vector-memory

You can add semantic memory by storing text data as vector embeddings, which enables AI agents to retrieve past information based on meaning and intent rather than exact keyword matches. This allows contextual recall of related topics.

Semantic search uses vector embeddings to find information by understanding the meaning of a query. By converting text into mathematical representations, it retrieves similar documents and related context even when exact words do not match.

Yes, generating vector embeddings for semantic memory storage requires an OpenAI API key. The Skill relies on OpenAI's embedding models to process text into vectors for subsequent retrieval and contextual recall.

Yes, semantic memory and their corresponding vector embeddings can be stored locally in a JSON file. This approach provides a lightweight data layer for AI agents to query past conversations and retrieve similar documents.

The best way to retrieve similar documents by meaning is using vector-based semantic search. By comparing the mathematical distance between query embeddings and stored memory embeddings, you can fetch contextually relevant information.

Using local JSON files for vector memory storage limits scalability and query speed compared to dedicated vector databases. This approach is best suited for smaller-scale AI memory applications rather than large production datasets.