Chain that generates key insights/facts of a user based on a a chat conversation with an AI.

Hierarchy

Implements

Constructors

Properties

chatHistoryKey: string = "chat_history"
jsonOutputParser: JsonOutputFunctionsParser

The LLM to use

retriever: BaseRetriever

The retriever to use for retrieving stored thoughts and insights.

stringOutputParser: StringOutputParser
thoughtsKey: string = "thoughts"
verbose: boolean

Whether to print out response text.

callbacks?: Callbacks
memory?: BaseMemory
metadata?: Record<string, unknown>
tags?: string[]

Accessors

Methods

  • This method breaks down the chat history into chunks of messages. Each chunk consists of a sequence of messages ending with an AI message and the subsequent user response, if any.

    Parameters

    • chatHistory: BaseMessage[]

      The chat history to be chunked.

    Returns MessageChunkResult[]

    An array of message chunks. Each chunk includes a sequence of messages and the subsequent user response.

    Description

    The method iterates over the chat history and pushes each message into a temporary array. When it encounters an AI message, it checks for a subsequent user message. If a user message is found, it is considered as the user response to the AI message. If no user message is found after the AI message, the user response is undefined. The method then pushes the chunk (sequence of messages and user response) into the result array. This process continues until all messages in the chat history have been processed.

  • Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run. The jsonpatch ops can be applied in order to construct state.

    Parameters

    Returns AsyncGenerator<RunLogPatch, any, unknown>

Generated using TypeDoc