62 lines
2.7 KiB
TypeScript
62 lines
2.7 KiB
TypeScript
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import { InputValues, MemoryVariables } from "@langchain/core/memory";
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import { BaseChatMemory, BaseChatMemoryInput } from "./chat_memory.js";
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/**
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* Interface for the input parameters of the `BufferMemory` class.
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*/
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export interface BufferMemoryInput extends BaseChatMemoryInput {
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humanPrefix?: string;
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aiPrefix?: string;
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memoryKey?: string;
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}
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/**
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* The `BufferMemory` class is a type of memory component used for storing
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* and managing previous chat messages. It is a wrapper around
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* `ChatMessageHistory` that extracts the messages into an input variable.
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* This class is particularly useful in applications like chatbots where
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* it is essential to remember previous interactions. Note: The memory
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* instance represents the history of a single conversation. Therefore, it
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* is not recommended to share the same history or memory instance between
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* two different chains. If you deploy your LangChain app on a serverless
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* environment, do not store memory instances in a variable, as your
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* hosting provider may reset it by the next time the function is called.
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* @example
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* ```typescript
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* // Initialize the memory to store chat history and set up the language model with a specific temperature.
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* const memory = new BufferMemory({ memoryKey: "chat_history" });
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* const model = new ChatOpenAI({ temperature: 0.9 });
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*
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* // Create a prompt template for a friendly conversation between a human and an AI.
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* const prompt =
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* PromptTemplate.fromTemplate(`The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
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*
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* Current conversation:
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* {chat_history}
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* Human: {input}
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* AI:`);
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*
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* // Set up the chain with the language model, prompt, and memory.
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* const chain = new LLMChain({ llm: model, prompt, memory });
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*
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* // Example usage of the chain to continue the conversation.
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* // The `call` method sends the input to the model and returns the AI's response.
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* const res = await chain.call({ input: "Hi! I'm Jim." });
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* console.log({ res });
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*
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* ```
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*/
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export declare class BufferMemory extends BaseChatMemory implements BufferMemoryInput {
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humanPrefix: string;
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aiPrefix: string;
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memoryKey: string;
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constructor(fields?: BufferMemoryInput);
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get memoryKeys(): string[];
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/**
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* Loads the memory variables. It takes an `InputValues` object as a
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* parameter and returns a `Promise` that resolves with a
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* `MemoryVariables` object.
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* @param _values `InputValues` object.
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* @returns A `Promise` that resolves with a `MemoryVariables` object.
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*/
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loadMemoryVariables(_values: InputValues): Promise<MemoryVariables>;
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}
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