Spaces:
Sleeping
Sleeping
File size: 12,142 Bytes
4e3c158 | 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 | #!/usr/bin/env python3
"""
LanceDB Conversation Retrieval Script
This script demonstrates how to retrieve conversations from LanceDB
using the existing memory system integration in Atom.
Features:
- Retrieve conversation history for specific users
- Search conversations using semantic similarity
- Export conversation data in various formats
- Test LanceDB connectivity and health
"""
import argparse
import asyncio
from datetime import datetime
import json
import os
import sys
from typing import Dict, List, Optional
# Add backend to path to import the necessary modules
sys.path.append(os.path.join(os.path.dirname(__file__), "backend"))
try:
from backend.python_api_service.lancedb_handler import (
get_conversation_history,
get_lancedb_connection,
search_conversation_context,
store_conversation_context,
)
LANCEDB_AVAILABLE = True
except ImportError as e:
print(f"Warning: LanceDB modules not available: {e}")
LANCEDB_AVAILABLE = False
class LanceDBConversationRetriever:
"""Class to handle conversation retrieval from LanceDB"""
def __init__(self, db_path: str = "data/lancedb"):
self.db_path = db_path
self.db_connection = None
async def initialize(self):
"""Initialize LanceDB connection"""
if not LANCEDB_AVAILABLE:
print("LanceDB is not available. Please check the installation.")
return False
try:
self.db_connection = await get_lancedb_connection(self.db_path)
print(f"✅ Successfully connected to LanceDB at {self.db_path}")
return True
except Exception as e:
print(f"❌ Failed to connect to LanceDB: {e}")
return False
async def get_user_conversations(
self,
user_id: str,
session_id: Optional[str] = None,
limit: int = 50,
offset: int = 0,
) -> Dict:
"""Get conversation history for a specific user"""
if not self.db_connection:
return {"status": "error", "message": "LanceDB not connected"}
try:
result = await get_conversation_history(
self.db_connection, user_id, session_id, limit, offset
)
return result
except Exception as e:
return {"status": "error", "message": f"Failed to get conversations: {e}"}
async def search_conversations(
self,
query_text: str,
user_id: str,
session_id: Optional[str] = None,
limit: int = 10,
) -> Dict:
"""Search conversations using semantic similarity"""
if not self.db_connection:
return {"status": "error", "message": "LanceDB not connected"}
try:
# Generate a simple embedding for the query (placeholder)
# In production, this would use a proper embedding model
query_embedding = [0.1] * 384 # Standard embedding dimension
result = await search_conversation_context(
self.db_connection, query_embedding, user_id, session_id, limit
)
return result
except Exception as e:
return {
"status": "error",
"message": f"Failed to search conversations: {e}",
}
async def get_conversation_stats(self, user_id: str) -> Dict:
"""Get conversation statistics for a user"""
if not self.db_connection:
return {"status": "error", "message": "LanceDB not connected"}
try:
# Get all conversations for the user
result = await get_conversation_history(
self.db_connection, user_id, limit=1000
)
if result.get("status") != "success":
return result
conversations = result.get("conversations", [])
# Calculate statistics
stats = {
"total_conversations": len(conversations),
"user_id": user_id,
"first_conversation": None,
"last_conversation": None,
"message_counts": {"user": 0, "assistant": 0, "system": 0},
"timeline": [],
}
if conversations:
# Sort by timestamp
sorted_conv = sorted(
conversations, key=lambda x: x.get("timestamp", "")
)
stats["first_conversation"] = sorted_conv[0].get("timestamp")
stats["last_conversation"] = sorted_conv[-1].get("timestamp")
# Count messages by role
for conv in conversations:
role = conv.get("role", "user")
stats["message_counts"][role] = (
stats["message_counts"].get(role, 0) + 1
)
# Add to timeline
stats["timeline"].append(
{
"timestamp": conv.get("timestamp"),
"role": role,
"content_preview": conv.get("content", "")[:100] + "..."
if len(conv.get("content", "")) > 100
else conv.get("content", ""),
}
)
return {"status": "success", "stats": stats}
except Exception as e:
return {"status": "error", "message": f"Failed to get stats: {e}"}
async def test_lancedb_connection():
"""Test LanceDB connection and basic functionality"""
print("🧪 Testing LanceDB Connection...")
retriever = LanceDBConversationRetriever()
connected = await retriever.initialize()
if not connected:
print("❌ LanceDB connection test failed")
return False
print("✅ LanceDB connection test passed")
return True
async def retrieve_user_conversations(user_id: str, limit: int = 20):
"""Retrieve and display conversations for a specific user"""
print(f"📝 Retrieving conversations for user: {user_id}")
retriever = LanceDBConversationRetriever()
await retriever.initialize()
# Get conversation history
result = await retriever.get_user_conversations(user_id, limit=limit)
if result.get("status") == "success":
conversations = result.get("conversations", [])
total_count = result.get("total_count", 0)
print(f"📊 Found {len(conversations)} conversations (total: {total_count})")
print("-" * 80)
for i, conv in enumerate(conversations, 1):
timestamp = conv.get("timestamp", "Unknown")
role = conv.get("role", "unknown").upper()
content = conv.get("content", "")
session_id = conv.get("session_id", "N/A")
print(f"{i}. [{timestamp}] {role} (Session: {session_id})")
print(f" {content[:200]}{'...' if len(content) > 200 else ''}")
print()
else:
print(f"❌ Failed to retrieve conversations: {result.get('message')}")
async def search_user_conversations(user_id: str, query: str, limit: int = 10):
"""Search conversations for a specific user"""
print(f"🔍 Searching conversations for user '{user_id}': '{query}'")
retriever = LanceDBConversationRetriever()
await retriever.initialize()
# Search conversations
result = await retriever.search_conversations(query, user_id, limit=limit)
if result.get("status") == "success":
results = result.get("results", [])
print(f"📊 Found {len(results)} relevant conversations")
print("-" * 80)
for i, res in enumerate(results, 1):
timestamp = res.get("timestamp", "Unknown")
role = res.get("role", "unknown").upper()
content = res.get("content", "")
similarity = res.get("similarity_score", 0)
session_id = res.get("session_id", "N/A")
print(f"{i}. [{timestamp}] {role} (Session: {session_id})")
print(f" Similarity: {similarity:.3f}")
print(f" {content[:200]}{'...' if len(content) > 200 else ''}")
print()
else:
print(f"❌ Failed to search conversations: {result.get('message')}")
async def export_conversations(user_id: str, output_file: str):
"""Export conversations to JSON file"""
print(f"💾 Exporting conversations for user '{user_id}' to {output_file}")
retriever = LanceDBConversationRetriever()
await retriever.initialize()
# Get all conversations (with large limit)
result = await retriever.get_user_conversations(user_id, limit=1000)
if result.get("status") == "success":
conversations = result.get("conversations", [])
# Prepare export data
export_data = {
"export_timestamp": datetime.now().isoformat(),
"user_id": user_id,
"total_conversations": len(conversations),
"conversations": conversations,
}
# Write to file
with open(output_file, "w", encoding="utf-8") as f:
json.dump(export_data, f, indent=2, ensure_ascii=False)
print(
f"✅ Successfully exported {len(conversations)} conversations to {output_file}"
)
else:
print(f"❌ Failed to export conversations: {result.get('message')}")
async def get_user_stats(user_id: str):
"""Get conversation statistics for a user"""
print(f"📊 Getting conversation statistics for user: {user_id}")
retriever = LanceDBConversationRetriever()
await retriever.initialize()
result = await retriever.get_conversation_stats(user_id)
if result.get("status") == "success":
stats = result.get("stats", {})
print(f"📈 Conversation Statistics for {user_id}:")
print(f" Total Conversations: {stats.get('total_conversations', 0)}")
print(f" First Conversation: {stats.get('first_conversation', 'N/A')}")
print(f" Last Conversation: {stats.get('last_conversation', 'N/A')}")
print(f" Message Counts:")
for role, count in stats.get("message_counts", {}).items():
print(f" - {role.capitalize()}: {count}")
else:
print(f"❌ Failed to get statistics: {result.get('message')}")
def main():
"""Main function with command line interface"""
parser = argparse.ArgumentParser(description="Retrieve conversations from LanceDB")
parser.add_argument(
"--user-id", required=True, help="User ID to retrieve conversations for"
)
parser.add_argument(
"--action",
choices=["retrieve", "search", "export", "stats", "test"],
default="retrieve",
help="Action to perform",
)
parser.add_argument("--query", help="Search query (for search action)")
parser.add_argument(
"--limit", type=int, default=20, help="Number of conversations to retrieve"
)
parser.add_argument("--output", help="Output file for export")
args = parser.parse_args()
if not LANCEDB_AVAILABLE:
print("❌ LanceDB is not available. Please ensure:")
print(" - LanceDB is installed: pip install lancedb")
print(" - The backend modules are accessible")
sys.exit(1)
# Perform the requested action
if args.action == "test":
asyncio.run(test_lancedb_connection())
elif args.action == "retrieve":
asyncio.run(retrieve_user_conversations(args.user_id, args.limit))
elif args.action == "search":
if not args.query:
print("❌ Please provide a search query with --query")
sys.exit(1)
asyncio.run(search_user_conversations(args.user_id, args.query, args.limit))
elif args.action == "export":
output_file = (
args.output
or f"conversations_{args.user_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
)
asyncio.run(export_conversations(args.user_id, output_file))
elif args.action == "stats":
asyncio.run(get_user_stats(args.user_id))
if __name__ == "__main__":
main()
|