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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.

This guide provides a quick overview for getting started with Chroma vector stores. For detailed documentation of all Chroma features and configurations head to the API reference.

Overview​

Integration details​

ClassPackagePY supportPackage latest
Chroma@langchain/community✅NPM - Version

Setup​

To use Chroma vector stores, you’ll need to install the @langchain/community integration package along with the Chroma JS SDK as a peer dependency.

This guide will also use OpenAI embeddings, which require you to install the @langchain/openai integration package. You can also use other supported embeddings models if you wish.

tip

See this section for general instructions on installing integration packages.

yarn add @langchain/community @langchain/openai @langchain/core chromadb

Next, follow the following instructions to run Chroma with Docker on your computer:

docker pull chromadb/chroma
docker run -p 8000:8000 chromadb/chroma

You can see alternative setup instructions in this guide.

Credentials​

If you are running Chroma through Docker, you do not need to provide any credentials.

If you are using OpenAI embeddings for this guide, you’ll need to set your OpenAI key as well:

process.env.OPENAI_API_KEY = "YOUR_API_KEY";

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

// process.env.LANGCHAIN_TRACING_V2="true"
// process.env.LANGCHAIN_API_KEY="your-api-key"

Instantiation​

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

const embeddings = new OpenAIEmbeddings({
  model: "text-embedding-3-small",
});

const vectorStore = new Chroma(embeddings, {
  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
});

Manage vector store​

Add items to vector store​

import type { Document } from "@langchain/core/documents";

const document1: Document = {
  pageContent: "The powerhouse of the cell is the mitochondria",
  metadata: { source: "https://example.com" },
};

const document2: Document = {
  pageContent: "Buildings are made out of brick",
  metadata: { source: "https://example.com" },
};

const document3: Document = {
  pageContent: "Mitochondria are made out of lipids",
  metadata: { source: "https://example.com" },
};

const document4: Document = {
  pageContent: "The 2024 Olympics are in Paris",
  metadata: { source: "https://example.com" },
};

const documents = [document1, document2, document3, document4];

await vectorStore.addDocuments(documents, { ids: ["1", "2", "3", "4"] });
[ '1', '2', '3', '4' ]

Delete items from vector store​

You can delete documents from Chroma by id as follows:

await vectorStore.delete({ ids: ["4"] });

Query vector store​

Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.

Query directly​

Performing a simple similarity search can be done as follows:

const filter = { source: "https://example.com" };

const similaritySearchResults = await vectorStore.similaritySearch(
  "biology",
  2,
  filter
);

for (const doc of similaritySearchResults) {
  console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
* The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* Mitochondria are made out of lipids [{"source":"https://example.com"}]

See this page for more on Chroma filter syntax.

If you want to execute a similarity search and receive the corresponding scores you can run:

const similaritySearchWithScoreResults =
  await vectorStore.similaritySearchWithScore("biology", 2, filter);

for (const [doc, score] of similaritySearchWithScoreResults) {
  console.log(
    `* [SIM=${score.toFixed(3)}] ${doc.pageContent} [${JSON.stringify(
      doc.metadata
    )}]`
  );
}
* [SIM=0.835] The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* [SIM=0.852] Mitochondria are made out of lipids [{"source":"https://example.com"}]

Query by turning into retriever​

You can also transform the vector store into a retriever for easier usage in your chains.

const retriever = vectorStore.asRetriever({
  // Optional filter
  filter: filter,
  k: 2,
});
await retriever.invoke("biology");
[
  Document {
    pageContent: 'The powerhouse of the cell is the mitochondria',
    metadata: { source: 'https://example.com' },
    id: undefined
  },
  Document {
    pageContent: 'Mitochondria are made out of lipids',
    metadata: { source: 'https://example.com' },
    id: undefined
  }
]

Usage for retrieval-augmented generation​

For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:

API reference​

For detailed documentation of all Chroma features and configurations head to the API reference

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