IBM Cloud RAG MVP

Streamlit single-page app with RAG pipeline using watsonx.ai (Granite-13B for answers, ibm/granite-embedding-30m-english for embeddings), FAISS vector search, and IBM Cloud Object Storage.

Prerequisites

• Python 3.11+
• IBM Cloud account with watsonx.ai, COS, watsonx.data (Milvus), Code Engine
• uv package manager

Environment

Set required variables via .env or environment. See app/config.py and the project plan for details.

Run locally

uv run streamlit run app/main.py --server.port 8080 --server.address 0.0.0.0

Container

docker build -t ibm-rag-mvp:latest .

Deploy (IBM Cloud Code Engine)

• Push image to IBM Cloud Container Registry (us.icr.io/<namespace>/ibm-rag-mvp:latest).
• Create a Code Engine project and app, set env vars and bind secrets.
• Expose port 8080.

Notes

• FAISS index and metadata are persisted to local files (see FAISS_INDEX_PATH, FAISS_META_PATH). For persistence across deploys, back them up to COS.
• COS can use IAM (no HMAC). Presigned URLs require HMAC; IAM mode uses internal access.
• Keep services in the same region for best latency.