mirror of
https://github.com/hwchase17/langchain
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54 lines
1.7 KiB
Python
54 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 import AstraDB
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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.runnables import RunnablePassthrough
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from .populate_vector_store import populate
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# inits
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llm = ChatOpenAI()
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embeddings = OpenAIEmbeddings()
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vector_store = AstraDB(
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embedding=embeddings,
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collection_name="langserve_rag_demo",
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token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
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api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"],
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namespace=os.environ.get("ASTRA_DB_KEYSPACE"),
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)
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retriever = vector_store.as_retriever(search_kwargs={"k": 3})
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# For demo reasons, let's ensure there are rows on the vector store.
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# Please remove this and/or adapt to your use case!
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inserted_lines = populate(vector_store)
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if inserted_lines:
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print(f"Done ({inserted_lines} lines inserted).") # noqa: T201
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entomology_template = """
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You are an expert entomologist, tasked with answering enthusiast biologists' questions.
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You must answer based only on the provided context, do not make up any fact.
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Your answers must be concise and to the point, but strive to provide scientific details
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(such as family, order, Latin names, and so on when appropriate).
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You MUST refuse to answer questions on other topics than entomology,
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as well as questions whose answer is not found in the provided context.
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CONTEXT:
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{context}
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QUESTION: {question}
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YOUR ANSWER:"""
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entomology_prompt = ChatPromptTemplate.from_template(entomology_template)
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chain = (
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{"context": retriever, "question": RunnablePassthrough()}
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| entomology_prompt
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| llm
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| StrOutputParser()
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)
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