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mbudisic/PsTuts-RAG

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App README

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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

🤖 PsTuts RAG System

An agentic RAG system for the PsTuts dataset that provides AI-powered answers to Adobe Photoshop questions using video tutorial transcripts.

🎨 Visual Theme

The application features a beautiful sepia-toned color scheme that gives it a vintage, artistic feel perfect for Adobe Photoshop tutorials:

—🌅 Light Theme (default): Warm cream and tan colors reminiscent of old photography
—🌙 Dark Theme: Rich coffee and amber tones for comfortable nighttime usage
—🖼️ Sepia Filter: Subtle sepia treatment on images for visual consistency
—⚡ Smooth Transitions: Elegant animations when switching between themes

Users can toggle between light and dark variants using the theme switcher in the interface.

🤗 HuggingFace is updated from Github.

🚀 Getting Started

1.Install dependencies:

bash

# Basic installation (includes Jupyter support)
pip install -e .

# With development tools
pip install -e ".[dev]"

# With web server components
pip install -e ".[web]"

# With additional extras (numpy, ragas, tavily)
pip install -e ".[extras]"

# Full installation with all features
pip install -e ".[dev,web,extras]"

1.Run the app:

bash

chainlit run app.py

1.Open your browser and navigate to http://localhost:8000

💡 Features

—Retrieval-augmented generation (RAG) for Photoshop tutorials
—Multi-agent system with team supervisor
—Web search integration via Tavily
—Semantic chunking for better context retrieval
—Interactive chat interface through Chainlit
—🌐 Web Search Permission Control: You can now set whether the AI is allowed to perform web searches directly from the chat settings (top right ⚙️). Choose between "Ask every time", "Always allow", or "Never allow" for full control!

⚙️ Configuration Options

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:

Env Var	Description
`EVA_WORKFLOW_NAME`	🏷️ Name of the EVA workflow. Default: `EVA_workflow`
`EVA_LOG_LEVEL`	🪵 Logging level for EVA. Default: `INFO`
`TRANSCRIPT_GLOB`	📄 Glob pattern for transcript JSON files (supports multiple files separated by `:`). Default: `data/test.json`
`EMBEDDING_MODEL`	🧊 Name of the embedding model to use (default: custom fine-tuned snowflake model). Default: `mbudisic/snowflake-arctic-embed-s-ft-pstuts`
`EVA_STRIP_THINK`	💭 If set (present in env), strips 'think' steps from EVA output.
`EMBEDDING_API`	🔌 API provider for embeddings (`OPENAI`, `HUGGINGFACE`, or `OLLAMA`). Default: `HUGGINGFACE`
`LLM_API`	🤖 API provider for LLM (`OPENAI`, `HUGGINGFACE`, or `OLLAMA`). Default: `OLLAMA`
`MAX_RESEARCH_LOOPS`	🔁 Maximum number of research loops to perform. Default: `3`
`LLM_TOOL_MODEL`	🛠️ Name of the LLM model to use for tool calling. Default: `smollm2:1.7b-instruct-q2_K`
`N_CONTEXT_DOCS`	📚 Number of context documents to retrieve for RAG. Default: `2`
`EVA_SEARCH_PERMISSION`	🌐 Permission for search (`yes`, `no`, or `ask`). Default: `no`. Can also be set in the chat UI!
`EVA_DB_PERSIST`	💾 Path or flag for DB persistence. Default: unset
`EVA_REINITIALIZE`	🔄 If true, reinitializes EVA DB. Default: `False`
`THREAD_ID`	🧵 Thread ID for the current session. Default: unset

Set these variables to control model selection, logging, search permissions, and more. For advanced usage, see the developer documentation.