基于向量库的内存

VectorStoreRetrieverMemory将记忆存储在向量数据库中，并在每次调用时查询最“显著”的前K个文档。

这与大多数其他内存类不同，它没有显式跟踪交互的顺序。

在这种情况下，“文档”是先前的会话片段。这可以用来提到AI之前在对话中被告知的相关信息。

import { OpenAI } from "langchain/llms/openai";
import { VectorStoreRetrieverMemory } from "langchain/memory";
import { LLMChain } from "langchain/chains";
import { PromptTemplate } from "langchain/prompts";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";

const vectorStore = new MemoryVectorStore(new OpenAIEmbeddings());
const memory = new VectorStoreRetrieverMemory({
  // 1 is how many documents to return, you might want to return more, eg. 4
  vectorStoreRetriever: vectorStore.asRetriever(1),
  memoryKey: "history",
});

// First let's save some information to memory, as it would happen when
// used inside a chain.
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" });

// Now let's use the memory to retrieve the information we saved.
console.log(
  await memory.loadMemoryVariables({ prompt: "what sport should i watch?" })
);
/*
{ history: 'input: My favorite sport is soccer\noutput: ...' }
*/

// Now let's use it in a chain.
const model = new OpenAI({ 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 });

const res1 = await chain.call({ input: "Hi, my name is Perry, what's up?" });
console.log({ res1 });
/*
{
  res1: {
    text: " Hi Perry, I'm doing great! I'm currently exploring different topics related to artificial intelligence like natural language processing and machine learning. What about you? What have you been up to lately?"
  }
}
*/

const res2 = await chain.call({ input: "what's my favorite sport?" });
console.log({ res2 });
/*
{ res2: { text: ' You said your favorite sport is soccer.' } }
*/

const res3 = await chain.call({ input: "what's my name?" });
console.log({ res3 });
/*
{ res3: { text: ' Your name is Perry.' } }
*/