diff --git a/docs/components/vectordbs/dbs/elasticsearch.mdx b/docs/components/vectordbs/dbs/elasticsearch.mdx new file mode 100644 index 00000000..fa33a929 --- /dev/null +++ b/docs/components/vectordbs/dbs/elasticsearch.mdx @@ -0,0 +1,58 @@ +[Elasticsearch](https://www.elastic.co/) 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 additional dependencies. Install them with: + +```bash +pip install elasticsearch>=8.0.0 +``` + +### Usage + +```python +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) +m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"}) +``` + +### Config + +Let's see the available parameters for the `elasticsearch` 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 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` | +| `verify_certs` | Whether to verify SSL certificates | `True` | +| `auto_create_index` | Whether to automatically create the index | `True` | + +### 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 diff --git a/mem0/configs/vector_stores/elasticsearch.py b/mem0/configs/vector_stores/elasticsearch.py new file mode 100644 index 00000000..0406dc9f --- /dev/null +++ b/mem0/configs/vector_stores/elasticsearch.py @@ -0,0 +1,43 @@ +from typing import Any, Dict, Optional + +from pydantic import BaseModel, Field, model_validator + + +class ElasticsearchConfig(BaseModel): + collection_name: str = Field("mem0", description="Name of the index") + host: str = Field("localhost", description="Elasticsearch host") + port: int = Field(9200, description="Elasticsearch port") + user: Optional[str] = Field(None, description="Username for authentication") + password: Optional[str] = Field(None, description="Password for authentication") + cloud_id: Optional[str] = Field(None, description="Cloud ID for Elastic Cloud") + api_key: Optional[str] = Field(None, description="API key for authentication") + embedding_model_dims: int = Field(1536, description="Dimension of the embedding vector") + verify_certs: bool = Field(True, description="Verify SSL certificates") + use_ssl: bool = Field(True, description="Use SSL for connection") + 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 either cloud_id or host/port is provided + if not values.get("cloud_id") and not values.get("host"): + raise ValueError("Either cloud_id or host must be provided") + + # Check if authentication is 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") + + 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"Please input only the following fields: {', '.join(allowed_fields)}" + ) + return values diff --git a/mem0/utils/factory.py b/mem0/utils/factory.py index bdff8fe2..0489bfa6 100644 --- a/mem0/utils/factory.py +++ b/mem0/utils/factory.py @@ -66,6 +66,7 @@ class VectorStoreFactory: "milvus": "mem0.vector_stores.milvus.MilvusDB", "azure_ai_search": "mem0.vector_stores.azure_ai_search.AzureAISearch", "redis": "mem0.vector_stores.redis.RedisDB", + "elasticsearch": "mem0.vector_stores.elasticsearch.ElasticsearchDB", } @classmethod diff --git a/mem0/vector_stores/configs.py b/mem0/vector_stores/configs.py index 75768d96..4a020f50 100644 --- a/mem0/vector_stores/configs.py +++ b/mem0/vector_stores/configs.py @@ -17,6 +17,7 @@ class VectorStoreConfig(BaseModel): "milvus": "MilvusDBConfig", "azure_ai_search": "AzureAISearchConfig", "redis": "RedisDBConfig", + "elasticsearch": "ElasticsearchConfig", } @model_validator(mode="after") diff --git a/mem0/vector_stores/elasticsearch.py b/mem0/vector_stores/elasticsearch.py new file mode 100644 index 00000000..0f7c0f08 --- /dev/null +++ b/mem0/vector_stores/elasticsearch.py @@ -0,0 +1,224 @@ +import logging +from typing import Any, Dict, List, Optional + +try: + from elasticsearch import Elasticsearch + from elasticsearch.helpers import bulk +except ImportError: + raise ImportError( + "Elasticsearch requires extra dependencies. Install with `pip install elasticsearch`" + ) from None + +from pydantic import BaseModel + +from mem0.configs.vector_stores.elasticsearch import ElasticsearchConfig +from mem0.vector_stores.base import VectorStoreBase + +logger = logging.getLogger(__name__) + + +class OutputData(BaseModel): + id: str + score: float + payload: Dict + + +class ElasticsearchDB(VectorStoreBase): + def __init__(self, **kwargs): + config = ElasticsearchConfig(**kwargs) + + # Initialize Elasticsearch client + if config.cloud_id: + self.client = Elasticsearch( + cloud_id=config.cloud_id, + api_key=config.api_key, + verify_certs=config.verify_certs, + ) + else: + self.client = Elasticsearch( + hosts=[f"{config.host}" if config.port is None else f"{config.host}:{config.port}"], + basic_auth=(config.user, config.password) if (config.user and config.password) else None, + 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 Elasticsearch index with proper mappings if it doesn't exist""" + index_settings = { + "mappings": { + "properties": { + "text": {"type": "text"}, + "embedding": { + "type": "dense_vector", + "dims": self.vector_dim, + "index": True, + "similarity": "cosine", + }, + "metadata": {"type": "object"}, + "user_id": {"type": "keyword"}, + "hash": {"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, distance: str = "cosine") -> None: + """Create a new collection (index in Elasticsearch).""" + index_settings = { + "mappings": { + "properties": { + "vector": {"type": "dense_vector", "dims": vector_size, "index": True, "similarity": "cosine"}, + "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_, "vector": vec, "payload": payloads[i]} + actions.append(action) + + bulk(self.client, actions) + + # Return OutputData objects for inserted documents + results = [] + for i, id_ in enumerate(ids): + results.append( + OutputData( + id=id_, + score=1.0, # Default score for inserts + 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 KNN search with pre-filtering.""" + search_query = { + "query": { + "bool": { + "must": [ + # Exact match filters for memory isolation + *({"term": {f"payload.{k}": v}} for k, v in (filters or {}).items()), + # KNN vector search + { + "knn": { + "vector": { + "vector": query, + "k": limit + } + } + } + ] + } + } + } + + response = self.client.search(index=self.collection_name, body=search_query) + + results = [] + for hit in response["hits"]["hits"]: + results.append( + OutputData( + id=hit["_id"], + score=hit["_score"], + payload=hit["_source"].get("payload", {}) + ) + ) + + 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["payload"] = 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, # Default score for direct get + payload=response["_source"].get("payload", {}), + ) + except KeyError as e: + logger.warning(f"Missing key in Elasticsearch response: {e}") + return None + except TypeError as e: + logger.warning(f"Invalid response type from Elasticsearch: {e}") + return None + except Exception as e: + logger.error(f"Unexpected error while parsing Elasticsearch response: {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: Dict[str, Any] = {"query": {"match_all": {}}} + + if filters: + query["query"] = {"bool": {"must": [{"match": {f"payload.{k}": v}} for k, v in filters.items()]}} + + if limit: + query["size"] = limit + + response = self.client.search(index=self.collection_name, body=query) + + results = [] + for hit in response["hits"]["hits"]: + results.append( + OutputData( + id=hit["_id"], + score=1.0, # 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unittest.mock import MagicMock, patch + +import dotenv + +try: + from elasticsearch import Elasticsearch +except ImportError: + raise ImportError( + "Elasticsearch requires extra dependencies. Install with `pip install elasticsearch`" + ) from None + +from mem0.vector_stores.elasticsearch import ElasticsearchDB, OutputData + + +class TestElasticsearchDB(unittest.TestCase): + @classmethod + def setUpClass(cls): + # Load environment variables before any test + dotenv.load_dotenv() + + # Save original environment variables + cls.original_env = { + 'ES_URL': os.getenv('ES_URL', 'http://localhost:9200'), + 'ES_USERNAME': os.getenv('ES_USERNAME', 'test_user'), + 'ES_PASSWORD': os.getenv('ES_PASSWORD', 'test_password'), + 'ES_CLOUD_ID': os.getenv('ES_CLOUD_ID', 'test_cloud_id') + } + + # Set test environment variables + os.environ['ES_URL'] = 'http://localhost' + os.environ['ES_USERNAME'] = 'test_user' + os.environ['ES_PASSWORD'] = 'test_password' + + def setUp(self): + # Create a mock Elasticsearch client with proper attributes + self.client_mock = MagicMock(spec=Elasticsearch) + self.client_mock.indices = MagicMock() + self.client_mock.indices.exists = MagicMock(return_value=False) + self.client_mock.indices.create = MagicMock() + self.client_mock.indices.delete = MagicMock() + self.client_mock.indices.get_alias = MagicMock() + + # Start patches BEFORE creating ElasticsearchDB instance + patcher = patch('mem0.vector_stores.elasticsearch.Elasticsearch', return_value=self.client_mock) + self.mock_es = patcher.start() + self.addCleanup(patcher.stop) + + # Initialize ElasticsearchDB with test config and auto_create_index=False + self.es_db = ElasticsearchDB( + host=os.getenv('ES_URL'), + port=9200, + collection_name="test_collection", + embedding_model_dims=1536, + user=os.getenv('ES_USERNAME'), + password=os.getenv('ES_PASSWORD'), + verify_certs=False, + use_ssl=False, + auto_create_index=False # Disable auto creation for tests + ) + + # Reset mock counts after initialization + self.client_mock.reset_mock() + + @classmethod + def tearDownClass(cls): + # Restore original environment variables + for key, value in cls.original_env.items(): + if value is not None: + os.environ[key] = value + else: + os.environ.pop(key, None) + + def tearDown(self): + self.client_mock.reset_mock() + # No need to stop patches here as we're using addCleanup + + def test_create_index(self): + # Test when index doesn't exist + self.client_mock.indices.exists.return_value = False + self.es_db.create_index() + + # Verify index creation was called with correct settings + self.client_mock.indices.create.assert_called_once() + create_args = self.client_mock.indices.create.call_args[1] + + # Verify basic index settings + self.assertEqual(create_args["index"], "test_collection") + self.assertIn("mappings", create_args["body"]) + + # Verify field mappings + mappings = create_args["body"]["mappings"]["properties"] + self.assertEqual(mappings["text"]["type"], "text") + self.assertEqual(mappings["embedding"]["type"], "dense_vector") + self.assertEqual(mappings["embedding"]["dims"], 1536) + self.assertEqual(mappings["embedding"]["index"], True) + self.assertEqual(mappings["embedding"]["similarity"], "cosine") + self.assertEqual(mappings["metadata"]["type"], "object") + self.assertEqual(mappings["user_id"]["type"], "keyword") + self.assertEqual(mappings["hash"]["type"], "keyword") + + # Reset mocks for next test + self.client_mock.reset_mock() + + # Test when index already exists + self.client_mock.indices.exists.return_value = True + self.es_db.create_index() + + # Verify create was not called when index exists + self.client_mock.indices.create.assert_not_called() + + def test_auto_create_index(self): + # Reset mock + self.client_mock.reset_mock() + + # Test with auto_create_index=True + ElasticsearchDB( + host=os.getenv('ES_URL'), + port=9200, + collection_name="test_collection", + embedding_model_dims=1536, + user=os.getenv('ES_USERNAME'), + password=os.getenv('ES_PASSWORD'), + verify_certs=False, + use_ssl=False, + auto_create_index=True + ) + + # Verify create_index was called during initialization + self.client_mock.indices.exists.assert_called_once() + + # Reset mock + self.client_mock.reset_mock() + + # Test with auto_create_index=False + ElasticsearchDB( + host=os.getenv('ES_URL'), + port=9200, + collection_name="test_collection", + embedding_model_dims=1536, + user=os.getenv('ES_USERNAME'), + password=os.getenv('ES_PASSWORD'), + verify_certs=False, + use_ssl=False, + auto_create_index=False + ) + + # Verify create_index was not called during initialization + self.client_mock.indices.exists.assert_not_called() + + def test_insert(self): + # Test data + vectors = [[0.1] * 1536, [0.2] * 1536] + payloads = [{"key1": "value1"}, {"key2": "value2"}] + ids = ["id1", "id2"] + + # Mock bulk operation + with patch('mem0.vector_stores.elasticsearch.bulk') as mock_bulk: + mock_bulk.return_value = (2, []) # Simulate successful bulk insert + + # Perform insert + results = self.es_db.insert(vectors=vectors, payloads=payloads, ids=ids) + + # Verify bulk was called + mock_bulk.assert_called_once() + + # Verify bulk actions format + actions = mock_bulk.call_args[0][1] + self.assertEqual(len(actions), 2) + self.assertEqual(actions[0]["_index"], "test_collection") + self.assertEqual(actions[0]["_id"], "id1") + self.assertEqual(actions[0]["vector"], vectors[0]) + self.assertEqual(actions[0]["payload"], payloads[0]) + + # Verify returned objects + self.assertEqual(len(results), 2) + self.assertIsInstance(results[0], OutputData) + self.assertEqual(results[0].id, "id1") + self.assertEqual(results[0].payload, payloads[0]) + + def test_search(self): + # Mock search response + mock_response = { + "hits": { + "hits": [ + { + "_id": "id1", + "_score": 0.8, + "_source": { + "vector": [0.1] * 1536, + "payload": {"key1": "value1"} + } + } + ] + } + } + self.client_mock.search.return_value = mock_response + + # Perform search + query_vector = [0.1] * 1536 + results = self.es_db.search(query=query_vector, limit=5) + + # Verify search call + self.client_mock.search.assert_called_once() + search_args = self.client_mock.search.call_args[1] + + # Verify search parameters + self.assertEqual(search_args["index"], "test_collection") + body = search_args["body"] + self.assertIn("script_score", body["query"]) + self.assertEqual( + body["query"]["script_score"]["script"]["params"]["query_vector"], + query_vector + ) + + # Verify results + self.assertEqual(len(results), 1) + self.assertIsInstance(results[0], OutputData) + self.assertEqual(results[0].id, "id1") + self.assertEqual(results[0].score, 0.8) + self.assertEqual(results[0].payload, {"key1": "value1"}) + + def test_get(self): + # Mock get response with correct structure + mock_response = { + "_id": "id1", + "_source": { + "vector": [0.1] * 1536, + "payload": {"key": "value"}, + "text": "sample text", + "user_id": "test_user", + "hash": "sample_hash" + } + } + self.client_mock.get.return_value = mock_response + + # Perform get + result = self.es_db.get(vector_id="id1") + + # Verify get call + self.client_mock.get.assert_called_once_with( + index="test_collection", + id="id1" + ) + + # Basic assertions that should pass if OutputData is created correctly + self.assertIsNotNone(result) + self.assertTrue(hasattr(result, 'id')) + self.assertTrue(hasattr(result, 'score')) + self.assertTrue(hasattr(result, 'payload')) + + # If the above assertions pass, we can safely check the values + if result is not None: # This satisfies the linter + self.assertEqual(result.id, "id1") + self.assertEqual(result.score, 1.0) + self.assertEqual(result.payload, {"key": "value"}) + + def test_get_not_found(self): + # Mock get raising exception + self.client_mock.get.side_effect = Exception("Not found") + + # Verify get returns None when document not found + result = self.es_db.get(vector_id="nonexistent") + self.assertIsNone(result) + + def test_list(self): + # Mock search response with scores + mock_response = { + "hits": { + "hits": [ + { + "_id": "id1", + "_source": { + "vector": [0.1] * 1536, + "payload": {"key1": "value1"} + }, + "_score": 1.0 + }, + { + "_id": "id2", + "_source": { + "vector": [0.2] * 1536, + "payload": {"key2": "value2"} + }, + "_score": 0.8 + } + ] + } + } + self.client_mock.search.return_value = mock_response + + # Perform list operation + results = self.es_db.list(limit=10) + + # Verify search call + self.client_mock.search.assert_called_once() + + # Verify results + self.assertEqual(len(results), 1) # Outer list + self.assertEqual(len(results[0]), 2) # Inner list + self.assertIsInstance(results[0][0], OutputData) + self.assertEqual(results[0][0].id, "id1") + self.assertEqual(results[0][0].payload, {"key1": "value1"}) + self.assertEqual(results[0][1].id, "id2") + self.assertEqual(results[0][1].payload, {"key2": "value2"}) + + def test_delete(self): + # Perform delete + self.es_db.delete(vector_id="id1") + + # Verify delete call + self.client_mock.delete.assert_called_once_with( + index="test_collection", + id="id1" + ) + + def test_list_cols(self): + # Mock indices response + mock_indices = {"index1": {}, "index2": {}} + self.client_mock.indices.get_alias.return_value = mock_indices + + # Get collections + result = self.es_db.list_cols() + + # Verify result + self.assertEqual(result, ["index1", "index2"]) + + def test_delete_col(self): + # Delete collection + self.es_db.delete_col() + + # Verify delete call + self.client_mock.indices.delete.assert_called_once_with( + index="test_collection" + ) + \ No newline at end of file