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Memory API


An "auto-RAG" system is a feature will automatically embed every discussion in a Vector Database, essentially turning the database into a memory system.

With this feature, all exchanges between the AI and a user are stored as embeddings in the Vector Database. This allows the AI to recall previous interactions and documents uploaded or generated like images , improving its understanding of the user's context and needs.

This "memory" can significantly enhance the AI's performance by informing its responses based on past interactions. It can help in providing more personalized and context-aware responses, making the AI more effective and user-friendly.

By acting as a memory system, the Vector Database not only stores data but also contributes to the AI's learning process, making it an integral part of the AI's operations. This is a testament to our commitment to continually enhance our services and provide users with a more efficient and personalized AI experience.


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API Docs


  • List memories
  • Create new memory database
  • Describe a memory database
  • Update a memory database
  • Delete a memory database
  • Add text to a memory database
  • Add file to a memory database