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How might a user go about building out their own context repository for working with LLM?

Generation Details

Date LLM Access Method
Nov 2024 GPT-4 Web UI

Prompt

Can you suggest any ways in which a user might go about developing a personal context for LLM usage that wouldn't only be useful in Lang Chain but which might be able to be easily imported into different LLMs. I'm thinking about something like setting up a structured store of data reflecting personal projects, code preferences, etc.

Based on your query and the search results, I can suggest several approaches for developing a personal context for LLM usage that could be versatile and easily importable into different LLMs, not just limited to LangChain. Here are some strategies:

  1. Knowledge Graph Approach: Create a personal knowledge graph that captures information about your projects, preferences, and other relevant personal context. This structured representation can be easily queried and integrated into various LLM systems[5].
  2. Use RDF (Resource Description Framework) or similar formats to structure your personal data.
  3. Include information about your projects, code preferences, work history, and other relevant personal details.
  4. Tools like Neo4j or RDFLib can be used to create and manage these knowledge graphs.
  5. Vector Database: Implement a vector database to store embeddings of your personal context[1]. This approach allows for efficient semantic search and retrieval of relevant information.
  6. Use tools like Pinecone, Weaviate, or Milvus to create and manage your vector database.
  7. Store embeddings of project descriptions, code snippets, personal notes, and other relevant information.
  8. This method enables semantic similarity search, making it easier to retrieve context-relevant information.
  9. Structured JSON or YAML Files: Create a set of structured JSON or YAML files that contain your personal context in a hierarchical format. This approach is simple and easily portable across different systems[5].
  10. Organize information into categories like "Projects", "Preferences", "Skills", etc.
  11. Include metadata for each entry to facilitate easy searching and filtering.
  12. Personal Context API: Develop a simple API that serves your personal context. This can be a lightweight server that different LLM systems can query to retrieve relevant information[3].
  13. Implement endpoints for different types of information (e.g., /projects, /preferences, /skills).
  14. Use authentication to ensure only authorized systems can access your personal data.
  15. Guided Profile Generation (GPG): Adopt a method similar to the Guided Profile Generation approach mentioned in the search results[2]. This involves:
  16. Creating a set of specific questions about your personal context.
  17. Using an LLM to generate descriptive natural language profiles based on your responses.
  18. Storing these generated profiles in a format that can be easily imported into different LLM systems.
  19. Contextual Compression Technique: Implement a system that compresses and summarizes your personal context, similar to the contextual compression mentioned in the search results[1]. This can help in managing large amounts of personal data efficiently.
  20. Use smaller LLMs to summarize and compress your personal context periodically.
  21. Store these summaries in a structured format that can be easily retrieved and used by different LLM systems.
  22. Hybrid Approach: Combine multiple methods for a more comprehensive solution. For example:
  23. Use a knowledge graph for structured relationships.
  24. Implement a vector database for semantic search capabilities.
  25. Maintain a set of compressed summaries for quick context injection.

Implementation Tips:

  • Ensure your personal context storage is easily updateable to reflect changes in your projects, preferences, or skills over time.
  • Implement a versioning system to track changes in your personal context.
  • Consider privacy and security measures to protect your personal information.
  • Create a standardized format for importing this personal context into different LLM systems, possibly using a custom prompt template that can be adapted for various LLMs.

By implementing one or a combination of these approaches, you can create a rich, structured store of personal context that can be easily imported and utilized across different LLM systems, enhancing the personalization and accuracy of your interactions with various AI models.

Extracted Prompts

Prompts/Portable personal contexts for LLMs.md#Prompt 1

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.