Spaces:
No application file
No application file
File size: 37,426 Bytes
4f24301 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 | import time
import json
import torch
import gc
from typing import Dict, List, Any, Optional, Generator
from deepforest_agent.agents.memory_agent import MemoryAgent
from deepforest_agent.agents.deepforest_detector_agent import DeepForestDetectorAgent
from deepforest_agent.agents.visual_analysis_agent import VisualAnalysisAgent
from deepforest_agent.agents.ecology_analysis_agent import EcologyAnalysisAgent
from deepforest_agent.utils.state_manager import session_state_manager
from deepforest_agent.utils.cache_utils import tool_call_cache
from deepforest_agent.utils.image_utils import check_image_resolution_for_deepforest
from deepforest_agent.utils.logging_utils import multi_agent_logger
from deepforest_agent.utils.detection_narrative_generator import DetectionNarrativeGenerator
class AgentOrchestrator:
"""
Orchestrates the multi-agent workflow with memory context + visual contexts + DeepForest detection context + ecological synthesis.
"""
def __init__(self):
"""Initialize the Agent Orchestrator."""
self.memory_agent = MemoryAgent()
self.detector_agent = DeepForestDetectorAgent()
self.visual_agent = VisualAnalysisAgent()
self.ecology_agent = EcologyAnalysisAgent()
self.execution_stats = {
"total_runs": 0,
"successful_runs": 0,
"average_execution_time": 0.0,
"memory_direct_answers": 0,
"deepforest_skipped": 0
}
def _log_gpu_memory(self, session_id: str, stage: str, agent_name: str):
"""
Log current GPU memory usage.
Args:
session_id (str): Unique identifier for the user session being processed
stage (str): Workflow stage identifier (e.g., "before", "after", "cleanup")
agent_name (str): Name of the agent being monitored (e.g., "Visual Analysis",
"DeepForest Detection", "Memory Agent")
"""
if torch.cuda.is_available():
allocated_gb = torch.cuda.memory_allocated() / 1024**3
cached_gb = torch.cuda.memory_reserved() / 1024**3
multi_agent_logger.log_agent_execution(
session_id=session_id,
agent_name=f"gpu_memory_{stage}",
agent_input=f"{agent_name} - {stage}",
agent_output=f"GPU Memory - Allocated: {allocated_gb:.2f} GB, Cached: {cached_gb:.2f} GB",
execution_time=0.0
)
print(f"Session {session_id} - {agent_name} {stage}: GPU Memory - Allocated: {allocated_gb:.2f} GB, Cached: {cached_gb:.2f} GB")
def cleanup_all_agents(self):
"""Cleanup models to manage memory."""
print("Orchestrator cleanup:")
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.synchronize()
torch.cuda.ipc_collect()
print(f"Final GPU memory after orchestrator cleanup: {torch.cuda.memory_allocated() / 1024**3:.2f} GB")
def _aggressive_gpu_cleanup(self, session_id: str, stage: str):
"""
Perform aggressive GPU memory cleanup.
Args:
session_id (str): Unique identifier for the user session
stage (str): Workflow stage identifier for logging context
"""
if torch.cuda.is_available():
for i in range(3):
gc.collect()
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
torch.cuda.synchronize()
try:
torch.cuda.reset_peak_memory_stats()
torch.cuda.reset_accumulated_memory_stats()
except:
pass
allocated = torch.cuda.memory_allocated() / 1024**3
cached = torch.cuda.memory_reserved() / 1024**3
print(f"Session {session_id} - {stage} aggressive cleanup: {allocated:.2f} GB allocated, {cached:.2f} GB cached")
def _format_detection_data_for_monitor(self, detection_narrative: str, detections_list: Optional[List[Dict[str, Any]]] = None) -> str:
"""
Format detection data for monitor display.
Args:
detection_narrative: Generated detection context from DeepForest Data
detections_list: Full DeepForest detection data
Returns:
Formatted detection data for monitor
"""
monitor_parts = []
if detections_list:
monitor_parts.append("=== DEEPFOREST DETECTIONS ===")
monitor_parts.append(json.dumps(detections_list, indent=2))
monitor_parts.append("")
if detection_narrative:
monitor_parts.append("=== DETECTION NARRATIVE ===")
monitor_parts.append(detection_narrative)
return "\n".join(monitor_parts) if monitor_parts else "No detection data available"
def _get_cached_detection_narrative(self, tool_cache_id: str) -> Optional[str]:
"""
Retrieve detection narrative using tool cache ID from the tool_call_cache.
Args:
tool_cache_id: Tool cache identifier
Returns:
Detection context from DeepForest Data if found, None otherwise
"""
try:
print(f"Looking up cached detection narrative for tool_cache_id: {tool_cache_id}")
# Handle multiple cache IDs
cache_ids = [id.strip() for id in tool_cache_id.split(",")] if tool_cache_id else []
all_narratives = []
for cache_id in cache_ids:
if cache_id in tool_call_cache.cache_data:
cached_entry = tool_call_cache.cache_data[cache_id]
cached_result = cached_entry.get("result", {})
tool_name = cached_entry.get("tool_name", "unknown")
tool_arguments = cached_entry.get("arguments", {})
# Get all possible arguments including defaults from Config
from deepforest_agent.conf.config import Config
all_arguments = Config.DEEPFOREST_DEFAULTS.copy()
all_arguments.update(tool_arguments)
# Format tool call info with all arguments
args_str = ", ".join([f"{k}={v}" for k, v in all_arguments.items()])
# Check if we have detections_list to generate narrative from
detections_list = cached_result.get("detections_list", [])
if detections_list:
print(f"Found {len(detections_list)} cached detections for cache ID {cache_id}")
# Get image dimensions for narrative generation
try:
session_keys = list(session_state_manager._sessions.keys())
if session_keys:
current_image = session_state_manager.get(session_keys[0], "current_image")
if current_image:
image_width, image_height = current_image.size
else:
image_width, image_height = 0, 0
else:
image_width, image_height = 0, 0
except:
image_width, image_height = 0, 0
# Generate fresh narrative from cached detection data
narrative_generator = DetectionNarrativeGenerator(image_width, image_height)
cached_detection_narrative = narrative_generator.generate_comprehensive_narrative(detections_list)
# Format with proper tool cache ID structure
formatted_narrative = f"**TOOL CACHE ID:** {cache_id}\nDeepForest tool run with arguments ({args_str}) and got the below narratives:\nDETECTION NARRATIVE:\n{cached_detection_narrative}"
all_narratives.append(formatted_narrative)
else:
detection_summary = cached_result.get("detection_summary", "")
if detection_summary:
formatted_summary = f"**TOOL CACHE ID:** {cache_id}\nDeepForest tool run with arguments ({args_str}) and got the below narratives:\nDETECTION NARRATIVE:\n{detection_summary}"
all_narratives.append(formatted_summary)
if all_narratives:
print(f"Generated {len(all_narratives)} cached detection narratives")
return "\n\n".join(all_narratives)
print(f"No cached data found for tool_cache_id(s): {tool_cache_id}")
return None
except Exception as e:
print(f"Error retrieving cached detection narrative for {tool_cache_id}: {e}")
return None
def process_user_message_streaming(
self,
user_message: str,
conversation_history: List[Dict[str, Any]],
session_id: str
) -> Generator[Dict[str, Any], None, None]:
"""
Orchestrate the multi-agent workflow with memory context and detection narrative flow.
Args:
user_message: Current user message/query to be processed
conversation_history: Full conversation history
session_id: Unique session identifier for this user's workflow
Yields:
Dict[str, Any]: Progress updates during processing
"""
start_time = time.perf_counter()
self.execution_stats["total_runs"] += 1
print(f"Session {session_id} - Query: {user_message}")
print(f"Session {session_id} - Conversation history length: {len(conversation_history)}")
agent_results = {}
execution_summary = {
"agents_executed": [],
"execution_order": [],
"timings": {},
"status": "in_progress",
"session_id": session_id,
"workflow_type": "memory_narrative_flow",
"memory_provided_direct_answer": False,
"deepforest_executed": False
}
memory_context = ""
visual_context = ""
detection_narrative = ""
memory_tool_cache_id = None
current_tool_cache_id = None
try:
if not session_state_manager.session_exists(session_id):
raise ValueError(f"Session {session_id} not found")
session_state_manager.set_processing_state(session_id, True)
session_state_manager.reset_cancellation(session_id)
yield {
"stage": "memory",
"message": "Analyzing conversation memory and context...",
"type": "progress"
}
if session_state_manager.is_cancelled(session_id):
raise Exception("Processing cancelled by user")
print(f"\nSTEP 1: Memory Agent Processing (Session {session_id})")
self._log_gpu_memory(session_id, "before", "Memory Agent")
memory_start = time.perf_counter()
memory_result = self.memory_agent.process_conversation_history_structured(
conversation_history=conversation_history,
latest_message=user_message,
session_id=session_id
)
memory_time = time.perf_counter() - memory_start
self._log_gpu_memory(session_id, "after", "Memory Agent")
self._aggressive_gpu_cleanup(session_id, "after_memory_agent")
execution_summary["timings"]["memory_agent"] = memory_time
execution_summary["agents_executed"].append("memory")
execution_summary["execution_order"].append("memory")
agent_results["memory"] = memory_result
# Extract memory context and tool cache ID
memory_context = memory_result.get("relevant_context", "No memory context available")
tool_cache_id = memory_result.get("tool_cache_id")
print(f"Session {session_id} - Memory Agent: Completed in {memory_time:.2f}s")
print(f"Session {session_id} - Memory Has Answer: {memory_result['answer_present']}")
print(f"Session {session_id} - Tool Cache ID: {tool_cache_id}")
if memory_result["answer_present"]:
print(f"Session {session_id} - Memory has direct answer - using cached data for synthesis")
self.execution_stats["memory_direct_answers"] += 1
execution_summary["memory_provided_direct_answer"] = True
# Get cached detection narrative if available
cached_detection_narrative = ""
if tool_cache_id:
cached_detection_narrative = self._get_cached_detection_narrative(tool_cache_id) or ""
yield {
"stage": "ecology",
"message": "Using memory context and cached detection narrative for synthesis...",
"type": "progress"
}
if session_state_manager.is_cancelled(session_id):
raise Exception("Processing cancelled by user")
print(f"\nSTEP 2 (MEMORY PATH): Ecology Agent with Memory Context (Session {session_id})")
self._log_gpu_memory(session_id, "before", "Ecology Agent (Memory Path)")
ecology_start = time.perf_counter()
# Prepare comprehensive context
comprehensive_context = self._prepare_comprehensive_context(
memory_context=memory_context,
visual_context="",
detection_narrative=cached_detection_narrative,
tool_cache_id=tool_cache_id
)
final_response = ""
for token_result in self.ecology_agent.synthesize_analysis_streaming(
user_message=user_message,
memory_context=comprehensive_context,
cached_json=None,
current_json=None,
session_id=session_id
):
if session_state_manager.is_cancelled(session_id):
raise Exception("Processing cancelled by user")
final_response += token_result["token"]
yield {
"stage": "ecology_streaming",
"message": final_response,
"type": "streaming",
"is_complete": token_result["is_complete"]
}
if token_result["is_complete"]:
ecology_time = time.perf_counter() - ecology_start
self._log_gpu_memory(session_id, "after", "Ecology Agent (Memory Path)")
execution_summary["timings"]["ecology_agent"] = ecology_time
execution_summary["agents_executed"].append("ecology")
execution_summary["execution_order"].append("ecology")
agent_results["ecology"] = {"final_response": final_response}
print(f"Session {session_id} - Ecology (Memory Path): Completed in {ecology_time:.2f}s")
break
total_time = time.perf_counter() - start_time
execution_summary["timings"]["total"] = total_time
execution_summary["status"] = "completed_via_memory"
detection_data_monitor = self._format_detection_data_for_monitor(
detection_narrative=cached_detection_narrative
)
yield {
"stage": "complete",
"message": final_response,
"type": "final",
"detection_data": detection_data_monitor,
"agent_results": agent_results,
"execution_summary": execution_summary,
"execution_time": total_time,
"status": "success"
}
return
else:
for result in self._execute_full_pipeline_with_narrative_flow(
user_message=user_message,
conversation_history=conversation_history,
session_id=session_id,
memory_context=memory_context,
memory_tool_cache_id=memory_result.get("tool_cache_id"),
start_time=start_time
):
yield result
if result["type"] == "final":
return
except Exception as e:
error_msg = f"Orchestrator error (Session {session_id}): {str(e)}"
print(f"ORCHESTRATOR ERROR: {error_msg}")
try:
self._aggressive_gpu_cleanup(session_id, "emergency")
except Exception as cleanup_error:
print(f"Emergency cleanup error: {cleanup_error}")
partial_time = time.perf_counter() - start_time
execution_summary["timings"]["total"] = partial_time
execution_summary["status"] = "error"
execution_summary["error"] = error_msg
fallback_response = self._create_fallback_response(
user_message=user_message,
agent_results=agent_results,
error=error_msg,
session_id=session_id
)
yield {
"stage": "error",
"message": fallback_response,
"type": "final",
"detection_data": "Error occurred - no detection data available",
"agent_results": agent_results,
"execution_summary": execution_summary,
"execution_time": partial_time,
"status": "error",
"error": error_msg
}
finally:
session_state_manager.set_processing_state(session_id, False)
def _execute_full_pipeline_with_narrative_flow(
self,
user_message: str,
conversation_history: List[Dict[str, Any]],
session_id: str,
memory_context: str,
memory_tool_cache_id: Optional[str],
start_time: float
) -> Generator[Dict[str, Any], None, None]:
"""
Execute the complete pipeline using memory context, visual contexts, and detection narratives.
Args:
user_message: Current user query
conversation_history: Complete conversation context
session_id: Unique session identifier
memory_context: Context from memory agent
memory_tool_cache_id (Optional[str]): Cache identifier from memory agent
start_time: Start time for total execution calculation
Yields:
Dict[str, Any]: Progress updates during processing containing:
- stage (str): Current workflow stage ("visual_analysis", "detector", etc.)
- message (str): Human-readable progress message
- type (str): Update type ("progress", "streaming", "final")
- Additional stage-specific data (detection_data, agent_results, etc.)
"""
agent_results = {}
execution_summary = {
"agents_executed": [],
"execution_order": [],
"timings": {},
"status": "in_progress",
"session_id": session_id,
"workflow_type": "Full Pipeline with Narrative Flow",
"memory_provided_direct_answer": False,
"deepforest_executed": False
}
visual_context = ""
detection_narrative = ""
yield {"stage": "visual_analysis", "message": "Analyzing image with unified full/tiled approach...", "type": "progress"}
if session_state_manager.is_cancelled(session_id):
raise Exception("Processing cancelled by user")
print(f"\nSTEP 1: Visual Analysis (Session {session_id})")
self._log_gpu_memory(session_id, "before", "Visual Analysis")
visual_start = time.perf_counter()
# Unified visual analysis
visual_analysis_result = self.visual_agent.analyze_full_image(
user_message=user_message,
session_id=session_id
)
visual_time = time.perf_counter() - visual_start
self._log_gpu_memory(session_id, "after", "Visual Analysis")
self._aggressive_gpu_cleanup(session_id, "after_visual_analysis")
execution_summary["timings"]["visual_analysis"] = visual_time
execution_summary["agents_executed"].append("visual_analysis")
execution_summary["execution_order"].append("visual_analysis")
agent_results["visual_analysis"] = visual_analysis_result
# Extract visual context
visual_context = visual_analysis_result.get("visual_analysis", "No visual analysis available")
print(f"Session {session_id} - Visual Analysis: {visual_analysis_result.get('status')}")
print(f"Session {session_id} - Analysis Type: {visual_analysis_result.get('analysis_type')}")
yield {"stage": "resolution_check", "message": "Checking image resolution for DeepForest suitability...", "type": "progress"}
if session_state_manager.is_cancelled(session_id):
raise Exception("Processing cancelled by user")
print(f"\nSTEP 2: Resolution Check (Session {session_id})")
resolution_start = time.perf_counter()
image_file_path = session_state_manager.get(session_id, "image_file_path")
resolution_result = None
if image_file_path:
resolution_result = check_image_resolution_for_deepforest(image_file_path)
resolution_time = time.perf_counter() - resolution_start
multi_agent_logger.log_resolution_check(
session_id=session_id,
image_file_path=image_file_path,
resolution_result=resolution_result,
execution_time=resolution_time
)
else:
resolution_result = {
"is_suitable": True,
"resolution_info": "No file path available for resolution check",
"error": None
}
resolution_time = time.perf_counter() - resolution_start
execution_summary["timings"]["resolution_check"] = resolution_time
execution_summary["agents_executed"].append("resolution_check")
execution_summary["execution_order"].append("resolution_check")
agent_results["resolution_check"] = resolution_result
# Determine if DeepForest should run
detection_result = None
image_quality_good = visual_analysis_result.get("image_quality_for_deepforest", "No").lower() == "yes"
resolution_suitable = resolution_result.get("is_suitable", True)
if resolution_suitable and image_quality_good:
yield {"stage": "detector", "message": "Quality and resolution good - executing DeepForest detection with narrative generation...", "type": "progress"}
if session_state_manager.is_cancelled(session_id):
raise Exception("Processing cancelled by user")
print(f"\nSTEP 3: DeepForest Detection with R-tree and Narrative (Session {session_id})")
self._log_gpu_memory(session_id, "before", "DeepForest Detection")
detector_start = time.perf_counter()
visual_objects = visual_analysis_result.get("deepforest_objects_present", [])
try:
detection_result = self.detector_agent.execute_detection_with_context(
user_message=user_message,
session_id=session_id,
visual_objects_detected=visual_objects,
memory_context=memory_context
)
detector_time = time.perf_counter() - detector_start
self._log_gpu_memory(session_id, "after", "DeepForest Detection")
self._aggressive_gpu_cleanup(session_id, "after_deepforest_detection")
execution_summary["timings"]["detector_agent"] = detector_time
execution_summary["agents_executed"].append("detector")
execution_summary["execution_order"].append("detector")
execution_summary["deepforest_executed"] = True
agent_results["detector"] = detection_result
# Extract detection narrative and tool cache ID from current run
current_detection_narrative = detection_result.get("detection_narrative", "No detection narrative available")
# Combine cached narratives from memory with current detection narrative
combined_narratives = []
# Add cached narratives from memory's tool cache IDs (if any)
if memory_tool_cache_id:
cached_narrative = self._get_cached_detection_narrative(memory_tool_cache_id)
if cached_narrative:
combined_narratives.append(cached_narrative)
# Add current detection narratives for ALL tool results
tool_results = detection_result.get("tool_results", [])
if tool_results:
for tool_result in tool_results:
cache_key = tool_result.get("cache_key")
tool_arguments = tool_result.get("tool_arguments", {})
if cache_key and tool_arguments:
# Get all possible arguments including defaults from Config
from deepforest_agent.conf.config import Config
all_arguments = Config.DEEPFOREST_DEFAULTS.copy()
all_arguments.update(tool_arguments)
# Format tool call info with all arguments
args_str = ", ".join([f"{k}={v}" for k, v in all_arguments.items()])
formatted_current = f"**TOOL CACHE ID:** {cache_key}\nDeepForest tool run with arguments ({args_str}) and got the below narratives:\nDETECTION NARRATIVE:\n{current_detection_narrative}"
combined_narratives.append(formatted_current)
# If no tool results but we have narrative, add it without formatting
if not tool_results and current_detection_narrative and current_detection_narrative != "No detection narrative available":
combined_narratives.append(current_detection_narrative)
# Combine all narratives
detection_narrative = "\n\n".join(combined_narratives) if combined_narratives else "No detection narrative available"
print(f"Session {session_id} - DeepForest Detection completed with narrative")
except Exception as detector_error:
print(f"Session {session_id} - DeepForest Detection FAILED: {detector_error}")
detection_result = None
detection_narrative = f"DeepForest detection failed: {str(detector_error)}"
else:
skip_reasons = []
if not resolution_suitable:
skip_reasons.append("insufficient resolution")
if not image_quality_good:
skip_reasons.append("poor image quality")
print(f"Session {session_id} - Skipping DeepForest detection: {', '.join(skip_reasons)}")
execution_summary["deepforest_executed"] = False
execution_summary["deepforest_skip_reason"] = ", ".join(skip_reasons)
detection_narrative = f"DeepForest detection was skipped due to: {', '.join(skip_reasons)}"
yield {"stage": "ecology", "message": "Synthesizing ecological insights from all contexts...", "type": "progress"}
if session_state_manager.is_cancelled(session_id):
raise Exception("Processing cancelled by user")
print(f"\nSTEP 4: Ecology Analysis with Comprehensive Context (Session {session_id})")
self._log_gpu_memory(session_id, "before", "Ecology Analysis")
ecology_start = time.perf_counter()
# Prepare comprehensive context for ecology agent
comprehensive_context = self._prepare_comprehensive_context(
memory_context=memory_context,
visual_context=visual_context,
detection_narrative=detection_narrative,
tool_cache_id=memory_tool_cache_id
)
final_response = ""
try:
for token_result in self.ecology_agent.synthesize_analysis_streaming(
user_message=user_message,
memory_context=comprehensive_context,
cached_json=None,
current_json=None,
session_id=session_id
):
if session_state_manager.is_cancelled(session_id):
raise Exception("Processing cancelled by user")
final_response += token_result["token"]
yield {
"stage": "ecology_streaming",
"message": final_response,
"type": "streaming",
"is_complete": token_result["is_complete"]
}
if token_result["is_complete"]:
break
except Exception as ecology_error:
print(f"Session {session_id} - Ecology streaming error: {ecology_error}")
if not final_response:
final_response = f"Ecology analysis failed: {str(ecology_error)}"
finally:
ecology_time = time.perf_counter() - ecology_start
self._log_gpu_memory(session_id, "after", "Ecology Analysis")
self._aggressive_gpu_cleanup(session_id, "after_ecology_analysis")
execution_summary["timings"]["ecology_agent"] = ecology_time
execution_summary["agents_executed"].append("ecology")
execution_summary["execution_order"].append("ecology")
agent_results["ecology"] = {"final_response": final_response}
# Store context data for memory agent's next turn
current_turn = len(session_state_manager.get(session_id, "conversation_history", [])) // 2 + 1
all_tool_cache_ids = []
if memory_tool_cache_id:
all_tool_cache_ids.extend([id.strip() for id in memory_tool_cache_id.split(",")])
# Add all current tool cache IDs
tool_results = detection_result.get("tool_results", []) if detection_result else []
for tool_result in tool_results:
cache_key = tool_result.get("cache_key")
if cache_key:
all_tool_cache_ids.append(cache_key)
combined_tool_cache_id = ", ".join(all_tool_cache_ids) if all_tool_cache_ids else None
self.memory_agent.store_turn_context(
session_id=session_id,
turn_number=current_turn,
visual_context=visual_context,
detection_narrative=detection_narrative,
tool_cache_id=combined_tool_cache_id
)
# Final result
total_time = time.perf_counter() - start_time
execution_summary["timings"]["total"] = total_time
execution_summary["status"] = "completed_narrative_flow"
detection_data_monitor = self._format_detection_data_for_monitor(
detection_narrative=detection_narrative,
detections_list=detection_result.get("detections_list", []) if detection_result else None
)
print(f"Session {session_id} - NARRATIVE FLOW WORKFLOW COMPLETED")
yield {
"stage": "complete",
"message": final_response,
"type": "final",
"detection_data": detection_data_monitor,
"agent_results": agent_results,
"execution_summary": execution_summary,
"execution_time": total_time,
"status": "success"
}
def _prepare_comprehensive_context(
self,
memory_context: str,
visual_context: str,
detection_narrative: str,
tool_cache_id: Optional[str]
) -> str:
"""
Prepare comprehensive context combining all data sources with better formatting.
Args:
memory_context: Context from memory agent
visual_context: Visual analysis context
detection_narrative: R-tree based detection narrative
tool_cache_id: Tool cache reference if available
Returns:
Combined context string for ecology agent
"""
context_parts = []
# Memory context section
if memory_context and memory_context != "No memory context available":
context_parts.append("--- START OF MEMORY CONTEXT ---")
context_parts.append(memory_context)
context_parts.append("--- END OF MEMORY CONTEXT ---")
context_parts.append("")
# Tool cache reference
if tool_cache_id:
context_parts.append(f"**TOOL CACHE ID:** {tool_cache_id}")
context_parts.append("")
# Detection narrative section
if detection_narrative and detection_narrative not in ["No detection analysis available", ""]:
context_parts.append("--- START OF DETECTION ANALYSIS ---")
context_parts.append(detection_narrative)
context_parts.append("--- END OF DETECTION ANALYSIS ---")
context_parts.append("")
# Visual context section
if visual_context and visual_context != "No visual analysis available":
context_parts.append("--- START OF VISUAL ANALYSIS ---")
context_parts.append(visual_context)
context_parts.append("There may be information that are not clear or accurate in this visual analysis. So make sure to mention that this analysis is provided by a visual analysis agent and it may not be very accurate as there is no confidence score associated with it. You can only provide this analysis seperately in a different section and inform the user that you are not very confident about this analysis.")
context_parts.append("--- END OF VISUAL ANALYSIS ---")
context_parts.append("")
# If we have very little context, provide a meaningful message
if not context_parts or len("".join(context_parts)) < 50:
return "No comprehensive context available for this query. Please provide more information or try a different approach."
result_context = "\n".join(context_parts)
print(f"Prepared comprehensive context ({len(result_context)} characters)")
print(f"Context preview: {result_context[:200]}...")
return result_context
def _create_fallback_response(
self,
user_message: str,
agent_results: Dict[str, Any],
error: str,
session_id: str
) -> str:
"""Create a fallback response when the orchestrator encounters errors."""
response_parts = []
response_parts.append(f"I encountered some processing issues but can provide analysis based on available data:")
response_parts.append("")
memory_result = agent_results.get("memory", {})
if memory_result and memory_result.get("relevant_context"):
response_parts.append(f"**Memory Context**: {memory_result['relevant_context']}")
response_parts.append("")
visual_result = agent_results.get("visual_analysis", {})
if visual_result and visual_result.get("visual_analysis"):
response_parts.append(f"**Visual Analysis**: {visual_result['visual_analysis']}")
response_parts.append("")
detector_result = agent_results.get("detector", {})
if detector_result and detector_result.get("detection_narrative"):
response_parts.append(f"**Detection Results**: {detector_result['detection_narrative']}")
response_parts.append("")
response_parts.append(f"Note: Workflow was interrupted ({error}). Please try your query again for full results.")
return "\n".join(response_parts) |