| --- |
| title: BuildSmall KnowledgeHub |
| emoji: 📚 |
| colorFrom: blue |
| colorTo: purple |
| sdk: gradio |
| app_file: app.py |
| pinned: true |
| license: mit |
| short_description: AI knowledge hub for groups, powered by Nvidia |
|
|
| tags: |
| - track:backyard |
| - sponsor:openai |
| - sponsor:nvidia |
| - achievement:offbrand |
| - achievement:sharing |
| - achievement:fieldnotes |
|
|
| --- |
| |
| # BuildSmall KnowledgeHub - https://huggingface.co/pkheria |
|
|
| BuildSmall KnowledgeHub is a modular Gradio app for loading knowledge from: |
|
|
| - Medium article links through Freedium |
| - arXiv links or IDs |
| - PDF documents |
|
|
| It extracts text, captures Medium image references/captions when available, chunks the content, 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. |
|
|
| ## 🔗 Resources & Links |
|
|
| - **Demo Video:** [Watch the Product Demo]([YOUR_DEMO_VIDEO_LINK_HERE](https://youtu.be/aDlKNW10pnw)) |
| - **Blog Post:** [Read the Full Write-up](https://huggingface.co/blog/pkheria/knowledgemesh) |
| - **Social Post :** [Linkedin Post](https://www.linkedin.com/posts/piyushkheria7_buildsmall-generativeai-rag-share-7472326307721437184-pFrz/) |
| ## 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 |
|
|
| 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. |
|
|
| ## Qdrant Collection Name |
|
|
| The Ingest and Retrieve tabs each have their own collection-name field. Set both to the same Qdrant collection when you want to search what you just ingested. The fields are intentionally not auto-synced because auto-sync can cause continuous refreshes in hosted Gradio Spaces. |
|
|
| ## Setup |
|
|
| ```bash |
| python3 -m venv .venv |
| source .venv/bin/activate |
| pip install -r requirements.txt |
| cp .env.example .env |
| ``` |
|
|
| Add `NVIDIA_API_KEY` to `.env` for chat completions. Start Qdrant locally or point `QDRANT_URL` to your hosted instance. |
|
|
| The default model split is: |
|
|
| - Local parsing model: `Qwen/Qwen2-VL-2B-Instruct` |
| - Local embedding model: `nvidia/llama-nemotron-colembed-vl-3b-v2` |
| - NVIDIA API chat model: `nvidia/nvidia-nemotron-nano-9b-v2` |
|
|
| ## Run |
|
|
| ```bash |
| python app.py |
| ``` |
|
|
| Open the local Gradio URL printed in the terminal, usually `http://127.0.0.1:7860`. |
|
|
| The app binds to `0.0.0.0:7860`, which is suitable for Hugging Face Spaces and container deployments. |
|
|
