Class VectorStoreRetrieverMemory

Class for managing long-term memory in Large Language Model (LLM) applications. It provides a way to persist and retrieve relevant documents from a vector store database, which can be useful for maintaining conversation history or other types of memory in an LLM application.

Example

const vectorStore = new MemoryVectorStore(new OpenAIEmbeddings());
const memory = new VectorStoreRetrieverMemory({
  vectorStoreRetriever: vectorStore.asRetriever(1),
  memoryKey: "history",
});

// Saving context to memory
await memory.saveContext(
  { input: "My favorite food is pizza" },
  { output: "thats good to know" },
);
await memory.saveContext(
  { input: "My favorite sport is soccer" },
  { output: "..." },
);
await memory.saveContext({ input: "I don't the Celtics" }, { output: "ok" });

// Loading memory variables
console.log(
  await memory.loadMemoryVariables({ prompt: "what sport should i watch?" }),
);

const model = new ChatAnthropic({ temperature: 0.9 });
const prompt =
  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.

Relevant pieces of previous conversation:
{history}

(You do not need to use these pieces of information if not relevant)

Current conversation:
Human: {input}
AI:`);
const chain = new LLMChain({ llm: model, prompt, memory });

// Making calls to the chain with different inputs
const res1 = await chain.call({ input: "Hi, my name is Perry, what's up?" });
console.log({ res1 });

const res2 = await chain.call({ input: "what's my favorite sport?" });
console.log({ res2 });

const res3 = await chain.call({ input: "what's my name?" });
console.log({ res3 });

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