mirror of
https://github.com/hwchase17/langchain
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56 lines
1.4 KiB
Python
56 lines
1.4 KiB
Python
import os
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from langchain.embeddings import BedrockEmbeddings
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from langchain.llms.bedrock import Bedrock
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from langchain.prompts import ChatPromptTemplate
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from langchain.pydantic_v1 import BaseModel
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from langchain.schema.output_parser import StrOutputParser
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from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
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from langchain.vectorstores import FAISS
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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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# 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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bedrock_embeddings = BedrockEmbeddings(model_id="amazon.titan-embed-text-v1")
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# Add to vectorDB
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vectorstore = FAISS.from_texts(
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["harrison worked at kensho"], embedding=bedrock_embeddings
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)
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retriever = vectorstore.as_retriever()
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# Get retriever from vectorstore
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retriever = vectorstore.as_retriever()
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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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