The LLM Extended Memory Framework is an open-source project designed to enhance the memory capabilities of large language models like OpenAI's GPT-series. By implementing an efficient encoding, storage, indexing, recall, and decoding system, this project aims to overcome the memory limitations associated with typical input+context character limits.
- LLM API Calling Tools : Tools for calling LLM APIs to get and decode responses for user inputs
- Memory Encoder : Compact semantic representations of conversation and context
- External Memory Storage: Support for storing data on various storage media (local files, databases, etc.)
- Indexing System: Efficient retrieval of required information based on keyword, topic, or semantic searches
- Memory Recall: Seamless retrieval and reintegration of essential data into ongoing tasks or conversations
- Memory Decoder: Reconstruction of original data from their encoded representations for LLM model compatibility
- Integration with LLM: Working in conjunction with LLM models for improved performance in complex tasks and long conversations
To run tests, execute the following command from the project directory:
python llm_extended_memory/run_tests.py