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Insert embedding in Chroma

examples/basics/rag/insert-embedding-in-chroma/README.md

2025-12-182.5 KB
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Insert embedding in Chroma

This example demonstrates how to store embeddings in Chroma using Mastra. It shows how to:

  1. Create a document and chunk it
  2. Generate embeddings using OpenAI
  3. Store the embeddings in Chroma for similarity search
  4. Use Chroma-specific features like document storage and filtering

Prerequisites

  • Node.js 22.13.0 or later
  • pnpm (recommended) or npm
  • OpenAI API key
  • Optional: Chroma Cloud API key

Getting Started

  1. Clone the repository and navigate to the project directory:

    bash
    git clone https://github.com/mastra-ai/mastra
cd examples/basics/rag/insert-embedding-in-chroma

  2. Install dependencies:

    pnpm install

  3. Copy the environment variables file and add your OpenAI API key:

    bash
    cp .env.example .env

    Then edit .env and add your OpenAI API key:

    env
    OPENAI_API_KEY=sk-your-api-key-here

    If you want to use Chroma Cloud, add your Chroma Cloud API Key, tenant, and database. When you install your dependencies, you get the Chroma CLI, which can output these variables for you with chroma db connect [DB-NAME] --env-file

    env
    CHROMA_API_KEY=your-chroma-cloud-api-key
CHROMA_TENANT=your-tenant-id
CHROMA_DATABASE=your-database-name

  4. Set up your Chroma server.

    • If you are using Chroma Cloud, you are ready to go.
    • For running locally, start a Chroma server with the CLI: chroma run. See more configurations on the Chroma docs.
    • If you deployed a Chroma server yourself, edit the ChromaVector instantiation with your specific connection requirements.

  5. Run the example:

    bash
    pnpm start

How it works

The example:

  1. Creates a document from text and chunks it
  2. Generates embeddings for each chunk using OpenAI's text-embedding-3-small model
  3. Creates a collection in Chroma with the appropriate dimensions (1536 for text-embedding-3-small)
  4. Inserts the embeddings along with their metadata into Chroma

Chroma-specific Features

This example also demonstrates features specific to ChromaDB:

Document Storage: Along with embeddings and metadata, you can store the original text documents in Chroma. This is useful for retrieving the full text during queries without needing a separate document store.

The stored embeddings and documents can then be used for similarity search and retrieval in your RAG applications.