Feat/mem0 support es (#2125)
This commit is contained in:
Yunsung Lee
@@ -17,6 +17,7 @@ class VectorStoreConfig(BaseModel):
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"milvus": "MilvusDBConfig",
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"azure_ai_search": "AzureAISearchConfig",
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"redis": "RedisDBConfig",
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"elasticsearch": "ElasticsearchConfig",
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}
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@model_validator(mode="after")
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224
mem0/vector_stores/elasticsearch.py
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224
mem0/vector_stores/elasticsearch.py
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@@ -0,0 +1,224 @@
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import logging
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from typing import Any, Dict, List, Optional
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try:
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from elasticsearch import Elasticsearch
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from elasticsearch.helpers import bulk
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except ImportError:
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raise ImportError(
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"Elasticsearch requires extra dependencies. Install with `pip install elasticsearch`"
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) from None
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from pydantic import BaseModel
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from mem0.configs.vector_stores.elasticsearch import ElasticsearchConfig
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from mem0.vector_stores.base import VectorStoreBase
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logger = logging.getLogger(__name__)
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class OutputData(BaseModel):
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id: str
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score: float
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payload: Dict
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class ElasticsearchDB(VectorStoreBase):
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def __init__(self, **kwargs):
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config = ElasticsearchConfig(**kwargs)
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# Initialize Elasticsearch client
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if config.cloud_id:
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self.client = Elasticsearch(
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cloud_id=config.cloud_id,
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api_key=config.api_key,
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verify_certs=config.verify_certs,
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)
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else:
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self.client = Elasticsearch(
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hosts=[f"{config.host}" if config.port is None else f"{config.host}:{config.port}"],
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basic_auth=(config.user, config.password) if (config.user and config.password) else None,
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verify_certs=config.verify_certs,
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)
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self.collection_name = config.collection_name
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self.vector_dim = config.embedding_model_dims
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# Create index only if auto_create_index is True
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if config.auto_create_index:
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self.create_index()
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def create_index(self) -> None:
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"""Create Elasticsearch index with proper mappings if it doesn't exist"""
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index_settings = {
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"mappings": {
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"properties": {
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"text": {"type": "text"},
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"embedding": {
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"type": "dense_vector",
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"dims": self.vector_dim,
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"index": True,
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"similarity": "cosine",
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},
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"metadata": {"type": "object"},
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"user_id": {"type": "keyword"},
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"hash": {"type": "keyword"},
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}
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}
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}
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if not self.client.indices.exists(index=self.collection_name):
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self.client.indices.create(index=self.collection_name, body=index_settings)
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logger.info(f"Created index {self.collection_name}")
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else:
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logger.info(f"Index {self.collection_name} already exists")
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def create_col(self, name: str, vector_size: int, distance: str = "cosine") -> None:
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"""Create a new collection (index in Elasticsearch)."""
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index_settings = {
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"mappings": {
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"properties": {
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"vector": {"type": "dense_vector", "dims": vector_size, "index": True, "similarity": "cosine"},
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"payload": {"type": "object"},
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"id": {"type": "keyword"},
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}
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}
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}
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if not self.client.indices.exists(index=name):
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self.client.indices.create(index=name, body=index_settings)
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logger.info(f"Created index {name}")
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def insert(
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self, vectors: List[List[float]], payloads: Optional[List[Dict]] = None, ids: Optional[List[str]] = None
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) -> List[OutputData]:
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"""Insert vectors into the index."""
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if not ids:
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ids = [str(i) for i in range(len(vectors))]
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if payloads is None:
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payloads = [{} for _ in range(len(vectors))]
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actions = []
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for i, (vec, id_) in enumerate(zip(vectors, ids)):
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action = {"_index": self.collection_name, "_id": id_, "vector": vec, "payload": payloads[i]}
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actions.append(action)
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bulk(self.client, actions)
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# Return OutputData objects for inserted documents
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results = []
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for i, id_ in enumerate(ids):
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results.append(
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OutputData(
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id=id_,
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score=1.0, # Default score for inserts
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payload=payloads[i],
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)
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)
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return results
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def search(self, query: List[float], limit: int = 5, filters: Optional[Dict] = None) -> List[OutputData]:
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"""Search for similar vectors using KNN search with pre-filtering."""
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search_query = {
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"query": {
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"bool": {
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"must": [
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# Exact match filters for memory isolation
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*({"term": {f"payload.{k}": v}} for k, v in (filters or {}).items()),
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# KNN vector search
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{
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"knn": {
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"vector": {
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"vector": query,
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"k": limit
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}
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}
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}
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]
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}
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}
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}
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response = self.client.search(index=self.collection_name, body=search_query)
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results = []
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for hit in response["hits"]["hits"]:
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results.append(
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OutputData(
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id=hit["_id"],
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score=hit["_score"],
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payload=hit["_source"].get("payload", {})
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)
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)
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return results
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def delete(self, vector_id: str) -> None:
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"""Delete a vector by ID."""
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self.client.delete(index=self.collection_name, id=vector_id)
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def update(self, vector_id: str, vector: Optional[List[float]] = None, payload: Optional[Dict] = None) -> None:
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"""Update a vector and its payload."""
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doc = {}
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if vector is not None:
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doc["vector"] = vector
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if payload is not None:
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doc["payload"] = payload
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self.client.update(index=self.collection_name, id=vector_id, body={"doc": doc})
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def get(self, vector_id: str) -> Optional[OutputData]:
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"""Retrieve a vector by ID."""
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try:
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response = self.client.get(index=self.collection_name, id=vector_id)
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return OutputData(
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id=response["_id"],
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score=1.0, # Default score for direct get
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payload=response["_source"].get("payload", {}),
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)
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except KeyError as e:
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logger.warning(f"Missing key in Elasticsearch response: {e}")
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return None
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except TypeError as e:
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logger.warning(f"Invalid response type from Elasticsearch: {e}")
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return None
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except Exception as e:
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logger.error(f"Unexpected error while parsing Elasticsearch response: {e}")
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return None
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def list_cols(self) -> List[str]:
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"""List all collections (indices)."""
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return list(self.client.indices.get_alias().keys())
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def delete_col(self) -> None:
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"""Delete a collection (index)."""
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self.client.indices.delete(index=self.collection_name)
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def col_info(self, name: str) -> Any:
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"""Get information about a collection (index)."""
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return self.client.indices.get(index=name)
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def list(self, filters: Optional[Dict] = None, limit: Optional[int] = None) -> List[List[OutputData]]:
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"""List all memories."""
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query: Dict[str, Any] = {"query": {"match_all": {}}}
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if filters:
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query["query"] = {"bool": {"must": [{"match": {f"payload.{k}": v}} for k, v in filters.items()]}}
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if limit:
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query["size"] = limit
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response = self.client.search(index=self.collection_name, body=query)
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results = []
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for hit in response["hits"]["hits"]:
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results.append(
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OutputData(
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id=hit["_id"],
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score=1.0, # Default score for list operation
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payload=hit["_source"].get("payload", {}),
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)
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)
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return [results]
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