mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
import os
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from langchain.retrievers import AmazonKendraRetriever
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from langchain_community.llms.bedrock import Bedrock
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough
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# Get region and profile from env
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region = os.environ.get("AWS_DEFAULT_REGION", "us-east-1")
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profile = os.environ.get("AWS_PROFILE", "default")
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kendra_index = os.environ.get("KENDRA_INDEX_ID", None)
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if not kendra_index:
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raise ValueError(
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"No value provided in env variable 'KENDRA_INDEX_ID'. "
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"A Kendra index is required to run this application."
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)
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# Set LLM and embeddings
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model = Bedrock(
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model_id="anthropic.claude-v2",
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region_name=region,
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credentials_profile_name=profile,
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model_kwargs={"max_tokens_to_sample": 200},
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)
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# Create Kendra retriever
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retriever = AmazonKendraRetriever(index_id=kendra_index, top_k=5, region_name=region)
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# RAG prompt
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template = """Answer the question based only on the following context:
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{context}
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Question: {question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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# RAG
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chain = (
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RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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| prompt
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| model
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| StrOutputParser()
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)
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# Add typing for input
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class Question(BaseModel):
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__root__: str
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chain = chain.with_types(input_type=Question)
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