langchain/templates/rag-redis/rag_redis/chain.py

from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Redis
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnableParallel, RunnablePassthrough

from rag_redis.config import (
    EMBED_MODEL,
    INDEX_NAME,
    INDEX_SCHEMA,
    REDIS_URL,
)


# Make this look better in the docs.
class Question(BaseModel):
    __root__: str


# Init Embeddings
embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL)

# Connect to pre-loaded vectorstore
# run the ingest.py script to populate this
vectorstore = Redis.from_existing_index(
    embedding=embedder, index_name=INDEX_NAME, schema=INDEX_SCHEMA, redis_url=REDIS_URL
)
# TODO allow user to change parameters
retriever = vectorstore.as_retriever(search_type="mmr")


# Define our prompt
template = """
Use the following pieces of context from Nike's financial 10k filings
dataset to answer the question. Do not make up an answer if there is no
context provided to help answer it. Include the 'source' and 'start_index'
from the metadata included in the context you used to answer the question

Context:
---------
{context}

---------
Question: {question}
---------

Answer:
"""


prompt = ChatPromptTemplate.from_template(template)


# RAG Chain
model = ChatOpenAI(model_name="gpt-3.5-turbo-16k")
chain = (
    RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
    | prompt
    | model
    | StrOutputParser()
).with_types(input_type=Question)