Chroma - Mem0 — ContextQMD

Chroma is an AI-native open-source vector database that simplifies building LLM apps by providing tools for storing, embedding, and searching embeddings with a focus on simplicity and speed. It supports both local deployment and cloud hosting through ChromaDB Cloud.

Usage

<CodeGroup> ```python Python import os from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"

config = { "vector_store": { "provider": "chroma", "config": { "collection_name": "test", "path": "db", # Optional: ChromaDB Cloud configuration # "api_key": "your-chroma-cloud-api-key", # "tenant": "your-chroma-cloud-tenant-id", } } }

m = Memory.from_config(config) messages = [ {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"}, {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."}, {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."}, {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."} ] m.add(messages, user_id="alice", metadata={"category": "movies"})


```typescript TypeScript
import { Memory } from 'mem0ai/oss';

// The Node.js client connects to a running Chroma server.
// Start one locally with: chroma run --host localhost --port 8000
const config = {
  vectorStore: {
    provider: 'chroma',
    config: {
      collectionName: 'memories',
      host: 'localhost',
      port: 8000,
      // Optional: ChromaDB Cloud configuration
      // apiKey: 'your-chroma-cloud-api-key',
      // tenant: 'your-chroma-cloud-tenant-id',
    },
  },
};

const memory = new Memory(config);
const messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });

</CodeGroup> <Note> The Node.js SDK uses the `chromadb` v3 client, which talks to a Chroma server over HTTP (local server or ChromaDB Cloud). Install it with `npm install chromadb`. Mem0 supplies the embeddings, so the collection is created without an embedding function. </Note>

Config

Here are the parameters available for configuring Chroma:

<Tabs> <Tab title="Python"> | Parameter | Description | Default Value | | --- | --- | --- | | `collection_name` | The name of the collection | `mem0` | | `client` | Custom client for Chroma | `None` | | `path` | Path for the Chroma database | `db` | | `host` | The host where the Chroma server is running | `None` | | `port` | The port where the Chroma server is running | `None` | | `api_key` | ChromaDB Cloud API key (for cloud usage) | `None` | | `tenant` | ChromaDB Cloud tenant ID (for cloud usage) | `None` | </Tab> <Tab title="TypeScript"> | Parameter | Description | Default Value | | --- | --- | --- | | `collectionName` | The name of the collection | `mem0` | | `client` | Pre-configured `ChromaClient` or `CloudClient` instance | `None` | | `host` | The host where the Chroma server is running | `None` | | `port` | The port where the Chroma server is running | `None` | | `ssl` | Whether to use SSL when connecting to the Chroma server | `false` | | `path` | Full URL of a Chroma server, e.g. `http://localhost:8000` (alternative to `host` and `port`) | `None` | | `apiKey` | ChromaDB Cloud API key (for cloud usage) | `None` | | `tenant` | ChromaDB Cloud tenant ID (for cloud usage) | `None` | | `database` | ChromaDB Cloud database name (for cloud usage) | `mem0` | </Tab> </Tabs>