mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
d39b4b61b6
Ran the following bash script for all templates
```bash
#!/bin/bash
set -e
current_dir="$(pwd)"
for directory in */; do
if [ -d "$directory" ]; then
(cd "$directory" && poetry lock --no-update)
fi
done
cd "$current_dir"
```
Co-authored-by: Bagatur <baskaryan@gmail.com>
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|---|---|---|
| .. | ||
| rag_pinecone_multi_query | ||
| tests | ||
| LICENSE | ||
| poetry.lock | ||
| pyproject.toml | ||
| rag_pinecone_multi_query.ipynb | ||
| README.md | ||
RAG Pinecone multi query
This template performs RAG using Pinecone and OpenAI with the multi-query retriever.
This will use an LLM to generate multiple queries from different perspectives for a given user input query.
For each query, it retrieves a set of relevant documents and takes the unique union across all queries for answer synthesis.
Pinecone
This template uses Pinecone as a vectorstore and requires that PINECONE_API_KEY, PINECONE_ENVIRONMENT, and PINECONE_INDEX are set.
LLM
Be sure that OPENAI_API_KEY is set in order to the OpenAI models.
App
Example server.py:
from fastapi import FastAPI
from langserve import add_routes
from rag_pinecone_multi_query.chain import chain
app = FastAPI()
# Edit this to add the chain you want to add
add_routes(app, chain, path="rag_pinecone_multi_query")
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8001)
Run:
python app/server.py
Check endpoint:
http://0.0.0.0:8001/docs
See rag_pinecone_multi_query.ipynb for example usage -
from langserve.client import RemoteRunnable
rag_app_pinecone = RemoteRunnable('http://0.0.0.0:8001/rag_pinecone_multi_query')
rag_app_pinecone.invoke("What are the different types of agent memory")