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Chainlit.md updated with README.md info
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chainlit.md
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- **Discord Community:** Join our friendly [Chainlit Discord](https://discord.gg/k73SQ3FyUh) to ask questions, share your projects, and connect with other developers! π¬
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---
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title: PsTuts RAG
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emoji: π»
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colorFrom: yellow
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colorTo: indigo
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sdk: docker
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pinned: false
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license: mit
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short_description: Agentic RAG that interrogates the PsTuts dataset.
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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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# π€ PsTuts RAG System
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An agentic RAG system for the PsTuts dataset that provides AI-powered answers to Adobe Photoshop questions using video tutorial transcripts.
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## π¨ Visual Theme
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The application features a beautiful **sepia-toned color scheme** that gives it a vintage, artistic feel perfect for Adobe Photoshop tutorials:
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- π
**Light Theme** (default): Warm cream and tan colors reminiscent of old photography
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- π **Dark Theme**: Rich coffee and amber tones for comfortable nighttime usage
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- πΌοΈ **Sepia Filter**: Subtle sepia treatment on images for visual consistency
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- β‘ **Smooth Transitions**: Elegant animations when switching between themes
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Users can toggle between light and dark variants using the theme switcher in the interface.
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## π Getting Started
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1. Install dependencies:
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```bash
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# Basic installation (includes Jupyter support)
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pip install -e .
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# With development tools
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pip install -e ".[dev]"
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# With web server components
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pip install -e ".[web]"
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# With additional extras (numpy, ragas, tavily)
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pip install -e ".[extras]"
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# Full installation with all features
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pip install -e ".[dev,web,extras]"
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```
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2. Run the app:
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```bash
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chainlit run app.py
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```
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3. Open your browser and navigate to `http://localhost:8000`
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## π‘ Features
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- Retrieval-augmented generation (RAG) for Photoshop tutorials
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- Multi-agent system with team supervisor
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- Web search integration via Tavily
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- Semantic chunking for better context retrieval
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- Interactive chat interface through Chainlit
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## βοΈ Configuration Options
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You can customize the behavior of PsTuts RAG using environment variables. Set these in your shell, `.env` file, or deployment environment. Here are the available options:
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| Env Var | Description |
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|---------|-------------|
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| `EVA_WORKFLOW_NAME` | π·οΈ Name of the EVA workflow. Default: `EVA_workflow` |
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| `EVA_LOG_LEVEL` | πͺ΅ Logging level for EVA. Default: `INFO` |
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| `TRANSCRIPT_GLOB` | π Glob pattern for transcript JSON files (supports multiple files separated by `:`). Default: `data/test.json` |
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| `EMBEDDING_MODEL` | π§ Name of the embedding model to use (default: custom fine-tuned snowflake model). Default: `mbudisic/snowflake-arctic-embed-s-ft-pstuts` |
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| `EVA_STRIP_THINK` | π If set (present in env), strips 'think' steps from EVA output. |
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| `EMBEDDING_API` | π API provider for embeddings (`OPENAI`, `HUGGINGFACE`, or `OLLAMA`). Default: `HUGGINGFACE` |
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| `LLM_API` | π€ API provider for LLM (`OPENAI`, `HUGGINGFACE`, or `OLLAMA`). Default: `OLLAMA` |
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| `MAX_RESEARCH_LOOPS` | π Maximum number of research loops to perform. Default: `3` |
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| `LLM_TOOL_MODEL` | π οΈ Name of the LLM model to use for tool calling. Default: `smollm2:1.7b-instruct-q2_K` |
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| `N_CONTEXT_DOCS` | π Number of context documents to retrieve for RAG. Default: `2` |
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| `EVA_SEARCH_PERMISSION` | π Permission for search (`yes`, `no`, or `ask`). Default: `no` |
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| `EVA_DB_PERSIST` | πΎ Path or flag for DB persistence. Default: unset |
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| `EVA_REINITIALIZE` | π If true, reinitializes EVA DB. Default: `False` |
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| `THREAD_ID` | π§΅ Thread ID for the current session. Default: unset |
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Set these variables to control model selection, logging, search permissions, and more. For advanced usage, see the developer documentation.
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