Zep 云

import { ZepCloudVectorStore } from "@langchain/community/vectorstores/zep_cloud";
import { FakeEmbeddings } from "@langchain/core/utils/testing";
import { TextLoader } from "@langchain/classic/document_loaders/fs/text";
import { randomUUID } from "crypto";

const loader = new TextLoader("src/document_loaders/example_data/example.txt");
const docs = await loader.load();
const collectionName = `collection${randomUUID().split("-")[0]}`;

const zepConfig = {
  // Your Zep Cloud Project API key https://help.getzep.com/projects
  apiKey: "<Zep Api Key>",
  collectionName,
};

// We're using fake embeddings here, because Zep Cloud handles embedding for you
const embeddings = new FakeEmbeddings();

const vectorStore = await ZepCloudVectorStore.fromDocuments(
  docs,
  embeddings,
  zepConfig
);

// Wait for the documents to be embedded
// eslint-disable-next-line no-constant-condition
while (true) {
  const c = await vectorStore.client.document.getCollection(collectionName);
  console.log(
    `Embedding status: ${c.documentEmbeddedCount}/${c.documentCount} documents embedded`
  );
  // eslint-disable-next-line no-promise-executor-return
  await new Promise((resolve) => setTimeout(resolve, 1000));
  if (c.documentEmbeddedCount === c.documentCount) {
    break;
  }
}

const results = await vectorStore.similaritySearchWithScore("bar", 3);

console.log("Similarity Results:");
console.log(JSON.stringify(results));

const results2 = await vectorStore.maxMarginalRelevanceSearch("bar", {
  k: 3,
});

console.log("MMR Results:");
console.log(JSON.stringify(results2));

import { ZepClient } from "@getzep/zep-cloud";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { ConsoleCallbackHandler } from "@langchain/core/tracers/console";
import { ChatOpenAI } from "@langchain/openai";
import { Document } from "@langchain/core/documents";
import {
  RunnableLambda,
  RunnableMap,
  RunnablePassthrough,
} from "@langchain/core/runnables";
import { ZepCloudVectorStore } from "@langchain/community/vectorstores/zep_cloud";
import { StringOutputParser } from "@langchain/core/output_parsers";

async function combineDocuments(docs: Document[], documentSeparator = "\n\n") {
  const docStrings: string[] = await Promise.all(
    docs.map((doc) => doc.pageContent)
  );
  return docStrings.join(documentSeparator);
}

// Your Zep Collection Name
const collectionName = "<Zep Collection Name>";

const zepClient = new ZepClient({
  // Your Zep Cloud Project API key https://help.getzep.com/projects
  apiKey: "<Zep Api Key>",
});

const vectorStore = await ZepCloudVectorStore.init({
  client: zepClient,
  collectionName,
});

const prompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    `Answer the question based only on the following context: {context}`,
  ],
  ["human", "{question}"],
]);

const model = new ChatOpenAI({
  temperature: 0.8,
  model: "gpt-3.5-turbo-1106",
});
const retriever = vectorStore.asRetriever();

const setupAndRetrieval = RunnableMap.from({
  context: new RunnableLambda({
    func: (input: string) => retriever.invoke(input).then(combineDocuments),
  }),
  question: new RunnablePassthrough(),
});
const outputParser = new StringOutputParser();

const chain = setupAndRetrieval
  .pipe(prompt)
  .pipe(model)
  .pipe(outputParser)
  .withConfig({
    callbacks: [new ConsoleCallbackHandler()],
  });

const result = await chain.invoke("Project Gutenberg?");

console.log("result", result);