Adding Native OpenSearch support for Mem0 (#2211)

Makefile

@@ -13,7 +13,7 @@ install:
 install_all:
 	poetry install
 	poetry run pip install groq together boto3 litellm ollama chromadb sentence_transformers vertexai \
-	                        google-generativeai elasticsearch
+	                        google-generativeai elasticsearch opensearch-py
 
 # Format code with ruff
 format:

docs/components/vectordbs/dbs/opensearch.mdx

@@ -0,0 +1,59 @@
+[OpenSearch](https://opensearch.org/) is an open-source, enterprise-grade search and observability suite that brings order to unstructured data at scale. OpenSearch supports k-NN (k-Nearest Neighbors) and allows you to store and retrieve high-dimensional vector embeddings efficiently.
+
+### Installation
+
+OpenSearch support requires additional dependencies. Install them with:
+
+```bash
+pip install opensearch>=2.8.0
+```
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "opensearch",
+        "config": {
+            "collection_name": "mem0",
+            "host": "localhost",
+            "port": 9200,
+            "embedding_model_dims": 1536
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+### Config
+
+Let's see the available parameters for the `opensearch` config:
+
+| 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 OpenSearch server is running    | `localhost`   |
+| `port`                 | The port where the OpenSearch server is running    | `9200`        |
+| `api_key`              | API key for authentication                         | `None`        |
+| `user`                 | Username for basic authentication                  | `None`        |
+| `password`             | Password for basic authentication                  | `None`        |
+| `verify_certs`         | Whether to verify SSL certificates                 | `False`        |
+| `auto_create_index`    | Whether to automatically create the index          | `True`        |
+| `use_ssl`              | Whether to use SSL for connection                  | `False`        |
+
+### Features
+
+- Fast and Efficient Vector Search
+- Can be deployed on-premises, in containers, or on cloud platforms like AWS OpenSearch Service.
+- Multiple Authentication and Security Methods (Basic Authentication, API Keys, LDAP, SAML, and OpenID Connect)
+- Automatic index creation with optimized mappings for vector search
+- Memory Optimization through Disk-Based Vector Search and Quantization
+- Real-Time Analytics and Observability

docs/components/vectordbs/overview.mdx

@@ -18,6 +18,7 @@ See the list of supported vector databases below.
   <Card title="Azure AI Search" href="/components/vectordbs/dbs/azure_ai_search"></Card>
   <Card title="Redis" href="/components/vectordbs/dbs/redis"></Card>
   <Card title="Elasticsearch" href="/components/vectordbs/dbs/elasticsearch"></Card>
+  <Card title="OpenSearch" href="/components/vectordbs/dbs/opensearch"></Card>
 </CardGroup>
 
 ## Usage

docs/docs.json

@@ -122,7 +122,8 @@
                           "components/vectordbs/dbs/milvus",
                           "components/vectordbs/dbs/azure_ai_search",
                           "components/vectordbs/dbs/redis",
-                          "components/vectordbs/dbs/elasticsearch"
+                          "components/vectordbs/dbs/elasticsearch",
+                          "components/vectordbs/dbs/opensearch"
                         ]
                       }
                     ]

mem0/configs/vector_stores/opensearch.py

@@ -0,0 +1,42 @@
+from typing import Any, Dict, Optional
+
+from pydantic import BaseModel, Field, model_validator
+
+
+class OpenSearchConfig(BaseModel):
+    collection_name: str = Field("mem0", description="Name of the index")
+    host: str = Field("localhost", description="OpenSearch host")
+    port: int = Field(9200, description="OpenSearch port")
+    user: Optional[str] = Field(None, description="Username for authentication")
+    password: Optional[str] = Field(None, description="Password for authentication")
+    api_key: Optional[str] = Field(None, description="API key for authentication (if applicable)")
+    embedding_model_dims: int = Field(1536, description="Dimension of the embedding vector")
+    verify_certs: bool = Field(False, description="Verify SSL certificates (default False for OpenSearch)")
+    use_ssl: bool = Field(False, description="Use SSL for connection (default False for OpenSearch)")
+    auto_create_index: bool = Field(True, description="Automatically create index during initialization")
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_auth(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        # Check if host is provided
+        if not values.get("host"):
+            raise ValueError("Host must be provided for OpenSearch")
+
+        # Authentication: Either API key or user/password must be provided
+        if not any([values.get("api_key"), (values.get("user") and values.get("password"))]):
+            raise ValueError("Either api_key or user/password must be provided for OpenSearch authentication")
+
+        return values
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_extra_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        allowed_fields = set(cls.model_fields.keys())
+        input_fields = set(values.keys())
+        extra_fields = input_fields - allowed_fields
+        if extra_fields:
+            raise ValueError(
+                f"Extra fields not allowed: {', '.join(extra_fields)}. "
+                f"Allowed fields: {', '.join(allowed_fields)}"
+            )
+        return values

mem0/utils/factory.py

@@ -68,6 +68,7 @@ class VectorStoreFactory:
         "azure_ai_search": "mem0.vector_stores.azure_ai_search.AzureAISearch",
         "redis": "mem0.vector_stores.redis.RedisDB",
         "elasticsearch": "mem0.vector_stores.elasticsearch.ElasticsearchDB",
+        "opensearch": "mem0.vector_stores.opensearch.OpenSearchDB"
     }
 
     @classmethod

mem0/vector_stores/configs.py

@@ -18,6 +18,7 @@ class VectorStoreConfig(BaseModel):
         "azure_ai_search": "AzureAISearchConfig",
         "redis": "RedisDBConfig",
         "elasticsearch": "ElasticsearchConfig",
+        "opensearch": "OpenSearchConfig",
     }
 
     @model_validator(mode="after")

mem0/vector_stores/opensearch.py

@@ -0,0 +1,189 @@
+import logging
+from typing import Any, Dict, List, Optional
+
+try:
+    from opensearchpy import OpenSearch
+    from opensearchpy.helpers import bulk
+except ImportError:
+    raise ImportError("OpenSearch requires extra dependencies. Install with `pip install opensearch-py`") from None
+
+from pydantic import BaseModel
+
+from mem0.configs.vector_stores.opensearch import OpenSearchConfig
+from mem0.vector_stores.base import VectorStoreBase
+
+logger = logging.getLogger(__name__)
+
+
+class OutputData(BaseModel):
+    id: str
+    score: float
+    payload: Dict
+
+
+class OpenSearchDB(VectorStoreBase):
+    def __init__(self, **kwargs):
+        config = OpenSearchConfig(**kwargs)
+
+        # Initialize OpenSearch client
+        self.client = OpenSearch(
+            hosts=[{"host": config.host, "port": config.port or 9200}],
+            http_auth=(config.user, config.password) if (config.user and config.password) else None,
+            use_ssl=config.use_ssl,
+            verify_certs=config.verify_certs,
+        )
+
+        self.collection_name = config.collection_name
+        self.vector_dim = config.embedding_model_dims
+
+        # Create index only if auto_create_index is True
+        if config.auto_create_index:
+            self.create_index()
+
+    def create_index(self) -> None:
+        """Create OpenSearch index with proper mappings if it doesn't exist."""
+        index_settings = {
+            # ToDo change replicas to 1
+            "settings": {
+                "index": {"number_of_replicas": 1, "number_of_shards": 5, "refresh_interval": "1s", "knn": True}
+            },
+            "mappings": {
+                "properties": {
+                    "text": {"type": "text"},
+                    "vector": {
+                        "type": "knn_vector",
+                        "dimension": self.vector_dim
+                    },
+                    "metadata": {"type": "object", "properties": {"user_id": {"type": "keyword"}}},
+                }
+            },
+        }
+
+        if not self.client.indices.exists(index=self.collection_name):
+            self.client.indices.create(index=self.collection_name, body=index_settings)
+            logger.info(f"Created index {self.collection_name}")
+        else:
+            logger.info(f"Index {self.collection_name} already exists")
+
+    def create_col(self, name: str, vector_size: int) -> None:
+        """Create a new collection (index in OpenSearch)."""
+        index_settings = {
+            "mappings": {
+                "properties": {
+                    "vector": {
+                        "type": "knn_vector",
+                        "dimension": vector_size,
+                        "method": { "engine": "lucene", "name": "hnsw", "space_type": "cosinesimil"},
+                    },
+                    "payload": {"type": "object"},
+                    "id": {"type": "keyword"},
+                }
+            }
+        }
+
+        if not self.client.indices.exists(index=name):
+            self.client.indices.create(index=name, body=index_settings)
+            logger.info(f"Created index {name}")
+
+    def insert(
+        self, vectors: List[List[float]], payloads: Optional[List[Dict]] = None, ids: Optional[List[str]] = None
+    ) -> List[OutputData]:
+        """Insert vectors into the index."""
+        if not ids:
+            ids = [str(i) for i in range(len(vectors))]
+
+        if payloads is None:
+            payloads = [{} for _ in range(len(vectors))]
+
+        actions = []
+        for i, (vec, id_) in enumerate(zip(vectors, ids)):
+            action = {
+                "_index": self.collection_name,
+                "_id": id_,
+                "_source": {
+                    "vector": vec,
+                    "metadata": payloads[i],  # Store metadata in the metadata field
+                },
+            }
+            actions.append(action)
+
+        bulk(self.client, actions)
+
+        results = []
+        for i, id_ in enumerate(ids):
+            results.append(OutputData(id=id_, score=1.0, payload=payloads[i]))
+        return results
+
+    def search(self, query: List[float], limit: int = 5, filters: Optional[Dict] = None) -> List[OutputData]:
+        """Search for similar vectors using OpenSearch k-NN search with pre-filtering."""
+        search_query = {
+            "size": limit,
+            "query": {
+                "knn": {
+                    "vector": {
+                        "vector": query,
+                        "k": limit,
+                    }
+                }
+            }
+        }
+
+        if filters:
+            filter_conditions = [{"term": {f"metadata.{key}": value}} for key, value in filters.items()]
+            search_query["query"]["knn"]["vector"]["filter"] = { "bool": {"filter": filter_conditions} }
+
+        response = self.client.search(index=self.collection_name, body=search_query)
+
+        results = [
+            OutputData(id=hit["_id"], score=hit["_score"], payload=hit["_source"].get("metadata", {}))
+            for hit in response["hits"]["hits"]
+        ]
+        return results
+
+    def delete(self, vector_id: str) -> None:
+        """Delete a vector by ID."""
+        self.client.delete(index=self.collection_name, id=vector_id)
+
+    def update(self, vector_id: str, vector: Optional[List[float]] = None, payload: Optional[Dict] = None) -> None:
+        """Update a vector and its payload."""
+        doc = {}
+        if vector is not None:
+            doc["vector"] = vector
+        if payload is not None:
+            doc["metadata"] = payload
+
+        self.client.update(index=self.collection_name, id=vector_id, body={"doc": doc})
+
+    def get(self, vector_id: str) -> Optional[OutputData]:
+        """Retrieve a vector by ID."""
+        try:
+            response = self.client.get(index=self.collection_name, id=vector_id)
+            return OutputData(id=response["_id"], score=1.0, payload=response["_source"].get("metadata", {}))
+        except Exception as e:
+            logger.error(f"Error retrieving vector {vector_id}: {e}")
+            return None
+
+    def list_cols(self) -> List[str]:
+        """List all collections (indices)."""
+        return list(self.client.indices.get_alias().keys())
+
+    def delete_col(self) -> None:
+        """Delete a collection (index)."""
+        self.client.indices.delete(index=self.collection_name)
+
+    def col_info(self, name: str) -> Any:
+        """Get information about a collection (index)."""
+        return self.client.indices.get(index=name)
+
+    def list(self, filters: Optional[Dict] = None, limit: Optional[int] = None) -> List[List[OutputData]]:
+        """List all memories."""
+        query = {"query": {"match_all": {}}}
+
+        if filters:
+            query["query"] = {"bool": {"must": [{"term": {f"metadata.{key}": value}} for key, value in filters.items()]}}
+
+        if limit:
+            query["size"] = limit
+
+        response = self.client.search(index=self.collection_name, body=query)
+        return [[OutputData(id=hit["_id"], score=1.0, payload=hit["_source"].get("metadata", {})) for hit in response["hits"]["hits"]]]