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qualityFacilitates searching and accessing programming resources across platforms like Stack Overflow, MDN, GitHub, npm, and PyPI, aiding LLMs in finding code examples and documentation.
Last updated -
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AGPL 3.0
Uses OpenAI's API for generating embeddings to power the semantic search functionality over PyTorch documentation
Provides semantic search capabilities over PyTorch documentation, allowing users to find relevant documentation, APIs, code examples, and error messages using vector embeddings and semantic similarity
PyTorch Documentation Search Tool (Project Paused)
A semantic search prototype for PyTorch documentation with command-line capabilities.
Current Status (April 19, 2025)
⚠️ This project is currently paused for significant redesign.
The tool provides a basic command-line search interface for PyTorch documentation but requires substantial improvements in several areas. While the core embedding and search functionality works at a basic level, both relevance quality and MCP integration require additional development.
Example Output
$ python scripts/search.py "How are multi-attention heads plotted out in PyTorch?"
Found 5 results for 'How are multi-attention heads plotted out in PyTorch?':
--- Result 1 (code) ---
Title: plot_visualization_utils.py
Source: plot_visualization_utils.py
Score: 0.3714
Snippet: # models. Let's start by analyzing the output of a Mask-RCNN model. Note that...
--- Result 2 (code) ---
Title: plot_transforms_getting_started.py
Source: plot_transforms_getting_started.py
Score: 0.3571
Snippet: https://github.com/pytorch/vision/tree/main/gallery/...
What Works
✅ Basic Semantic Search: Command-line interface for querying PyTorch documentation
✅ Vector Database: Functional ChromaDB integration for storing and querying embeddings
✅ Content Differentiation: Distinguishes between code and text content
✅ Interactive Mode: Option to run continuous interactive queries in a session
What Needs Improvement
❌ Relevance Quality: Moderate similarity scores (0.35-0.37) indicate suboptimal results
❌ Content Coverage: Specialized topics may have insufficient representation in the database
❌ Chunking Strategy: Current approach breaks documentation at arbitrary points
❌ Result Presentation: Snippets are too short and lack sufficient context
❌ MCP Integration: Connection timeout issues prevent Claude Code integration
Getting Started
Environment Setup
Create a conda environment with all dependencies:
conda env create -f environment.yml
conda activate pytorch_docs_search
API Key Setup
The tool requires an OpenAI API key for embedding generation:
export OPENAI_API_KEY=your_key_here
Command-line Usage
# Search with a direct query
python scripts/search.py "your search query here"
# Run in interactive mode
python scripts/search.py --interactive
# Additional options
python scripts/search.py "query" --results 5 # Limit to 5 results
python scripts/search.py "query" --filter code # Only code results
python scripts/search.py "query" --json # Output in JSON format
Project Architecture
ptsearch/core/: Core search functionality (database, embedding, search)ptsearch/config/: Configuration managementptsearch/utils/: Utility functions and loggingscripts/: Command-line toolsdata/: Embedded documentation and databaseptsearch/protocol/: MCP protocol handling (currently unused)ptsearch/transport/: Transport implementations (STDIO, SSE) (currently unused)
Why This Project Is Paused
After evaluating the current implementation, we've identified several challenges that require significant redesign:
Data Quality Issues: The current embedding approach doesn't capture semantic relationships between PyTorch concepts effectively enough. Relevance scores around 0.35-0.37 are too low for a quality user experience.
Chunking Limitations: Our current method divides documentation into chunks based on character count rather than conceptual boundaries, leading to fragmented results.
MCP Integration Problems: Despite multiple implementation approaches, we encountered persistent timeout issues when attempting to integrate with Claude Code:
STDIO integration failed at connection establishment
Flask server with SSE transport couldn't maintain stable connections
UVX deployment experienced similar timeout issues
Future Roadmap
When development resumes, we plan to focus on:
Improved Chunking Strategy: Implement semantic chunking that preserves conceptual boundaries
Enhanced Result Formatting: Provide more context and better snippet selection
Expanded Documentation Coverage: Ensure comprehensive representation of all PyTorch topics
MCP Integration Redesign: Work with the Claude team to resolve timeout issues
Development
Running Tests
pytest -v tests/
Format Code
black .
License
MIT License
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