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

from langchain.chat_models import ChatOpenAI
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.prompts import ChatPromptTemplate
from langchain.pydantic_v1 import BaseModel
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
from langchain.vectorstores import Redis

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)