chat-memory

Long-term memory persists user conversations across sessions using a vector store and embedding model. Store conversations with user IDs and retrieve relevant past chats to maintain context and personalization between interactions.

Contextual retrieval uses embeddings to search stored conversations and surface relevant past interactions. This approach matches user intent and history to provide informed, personalized responses without losing context across sessions.

Yes, Milvus functions as a vector store for chat embeddings in memory pipelines. It supports fast retrieval of past conversations indexed by embedding similarity, enabling efficient multi-session chat personalization.

Yes, chat memory requires both a vector store to persist embeddings and an embedding model to vectorize conversations. Together they enable semantic search and retrieval of relevant past chats across user sessions.

Memory pipelines manage decay policies to age older interactions and storage strategies to retain high-value conversations. This balances personalization depth with computational efficiency across extended chat histories.

Session memory persists only within a single conversation; long-term memory retains interactions across multiple sessions to build user profiles and adapt responses. Long-term memory enables true personalization through historical recall and preference learning.