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#1	from typing import Dict, List, Optional
#2
#3	from mem0.configs.llms.base import BaseLlmConfig
#4	from mem0.llms.base import LLMBase
#5
#6	try:
#7	from langchain.chat_models.base import BaseChatModel
#8	from langchain_core.messages import AIMessage
#9	except ImportError:
#10	raise ImportError("langchain is not installed. Please install it using `pip install langchain`")
#11
#12
#13	class LangchainLLM(LLMBase):
#14	def __init__(self, config: Optional[BaseLlmConfig] = None):
#15	super().__init__(config)
#16
#17	if self.config.model is None:
#18	raise ValueError("`model` parameter is required")
#19
#20	if not isinstance(self.config.model, BaseChatModel):
#21	raise ValueError("`model` must be an instance of BaseChatModel")
#22
#23	self.langchain_model = self.config.model
#24
#25	def _parse_response(self, response: AIMessage, tools: Optional[List[Dict]]):
#26	"""
#27	Process the response based on whether tools are used or not.
#28
#29	Args:
#30	response: AI Message.
#31	tools: The list of tools provided in the request.
#32
#33	Returns:
#34	str or dict: The processed response.
#35	"""
#36	if not tools:
#37	return response.content
#38
#39	processed_response = {
#40	"content": response.content,
#41	"tool_calls": [],
#42	}
#43
#44	for tool_call in response.tool_calls:
#45	processed_response["tool_calls"].append(
#46	{
#47	"name": tool_call["name"],
#48	"arguments": tool_call["args"],
#49	}
#50	)
#51
#52	return processed_response
#53
#54	def generate_response(
#55	self,
#56	messages: List[Dict[str, str]],
#57	response_format=None,
#58	tools: Optional[List[Dict]] = None,
#59	tool_choice: str = "auto",
#60	):
#61	"""
#62	Generate a response based on the given messages using langchain_community.
#63
#64	Args:
#65	messages (list): List of message dicts containing 'role' and 'content'.
#66	response_format (str or object, optional): Format of the response. Not used in Langchain.
#67	tools (list, optional): List of tools that the model can call.
#68	tool_choice (str, optional): Tool choice method.
#69
#70	Returns:
#71	str: The generated response.
#72	"""
#73	# Convert the messages to LangChain's tuple format
#74	langchain_messages = []
#75	for message in messages:
#76	role = message["role"]
#77	content = message["content"]
#78
#79	if role == "system":
#80	langchain_messages.append(("system", content))
#81	elif role == "user":
#82	langchain_messages.append(("human", content))
#83	elif role == "assistant":
#84	langchain_messages.append(("ai", content))
#85
#86	if not langchain_messages:
#87	raise ValueError("No valid messages found in the messages list")
#88
#89	langchain_model = self.langchain_model
#90	if tools:
#91	langchain_model = langchain_model.bind_tools(tools=tools, tool_choice=tool_choice)
#92
#93	response: AIMessage = langchain_model.invoke(langchain_messages)
#94	return self._parse_response(response, tools)
#95

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