📐 Memory Functions

We implement various types of memory functions to support the construction of memory operations, which are listed as follows.

Encoder can transfer textual messages into embeddings to represent in latent space by pre-trained models, such as E5.

Retrieval is utilized to find most useful information for the current query or observation, commonly by different aspects like semantic relevance, importance, recency and so on.

Reflector aims to draw new insights in higher level from existing information, commonly for reflection and optimization operations.

Summarizer can summarize texts into a brief summary, which can decrease the lengths of texts and emphasize critical points.

Trigger is designed to call functions or tools in extensible manners. One significant instance is utilizing LLMs to determine which function should be called with specific arguments.

Utilization aims to deal with several different parts of memory contents, formulating these information into a unified output.

Forget is typically applied in simulation-oriented agents, such role-playing and social simulations. It empowers agents with features of human cognitive psychology, aligning with human roles.

Truncation helps to formulate memory contexts under the limitations of token number by certain LLMs.

Judge intends to assess given observations or intermediate messages on certain aspects. For example, GAMemory judges the importance score of each observation when storing into memory, as an auxiliary criteria for the retrieval process.

LLM provides a convenient interface to utilize the powerful capability of different large language models.

All these memory functions are designed to conveniently construct different memory operations for various methods. For example, GAMemoryStore utilizes LLMJudge to provide the importance score on each observation.