first commit
This commit is contained in:
292
vector_stores/opensearch.py
Normal file
292
vector_stores/opensearch.py
Normal file
@@ -0,0 +1,292 @@
|
||||
import logging
|
||||
import time
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
try:
|
||||
from opensearchpy import OpenSearch, RequestsHttpConnection
|
||||
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.http_auth
|
||||
if config.http_auth
|
||||
else ((config.user, config.password) if (config.user and config.password) else None),
|
||||
use_ssl=config.use_ssl,
|
||||
verify_certs=config.verify_certs,
|
||||
connection_class=RequestsHttpConnection,
|
||||
pool_maxsize=20,
|
||||
)
|
||||
|
||||
self.collection_name = config.collection_name
|
||||
self.embedding_model_dims = config.embedding_model_dims
|
||||
self.create_col(self.collection_name, self.embedding_model_dims)
|
||||
|
||||
def create_index(self) -> None:
|
||||
"""Create OpenSearch index with proper mappings if it doesn't exist."""
|
||||
index_settings = {
|
||||
"settings": {
|
||||
"index": {"number_of_replicas": 1, "number_of_shards": 5, "refresh_interval": "10s", "knn": True}
|
||||
},
|
||||
"mappings": {
|
||||
"properties": {
|
||||
"text": {"type": "text"},
|
||||
"vector_field": {
|
||||
"type": "knn_vector",
|
||||
"dimension": self.embedding_model_dims,
|
||||
"method": {"engine": "nmslib", "name": "hnsw", "space_type": "cosinesimil"},
|
||||
},
|
||||
"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 = {
|
||||
"settings": {"index.knn": True},
|
||||
"mappings": {
|
||||
"properties": {
|
||||
"vector_field": {
|
||||
"type": "knn_vector",
|
||||
"dimension": vector_size,
|
||||
"method": {"engine": "nmslib", "name": "hnsw", "space_type": "cosinesimil"},
|
||||
},
|
||||
"payload": {"type": "object"},
|
||||
"id": {"type": "keyword"},
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
if not self.client.indices.exists(index=name):
|
||||
logger.warning(f"Creating index {name}, it might take 1-2 minutes...")
|
||||
self.client.indices.create(index=name, body=index_settings)
|
||||
|
||||
# Wait for index to be ready
|
||||
max_retries = 180 # 3 minutes timeout
|
||||
retry_count = 0
|
||||
while retry_count < max_retries:
|
||||
try:
|
||||
# Check if index is ready by attempting a simple search
|
||||
self.client.search(index=name, body={"query": {"match_all": {}}})
|
||||
time.sleep(1)
|
||||
logger.info(f"Index {name} is ready")
|
||||
return
|
||||
except Exception:
|
||||
retry_count += 1
|
||||
if retry_count == max_retries:
|
||||
raise TimeoutError(f"Index {name} creation timed out after {max_retries} seconds")
|
||||
time.sleep(0.5)
|
||||
|
||||
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))]
|
||||
|
||||
results = []
|
||||
for i, (vec, id_) in enumerate(zip(vectors, ids)):
|
||||
body = {
|
||||
"vector_field": vec,
|
||||
"payload": payloads[i],
|
||||
"id": id_,
|
||||
}
|
||||
try:
|
||||
self.client.index(index=self.collection_name, body=body)
|
||||
# Force refresh to make documents immediately searchable for tests
|
||||
self.client.indices.refresh(index=self.collection_name)
|
||||
|
||||
results.append(OutputData(
|
||||
id=id_,
|
||||
score=1.0, # No score for inserts
|
||||
payload=payloads[i]
|
||||
))
|
||||
except Exception as e:
|
||||
logger.error(f"Error inserting vector {id_}: {e}")
|
||||
raise
|
||||
|
||||
return results
|
||||
|
||||
def search(
|
||||
self, query: str, vectors: List[float], limit: int = 5, filters: Optional[Dict] = None
|
||||
) -> List[OutputData]:
|
||||
"""Search for similar vectors using OpenSearch k-NN search with optional filters."""
|
||||
|
||||
# Base KNN query
|
||||
knn_query = {
|
||||
"knn": {
|
||||
"vector_field": {
|
||||
"vector": vectors,
|
||||
"k": limit * 2,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
# Start building the full query
|
||||
query_body = {"size": limit * 2, "query": None}
|
||||
|
||||
# Prepare filter conditions if applicable
|
||||
filter_clauses = []
|
||||
if filters:
|
||||
for key in ["user_id", "run_id", "agent_id"]:
|
||||
value = filters.get(key)
|
||||
if value:
|
||||
filter_clauses.append({"term": {f"payload.{key}.keyword": value}})
|
||||
|
||||
# Combine knn with filters if needed
|
||||
if filter_clauses:
|
||||
query_body["query"] = {"bool": {"must": knn_query, "filter": filter_clauses}}
|
||||
else:
|
||||
query_body["query"] = knn_query
|
||||
|
||||
try:
|
||||
# Execute search
|
||||
response = self.client.search(index=self.collection_name, body=query_body)
|
||||
|
||||
hits = response["hits"]["hits"]
|
||||
results = [
|
||||
OutputData(id=hit["_source"].get("id"), score=hit["_score"], payload=hit["_source"].get("payload", {}))
|
||||
for hit in hits[:limit] # Ensure we don't exceed limit
|
||||
]
|
||||
return results
|
||||
except Exception as e:
|
||||
logger.error(f"Error during search: {e}")
|
||||
return []
|
||||
|
||||
def delete(self, vector_id: str) -> None:
|
||||
"""Delete a vector by custom ID."""
|
||||
# First, find the document by custom ID
|
||||
search_query = {"query": {"term": {"id": vector_id}}}
|
||||
|
||||
response = self.client.search(index=self.collection_name, body=search_query)
|
||||
hits = response.get("hits", {}).get("hits", [])
|
||||
|
||||
if not hits:
|
||||
return
|
||||
|
||||
opensearch_id = hits[0]["_id"]
|
||||
|
||||
# Delete using the actual document ID
|
||||
self.client.delete(index=self.collection_name, id=opensearch_id)
|
||||
|
||||
def update(self, vector_id: str, vector: Optional[List[float]] = None, payload: Optional[Dict] = None) -> None:
|
||||
"""Update a vector and its payload using the custom 'id' field."""
|
||||
|
||||
# First, find the document by custom ID
|
||||
search_query = {"query": {"term": {"id": vector_id}}}
|
||||
|
||||
response = self.client.search(index=self.collection_name, body=search_query)
|
||||
hits = response.get("hits", {}).get("hits", [])
|
||||
|
||||
if not hits:
|
||||
return
|
||||
|
||||
opensearch_id = hits[0]["_id"] # The actual document ID in OpenSearch
|
||||
|
||||
# Prepare updated fields
|
||||
doc = {}
|
||||
if vector is not None:
|
||||
doc["vector_field"] = vector
|
||||
if payload is not None:
|
||||
doc["payload"] = payload
|
||||
|
||||
if doc:
|
||||
try:
|
||||
response = self.client.update(index=self.collection_name, id=opensearch_id, body={"doc": doc})
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
def get(self, vector_id: str) -> Optional[OutputData]:
|
||||
"""Retrieve a vector by ID."""
|
||||
try:
|
||||
search_query = {"query": {"term": {"id": vector_id}}}
|
||||
response = self.client.search(index=self.collection_name, body=search_query)
|
||||
|
||||
hits = response["hits"]["hits"]
|
||||
|
||||
if not hits:
|
||||
return None
|
||||
|
||||
return OutputData(id=hits[0]["_source"].get("id"), score=1.0, payload=hits[0]["_source"].get("payload", {}))
|
||||
except Exception as e:
|
||||
logger.error(f"Error retrieving vector {vector_id}: {str(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[OutputData]:
|
||||
try:
|
||||
"""List all memories with optional filters."""
|
||||
query: Dict = {"query": {"match_all": {}}}
|
||||
|
||||
filter_clauses = []
|
||||
if filters:
|
||||
for key in ["user_id", "run_id", "agent_id"]:
|
||||
value = filters.get(key)
|
||||
if value:
|
||||
filter_clauses.append({"term": {f"payload.{key}.keyword": value}})
|
||||
|
||||
if filter_clauses:
|
||||
query["query"] = {"bool": {"filter": filter_clauses}}
|
||||
|
||||
if limit:
|
||||
query["size"] = limit
|
||||
|
||||
response = self.client.search(index=self.collection_name, body=query)
|
||||
hits = response["hits"]["hits"]
|
||||
|
||||
# Return a flat list, not a nested array
|
||||
results = [
|
||||
OutputData(id=hit["_source"].get("id"), score=1.0, payload=hit["_source"].get("payload", {}))
|
||||
for hit in hits
|
||||
]
|
||||
return [results] # VectorStore expects tuple/list format
|
||||
except Exception as e:
|
||||
logger.error(f"Error listing vectors: {e}")
|
||||
return []
|
||||
|
||||
|
||||
def reset(self):
|
||||
"""Reset the index by deleting and recreating it."""
|
||||
logger.warning(f"Resetting index {self.collection_name}...")
|
||||
self.delete_col()
|
||||
self.create_col(self.collection_name, self.embedding_model_dims)
|
||||
Reference in New Issue
Block a user