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https://github.com/hwchase17/langchain
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…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```
54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
from typing import List, Tuple
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from langchain.agents import AgentExecutor
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from langchain.agents.format_scratchpad import format_xml
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from langchain.schema import AIMessage, HumanMessage
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from langchain.tools import DuckDuckGoSearchRun
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from langchain.tools.render import render_text_description
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from langchain_community.chat_models import ChatAnthropic
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from langchain_core.pydantic_v1 import BaseModel, Field
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from xml_agent.prompts import conversational_prompt, parse_output
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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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tools = [DuckDuckGoSearchRun()]
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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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{
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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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}
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| prompt
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| llm_with_stop
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| parse_output
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)
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class AgentInput(BaseModel):
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question: str
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chat_history: List[Tuple[str, str]] = Field(..., extra={"widget": {"type": "chat"}})
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agent_executor = AgentExecutor(
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agent=agent, tools=tools, verbose=True, handle_parsing_errors=True
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).with_types(input_type=AgentInput)
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agent_executor = agent_executor | (lambda x: x["output"])
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