt_management_deployment["litellm_params"].get(
|
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"model", None
|
||||
)
|
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data = prompt_management_deployment["litellm_params"].copy()
|
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|
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litellm_model = data.get("model", None)
|
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|
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prompt_id = kwargs.get("prompt_id") or prompt_management_deployment[
|
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"litellm_params"
|
||||
].get("prompt_id", None)
|
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@@ -1715,9 +1711,6 @@ class Router:
|
||||
) or prompt_management_deployment["litellm_params"].get(
|
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"prompt_variables", None
|
||||
)
|
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prompt_label = kwargs.get("prompt_label", None) or prompt_management_deployment[
|
||||
"litellm_params"
|
||||
].get("prompt_label", None)
|
||||
|
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if prompt_id is None or not isinstance(prompt_id, str):
|
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raise ValueError(
|
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@@ -1738,16 +1731,14 @@ class Router:
|
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non_default_params=get_non_default_completion_params(kwargs=kwargs),
|
||||
prompt_id=prompt_id,
|
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prompt_variables=prompt_variables,
|
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prompt_label=prompt_label,
|
||||
)
|
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|
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kwargs = {**data, **kwargs, **optional_params}
|
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kwargs = {**kwargs, **optional_params}
|
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kwargs["model"] = model
|
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kwargs["messages"] = messages
|
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kwargs["litellm_logging_obj"] = litellm_logging_object
|
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kwargs["prompt_id"] = prompt_id
|
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kwargs["prompt_variables"] = prompt_variables
|
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kwargs["prompt_label"] = prompt_label
|
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|
||||
_model_list = self.get_model_list(model_name=model)
|
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if _model_list is None or len(_model_list) == 0: # if direct call to model
|
||||
|
||||
@@ -2117,7 +2117,6 @@ all_litellm_params = [
|
||||
"allowed_openai_params",
|
||||
"litellm_session_id",
|
||||
"use_litellm_proxy",
|
||||
"prompt_label",
|
||||
] + list(StandardCallbackDynamicParams.__annotations__.keys())
|
||||
|
||||
|
||||
|
||||
@@ -6835,14 +6835,6 @@ def _add_path_to_api_base(api_base: str, ending_path: str) -> str:
|
||||
return str(modified_url.copy_with(params=original_url.params))
|
||||
|
||||
|
||||
def get_standard_openai_params(params: dict) -> dict:
|
||||
return {
|
||||
k: v
|
||||
for k, v in params.items()
|
||||
if k in litellm.OPENAI_CHAT_COMPLETION_PARAMS and v is not None
|
||||
}
|
||||
|
||||
|
||||
def get_non_default_completion_params(kwargs: dict) -> dict:
|
||||
openai_params = litellm.OPENAI_CHAT_COMPLETION_PARAMS
|
||||
default_params = openai_params + all_litellm_params
|
||||
|
||||
@@ -33,7 +33,6 @@ class TestCustomPromptManagement(CustomPromptManagement):
|
||||
prompt_id: Optional[str],
|
||||
prompt_variables: Optional[dict],
|
||||
dynamic_callback_params: StandardCallbackDynamicParams,
|
||||
prompt_label: Optional[str],
|
||||
) -> Tuple[str, List[AllMessageValues], dict]:
|
||||
print(
|
||||
"TestCustomPromptManagement: running get_chat_completion_prompt for prompt_id: ",
|
||||
|
||||
@@ -62,7 +62,9 @@
|
||||
"endTime": "2025-01-16T11:28:55.124353-08:00",
|
||||
"completionStartTime": "2025-01-16T11:28:55.124353-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 20,
|
||||
|
||||
+3
-1
@@ -103,7 +103,9 @@
|
||||
"endTime": "2025-01-22T09:27:51.702048-08:00",
|
||||
"completionStartTime": "2025-01-22T09:27:51.702048-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 20,
|
||||
|
||||
+3
-1
@@ -81,7 +81,9 @@
|
||||
"endTime": "2025-01-22T09:19:11.234200-08:00",
|
||||
"completionStartTime": "2025-01-22T09:19:11.234200-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 20,
|
||||
|
||||
+3
-1
@@ -52,7 +52,9 @@
|
||||
"endTime": "2025-02-06T16:23:27.644253-08:00",
|
||||
"completionStartTime": "2025-02-06T16:23:27.644253-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 10,
|
||||
|
||||
+3
-1
@@ -71,7 +71,9 @@
|
||||
"endTime": "2025-01-22T07:31:28.962389-08:00",
|
||||
"completionStartTime": "2025-01-22T07:31:28.962389-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 20,
|
||||
|
||||
+3
-1
@@ -71,7 +71,9 @@
|
||||
"endTime": "2025-01-22T08:38:26.015666-08:00",
|
||||
"completionStartTime": "2025-01-22T08:38:26.015666-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 20,
|
||||
|
||||
@@ -78,7 +78,9 @@
|
||||
"endTime": "2025-01-22T09:59:39.365756-08:00",
|
||||
"completionStartTime": "2025-01-22T09:59:39.365756-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 20,
|
||||
|
||||
@@ -70,7 +70,9 @@
|
||||
"endTime": "2025-01-22T10:06:50.958374-08:00",
|
||||
"completionStartTime": "2025-01-22T10:06:50.958374-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 20,
|
||||
|
||||
@@ -64,7 +64,9 @@
|
||||
"endTime": "2025-01-22T09:59:32.880691-08:00",
|
||||
"completionStartTime": "2025-01-22T09:59:32.880691-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 20,
|
||||
|
||||
+3
-1
@@ -64,7 +64,9 @@
|
||||
"endTime": "2025-01-22T09:59:36.161959-08:00",
|
||||
"completionStartTime": "2025-01-22T09:59:36.161959-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 20,
|
||||
|
||||
@@ -64,7 +64,9 @@
|
||||
"endTime": "2025-01-22T09:59:32.880691-08:00",
|
||||
"completionStartTime": "2025-01-22T09:59:32.880691-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 20,
|
||||
|
||||
@@ -70,7 +70,9 @@
|
||||
"endTime": "2025-01-22T09:55:28.853979-08:00",
|
||||
"completionStartTime": "2025-01-22T09:55:28.853979-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 20,
|
||||
|
||||
@@ -70,7 +70,9 @@
|
||||
"endTime": "2025-01-22T09:53:53.753431-08:00",
|
||||
"completionStartTime": "2025-01-22T09:53:53.753431-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 20,
|
||||
|
||||
@@ -74,7 +74,9 @@
|
||||
"endTime": "2025-01-22T09:56:35.476236-08:00",
|
||||
"completionStartTime": "2025-01-22T09:56:35.476236-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 20,
|
||||
|
||||
@@ -78,7 +78,9 @@
|
||||
"endTime": "2025-01-22T09:56:38.785762-08:00",
|
||||
"completionStartTime": "2025-01-22T09:56:38.785762-08:00",
|
||||
"model": "gpt-3.5-turbo",
|
||||
"modelParameters": {},
|
||||
"modelParameters": {
|
||||
"extra_body": "{}"
|
||||
},
|
||||
"usage": {
|
||||
"input": 10,
|
||||
"output": 20,
|
||||
|
||||
Reference in New Issue
Block a user