Spaces:
Sleeping
Sleeping
File size: 17,477 Bytes
cf02b2b df5eb6f cf02b2b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 |
import lancedb
import pandas as pd
from langchain_huggingface import HuggingFaceEmbeddings
from config import EMBEDDING_MODEL_NAME, LANCEDB_PATH
from typing import List, Dict, Any, Optional
import logging
import os
import uuid
from datetime import datetime
import json
logger = logging.getLogger("voicebot")
# Lazy load embedding model to reduce startup time and memory usage
embedding_model = None
def get_embedding_model():
"""Lazy load the embedding model"""
global embedding_model
if embedding_model is None:
logger.info(f"Loading embedding model: {EMBEDDING_MODEL_NAME}")
embedding_model = HuggingFaceEmbeddings(
model_name=EMBEDDING_MODEL_NAME,
model_kwargs={
"device": "cpu",
"trust_remote_code": True
},
encode_kwargs={
"normalize_embeddings": True
}
)
return embedding_model
class LanceDBService:
def __init__(self):
self.db_path = LANCEDB_PATH
self.db = None
self.embedding_model = get_embedding_model()
self._initialize_db()
def _initialize_db(self):
"""Initialize LanceDB connection and create tables if they don't exist"""
try:
os.makedirs(self.db_path, exist_ok=True)
self.db = lancedb.connect(self.db_path)
# Initialize tables
self._init_documents_table()
self._init_personas_table()
self._init_mcp_servers_table()
self._init_sessions_table()
logger.info("✅ LanceDB initialized successfully")
except Exception as e:
logger.error(f"❌ Error initializing LanceDB: {e}")
raise
def _init_documents_table(self):
"""Initialize documents table for vector storage"""
try:
if "documents" not in self.db.table_names():
# Create sample data to define schema
sample_data = pd.DataFrame({
"id": [str(uuid.uuid4())],
"content": ["sample"],
"metadata": [json.dumps({})],
"user_id": ["sample"],
"knowledge_base": ["sample"],
"filename": ["sample"],
"upload_date": [datetime.now().isoformat()],
"vector": [get_embedding_model().embed_query("sample")]
})
self.db.create_table("documents", sample_data)
# Delete sample data
tbl = self.db.open_table("documents")
tbl.delete("id = 'sample'")
except Exception as e:
logger.error(f"❌ Error initializing documents table: {e}")
def _init_personas_table(self):
"""Initialize personas table"""
try:
if "personas" not in self.db.table_names():
sample_data = pd.DataFrame({
"id": [str(uuid.uuid4())],
"user_id": ["sample"],
"name": ["sample"],
"description": ["sample"],
"icon": ["sample"],
"custom_prompt": ["sample"],
"knowledge_base": ["none"],
"language": ["en"],
"created_at": [datetime.now().isoformat()],
"updated_at": [datetime.now().isoformat()]
})
self.db.create_table("personas", sample_data)
tbl = self.db.open_table("personas")
tbl.delete("id = 'sample'")
except Exception as e:
logger.error(f"❌ Error initializing personas table: {e}")
def _init_mcp_servers_table(self):
"""Initialize MCP servers table"""
try:
if "mcp_servers" not in self.db.table_names():
sample_data = pd.DataFrame({
"id": [str(uuid.uuid4())],
"user_id": ["sample"],
"name": ["sample"],
"url": ["sample"],
"bearer_token": ["sample"],
"created_at": [datetime.now().isoformat()]
})
self.db.create_table("mcp_servers", sample_data)
tbl = self.db.open_table("mcp_servers")
tbl.delete("id = 'sample'")
except Exception as e:
logger.error(f"❌ Error initializing mcp_servers table: {e}")
def _init_sessions_table(self):
"""Initialize sessions table"""
try:
if "sessions" not in self.db.table_names():
sample_data = pd.DataFrame({
"id": [str(uuid.uuid4())],
"user_id": ["sample"],
"persona_id": ["sample"],
"persona_source": ["sample"],
"session_summary": ["sample"],
"created_at": [datetime.now().isoformat()],
"updated_at": [datetime.now().isoformat()]
})
self.db.create_table("sessions", sample_data)
tbl = self.db.open_table("sessions")
tbl.delete("id = 'sample'")
except Exception as e:
logger.error(f"❌ Error initializing sessions table: {e}")
async def add_documents(self, docs, user_id: str, knowledge_base: str, filename: str):
"""Add documents to LanceDB vector store"""
try:
documents_to_insert = []
for doc in docs:
embedding = self.embedding_model.embed_query(doc.page_content)
doc_data = {
"id": str(uuid.uuid4()),
"content": doc.page_content,
"metadata": json.dumps(doc.metadata),
"user_id": user_id,
"knowledge_base": knowledge_base,
"filename": filename,
"upload_date": datetime.now().isoformat(),
"vector": embedding
}
documents_to_insert.append(doc_data)
# Insert documents
tbl = self.db.open_table("documents")
df = pd.DataFrame(documents_to_insert)
tbl.add(df)
logger.info(f"✅ Added {len(docs)} documents to LanceDB")
return len(docs)
except Exception as e:
logger.error(f"❌ Error adding documents to LanceDB: {e}")
raise e
async def similarity_search(self, query: str, user_id: str, knowledge_base: str, k: int = 4):
"""Search for similar documents"""
try:
query_embedding = self.embedding_model.embed_query(query)
tbl = self.db.open_table("documents")
# Search with filters
results = tbl.search(query_embedding)\
.where(f"user_id = '{user_id}' AND knowledge_base = '{knowledge_base}'")\
.limit(k)\
.to_list()
docs = []
for result in results:
docs.append(type('Document', (), {
'page_content': result['content'],
'metadata': json.loads(result['metadata']) if result['metadata'] else {}
})())
return docs
except Exception as e:
logger.error(f"❌ Error searching LanceDB: {e}")
return []
async def get_user_knowledge_bases(self, user_id: str) -> List[str]:
"""Get all knowledge bases for a user"""
try:
tbl = self.db.open_table("documents")
df = tbl.search().where(f"user_id = '{user_id}'").to_pandas()
if df.empty:
return []
knowledge_bases = df['knowledge_base'].unique().tolist()
return [kb for kb in knowledge_bases if kb and kb != "none"]
except Exception as e:
logger.error(f"❌ Error fetching knowledge bases: {e}")
return []
async def get_kb_documents(self, user_id: str, kb_name: str):
"""Get all documents in a knowledge base"""
try:
tbl = self.db.open_table("documents")
df = tbl.search().where(f"user_id = '{user_id}' AND knowledge_base = '{kb_name}'").to_pandas()
documents = []
for _, row in df.iterrows():
documents.append({
"id": row['id'],
"filename": row['filename'],
"knowledge_base": row['knowledge_base'],
"upload_date": row['upload_date']
})
return documents
except Exception as e:
logger.error(f"❌ Error fetching documents: {e}")
return []
async def delete_document_from_kb(self, user_id: str, kb_name: str, filename: str):
"""Delete a document from knowledge base"""
try:
tbl = self.db.open_table("documents")
tbl.delete(f"user_id = '{user_id}' AND knowledge_base = '{kb_name}' AND filename = '{filename}'")
return True
except Exception as e:
logger.error(f"❌ Error deleting document: {e}")
return False
# Persona management methods
async def insert_persona(self, name: str, description: str, icon: str, custom_prompt: str, user_id: str):
"""Insert a new persona"""
try:
persona_data = {
"id": str(uuid.uuid4()),
"user_id": user_id,
"name": name,
"description": description,
"icon": icon,
"custom_prompt": custom_prompt,
"knowledge_base": "none",
"language": "en",
"created_at": datetime.now().isoformat(),
"updated_at": datetime.now().isoformat()
}
tbl = self.db.open_table("personas")
df = pd.DataFrame([persona_data])
tbl.add(df)
return persona_data
except Exception as e:
logger.error(f"❌ Error inserting persona: {e}")
raise e
async def get_user_personas(self, user_id: str):
"""Get all personas for a user"""
try:
tbl = self.db.open_table("personas")
df = tbl.search().where(f"user_id = '{user_id}'").to_pandas()
return df.to_dict('records')
except Exception as e:
logger.error(f"❌ Error fetching personas: {e}")
return []
# MCP Server methods
async def create_mcp_server(self, user_id: str, name: str, url: str, bearer_token: str = None):
"""Create MCP server entry"""
try:
server_data = {
"id": str(uuid.uuid4()),
"user_id": user_id,
"name": name,
"url": url,
"bearer_token": bearer_token,
"created_at": datetime.now().isoformat()
}
tbl = self.db.open_table("mcp_servers")
df = pd.DataFrame([server_data])
tbl.add(df)
return server_data
except Exception as e:
logger.error(f"❌ Error creating MCP server: {e}")
raise e
async def get_mcp_servers_for_user(self, user_id: str):
"""Get MCP servers for user"""
try:
tbl = self.db.open_table("mcp_servers")
df = tbl.search().where(f"user_id = '{user_id}'").to_pandas()
return df.to_dict('records')
except Exception as e:
logger.error(f"❌ Error fetching MCP servers: {e}")
return []
async def delete_mcp_server(self, user_id: str, server_id: str):
"""Delete MCP server"""
try:
tbl = self.db.open_table("mcp_servers")
tbl.delete(f"user_id = '{user_id}' AND id = '{server_id}'")
return True
except Exception as e:
logger.error(f"❌ Error deleting MCP server: {e}")
return False
# Session management
async def upsert_session_summary(self, user_id: str, persona_id: str, persona_source: str, summary: str):
"""Create or update session summary"""
try:
session_data = {
"id": str(uuid.uuid4()),
"user_id": user_id,
"persona_id": persona_id,
"persona_source": persona_source,
"session_summary": summary,
"created_at": datetime.now().isoformat(),
"updated_at": datetime.now().isoformat()
}
tbl = self.db.open_table("sessions")
df = pd.DataFrame([session_data])
tbl.add(df)
return session_data
except Exception as e:
logger.error(f"❌ Error upserting session: {e}")
return None
async def get_knowledge_bases(self) -> List[str]:
"""Get all unique knowledge bases"""
try:
tbl = self.db.open_table("documents")
df = tbl.search().to_pandas()
if df.empty:
return []
knowledge_bases = df['knowledge_base'].unique().tolist()
return [kb for kb in knowledge_bases if kb and kb != "none"]
except Exception as e:
logger.error(f"❌ Error getting knowledge bases: {e}")
return []
async def get_documents_by_knowledge_base(self, knowledge_base: str) -> List[dict]:
"""Get list of documents in a specific knowledge base"""
try:
tbl = self.db.open_table("documents")
df = tbl.search().where(f"knowledge_base = '{knowledge_base}'").to_pandas()
if df.empty:
return []
# Group by filename and get document info
documents = []
for filename in df['filename'].unique():
file_docs = df[df['filename'] == filename]
documents.append({
"filename": filename,
"knowledge_base": knowledge_base,
"chunks": len(file_docs),
"upload_date": file_docs['upload_date'].iloc[0] if not file_docs.empty else None
})
return documents
except Exception as e:
logger.error(f"❌ Error getting documents by knowledge base: {e}")
return []
async def delete_document(self, filename: str, knowledge_base: str, user_id: str = None):
"""Delete a document from the knowledge base"""
try:
tbl = self.db.open_table("documents")
where_clause = f"filename = '{filename}' AND knowledge_base = '{knowledge_base}'"
if user_id:
where_clause += f" AND user_id = '{user_id}'"
# Delete the document chunks
tbl.delete(where_clause)
logger.info(f"✅ Deleted document {filename} from knowledge base {knowledge_base}")
return True
except Exception as e:
logger.error(f"❌ Error deleting document: {e}")
return False
async def search_documents(self, query: str, limit: int = 5, knowledge_base: str = None):
"""Search for documents with specific query and limit"""
try:
query_embedding = self.embedding_model.embed_query(query)
tbl = self.db.open_table("documents")
# Build search query
search_query = tbl.search(query_embedding).limit(limit)
# Add knowledge base filter if specified
if knowledge_base:
search_query = search_query.where(f"knowledge_base = '{knowledge_base}'")
results = search_query.to_list()
docs = []
for result in results:
docs.append({
'content': result['content'],
'metadata': json.loads(result['metadata']) if result['metadata'] else {},
'score': result.get('_distance', 0.0),
'knowledge_base': result.get('knowledge_base', 'unknown')
})
return docs
except Exception as e:
logger.error(f"❌ Error searching documents: {e}")
return []
async def search_all_knowledge_bases(self, query: str, k: int = 4):
"""Search across all knowledge bases"""
try:
query_embedding = self.embedding_model.embed_query(query)
tbl = self.db.open_table("documents")
# Search without user filters for system-wide search
results = tbl.search(query_embedding).limit(k).to_list()
docs = []
for result in results:
docs.append(type('Document', (), {
'page_content': result['content'],
'metadata': json.loads(result['metadata']) if result['metadata'] else {}
})())
return docs
except Exception as e:
logger.error(f"❌ Error searching all knowledge bases: {e}")
return []
# Global instance
lancedb_service = LanceDBService()
|