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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 ```
39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
from langchain.agents import AgentExecutor
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from langchain.agents.format_scratchpad import format_xml
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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.llms import OpenAI
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from langchain_core.pydantic_v1 import BaseModel
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from solo_performance_prompting_agent.parser import parse_output
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from solo_performance_prompting_agent.prompts import conversational_prompt
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_model = OpenAI()
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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>", "</final_answer>"])
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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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}
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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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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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