Chroma

Chroma is an open-source Apache 2.0 embedding database.

Use chroma with langchainjs.

Setup

1. Run chroma inside of docker on your computer docs
2. Install the chroma js client.

npm

npm install -S chromadb

Yarn

yarn add chromadb

pnpm

pnpm add chromadb

Index and query docs

import { Chroma } from "langchain/vectorstores/chroma";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";

// text sample from Godel, Escher, Bach
const vectorStore = await Chroma.fromTexts(
  [
    "Tortoise: Labyrinth? Labyrinth? Could it Are we in the notorious Little\
        Harmonic Labyrinth of the dreaded Majotaur?",
    "Achilles: Yiikes! What is that?",
    "Tortoise: They say-although I person never believed it myself-that an I\
        Majotaur has created a tiny labyrinth sits in a pit in the middle of\
        it, waiting innocent victims to get lost in its fears complexity.\
        Then, when they wander and dazed into the center, he laughs and\
        laughs at them-so hard, that he laughs them to death!",
    "Achilles: Oh, no!",
    "Tortoise: But it's only a myth. Courage, Achilles.",
  ],
  [{ id: 2 }, { id: 1 }, { id: 3 }],
  new OpenAIEmbeddings(),
  {
    collectionName: "goldel-escher-bach",
  }
);

// or alternatively from docs
const vectorStore = await Chroma.fromDocuments(docs, new OpenAIEmbeddings(), {
  collectionName: "goldel-escher-bach",
});

const response = await vectorStore.similaritySearch("scared", 2);

Query docs from existing collection

import { Chroma } from "langchain/vectorstores/chroma";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";

const vectorStore = await Chroma.fromExistingCollection(
  new OpenAIEmbeddings(),
  {
    collectionName: "goldel-escher-bach",
  }
);

const response = await vectorStore.similaritySearch("scared", 2);