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Chroma

Chroma is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0.

Discord  License  Integration Tests

Setup​

  1. Run Chroma with Docker on your computer
git clone git@github.com:chroma-core/chroma.git
cd chroma
docker-compose up -d --build
  1. Install the Chroma JS SDK.
npm install -S chromadb

Chroma is fully-typed, fully-tested and fully-documented.

Like any other database, you can:

  • .add
  • .get
  • .update
  • .upsert
  • .delete
  • .peek
  • and .query runs the similarity search.

View full docs at docs.

Usage, Index and query Documents​

tip

See this section for general instructions on installing integration packages.

npm install @langchain/openai @langchain/community
import { Chroma } from "@langchain/community/vectorstores/chroma";
import { OpenAIEmbeddings } from "@langchain/openai";
import { TextLoader } from "langchain/document_loaders/fs/text";

// Create docs with a loader
const loader = new TextLoader("src/document_loaders/example_data/example.txt");
const docs = await loader.load();

// Create vector store and index the docs
const vectorStore = await Chroma.fromDocuments(docs, new OpenAIEmbeddings(), {
  collectionName: "a-test-collection",
  url: "http://localhost:8000", // Optional, will default to this value
  collectionMetadata: {
    "hnsw:space": "cosine",
  }, // Optional, can be used to specify the distance method of the embedding space https://docs.trychroma.com/usage-guide#changing-the-distance-function
});

// Search for the most similar document
const response = await vectorStore.similaritySearch("hello", 1);

console.log(response);
/*
[
  Document {
    pageContent: 'Foo\nBar\nBaz\n\n',
    metadata: { source: 'src/document_loaders/example_data/example.txt' }
  }
]
*/

API Reference:

Usage, Index and query texts​

import { Chroma } from "@langchain/community/vectorstores/chroma";
import { OpenAIEmbeddings } from "@langchain/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: "godel-escher-bach",
  }
);

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

console.log(response);
/*
[
  Document { pageContent: 'Achilles: Oh, no!', metadata: {} },
  Document {
    pageContent: 'Achilles: Yiikes! What is that?',
    metadata: { id: 1 }
  }
]
*/

// You can also filter by metadata
const filteredResponse = await vectorStore.similaritySearch("scared", 2, {
  id: 1,
});

console.log(filteredResponse);
/*
[
  Document {
    pageContent: 'Achilles: Yiikes! What is that?',
    metadata: { id: 1 }
  }
]
*/

API Reference:

Usage, Query docs from existing collection​

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

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

const response = await vectorStore.similaritySearch("scared", 2);
console.log(response);
/*
[
  Document { pageContent: 'Achilles: Oh, no!', metadata: {} },
  Document {
    pageContent: 'Achilles: Yiikes! What is that?',
    metadata: { id: 1 }
  }
]
*/

API Reference:

Usage, delete docs​

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

const embeddings = new OpenAIEmbeddings();
const vectorStore = new Chroma(embeddings, {
  collectionName: "test-deletion",
});

const documents = [
  {
    pageContent: `Tortoise: Labyrinth? Labyrinth? Could it Are we in the notorious Little
    Harmonic Labyrinth of the dreaded Majotaur?`,
    metadata: {
      speaker: "Tortoise",
    },
  },
  {
    pageContent: "Achilles: Yiikes! What is that?",
    metadata: {
      speaker: "Achilles",
    },
  },
  {
    pageContent: `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!`,
    metadata: {
      speaker: "Tortoise",
    },
  },
  {
    pageContent: "Achilles: Oh, no!",
    metadata: {
      speaker: "Achilles",
    },
  },
  {
    pageContent: "Tortoise: But it's only a myth. Courage, Achilles.",
    metadata: {
      speaker: "Tortoise",
    },
  },
];

// Also supports an additional {ids: []} parameter for upsertion
const ids = await vectorStore.addDocuments(documents);

const response = await vectorStore.similaritySearch("scared", 2);
console.log(response);
/*
[
  Document { pageContent: 'Achilles: Oh, no!', metadata: {} },
  Document {
    pageContent: 'Achilles: Yiikes! What is that?',
    metadata: { id: 1 }
  }
]
*/

// You can also pass a "filter" parameter instead
await vectorStore.delete({ ids });

const response2 = await vectorStore.similaritySearch("scared", 2);
console.log(response2);

/*
  []
*/

API Reference:

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