[Proposal] Multi-Tenant RAG, pgvector Hybrid Search & LLM Streaming Integration

[VimalN2005]

First Check

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Example Code

from app.services.rag import hybrid_search

# Native multi-tenant pgvector hybrid search (HNSW cosine + full-text RRF)
results = hybrid_search(
    session=session,
    user_id=current_user.id,
    query="system architecture",
    top_k=5,
    rerank=True,
)

Description

Proposing to add native multi-tenant RAG, pgvector hybrid search, and real-time LLM token streaming to the official full-stack template.

I have implemented and verified this entire pipeline with 90/90 tests passing:
https://github.com/VimalN2005/full-stack-fastapi-template

Key features added:

1. Native pgvector hybrid search (HNSW cosine distance + PostgreSQL full-text search with Reciprocal Rank Fusion).
2. Two-stage retrieval with cross-encoder re-ranking.
3. Abort-aware SSE token streaming with client disconnect detection (request.is_disconnected()).
4. Real-time monthly AI token metering & HTTP 429 budget quota guardrails.
5. Persistent multi-turn conversation memory with chat sessions.
6. Async background document ingestion via FastAPI BackgroundTasks with live status polling.
7. React 19 + Tailwind + shadcn Knowledge Base and streaming AI Chat UI.

Operating System

Windows

Operating System Details

Windows 11

Project Version

latest (master)

Python Version

3.10+

Additional Context

All 90/90 pytest tests pass cleanly with zero external API key requirements (deterministic offline embedding fallback included).

Full working fork & architecture documentation:
https://github.com/VimalN2005/full-stack-fastapi-template