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

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Installation

Elasticsearch support requires additional dependencies. Install them with: 
pip install elasticsearch>=8.0.0

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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 a 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"})

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Config

Let’s see the available parameters for the elasticsearch config:
ParameterDescriptionDefault Value
collection_nameThe name of the index to store the vectorsmem0
embedding_model_dimsDimensions of the embedding model1536
hostThe host where the Elasticsearch server is runninglocalhost
portThe port where the Elasticsearch server is running9200
cloud_idCloud ID for Elastic Cloud deploymentNone
api_keyAPI key for authenticationNone
userUsername for basic authenticationNone
passwordPassword for basic authenticationNone
verify_certsWhether to verify SSL certificatesTrue
auto_create_indexWhether to automatically create the indexTrue
custom_search_queryFunction returning a custom search queryNone

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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

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Custom Search Query

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.