## Pinecone-Serverless

We see demand for tools that bridge the gap between prototyping and production. With usage based pricing and support for unlimited scaling, Pinecone Serverless helps to address pain points with vectorstore productionization that we've seen from the community. This repo builds a RAG chain that connects to Pinecone Serverless index using LCEL, turns it into an a web service with LangServe, uses Hosted LangServe deploy it, and uses LangSmith to monitor the input / outputs.  

![chain](https://github.com/langchain-ai/pinecone-serverless/assets/122662504/454266ba-727c-4ce0-ae56-7d004c0fb5d4)

### Index

Follow [instructions from Pinecone](https://www.pinecone.io/blog/serverless/) on setting up your serverless index.

### API keys

Ensure these are set:

* PINECONE_API_KEY
* PINECONE_ENVIRONMENT
* PINECONE_INDEX_NAME 
* OPENAI_API_KEY

Note: the choice of embedding model may require additional API keys, such as:
* COHERE_API_KEY

### Notebook

For prototyping:
```
poetry run jupyter notebook
```

### Deployment

This repo was created by following these steps:

**(1) Create a LangChain app.**

Run:
```
langchain app new .  
```

This creates two folders:
```
app: This is where LangServe code will live
packages: This is where your chains or agents will live
```

It also creates:
```
Dockerfile: App configurations
pyproject.toml: Project configurations
```

Add your app dependencies to `pyproject.toml` and `poetry.lock` to support Pinecone serverless:
```
poetry add pinecone-client==3.0.0.dev8
poetry add langchain-community==0.0.12
poetry add cohere
poetry add openai
poetry add jupyter
```

Update enviorment based on the updated lock file:
```
poetry install
```

**(2) Add your runnable (RAG app)**

Create a file, `chain.py` with a runnable named `chain` that you want to execute. 

This is our RAG logic (e.g., that we prototyped in our notebook).

Add `chain.py` to `app` directory.

Import the LCEL object in `server.py`:
```
from app.chain import chain as pinecone_wiki_chain
add_routes(app, pinecone_wiki_chain, path="/pinecone-wikipedia")
```

Run locally
```
poetry run langchain serve
```

**(3) Deploy it with hosted LangServe**

Go to your LangSmith console.

Select `New Deployment`.

Specify this Github url.

Add the abovementioned API keys as secrets.