64 lines
2.8 KiB
Markdown
64 lines
2.8 KiB
Markdown
# Kirana AI — RAG-Powered Brand Product Search Engine
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This is the v2.0 upgrade of the Indian FMCG product catalog project: the
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same discovery/enrichment pipeline (Ollama + pgvector + S3 image
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storage), now with a **Retrieval-Augmented Generation (RAG) layer** and a
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**React UI** in place of the old Streamlit app.
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Two independent pieces:
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- **`backend/`** - FastAPI service. Browse/search/chat endpoints over a
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pgvector-backed catalog; a RAG chat endpoint that retrieves relevant
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products and asks a local Ollama model to answer grounded in them.
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- **`frontend/`** - React + Vite single-page app: product search/browse
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grid, a conversational "Ask AI" panel with source citations, and an
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admin page to ingest new brands.
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See **`docs/`** for the full architecture write-up, setup guide, and API
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reference — including **`docs/SKU_RESOLVER.md`** for the Product
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Resolver library (`POST /api/resolve`), which accepts any BigBasket
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product ID, Amazon ASIN, or Internal SKU, resolves it against the
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database or the matching external source, and stores it for next time.
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The two component READMEs (`backend/README.md`,
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`frontend/README.md`) are quick local references once you've read the
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main docs once.
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## Fastest path to a working demo
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```bash
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# 1. Backend
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cd backend
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python3 -m venv .venv && source .venv/bin/activate # Windows: .venv\Scripts\activate
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pip install -r requirements.txt
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cp .env.example .env # edit DB_PASSWORD, see docs
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docker compose -f ../docker-compose.yml up -d # local Postgres+pgvector (skip if you already have one)
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python scripts/seed_sample_data.py # loads bundled sample catalogs - no LLM needed
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ollama pull qwen2.5:1.5b # one-time
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uvicorn app.main:app --reload --port 8000
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# 2. Frontend (new terminal)
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cd frontend
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npm install
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npm run dev
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```
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Open http://localhost:5173, pick a brand, try the search bar, then switch
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to **Ask AI** and ask something like "recommend a low sugar biscuit".
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## Why this exists (what changed from v1.0)
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The original project (`Project_LLM`) could discover, enrich, and store
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products with embeddings in pgvector - but nothing ever read those
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embeddings back out. There was no similarity-search function, no API
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layer (`app/api/` was an empty folder), and the UI was a Streamlit app
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that only ever queried the catalog by exact brand/category, never by
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meaning. This project adds the missing retrieval layer
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(`vector_store.semantic_search`), a RAG orchestration service
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(`rag_service.py`), a full FastAPI surface, and a React UI to use it.
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It also fixes a real security issue: `app/infrastructure/settings.py`
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used to hard-code a live database host/port/password as Python literal
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fallbacks. Every credential now comes only from environment
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variables/`.env` - see `backend/app/infrastructure/settings.py` for
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details, and `docs/` for the full write-up.
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