Memory modules for conversation prompts.
Combining multiple memories' data together.
A basic memory implementation that simply stores the conversation history.
This stores the entire conversation history in memory without any additional processing.
Note that additional processing may be required in some situations when the conversation history is too large to fit in the context window of the model.
A basic memory implementation that simply stores the conversation history.
This stores the entire conversation history in memory without any additional processing.
Equivalent to ConversationBufferMemory but tailored more specifically for string-based conversations rather than chat models.
Note that additional processing may be required in some situations when the conversation history is too large to fit in the context window of the model.
Use to keep track of the last k turns of a conversation.
If the number of messages in the conversation is more than the maximum number of messages to keep, the oldest messages are dropped.
Entity extractor & summarizer memory.
Extracts named entities from the recent chat history and generates summaries. With a swappable entity store, persisting entities across conversations. Defaults to an in-memory entity store, and can be swapped out for a Redis, SQLite, or other entity store.
Continually summarizes the conversation history.
The summary is updated after each conversation turn. The implementations returns a summary of the conversation history which can be used to provide context to the model.
Buffer with summarizer for storing conversation memory.
Provides a running summary of the conversation together with the most recent messages in the conversation under the constraint that the total number of tokens in the conversation does not exceed a certain limit.
