File size: 9,663 Bytes
eefb354 3b5d2e9 eefb354 5dd2ee5 4cab845 5dd2ee5 4cab845 5dd2ee5 4cab845 5dd2ee5 eefb354 3b5d2e9 4cab845 3b5d2e9 4cab845 3b5d2e9 4cab845 3b5d2e9 eefb354 43fe2fe eefb354 3b5d2e9 c72956b 3b5d2e9 c72956b 3b5d2e9 c72956b 3b5d2e9 |
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 |
from qdrant_client import QdrantClient, models
import os
import uuid
import logging
logger = logging.getLogger(__name__)
# --- Qdrant Client Initialization ---
def get_qdrant_client():
"""Initializes and returns the Qdrant client, prioritizing Cloud over local."""
qdrant_url = os.environ.get("QDRANT_URL")
qdrant_api_key = os.environ.get("QDRANT_API_KEY")
# Priority 1: Qdrant Cloud (production)
if qdrant_url and qdrant_api_key:
try:
client = QdrantClient(url=qdrant_url, api_key=qdrant_api_key)
logger.info(f"Connected to Qdrant Cloud at {qdrant_url}")
return client
except Exception as e:
logger.error(f"Failed to connect to Qdrant Cloud with provided credentials: {e}")
raise # If cloud credentials are provided, failure should be fatal.
# Priority 2: Local Docker container
qdrant_host = os.environ.get("QDRANT_HOST")
if qdrant_host and qdrant_host != "localhost":
try:
client = QdrantClient(host=qdrant_host, port=6333)
logger.info(f"Connected to local Qdrant server at {qdrant_host}")
return client
except Exception as e:
logger.warning(f"Failed to connect to local Qdrant server at {qdrant_host}: {e}")
# Priority 3: Local file-based storage (fallback for development)
try:
data_dir = "/app/data/qdrant"
os.makedirs(data_dir, exist_ok=True)
client = QdrantClient(path=data_dir)
logger.info(f"Using file-based Qdrant client at {data_dir}")
return client
except Exception as e:
logger.warning(f"Failed to create file-based Qdrant client: {e}")
# Final fallback: in-memory
client = QdrantClient(":memory:")
logger.info("Using in-memory Qdrant client as final fallback")
return client
# --- Collection Management ---
def create_collection_if_not_exists(client: QdrantClient, collection_name: str, vector_size: int):
"""Creates a Qdrant collection if it doesn't already exist."""
try:
client.get_collection(collection_name=collection_name)
logger.info(f"Collection '{collection_name}' already exists")
except Exception as e:
# If the collection does not exist, this will raise an exception
logger.info(f"Collection '{collection_name}' does not exist, creating it...")
try:
client.create_collection(
collection_name=collection_name,
vectors_config=models.VectorParams(size=vector_size, distance=models.Distance.COSINE),
)
logger.info(f"Created new collection '{collection_name}'")
except Exception as create_error:
logger.error(f"Failed to create collection '{collection_name}': {str(create_error)}")
raise
# --- User-Specific Collection Management ---
def get_user_collection_name(user_id: uuid.UUID) -> str:
"""
Generate a user-specific collection name.
Args:
user_id: The user's UUID
Returns:
Collection name in format 'user_{user_id_without_hyphens}'
"""
# Convert UUID to string and remove hyphens for valid collection name
user_id_str = str(user_id).replace('-', '_')
return f"user_{user_id_str}"
def ensure_user_collection_exists(client: QdrantClient, user_id: uuid.UUID, vector_size: int) -> str:
"""
Ensure that a user-specific collection exists in Qdrant.
Args:
client: Qdrant client instance
user_id: The user's UUID
vector_size: Size of the embedding vectors
Returns:
The collection name that was created or verified
"""
try:
collection_name = get_user_collection_name(user_id)
logger.info(f"Ensuring collection exists for user {user_id}: {collection_name}")
try:
# Check if collection exists
client.get_collection(collection_name=collection_name)
logger.info(f"User collection '{collection_name}' already exists for user {user_id}")
except Exception as get_error:
# Collection doesn't exist, create it
logger.info(f"Collection '{collection_name}' does not exist, creating it for user {user_id}")
try:
client.create_collection(
collection_name=collection_name,
vectors_config=models.VectorParams(size=vector_size, distance=models.Distance.COSINE),
)
logger.info(f"Created new user collection '{collection_name}' for user {user_id}")
except Exception as create_error:
logger.error(f"Failed to create collection '{collection_name}' for user {user_id}: {str(create_error)}")
raise create_error
return collection_name
except Exception as e:
logger.error(f"Error in ensure_user_collection_exists: {str(e)}")
logger.error(f"Function called with client={type(client)}, user_id={user_id}, vector_size={vector_size}")
raise
def collection_exists(client: QdrantClient, collection_name: str) -> bool:
"""
Check if a collection exists in Qdrant.
Args:
client: Qdrant client instance
collection_name: Name of the collection to check
Returns:
True if collection exists, False otherwise
"""
try:
client.get_collection(collection_name=collection_name)
return True
except Exception:
return False
# --- Vector Operations ---
def upsert_vectors(client: QdrantClient, collection_name: str, vectors, payloads):
"""Upserts vectors and their payloads into the specified collection."""
client.upsert(
collection_name=collection_name,
points=models.Batch(
ids=list(range(len(vectors))), # Generate sequential integer IDs
vectors=vectors,
payloads=payloads
),
wait=True
)
def search_vectors(client: QdrantClient, collection_name: str, query_vector, limit: int = 5):
"""
Searches for similar vectors in the collection.
Args:
client: Qdrant client instance
collection_name: Name of the collection to search
query_vector: Query vector for similarity search
limit: Maximum number of results to return
Returns:
Search results, or empty list if collection doesn't exist or is empty
"""
try:
# Check if collection exists first
if not collection_exists(client, collection_name):
logger.warning(f"Collection '{collection_name}' does not exist")
return []
# Check if collection has any points
collection_info = client.get_collection(collection_name)
if collection_info.points_count == 0:
logger.info(f"Collection '{collection_name}' is empty")
return []
# Convert numpy array to list if needed
query_vector_list = query_vector.tolist() if hasattr(query_vector, 'tolist') else query_vector
# Qdrant Cloud uses the newer API (v1.7+)
# Use query_points which is the current method
try:
logger.debug(f"Attempting query_points on collection '{collection_name}'")
result = client.query_points(
collection_name=collection_name,
query=query_vector_list,
limit=limit,
with_payload=True
)
# Extract points from QueryResponse
results = result.points if hasattr(result, 'points') else result
logger.info(f"Found {len(results)} results using query_points in collection '{collection_name}'")
return results
except AttributeError as attr_err:
# Fallback to older search method for backward compatibility
logger.warning(f"query_points failed ({attr_err}), falling back to search method")
try:
results = client.search(
collection_name=collection_name,
query_vector=query_vector_list,
limit=limit,
with_payload=True
)
logger.info(f"Found {len(results)} results using search in collection '{collection_name}'")
return results
except Exception as search_err:
logger.error(f"Both query_points and search failed. search error: {search_err}")
raise
except Exception as e:
logger.error(f"Error searching collection '{collection_name}': {str(e)}")
logger.error(f"Error type: {type(e).__name__}")
import traceback
logger.error(f"Traceback: {traceback.format_exc()}")
return []
def get_collection_info(client: QdrantClient, collection_name: str) -> dict:
"""
Get information about a collection.
Args:
client: Qdrant client instance
collection_name: Name of the collection
Returns:
Dictionary with collection information or None if collection doesn't exist
"""
try:
collection_info = client.get_collection(collection_name)
return {
"name": collection_name,
"points_count": collection_info.points_count,
"status": collection_info.status,
"vectors_count": collection_info.vectors_count if hasattr(collection_info, 'vectors_count') else None
}
except Exception as e:
logger.error(f"Error getting collection info for '{collection_name}': {str(e)}")
return None
|