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Data: Ecosystem news, GitHub updates, discussion summaries, and other useful bits for knowledge / RAG systems

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Knowledge Repository

This repository serves as a centralized storage of markdown and text files gathered from various sources, organized to be easily ingested into Retrieval-Augmented Generation (RAG) systems for AI agents. The primary purpose is to maintain an up-to-date collection of knowledge that can be quickly referenced and utilized by AI systems.

Repository Structure

The repository is organized into several main directories, each containing information from different sources:

.
├── discord/            # Discord discussions (legacy, to be deprecated)
├── docs/               # Documentation from ElizaOS (from elizaOS/eliza repo)
├── github/             # GitHub activity logs organized by day/week/month
├── news/               # Daily AI news summaries
├── daily-silk/         # Daily AI news from Discord channel using SILK
├── plugins/            # Documentation from ElizaOS plugin ecosystem
└── partners/           # Information about ElizaOS partners and integrations

Data Sources

Discord

Archive of Discord discussions from various channels related to AI development and communities. This section is being deprecated in favor of more structured data sources.

Docs

Technical documentation from the ElizaOS/eliza repository, specifically from its packages/docs folder. These files contain guides, tutorials, API references, and technical specifications for the ElizaOS system.

GitHub

Activity logs from ElizaOS/elizaos.github.io (_data branch), organized by day, week, and month. This provides a chronological view of development activities and changes.

News

Daily AI news summaries generated by M3-org/ai-news, an AI-powered news aggregation platform that collects, analyzes, and summarizes information from multiple sources in real-time. The news data is stored in the gh-pages branch of the original repository.

Daily Silk

Daily AI news collected from a Discord channel using SILK and discord.py. The data is automatically fetched, processed, and stored in markdown format, with each file representing a day's worth of AI news and updates. The content is organized chronologically and includes timestamps for each entry. The data is collected daily and provides a comprehensive view of AI developments and announcements.

Plugins

Documentation from the ElizaOS plugin ecosystem, which includes a collection of adapters, clients, and plugins that extend the functionality of the ElizaOS platform. This directory contains detailed information about each plugin's features, configuration, and integration methods.

Partners

Information about ElizaOS partners and integrations, including details about official partnerships, supported platforms, and integration capabilities. This documentation helps users understand the broader ecosystem of services and platforms that work with ElizaOS.

Usage

This repository is designed to be used as a knowledge source for RAG systems. The markdown files can be ingested into vector databases or other retrieval systems to provide context for AI agents.

For AI Researchers and Developers

  1. Clone this repository to your local machine or server
  2. Use the files as a corpus for training or fine-tuning AI models
  3. Index the content for retrieval in RAG systems
  4. Reference specific sections in your AI prompts for domain-specific knowledge

Contributing

Adding New Sources

To add a new source to the knowledge repository:

  1. Create a dedicated directory for the source
  2. Ensure all files are in markdown (.md) format when possible
  3. Update this README with information about the new source
  4. Create a GitHub action to keep the source updated (see below)

GitHub Actions

This repository uses GitHub Actions to automatically update content from various sources. To contribute a new action:

  1. Create a new workflow file in .github/workflows/
  2. Configure the action to fetch and format data from the source
  3. Set an appropriate schedule for updates
  4. Test the action to ensure it correctly updates the repository

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Data: Ecosystem news, GitHub updates, discussion summaries, and other useful bits for knowledge / RAG systems

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