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[DOCS] v1.5 and v2.0 roadmaps, restore known limitations
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README.md
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title: rag-uigreenmetric
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emoji: 📚
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colorFrom: green
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colorTo: blue
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sdk: gradio
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sdk_version: "6.14.0"
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app_file: app.py
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pinned: false
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# 📚 RAG — Multi Source Information Retrieval
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> A hybrid Retrieval-Augmented Generation (RAG) project built to learn and implement context injection using the DeepSeek API.
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##
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| # | Feature | Description |
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| 1 | Show retrieved context | Collapsible view of the chunks that fed each answer — full transparency into what the system based its response on |
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| 2 | Source citation | Display which data source answered the query (PDF guidelines, Appendix 1, etc.) |
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| 3 | Token cost per query | Show exact token usage and estimated cost under each answer |
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| 4 | Pipeline timing | Display response generation time to set latency expectations |
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| 5 | Route badge color-coding | Green/blue/purple/gray badges for pdf/csv/both/none sources |
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| 6 | Welcome + example chips | Clickable sample questions on empty chat for better first impression |
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| 7 | Feedback thumbs | ±1 per answer, logged for future evaluation |
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| 8 | Markdown rendering | Properly formatted lists, tables, and bold text in answers |
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| 9 | Copy answer button | One-click clipboard copy on each response |
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| 10 | Dark mode toggle | OS preference detection + manual toggle |
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---
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## ⚠️ Known Limitations
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# 📚 RAG — Multi Source Information Retrieval
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> A hybrid Retrieval-Augmented Generation (RAG) project built to learn and implement context injection using the DeepSeek API.
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---
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## 🗺️ v1.5 Roadmap
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- [ ] Show retrieved context (collapsible view of chunks used per answer)
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- [ ] Source citation (display which data source answered the query)
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- [ ] Token cost per query (exact usage and estimated cost under each answer)
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- [ ] Pipeline timing (response generation time displayed)
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- [ ] Route badge color-coding (green/blue/purple/gray for pdf/csv/both/none)
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- [ ] Markdown rendering (properly formatted lists, tables, bold text)
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- [ ] Copy answer button (clipboard copy on each response)
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- [ ] Welcome + example chips (clickable sample questions on empty chat)
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- [ ] Feedback thumbs (±1 per answer, logged for future evaluation)
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- [ ] Dark mode toggle (OS preference detection + manual toggle)
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## 🚀 v2.0 Roadmap
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- [ ] FastAPI backend (`POST /ask`, `GET /budget`, `GET /health`)
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- [ ] Streaming generator (DeepSeek `stream=True` → `StreamingResponse`)
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- [ ] Conversation history (generator-only, router stays single-query)
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- [ ] Dockerize backend (`Dockerfile` + `docker-compose.yml`)
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- [ ] Svelte chat UI with Vercel AI SDK (replaces Gradio)
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- [ ] Strip Gradio (`app.py`, `gradio` from requirements)
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---
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## ⚠️ Known Limitations
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