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Multi-tenant vector store for SimpleMem MCP Server
Uses LanceDB for vector storage with per-user table isolation
"""
import os
from typing import List, Optional, Dict, Any
from datetime import datetime
import lancedb
import pyarrow as pa
from ..auth.models import MemoryEntry
# LanceDB schema for memory entries
def get_memory_schema(embedding_dimension: int = 2560) -> pa.Schema:
"""Get PyArrow schema for memory entries"""
return pa.schema([
pa.field("entry_id", pa.string()),
pa.field("lossless_restatement", pa.string()),
pa.field("keywords", pa.list_(pa.string())),
pa.field("timestamp", pa.string()),
pa.field("location", pa.string()),
pa.field("persons", pa.list_(pa.string())),
pa.field("entities", pa.list_(pa.string())),
pa.field("topic", pa.string()),
pa.field("vector", pa.list_(pa.float32(), embedding_dimension)),
pa.field("created_at", pa.string()),
])
class MultiTenantVectorStore:
"""
Multi-tenant vector storage with per-user table isolation.
Each user gets their own LanceDB table for complete data isolation.
"""
def __init__(
self,
db_path: str = "./data/lancedb",
embedding_dimension: int = 2560,
):
self.db_path = db_path
self.embedding_dimension = embedding_dimension
os.makedirs(db_path, exist_ok=True)
# Connect to LanceDB
self.db = lancedb.connect(db_path)
# Cache for opened tables
self._tables: Dict[str, Any] = {}
def _get_table(self, table_name: str) -> Any:
"""Get or create a user's table"""
if table_name not in self._tables:
if table_name in self.db.table_names():
self._tables[table_name] = self.db.open_table(table_name)
else:
# Create new table with schema
schema = get_memory_schema(self.embedding_dimension)
self._tables[table_name] = self.db.create_table(
table_name,
schema=schema,
)
return self._tables[table_name]
async def add_entries(
self,
table_name: str,
entries: List[MemoryEntry],
embeddings: List[List[float]],
) -> int:
"""
Add memory entries to a user's table
Args:
table_name: User's table name
entries: List of MemoryEntry objects
embeddings: List of embedding vectors
Returns:
Number of entries added
"""
if len(entries) != len(embeddings):
raise ValueError("Number of entries must match number of embeddings")
if not entries:
return 0
table = self._get_table(table_name)
created_at = datetime.utcnow().isoformat()
# Build records
records = []
for entry, embedding in zip(entries, embeddings):
records.append({
"entry_id": entry.entry_id,
"lossless_restatement": entry.lossless_restatement,
"keywords": entry.keywords or [],
"timestamp": entry.timestamp or "",
"location": entry.location or "",
"persons": entry.persons or [],
"entities": entry.entities or [],
"topic": entry.topic or "",
"vector": embedding,
"created_at": created_at,
})
# Add to table
table.add(records)
return len(records)
async def semantic_search(
self,
table_name: str,
query_embedding: List[float],
top_k: int = 25,
) -> List[MemoryEntry]:
"""
Perform semantic search using vector similarity
Args:
table_name: User's table name
query_embedding: Query embedding vector
top_k: Number of results to return
Returns:
List of matching MemoryEntry objects
"""
table = self._get_table(table_name)
try:
# Check if table has data
if table.count_rows() == 0:
return []
results = (
table.search(query_embedding)
.limit(top_k)
.to_pandas()
)
entries = []
for _, row in results.iterrows():
entries.append(MemoryEntry(
entry_id=row["entry_id"],
lossless_restatement=row["lossless_restatement"],
keywords=list(row["keywords"]) if row["keywords"] is not None else [],
timestamp=row["timestamp"] if row["timestamp"] else None,
location=row["location"] if row["location"] else None,
persons=list(row["persons"]) if row["persons"] is not None else [],
entities=list(row["entities"]) if row["entities"] is not None else [],
topic=row["topic"] if row["topic"] else None,
))
return entries
except Exception as e:
print(f"Semantic search error: {e}")
return []
async def keyword_search(
self,
table_name: str,
keywords: List[str],
top_k: int = 5,
) -> List[MemoryEntry]:
"""
Perform keyword-based search (BM25-style matching)
Args:
table_name: User's table name
keywords: List of keywords to match
top_k: Number of results to return
Returns:
List of matching MemoryEntry objects
"""
table = self._get_table(table_name)
try:
if table.count_rows() == 0:
return []
# Load all entries for keyword matching
df = table.to_pandas()
# Score each entry
scores = []
for idx, row in df.iterrows():
score = 0
entry_keywords = set(k.lower() for k in (row["keywords"] or []))
entry_text = row["lossless_restatement"].lower()
for kw in keywords:
kw_lower = kw.lower()
# Keyword list match: 2 points
if kw_lower in entry_keywords:
score += 2
# Text match: 1 point
if kw_lower in entry_text:
score += 1
scores.append((idx, score))
# Sort by score and get top-k
scores.sort(key=lambda x: x[1], reverse=True)
top_indices = [idx for idx, score in scores[:top_k] if score > 0]
entries = []
for idx in top_indices:
row = df.iloc[idx]
entries.append(MemoryEntry(
entry_id=row["entry_id"],
lossless_restatement=row["lossless_restatement"],
keywords=list(row["keywords"]) if row["keywords"] is not None else [],
timestamp=row["timestamp"] if row["timestamp"] else None,
location=row["location"] if row["location"] else None,
persons=list(row["persons"]) if row["persons"] is not None else [],
entities=list(row["entities"]) if row["entities"] is not None else [],
topic=row["topic"] if row["topic"] else None,
))
return entries
except Exception as e:
print(f"Keyword search error: {e}")
return []
async def structured_search(
self,
table_name: str,
persons: Optional[List[str]] = None,
location: Optional[str] = None,
entities: Optional[List[str]] = None,
timestamp_start: Optional[str] = None,
timestamp_end: Optional[str] = None,
top_k: int = 5,
) -> List[MemoryEntry]:
"""
Perform structured/metadata-based search
Args:
table_name: User's table name
persons: Filter by person names
location: Filter by location
entities: Filter by entities
timestamp_start: Start of timestamp range
timestamp_end: End of timestamp range
top_k: Number of results to return
Returns:
List of matching MemoryEntry objects
"""
table = self._get_table(table_name)
try:
if table.count_rows() == 0:
return []
df = table.to_pandas()
# Apply filters
mask = [True] * len(df)
if persons:
persons_lower = set(p.lower() for p in persons)
for i, row in df.iterrows():
row_persons = set(p.lower() for p in (row["persons"] or []))
if not persons_lower.intersection(row_persons):
mask[i] = False
if location:
location_lower = location.lower()
for i, row in df.iterrows():
if mask[i] and row["location"]:
if location_lower not in row["location"].lower():
mask[i] = False
elif mask[i]:
mask[i] = False
if entities:
entities_lower = set(e.lower() for e in entities)
for i, row in df.iterrows():
if mask[i]:
row_entities = set(e.lower() for e in (row["entities"] or []))
if not entities_lower.intersection(row_entities):
mask[i] = False
if timestamp_start:
for i, row in df.iterrows():
if mask[i] and row["timestamp"]:
if row["timestamp"] < timestamp_start:
mask[i] = False
if timestamp_end:
for i, row in df.iterrows():
if mask[i] and row["timestamp"]:
if row["timestamp"] > timestamp_end:
mask[i] = False
# Get filtered results
filtered_df = df[[m for m in mask]][:top_k]
entries = []
for _, row in filtered_df.iterrows():
entries.append(MemoryEntry(
entry_id=row["entry_id"],
lossless_restatement=row["lossless_restatement"],
keywords=list(row["keywords"]) if row["keywords"] is not None else [],
timestamp=row["timestamp"] if row["timestamp"] else None,
location=row["location"] if row["location"] else None,
persons=list(row["persons"]) if row["persons"] is not None else [],
entities=list(row["entities"]) if row["entities"] is not None else [],
topic=row["topic"] if row["topic"] else None,
))
return entries
except Exception as e:
print(f"Structured search error: {e}")
return []
async def get_all_entries(self, table_name: str) -> List[MemoryEntry]:
"""Get all entries from a user's table"""
table = self._get_table(table_name)
try:
if table.count_rows() == 0:
return []
df = table.to_pandas()
entries = []
for _, row in df.iterrows():
entries.append(MemoryEntry(
entry_id=row["entry_id"],
lossless_restatement=row["lossless_restatement"],
keywords=list(row["keywords"]) if row["keywords"] is not None else [],
timestamp=row["timestamp"] if row["timestamp"] else None,
location=row["location"] if row["location"] else None,
persons=list(row["persons"]) if row["persons"] is not None else [],
entities=list(row["entities"]) if row["entities"] is not None else [],
topic=row["topic"] if row["topic"] else None,
))
return entries
except Exception as e:
print(f"Get all entries error: {e}")
return []
async def count_entries(self, table_name: str) -> int:
"""Count entries in a user's table"""
table = self._get_table(table_name)
try:
return table.count_rows()
except Exception:
return 0
async def clear_table(self, table_name: str) -> bool:
"""Clear all entries from a user's table"""
try:
if table_name in self._tables:
del self._tables[table_name]
if table_name in self.db.table_names():
self.db.drop_table(table_name)
# Recreate empty table
self._get_table(table_name)
return True
except Exception as e:
print(f"Clear table error: {e}")
return False
async def delete_table(self, table_name: str) -> bool:
"""Completely delete a user's table"""
try:
if table_name in self._tables:
del self._tables[table_name]
if table_name in self.db.table_names():
self.db.drop_table(table_name)
return True
except Exception as e:
print(f"Delete table error: {e}")
return False
def get_stats(self, table_name: str) -> Dict[str, Any]:
"""Get statistics for a user's table"""
try:
table = self._get_table(table_name)
count = table.count_rows()
return {
"table_name": table_name,
"entry_count": count,
"embedding_dimension": self.embedding_dimension,
}
except Exception as e:
return {
"table_name": table_name,
"entry_count": 0,
"embedding_dimension": self.embedding_dimension,
"error": str(e),
}
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