mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
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from langchain.chat_models import ChatAnthropic
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from langchain.tools.render import render_text_description
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from langchain.agents.format_scratchpad import format_xml
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from langchain.agents import AgentExecutor
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from langchain.retrievers.you import YouRetriever
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from langchain.agents.agent_toolkits.conversational_retrieval.tool import create_retriever_tool
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from langchain.pydantic_v1 import BaseModel
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from xml_agent.prompts import conversational_prompt, parse_output
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from langchain.schema import AIMessage, HumanMessage
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from typing import List, Tuple
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def _format_chat_history(chat_history: List[Tuple[str, str]]):
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buffer = []
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for human, ai in chat_history:
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buffer.append(HumanMessage(content=human))
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buffer.append(AIMessage(content=ai))
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return buffer
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model = ChatAnthropic(model="claude-2")
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# Fake Tool
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retriever = YouRetriever(k=5)
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retriever_tool = create_retriever_tool(retriever, "search", "Use this to search for current events.")
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tools = [retriever_tool]
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prompt = conversational_prompt.partial(
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tools=render_text_description(tools),
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tool_names=", ".join([t.name for t in tools]),
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)
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llm_with_stop = model.bind(stop=["</tool_input>"])
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agent = {
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"question": lambda x: x["question"],
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"agent_scratchpad": lambda x: format_xml(x['intermediate_steps']),
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"chat_history": lambda x: _format_chat_history(x["chat_history"]),
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} | prompt | llm_with_stop | parse_output
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class AgentInput(BaseModel):
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question: str
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chat_history: List[Tuple[str, str]]
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agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True).with_types(
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input_type=AgentInput
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)
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agent_executor = agent_executor | (lambda x: x["output"])
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