mirror of
https://github.com/hwchase17/langchain
synced 2024-11-20 03:25:56 +00:00
70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
import os
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import cassio
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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 Cassandra
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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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use_cassandra = int(os.environ.get("USE_CASSANDRA_CLUSTER", "0"))
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if use_cassandra:
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from .cassandra_cluster_init import get_cassandra_connection
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session, keyspace = get_cassandra_connection()
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cassio.init(
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session=session,
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keyspace=keyspace,
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)
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else:
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cassio.init(
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token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
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database_id=os.environ["ASTRA_DB_ID"],
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keyspace=os.environ.get("ASTRA_DB_KEYSPACE"),
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)
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# inits
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llm = ChatOpenAI()
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embeddings = OpenAIEmbeddings()
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vector_store = Cassandra(
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session=None,
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keyspace=None,
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embedding=embeddings,
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table_name="langserve_rag_demo",
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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).")
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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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