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9c13f8a af44a61 9c13f8a af44a61 c534a3c af44a61 9c13f8a af44a61 c534a3c af44a61 9c13f8a | 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 | from qdrant_client import AsyncQdrantClient
from qdrant_client.http import models
from qdrant_client.http.models import Distance, VectorParams
from typing import List, Dict, Any, Optional
import os
import logging
from uuid import UUID
logger = logging.getLogger(__name__)
class QdrantManager:
def __init__(self):
# Get Qdrant configuration from environment
self.url = os.getenv("QDRANT_URL", "http://localhost:6333")
self.api_key = os.getenv("QDRANT_API_KEY")
self.collection_name = os.getenv("QDRANT_COLLECTION_NAME", "textbook_content_embeddings")
# Initialize async Qdrant client
# For Qdrant Cloud, use HTTP API instead of gRPC to avoid compatibility issues
if self.api_key:
self.client = AsyncQdrantClient(
url=self.url,
api_key=self.api_key,
prefer_grpc=False, # Use HTTP API instead of gRPC for cloud
https=True, # Explicitly enable HTTPS for cloud
verify=True, # Verify SSL certificates
timeout=30, # Add timeout to prevent hanging
check_compatibility=False # Disable compatibility check causing warnings
)
else:
self.client = AsyncQdrantClient(
url=self.url,
prefer_grpc=False, # Use HTTP API instead of gRPC for cloud
https=True, # Explicitly enable HTTPS for cloud
verify=True, # Verify SSL certificates
timeout=30, # Add timeout to prevent hanging
check_compatibility=False # Disable compatibility check causing warnings
)
async def health(self) -> bool:
"""
Check if Qdrant is available
"""
try:
# Use get_collections() to check connectivity
await self.client.get_collections()
return True
except Exception as e:
logger.error(f"Qdrant health check failed: {e}")
return False
async def create_collection(self):
"""
Create the textbook content embeddings collection if it doesn't exist
"""
try:
# Check if collection exists
collections = await self.client.get_collections()
collection_names = [collection.name for collection in collections.collections]
if self.collection_name not in collection_names:
# Create collection with specified vector size and payload schema
await self.client.create_collection(
collection_name=self.collection_name,
vectors_config=VectorParams(size=768, distance=Distance.COSINE), # Using 768 for Gemini embeddings
)
# Create payload index for faster filtering
await self.client.create_payload_index(
collection_name=self.collection_name,
field_name="textbook_content_id",
field_schema=models.PayloadSchemaType.KEYWORD
)
await self.client.create_payload_index(
collection_name=self.collection_name,
field_name="chapter_id",
field_schema=models.PayloadSchemaType.KEYWORD
)
await self.client.create_payload_index(
collection_name=self.collection_name,
field_name="section_path",
field_schema=models.PayloadSchemaType.KEYWORD
)
logger.info(f"Created Qdrant collection: {self.collection_name}")
else:
logger.info(f"Qdrant collection {self.collection_name} already exists")
except Exception as e:
logger.error(f"Failed to create Qdrant collection: {e}")
raise
async def store_embedding(self,
embedding_id: str,
vector: List[float],
textbook_content_id: str,
chapter_id: str,
section_path: str,
token_count: int,
content_type: str,
chunk_index: int,
content: str = "") -> bool:
"""
Store an embedding in Qdrant with content text in payload
"""
try:
points = [
models.PointStruct(
id=embedding_id,
vector=vector,
payload={
"textbook_content_id": textbook_content_id,
"chapter_id": chapter_id,
"section_path": section_path,
"token_count": token_count,
"content_type": content_type,
"chunk_index": chunk_index,
"content": content # Store actual text for RAG retrieval
}
)
]
await self.client.upsert(
collection_name=self.collection_name,
points=points
)
return True
except Exception as e:
logger.error(f"Failed to store embedding: {e}")
return False
async def search_similar(self,
query_vector: List[float],
top_k: int = 5,
min_score: float = 0.3,
filters: Optional[Dict[str, Any]] = None) -> List[Dict[str, Any]]:
"""
Search for similar embeddings in Qdrant using query_points
"""
try:
# Build filters if provided
qdrant_filters = None
if filters:
filter_conditions = []
for key, value in filters.items():
filter_conditions.append(
models.FieldCondition(
key=key,
match=models.MatchValue(value=value)
)
)
if filter_conditions:
qdrant_filters = models.Filter(
must=filter_conditions
)
# Use query_points for AsyncQdrantClient
search_results = await self.client.query_points(
collection_name=self.collection_name,
query=query_vector,
limit=top_k,
score_threshold=min_score,
with_payload=True,
query_filter=qdrant_filters
)
# Format results - query_points returns QueryResponse with .points attribute
formatted_results = []
for result in search_results.points:
formatted_results.append({
"id": result.id,
"score": result.score,
"payload": result.payload
})
return formatted_results
except Exception as e:
logger.error(f"Search failed: {e}")
import traceback
traceback.print_exc()
return []
async def get_embedding(self, embedding_id: str) -> Optional[Dict[str, Any]]:
"""
Get a specific embedding by ID
"""
try:
records = await self.client.retrieve(
collection_name=self.collection_name,
ids=[embedding_id],
with_payload=True,
with_vectors=True
)
if records:
record = records[0]
return {
"id": record.id,
"vector": record.vector,
"payload": record.payload
}
return None
except Exception as e:
logger.error(f"Failed to get embedding: {e}")
return None
async def delete_embedding(self, embedding_id: str) -> bool:
"""
Delete an embedding by ID
"""
try:
await self.client.delete(
collection_name=self.collection_name,
points_selector=models.PointIdsList(
points=[embedding_id]
)
)
return True
except Exception as e:
logger.error(f"Failed to delete embedding: {e}")
return False
async def close(self):
"""
Close the Qdrant client connection
"""
await self.client.close()
# Lazy-initialized global instance
_qdrant_manager: Optional[QdrantManager] = None
def get_qdrant_manager() -> QdrantManager:
"""Get or create the Qdrant manager instance (lazy initialization)"""
global _qdrant_manager
if _qdrant_manager is None:
_qdrant_manager = QdrantManager()
return _qdrant_manager
# For backward compatibility, use property pattern
class _QdrantManagerProxy:
"""Proxy class for lazy initialization of QdrantManager"""
def __getattr__(self, name):
return getattr(get_qdrant_manager(), name)
qdrant_manager = _QdrantManagerProxy() |