Skip to main content

A tool for managing embeddings for code analysis

Project description

PyPI version License: MIT Downloads

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

EmbedMan-0.0.2.tar.gz (5.4 kB view details)

Uploaded Source

Built Distribution

EmbedMan-0.0.2-py3-none-any.whl (6.3 kB view details)

Uploaded Python 3

File details

Details for the file EmbedMan-0.0.2.tar.gz.

File metadata

  • Download URL: EmbedMan-0.0.2.tar.gz
  • Upload date:
  • Size: 5.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for EmbedMan-0.0.2.tar.gz
Algorithm Hash digest
SHA256 8d8ae80be149aa68d0b0d1df04ed7f17505e1647f8703ecf40a850f0a6b36829
MD5 43f4c40a7ec60d78408efd4a7507c497
BLAKE2b-256 72470a4e43123b0b24346f7cd12ac81c5549132ad23f232edd6571c2d1ea3701

See more details on using hashes here.

File details

Details for the file EmbedMan-0.0.2-py3-none-any.whl.

File metadata

  • Download URL: EmbedMan-0.0.2-py3-none-any.whl
  • Upload date:
  • Size: 6.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for EmbedMan-0.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 f78dd1ddf0996bdbc09edd575d5bef17db5634c0673713762904c8898119eeab
MD5 fff664474266625e32e53a22b123f874
BLAKE2b-256 4fb6cf4f524ac6e2e933e95b8b7e0707915baeaf81a8c2374d5df59f99467887

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page