mirror of
https://github.com/hwchase17/langchain
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64 lines
1.6 KiB
Python
64 lines
1.6 KiB
Python
from langchain_community.chat_models import ChatOpenAI
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain_community.vectorstores import Redis
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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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from rag_redis.config import (
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EMBED_MODEL,
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INDEX_NAME,
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INDEX_SCHEMA,
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REDIS_URL,
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)
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# Make this look better in the docs.
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class Question(BaseModel):
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__root__: str
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# Init Embeddings
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embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
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# Connect to pre-loaded vectorstore
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# run the ingest.py script to populate this
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vectorstore = Redis.from_existing_index(
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embedding=embedder, index_name=INDEX_NAME, schema=INDEX_SCHEMA, redis_url=REDIS_URL
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)
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# TODO allow user to change parameters
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retriever = vectorstore.as_retriever(search_type="mmr")
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# Define our prompt
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template = """
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Use the following pieces of context from Nike's financial 10k filings
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dataset to answer the question. Do not make up an answer if there is no
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context provided to help answer it. Include the 'source' and 'start_index'
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from the metadata included in the context you used to answer the question
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Context:
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---------
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{context}
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---------
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Question: {question}
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---------
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Answer:
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"""
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prompt = ChatPromptTemplate.from_template(template)
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# RAG Chain
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model = ChatOpenAI(model="gpt-3.5-turbo-16k")
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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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).with_types(input_type=Question)
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