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Running on Zero
| title: Customer Support RAG Assistant | |
| emoji: 💬 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| app_file: app.py | |
| # Customer Support RAG Assistant | |
| A Gradio demo for a customer-support Retrieval-Augmented Generation (RAG) pipeline. | |
| ## Components | |
| - Sentence Transformers for embeddings | |
| - FAISS for FAQ retrieval | |
| - LangGraph for the workflow | |
| - Groq API for hosted language-model generation | |
| - Gradio for the web interface | |
| ## Configure the Groq API key | |
| In the Space, open **Settings → Secrets** and add: | |
| - Name: `GROQ_API_KEY` | |
| - Value: your Groq API key | |
| Optionally add `GROQ_MODEL` as a Space variable if you want to select a different model enabled for your Groq account. | |
| Do not put API keys in source code or upload them in `.env`. | |
| ## Notes | |
| The first question may take longer because the embedding model and FAISS index are initialized on first use. The language model itself is accessed through Groq. | |