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A tool for managing embeddings for code analysis

Project description

PyPI version License: MIT Downloads LinkedIn

EmbedMan

EmbedMan is a Python package designed to manage embeddings for code analysis efficiently. It facilitates the process of generating and retrieving embeddings from a specified directory of code files, utilizing the power of language models and embedding storage solutions.

Installation

To install EmbedMan, you can use pip:

pip install embedman

Usage

As a Python Module

After installation, EmbedMan can be imported and used in your Python projects.

Example:

from embed_man import EmbedManager

# Initialize the EmbedManager with desired parameters
embed_manager = EmbedManager(
    path="path/to/your/code/directory",
    glob_rule="**/*.py",
    use_cache=True
)

# Run the embedding process and get a retriever for querying embeddings
retriever = embed_manager.run()

# You can now use the retriever to query embeddings

Configurable Parameters

EmbedMan allows various configurations to tailor the embedding process to your needs, including:

  • path: The directory path to scan for documents.
  • glob_rule: Glob pattern to match files within the directory.
  • suffixes: List of file suffixes to include.
  • exclude: List of patterns to exclude.
  • language: Programming language of the documents.
  • parser_threshold: Threshold for the parser to consider a document valid.
  • chunk_size: Size of chunks to split documents into for embedding.
  • chunk_overlap: Overlap between consecutive chunks.
  • cache_dir: Directory path for caching embeddings.
  • namespace_cache: Namespace for the cache to avoid collisions.

Contributing

Contributions, issues, and feature requests are welcome! Feel free to check issues page.

License

MIT

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