Skip to main content

Summarize information from vectorized files using OpenAI's powerful models, and explore data further with an interactive prompt for deep dives into content.

Project description

summawise

PyPI - Version

Summarize information from vectorized files using OpenAI's powerful models, and explore data further with an interactive prompt for deep dives into content.

Installation

For the sake of convenience (and to be added to your $PATH), the program is available via PyPI:

pip install --upgrade summawise

Notes

This tool uses OpenAI API features which are currently in beta.

Resources:

Information

The following inputs are supported:

  • YouTube video URLs. (Transcript is extracted and used as text)
  • Local files. (Any type of content, file will be uploaded byte for byte)
  • Other URLs, depending on the response content. (Text content, PDF files, and HTML are all supported)

Support for a wider variety of input may be added in the future.

Potential improvements:

  • Directory support (Enter a local directory path, contents are uploaded recursively)
  • Archive support (Both URLs and local files - automatically extract/upload contents of .zip/.tar.gz files)
  • VectorStore caching (already supported for youtube videos, the goal is to implement this for all inputs)
  • Multiple "assistants" with more intricate instructions, which would be selected based on the type of information being analyzed.
    • A custom assistant can already be used by manually changing the "assistant_id" property in settings file.
  • User friendly command line interface.

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

summawise-0.2.3.tar.gz (28.4 kB view details)

Uploaded Source

Built Distribution

summawise-0.2.3-py3-none-any.whl (31.3 kB view details)

Uploaded Python 3

File details

Details for the file summawise-0.2.3.tar.gz.

File metadata

  • Download URL: summawise-0.2.3.tar.gz
  • Upload date:
  • Size: 28.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.19

File hashes

Hashes for summawise-0.2.3.tar.gz
Algorithm Hash digest
SHA256 565f8c17c0f7a4210e61d6c25dbd4793f8b223d32d6e6855be58b6020bb3b8b7
MD5 ab91cdc8f605c07566496012bd91104c
BLAKE2b-256 100ddd80fea7faff9ffc62d65c02832ba798f6b3577aab5c24d475b9f17b3dad

See more details on using hashes here.

File details

Details for the file summawise-0.2.3-py3-none-any.whl.

File metadata

  • Download URL: summawise-0.2.3-py3-none-any.whl
  • Upload date:
  • Size: 31.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.19

File hashes

Hashes for summawise-0.2.3-py3-none-any.whl
Algorithm Hash digest
SHA256 0968ba4d314f016153b42b6003098235f952044b900741aca2881028f6cd8beb
MD5 875deee871748d1d1e11c4de9e13bd72
BLAKE2b-256 d905d06e3cc7b5f8296f701dcdbc410a782cbe2c8a61fa6dccc17b646c8befbe

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