MongoDB

MongoDB is a versatile document database that supports vector search capabilities, allowing for efficient high-dimensional similarity searches over large datasets with robust scalability and performance.

Usage

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

config = { "vector_store": { "provider": "mongodb", "config": { "db_name": "mem0-db", "collection_name": "mem0-collection", "mongo_uri": "mongodb://username:password@localhost:27017" } } }

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";

const config = {
  vectorStore: {
    provider: "mongodb",
    config: {
      dbName: "mem0-db",
      collectionName: "mem0-collection",
      url: "mongodb://username:password@localhost:27017",
    },
  },
};

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",
  },
});

Config

Here are the parameters available for configuring MongoDB:

Note: If mongo_uri (Python) or url (TypeScript) is not provided, it defaults to mongodb://localhost:27017. A local instance must be running MongoDB v8.2+ for vector search to work.

Note: The vector search index builds asynchronously after the first write. A search issued right after the first add() may return no results (and log an "index not initialized" message) until the index finishes building. This takes a few seconds on a local deployment and up to about a minute on Atlas. This is expected; the search returns results once the index is ready.