readME upadted

README.md

@@ -1,12 +1,97 @@
-# KnowledgeHub Ingestor
-
-KnowledgeHub Ingestor is a modular Gradio app for loading knowledge from:
+---
+title: BuildSmall KnowledgeHub
+emoji: 📚
+colorFrom: cyan
+colorTo: lime
+sdk: gradio
+sdk_version: 6.17.3
+app_file: app.py
+pinned: false
+license: mit
+short_description: Ingest PDFs, arXiv papers, and YouTube transcripts into Qdrant with NVIDIA-powered RAG.
+---
+
+# BuildSmall KnowledgeHub
+
+BuildSmall KnowledgeHub is a modular Gradio app for loading knowledge from:
 
 - YouTube links with public transcripts/captions
 - arXiv links or IDs
 - PDF documents
 
-It extracts text, chunks it, embeds chunks locally with your embedding model, and uploads vectors into Qdrant for retrieval. The answer generation step uses NVIDIA's OpenAI-compatible chat API.
+It extracts text, chunks it, embeds chunks locally with the configured NVIDIA Nemotron embedding model, uploads vectors into Qdrant, and generates grounded answers with NVIDIA's OpenAI-compatible chat API.
+
+## NVIDIA Usage
+
+This project explicitly uses NVIDIA in two places:
+
+- Local retrieval embedding model: `nvidia/llama-nemotron-colembed-vl-3b-v2`
+- NVIDIA API chat model: `nvidia/nvidia-nemotron-nano-9b-v2`
+
+The chat client calls:
+
+```text
+https://integrate.api.nvidia.com/v1
+```
+
+You must provide `NVIDIA_API_KEY` as a Hugging Face Space secret or in your local `.env`.
+
+## Hugging Face Spaces Deployment
+
+Create a new Hugging Face Space with:
+
+- SDK: `Gradio`
+- App file: `app.py`
+- Hardware: `ZeroGPU` if available, otherwise CPU/GPU according to your quota
+- Python dependencies: installed from `requirements.txt`
+
+Push this repository to the Space repo:
+
+```bash
+git remote add space https://huggingface.co/spaces/<your-username>/<your-space-name>
+git push space main
+```
+
+For ZeroGPU Spaces, add these Space variables:
+
+```bash
+ENABLE_ZEROGPU=true
+EMBEDDING_DEVICE=cuda
+ZEROGPU_DURATION_SECONDS=180
+```
+
+For local Apple Silicon development, keep:
+
+```bash
+EMBEDDING_DEVICE=cpu
+```
+
+The Gradio ingest, search, and answer callbacks are decorated with `spaces.GPU` when running on Hugging Face Spaces. Locally, the decorator becomes a no-op.
+
+## Hugging Face Secrets
+
+Add these in your Space settings under **Settings → Variables and secrets**.
+
+Required secrets:
+
+```bash
+NVIDIA_API_KEY=<your-nvidia-api-key>
+QDRANT_URL=<your-qdrant-url>
+QDRANT_API_KEY=<your-qdrant-api-key>
+```
+
+Optional variables:
+
+```bash
+QDRANT_COLLECTION_NAME=knowledge_base
+NVIDIA_API_URL=https://integrate.api.nvidia.com/v1
+NVIDIA_CHAT_MODEL=nvidia/nvidia-nemotron-nano-9b-v2
+NEMOTRON_EMBED_MODEL=nvidia/llama-nemotron-colembed-vl-3b-v2
+NEMOTRON_PARSE_MODEL=Qwen/Qwen2-VL-2B-Instruct
+HF_TOKEN=<token-if-needed-for-gated-model-downloads>
+```
+
+Use a hosted Qdrant instance for Hugging Face Spaces. `localhost:6333` only works for local development.
 
 ## Setup
 
@@ -35,15 +120,6 @@ Open the local Gradio URL printed in the terminal, usually `http://127.0.0.1:786
 
 The app binds to `0.0.0.0:7860`, which is suitable for Hugging Face Spaces and container deployments.
 
-For Hugging Face ZeroGPU Spaces, set:
-
-```bash
-ENABLE_ZEROGPU=true
-EMBEDDING_DEVICE=cuda
-```
-
-The Gradio ingest/search/answer callbacks are decorated with `spaces.GPU` when running on Spaces. Locally, the decorator becomes a no-op.
-
 ## Project Structure
 
 ```text