Skip to main content

A Machine With Human-Like Memory Systems.

Project description

humemai

DOI

This repo hosts a package humemai, a human-like memory systems that are modeled with knowledge knoweldge graphs (KGs). At the moment they are nothing but a Python list of quadruples, but soon it'll be a better object type so that they can be compatible with graph databases, e.g., GraphDB, Neo4j, etc. There have been both academic papers and applications that have used this package.

List of academic papers that use HumemAI

List of applications that use HumemAI

pdoc documentation

Click on this link to see the HTML rendered docstrings

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Run make test && make style && make quality in the root repo directory, to ensure code quality.
  4. Commit your Changes (git commit -m 'Add some AmazingFeature')
  5. Push to the Branch (git push origin feature/AmazingFeature)
  6. Open a Pull Request

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

humemai_research-1.0.0.post2.tar.gz (23.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

humemai_research-1.0.0.post2-py3-none-any.whl (18.4 kB view details)

Uploaded Python 3

File details

Details for the file humemai_research-1.0.0.post2.tar.gz.

File metadata

  • Download URL: humemai_research-1.0.0.post2.tar.gz
  • Upload date:
  • Size: 23.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for humemai_research-1.0.0.post2.tar.gz
Algorithm Hash digest
SHA256 2367b3f05b24b129a865c2255c78ccfa97dd5ac3461a25a166037cf0571a0870
MD5 ee8347674abd57ef569d4cdbb017459d
BLAKE2b-256 f39aa612f937dfa7c88d1871223dc755a99c123d72768cfbb426b14814580d09

See more details on using hashes here.

Provenance

The following attestation bundles were made for humemai_research-1.0.0.post2.tar.gz:

Publisher: publish-pypi.yml on humemai/humemai-research

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file humemai_research-1.0.0.post2-py3-none-any.whl.

File metadata

File hashes

Hashes for humemai_research-1.0.0.post2-py3-none-any.whl
Algorithm Hash digest
SHA256 031a6bf34515607f65b2182e9377b69947f82286a071ea4c3ae2a2be11f9c3e1
MD5 91b3bb490b86499a19c3ae5153e63a6d
BLAKE2b-256 33b12da24896e35e26cb91f401f64f74133de2967247ae6ad69d90a05bcfdba8

See more details on using hashes here.

Provenance

The following attestation bundles were made for humemai_research-1.0.0.post2-py3-none-any.whl:

Publisher: publish-pypi.yml on humemai/humemai-research

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

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