🔌 Integrations

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Python

JavaScript

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Python

🦜️🔗 Langchain - python

Learn more at LangChain's Chroma Documentation

🦙 GPT-index / LlamaIndex

Learn more on GPT-index/LlamaIndex Vector Store page.

JavaScript

🦜️🔗 LangchainJS

Here is an example in LangChainJS

import { OpenAI } from "langchain/llms";
import { ChatVectorDBQAChain } from "langchain/chains";
import { Chroma } from "langchain/vectorstores";
import { OpenAIEmbeddings } from "langchain/embeddings";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import * as fs from "fs";

// to run this first run chroma's docker-container with `docker-compose up -d --build`

export const run = async () => {
  /* Initialize the LLM to use to answer the question */
  const model = new OpenAI();
  /* Load in the file we want to do question answering over */
  const text = fs.readFileSync("state_of_the_union.txt", "utf8");
  /* Split the text into chunks */
  const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000 });
  const docs = await textSplitter.createDocuments([text]);
  /* Create the vectorstore */
  const vectorStore = await Chroma.fromDocuments(
    docs,
    new OpenAIEmbeddings(),
    { collectionName: "state_of_the_union" }
  );
  /* Create the chain */
  const chain = ChatVectorDBQAChain.fromLLM(model, vectorStore);
  /* Ask it a question */
  const question = "What did the president say about Justice Breyer?";
  const res = await chain.call({ question, chat_history: [] });
  console.log(res);
};