95 lines
3.1 KiB
Python
95 lines
3.1 KiB
Python
from typing import Dict, List, Optional
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from mem0.configs.llms.base import BaseLlmConfig
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from mem0.llms.base import LLMBase
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try:
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from langchain.chat_models.base import BaseChatModel
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from langchain_core.messages import AIMessage
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except ImportError:
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raise ImportError("langchain is not installed. Please install it using `pip install langchain`")
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class LangchainLLM(LLMBase):
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def __init__(self, config: Optional[BaseLlmConfig] = None):
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super().__init__(config)
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if self.config.model is None:
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raise ValueError("`model` parameter is required")
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if not isinstance(self.config.model, BaseChatModel):
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raise ValueError("`model` must be an instance of BaseChatModel")
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self.langchain_model = self.config.model
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def _parse_response(self, response: AIMessage, tools: Optional[List[Dict]]):
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"""
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Process the response based on whether tools are used or not.
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Args:
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response: AI Message.
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tools: The list of tools provided in the request.
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Returns:
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str or dict: The processed response.
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"""
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if not tools:
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return response.content
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processed_response = {
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"content": response.content,
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"tool_calls": [],
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}
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for tool_call in response.tool_calls:
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processed_response["tool_calls"].append(
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{
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"name": tool_call["name"],
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"arguments": tool_call["args"],
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}
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)
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return processed_response
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def generate_response(
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self,
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messages: List[Dict[str, str]],
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response_format=None,
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tools: Optional[List[Dict]] = None,
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tool_choice: str = "auto",
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):
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"""
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Generate a response based on the given messages using langchain_community.
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Args:
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messages (list): List of message dicts containing 'role' and 'content'.
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response_format (str or object, optional): Format of the response. Not used in Langchain.
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tools (list, optional): List of tools that the model can call.
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tool_choice (str, optional): Tool choice method.
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Returns:
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str: The generated response.
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"""
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# Convert the messages to LangChain's tuple format
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langchain_messages = []
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for message in messages:
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role = message["role"]
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content = message["content"]
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if role == "system":
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langchain_messages.append(("system", content))
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elif role == "user":
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langchain_messages.append(("human", content))
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elif role == "assistant":
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langchain_messages.append(("ai", content))
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if not langchain_messages:
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raise ValueError("No valid messages found in the messages list")
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langchain_model = self.langchain_model
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if tools:
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langchain_model = langchain_model.bind_tools(tools=tools, tool_choice=tool_choice)
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response: AIMessage = langchain_model.invoke(langchain_messages)
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return self._parse_response(response, tools)
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