Zǻi%ddlmZddlZddlmZmZddlmZmZm Z m Z m Z m Z ddl mZddlmZddlmZddlmZdd lmZmZd d lmZmZej6eZGd d eZGddeZy)) annotationsN)ABCabstractmethod)AnyListOptionalSequenceTypeUnion) BaseModel)MessageHistory)Tool) LLMMessage)DEFAULT_RATE_LIMIT_HANDLERRateLimitHandler) LLMResponseToolCallResponseceZdZUdZdZded< d d dZe d d dZe d d dZ d dd Z d dd Z y) LLMInterfaceaInterface for large language models. Args: model_name (str): The name of the language model. model_params (Optional[dict]): Additional parameters passed to the model when text is sent to it. Defaults to None. rate_limit_handler (Optional[RateLimitHandler]): Handler for rate limiting. Defaults to retry with exponential backoff. **kwargs (Any): Arguments passed to the model when for the class is initialised. Defaults to None. Fboolsupports_structured_outputNc |tjd||_|xsi|_|||_yt |_y)NzeLLMInterface is deprecated and will be removed in future versions. Please use LLMInterfaceV2 instead.)loggerwarning model_name model_params_rate_limit_handlerrselfrrrate_limit_handlerkwargss U/opt/lhia/marcimex/agent/venv/lib/python3.12/site-packages/neo4j_graphrag/llm/base.py__init__zLLMInterface.__init__3s?  s %(.B  )'9D $'AD $cy)a3Sends a text input to the LLM and retrieves a response. Args: input (str): Text sent to the LLM. message_history (Optional[Union[List[LLMMessage], MessageHistory]]): A collection previous messages, with each message having a specific role assigned. system_instruction (Optional[str]): An option to override the llm system message for this invocation. Returns: LLMResponse: The response from the LLM. Raises: LLMGenerationError: If anything goes wrong. Nr inputmessage_historysystem_instructions r#invokezLLMInterface.invokeEr%c Kyw)aBAsynchronously sends a text input to the LLM and retrieves a response. Args: input (str): Text sent to the LLM. message_history (Optional[Union[List[LLMMessage], MessageHistory]]): A collection previous messages, with each message having a specific role assigned. system_instruction (Optional[str]): An option to override the llm system message for this invocation. Returns: LLMResponse: The response from the LLM. Raises: LLMGenerationError: If anything goes wrong. Nr'r(s r#ainvokezLLMInterface.ainvoke[ctd)aSends a text input to the LLM with tool definitions and retrieves a tool call response. This is a default implementation that should be overridden by LLM providers that support tool/function calling. Args: input (str): Text sent to the LLM. tools (Sequence[Tool]): Sequence of Tools for the LLM to choose from. Each LLM implementation should handle the conversion to its specific format. message_history (Optional[Union[List[LLMMessage], MessageHistory]]): A collection previous messages, with each message having a specific role assigned. system_instruction (Optional[str]): An option to override the llm system message for this invocation. Returns: ToolCallResponse: The response from the LLM containing a tool call. Raises: LLMGenerationError: If anything goes wrong. NotImplementedError: If the LLM provider does not support tool calling. 0This LLM provider does not support tool calling.NotImplementedErrorr r)toolsr*r+s r#invoke_with_toolszLLMInterface.invoke_with_toolsqs2""TUUr%c Ktdw)aAsynchronously sends a text input to the LLM with tool definitions and retrieves a tool call response. This is a default implementation that should be overridden by LLM providers that support tool/function calling. Args: input (str): Text sent to the LLM. tools (Sequence[Tool]): Sequence of Tools for the LLM to choose from. Each LLM implementation should handle the conversion to its specific format. message_history (Optional[Union[List[LLMMessage], MessageHistory]]): A collection previous messages, with each message having a specific role assigned. system_instruction (Optional[str]): An option to override the llm system message for this invocation. Returns: ToolCallResponse: The response from the LLM containing a tool call. Raises: LLMGenerationError: If anything goes wrong. NotImplementedError: If the LLM provider does not support tool calling. r3r4r6s r#ainvoke_with_toolszLLMInterface.ainvoke_with_toolss2""TUUs NNrstrrzOptional[dict[str, Any]]r!zOptional[RateLimitHandler]r"r)r)r=r*1Optional[Union[List[LLMMessage], MessageHistory]]r+ Optional[str]returnr) r)r=r7zSequence[Tool]r*r>r+r?r@r) __name__ __module__ __qualname____doc__r__annotations__r$rr,r/r8r:r'r%r#rr&se(-,l 269= BB/B7 B  B$NR,0   K *     *NR,0   K *     2NR,0 VVVK V * V  V>NR,0 VVVK V * V  Vr%rcteZdZdZ d ddZe d d dZe d d dZy) LLMInterfaceV2aInterface for large language models compatible with LangChain. Args: model_name (str): The name of the language model. model_params (Optional[dict]): Additional parameters passed to the model when text is sent to it. Defaults to None. rate_limit_handler (Optional[RateLimitHandler]): Handler for rate limiting. Defaults to retry with exponential backoff. **kwargs (Any): Arguments passed to the model when for the class is initialised. Defaults to None. Nc R||_|xsi|_|||_yt|_yN)rrrrrs r#r$zLLMInterfaceV2.__init__s.%(.B  )'9D $'AD $r%c y)aSends a list of messages to the LLM and retrieves a response. Args: input (List[LLMMessage]): Text sent to the LLM as a list of LLMMessage objects. response_format (Optional[Union[Type[BaseModel], dict[str, Any]]]): Optional response format specification. Can be a Pydantic model class for structured output or a dict for provider-specific formats. Defaults to None. Returns: LLMResponse: The response from the LLM. Raises: LLMGenerationError: If anything goes wrong. NotImplementedError: If the LLM provider does not support structured output. Nr'r r)response_formatr"s r#r,zLLMInterfaceV2.invoker-r%c Kyw)aAsynchronously sends a list of messages to the LLM and retrieves a response. Args: input (List[LLMMessage]): List of messages sent to the LLM. response_format (Optional[Union[Type[BaseModel], dict[str, Any]]]): Optional response format specification. Can be a Pydantic model class for structured output or a dict for provider-specific formats. Defaults to None. Returns: LLMResponse: The response from the LLM. Raises: LLMGenerationError: If anything goes wrong. NotImplementedError: If the LLM provider does not support structured output. Nr'rKs r#r/zLLMInterfaceV2.ainvoker0r1r;r<rI)r)zList[LLMMessage]rLz0Optional[Union[Type[BaseModel], dict[str, Any]]]r"rr@r)rArBrCrDr$rr,r/r'r%r#rGrGs269= B B/ B7 B  BMQ  J      ,MQ  J      r%rG) __future__rloggingabcrrtypingrrrr r r pydanticr neo4j_graphrag.message_historyr neo4j_graphrag.toolrneo4j_graphrag.typesrneo4j_graphrag.utils.rate_limitrrtypesrr getLoggerrArrrGr'r%r#rYs_##==9$+ 1   8 $V3VDE SE r%