Skip to content

Are there existing frameworks for building repositories of contextual information?

Note: the context for this output is Creating A Personal Context Repo

Generation Details

Date LLM Access Method
Nov 2024 GPT-4 Web UI

Yes, there are several existing frameworks and approaches for creating personal contexts to interface with LLMs. Based on the search results and additional context, here are some key options:

  1. LlamaIndex: LlamaIndex is a data framework specifically designed for connecting custom data sources to large language models (LLMs). It allows you to:

  2. Load data from over 160 sources and formats, including personal documents, APIs, and databases.

  3. Index your personal data for efficient retrieval.
  4. Create personalized query engines and chat interfaces using your data.

  5. Build agents that can interact with your personal information.

  6. LangChain's Memory Module: LangChain provides a Memory module that helps in persisting state between interactions with an LLM. It offers:

  7. Various memory types, including ConversationBufferMemory, which stores chat messages.

  8. The ability to store and query personal context across multiple interactions.

  9. Flexibility in how memory is stored and retrieved, from in-memory lists to databases.

  10. Guided Profile Generation (GPG): This is a method proposed in research for generating personal profiles in natural language. It involves:

  11. Posing specific questions about personal context.

  12. Using an LLM to generate descriptive natural language profiles based on responses.

  13. Employing these profiles in downstream tasks for personalization.

  14. Custom Knowledge Graphs: While not a specific framework, creating a personal knowledge graph is an approach that can be used across different LLM systems. This involves:

  15. Structuring personal data in a graph format.

  16. Using tools like Neo4j or RDFLib to manage the knowledge graph.

  17. Querying the graph to provide context to LLMs.

  18. Vector Databases: Tools like Pinecone, Weaviate, or Milvus can be used to create personal vector databases. This approach allows for:

  19. Storing embeddings of personal information.

  20. Efficient semantic search and retrieval of relevant personal context.

  21. Structured Data Storage: Using structured JSON or YAML files to store personal context in a hierarchical format. This simple approach can be easily integrated with various LLM systems.

  22. Personal Context APIs: Developing a custom API that serves personal context, which different LLM systems can query to retrieve relevant information.

These frameworks and approaches offer various ways to create and manage personal contexts for LLM interactions. The choice depends on the specific requirements of your project, the type of personal data you're working with, and the level of integration you need with different LLM systems. LlamaIndex and LangChain stand out as comprehensive frameworks specifically designed for this purpose, offering robust tools for personal data integration with LLMs.

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.