mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
ebf998acb6
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Lance Martin <lance@langchain.dev> Co-authored-by: Jacob Lee <jacoblee93@gmail.com>
30 lines
1.1 KiB
Python
30 lines
1.1 KiB
Python
from langchain.chat_models import ChatAnthropic
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from langchain.prompts import ChatPromptTemplate
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from langchain.schema.runnable import RunnablePassthrough, RunnableMap
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from langchain.schema.output_parser import StrOutputParser
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from langchain.agents import AgentExecutor
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from .retriever import search, RETRIEVER_TOOL_NAME, retriever_description
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from .prompts import retrieval_prompt
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from .agent_scratchpad import format_agent_scratchpad
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from .output_parser import parse_output
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prompt = ChatPromptTemplate.from_messages([
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("user", retrieval_prompt),
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("ai", "{agent_scratchpad}"),
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])
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prompt = prompt.partial(retriever_description=retriever_description)
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model = ChatAnthropic(model="claude-2", temperature=0, max_tokens_to_sample=1000)
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chain = RunnablePassthrough.assign(
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agent_scratchpad=lambda x: format_agent_scratchpad(x['intermediate_steps'])
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) | prompt | model.bind( stop_sequences=['</search_query>']) | StrOutputParser()
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agent_chain = RunnableMap({
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"partial_completion": chain,
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"intermediate_steps": lambda x: x['intermediate_steps']
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}) | parse_output
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executor = AgentExecutor(agent=agent_chain, tools = [search], verbose=True)
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