mirror of
https://github.com/hwchase17/langchain
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63 lines
1.7 KiB
Python
63 lines
1.7 KiB
Python
import os
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from langchain_community.chat_models import ChatOpenAI
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from langchain_community.embeddings import OpenAIEmbeddings
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from langchain_community.vectorstores.opensearch_vector_search import (
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OpenSearchVectorSearch,
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)
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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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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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OPENSEARCH_URL = os.getenv("OPENSEARCH_URL", "https://localhost:9200")
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OPENSEARCH_USERNAME = os.getenv("OPENSEARCH_USERNAME", "admin")
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OPENSEARCH_PASSWORD = os.getenv("OPENSEARCH_PASSWORD", "admin")
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OPENSEARCH_INDEX_NAME = os.getenv("OPENSEARCH_INDEX_NAME", "langchain-test")
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embedding_function = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
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vector_store = OpenSearchVectorSearch(
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opensearch_url=OPENSEARCH_URL,
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http_auth=(OPENSEARCH_USERNAME, OPENSEARCH_PASSWORD),
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index_name=OPENSEARCH_INDEX_NAME,
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embedding_function=embedding_function,
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verify_certs=False,
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)
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retriever = vector_store.as_retriever()
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def format_docs(docs):
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return "\n\n".join([d.page_content for d in docs])
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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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model = ChatOpenAI(openai_api_key=OPENAI_API_KEY)
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chain = (
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RunnableParallel(
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{"context": retriever | format_docs, "question": RunnablePassthrough()}
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)
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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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