Pinecone Integration

Wire Pinecone in as a third vector store option alongside pgvector/Qdrant.

FastAPI AI Kit ships pgvector and Qdrant adapters behind a common RAG interface. Pinecone isn't a pre-wired option, but implementing a third adapter against the same interface is a well-defined, contained change if you're already committed to Pinecone.

Setup in 4 steps

1Add PINECONE_API_KEY and PINECONE_INDEX to your .env
2Implement a PineconeStore class matching the kit's VectorStore interface (upsert, query, delete)
3Register it in the RAG factory next to the pgvector/Qdrant adapters
4Point VECTOR_STORE=pinecone once the adapter is registered

What's included

Reuses the same rag.ingest() / rag.query() call sites as pgvector/Qdrant
Namespace-per-collection maps cleanly onto Pinecone's namespace model
Serverless Pinecone indexes avoid managing your own vector infra
Good option if you're already standardized on Pinecone elsewhere in your stack
Keep pgvector as a local/dev fallback while Pinecone serves production

Code example

example.py

# app/rag/stores/pinecone_store.py
from pinecone import Pinecone

class PineconeStore:
    def __init__(self, api_key: str, index_name: str):
        self.index = Pinecone(api_key=api_key).Index(index_name)

    async def upsert(self, collection: str, chunks: list[dict]):
        self.index.upsert(
            vectors=[(c["id"], c["embedding"], c["metadata"]) for c in chunks],
            namespace=collection,
        )

    async def query(self, collection: str, embedding: list[float], top_k: int):
        return self.index.query(
            vector=embedding, top_k=top_k, namespace=collection, include_metadata=True,
        )

Full documentation: /docs/rag-pipeline

Pinecone integration pre-wired and ready.

FastAPI AI Kit ships with Pinecone configured out of the box. No manual setup required.

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