Spaces:
Sleeping
Sleeping
chore: Reorganize dependencies structure and fix deptry configuration
Browse files- DEVELOPER.md +52 -13
- README.md +22 -5
- pyproject.toml +64 -38
- requirements.txt +2 -1
- uv.lock +80 -470
DEVELOPER.md
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```
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βββ app.py
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βββ
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```
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## π§ Technical Details
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The application uses Chainlit
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### Key Components
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## π Running Locally
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2. Install dependencies:
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```bash
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pip install -
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```
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3.
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```bash
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chainlit run app.py
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```
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##
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```bash
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```
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## π Resources
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- [Chainlit Documentation](https://docs.chainlit.io)
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```
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βββ app.py # Main Chainlit application
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βββ app_simple_rag.py # Simplified RAG application
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βββ pyproject.toml # Project configuration and dependencies
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βββ pstuts_rag/ # Core package
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β βββ pstuts_rag/ # Source code
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β βββ __init__.py
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β βββ datastore.py # Vector database management
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β βββ loader.py # Data loading utilities
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β βββ rag.py # RAG implementation
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β βββ agents.py # Team agent implementation
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β βββ ...
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βββ data/ # Dataset files
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βββ README.md # User documentation
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```
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## π§© Dependency Structure
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Dependencies are organized into logical groups:
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- **Core**: Basic dependencies needed for the RAG system (includes Jupyter support)
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- **Dev**: Development tools (linting, testing, etc.)
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- **Web**: Dependencies for web server functionality
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- **Extras**: Additional optional packages (numpy, ragas, tavily)
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You can install different combinations using pip's extras syntax:
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```bash
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pip install -e ".[dev,web]" # Install core + dev + web dependencies
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```
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## π§ Technical Details
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The application uses LangChain, LangGraph, and Chainlit to create an agentic RAG system:
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### Key Components
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- **DatastoreManager**: Manages the Qdrant vector store and document retrieval
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- **RAGChainFactory**: Creates retrieval-augmented generation chains
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- **PsTutsTeamState**: Manages the state of the agent-based system
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- **Langgraph**: Implements the routing logic between different agents
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## π Running Locally
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2. Install dependencies:
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```bash
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pip install -e ".[dev]" # Install with development tools
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```
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3. Set up API keys:
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```bash
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export OPENAI_API_KEY="your-openai-key"
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export TAVILY_API_KEY="your-tavily-key" # Optional, for web search
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```
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4. Run the application:
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```bash
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chainlit run app.py
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```
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## π§ͺ Code Quality
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To check for dependency issues:
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```bash
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deptry .
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```
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For linting:
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```bash
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black .
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ruff check .
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mypy .
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```
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## π Resources
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- [Chainlit Documentation](https://docs.chainlit.io)
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- [LangChain Documentation](https://python.langchain.com/docs/get_started/introduction)
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- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/)
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README.md
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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# π€
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## π Getting Started
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1. Install dependencies:
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```bash
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```
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2. Run the app:
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## π‘ Features
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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 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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## 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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pyproject.toml
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"
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"
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"fastapi>=0.115.3,<0.116",
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"httpx==0.27.0",
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"ipykernel>=6.29.5",
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"ipywidgets>=8.1.7",
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"jq>=1.8.0",
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"jupyter>=1.1.1",
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"langchain-community>=0.3.23",
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"langchain-experimental>=0.3.4",
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"langchain-openai",
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"langchain-qdrant>=0.2.0",
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"langchain>=0.3.25",
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"langgraph>=0.4.3",
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"
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"
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"python-multipart>=0.0.18,<0.0.19",
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"ragas>=0.2.15",
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"requests>=2.31.0",
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"grandalf>=0.8",
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"jupyter-contrib-nbextensions>=0.7.0",
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authors = [{ name = "Marko Budisic", email = "[email protected]" }]
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"black>=22.0.0",
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"flake8>=4.0.0",
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"mypy>=0.900",
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]
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[tool.ruff]
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line-length = 79
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target-version = ["py311"]
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# [tool.mypy]
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# python_version = "3.13"
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# warn_return_any = true
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# warn_unused_configs = true
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# disallow_untyped_defs = true
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# mypy_path = ["pstuts_rag/pstuts_rag"]
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# namespace_packages = true
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# explicit_package_bases = true
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[tool.mypy]
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ignore_errors = true
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[tool.pylint.TYPECHECK]
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ignored-modules = "pkg_resources" # suppress noisy vendored imports
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[
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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# Core RAG and LLM dependencies
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"langchain>=0.3.25",
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"langchain-core>=0.3.59",
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"langchain-community>=0.3.23",
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"langchain-experimental>=0.3.4",
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"langchain-openai",
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"langchain-qdrant>=0.2.0",
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"langgraph>=0.4.3",
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"qdrant-client>=1.8.0",
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# API integration
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"chainlit==2.0.4",
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"requests>=2.31.0",
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"python-dotenv>=0.9.9",
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# Utilities
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"nest-asyncio>=1.5.6",
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"aiofiles>=23.1.0",
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# Jupyter/notebook dependencies
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"ipykernel>=6.29.5",
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"ipywidgets>=8.1.7",
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"jupyter>=1.1.1",
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"jupyter-contrib-nbextensions>=0.7.0",
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]
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authors = [{ name = "Marko Budisic", email = "[email protected]" }]
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"black>=22.0.0",
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"flake8>=4.0.0",
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"mypy>=0.900",
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"isort>=6.0.1",
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"pylint-venv>=3.0.4",
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"ipdb>=0.13.13",
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"deptry>=0.23.0",
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]
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web = [
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"fastapi>=0.115.3,<0.116",
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"uvicorn>=0.25.0,<0.26.0",
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"python-multipart>=0.0.18,<0.0.19",
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"websockets==14.2",
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"httpx==0.27.0",
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]
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extras = [
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"numpy==2.2.2",
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"ragas>=0.2.15",
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"tavily-python>=0.7.2",
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]
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[tool.ruff]
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line-length = 79
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target-version = ["py311"]
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[tool.mypy]
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ignore_errors = true
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[tool.pylint.TYPECHECK]
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ignored-modules = "pkg_resources" # suppress noisy vendored imports
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[tool.deptry.per_rule_ignores]
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DEP002 = [
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# Jupyter packages
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"ipykernel",
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"ipywidgets",
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"jupyter",
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"jupyter-contrib-nbextensions",
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# Development packages
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"pytest",
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"black",
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"flake8",
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"mypy",
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"isort",
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"pylint-venv",
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"ipdb",
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"deptry",
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# Web packages
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"fastapi",
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"uvicorn",
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"python-multipart",
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"websockets",
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"httpx",
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# Other packages
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"numpy",
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"tavily-python",
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]
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DEP003 = [
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"huggingface_hub",
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"setuptools",
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]
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requirements.txt
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chainlit>=0.7.700
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chainlit>=0.7.700
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-e .
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uv.lock
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[[package]]
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name = "beautifulsoup4"
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version = "4.13.4"
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[[package]]
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name = "chardet"
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version = "5.2.0"
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source = { registry = "https://pypi.org/simple" }
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[[package]]
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name = "charset-normalizer"
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version = "3.4.2"
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[[package]]
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name = "cryptography"
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version = "44.0.3"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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