langchain/templates/rag-pinecone/README.md
Albert Gil López 1710423de3
docs: correct path in readme (#22383)
Description: Fix incorrect path in README instructions.
Issue: N/A
Dependencies: None
Twitter handle: @jddam

---------

Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
2024-06-10 17:47:39 -07:00

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Markdown

# rag-pinecone
This template performs RAG using Pinecone and OpenAI.
## Environment Setup
This template uses Pinecone as a vectorstore and requires that `PINECONE_API_KEY`, `PINECONE_ENVIRONMENT`, and `PINECONE_INDEX` are set.
Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
## Usage
To use this package, you should first have the LangChain CLI installed:
```shell
pip install -U langchain-cli
```
To create a new LangChain project and install this as the only package, you can do:
```shell
langchain app new my-app --package rag-pinecone
```
If you want to add this to an existing project, you can just run:
```shell
langchain app add rag-pinecone
```
And add the following code to your `server.py` file:
```python
from rag_pinecone import chain as rag_pinecone_chain
# Be careful with this, in the console, when you create the project add_routes(app, rag_pinecone_chain, path="\rag-pinecone") appears with a backslash, the correct route is "/rag-pinecone"
add_routes(app, rag_pinecone_chain, path="/rag-pinecone")
```
(Optional) Let's now configure LangSmith.
LangSmith will help us trace, monitor and debug LangChain applications.
You can sign up for LangSmith [here](https://smith.langchain.com/).
If you don't have access, you can skip this section
```shell
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
```
If you are inside this directory, then you can spin up a LangServe instance directly by:
```shell
langchain serve
```
This will start the FastAPI app with a server is running locally at
[http://localhost:8000](http://localhost:8000)
We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
We can access the playground at [http://127.0.0.1:8000/rag-pinecone/playground](http://127.0.0.1:8000/rag-pinecone/playground)
We can access the template from code with:
```python
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-pinecone")
```