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8a682b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 | from supabase import create_client, Client
from contextlib import asynccontextmanager
import asyncio
from typing import Optional, List, Dict, Any
import aiohttp
from dataclasses import dataclass
from langchain.schema import Document
import numpy as np
import logging
import os
from functools import lru_cache
try:
from .config.integrations import integration_config
except ImportError:
try:
from config.integrations import integration_config
except ImportError:
# Fallback for when running as standalone script
integration_config = None
logging.warning("Could not import integration_config - using defaults")
# Import centralized embedding manager
from .embedding_manager import get_embedding_manager
logger = logging.getLogger(__name__)
@dataclass
class SearchResult:
"""Structured search result"""
content: str
metadata: Dict[str, Any]
score: float
source: str
class SupabaseConnectionPool:
"""Enhanced Supabase client with connection pooling"""
def __init__(self, url: str, key: str, pool_size: int = 10):
self.url = url
self.key = key
self.pool_size = pool_size
self._pool = asyncio.Queue(maxsize=pool_size)
self._session = None
self._initialized = False
async def initialize(self):
"""Initialize connection pool"""
if self._initialized:
return
# Create custom aiohttp session with connection pooling
connector = aiohttp.TCPConnector(
limit=self.pool_size,
limit_per_host=self.pool_size,
ttl_dns_cache=300,
keepalive_timeout=30
)
self._session = aiohttp.ClientSession(connector=connector)
# Pre-create clients
for _ in range(self.pool_size):
client = create_client(self.url, self.key)
await self._pool.put(client)
self._initialized = True
logger.info(f"Supabase connection pool initialized with {self.pool_size} connections")
@asynccontextmanager
async def get_client(self):
"""Get client from pool"""
if not self._initialized:
await self.initialize()
client = await self._pool.get()
try:
yield client
finally:
await self._pool.put(client)
async def close(self):
"""Close all connections"""
if self._session:
await self._session.close()
self._initialized = False
class OptimizedVectorStore:
"""Optimized vector store with batch operations and caching"""
def __init__(self, pool: SupabaseConnectionPool):
self.pool = pool
self.config = integration_config
# Use centralized embedding manager instead of local initialization
self.embedding_manager = get_embedding_manager()
# Use functools.lru_cache for proper caching
self._embedding_cache = lru_cache(maxsize=1000)(self._compute_embedding)
self._batch_size = config.supabase.batch_size if config else 100
def _compute_embedding(self, text: str) -> np.ndarray:
"""Compute actual embeddings using centralized manager"""
embedding = self.embedding_manager.embed(text)
return np.array(embedding)
async def _get_cached_embedding(self, text: str) -> np.ndarray:
"""Get embedding with caching"""
# Use the LRU cached method
return self._embedding_cache(text)
async def batch_insert_embeddings(
self,
documents: List[Document],
batch_size: int = None
):
"""Batch insert for better performance"""
if batch_size is None:
batch_size = self._batch_size
async with self.pool.get_client() as client:
for i in range(0, len(documents), batch_size):
batch = documents[i:i + batch_size]
# Prepare batch data
batch_data = []
for doc in batch:
embedding = await self._get_cached_embedding(doc.page_content)
batch_data.append({
"node_id": doc.metadata.get("id", str(hash(doc.page_content))),
"embedding": embedding.tolist(),
"text": doc.page_content,
"metadata_": doc.metadata
})
# Use upsert for conflict resolution
try:
result = await client.table("knowledge_base").upsert(batch_data).execute()
logger.info(f"Inserted {len(batch_data)} documents")
except Exception as e:
logger.error(f"Batch insert failed: {e}")
raise
class HybridVectorSearch:
"""Combine vector similarity with metadata filtering and BM25"""
def __init__(self, pool: SupabaseConnectionPool):
self.pool = pool
# Use centralized embedding manager
self.embedding_manager = get_embedding_manager()
async def get_embedding(self, text: str) -> np.ndarray:
"""Get embedding for query using centralized manager"""
# FIXED: Use real embeddings instead of random
embedding = self.embedding_manager.embed(text)
return np.array(embedding)
async def hybrid_search(
self,
query: str,
top_k: int = 5,
metadata_filter: Optional[Dict] = None,
rerank: bool = True
) -> List[SearchResult]:
"""Enhanced search with multiple ranking strategies"""
# 1. Vector similarity search
query_embedding = await self.get_embedding(query)
async with self.pool.get_client() as client:
try:
# Use RPC for complex queries
results = await client.rpc(
'hybrid_match_documents',
{
'query_embedding': query_embedding.tolist(),
'match_count': top_k * 3, # Get more for reranking
'metadata_filter': metadata_filter or {},
'query_text': query # For BM25
}
).execute()
# Convert to SearchResult objects
search_results = []
for result in results.data:
search_results.append(SearchResult(
content=result.get('text', ''),
metadata=result.get('metadata_', {}),
score=result.get('similarity', 0.0),
source=result.get('source', 'unknown')
))
if rerank:
search_results = await self._rerank_results(query, search_results)
return search_results[:top_k]
except Exception as e:
logger.error(f"Hybrid search failed: {e}")
# Fallback to simple vector search
return await self._fallback_search(client, query_embedding, top_k)
async def _rerank_results(self, query: str, results: List[SearchResult]) -> List[SearchResult]:
"""Rerank results using additional signals"""
# Simple reranking based on content length and metadata
for result in results:
# Boost results with more metadata
metadata_boost = len(result.metadata) * 0.1
result.score += metadata_boost
# Sort by score
results.sort(key=lambda x: x.score, reverse=True)
return results
async def _fallback_search(self, client, query_embedding: np.ndarray, top_k: int) -> List[SearchResult]:
"""Fallback to simple vector similarity search"""
try:
# Simple vector similarity search
results = await client.rpc(
'match_documents',
{
'query_embedding': query_embedding.tolist(),
'match_count': top_k
}
).execute()
search_results = []
for result in results.data:
search_results.append(SearchResult(
content=result.get('text', ''),
metadata=result.get('metadata_', {}),
score=result.get('similarity', 0.0),
source=result.get('source', 'unknown')
))
return search_results
except Exception as e:
logger.error(f"Fallback search also failed: {e}")
return []
class SupabaseRealtimeManager:
"""Manage realtime subscriptions"""
def __init__(self, client: Client):
self.client = client
self.subscriptions = {}
async def subscribe_to_tool_metrics(self, callback):
"""Subscribe to tool execution metrics"""
try:
subscription = self.client.table('tool_metrics').on('INSERT', callback).subscribe()
self.subscriptions['tool_metrics'] = subscription
logger.info("Subscribed to tool metrics")
except Exception as e:
logger.error(f"Failed to subscribe to tool metrics: {e}")
async def subscribe_to_knowledge_updates(self, callback):
"""Subscribe to knowledge base updates"""
try:
subscription = self.client.table('knowledge_base').on('INSERT', callback).subscribe()
self.subscriptions['knowledge_updates'] = subscription
logger.info("Subscribed to knowledge updates")
except Exception as e:
logger.error(f"Failed to subscribe to knowledge updates: {e}")
async def unsubscribe_all(self):
"""Unsubscribe from all subscriptions"""
for name, subscription in self.subscriptions.items():
try:
await subscription.unsubscribe()
logger.info(f"Unsubscribed from {name}")
except Exception as e:
logger.error(f"Failed to unsubscribe from {name}: {e}")
self.subscriptions.clear()
async def initialize_supabase_enhanced(url: Optional[str] = None, key: Optional[str] = None):
"""Initialize enhanced Supabase components"""
# Use provided values or get from config
if url is None or key is None:
if integration_config and integration_config.supabase.is_configured():
url = integration_config.supabase.url
key = integration_config.supabase.key
else:
raise ValueError("Supabase URL and key must be provided or configured")
try:
# Initialize connection pool
pool = SupabaseConnectionPool(url, key)
await pool.initialize()
# Initialize vector store
vector_store = OptimizedVectorStore(pool)
# Initialize search
hybrid_search = HybridVectorSearch(pool)
# Initialize realtime manager
client = create_client(url, key)
realtime_manager = SupabaseRealtimeManager(client)
logger.info("Enhanced Supabase components initialized successfully")
return {
'connection_pool': pool,
'vector_store': vector_store,
'hybrid_search': hybrid_search,
'realtime_manager': realtime_manager,
'client': client
}
except Exception as e:
logger.error(f"Failed to initialize Supabase: {e}")
raise
# Global instances for backward compatibility
vector_store = None
hybrid_search = None |