Elasticsearch

Elasticsearch is a distributed, RESTful search and analytics engine that can efficiently store and search vector data using dense vectors and k-NN search.

Installation

Elasticsearch support requires the Elasticsearch client as an extra dependency.

pip install elasticsearch>=8.0.0

npm install mem0ai @elastic/elasticsearch

Usage

import os
from mem0 import Memory

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

config = {
    "vector_store": {
        "provider": "elasticsearch",
        "config": {
            "collection_name": "mem0",
            "host": "localhost",
            "port": 9200,
            "embedding_model_dims": 1536
        }
    }
}

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"})

import { Memory } from "mem0ai/oss";

// Set OPENAI_API_KEY in your environment.
const config = {
  embedder: {
    provider: "openai",
    config: {
      apiKey: process.env.OPENAI_API_KEY,
      model: "text-embedding-3-small",
    },
  },
  vectorStore: {
    provider: "elasticsearch",
    config: {
      collectionName: "mem0",
      embeddingModelDims: 1536,
      host: "localhost",
      port: 9200,
      // For Elastic Cloud, pass cloudId and apiKey instead of host/port.
      // For basic auth, pass username and password.
    },
  },
};

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" } });

The TypeScript SDK uses camelCase config keys: collectionName, embeddingModelDims, cloudId, apiKey, useSsl, verifyCerts, caCerts, autoCreateIndex, and username (in place of the Python user). collectionName and embeddingModelDims are required. Because the vector store embeds text with your configured embedder before writing, set an embedder in the config as shown above.

Config

Here are the parameters available for configuring Elasticsearch:

Parameter	Description	Default Value
`collection_name`	The name of the index to store the vectors	`mem0`
`embedding_model_dims`	Dimensions of the embedding model	`1536`
`host`	The host where the Elasticsearch server is running	`localhost`
`port`	The port where the Elasticsearch server is running	`9200`
`cloud_id`	Cloud ID for Elastic Cloud deployment	`None`
`api_key`	API key for authentication	`None`
`user`	Username for basic authentication	`None`
`password`	Password for basic authentication	`None`
`use_ssl`	Whether to use SSL for the connection	`True`
`ca_certs`	Path to CA bundle for SSL certificate verification	`None`
`verify_certs`	Whether to verify SSL certificates	`True`
`auto_create_index`	Whether to automatically create the index	`True`
`custom_search_query`	Function returning a custom search query	`None`
`headers`	Custom headers to include in requests	`None`

Features

Efficient vector search using Elasticsearch’s native k-NN search
Support for both local and cloud deployments (Elastic Cloud)
Multiple authentication methods (Basic Auth, API Key)
Automatic index creation with optimized mappings for vector search
Memory isolation through payload filtering
Custom search query function to customize the search query

Custom Search Query

custom_search_query is available in the Python SDK only. The TypeScript SDK runs a fixed k-NN query with optional metadata filters.

The custom_search_query parameter allows you to customize the search query when Memory.search is called. Example

import os
from typing import List, Optional, Dict
from mem0 import Memory

def custom_search_query(query: List[float], limit: int, filters: Optional[Dict]) -> Dict:
    return {
        "knn": {
            "field": "vector", 
            "query_vector": query, 
            "k": limit, 
            "num_candidates": limit * 2
        }
    }

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

config = {
    "vector_store": {
        "provider": "elasticsearch",
        "config": {
            "collection_name": "mem0",
            "host": "localhost",
            "port": 9200,
            "embedding_model_dims": 1536,
            "custom_search_query": custom_search_query
        }
    }
}

It should be a function that takes the following parameters:

query: a query vector used in Memory.search
limit: a number of results used in Memory.search
filters: a dictionary of key-value pairs used in Memory.search. You can add custom pairs for the custom search query.

The function should return a query body for the Elasticsearch search API.

​Installation

​Usage

​Config

​Features

​Custom Search Query

Installation

Usage

Config

Features

Custom Search Query