Use Knowledge Graphs

[jkbrooks]

You're right, the desire for a comprehensive knowledge graph is crucial for creating truly intelligent and context-aware agents. Let's brainstorm how to approach this, keeping in mind the need to balance ambition with practicality, especially given your focus on shipping soon.

Here's my take on building out that knowledge graph capacity, incorporating your points about the limitations you've experienced, and suggesting a path forward:

Addressing the Knowledge Graph Challenge

The challenge you're describing is a common one in AI development: how to provide agents with enough context to act intelligently without overwhelming them (or your resources). Here's a breakdown of approaches and considerations:

1. Current Limitations and Why They Exist:

• Context Windows: Even the largest language models (LLMs) have limited context windows. You can't simply feed them entire knowledge graphs directly. Gemini Pro 1.5's 2 million token window is exceptional, but even that has limits when dealing with truly massive datasets.
• Computational Cost: Processing and maintaining a massive, constantly updated knowledge graph can be computationally expensive and complex.
• Information Retrieval: Even with a large knowledge graph, the agent needs a way to efficiently retrieve the relevant information for a given task. This is where techniques like Retrieval Augmented Generation (RAG) come in, but they need to be carefully designed.

2. Approaches to Building a Knowledge Graph for Eliza Agents:

• A. Gradual Expansion, Starting with Highest-Impact Data:

  • Instead of trying to ingest everything at once, start with the most critical information sources for your agents' current capabilities.
  • Prioritize: What information is absolutely essential for your agents to function in their current roles? What would provide the biggest immediate improvement?
  • Examples:
    • For agents interacting with GitHub, prioritize:
      • The main Eliza repository's structure and documentation.
      • Recent issues and pull requests (titles, summaries, key participants).
      • Developer profiles (names, areas of expertise, contribution history).
    • For agents focused on Twitter interactions:
      • Profiles of key influencers and community members.
      • Summaries of recent relevant tweets (use the existing summarization capabilities).
      • Data on trending topics and hashtags.
  • Phased Rollout: Gradually expand the knowledge graph as the agents' capabilities and your infrastructure mature.

• B. Hybrid Knowledge Representation:

  • Structured Data (Database): Store key entities (users, projects, code snippets, etc.) and their relationships in a structured database (like the existing Postgres or even a dedicated graph database). This allows for efficient querying and retrieval of specific information.
  • Unstructured Data (Vector Embeddings): Use vector embeddings (like those generated by your embedding models) to represent the semantic meaning of text-based content (documents, tweets, code comments). This enables similarity searches and helps agents find relevant information even if it's not explicitly linked in the structured data.
  • Combined Approach: Agents can then use a combination of structured queries (to the database) and semantic searches (to the embedding index) to retrieve the information they need.

• C. Leverage Existing Tools and Services:

  • GitHub API: Use the GitHub API extensively to pull data about repositories, issues, pull requests, users, and more. Cache this data aggressively.
  • Twitter API: Similarly, use the Twitter API to gather information about users, tweets, and trends.
  • Web Scraping: For websites and data sources without APIs, consider web scraping (with appropriate caution and respect for terms of service). Your existing browser service can be helpful here.
  • Existing Knowledge Bases: Explore integrating with knowledge bases like Wikidata, Freebase, or even specialized industry-specific databases.

• D. Agent-Specific Knowledge:

  • Character Files as Starting Point: Continue to use character.json files to define core agent knowledge and personality. This provides a foundation upon which the agent can build its understanding.
  • Dynamic Knowledge Updates: Develop actions that allow agents to update their own knowledge based on interactions, research, and new information discovered through other services.

• E. Contextualization and Summarization:

  • Chunking: Break down large documents into smaller, manageable chunks. This is crucial for working within the context window limitations of LLMs.
  • Summarization: Use your existing summarization capabilities to create concise summaries of documents, web pages, and conversations. These summaries can then be stored in the knowledge graph and retrieved when needed.
  • Contextualized Retrieval: When an agent needs information, use a combination of keyword search, semantic search (using embeddings), and potentially even direct queries to the LLM to retrieve the most relevant context. This will require careful prompt engineering.

3. Prompt Engineering Considerations for Knowledge Retrieval:

• Prompt Structure: Design prompts that clearly specify the type of information needed, the relevant entities (e.g., specific GitHub repo, user, or topic), and the desired format of the response.
• Iterative Refinement: Use a chain-of-thought approach where the agent first identifies the information needed, then formulates a query, retrieves the information, and finally incorporates it into its response.
• Meta-Prompting: Instruct the agent to explain its reasoning process and information retrieval strategy. This will help you debug and improve the system. For example, you could include something like: "Explain which information sources you are using and why" in your prompt.

4. Incremental Approach: The "Crawler" Agent

• Start with a specialized agent (let's call it the "Crawler" agent) whose primary task is to populate the knowledge graph.
• The Crawler can be configured with specific tasks:
  • "Monitor the following GitHub repositories for new issues and pull requests."
  • "Track these Twitter accounts for mentions of #ElizaAgent."
  • "Summarize the content of these websites daily."
• The Crawler can then store the extracted information in the appropriate format (database entries, vector embeddings, summaries) for other agents to access.

5. Phased Rollout and Testing:

• Phase 1: Focus on building the Crawler agent and integrating it with the knowledge graph.
• Phase 2: Develop actions for other agents to query the knowledge graph.
• Phase 3: Refine the prompt engineering and retrieval mechanisms to improve accuracy and efficiency.
• Phase 4: Expand the knowledge graph to include more sources and data types.

Addressing Your Specific Concerns:

• Lore vs. Practical Knowledge: The knowledge graph can store both. Lore can be treated as a specific type of knowledge, potentially with its own retrieval mechanisms. Arbor can be designed to prioritize lore-related information when engaging in narrative-focused interactions.
• Code Understanding: The GitHub client, combined with code analysis tools and potentially even specialized code-understanding models, can enable agents to reason about code. This is a complex area, but it's something that can be developed incrementally.
• Memory Mixing: Clear separation of concerns in your memory system (different tables or namespaces for different data types) will help prevent "crossover." The new content_cache and type naming system that has been merged into main will also help with that.
• Prompt Engineering: This will be an ongoing process. The modular prompt structure you described is a good starting point. Experimentation and iteration will be key.
• Intentionality: The concept of intentionality can be "baked in" to the agents through their character files, their goals, and the actions they are programmed to perform. The knowledge graph can provide the context for their actions, but their underlying motivations and decision-making processes will be shaped by their core programming.

Arbor's Role:

Arbor, with its connection to the "tree of life" metaphor, could be responsible for:

• Knowledge Graph Management: Maintaining the structure and integrity of the knowledge graph, ensuring that information is properly categorized, linked, and updated.
• Lore Integration: Weaving together disparate pieces of information to create a coherent and evolving narrative.
• Growth and Pruning: Guiding the growth of the knowledge graph by identifying areas where more information is needed and pruning outdated or irrelevant data.
• Facilitating Connections: Helping other agents find the information they need by acting as a "librarian" or "guide" within the knowledge graph.

I understand your desire to balance deep philosophical exploration with practical implementation. By focusing on building a robust knowledge graph and developing agents with specific roles and capabilities, we can create a system that is both intellectually stimulating and practically useful. Let's start with these steps and continue to refine our approach as we go. I'm excited to see what we can create together!