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short_description: Minimal RAG API with MiniLM embeddings and FAISS
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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short_description: Minimal RAG API with MiniLM embeddings and FAISS
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---
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# RAG API (Minimal) β MiniLM + FAISS (Gradio)
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Minimal Retrieval-Augmented Generation (RAG) service built with:
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- **Sentence-Transformers MiniLM** for embeddings
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- **FAISS** for vector search (cosine similarity)
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- **Gradio** for both UI and API exposure
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---
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## Features
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- Ingest documents (one per line) with configurable chunk size/overlap
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- Query top-K relevant chunks with similarity search
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- Get concise answers composed from retrieved context
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- Reset index at any time
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- Call endpoints via **UI or API** (`/api/ingest`, `/api/answer`, `/api/reset`)
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---
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## Quick Start
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1. **Load sample docs β Ingest β Ask a query** using the Gradio UI.
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2. Programmatic access:
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## ```bash
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## Ingest
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curl -s -X POST https://<your-space>.hf.space/api/ingest \
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-H "content-type: application/json" \
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-d '{"data": ["PySpark scales ETL across clusters.\nFAISS powers fast vector similarity search used in retrieval.", 256, 32]}'
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# Answer
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curl -s -X POST https://<your-space>.hf.space/api/answer \
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-H "content-type: application/json" \
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-d '{"data": ["What does FAISS do?", 5, 1000]}'
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## Python Client
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from gradio_client import Client
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client = Client("https://<your-space>.hf.space")
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status, size = client.predict("FAISS powers fast vector search.", 256, 32, api_name="/ingest")
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res = client.predict("What does FAISS do?", 5, 1000, api_name="/answer")
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print(res["answer"])
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## Tech Stack
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- Embeddings: sentence-transformers/all-MiniLM-L6-v2 (384-dim)
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- Vector DB: FAISS (FlatIP index, normalized vectors)
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- UI & API: Gradio Blocks
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## Notes
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- In-memory index only; resets when Space sleeps.
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- For persistence, extend with save/load to ./data/.
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- Demo-focused β fast, light, minimal surface.
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## Author/Developer: Naga Adithya Kaushik (GenAIDevTOProd)
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## Utilized AI CoPilot for development purpose : Yes (minimal) - Debug, test cases, experimentation only
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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