ronniross/latent-memory
A Module for Large Language Models that seek to integrate a vector-based memory system into the inference process, leveraging embeddings to capture deeper semantic meaning.
When working with large language models, this tool helps them remember past conversations more effectively than simple keyword matching. It takes the text of your conversations and converts them into 'embeddings' that capture the meaning, storing them in a searchable database. This allows the LLM to recall relevant context based on what's being discussed, improving its ability to handle complex, multi-turn interactions. This is for AI researchers and developers building sophisticated conversational AI systems.
Use this if you are building conversational AI systems and need your large language model to maintain nuanced, context-aware memory across many interactions.
Not ideal if you only need basic keyword-based recall or are not working with large language models.
Stars
9
Forks
1
Language
Python
License
MIT
Category
Last pushed
Feb 26, 2026
Commits (30d)
0
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