KaiserShultz/Ankelodon_AI_Multi_task_agentic_system
1
1{2 "cells": [3 {4 "cell_type": "code",5 "execution_count": 1,6 "metadata": {},7 "outputs": [8 {9 "name": "stderr",10 "output_type": "stream",11 "text": [12 "d:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",13 " from .autonotebook import tqdm as notebook_tqdm\n"14 ]15 }16 ],17 "source": [18 "import math\n",19 "from langgraph.checkpoint.memory import MemorySaver\n",20 "from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage\n",21 "import uuid\n",22 "\n",23 "from typing import Sequence\n",24 "from langchain_core.messages import BaseMessage\n",25 "from langgraph.graph.message import add_messages\n",26 "\n",27 "import os, io, json, base64\n",28 "from typing import Optional, Dict, Any, List\n",29 "from langchain_core.tools import tool\n",30 "\n",31 "# pip install google-generativeai pillow\n",32 "import google.generativeai as genai\n",33 "from PIL import Image\n",34 "from langgraph.prebuilt import ToolNode\n",35 "\n",36 "from dotenv import load_dotenv\n",37 "import pandas as pd\n",38 "from IPython.display import display, Image\n",39 "from langchain_community.document_loaders import DataFrameLoader, TextLoader\n",40 "from langchain_community.vectorstores import Chroma\n",41 "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",42 "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",43 "from langchain.schema import Document\n",44 "from langchain.schema.output_parser import StrOutputParser\n",45 "import pickle \n",46 "\n",47 "\n",48 "from langchain_core.prompts import ChatPromptTemplate, PromptTemplate\n",49 "from pydantic import BaseModel, Field\n",50 "\n",51 "from typing import List, TypedDict, Annotated, Literal, Optional, Union\n",52 "\n",53 "from langgraph.graph import StateGraph, END\n",54 "\n",55 "load_dotenv()\n",56 "import os\n",57 "import json\n",58 "import re\n",59 "import operator\n",60 "\n",61 "from langgraph.store.memory import InMemoryStore\n",62 "in_memory_store = InMemoryStore() #сохраняем состояние между запусками\n",63 "\n",64 "from IPython.display import Image, display\n",65 "\n",66 "from langgraph.checkpoint.memory import MemorySaver\n",67 "from langgraph.graph import StateGraph, MessagesState, START, END\n",68 "from langgraph.store.base import BaseStore\n",69 "\n",70 "from langchain_core.messages import HumanMessage, SystemMessage\n",71 "from langchain_core.runnables.config import RunnableConfig\n",72 "from PIL import Image, ImageStat, ExifTags\n",73 "import pandas as pd\n",74 "\n",75 "\n",76 "#TOOLS\n",77 "\n",78 "from tools import (web_search, arxiv_search, wiki_search, add, subtract, multiply, divide, power, \n",79 "analyze_csv_file, analyze_docx_file, analyze_pdf_file, analyze_txt_file, analyze_image_file, vision_qa_gemma, analyze_excel_file, preprocess_files, save_and_read_file, download_file_from_url)\n",80 "\n",81 "from code_interpreter import safe_code_run\n"82 ]83 },84 {85 "cell_type": "code",86 "execution_count": 2,87 "metadata": {},88 "outputs": [],89 "source": [90 "# === НОВЫЕ PYDANTIC МОДЕЛИ ===\n",91 "\n",92 "class ComplexityLevel(BaseModel):\n",93 " level: Literal[\"simple\", \"moderate\", \"complex\"] = Field(description=\"Complexity level of the query\")\n",94 " reasoning: str = Field(description=\"Explanation for the complexity assessment\")\n",95 " needs_planning: bool = Field(description=\"Whether this query requires detailed planning\")\n",96 " suggested_approach: str = Field(description=\"Recommended approach for handling this query\")\n",97 "\n",98 "class CritiqueFeedback(BaseModel):\n",99 " quality_score: int = Field(ge=1, le=10, description=\"Quality score from 1-10\")\n",100 " is_complete: bool = Field(description=\"Whether the answer is complete\")\n",101 " is_accurate: bool = Field(description=\"Whether the answer appears accurate\")\n",102 " missing_elements: List[str] = Field(default_factory=list, description=\"What's missing from the answer\")\n",103 " errors_found: List[str] = Field(default_factory=list, description=\"Potential errors identified\")\n",104 " suggested_improvements: List[str] = Field(default_factory=list, description=\"Suggestions for improvement\")\n",105 " needs_replanning: bool = Field(description=\"Whether the plan should be revised\")\n",106 " replan_instructions: Optional[str] = Field(default=None, description=\"Instructions for replanning\")"107 ]108 },109 {110 "cell_type": "code",111 "execution_count": 3,112 "metadata": {},113 "outputs": [],114 "source": [115 "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0.25)\n",116 "TOOLS = [download_file_from_url, web_search, arxiv_search, wiki_search, add, subtract, multiply, divide, power, analyze_excel_file, analyze_csv_file, analyze_docx_file, analyze_pdf_file, analyze_txt_file, analyze_image_file, vision_qa_gemma, safe_code_run]"117 ]118 },119 {120 "cell_type": "code",121 "execution_count": 4,122 "metadata": {},123 "outputs": [],124 "source": [125 "SYSTEM_PROMPT_PLANNER = \"\"\"\n",126 "You are the PLANNER of a multi-tool agent (GAIA I–II level). \n",127 "Your job is to produce a minimal, reliable, reproducible plan to solve the user’s request using available tools.\n",128 "You DO NOT call tools yourself; you only output a plan. The executor will run the plan.\n",129 "Tools are already bound to the model via .bind_tools(), so use EXACT tool names.\n",130 "\n",131 "Principles\n",132 "- Goal: a correct, verifiable answer (with citations/artifacts where appropriate).\n",133 "- Minimality: use as few steps/tool calls as possible.\n",134 "- Proper routing: pick the right branch: info | calc | table | doc_qa | image_qa | multi_hop.\n",135 "- Files first: never send raw files to the code interpreter. First extract with specialized tools (CSV/XLSX/PDF/DOCX/TXT/IMG). \n",136 " Only then compute on the extracted data (if needed) with the safe code interpreter.\n",137 "- Units & rounding: be explicit about units and rounding rules when numbers are involved.\n",138 "- Evidence: require sources (URL/page/figure caption) for external facts.\n",139 "- Fallbacks: define success criteria per step and a failure policy (“replan”, “stop”, or jump to another step-id).\n",140 "- Cost aware: start with cheap preview/metadata tools before heavy steps.\n",141 "\n",142 "\n",143 "Patterns / Routing\n",144 "- info/web: web_search/wiki_search/arxiv_search → gather citations.\n",145 "- calc: ensure data is available → safe_code_run only on extracted data; request plots/dataframes only if needed.\n",146 "- table (CSV/XLSX): analyze_* to confirm columns/shape → aggregate via safe_code_run (or SQL tool if available).\n",147 "- doc_qa (PDF/DOCX/TXT): analyze_* for pages/preview → extract_text or OCR if needed → answer with page/quote.\n",148 "- image_qa: analyze_image_* for metadata/OCR, or vision_qa_* for visual questions; for chart numbers, convert figure→table and verify with computation.\n",149 "- multi_hop: decompose into sub-queries, retrieve per modality, then synthesize with citations.\n",150 "\n",151 "Output format\n",152 "Return ONLY a single JSON object following this schema:\n",153 "{\n",154 " \"task_type\": \"info | calc | table | doc_qa | image_qa | multi_hop\",\n",155 " \"assumptions\": [\"string\", \"...\"],\n",156 " \"plan_rationale\": \"why this route and which tools are needed\",\n",157 " \"steps\": [\n",158 " {\n",159 " \"id\": \"s1\",\n",160 " \"description\": \"what and why\",\n",161 " \"evidence_needed\": [\"citations|page_numbers|figure_captions|stats_check|unit_check\"],\n",162 " \"success_criteria\": \"how we know the step succeeded\",\n",163 " \"on_fail\": \"replan | stop | sN\",\n",164 " \"outputs_to_state\": [\"what we expect to store for later steps\"]\n",165 " }\n",166 " ],\n",167 " \"answer_guidelines\": {\n",168 " \"final_answer_template\": \"how to form the final answer\",\n",169 " \"citations_required\": true,\n",170 " \"min_citations\": 1,\n",171 " \"units_policy\": \"what units to report and conversions\",\n",172 " \"rounding_policy\": \"how to round numbers\",\n",173 " \"include_artifacts\": [\"plots\",\"tables\",\"snippets\"]\n",174 " }\n",175 "}\n",176 "\n",177 "Constraints\n",178 "- Output must be valid JSON only. No markdown, no comments, no tool calls.\n",179 "- Use exact tool names from the injected catalog (tools are already bound via .bind_tools()).\n",180 "- Prefer a single-pass plan; add a fallback step only when necessary.\n",181 "- Do not assume file I/O inside the code interpreter beyond its sandboxed read-only rules; data must be staged beforehand by extract tools.\n",182 "\"\"\""183 ]184 },185 {186 "cell_type": "code",187 "execution_count": 5,188 "metadata": {},189 "outputs": [],190 "source": [191 "SYSTEM_EXECUTOR_PROMPT = \"\"\"\n",192 "ROLE: You are the EXECUTOR of a multi-tool agent system (GAIA I–II level).\n",193 "MISSION:\n",194 "\n",195 "Your only responsibility is to EXECUTE the steps of the plan generated by the PLANNER.\n",196 "You never change, reinterpret, or optimize the plan — you just follow it exactly as given.\n",197 "You can use the available tools strictly in the order and manner specified in {plan}.\n",198 "\n",199 "CRITICAL EXECUTION PROTOCOL:\n",200 "\n",201 "MANDATORY: Before ANY tool call, you MUST output reasoning inside <REASONING> ... </REASONING> tags.\n",202 "MANDATORY: Each <REASONING> block must contain:\n",203 "\n",204 "What step you are about to execute\n",205 "Why this tool is needed for this step\n",206 "What specific inputs you will provide to the tool\n",207 "What output you expect from the tool\n",208 "\n",209 "\n",210 "MANDATORY: Only after completing the <REASONING> block, proceed with the actual tool call.\n",211 "FORBIDDEN: Making any tool call without a preceding <REASONING> block.\n",212 "\n",213 "EXECUTION RULES:\n",214 "\n",215 "Do NOT invent new steps or modify the plan.\n",216 "BEFORE EACH TOOL CALL — MANDATORY <REASONING> STEP (NO EXCEPTIONS)\n",217 "If a step requires a tool — first reason, then call that tool with exactly the required inputs.\n",218 "If a step can be solved without a tool — just provide the direct output (no reasoning needed for non-tool steps).\n",219 "If a step fails, you may retry it, but never alter its intent.\n",220 "At the end: if you have all required results -> generate the FINAL ANSWER to the user.\n",221 "\n",222 "REASONING REQUIREMENTS:\n",223 "\n",224 "IMPERATIVE: NO TOOL CALLS WITHOUT <REASONING> TAGS FIRST\n",225 "Keep reasoning concise but complete (2-4 sentences)\n",226 "Be logical, precise and consistent\n",227 "Always specify: current step + tool choice + expected outcome\n",228 "After receiving tool results, you may add clarifying reasoning if needed\n",229 "\n",230 "EXECUTION FLOW EXAMPLE:\n",231 "<REASONING>\n",232 "I need to execute step 1 of the plan which requires searching for information about X. I will use the web_search tool with query \"X\" to gather relevant data that the next steps depend on.\n",233 "</REASONING>\n",234 "[tool call here]\n",235 "<REASONING>\n",236 "The search returned relevant information about X. Now proceeding to step 2 which requires...\n",237 "</REASONING>\n",238 "[next tool call here]\n",239 "OUTPUT GUIDELINES:\n",240 "\n",241 "For intermediate steps: return only the results (with mandatory <REASONING> before each tool).\n",242 "For the final answer: provide a clear, concise solution to the user's request, formatted for readability.\n",243 "MANDATORY: End final solution with <FINAL_ANSWER> marker\n",244 "Do not expose internal IDs, tool errors, or system details.\n",245 "\n",246 "FAILSAFE:\n",247 "\n",248 "If the plan is empty or invalid -> return \"\" (empty string).\n",249 "If the requested task is already trivially solvable without tools -> skip execution and answer directly.\n",250 "\n",251 "COMPLIANCE CHECK:\n",252 "\n",253 "Before submitting any response, verify: \"Did I include <REASONING> before EVERY tool call?\"\n",254 "If no: add the missing reasoning blocks\n",255 "If yes: proceed with response\n",256 "\n",257 "CRITICAL REMINDERS:\n",258 "\n",259 "NO TOOL CALLS WITHOUT <REASONING> TAGS — ZERO EXCEPTIONS\n",260 "EVERY TOOL CALL MUST BE PRECEDED BY REASONING\n",261 "ADD <FINAL_ANSWER> MARKER AT THE END\n",262 "\"\"\"\n",263 "\n",264 "\n",265 "COMPLEXITY_ASSESSOR_PROMPT = \"\"\"\n",266 "You are a COMPLEXITY ASSESSOR for a multi-tool agent system.\n",267 "Your job is to analyze user queries and determine their complexity level and processing requirements.\n",268 "\n",269 "COMPLEXITY LEVELS:\n",270 "1. SIMPLE: Direct questions that can be answered immediately without tools or with single tool use\n",271 " - Examples: \"What is 2+2?\", \"Define photosynthesis\", \"What's the capital of France?\"\n",272 " \n",273 "2. MODERATE: Questions requiring 1-3 tool calls or basic analysis\n",274 " - Examples: \"Search for recent news about AI\", \"Analyze this CSV file\", \"What's the weather tomorrow?\"\n",275 " \n",276 "3. COMPLEX: Multi-step problems requiring planning, multiple tools, or sophisticated reasoning\n",277 " - Examples: Research tasks, multi-file analysis, calculations with dependencies, creative projects\n",278 "\n",279 "ASSESSMENT CRITERIA:\n",280 "- Number of steps likely needed\n",281 "- Tool complexity and dependencies\n",282 "- Data processing requirements\n",283 "- Need for intermediate reasoning\n",284 "- Risk of failure without proper planning\n",285 "\n",286 "RULES:\n",287 "- SIMPLE queries bypass planning entirely\n",288 "- MODERATE queries may use lightweight planning\n",289 "- COMPLEX queries require full planning with fallbacks\n",290 "- When in doubt, err toward higher complexity\n",291 "\n",292 "Analyze the query and respond with your assessment.\n",293 "\"\"\"\n",294 "\n",295 "CRITIC_PROMPT = \"\"\"\n",296 "You are the CRITIC of a multi-tool agent system.\n",297 "Your job is to evaluate execution reports and provide detailed feedback.\n",298 "\n",299 "EVALUATION FRAMEWORK:\n",300 "\n",301 "1. COMPLETENESS (0-3 points):\n",302 " - 3: Fully addresses all aspects of the query\n",303 " - 2: Addresses main aspects, minor gaps\n",304 " - 1: Partial answer, significant gaps\n",305 " - 0: Incomplete or off-topic\n",306 "\n",307 "2. ACCURACY (0-3 points):\n",308 " - 3: All information appears accurate and well-sourced\n",309 " - 2: Mostly accurate, minor issues\n",310 " - 1: Some accuracy concerns\n",311 " - 0: Significant accuracy problems\n",312 "\n",313 "3. METHODOLOGY (0-2 points):\n",314 " - 2: Appropriate tools and approach used\n",315 " - 1: Acceptable approach, could be better\n",316 " - 0: Poor methodology or tool selection\n",317 "\n",318 "4. EVIDENCE (0-2 points):\n",319 " - 2: Strong evidence and sources provided\n",320 " - 1: Some evidence provided\n",321 " - 0: Insufficient evidence\n",322 "\n",323 "TOTAL SCORE: /10 points\n",324 "\n",325 "DECISION THRESHOLDS:\n",326 "- 8-10: Accept (excellent quality)\n",327 "- 6-7: Accept with minor notes\n",328 "- 4-5: Marginal, consider replanning\n",329 "- 0-3: Reject, requires replanning\n",330 "\n",331 "EXECUTION REPORT TO EVALUATE:\n",332 "Query: {query}\n",333 "Approach: {approach}\n",334 "Tools Used: {tools}\n",335 "Key Findings: {findings}\n",336 "Sources: {sources}\n",337 "Confidence: {confidence}\n",338 "Limitations: {limitations}\n",339 "Final Answer: {answer}\n",340 "\n",341 "Provide detailed critique focusing on what works well and what could be improved.\n",342 "\"\"\""343 ]344 },345 {346 "cell_type": "code",347 "execution_count": 6,348 "metadata": {},349 "outputs": [],350 "source": [351 "#PLANNER PYDANTIC MODELS\n",352 "\n",353 "from typing import Any, Dict, List, Optional, Literal, Iterable\n",354 "from pydantic import BaseModel, Field, ValidationError\n",355 "\n",356 "TaskType = Literal[\"info\", \"calc\", \"table\", \"doc_qa\", \"image_qa\", \"multi_hop\"]\n",357 "EvidenceTag = Literal[\"citations\", \"page_numbers\", \"figure_captions\", \"stats_check\", \"unit_check\"]\n",358 "\n",359 "class PlanStep(BaseModel):\n",360 " id: str\n",361 " description: str\n",362 " #tool: Optional[str] = Field(default=None, description=\"Exact tool name or null for reasoning step\")\n",363 " #args_hint: Dict[str, Any] = Field(default_factory=dict)\n",364 " evidence_needed: List[EvidenceTag] = Field(default_factory=list)\n",365 " success_criteria: str\n",366 " on_fail: str = Field(default=\"replan\", description=\"One of: 'replan' | 'stop' | step-id\")\n",367 " outputs_to_state: List[str] = Field(default_factory=list)\n",368 "\n",369 "class AnswerGuidelines(BaseModel):\n",370 " final_answer_template: str\n",371 " citations_required: bool = False\n",372 " min_citations: int = 0\n",373 " units_policy: Optional[str] = None\n",374 " rounding_policy: Optional[str] = None\n",375 " include_artifacts: List[str] = Field(default_factory=list)\n",376 "\n",377 "class PlannerPlan(BaseModel):\n",378 " task_type: TaskType\n",379 " assumptions: List[str] = Field(default_factory=list)\n",380 " plan_rationale: str\n",381 " steps: List[PlanStep]\n",382 " answer_guidelines: AnswerGuidelines"383 ]384 },385 {386 "cell_type": "code",387 "execution_count": 7,388 "metadata": {},389 "outputs": [],390 "source": [391 "llm_with_tools = llm.bind_tools(TOOLS)\n",392 "config = {\"configurable\": {\"thread_id\": \"1\"}, \"recursion_limit\" : 50}\n",393 "TOOL_NODE = ToolNode(TOOLS)\n",394 "planner_llm = llm.with_structured_output(PlannerPlan)\n",395 "\n",396 "class ToolExecution(BaseModel):\n",397 " tool_name: str\n",398 " arguments: str\n",399 " call_id: str\n",400 " \n",401 " class Config:\n",402 " extra = \"forbid\"\n",403 "\n",404 "class ExecutionReport(BaseModel):\n",405 " \"\"\"Structured report for critic evaluation.\"\"\"\n",406 " query_summary: str = Field(description=\"Brief summary of the user's query\")\n",407 " approach_used: str = Field(description=\"What approach/strategy was used\")\n",408 " tools_executed: List[ToolExecution] = Field(default_factory=list, description=\"List of tools used with results\")\n",409 " key_findings: List[str] = Field(default_factory=list, description=\"Main findings or results\")\n",410 " data_sources: List[str] = Field(default_factory=list, description=\"Sources of information used\")\n",411 " assumptions_made: List[str] = Field(default_factory=list, description=\"Any assumptions made during execution\")\n",412 " confidence_level: Literal[\"low\", \"medium\", \"high\"] = Field(description=\"Confidence in the answer\")\n",413 " limitations: List[str] = Field(default_factory=list, description=\"Known limitations or caveats\")\n",414 " final_answer: str = Field(description=\"The actual answer to the user's query\")\n",415 "\n",416 " class Config:\n",417 " extra = \"forbid\"\n",418 "\n",419 "\n",420 "class AgentState(MessagesState):\n",421 " query: str\n",422 " final_answer: str\n",423 " plan: Optional[PlannerPlan]\n",424 " complexity_assessment: ComplexityLevel\n",425 " current_step: int\n",426 " reasoning_done: bool\n",427 " messages : Annotated[Sequence[BaseMessage], add_messages]\n",428 " files: List[str]\n",429 " file_contents: Dict[str, Any]\n",430 " critique_feedback: Optional[CritiqueFeedback]\n",431 " iteration_count :int\n",432 " max_iterations: int\n",433 " execution_report : ExecutionReport\n",434 "\n",435 "\n",436 "def query_input(state : AgentState) -> AgentState:\n",437 " print(\"=== USER QUERY TRANSFERED TO AGENT ===\")\n",438 "\n",439 " files = state.get(\"files\", [])\n",440 " if files:\n",441 " print(f\"Processing {len(files)} files:\")\n",442 " file_info = preprocess_files(files)\n",443 " \n",444 " for file_path, info in file_info.items():\n",445 " print(f\" - {file_path}: {info['type']} ({info['size']} bytes) -> {info['suggested_tool']}\")\n",446 "\n",447 " state[\"file_contents\"] = file_info\n",448 " file_context = \"\\n\\n=== AVAILABLE FILES FOR ANALYSIS ===\\n\"\n",449 " for file_path, info in file_info.items():\n",450 " filename = os.path.basename(file_path)\n",451 " file_context += f\"File: {filename}\\n\"\n",452 " file_context += f\" - Type: {info['type']}\\n\" \n",453 " file_context += f\" - Size: {info['size']} bytes\\n\"\n",454 " file_context += f\" - Suggested tool: {info['suggested_tool']}\\n\"\n",455 " if info.get(\"preview\"):\n",456 " file_context += f\" - Preview: {info['preview']}\\n\"\n",457 " file_context += \"\\n\"\n",458 " \n",459 " # Добавляем инструкции по работе с файлами\n",460 " file_context += \"IMPORTANT: Use the suggested tools to analyze these files before processing their data.\\n\"\n",461 " file_context += \"File paths are available in the agent state and can be passed directly to analysis tools.\\n\"\n",462 " \n",463 " original_query = state.get(\"query\", \"\")\n",464 " state[\"query\"] = original_query + file_context\n",465 " return state\n",466 "\n",467 "\n",468 "def planner(state : AgentState) -> AgentState:\n",469 " sys_stack = [\n",470 " SystemMessage(content=SYSTEM_PROMPT_PLANNER.strip()),\n",471 " HumanMessage(content=state[\"query\"]),\n",472 " ]\n",473 " plan: PlannerPlan = planner_llm.invoke(sys_stack)\n",474 " \n",475 " print(\"=== GENERATED PLAN ===\")\n",476 " return {\"messages\" : sys_stack + state[\"messages\"],\n",477 " \"plan\": plan,\n",478 " \"current_step \": 0,\n",479 " \"reasoning_done\": False}\n",480 "\n",481 "def agent(state: AgentState) -> AgentState:\n",482 " \n",483 " \"\"\"\n",484 " sys_msg = SystemMessage(\n",485 " content=SYSTEM_EXECUTOR_PROMPT.strip().format(\n",486 " plan=json.dumps(state[\"plan\"], indent=2)\n",487 " )\n",488 " )\n",489 " \"\"\"\n",490 " current_step = state.get(\"current_step\", 0)\n",491 " reasoning_done = state.get(\"reasoning_done\", False)\n",492 " plan = state.get(\"plan\", {})\n",493 " steps = state[\"plan\"].steps\n",494 "\n",495 " if current_step >= len(steps):\n",496 " return {\n",497 " \"messages\": state[\"messages\"] + [AIMessage(content=\"All steps completed. <FINAL_ANSWER>\")],\n",498 " \"reasoning_done\": False\n",499 " }\n",500 "\n",501 " current_step_info = steps[current_step]\n",502 "\n",503 " if not reasoning_done:\n",504 "\n",505 " # ✅ ДОБАВЛЕНО: Специальный контекст для файлов\n",506 " file_context = \"\"\n",507 " file_contents = state.get(\"file_contents\", {})\n",508 " if file_contents:\n",509 " file_context = \"\\n\\nAVAILABLE FILES IN CURRENT SESSION:\\n\"\n",510 " for filepath, info in file_contents.items():\n",511 " filename = os.path.basename(filepath)\n",512 " file_context += f\"- {filename}: {info['type']} file, suggested tool: {info['suggested_tool']}\\n\"\n",513 " file_context += f\" Path: {filepath}\\n\"\n",514 "\n",515 " reasoning_prompt = f\"\"\"\n",516 " {SYSTEM_EXECUTOR_PROMPT}\n",517 " \n",518 " CURRENT TASK: You must perform reasoning for step {current_step + 1}.\n",519 " \n",520 " STEP INFO: {current_step_info}\\n\\n\n",521 "\n",522 " FILE CONTEXT: {file_contents}\n",523 " \n",524 " CRITICAL: You MUST output your reasoning in <REASONING> tags, but DO NOT call any tools yet.\n",525 " Explain what you need to do and why, then end your response.\n",526 "\n",527 " REASONING IS IMPERATIVE BEFORE ANY TOOL CALLS.\n",528 " \"\"\"\n",529 "\n",530 " sys_msg = SystemMessage(content = reasoning_prompt)\n",531 " stack = [sys_msg] + state[\"messages\"]\n",532 "\n",533 " step = llm.invoke(stack)\n",534 " print(\"=== REASONING STEP ===\")\n",535 " print(step.content)\n",536 "\n",537 " return {\n",538 " \"messages\" : state[\"messages\"] + [step],\n",539 " \"reasoning_done\" : True\n",540 " }\n",541 " \n",542 " else:\n",543 " tool_prompt = f\"\"\"\n",544 " Now execute the tool for step {current_step + 1}.\n",545 " \n",546 " STEP INFO: {current_step_info}\n",547 " \n",548 " You have already done the reasoning. Now call the appropriate tool with the correct parameters.\n",549 " Available file paths: {list(state.get(\"file_contents\", {}).keys())}\\n\n",550 " IMPORTANT NOTE: IF YOU DECIDED TO USE safe_code_run, MAKE SURE TO FINISH CALCULATIONS WITH print() or saving to a variable NAMED 'result' so that the output can be captured!\n",551 " \"\"\" \n",552 "\n",553 " sys_msg = SystemMessage(content=tool_prompt)\n",554 " stack = [sys_msg] + state[\"messages\"] # Берем последние сообщения включая reasoning\n",555 " \n",556 " # Используем модель С инструментами для выполнения\n",557 " step = llm_with_tools.invoke(stack)\n",558 " print(\"=== TOOL EXECUTION ===\")\n",559 " print(f\"Tool calls: {step.tool_calls}\")\n",560 " \n",561 " return {\n",562 " \"messages\": state[\"messages\"] + [step],\n",563 " \"current_step\": current_step + 1 if step.tool_calls else current_step,\n",564 " \"reasoning_done\": False # Сбрасываем для следующего шага\n",565 " }\n",566 "\n",567 "\n",568 "def should_continue(state : AgentState) -> bool:\n",569 " \n",570 " last_message = state[\"messages\"][-1]\n",571 " reasoning_done = state.get(\"reasoning_done\", False)\n",572 " if \"<FINAL_ANSWER>\" in last_message.content:\n",573 " return \"final_answer\"\n",574 " elif last_message.tool_calls:\n",575 " return \"tools\" \n",576 " elif not reasoning_done and \"<REASONING>\" in last_message.content:\n",577 " # Reasoning выполнен, но инструменты еще не вызваны\n",578 " return \"agent\"\n",579 " elif reasoning_done:\n",580 " # Reasoning выполнен, теперь нужно вызвать инструменты\n",581 " return \"agent\"\n",582 " else:\n",583 " # Нужно сделать reasoning\n",584 " return \"agent\"\n",585 "\n",586 "# 6. Добавить отладочную информацию в TOOL_NODE\n",587 "class DebuggingToolNode(ToolNode):\n",588 " def __init__(self, tools):\n",589 " super().__init__(tools)\n",590 " \n",591 " def __call__(self, state):\n",592 " print(\"=== TOOL EXECUTION STARTED ===\")\n",593 " result = super().__call__(state)\n",594 " print(\"=== TOOL EXECUTION COMPLETED ===\")\n",595 " return result\n",596 "\n",597 "DEBUGGING_TOOL_NODE = DebuggingToolNode(TOOLS)\n",598 "\n",599 "\n",600 "\n",601 "\"\"\"\n",602 "def summary(state : AgentState) -> AgentState:\n",603 " print(\"=== FINAL ANSWER ===\")\n",604 " summarizer_prompt = \n",605 " Now you have to provide final answer for the user query : {query}\n",606 " In messages below you have all the context you need.\n",607 "\n",608 " YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, Apply the rules above for each element (number or string), ensure there is exactly one space after each comma.\n",609 " Your answer should only start with \"FINAL ANSWER: \", then follows with the answer.\n",610 "\n",611 " Here is the context:\n",612 " {messages}\n",613 "\n",614 " REMEMBER AND STRICTLY FOLLOW THE FORMATTING RULES ABOVE. ALWAYS USE THIS FORMAT:\n",615 " FINAL ANSWER: ...\n",616 " \n",617 "\n",618 " state[\"final_answer\"] = llm.invoke([SystemMessage(content=summarizer_prompt.strip().format(query=state[\"query\"], messages = state[\"messages\"]))])\n",619 " return state\n",620 "\"\"\"\n",621 "\n",622 "def enhanced_finalizer(state: AgentState) -> AgentState:\n",623 " \"\"\"Generate comprehensive execution report for critic evaluation.\"\"\"\n",624 " print(\"=== GENERATING EXECUTION REPORT ===\")\n",625 " \n",626 " # Extract tool execution information\n",627 " tools_executed = []\n",628 " data_sources = []\n",629 " \n",630 " for msg in state[\"messages\"]:\n",631 " if hasattr(msg, 'tool_calls') and msg.tool_calls:\n",632 " for tool_call in msg.tool_calls:\n",633 " tools_executed.append(ToolExecution(\n",634 " tool_name=tool_call['name'],\n",635 " arguments=str(tool_call['args']),\n",636 " call_id=tool_call['id']\n",637 " ))\n",638 " \n",639 " # Extract data sources from tool results\n",640 " if hasattr(msg, 'content') and isinstance(msg.content, str):\n",641 " # Look for URLs, file names, or other sources\n",642 " import re\n",643 " urls = re.findall(r'https?://[^\\s]+', msg.content)\n",644 " data_sources.extend(urls)\n",645 " \n",646 " # Get plan information if available\n",647 " plan = state.get(\"plan\")\n",648 " approach_used = \"Direct execution\"\n",649 " assumptions_made = []\n",650 " \n",651 " if plan:\n",652 " approach_used = f\"{plan.task_type} approach with {len(plan.steps)} steps\"\n",653 " assumptions_made = plan.assumptions\n",654 " \n",655 " # Generate structured report (КОСТЫЛЬ ЗДЕСЬ!)\n",656 " report_generator_prompt = f\"\"\"\n",657 " Generate a comprehensive execution report for the following query processing:\n",658 "\n",659 " ORIGINAL QUERY: {state['query']}\n",660 " \n",661 " EXECUTION CONTEXT:\n",662 " - Complexity Level: {state.get('complexity_assessment', {}).level}\n",663 " - Plan Used: {plan if plan else {}}\n",664 " - Tools Executed: {tools_executed}\n",665 " - Available Files: {list(state.get('file_contents', {}).keys())}\n",666 " \n",667 " CONVERSATION HISTORY:\n",668 " {[msg.content[:200] + \"...\" if len(msg.content) > 200 else msg.content \n",669 " for msg in state['messages'][-5:]]} # Last 5 messages for context\n",670 " \n",671 " Based on this information, create a structured execution report that includes:\n",672 " 1. Query summary\n",673 " 2. Approach used\n",674 " 3. Key findings from the execution\n",675 " 4. Data sources used\n",676 " 5. Your confidence level in the results\n",677 " 6. Any limitations or caveats\n",678 " 7. The final answer\n",679 " \n",680 " Be thorough but concise. This report will be evaluated by a critic for quality assurance.\n",681 " \"\"\"\n",682 " \n",683 " report_llm = llm.with_structured_output(ExecutionReport)\n",684 " \n",685 " execution_report = report_llm.invoke([\n",686 " SystemMessage(content=report_generator_prompt),\n",687 " HumanMessage(content=\"Generate the execution report.\")\n",688 " ])\n",689 " \n",690 " print(f\"Report generated - Confidence: {execution_report.confidence_level}\")\n",691 " print(f\"Key findings: {len(execution_report.key_findings)}\")\n",692 " print(f\"Data sources: {len(execution_report.data_sources)}\")\n",693 " \n",694 " # Format final answer for user\n",695 " formatted_answer = format_final_answer(execution_report, state.get('complexity_assessment', {}))\n",696 " print(execution_report)\n",697 " return {\n",698 " \"execution_report\": execution_report,\n",699 " \"final_answer\": formatted_answer\n",700 " }\n",701 "\n",702 "def format_final_answer(report: ExecutionReport, complexity: dict) -> str:\n",703 " \"\"\"Format the final answer based on complexity and report content.\"\"\"\n",704 " \n",705 " if complexity.level == 'simple':\n",706 " # For simple queries, just return the answer\n",707 " return f\"FINAL ANSWER: {report.final_answer}\"\n",708 " \n",709 " # For complex queries, provide more detailed response\n",710 " formatted = f\"\"\"FINAL ANSWER: {report.final_answer}\n",711 "\n",712 "SUMMARY:\n",713 "{report.query_summary}\n",714 "\n",715 "KEY FINDINGS:\n",716 "{chr(10).join(f\"• {finding}\" for finding in report.key_findings)}\"\"\"\n",717 " \n",718 " if report.data_sources:\n",719 " formatted += f\"\"\"\n",720 "\n",721 "SOURCES:\n",722 "{chr(10).join(f\"• {source}\" for source in report.data_sources[:5])}\"\"\" # Limit to 5 sources\n",723 " \n",724 " if report.limitations:\n",725 " formatted += f\"\"\"\n",726 "\n",727 "LIMITATIONS:\n",728 "{chr(10).join(f\"• {limitation}\" for limitation in report.limitations)}\"\"\"\n",729 " \n",730 " return formatted\n",731 "\n",732 "\n",733 "def complexity_assessor(state: AgentState) -> AgentState:\n",734 " \"\"\"Assess query complexity and determine if planning is needed.\"\"\"\n",735 " print(\"=== COMPLEXITY ASSESSMENT ===\")\n",736 " \n",737 " complexity_llm = llm.with_structured_output(ComplexityLevel)\n",738 " \n",739 " assessment_message = [\n",740 " SystemMessage(content=COMPLEXITY_ASSESSOR_PROMPT.strip()),\n",741 " HumanMessage(content=f\"Query: {state['query']}\")\n",742 " ]\n",743 " \n",744 " assessment = complexity_llm.invoke(assessment_message)\n",745 " \n",746 " print(f\"Complexity: {assessment.level}\")\n",747 " print(f\"Needs planning: {assessment.needs_planning}\")\n",748 " print(f\"Reasoning: {assessment.reasoning}\")\n",749 " \n",750 " return {\n",751 " \"complexity_assessment\": assessment,\n",752 " \"messages\": state[\"messages\"] + assessment_message\n",753 " }\n",754 "\n",755 "\n",756 "def simple_executor(state: AgentState) -> AgentState:\n",757 " \"\"\"Handle simple queries directly without planning.\"\"\"\n",758 " print(\"=== SIMPLE EXECUTION ===\")\n",759 " \n",760 " # For simple queries, use the LLM with tools directly\n",761 " simple_prompt = f\"\"\"\n",762 " Answer this simple query directly and efficiently: {state['query']}\n",763 " \n",764 " You have access to tools if needed, but try to answer directly when possible.\n",765 " If you need files, they are available at: {list(state.get('file_contents', {}).keys())}\n",766 " \n",767 " Provide a clear, concise answer.\n",768 " \"\"\"\n",769 " \n",770 " response = llm_with_tools.invoke([\n",771 " SystemMessage(content=simple_prompt),\n",772 " HumanMessage(content=state['query'])\n",773 " ])\n",774 " \n",775 " return {\n",776 " \"messages\": state[\"messages\"] + [response],\n",777 " \"final_answer\": response.content\n",778 " }\n",779 "\n",780 "\n",781 "def should_use_planning(state: AgentState) -> str:\n",782 " \"\"\"Route based on complexity assessment.\"\"\"\n",783 " complexity = state[\"complexity_assessment\"]\n",784 " \n",785 " if complexity.level == \"simple\" and not complexity.needs_planning:\n",786 " return \"simple_executor\"\n",787 " else:\n",788 " return \"planner\"\n",789 " \n",790 "\"\"\" \n",791 "def critic_evaluator(state: AgentState) -> AgentState:\n",792 " \n",793 " print(\"=== ANSWER CRITIQUE ===\")\n",794 " \n",795 " critic_llm = llm.with_structured_output(CritiqueFeedback)\n",796 " \n",797 " # Gather tool execution results for context\n",798 " tool_results = []\n",799 " for msg in state[\"messages\"]:\n",800 " if hasattr(msg, 'tool_calls') and msg.tool_calls:\n",801 " tool_results.extend([f\"Tool: {tc['name']}, Args: {tc['args']}\" for tc in msg.tool_calls])\n",802 " \n",803 " if state.get(\"plan\"):\n",804 " terra = state.get(\"plan\")\n",805 " else:\n",806 " terra = \"No plan used\"\n",807 " critique_prompt = CRITIC_PROMPT.format(\n",808 " query=state[\"query\"],\n",809 " plan=terra,\n",810 " answer=state[\"final_answer\"],\n",811 " tool_results=tool_results[:5] #Limit context\n",812 " )\n",813 " \n",814 " critique = critic_llm.invoke([\n",815 " SystemMessage(content=critique_prompt),\n",816 " HumanMessage(content=\"Please evaluate this answer.\")\n",817 " ])\n",818 " \n",819 " print(f\"Quality Score: {critique.quality_score}/10\")\n",820 " print(f\"Complete: {critique.is_complete}\")\n",821 " print(f\"Accurate: {critique.is_accurate}\")\n",822 " if critique.errors_found:\n",823 " print(f\"Errors: {critique.errors_found}\")\n",824 " if critique.needs_replanning:\n",825 " print(f\"Needs replanning: {critique.replan_instructions}\")\n",826 " \n",827 " return {\n",828 " \"critique_feedback\": critique,\n",829 " \"iteration_count\": state.get(\"iteration_count\", 0) + 1\n",830 " }\n",831 "\"\"\"\n",832 "\n",833 "def critic_evaluator(state: AgentState) -> AgentState:\n",834 " \"\"\"Enhanced critic that evaluates execution reports.\"\"\"\n",835 " print(\"=== ENHANCED ANSWER CRITIQUE ===\")\n",836 " \n",837 " report = state.get(\"execution_report\")\n",838 " critic_llm = llm.with_structured_output(CritiqueFeedback)\n",839 " \n",840 " critique_prompt = CRITIC_PROMPT.format(\n",841 " query=report.query_summary,\n",842 " approach=report.approach_used,\n",843 " tools=report.tools_executed,\n",844 " findings=report.key_findings,\n",845 " sources=report.data_sources,\n",846 " confidence=report.confidence_level,\n",847 " limitations=report.limitations,\n",848 " answer=report.final_answer\n",849 " )\n",850 " \n",851 " critique = critic_llm.invoke([\n",852 " SystemMessage(content=critique_prompt),\n",853 " HumanMessage(content=\"Evaluate this execution report thoroughly.\")\n",854 " ])\n",855 " \n",856 " print(f\"Quality Score: {critique.quality_score}/10\")\n",857 " print(f\"Complete: {critique.is_complete}\")\n",858 " print(f\"Accurate: {critique.is_accurate}\")\n",859 " \n",860 " if critique.errors_found:\n",861 " print(f\"Issues found: {critique.errors_found}\")\n",862 " \n",863 " if critique.needs_replanning:\n",864 " print(f\"Replanning needed: {critique.replan_instructions}\")\n",865 " \n",866 " return {\n",867 " \"critique_feedback\": critique,\n",868 " \"iteration_count\": state.get(\"iteration_count\", 0) + 1\n",869 " }\n",870 "\n",871 "\n",872 "\n",873 "def should_replan(state: AgentState) -> str:\n",874 " \"\"\"Decide whether to accept answer, replan, or stop.\"\"\"\n",875 " critique = state.get(\"critique_feedback\")\n",876 " iteration_count = state.get(\"iteration_count\", 0)\n",877 " max_iterations = state.get(\"max_iterations\", 3)\n",878 " \n",879 " if not critique:\n",880 " return \"end\"\n",881 " \n",882 " # Stop if max iterations reached\n",883 " if iteration_count >= max_iterations:\n",884 " print(f\"Max iterations ({max_iterations}) reached. Accepting current answer.\")\n",885 " return \"end\"\n",886 " \n",887 " # Accept if quality is good enough\n",888 " if critique.quality_score >= 7 or not critique.needs_replanning:\n",889 " return \"end\"\n",890 " \n",891 " # Replan if quality is poor and we haven't exceeded max iterations\n",892 " if critique.needs_replanning and iteration_count < max_iterations:\n",893 " print(\"Replanning due to critic feedback...\")\n",894 " return \"replan\"\n",895 " \n",896 " return \"end\"\n",897 "\n",898 "def replanner(state: AgentState) -> AgentState:\n",899 " \"\"\"Create a revised plan based on critic feedback.\"\"\"\n",900 " print(\"=== REPLANNING ===\")\n",901 " \n",902 " critique = state[\"critique_feedback\"]\n",903 " previous_plan = state.get(\"plan\")\n",904 " \n",905 " replan_prompt = f\"\"\"\n",906 " {SYSTEM_PROMPT_PLANNER}\n",907 " \n",908 " REPLANNING CONTEXT:\n",909 " Original Query: {state['query']}\n",910 " Previous Plan: {previous_plan if previous_plan else {}}\n",911 " \n",912 " CRITIC FEEDBACK:\n",913 " - Quality Score: {critique.quality_score}/10\n",914 " - Issues Found: {critique.errors_found}\n",915 " - Missing Elements: {critique.missing_elements}\n",916 " - Improvement Suggestions: {critique.suggested_improvements}\n",917 " - Specific Instructions: {critique.replan_instructions}\n",918 " \n",919 " Create a REVISED plan that addresses these issues. Focus on fixing the identified problems.\n",920 " \"\"\"\n",921 " \n",922 " revised_plan = planner_llm.invoke([\n",923 " SystemMessage(content=replan_prompt),\n",924 " HumanMessage(content=\"Create a revised plan based on the feedback.\")\n",925 " ])\n",926 " \n",927 " print(\"Plan revised based on critic feedback\")\n",928 " \n",929 " return {\n",930 " \"plan\": revised_plan,\n",931 " \"current_step\": 0,\n",932 " \"reasoning_done\": False\n",933 " #\"messages\": [] Reset messages for fresh execution\n",934 " }\n"935 ]936 },937 {938 "cell_type": "code",939 "execution_count": 8,940 "metadata": {},941 "outputs": [],942 "source": [943 "#GRAPH BUILDING\n",944 "\n",945 "builder = StateGraph(AgentState)\n",946 "builder.add_node(\"INPUT\", query_input)\n",947 "builder.add_node(\"COMPLEXITY_ASSESSOR\", complexity_assessor)\n",948 "builder.add_node(\"PLANNING\", planner)\n",949 "builder.add_node(\"AGENT\", agent)\n",950 "builder.add_node(\"TOOLS\", DEBUGGING_TOOL_NODE)\n",951 "builder.add_node(\"FINALIZER\", enhanced_finalizer)\n",952 "builder.add_node(\"SIMPLE_EXECUTOR\", simple_executor)\n",953 "builder.add_node(\"CRITIC\", critic_evaluator)\n",954 "builder.add_node(\"REPLANNER\", replanner)\n",955 "\n",956 "builder.set_entry_point(\"INPUT\")\n",957 "builder.add_edge(\"INPUT\", \"COMPLEXITY_ASSESSOR\")\n",958 "\n",959 "builder.add_conditional_edges(\n",960 " \"COMPLEXITY_ASSESSOR\",\n",961 " should_use_planning,\n",962 " {\"simple_executor\": \"SIMPLE_EXECUTOR\", \"planner\": \"PLANNING\"},\n",963 " )\n",964 "builder.add_edge(\"SIMPLE_EXECUTOR\", \"FINALIZER\")\n",965 "\n",966 "\n",967 "builder.add_edge(\"PLANNING\", \"AGENT\")\n",968 "builder.add_conditional_edges(\n",969 " \"AGENT\",\n",970 " should_continue,\n",971 " {\"tools\": \"TOOLS\", \"agent\": \"AGENT\", \"final_answer\": \"FINALIZER\"},\n",972 " )\n",973 "builder.add_edge(\"TOOLS\", \"AGENT\")\n",974 "builder.add_edge(\"FINALIZER\", \"CRITIC\")\n",975 "builder.add_conditional_edges(\n",976 " \"CRITIC\",\n",977 " should_replan,\n",978 " {\"end\": END, \"replan\": \"REPLANNER\"},\n",979 " )\n",980 "builder.add_edge(\"REPLANNER\", \"AGENT\")\n",981 "\n",982 "\n",983 "system = builder.compile(checkpointer=MemorySaver())"984 ]985 },986 {987 "cell_type": "code",988 "execution_count": 9,989 "metadata": {},990 "outputs": [991 {992 "name": "stdout",993 "output_type": "stream",994 "text": [995 "=== USER QUERY TRANSFERED TO AGENT ===\n",996 "=== COMPLEXITY ASSESSMENT ===\n",997 "Complexity: complex\n",998 "Needs planning: True\n",999 "Reasoning: This query involves multiple steps, including identifying the specific kit version, locating the relevant paper, extracting data about the vials, and performing calculations to determine the cumulative volume of fluid. It requires sophisticated reasoning and potentially multiple tool calls to gather and analyze the necessary data.\n",1000 "=== GENERATED PLAN ===\n",1001 "=== REASONING STEP ===\n",1002 "{\n",1003 " \"task_type\": \"doc_qa\",\n",1004 " \"assumptions\": [\n",1005 " \"The paper contains specific information about the 114 version of the kit and the opaque-capped vials without stickers.\",\n",1006 " \"The cumulative milliliters of fluid in the vials can be extracted from the text.\"\n",1007 " ],\n",1008 " \"plan_rationale\": \"I will use the doc_qa tool to extract text from the paper to find details about the 114 version of the kit and the opaque-capped vials without stickers. This step is essential to gather the necessary information for calculating the cumulative fluid volume.\",\n",1009 " \"steps\": [\n",1010 " {\n",1011 " \"id\": \"s1\",\n",1012 " \"description\": \"Extract text from the paper to find details about the 114 version of the kit and the opaque-capped vials without stickers.\",\n",1013 " \"evidence_needed\": [\"page_numbers\"],\n",1014 " \"success_criteria\": \"Successfully extract relevant information about the vials and their fluid volumes.\",\n",1015 " \"on_fail\": \"stop\",\n",1016 " \"outputs_to_state\": [\"cumulative milliliters of fluid in opaque-capped vials without stickers\"]\n",1017 " }\n",1018 " ],\n",1019 " \"answer_guidelines\": {\n",1020 " \"final_answer_template\": \"Report the total cumulative milliliters of fluid found in the opaque-capped vials without stickers.\",\n",1021 " \"citations_required\": true,\n",1022 " \"min_citations\": 1,\n",1023 " \"units_policy\": \"Report in milliliters (mL).\",\n",1024 " \"rounding_policy\": \"Round to two decimal places.\",\n",1025 " \"include_artifacts\": [\"snippets\"]\n",1026 " }\n",1027 "}\n",1028 "=== TOOL EXECUTION ===\n",1029 "Tool calls: []\n",1030 "=== REASONING STEP ===\n",1031 "{\n",1032 " \"task_type\": \"doc_qa\",\n",1033 " \"assumptions\": [\n",1034 " \"The paper contains specific information about the 114 version of the kit and the opaque-capped vials without stickers.\",\n",1035 " \"The cumulative milliliters of fluid in the vials can be extracted from the text.\"\n",1036 " ],\n",1037 " \"plan_rationale\": \"I will use the doc_qa tool to extract text from the paper to find details about the 114 version of the kit and the opaque-capped vials without stickers. This step is essential to gather the necessary information for calculating the cumulative fluid volume.\",\n",1038 " \"steps\": [\n",1039 " {\n",1040 " \"id\": \"s1\",\n",1041 " \"description\": \"Extract text from the paper to find details about the 114 version of the kit and the opaque-capped vials without stickers.\",\n",1042 " \"evidence_needed\": [\"page_numbers\"],\n",1043 " \"success_criteria\": \"Successfully extract relevant information about the vials and their fluid volumes.\",\n",1044 " \"on_fail\": \"stop\",\n",1045 " \"outputs_to_state\": [\"cumulative milliliters of fluid in opaque-capped vials without stickers\"]\n",1046 " }\n",1047 " ],\n",1048 " \"answer_guidelines\": {\n",1049 " \"final_answer_template\": \"Report the total cumulative milliliters of fluid found in the opaque-capped vials without stickers.\",\n",1050 " \"citations_required\": true,\n",1051 " \"min_citations\": 1,\n",1052 " \"units_policy\": \"Report in milliliters (mL).\",\n",1053 " \"rounding_policy\": \"Round to two decimal places.\",\n",1054 " \"include_artifacts\": [\"snippets\"]\n",1055 " }\n",1056 "}\n",1057 "=== TOOL EXECUTION ===\n",1058 "Tool calls: []\n",1059 "=== REASONING STEP ===\n",1060 "<REASONING>\n",1061 "I need to execute step 1 of the plan, which involves extracting text from the paper to find details about the 114 version of the kit and the opaque-capped vials without stickers. I will use the doc_qa tool to gather this information, as it is essential for determining the cumulative milliliters of fluid in the vials. The specific input for this tool will be the reference to the paper mentioned in the query. I expect to receive relevant information about the vials and their fluid volumes as output.\n",1062 "</REASONING>\n",1063 "[tool call here]\n",1064 "=== TOOL EXECUTION ===\n",1065 "Tool calls: [{'name': 'arxiv_search', 'args': {'query': 'De Novo-Whole Genome Assembly of the Roborovski Dwarf Hamster (Phodopus roborovskii) Genome'}, 'id': 'call_eKQUtRMjgGsgO7ITT2afdsOA', 'type': 'tool_call'}]\n",1066 "=== GENERATING EXECUTION REPORT ===\n"1067 ]1068 },1069 {1070 "ename": "KeyboardInterrupt",1071 "evalue": "",1072 "output_type": "error",1073 "traceback": [1074 "\u001b[31m---------------------------------------------------------------------------\u001b[39m",1075 "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)",1076 "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m workflow = \u001b[43msystem\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mquery\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mHow many cumulative milliliters of fluid is in all the opaque-capped vials without stickers in the 114 version of the kit that was used for the PromethION long-read sequencing in the paper De Novo-Whole Genome Assembly of the Roborovski Dwarf Hamster (Phodopus roborovskii) Genome?\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcurrent_step\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mreasoning_done\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfiles\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfiles_contents\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43miteration_count\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_iterations\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mplan\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n",1077 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\langgraph\\pregel\\main.py:3026\u001b[39m, in \u001b[36mPregel.invoke\u001b[39m\u001b[34m(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, **kwargs)\u001b[39m\n\u001b[32m 3023\u001b[39m chunks: \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any] | Any] = []\n\u001b[32m 3024\u001b[39m interrupts: \u001b[38;5;28mlist\u001b[39m[Interrupt] = []\n\u001b[32m-> \u001b[39m\u001b[32m3026\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3027\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 3028\u001b[39m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3029\u001b[39m \u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3030\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mupdates\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mvalues\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[32m 3031\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mvalues\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n\u001b[32m 3032\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3033\u001b[39m \u001b[43m \u001b[49m\u001b[43mprint_mode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprint_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3034\u001b[39m \u001b[43m \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3035\u001b[39m \u001b[43m \u001b[49m\u001b[43minterrupt_before\u001b[49m\u001b[43m=\u001b[49m\u001b[43minterrupt_before\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3036\u001b[39m \u001b[43m \u001b[49m\u001b[43minterrupt_after\u001b[49m\u001b[43m=\u001b[49m\u001b[43minterrupt_after\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3037\u001b[39m \u001b[43m \u001b[49m\u001b[43mdurability\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdurability\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3038\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3039\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 3040\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mvalues\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\n\u001b[32m 3041\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m:\u001b[49m\n",1078 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\langgraph\\pregel\\main.py:2647\u001b[39m, in \u001b[36mPregel.stream\u001b[39m\u001b[34m(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, subgraphs, debug, **kwargs)\u001b[39m\n\u001b[32m 2645\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m task \u001b[38;5;129;01min\u001b[39;00m loop.match_cached_writes():\n\u001b[32m 2646\u001b[39m loop.output_writes(task.id, task.writes, cached=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m-> \u001b[39m\u001b[32m2647\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrunner\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtick\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2648\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mloop\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwrites\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2649\u001b[39m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstep_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2650\u001b[39m \u001b[43m \u001b[49m\u001b[43mget_waiter\u001b[49m\u001b[43m=\u001b[49m\u001b[43mget_waiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2651\u001b[39m \u001b[43m \u001b[49m\u001b[43mschedule_task\u001b[49m\u001b[43m=\u001b[49m\u001b[43mloop\u001b[49m\u001b[43m.\u001b[49m\u001b[43maccept_push\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2652\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 2653\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# emit output\u001b[39;49;00m\n\u001b[32m 2654\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01myield from\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_output\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2655\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubgraphs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqueue\u001b[49m\u001b[43m.\u001b[49m\u001b[43mEmpty\u001b[49m\n\u001b[32m 2656\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2657\u001b[39m loop.after_tick()\n",1079 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\langgraph\\pregel\\_runner.py:162\u001b[39m, in \u001b[36mPregelRunner.tick\u001b[39m\u001b[34m(self, tasks, reraise, timeout, retry_policy, get_waiter, schedule_task)\u001b[39m\n\u001b[32m 160\u001b[39m t = tasks[\u001b[32m0\u001b[39m]\n\u001b[32m 161\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m162\u001b[39m \u001b[43mrun_with_retry\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 163\u001b[39m \u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 164\u001b[39m \u001b[43m \u001b[49m\u001b[43mretry_policy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 165\u001b[39m \u001b[43m \u001b[49m\u001b[43mconfigurable\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 166\u001b[39m \u001b[43m \u001b[49m\u001b[43mCONFIG_KEY_CALL\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpartial\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 167\u001b[39m \u001b[43m \u001b[49m\u001b[43m_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 168\u001b[39m \u001b[43m \u001b[49m\u001b[43mweakref\u001b[49m\u001b[43m.\u001b[49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 169\u001b[39m \u001b[43m \u001b[49m\u001b[43mretry_policy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretry_policy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 170\u001b[39m \u001b[43m \u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m=\u001b[49m\u001b[43mweakref\u001b[49m\u001b[43m.\u001b[49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 171\u001b[39m \u001b[43m \u001b[49m\u001b[43mschedule_task\u001b[49m\u001b[43m=\u001b[49m\u001b[43mschedule_task\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 172\u001b[39m \u001b[43m \u001b[49m\u001b[43msubmit\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msubmit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 173\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 174\u001b[39m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 175\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 176\u001b[39m \u001b[38;5;28mself\u001b[39m.commit(t, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 177\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n",1080 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\langgraph\\pregel\\_retry.py:42\u001b[39m, in \u001b[36mrun_with_retry\u001b[39m\u001b[34m(task, retry_policy, configurable)\u001b[39m\n\u001b[32m 40\u001b[39m task.writes.clear()\n\u001b[32m 41\u001b[39m \u001b[38;5;66;03m# run the task\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m42\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtask\u001b[49m\u001b[43m.\u001b[49m\u001b[43mproc\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m.\u001b[49m\u001b[43minput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 43\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ParentCommand \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m 44\u001b[39m ns: \u001b[38;5;28mstr\u001b[39m = config[CONF][CONFIG_KEY_CHECKPOINT_NS]\n",1081 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\langgraph\\_internal\\_runnable.py:657\u001b[39m, in \u001b[36mRunnableSeq.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 655\u001b[39m \u001b[38;5;66;03m# run in context\u001b[39;00m\n\u001b[32m 656\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m set_config_context(config, run) \u001b[38;5;28;01mas\u001b[39;00m context:\n\u001b[32m--> \u001b[39m\u001b[32m657\u001b[39m \u001b[38;5;28minput\u001b[39m = \u001b[43mcontext\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 658\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 659\u001b[39m \u001b[38;5;28minput\u001b[39m = step.invoke(\u001b[38;5;28minput\u001b[39m, config)\n",1082 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\langgraph\\_internal\\_runnable.py:401\u001b[39m, in \u001b[36mRunnableCallable.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 399\u001b[39m run_manager.on_chain_end(ret)\n\u001b[32m 400\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m401\u001b[39m ret = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 402\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.recurse \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(ret, Runnable):\n\u001b[32m 403\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m ret.invoke(\u001b[38;5;28minput\u001b[39m, config)\n",1083 "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 295\u001b[39m, in \u001b[36menhanced_finalizer\u001b[39m\u001b[34m(state)\u001b[39m\n\u001b[32m 266\u001b[39m report_generator_prompt = \u001b[33mf\u001b[39m\u001b[33m\"\"\"\u001b[39m\n\u001b[32m 267\u001b[39m \u001b[33mGenerate a comprehensive execution report for the following query processing:\u001b[39m\n\u001b[32m 268\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 290\u001b[39m \u001b[33mBe thorough but concise. This report will be evaluated by a critic for quality assurance.\u001b[39m\n\u001b[32m 291\u001b[39m \u001b[33m\u001b[39m\u001b[33m\"\"\"\u001b[39m\n\u001b[32m 293\u001b[39m report_llm = llm.with_structured_output(ExecutionReport)\n\u001b[32m--> \u001b[39m\u001b[32m295\u001b[39m execution_report = \u001b[43mreport_llm\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\n\u001b[32m 296\u001b[39m \u001b[43m \u001b[49m\u001b[43mSystemMessage\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreport_generator_prompt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 297\u001b[39m \u001b[43m \u001b[49m\u001b[43mHumanMessage\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mGenerate the execution report.\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 298\u001b[39m \u001b[43m\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 300\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mReport generated - Confidence: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexecution_report.confidence_level\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 301\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mKey findings: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(execution_report.key_findings)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",1084 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\langchain_core\\runnables\\base.py:3243\u001b[39m, in \u001b[36mRunnableSequence.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 3241\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m set_config_context(config) \u001b[38;5;28;01mas\u001b[39;00m context:\n\u001b[32m 3242\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m i == \u001b[32m0\u001b[39m:\n\u001b[32m-> \u001b[39m\u001b[32m3243\u001b[39m input_ = \u001b[43mcontext\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3244\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 3245\u001b[39m input_ = context.run(step.invoke, input_, config)\n",1085 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\langchain_core\\runnables\\base.py:5710\u001b[39m, in \u001b[36mRunnableBindingBase.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 5703\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m 5704\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m 5705\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 5708\u001b[39m **kwargs: Optional[Any],\n\u001b[32m 5709\u001b[39m ) -> Output:\n\u001b[32m-> \u001b[39m\u001b[32m5710\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbound\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 5711\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 5712\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_merge_configs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5713\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m{\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5714\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",1086 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:395\u001b[39m, in \u001b[36mBaseChatModel.invoke\u001b[39m\u001b[34m(self, input, config, stop, **kwargs)\u001b[39m\n\u001b[32m 383\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m 384\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m 385\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 390\u001b[39m **kwargs: Any,\n\u001b[32m 391\u001b[39m ) -> BaseMessage:\n\u001b[32m 392\u001b[39m config = ensure_config(config)\n\u001b[32m 393\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[32m 394\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mChatGeneration\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m--> \u001b[39m\u001b[32m395\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate_prompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 396\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 397\u001b[39m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 398\u001b[39m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcallbacks\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 399\u001b[39m \u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtags\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 400\u001b[39m \u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 401\u001b[39m \u001b[43m \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrun_name\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 402\u001b[39m \u001b[43m \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrun_id\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 403\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 404\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m.generations[\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m],\n\u001b[32m 405\u001b[39m ).message\n",1087 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:1023\u001b[39m, in \u001b[36mBaseChatModel.generate_prompt\u001b[39m\u001b[34m(self, prompts, stop, callbacks, **kwargs)\u001b[39m\n\u001b[32m 1014\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m 1015\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mgenerate_prompt\u001b[39m(\n\u001b[32m 1016\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1020\u001b[39m **kwargs: Any,\n\u001b[32m 1021\u001b[39m ) -> LLMResult:\n\u001b[32m 1022\u001b[39m prompt_messages = [p.to_messages() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[32m-> \u001b[39m\u001b[32m1023\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_messages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",1088 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:840\u001b[39m, in \u001b[36mBaseChatModel.generate\u001b[39m\u001b[34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[39m\n\u001b[32m 837\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(input_messages):\n\u001b[32m 838\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 839\u001b[39m results.append(\n\u001b[32m--> \u001b[39m\u001b[32m840\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_generate_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 841\u001b[39m \u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 842\u001b[39m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 843\u001b[39m \u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 844\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 845\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 846\u001b[39m )\n\u001b[32m 847\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 848\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n",1089 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:1089\u001b[39m, in \u001b[36mBaseChatModel._generate_with_cache\u001b[39m\u001b[34m(self, messages, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m 1087\u001b[39m result = generate_from_stream(\u001b[38;5;28miter\u001b[39m(chunks))\n\u001b[32m 1088\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m inspect.signature(\u001b[38;5;28mself\u001b[39m._generate).parameters.get(\u001b[33m\"\u001b[39m\u001b[33mrun_manager\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m-> \u001b[39m\u001b[32m1089\u001b[39m result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1090\u001b[39m \u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m 1091\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1092\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1093\u001b[39m result = \u001b[38;5;28mself\u001b[39m._generate(messages, stop=stop, **kwargs)\n",1090 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\langchain_openai\\chat_models\\base.py:1152\u001b[39m, in \u001b[36mBaseChatOpenAI._generate\u001b[39m\u001b[34m(self, messages, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m 1149\u001b[39m payload.pop(\u001b[33m\"\u001b[39m\u001b[33mstream\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 1150\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 1151\u001b[39m raw_response = (\n\u001b[32m-> \u001b[39m\u001b[32m1152\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mroot_client\u001b[49m\u001b[43m.\u001b[49m\u001b[43mchat\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompletions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwith_raw_response\u001b[49m\u001b[43m.\u001b[49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1153\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpayload\u001b[49m\n\u001b[32m 1154\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1155\u001b[39m )\n\u001b[32m 1156\u001b[39m response = raw_response.parse()\n\u001b[32m 1157\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m openai.BadRequestError \u001b[38;5;28;01mas\u001b[39;00m e:\n",1091 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\openai\\_legacy_response.py:364\u001b[39m, in \u001b[36mto_raw_response_wrapper.<locals>.wrapped\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 360\u001b[39m extra_headers[RAW_RESPONSE_HEADER] = \u001b[33m\"\u001b[39m\u001b[33mtrue\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 362\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33mextra_headers\u001b[39m\u001b[33m\"\u001b[39m] = extra_headers\n\u001b[32m--> \u001b[39m\u001b[32m364\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(LegacyAPIResponse[R], \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n",1092 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\openai\\resources\\chat\\completions\\completions.py:183\u001b[39m, in \u001b[36mCompletions.parse\u001b[39m\u001b[34m(self, messages, model, audio, response_format, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, modalities, n, parallel_tool_calls, prediction, presence_penalty, prompt_cache_key, reasoning_effort, safety_identifier, seed, service_tier, stop, store, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, verbosity, web_search_options, extra_headers, extra_query, extra_body, timeout)\u001b[39m\n\u001b[32m 176\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mparser\u001b[39m(raw_completion: ChatCompletion) -> ParsedChatCompletion[ResponseFormatT]:\n\u001b[32m 177\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _parse_chat_completion(\n\u001b[32m 178\u001b[39m response_format=response_format,\n\u001b[32m 179\u001b[39m chat_completion=raw_completion,\n\u001b[32m 180\u001b[39m input_tools=chat_completion_tools,\n\u001b[32m 181\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m183\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 184\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/chat/completions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 185\u001b[39m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 186\u001b[39m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 187\u001b[39m \u001b[43m 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\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfunction_call\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 192\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfunctions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunctions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 193\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogit_bias\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 194\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogprobs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 195\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_completion_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_completion_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 196\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 197\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 198\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodalities\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodalities\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 199\u001b[39m \u001b[43m 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\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mprompt_cache_key\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mprompt_cache_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 204\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mreasoning_effort\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mreasoning_effort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 205\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mresponse_format\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m_type_to_response_format\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 206\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msafety_identifier\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m 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\u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 226\u001b[39m \u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 227\u001b[39m \u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 228\u001b[39m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 229\u001b[39m \u001b[43m \u001b[49m\u001b[43mpost_parser\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparser\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 230\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 231\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# we turn the `ChatCompletion` instance into a `ParsedChatCompletion`\u001b[39;49;00m\n\u001b[32m 232\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# in the `parser` function above\u001b[39;49;00m\n\u001b[32m 233\u001b[39m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mType\u001b[49m\u001b[43m[\u001b[49m\u001b[43mParsedChatCompletion\u001b[49m\u001b[43m[\u001b[49m\u001b[43mResponseFormatT\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mChatCompletion\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 234\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 235\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",1093 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\openai\\_base_client.py:1259\u001b[39m, in \u001b[36mSyncAPIClient.post\u001b[39m\u001b[34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[39m\n\u001b[32m 1245\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\n\u001b[32m 1246\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 1247\u001b[39m path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1254\u001b[39m stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 1255\u001b[39m ) -> ResponseT | _StreamT:\n\u001b[32m 1256\u001b[39m opts = FinalRequestOptions.construct(\n\u001b[32m 1257\u001b[39m method=\u001b[33m\"\u001b[39m\u001b[33mpost\u001b[39m\u001b[33m\"\u001b[39m, url=path, json_data=body, files=to_httpx_files(files), **options\n\u001b[32m 1258\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1259\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",1094 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\openai\\_base_client.py:982\u001b[39m, in \u001b[36mSyncAPIClient.request\u001b[39m\u001b[34m(self, cast_to, options, stream, stream_cls)\u001b[39m\n\u001b[32m 980\u001b[39m response = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 981\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m982\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_client\u001b[49m\u001b[43m.\u001b[49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 983\u001b[39m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 984\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_should_stream_response_body\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 985\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 986\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 987\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m httpx.TimeoutException \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m 988\u001b[39m log.debug(\u001b[33m\"\u001b[39m\u001b[33mEncountered httpx.TimeoutException\u001b[39m\u001b[33m\"\u001b[39m, exc_info=\u001b[38;5;28;01mTrue\u001b[39;00m)\n",1095 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\httpx\\_client.py:914\u001b[39m, in \u001b[36mClient.send\u001b[39m\u001b[34m(self, request, stream, auth, follow_redirects)\u001b[39m\n\u001b[32m 910\u001b[39m \u001b[38;5;28mself\u001b[39m._set_timeout(request)\n\u001b[32m 912\u001b[39m auth = \u001b[38;5;28mself\u001b[39m._build_request_auth(request, auth)\n\u001b[32m--> \u001b[39m\u001b[32m914\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_handling_auth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 915\u001b[39m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 916\u001b[39m \u001b[43m \u001b[49m\u001b[43mauth\u001b[49m\u001b[43m=\u001b[49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 917\u001b[39m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 918\u001b[39m \u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 919\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 920\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 921\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m stream:\n",1096 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\httpx\\_client.py:942\u001b[39m, in \u001b[36mClient._send_handling_auth\u001b[39m\u001b[34m(self, request, auth, follow_redirects, history)\u001b[39m\n\u001b[32m 939\u001b[39m request = \u001b[38;5;28mnext\u001b[39m(auth_flow)\n\u001b[32m 941\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m942\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_handling_redirects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 943\u001b[39m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 944\u001b[39m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 945\u001b[39m \u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 946\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 947\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 948\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n",1097 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\httpx\\_client.py:979\u001b[39m, in \u001b[36mClient._send_handling_redirects\u001b[39m\u001b[34m(self, request, follow_redirects, history)\u001b[39m\n\u001b[32m 976\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._event_hooks[\u001b[33m\"\u001b[39m\u001b[33mrequest\u001b[39m\u001b[33m\"\u001b[39m]:\n\u001b[32m 977\u001b[39m hook(request)\n\u001b[32m--> \u001b[39m\u001b[32m979\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_single_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 980\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 981\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._event_hooks[\u001b[33m\"\u001b[39m\u001b[33mresponse\u001b[39m\u001b[33m\"\u001b[39m]:\n",1098 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\httpx\\_client.py:1014\u001b[39m, in \u001b[36mClient._send_single_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 1009\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 1010\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mAttempted to send an async request with a sync Client instance.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1011\u001b[39m )\n\u001b[32m 1013\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m request_context(request=request):\n\u001b[32m-> \u001b[39m\u001b[32m1014\u001b[39m response = \u001b[43mtransport\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1016\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response.stream, SyncByteStream)\n\u001b[32m 1018\u001b[39m response.request = request\n",1099 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\httpx\\_transports\\default.py:250\u001b[39m, in \u001b[36mHTTPTransport.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 237\u001b[39m req = httpcore.Request(\n\u001b[32m 238\u001b[39m method=request.method,\n\u001b[32m 239\u001b[39m url=httpcore.URL(\n\u001b[32m (...)\u001b[39m\u001b[32m 247\u001b[39m extensions=request.extensions,\n\u001b[32m 248\u001b[39m )\n\u001b[32m 249\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[32m--> \u001b[39m\u001b[32m250\u001b[39m resp = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_pool\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 252\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp.stream, typing.Iterable)\n\u001b[32m 254\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m Response(\n\u001b[32m 255\u001b[39m status_code=resp.status,\n\u001b[32m 256\u001b[39m headers=resp.headers,\n\u001b[32m 257\u001b[39m stream=ResponseStream(resp.stream),\n\u001b[32m 258\u001b[39m extensions=resp.extensions,\n\u001b[32m 259\u001b[39m )\n",1100 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\httpcore\\_sync\\connection_pool.py:256\u001b[39m, in \u001b[36mConnectionPool.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 253\u001b[39m closing = \u001b[38;5;28mself\u001b[39m._assign_requests_to_connections()\n\u001b[32m 255\u001b[39m \u001b[38;5;28mself\u001b[39m._close_connections(closing)\n\u001b[32m--> \u001b[39m\u001b[32m256\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exc \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 258\u001b[39m \u001b[38;5;66;03m# Return the response. Note that in this case we still have to manage\u001b[39;00m\n\u001b[32m 259\u001b[39m \u001b[38;5;66;03m# the point at which the response is closed.\u001b[39;00m\n\u001b[32m 260\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response.stream, typing.Iterable)\n",1101 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\httpcore\\_sync\\connection_pool.py:236\u001b[39m, in \u001b[36mConnectionPool.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 232\u001b[39m connection = pool_request.wait_for_connection(timeout=timeout)\n\u001b[32m 234\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 235\u001b[39m \u001b[38;5;66;03m# Send the request on the assigned connection.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m236\u001b[39m response = \u001b[43mconnection\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 237\u001b[39m \u001b[43m \u001b[49m\u001b[43mpool_request\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\n\u001b[32m 238\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 239\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ConnectionNotAvailable:\n\u001b[32m 240\u001b[39m \u001b[38;5;66;03m# In some cases a connection may initially be available to\u001b[39;00m\n\u001b[32m 241\u001b[39m \u001b[38;5;66;03m# handle a request, but then become unavailable.\u001b[39;00m\n\u001b[32m 242\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 243\u001b[39m \u001b[38;5;66;03m# In this case we clear the connection and try again.\u001b[39;00m\n\u001b[32m 244\u001b[39m pool_request.clear_connection()\n",1102 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\httpcore\\_sync\\connection.py:103\u001b[39m, in \u001b[36mHTTPConnection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 100\u001b[39m \u001b[38;5;28mself\u001b[39m._connect_failed = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 101\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[32m--> \u001b[39m\u001b[32m103\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_connection\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n",1103 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\httpcore\\_sync\\http11.py:136\u001b[39m, in \u001b[36mHTTP11Connection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 134\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\u001b[33m\"\u001b[39m\u001b[33mresponse_closed\u001b[39m\u001b[33m\"\u001b[39m, logger, request) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[32m 135\u001b[39m \u001b[38;5;28mself\u001b[39m._response_closed()\n\u001b[32m--> \u001b[39m\u001b[32m136\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exc\n",1104 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\httpcore\\_sync\\http11.py:106\u001b[39m, in \u001b[36mHTTP11Connection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 97\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\n\u001b[32m 98\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mreceive_response_headers\u001b[39m\u001b[33m\"\u001b[39m, logger, request, kwargs\n\u001b[32m 99\u001b[39m ) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[32m 100\u001b[39m (\n\u001b[32m 101\u001b[39m http_version,\n\u001b[32m 102\u001b[39m status,\n\u001b[32m 103\u001b[39m reason_phrase,\n\u001b[32m 104\u001b[39m headers,\n\u001b[32m 105\u001b[39m trailing_data,\n\u001b[32m--> \u001b[39m\u001b[32m106\u001b[39m ) = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_receive_response_headers\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 107\u001b[39m trace.return_value = (\n\u001b[32m 108\u001b[39m http_version,\n\u001b[32m 109\u001b[39m status,\n\u001b[32m 110\u001b[39m reason_phrase,\n\u001b[32m 111\u001b[39m headers,\n\u001b[32m 112\u001b[39m )\n\u001b[32m 114\u001b[39m network_stream = \u001b[38;5;28mself\u001b[39m._network_stream\n",1105 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\httpcore\\_sync\\http11.py:177\u001b[39m, in \u001b[36mHTTP11Connection._receive_response_headers\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 174\u001b[39m timeout = timeouts.get(\u001b[33m\"\u001b[39m\u001b[33mread\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 176\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m177\u001b[39m event = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_receive_event\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 178\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(event, h11.Response):\n\u001b[32m 179\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n",1106 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\httpcore\\_sync\\http11.py:217\u001b[39m, in \u001b[36mHTTP11Connection._receive_event\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 214\u001b[39m event = \u001b[38;5;28mself\u001b[39m._h11_state.next_event()\n\u001b[32m 216\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m event \u001b[38;5;129;01mis\u001b[39;00m h11.NEED_DATA:\n\u001b[32m--> \u001b[39m\u001b[32m217\u001b[39m data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_network_stream\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 218\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mREAD_NUM_BYTES\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m 219\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 221\u001b[39m \u001b[38;5;66;03m# If we feed this case through h11 we'll raise an exception like:\u001b[39;00m\n\u001b[32m 222\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 223\u001b[39m \u001b[38;5;66;03m# httpcore.RemoteProtocolError: can't handle event type\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 227\u001b[39m \u001b[38;5;66;03m# perspective. Instead we handle this case distinctly and treat\u001b[39;00m\n\u001b[32m 228\u001b[39m \u001b[38;5;66;03m# it as a ConnectError.\u001b[39;00m\n\u001b[32m 229\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data == \u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m._h11_state.their_state == h11.SEND_RESPONSE:\n",1107 "\u001b[36mFile \u001b[39m\u001b[32md:\\REGNUM_SPECTRARUM\\.venv\\Lib\\site-packages\\httpcore\\_backends\\sync.py:128\u001b[39m, in \u001b[36mSyncStream.read\u001b[39m\u001b[34m(self, max_bytes, timeout)\u001b[39m\n\u001b[32m 126\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m map_exceptions(exc_map):\n\u001b[32m 127\u001b[39m \u001b[38;5;28mself\u001b[39m._sock.settimeout(timeout)\n\u001b[32m--> \u001b[39m\u001b[32m128\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_sock\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrecv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_bytes\u001b[49m\u001b[43m)\u001b[49m\n",1108 "\u001b[36mFile \u001b[39m\u001b[32mD:\\Anaconda\\Lib\\ssl.py:1296\u001b[39m, in \u001b[36mSSLSocket.recv\u001b[39m\u001b[34m(self, buflen, flags)\u001b[39m\n\u001b[32m 1292\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m flags != \u001b[32m0\u001b[39m:\n\u001b[32m 1293\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 1294\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mnon-zero flags not allowed in calls to recv() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m\"\u001b[39m %\n\u001b[32m 1295\u001b[39m \u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m1296\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuflen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1297\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1298\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().recv(buflen, flags)\n",1109 "\u001b[36mFile \u001b[39m\u001b[32mD:\\Anaconda\\Lib\\ssl.py:1169\u001b[39m, in \u001b[36mSSLSocket.read\u001b[39m\u001b[34m(self, len, buffer)\u001b[39m\n\u001b[32m 1167\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._sslobj.read(\u001b[38;5;28mlen\u001b[39m, buffer)\n\u001b[32m 1168\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1169\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_sslobj\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 1170\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m SSLError \u001b[38;5;28;01mas\u001b[39;00m x:\n\u001b[32m 1171\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m x.args[\u001b[32m0\u001b[39m] == SSL_ERROR_EOF \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m.suppress_ragged_eofs:\n",1110 "\u001b[31mKeyboardInterrupt\u001b[39m: "1111 ]1112 }1113 ],1114 "source": [1115 "workflow = system.invoke({\"query\" : \"How many cumulative milliliters of fluid is in all the opaque-capped vials without stickers in the 114 version of the kit that was used for the PromethION long-read sequencing in the paper De Novo-Whole Genome Assembly of the Roborovski Dwarf Hamster (Phodopus roborovskii) Genome?\", \"current_step\": 0, \"reasoning_done\": False, \"files\" : [], \"files_contents\" : {}, \"iteration_count\" : 0, \"max_iterations\" : 10, \"plan\" : None} , config = config)"1116 ]1117 },1118 {1119 "cell_type": "code",1120 "execution_count": null,1121 "metadata": {},1122 "outputs": [1123 {1124 "name": "stdout",1125 "output_type": "stream",1126 "text": [1127 "================================\u001b[1m System Message \u001b[0m================================\n",1128 "\n",1129 "You are a COMPLEXITY ASSESSOR for a multi-tool agent system.\n",1130 "Your job is to analyze user queries and determine their complexity level and processing requirements.\n",1131 "\n",1132 "COMPLEXITY LEVELS:\n",1133 "1. SIMPLE: Direct questions that can be answered immediately without tools or with single tool use\n",1134 " - Examples: \"What is 2+2?\", \"Define photosynthesis\", \"What's the capital of France?\"\n",1135 "\n",1136 "2. MODERATE: Questions requiring 1-3 tool calls or basic analysis\n",1137 " - Examples: \"Search for recent news about AI\", \"Analyze this CSV file\", \"What's the weather tomorrow?\"\n",1138 "\n",1139 "3. COMPLEX: Multi-step problems requiring planning, multiple tools, or sophisticated reasoning\n",1140 " - Examples: Research tasks, multi-file analysis, calculations with dependencies, creative projects\n",1141 "\n",1142 "ASSESSMENT CRITERIA:\n",1143 "- Number of steps likely needed\n",1144 "- Tool complexity and dependencies\n",1145 "- Data processing requirements\n",1146 "- Need for intermediate reasoning\n",1147 "- Risk of failure without proper planning\n",1148 "\n",1149 "RULES:\n",1150 "- SIMPLE queries bypass planning entirely\n",1151 "- MODERATE queries may use lightweight planning\n",1152 "- COMPLEX queries require full planning with fallbacks\n",1153 "- When in doubt, err toward higher complexity\n",1154 "\n",1155 "Analyze the query and respond with your assessment.\n",1156 "================================\u001b[1m Human Message \u001b[0m=================================\n",1157 "\n",1158 "Query: How many cumulative milliliters of fluid is in all the opaque-capped vials without stickers in the 114 version of the kit that was used for the PromethION long-read sequencing in the paper De Novo-Whole Genome Assembly of the Roborovski Dwarf Hamster (Phodopus roborovskii) Genome?\n",1159 "================================\u001b[1m System Message \u001b[0m================================\n",1160 "\n",1161 "You are the PLANNER of a multi-tool agent (GAIA I–II level). \n",1162 "Your job is to produce a minimal, reliable, reproducible plan to solve the user’s request using available tools.\n",1163 "You DO NOT call tools yourself; you only output a plan. The executor will run the plan.\n",1164 "Tools are already bound to the model via .bind_tools(), so use EXACT tool names.\n",1165 "\n",1166 "Principles\n",1167 "- Goal: a correct, verifiable answer (with citations/artifacts where appropriate).\n",1168 "- Minimality: use as few steps/tool calls as possible.\n",1169 "- Proper routing: pick the right branch: info | calc | table | doc_qa | image_qa | multi_hop.\n",1170 "- Files first: never send raw files to the code interpreter. First extract with specialized tools (CSV/XLSX/PDF/DOCX/TXT/IMG). \n",1171 " Only then compute on the extracted data (if needed) with the safe code interpreter.\n",1172 "- Units & rounding: be explicit about units and rounding rules when numbers are involved.\n",1173 "- Evidence: require sources (URL/page/figure caption) for external facts.\n",1174 "- Fallbacks: define success criteria per step and a failure policy (“replan”, “stop”, or jump to another step-id).\n",1175 "- Cost aware: start with cheap preview/metadata tools before heavy steps.\n",1176 "\n",1177 "\n",1178 "Patterns / Routing\n",1179 "- info/web: web_search/wiki_search/arxiv_search → gather citations.\n",1180 "- calc: ensure data is available → safe_code_run only on extracted data; request plots/dataframes only if needed.\n",1181 "- table (CSV/XLSX): analyze_* to confirm columns/shape → aggregate via safe_code_run (or SQL tool if available).\n",1182 "- doc_qa (PDF/DOCX/TXT): analyze_* for pages/preview → extract_text or OCR if needed → answer with page/quote.\n",1183 "- image_qa: analyze_image_* for metadata/OCR, or vision_qa_* for visual questions; for chart numbers, convert figure→table and verify with computation.\n",1184 "- multi_hop: decompose into sub-queries, retrieve per modality, then synthesize with citations.\n",1185 "\n",1186 "Output format\n",1187 "Return ONLY a single JSON object following this schema:\n",1188 "{\n",1189 " \"task_type\": \"info | calc | table | doc_qa | image_qa | multi_hop\",\n",1190 " \"assumptions\": [\"string\", \"...\"],\n",1191 " \"plan_rationale\": \"why this route and which tools are needed\",\n",1192 " \"steps\": [\n",1193 " {\n",1194 " \"id\": \"s1\",\n",1195 " \"description\": \"what and why\",\n",1196 " \"evidence_needed\": [\"citations|page_numbers|figure_captions|stats_check|unit_check\"],\n",1197 " \"success_criteria\": \"how we know the step succeeded\",\n",1198 " \"on_fail\": \"replan | stop | sN\",\n",1199 " \"outputs_to_state\": [\"what we expect to store for later steps\"]\n",1200 " }\n",