Spaces:
Sleeping
Sleeping
File size: 47,120 Bytes
ba5110e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 |
"""
LangGraph node implementations for the multi-agent algebra chatbot.
Agents: ocr_agent, planner, parallel_executor, synthetic_agent
Tools: wolfram_tool_node, code_tool_node
"""
import os
import time
import json
import re
import asyncio
from typing import List, Dict, Any, Optional
from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
from backend.agent.state import (
AgentState, ToolCall, ModelCall,
add_agent_used, add_tool_call, add_model_call
)
from backend.agent.models import model_manager, get_model
from backend.tools.wolfram import query_wolfram_alpha
from backend.tools.code_executor import CodeTool
from backend.utils.memory import (
memory_tracker, estimate_tokens, estimate_message_tokens,
TokenOverflowError, truncate_history_to_fit
)
from backend.agent.prompts import (
OCR_PROMPT,
SYNTHETIC_PROMPT,
CODEGEN_PROMPT,
CODEGEN_FIX_PROMPT,
PLANNER_SYSTEM_PROMPT,
PLANNER_USER_PROMPT
)
# ============================================================================
# HELPER FUNCTIONS FOR OUTPUT FORMATTING
# ============================================================================
def format_latex_for_markdown(text: str) -> str:
"""
Format LaTeX content for proper Markdown rendering.
Key principle:
- Add paragraph breaks (double newlines) OUTSIDE of $$...$$ blocks
- NEVER modify content INSIDE $$...$$ blocks (preserves aligned, matrix, etc.)
- Ensure $$ is on its own line for block rendering
Args:
text: Raw text containing LaTeX expressions
Returns:
Formatted text suitable for Markdown rendering
"""
if not text:
return text
# Split by $$ to separate math blocks from text
parts = text.split('$$')
formatted_parts = []
for i, part in enumerate(parts):
if i % 2 == 0:
# OUTSIDE math block (text content)
# Add paragraph spacing for better readability
# But be careful not to add excessive whitespace
formatted_parts.append(part)
else:
# INSIDE math block - preserve exactly as-is
# Just wrap with $$ and ensure it's on its own line
formatted_parts.append(f'\n$$\n{part.strip()}\n$$\n')
# Rejoin: even parts are text, odd parts are already formatted with $$
result = ''
for i, part in enumerate(formatted_parts):
if i % 2 == 0:
result += part
else:
# This is the formatted math block, append directly
result += part
# Clean up excessive whitespace (more than 2 consecutive newlines)
result = re.sub(r'\n{3,}', '\n\n', result)
return result.strip()
# ============================================================================
# AGENT NODES
# ============================================================================
async def ocr_agent_node(state: AgentState) -> AgentState:
"""
OCR Agent: Extract text from images using vision model.
Supports multiple images with parallel processing.
Primary: llama-4-maverick, Fallback: llama-4-scout
"""
import asyncio
add_agent_used(state, "ocr_agent")
# Check for images (new list or legacy single image)
image_list = state.get("image_data_list", [])
if not image_list and state.get("image_data"):
image_list = [state["image_data"]] # Backward compatibility
if not image_list:
# No images - proceed directly to planner (OCR skipped)
state["current_agent"] = "planner"
return state
start_time = time.time()
primary_model = "llama-4-maverick"
fallback_model = "llama-4-scout"
async def ocr_single_image(image_data: str, index: int) -> dict:
"""Process a single image and return result dict."""
content = [
{"type": "text", "text": OCR_PROMPT},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}}
]
messages = [HumanMessage(content=content)]
model_used = primary_model
try:
# Check rate limit for primary
can_use, error = model_manager.check_rate_limit(primary_model)
if not can_use:
model_used = fallback_model
can_use, error = model_manager.check_rate_limit(fallback_model)
if not can_use:
return {"image_index": index + 1, "text": None, "error": error}
llm = get_model(model_used)
response = await llm.ainvoke(messages)
return {"image_index": index + 1, "text": response.content, "error": None}
except Exception as e:
return {"image_index": index + 1, "text": None, "error": str(e)}
# Process all images in parallel
tasks = [ocr_single_image(img, i) for i, img in enumerate(image_list)]
results = await asyncio.gather(*tasks)
duration_ms = int((time.time() - start_time) * 1000)
# Store results
state["ocr_results"] = results
# Build combined OCR text for backward compatibility
successful_texts = []
for r in results:
if r["text"]:
if len(image_list) > 1:
successful_texts.append(f"[Ảnh {r['image_index']}]:\n{r['text']}")
else:
successful_texts.append(r["text"])
state["ocr_text"] = "\n\n".join(successful_texts) if successful_texts else None
# Log model calls
add_model_call(state, ModelCall(
model=primary_model,
agent="ocr_agent",
tokens_in=500 * len(image_list),
tokens_out=sum(len(r.get("text", "") or "") // 4 for r in results),
duration_ms=duration_ms,
success=any(r["text"] for r in results)
))
# Report any errors but continue
errors = [f"Ảnh {r['image_index']}: {r['error']}" for r in results if r["error"]]
if errors and not successful_texts:
state["error_message"] = "OCR failed: " + "; ".join(errors)
# Route to planner for multi-question analysis
state["current_agent"] = "planner"
return state
async def planner_node(state: AgentState) -> AgentState:
"""
Planner Node: Analyze all content (text + OCR) and identify individual questions.
Creates an execution plan for parallel processing.
NOW WITH FULL CONVERSATION HISTORY FOR MEMORY!
"""
import asyncio
add_agent_used(state, "planner")
start_time = time.time()
model_name = "kimi-k2"
# Get user text from last message
user_text = ""
for msg in reversed(state["messages"]):
if isinstance(msg, HumanMessage):
user_text = msg.content if isinstance(msg.content, str) else str(msg.content)
break
ocr_text = state.get("ocr_text") or "(Không có ảnh)"
# Build user prompt for current request
current_prompt = PLANNER_USER_PROMPT.format(
user_text=user_text or "(Không có text)",
ocr_text=ocr_text
)
# ========================================
# NEW: Build messages WITH conversation history
# ========================================
llm_messages = []
# 1. Add system prompt with memory-awareness instructions
llm_messages.append(SystemMessage(content=PLANNER_SYSTEM_PROMPT))
# 2. Add truncated conversation history (smart token management)
history_messages = state.get("messages", [])
# Exclude the last message since we'll add current_prompt separately
if history_messages:
history_to_include = history_messages[:-1] if len(history_messages) > 1 else []
else:
history_to_include = []
# Truncate history to fit within token limits
system_tokens = estimate_tokens(PLANNER_SYSTEM_PROMPT)
current_tokens = estimate_tokens(current_prompt)
truncated_history = truncate_history_to_fit(
history_to_include,
system_tokens=system_tokens,
current_tokens=current_tokens,
max_context_tokens=200000 # Leave room within 256K limit
)
# Add history messages
for msg in truncated_history:
llm_messages.append(msg)
# 3. Add current user request as last message
llm_messages.append(HumanMessage(content=current_prompt))
# Calculate total input tokens for tracking
total_input_tokens = system_tokens + estimate_message_tokens(truncated_history) + current_tokens
try:
llm = get_model(model_name)
response = await llm.ainvoke(llm_messages)
content = response.content.strip()
duration_ms = int((time.time() - start_time) * 1000)
add_model_call(state, ModelCall(
model=model_name,
agent="planner",
tokens_in=total_input_tokens,
tokens_out=len(content) // 4,
duration_ms=duration_ms,
success=True
))
# Parse JSON from response
# Handle markdown code blocks
if "```json" in content:
content = content.split("```json")[1].split("```")[0].strip()
elif "```" in content:
content = content.split("```")[1].split("```")[0].strip()
try:
# Try to parse JSON (Mixed/Tool Case)
plan = json.loads(content)
except json.JSONDecodeError:
try:
# Try repair: Fix invalid escapes for LaTeX (e.g., \frac -> \\frac)
# Matches backslash NOT followed by valid JSON escape chars (excluding \\ itself)
fixed_content = re.sub(r'\\(?![unrtbf"\/])', r'\\\\', content)
plan = json.loads(fixed_content)
except Exception:
# If JSON parsing fails completely, try Regex Fallback
# This catches cases where LLM returns valid-looking JSON but with syntax errors
if content.strip().startswith("{") and '"questions"' in content:
# Attempt to extract answers using Regex
# Pattern: "answer": "..." (handling escaped quotes is hard in regex, simplified)
import re
# Extract individual question blocks (simplified assumption)
# Use a rough scan for "answer": "..."
# Find all "answer": "(.*?)" where content is non-greedy until next quote
# Note: this is fragile but better than raw JSON
# Better fallback: Just treat it as raw text but tell user format error
pass
# If JSON fails, it means Planner returned Direct Text Answer (All Direct Case)
# OR malformed JSON that looks like text.
# Check directly if it looks like the raw JSON output
if content.strip().startswith('{') and '"type": "direct"' in content:
# This is likely the malformed JSON case the user saw
# Use Regex to extract answers
answers = re.findall(r'"answer":\s*"(.*?)(?<!\\)"', content, re.DOTALL)
if answers:
# Unescape the extracted string somewhat
final_parts = []
for i, ans in enumerate(answers):
# excessive backslashes might be present
clean_ans = ans.replace('\\"', '"').replace('\\n', '\n')
# Use helper to properly format LaTeX for Markdown
formatted_answer = format_latex_for_markdown(clean_ans)
final_parts.append(f"## Bài {i+1}:\n{formatted_answer}\n")
final_response = "\n".join(final_parts)
# Update memory & return
session_id = state["session_id"]
tokens_in = total_input_tokens
tokens_out = len(content) // 4
total_turn_tokens = tokens_in + tokens_out
memory_tracker.add_usage(session_id, total_turn_tokens)
new_status = memory_tracker.check_status(session_id)
state["session_token_count"] = new_status.used_tokens
state["context_status"] = new_status.status
state["context_message"] = new_status.message
state["execution_plan"] = None
state["final_response"] = final_response
state["messages"].append(AIMessage(content=final_response))
state["current_agent"] = "done"
return state
# Update memory tracking (consistent with other agents)
session_id = state["session_id"]
tokens_in = total_input_tokens
tokens_out = len(content) // 4
total_turn_tokens = tokens_in + tokens_out
memory_tracker.add_usage(session_id, total_turn_tokens)
new_status = memory_tracker.check_status(session_id)
state["session_token_count"] = new_status.used_tokens
state["context_status"] = new_status.status
state["context_message"] = new_status.message
# Check for memory overflow
if new_status.status == "blocked":
state["final_response"] = new_status.message
state["current_agent"] = "done"
return state
# CRITICAL: Check if content looks like JSON with tool questions
# If so, try to route to executor instead of displaying raw JSON
if content.strip().startswith('{') and '"questions"' in content:
# This is JSON that failed parsing but contains questions
# Try one more time with aggressive repair
try:
# Remove control characters and fix common issues
import re as regex_module
aggressive_fix = content
# Fix unescaped backslashes in LaTeX (including doubling existing ones)
aggressive_fix = regex_module.sub(r'\\(?![unrtbf"\/])', r'\\\\', aggressive_fix)
# Try parsing
parsed_plan = json.loads(aggressive_fix)
if parsed_plan.get("questions"):
# Success! Route to executor
state["execution_plan"] = parsed_plan
state["current_agent"] = "executor"
return state
except:
pass
# If still unparseable, try manual extraction
# Extract questions array manually with regex
try:
# Find id, content, type, tool_input for each question
q_matches = re.findall(r'"id"\s*:\s*(\d+).*?"content"\s*:\s*"([^"]*)".*?"type"\s*:\s*"(direct|wolfram|code)"', content, re.DOTALL)
if q_matches:
manual_plan = {"questions": []}
for q_id, q_content, q_type in q_matches:
q_entry = {"id": int(q_id), "content": q_content, "type": q_type, "answer": None}
if q_type in ["wolfram", "code"]:
q_entry["tool_input"] = q_content
manual_plan["questions"].append(q_entry)
state["execution_plan"] = manual_plan
state["current_agent"] = "executor"
return state
except:
pass
# Last resort: Show error message instead of raw JSON
state["execution_plan"] = None
state["final_response"] = "Xin lỗi, hệ thống gặp lỗi khi phân tích câu hỏi. Vui lòng thử lại hoặc diễn đạt câu hỏi khác đi."
state["current_agent"] = "done"
return state
# Treat as final answer (only if NOT JSON)
state["execution_plan"] = None
state["final_response"] = content
state["messages"].append(AIMessage(content=content))
state["current_agent"] = "done"
return state
# If JSON Valid -> Check if all questions are direct (LLM didn't follow prompt correctly)
all_direct = all(q.get("type") == "direct" for q in plan.get("questions", []))
if all_direct:
# LLM returned JSON for all-direct case (should have returned text)
# Check if answers are provided
questions = plan.get("questions", [])
has_valid_answers = all(q.get("answer") for q in questions)
if has_valid_answers:
# Answers are in the JSON, extract them
final_parts = []
for q in questions:
q_id = q.get("id", "?")
q_answer = q.get("answer", "")
# Use helper to properly format LaTeX for Markdown
formatted_answer = format_latex_for_markdown(q_answer)
final_parts.append(f"## Bài {q_id}:\n{formatted_answer}\n")
final_response = "\n".join(final_parts)
else:
# No answers provided - LLM didn't follow prompt correctly
# Route to executor to re-process these as direct questions
# For now, mark as needing tool (wolfram) so they get solved
for q in questions:
if not q.get("answer"):
q["type"] = "wolfram" # Force tool use
if not q.get("tool_input"):
q["tool_input"] = q.get("content", "")
state["execution_plan"] = plan
state["current_agent"] = "executor"
# Update memory tracking
session_id = state["session_id"]
tokens_in = total_input_tokens
tokens_out = len(content) // 4
total_turn_tokens = tokens_in + tokens_out
memory_tracker.add_usage(session_id, total_turn_tokens)
new_status = memory_tracker.check_status(session_id)
state["session_token_count"] = new_status.used_tokens
state["context_status"] = new_status.status
state["context_message"] = new_status.message
return state
state["execution_plan"] = None
state["final_response"] = final_response
state["messages"].append(AIMessage(content=final_response))
state["current_agent"] = "done"
# Update memory tracking
session_id = state["session_id"]
tokens_in = total_input_tokens
tokens_out = len(content) // 4
total_turn_tokens = tokens_in + tokens_out
memory_tracker.add_usage(session_id, total_turn_tokens)
new_status = memory_tracker.check_status(session_id)
state["session_token_count"] = new_status.used_tokens
state["context_status"] = new_status.status
state["context_message"] = new_status.message
return state
# Mixed/Tool Case -> Route to Executor
state["execution_plan"] = plan
state["current_agent"] = "executor"
# Update memory tracking (consistent with other agents)
session_id = state["session_id"]
tokens_in = total_input_tokens
tokens_out = len(content) // 4
total_turn_tokens = tokens_in + tokens_out
memory_tracker.add_usage(session_id, total_turn_tokens)
new_status = memory_tracker.check_status(session_id)
state["session_token_count"] = new_status.used_tokens
state["context_status"] = new_status.status
state["context_message"] = new_status.message
# Check for memory overflow
if new_status.status == "blocked":
state["final_response"] = new_status.message
state["current_agent"] = "done"
except Exception as e:
add_model_call(state, ModelCall(
model=model_name,
agent="planner",
tokens_in=0,
tokens_out=0,
duration_ms=int((time.time() - start_time) * 1000),
success=False,
error=str(e)
))
# Fallback: Planner failed, return error to user
error_msg = str(e)
user_friendly_msg = "Xin lỗi, đã có lỗi xảy ra khi phân tích câu hỏi."
if "413" in error_msg or "Request too large" in error_msg:
user_friendly_msg = "Nội dung lịch sử trò chuyện vượt quá giới hạn mô hình. Vui lòng tạo hội thoại mới để tiếp tục."
elif "rate_limit" in error_msg or "TPM" in error_msg:
user_friendly_msg = "Hệ thống đang quá tải (Rate Limit). Bạn vui lòng đợi khoảng 10-20 giây rồi thử lại nhé!"
elif "context_length_exceeded" in error_msg:
user_friendly_msg = "Hội thoại đã quá dài. Vui lòng tạo hội thoại mới để tiếp tục."
else:
user_friendly_msg = f"Xin lỗi, đã có lỗi kỹ thuật: {error_msg}."
state["execution_plan"] = None
state["final_response"] = user_friendly_msg
state["current_agent"] = "done"
return state
async def parallel_executor_node(state: AgentState) -> AgentState:
"""
Parallel Executor: Execute multiple questions in parallel.
- Direct questions: Process with kimi-k2
- Wolfram questions: Call API in parallel
- Code questions: Execute code in parallel
"""
import asyncio
add_agent_used(state, "parallel_executor")
plan = state.get("execution_plan")
if not plan or not plan.get("questions"):
# No plan - planner should have handled this, go to done
state["current_agent"] = "done"
return state
questions = plan["questions"]
start_time = time.time()
async def execute_single_question(q: dict) -> dict:
"""Execute a single question and return result."""
q_id = q.get("id", 0)
q_type = q.get("type", "direct")
q_content = q.get("content", "")
q_tool_input = q.get("tool_input", "")
result = {
"id": q_id,
"content": q_content,
"type": q_type,
"result": None,
"error": None
}
async def solve_with_code(task_description: str, retries: int = 3) -> dict:
"""Helper to run code tool with retries."""
code_tool = CodeTool()
out = {"result": None, "error": None}
last_code = ""
last_error = ""
for attempt in range(retries):
try:
llm = get_model("qwen3-32b")
# SMART RETRY: If we have an error, ask LLM to FIX it
if attempt > 0 and last_error:
code_prompt = CODEGEN_FIX_PROMPT.format(code=last_code, error=last_error)
else:
code_prompt = CODEGEN_PROMPT.format(task=task_description)
code_response = await llm.ainvoke([HumanMessage(content=code_prompt)])
# Extract code
code = code_response.content
if "```python" in code:
code = code.split("```python")[1].split("```")[0]
elif "```" in code:
code = code.split("```")[1].split("```")[0]
last_code = code # Save for next retry if needed
# Execute
exec_result = code_tool.execute(code)
if exec_result.get("success"):
out["result"] = exec_result.get("output", "")
return out
else:
last_error = exec_result.get("error", "Unknown error")
if attempt == retries - 1:
out["error"] = last_error
except Exception as e:
last_error = str(e)
if attempt == retries - 1:
out["error"] = str(e)
return out
try:
if q_type == "wolfram":
wolfram_done = False
# Call Wolfram Alpha (with retry logic)
# Call Wolfram Alpha (1 attempt only)
for attempt in range(1):
try:
can_use, err = model_manager.check_rate_limit("wolfram")
if not can_use:
if attempt == 0: break
await asyncio.sleep(1)
continue
wolfram_success, wolfram_result = await query_wolfram_alpha(q_tool_input)
if wolfram_success:
result["result"] = wolfram_result
wolfram_done = True
break
else:
# Treat logical failure as exception to trigger retry/fallback
if attempt == 0: raise Exception(wolfram_result)
except Exception as e:
if attempt == 0:
result["error"] = f"Wolfram failed: {str(e)}"
await asyncio.sleep(0.5)
# --- FALLBACK TO CODE IF WOLFRAM FAILED ---
if not wolfram_done:
# Append status to result
fallback_note = f"\n(Wolfram failed, tried Code fallback)"
code_out = await solve_with_code(q_tool_input)
if code_out["result"]:
result["result"] = code_out["result"] + fallback_note
result["error"] = None # Clear error if fallback succeeded
result["type"] = "wolfram+code" # Indicate hybrid path
else:
result["error"] += f" | Code Fallback also failed: {code_out['error']}"
elif q_type == "code":
# Execute code directly
code_out = await solve_with_code(q_tool_input)
result["result"] = code_out["result"]
result["error"] = code_out["error"]
else: # direct
# User Optimization: If planner provided answer, use it directly (Save API)
if q.get("answer"):
result["result"] = q.get("answer")
else:
# Fallback: Solve directly with kimi-k2 (if planner forgot answer)
llm = get_model("kimi-k2")
solve_prompt = f"Giải bài toán sau một cách chi tiết:\n{q_content}"
response = await llm.ainvoke([
SystemMessage(content="Bạn là chuyên gia giải toán. Trả lời ngắn gọn, đúng trọng tâm."),
HumanMessage(content=solve_prompt)
])
result["result"] = format_latex_for_markdown(response.content) # Direct result
except Exception as e:
result["error"] = str(e)
return result
# Execute all questions in parallel
tasks = [execute_single_question(q) for q in questions]
results = await asyncio.gather(*tasks, return_exceptions=True)
# Process results and collect metrics
question_results = []
total_tokens_in = 0
total_tokens_out = 0
for i, r in enumerate(results):
q = questions[i]
q_type = q.get("type", "direct")
# Prepare result entry
res_entry = {
"id": q.get("id", i+1),
"content": q.get("content", ""),
"result": None,
"error": None,
"type": q_type
}
if isinstance(r, Exception):
error_msg = str(r)
if "413" in error_msg or "Request too large" in error_msg:
friendly = "Nội dung quá dài, vui lòng gửi ngắn hơn."
elif "rate_limit" in error_msg or "TPM" in error_msg:
friendly = "Rate Limit (Quá tải), vui lòng đợi giây lát."
else:
friendly = f"Lỗi kỹ thuật: {error_msg}"
res_entry["error"] = friendly
success = False
r_content = friendly
else:
# r is the result dict from execute_single_question
res_entry.update(r)
success = not bool(r.get("error"))
r_content = str(r.get("result", ""))
# Use friendly error if present in result dict
raw_err = r.get("error")
if raw_err:
error_msg = str(raw_err)
if "413" in error_msg or "Request too large" in error_msg:
friendly = "Nội dung quá dài, vui lòng gửi ngắn hơn."
elif "rate_limit" in error_msg or "TPM" in error_msg:
friendly = "Rate Limit (Quá tải), vui lòng đợi giây lát."
else:
friendly = f"Lỗi kỹ thuật: {error_msg}"
res_entry["error"] = friendly
r_content = friendly
question_results.append(res_entry)
# Add individual model call trace for each parallel task
# This allows the frontend to show "Wolfram", "Code", "Kimi" calls clearly
# Estimate tokens for metrics (rough check)
t_in = len(q.get("content", "")) // 4
t_out = len(r_content) // 4
total_tokens_in += t_in
total_tokens_out += t_out
model_name_trace = "unknown"
if q_type == "wolfram": model_name_trace = "wolfram-alpha"
elif q_type == "code": model_name_trace = "python-code-executor"
else: model_name_trace = "kimi-k2"
add_model_call(state, ModelCall(
model=model_name_trace,
agent=f"parallel_executor_q{res_entry['id']}",
tokens_in=t_in,
tokens_out=t_out,
duration_ms=int((time.time() - start_time) * 1000), # Approx sharing total time
success=success,
tool_calls=[{
"tool": q_type,
"input": q.get("tool_input") or q.get("content"),
"output": r_content[:200] + "..." if len(r_content) > 200 else r_content
}]
))
state["question_results"] = question_results
# --- UI COMPATIBILITY FIX ---
# Populate legacy fields so the Tracing UI (which expects single tool per turn) shows SOMETHING.
# We aggregate all parallel results into a single string.
start_time_ms = int(start_time * 1000)
# 1. Selected Tool
tool_names = list(set(r["type"] for r in question_results))
state["selected_tool"] = f"parallel({','.join(tool_names)})"
state["should_use_tools"] = True
# 2. Tool Result (Aggregated)
agg_result = []
for r in question_results:
status = "✅" if not r.get("error") else "❌"
val = r.get("result") or r.get("error")
agg_result.append(f"[{status} {r['type'].upper()}]: {str(val)[:100]}...")
state["tool_result"] = "\n".join(agg_result)
# 3. Tools Called (List of ToolCall objects)
tools_called_list = []
for r in question_results:
tools_called_list.append({
"tool": r["type"],
"tool_input": str(questions[next((i for i, q in enumerate(questions) if q.get("id") == r["id"]), 0)].get("tool_input", "") or r.get("content")),
"tool_output": str(r.get("result") or r.get("error"))
})
state["tools_called"] = tools_called_list
state["tool_success"] = any(not r.get("error") for r in question_results)
# ---------------------------
duration_ms = int((time.time() - start_time) * 1000)
add_model_call(state, ModelCall(
model="parallel_orchestrator",
agent="parallel_executor",
tokens_in=total_tokens_in,
tokens_out=total_tokens_out,
duration_ms=duration_ms,
success=state["tool_success"]
))
# Go to synthesizer to combine results
state["current_agent"] = "synthetic"
return state
# NOTE: reasoning_agent_node has been DEPRECATED and REMOVED.
# The workflow now flows: OCR -> Planner -> Executor -> Synthetic
# (See user's workflow diagram for reference)
async def synthetic_agent_node(state: AgentState) -> AgentState:
"""
Synthetic Agent: Synthesize tool results into final response.
Handles both single-tool results and multi-question parallel results.
Uses kimi-k2.
"""
add_agent_used(state, "synthetic_agent")
start_time = time.time()
model_name = "kimi-k2"
session_id = state["session_id"]
# Check memory status before processing
mem_status = memory_tracker.check_status(session_id)
if mem_status.status == "blocked":
state["context_status"] = "blocked"
state["context_message"] = mem_status.message
state["final_response"] = mem_status.message
state["current_agent"] = "done"
return state
# Check if we have multi-question results from parallel executor
question_results = state.get("question_results", [])
if question_results:
# Multi-question mode: combine all results
# Use LLM to synthesize a natural response instead of raw concatenation
# Prepare context for synthesis
results_context = []
for r in question_results:
q_id = r.get("id", 0)
q_content = r.get("content", "")
q_result = r.get("result", "Không có kết quả")
q_error = r.get("error")
status = "Thành công" if not q_error else f"Lỗi: {q_error}"
results_context.append(f"--- BÀI TOÁN {q_id} ---\nNội dung: {q_content}\nTrạng thái: {status}\nKết quả gốc:\n{q_result}\n\n")
combined_context = "".join(results_context)
# Get original question text for context
original_q_text = "Nhiều câu hỏi (xem chi tiết bên trên)"
if state.get("ocr_text"):
original_q_text = f"[OCR]: {state['ocr_text']}"
elif state["messages"]:
for m in reversed(state["messages"]):
if isinstance(m, HumanMessage):
original_q_text = str(m.content)
break
# Use Standard SYNTHETIC_PROMPT
synth_prompt = SYNTHETIC_PROMPT.format(
tool_result=combined_context,
original_question=original_q_text
)
# ========================================
# NEW: Include recent conversation history for contextual synthesis
# ========================================
llm_messages = [
SystemMessage(content="""Bạn là chuyên gia toán học Việt Nam. Hãy giải thích lời giải một cách sư phạm, dễ hiểu.
VỀ BỘ NHỚ HỘI THOẠI:
- Bạn có thể tham chiếu đến các câu hỏi trước đó trong hội thoại.
- Nếu người dùng đề cập đến "bài trước", "câu đó", hãy hiểu ngữ cảnh.
- Trả lời tự nhiên như một cuộc trò chuyện liên tục."""),
]
# Add recent conversation history (last 3 turns = 6 messages)
recent_history = state.get("messages", [])[-6:]
for msg in recent_history:
llm_messages.append(msg)
# Add synthesis prompt
llm_messages.append(HumanMessage(content=synth_prompt))
try:
llm = get_model("kimi-k2")
response = await llm.ainvoke(llm_messages)
final_response = format_latex_for_markdown(response.content)
except Exception as e:
# Fallback manual synthesis if LLM fails
error_msg = str(e)
if "413" in error_msg or "Request too large" in error_msg:
friendly_err = "Nội dung quá dài để tổng hợp."
elif "rate_limit" in error_msg or "TPM" in error_msg:
friendly_err = "Hệ thống đang bận (Rate Limit)."
else:
friendly_err = f"Lỗi kỹ thuật: {error_msg}"
final_response = f"**Kết quả (Tổng hợp tự động thất bại do {friendly_err}):**\n\n" + combined_context
state["final_response"] = final_response
state["messages"].append(AIMessage(content=final_response))
state["current_agent"] = "done"
# Update memory
tokens_out = len(final_response) // 4
memory_tracker.add_usage(session_id, tokens_out)
new_status = memory_tracker.check_status(session_id)
state["session_token_count"] = new_status.used_tokens
state["context_status"] = new_status.status
state["context_message"] = new_status.message
return state
# Single-question mode: original logic
# Get original question
original_question = ""
if state["messages"]:
for msg in state["messages"]:
if hasattr(msg, "content") and isinstance(msg, HumanMessage):
original_question = msg.content if isinstance(msg.content, str) else str(msg.content)
break
# Add OCR context if available
if state.get("ocr_text"):
original_question = f"[Từ ảnh]: {state['ocr_text']}\n\n{original_question}"
# Build prompt
tool_result = state.get("tool_result", "Không có kết quả")
if not state.get("tool_success"):
tool_result = f"[Công cụ thất bại]: {state.get('error_message', 'Unknown error')}\n\nHãy cố gắng trả lời dựa trên kiến thức của bạn."
prompt = SYNTHETIC_PROMPT.format(
tool_result=tool_result,
original_question=original_question
)
messages = [HumanMessage(content=prompt)]
tokens_in = estimate_tokens(prompt)
try:
llm = get_model(model_name)
response = await llm.ainvoke(messages)
duration_ms = int((time.time() - start_time) * 1000)
tokens_out = len(response.content) // 4
add_model_call(state, ModelCall(
model=model_name,
agent="synthetic_agent",
tokens_in=tokens_in,
tokens_out=tokens_out,
duration_ms=duration_ms,
success=True
))
# Update session memory tracker
total_turn_tokens = tokens_in + tokens_out
memory_tracker.add_usage(session_id, total_turn_tokens)
new_status = memory_tracker.check_status(session_id)
state["session_token_count"] = new_status.used_tokens
state["context_status"] = new_status.status
state["context_message"] = new_status.message
# Format the synthesis with standard helper
formatted_response = format_latex_for_markdown(response.content)
state["final_response"] = formatted_response
state["messages"].append(AIMessage(content=formatted_response))
state["current_agent"] = "done"
except Exception as e:
# Fallback to raw tool result if synthesis fails
fallback_response = f"**Kết quả tính toán:**\n{state.get('tool_result', 'Không có kết quả')}"
state["final_response"] = fallback_response
state["messages"].append(AIMessage(content=fallback_response))
state["current_agent"] = "done"
return state
# ============================================================================
# TOOL NODES
# ============================================================================
async def wolfram_tool_node(state: AgentState) -> AgentState:
"""
Wolfram Tool: Query Wolfram Alpha.
Max 3 attempts (1 initial + 2 retries).
"""
add_agent_used(state, "wolfram_tool")
query = state.get("_tool_query", "")
state["wolfram_attempts"] += 1
start_time = time.time()
success, result = await query_wolfram_alpha(query)
duration_ms = int((time.time() - start_time) * 1000)
tool_call = ToolCall(
tool="wolfram",
input=query,
output=result if success else None,
success=success,
attempt=state["wolfram_attempts"],
duration_ms=duration_ms,
error=None if success else result
)
add_tool_call(state, tool_call)
if success:
state["tool_result"] = result
state["tool_success"] = True
state["current_agent"] = "synthetic"
else:
if state["wolfram_attempts"] < 1:
# Retry
state["current_agent"] = "wolfram"
else:
# Fallback to code tool
state["selected_tool"] = "code"
state["current_agent"] = "code"
return state
async def code_tool_node(state: AgentState) -> AgentState:
"""
Code Tool: Generate and execute Python code.
codegen_agent: qwen3-32b
codefix_agent: gpt-oss-120b (max 2 fixes)
"""
add_agent_used(state, "code_tool")
task = state.get("_tool_query", "")
state["code_attempts"] += 1
code_tool = CodeTool()
start_time = time.time()
# Generate code using qwen3-32b
codegen_start = time.time()
try:
llm = get_model("qwen3-32b")
prompt = CODEGEN_PROMPT.format(task=task)
response = await llm.ainvoke([HumanMessage(content=prompt)])
code = _extract_code(response.content)
add_model_call(state, ModelCall(
model="qwen3-32b",
agent="codegen_agent",
tokens_in=len(prompt) // 4,
tokens_out=len(response.content) // 4,
duration_ms=int((time.time() - codegen_start) * 1000),
success=True
))
except Exception as e:
add_model_call(state, ModelCall(
model="qwen3-32b",
agent="codegen_agent",
tokens_in=0,
tokens_out=0,
duration_ms=int((time.time() - codegen_start) * 1000),
success=False,
error=str(e)
))
state["error_message"] = f"Code generation failed: {str(e)}"
state["tool_success"] = False
state["current_agent"] = "synthetic"
return state
# Execute code with correction loop (max 2 fixes)
exec_result = code_tool.execute(code)
while not exec_result["success"] and state["codefix_attempts"] < 2:
state["codefix_attempts"] += 1
# Fix code using gpt-oss-120b
fix_start = time.time()
try:
llm = get_model("gpt-oss-120b")
fix_prompt = CODEGEN_FIX_PROMPT.format(code=code, error=exec_result["error"])
response = await llm.ainvoke([HumanMessage(content=fix_prompt)])
code = _extract_code(response.content)
add_model_call(state, ModelCall(
model="gpt-oss-120b",
agent="codefix_agent",
tokens_in=len(fix_prompt) // 4,
tokens_out=len(response.content) // 4,
duration_ms=int((time.time() - fix_start) * 1000),
success=True
))
exec_result = code_tool.execute(code)
except Exception as e:
add_model_call(state, ModelCall(
model="gpt-oss-120b",
agent="codefix_agent",
tokens_in=0,
tokens_out=0,
duration_ms=int((time.time() - fix_start) * 1000),
success=False,
error=str(e)
))
break
duration_ms = int((time.time() - start_time) * 1000)
tool_call = ToolCall(
tool="code",
input=task,
output=exec_result.get("output") if exec_result["success"] else None,
success=exec_result["success"],
attempt=state["code_attempts"],
duration_ms=duration_ms,
error=exec_result.get("error") if not exec_result["success"] else None
)
add_tool_call(state, tool_call)
if exec_result["success"]:
state["tool_result"] = exec_result["output"]
state["tool_success"] = True
else:
state["tool_result"] = f"Code execution failed after {state['codefix_attempts']} fixes: {exec_result.get('error')}"
state["tool_success"] = False
state["error_message"] = exec_result.get("error")
state["current_agent"] = "synthetic"
return state
def _extract_code(response: str) -> str:
"""Extract Python code from LLM response."""
if "```python" in response:
return response.split("```python")[1].split("```")[0].strip()
elif "```" in response:
return response.split("```")[1].split("```")[0].strip()
return response.strip()
# ============================================================================
# ROUTER
# ============================================================================
def route_agent(state: AgentState) -> str:
"""Route to the next agent/node based on current state."""
current = state.get("current_agent", "done")
if current == "ocr":
return "ocr_agent"
elif current == "planner":
return "planner"
elif current == "executor":
return "executor"
elif current == "wolfram":
return "wolfram_tool"
elif current == "code":
return "code_tool"
elif current == "synthetic":
return "synthetic_agent"
elif current == "done":
return "done"
else:
return "end"
|