From 6aee4b65c8923e121d4f77608d78084263654dd0 Mon Sep 17 00:00:00 2001 From: xFtagn Date: Sun, 23 Nov 2025 00:14:05 +0300 Subject: [PATCH 1/2] [WIP] Gigachat usage (https://github.com/vamplabAI/sgr-agent-core/issues/92) Added function use instead of tool call and an example of how it could be used to actually run gigachat with the framework --- demo_agent_Gigachat.ipynb | 525 ++++++++++++++++++ sgr_deep_research/core/agents/sgr_agent.py | 72 ++- .../core/agents/sgr_so_tool_calling_agent.py | 89 +-- .../core/agents/sgr_tool_calling_agent.py | 165 ++++-- .../core/agents/tool_calling_agent.py | 87 ++- 5 files changed, 797 insertions(+), 141 deletions(-) create mode 100644 demo_agent_Gigachat.ipynb diff --git a/demo_agent_Gigachat.ipynb b/demo_agent_Gigachat.ipynb new file mode 100644 index 00000000..afa38502 --- /dev/null +++ b/demo_agent_Gigachat.ipynb @@ -0,0 +1,525 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "ac38ba1c", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -e \".[dev]\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d56662b9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:__main__:--- envs've been successfully loaded ---\n", + "INFO:__main__:--- GIGA CONFIG: {'base_url': 'https://gigachat.devices.sberbank.ru/api/v1', 'verify_ssl_certs': False, 'main_model': 'GigaChat', 'temperature': 0.0} ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- GIGA CONFIG: {'base_url': 'https://gigachat.devices.sberbank.ru/api/v1', 'verify_ssl_certs': False, 'main_model': 'GigaChat', 'temperature': 0.0} ---\n" + ] + } + ], + "source": [ + "import os\n", + "import re\n", + "import logging\n", + "from dotenv import load_dotenv\n", + "from dataclasses import dataclass, asdict\n", + "from typing import Optional\n", + "\n", + "\n", + "_LOGGER = logging.getLogger(__name__)\n", + "\n", + "load_dotenv()\n", + "_LOGGER.info(\"--- envs've been successfully loaded ---\")\n", + "\n", + "@dataclass\n", + "class GigaSettings:\n", + " # --- ПАРАМЕТРЫ GIGACHAT API ---\n", + " base_url: str\n", + " verify_ssl_certs: bool=False\n", + " main_model: str=\"GigaChat\" # Изменил на Pro, так как Max может быть недоступен\n", + " temperature: float=0.0\n", + "\n", + "# Определяем URL. По умолчанию используем публичный API, который работает везде.\n", + "# Если нужен IFT контур, задайте переменную окружения GIGACHAT_API_URL\n", + "_api_url = os.getenv(\"GIGACHAT_API_URL\", \"https://gigachat.devices.sberbank.ru/api/v1\")\n", + "# Убираем дублирование /v1 если оно уже есть в переменной окружения\n", + "if not _api_url.endswith(\"/v1\") and \"/api\" not in _api_url:\n", + " _api_url = f\"{_api_url}/v1\"\n", + "\n", + "giga_settings = GigaSettings(\n", + " _api_url,\n", + " False\n", + ")\n", + "\n", + "_LOGGER.info(f\"--- GIGA CONFIG: {asdict(giga_settings)} ---\")\n", + "print(f\"--- GIGA CONFIG: {asdict(giga_settings)} ---\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1892e388-5f51-412f-96ca-328c1c16e326", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/embeddings \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Токен успешно получен: eyJjdHkiOi...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sgr_deep_research.agents.tool_calling_agent_28a9634c-f00e-4ae4-8819-f67c87a19a4b:🚀 Starting for task: 'Сколько стоит конструктор звездный крейсер и есть ли бесплатная доставка?'\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_28a9634c-f00e-4ae4-8819-f67c87a19a4b:Step 1 started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + ">>> ПОКУПАТЕЛЬ: Сколько стоит конструктор звездный крейсер и есть ли бесплатная доставка?\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_28a9634c-f00e-4ae4-8819-f67c87a19a4b:\n", + "###############################################\n", + "🛠️ TOOL EXECUTION DEBUG:\n", + " 🔧 Tool Name: search_catalog\n", + " 📋 Tool Model: {\n", + " \"query\": \"конструктор звездный крейсер цена\"\n", + "}\n", + " 🔍 Result: '\"Найдено в базе:\\n- Конструктор 'Звездный крейсер' стоит 5000 рублей. Подходит для детей от 10 лет.\\n- Настольная игра 'Монополия' стоит 2500 рублей.\"...'\n", + "###############################################\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_28a9634c-f00e-4ae4-8819-f67c87a19a4b:Step 2 started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [DEBUG] Поиск в каталоге: конструктор звездный крейсер цена\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_28a9634c-f00e-4ae4-8819-f67c87a19a4b:\n", + "###############################################\n", + "🛠️ TOOL EXECUTION DEBUG:\n", + " 🔧 Tool Name: search_catalog\n", + " 📋 Tool Model: {\n", + " \"query\": \"бесплатная доставка\"\n", + "}\n", + " 🔍 Result: '\"Найдено в базе:\\n- Доставка осуществляется бесплатно при заказе от 3000 рублей. Срок доставки 2-3 дня.\\n- Возврат товара возможен в течение 14 дней при сохранении упаковки.\"...'\n", + "###############################################\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_28a9634c-f00e-4ae4-8819-f67c87a19a4b:Step 3 started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [DEBUG] Поиск в каталоге: бесплатная доставка\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_28a9634c-f00e-4ae4-8819-f67c87a19a4b:\n", + "###############################################\n", + "🛠️ TOOL EXECUTION DEBUG:\n", + " 🔧 Tool Name: finalanswertool\n", + " 📋 Tool Model: {\n", + " \"reasoning\": \"Обе части запроса (цена конструктора и условия бесплатной доставки) были успешно найдены в каталоге магазина. Ответ содержит точные данные о цене и условиях доставки. 5000 рублей >= 3000 рублей, следовательно, доставка будет бесплатной.😊\",\n", + " \"completed_steps\": [\n", + " \"Поиск цены конструктора 'Звездный крейсер' в каталоге\",\n", + " \"Поиск информации о бесплатной доставке в каталоге\"\n", + " ],\n", + " \"answer\": \"Конструктор 'Звездный крейсер' стоит 5000 рублей. Бесплатная доставка предоставляется при заказе от 3000 рублей, срок доставки 2-3 дня.🚗🚀\",\n", + " \"status\": \"completed\"\n", + "}\n", + " 🔍 Result: '{\n", + " \"reasoning\": \"Обе части запроса (цена конструктора и условия бесплатной доставки) были успешно найдены в каталоге магазина. Ответ содержит точные данные о цене и условиях доставки. 5000 рублей >= 3000 рублей, следовательно, доставка будет бесплатной.😊\",\n", + " \"completed_steps\": [\n", + " \"Поиск цены конструктора 'Звездный крейсер' в каталоге\",\n", + " \"Поиск информации о бесплатной доставке в каталоге\"\n", + " ...'\n", + "###############################################\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_d4bd554b-a05e-4f7c-a5e5-9fa224c42e30:🚀 Starting for task: 'Хочу купить плюшевого медведя. Меня зовут Саша, телефон 89990000000. Оформите заказ.'\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_d4bd554b-a05e-4f7c-a5e5-9fa224c42e30:Step 1 started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AGENTS LOG:\n", + "\n", + ">>> ПОКУПАТЕЛЬ: Хочу купить плюшевого медведя. Меня зовут Саша, телефон 89990000000. Оформите заказ.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_d4bd554b-a05e-4f7c-a5e5-9fa224c42e30:\n", + "###############################################\n", + "🛠️ TOOL EXECUTION DEBUG:\n", + " 🔧 Tool Name: create_order\n", + " 📋 Tool Model: {\n", + " \"items\": \"плюшевый медведь 1\",\n", + " \"customer_info\": \"Саша, 89990000000\"\n", + "}\n", + " 🔍 Result: '{\"status\": \"success\", \"order_id\": \"ORD-999\", \"message\": \"Order created successfully\"}...'\n", + "###############################################\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_d4bd554b-a05e-4f7c-a5e5-9fa224c42e30:Step 2 started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [DEBUG] Создание заказа: плюшевый медведь 1 для Саша, 89990000000\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_d4bd554b-a05e-4f7c-a5e5-9fa224c42e30:\n", + "###############################################\n", + "🛠️ TOOL EXECUTION DEBUG:\n", + " 🔧 Tool Name: finalanswertool\n", + " 📋 Tool Model: {\n", + " \"reasoning\": \"Заказ на плюшевого медведя оформлен успешно и был присвоен номер заказа ORD-999. Ответ включает имя клиента и информацию о статусе заказа. Ответ позитивный и включает эмодзи для улучшения пользовательского опыта. 00000\",\n", + " \"completed_steps\": [\n", + " \"Оформлен заказ на плюшевого медведя на имя Саши с контактным телефоном 89990000000.00000\"\n", + " ],\n", + " \"answer\": \"Саша, Ваш заказ № ORD-999 на плюшевого медведя оформлен успешно. Пожалуйста, сохраняйте свой номер заказа. 🥰🧸\",\n", + " \"status\": \"completed\"\n", + "}\n", + " 🔍 Result: '{\n", + " \"reasoning\": \"Заказ на плюшевого медведя оформлен успешно и был присвоен номер заказа ORD-999. Ответ включает имя клиента и информацию о статусе заказа. Ответ позитивный и включает эмодзи для улучшения пользовательского опыта. 00000\",\n", + " \"completed_steps\": [\n", + " \"Оформлен заказ на плюшевого медведя на имя Саши с контактным телефоном 89990000000.00000\"\n", + " ],\n", + " \"answer\": \"Саша, Ваш заказ № ORD-999...'\n", + "###############################################\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AGENTS LOG:\n" + ] + } + ], + "source": [ + "import os\n", + "import json\n", + "import uuid\n", + "import logging\n", + "import requests\n", + "import urllib3\n", + "import httpx\n", + "from dotenv import load_dotenv\n", + "from typing import List, Optional, Dict, Any, ClassVar, Type\n", + "from pydantic import BaseModel, Field\n", + "\n", + "# Langchain imports\n", + "from langchain_community.vectorstores import FAISS\n", + "from langchain_core.documents import Document\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", + "from langchain_gigachat.embeddings.gigachat import GigaChatEmbeddings\n", + "\n", + "# Agent Core imports\n", + "from sgr_deep_research.core.agent_definition import (\n", + " AgentConfig, \n", + " LLMConfig, \n", + " PromptsConfig, \n", + " ExecutionConfig\n", + ")\n", + "from sgr_deep_research.core.agents.tool_calling_agent import ToolCallingAgent\n", + "from sgr_deep_research.core.tools import (\n", + " BaseTool, \n", + " ClarificationTool,\n", + " GeneratePlanTool,\n", + " AdaptPlanTool,\n", + " ReasoningTool,\n", + " FinalAnswerTool\n", + ")\n", + "from sgr_deep_research.core.models import ResearchContext\n", + "from openai import AsyncOpenAI\n", + "\n", + "# Настройка логирования\n", + "logging.basicConfig(level=logging.INFO)\n", + "urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n", + "\n", + "# Загрузка переменных (.env)\n", + "load_dotenv(override=True)\n", + "\n", + "# --- 1. Получение токена GigaChat ---\n", + "def get_gigachat_token(auth_key: str, scope: str = \"GIGACHAT_API_PERS\") -> str:\n", + " \"\"\"Получает токен доступа GigaChat с помощью ключа авторизации.\"\"\"\n", + " token_url = \"https://ngw.devices.sberbank.ru:9443/api/v2/oauth\"\n", + " \n", + " payload = {'scope': scope}\n", + " headers = {\n", + " 'Content-Type': 'application/x-www-form-urlencoded',\n", + " 'Accept': 'application/json',\n", + " 'RqUID': str(uuid.uuid4()),\n", + " 'Authorization': f'Basic {auth_key}'\n", + " }\n", + " \n", + " try:\n", + " response = requests.post(token_url, headers=headers, data=payload, verify=False)\n", + " response.raise_for_status()\n", + " return response.json()['access_token']\n", + " except Exception as e:\n", + " print(f\"Ошибка получения токена: {e}\")\n", + " if 'response' in locals():\n", + " print(f\"Ответ сервера: {response.text}\")\n", + " raise\n", + "\n", + "# Получаем ключ из .env\n", + "auth_key = os.getenv(\"GIGACHAT_CREDENTIALS\")\n", + "if not auth_key:\n", + " raise ValueError(\"GIGACHAT_CREDENTIALS не найден в .env! Проверьте файл .env\")\n", + "\n", + "# Получаем актуальный токен\n", + "ACCESS_TOKEN = get_gigachat_token(auth_key)\n", + "print(f\"Токен успешно получен: {ACCESS_TOKEN[:10]}...\")\n", + "\n", + "# Настройки для GigaChat (предполагаем наличие giga_settings как в оригинале, или задаем тут)\n", + "class GigaSettings:\n", + " base_url = \"https://gigachat.devices.sberbank.ru/api/v1\"\n", + " verify_ssl_certs = False\n", + " main_model = \"GigaChat-Pro\"\n", + " temperature = 0.0\n", + "\n", + "giga_settings = GigaSettings()\n", + "\n", + "\n", + "# --- 2. Настройка FAISS (Каталог товаров и правила магазина) ---\n", + "embedding_function = GigaChatEmbeddings(\n", + " base_url=giga_settings.base_url,\n", + " access_token=ACCESS_TOKEN,\n", + " verify_ssl_certs=giga_settings.verify_ssl_certs\n", + ")\n", + "\n", + "documents = [\n", + " Document(page_content=\"Конструктор 'Звездный крейсер' стоит 5000 рублей. Подходит для детей от 10 лет.\", metadata={\"id\": \"prod1\"}),\n", + " Document(page_content=\"Плюшевый медведь 'Мишутка' (50 см) стоит 1200 рублей.\", metadata={\"id\": \"prod2\"}),\n", + " Document(page_content=\"Настольная игра 'Монополия' стоит 2500 рублей.\", metadata={\"id\": \"prod3\"}),\n", + " Document(page_content=\"Доставка осуществляется бесплатно при заказе от 3000 рублей. Срок доставки 2-3 дня.\", metadata={\"id\": \"rule1\"}),\n", + " Document(page_content=\"Возврат товара возможен в течение 14 дней при сохранении упаковки.\", metadata={\"id\": \"rule2\"})\n", + "]\n", + "\n", + "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", + "splits = text_splitter.split_documents(documents)\n", + "vectorstore = FAISS.from_documents(splits, embedding_function)\n", + "retriever = vectorstore.as_retriever(search_kwargs={\"k\": 2})\n", + "\n", + "\n", + "# --- 3. Инструменты ---\n", + "class CatalogSearchTool(BaseTool):\n", + " \"\"\"Поиск информации о товарах и правилах магазина.\"\"\"\n", + " tool_name: ClassVar[str] = \"search_catalog\"\n", + " description: ClassVar[str] = \"Используй для поиска цен, описаний товаров и условий доставки/возврата.\"\n", + " query: str = Field(..., description=\"Поисковый запрос (например: 'цена конструктора' или 'условия доставки')\")\n", + "\n", + " async def __call__(self, context: ResearchContext) -> str:\n", + " print(f\" [DEBUG] Поиск в каталоге: {self.query}\")\n", + " results = retriever.invoke(self.query)\n", + " if results:\n", + " found_info = \"\\n\".join([f\"- {doc.page_content}\" for doc in results])\n", + " return json.dumps(f\"Найдено в базе:\\n{found_info}\", ensure_ascii=False)\n", + " return json.dumps(\"В каталоге информации не найдено.\", ensure_ascii=False)\n", + "\n", + "class OrderCreationTool(BaseTool):\n", + " \"\"\"Оформление заказа на игрушки.\"\"\"\n", + " tool_name: ClassVar[str] = \"create_order\"\n", + " description: ClassVar[str] = \"Создание заказа. Возвращает номер заказа.\"\n", + " items: str = Field(..., description=\"Список товаров и их количество\")\n", + " customer_info: str = Field(..., description=\"Имя и контактные данные покупателя\")\n", + "\n", + " async def __call__(self, context: ResearchContext) -> str:\n", + " print(f\" [DEBUG] Создание заказа: {self.items} для {self.customer_info}\")\n", + " return json.dumps({\"status\": \"success\", \"order_id\": \"ORD-999\", \"message\": \"Order created successfully\"}, ensure_ascii=False)\n", + "\n", + "class OrderStatusTool(BaseTool):\n", + " \"\"\"Проверка статуса заказа по номеру.\"\"\"\n", + " tool_name: ClassVar[str] = \"check_order_status\"\n", + " description: ClassVar[str] = \"Проверка статуса заказа по ID (например, ORD-999).\"\n", + " order_id: str = Field(..., description=\"Номер заказа\")\n", + "\n", + " async def __call__(self, context: ResearchContext) -> str:\n", + " print(f\" [DEBUG] Проверка статуса заказа: {self.order_id}\")\n", + " if self.order_id == \"ORD-999\":\n", + " return json.dumps(\"Статус: Передан в курьерскую службу. Ожидайте доставку завтра.\", ensure_ascii=False)\n", + " return json.dumps(\"Заказ с таким номером не найден.\", ensure_ascii=False)\n", + "\n", + "\n", + "# --- 4. Конфигурация Агента ---\n", + "http_client = httpx.AsyncClient(verify=False)\n", + "\n", + "llm_client = AsyncOpenAI(\n", + " api_key=ACCESS_TOKEN,\n", + " base_url=giga_settings.base_url,\n", + " http_client=http_client\n", + ")\n", + "\n", + "llm_config = LLMConfig(\n", + " api_key=ACCESS_TOKEN,\n", + " base_url=giga_settings.base_url,\n", + " model=giga_settings.main_model,\n", + " temperature=giga_settings.temperature,\n", + ")\n", + "\n", + "prompts_config = PromptsConfig(\n", + " system_prompt_str=(\n", + " \"Ты консультант магазина игрушек 'Детский Мир'. Отвечаешь на вопросы о товарах и помогаешь с заказами.\"\n", + " \"Список твоих инструментов = [CatalogSearchTool, OrderCreationTool, OrderStatusTool, ClarificationTool, GeneratePlanTool, AdaptPlanTool, ReasoningTool, FinalAnswerTool]\"\n", + " \"Когда ответ готов - вызови FinalAnswerTool.\"\n", + " \"ПРАВИЛА: \"\n", + " \"0. Будь вежлив и дружелюбен, используй эмодзи (🧸, 🚗).\"\n", + " \"1. Сначала ищи информацию в каталоге (CatalogSearchTool), не выдумывай цены.\"\n", + " \"2. Если клиент хочет купить, сначала оформи заказ (OrderCreationTool), потом скажи номер заказа.\"\n", + " \"3. Если спрашивают про условия доставки, тоже смотри в CatalogSearchTool.\"\n", + " ),\n", + " initial_user_request_str=None,\n", + " clarification_response_str=\"Уточните, пожалуйста, какую именно игрушку вы ищете?\"\n", + ")\n", + "\n", + "execution_config = ExecutionConfig(\n", + " max_iterations=5,\n", + " max_searches=0\n", + ")\n", + "\n", + "tools_list = [\n", + " CatalogSearchTool, OrderCreationTool, OrderStatusTool, \n", + " ClarificationTool, GeneratePlanTool, AdaptPlanTool, ReasoningTool, FinalAnswerTool\n", + "]\n", + "\n", + "\n", + "# --- 5. Запуск ---\n", + "async def run_agent_demo(user_input: str):\n", + " print(f\"\\n>>> ПОКУПАТЕЛЬ: {user_input}\")\n", + " \n", + " agent = ToolCallingAgent(\n", + " task=user_input,\n", + " openai_client=llm_client,\n", + " llm_config=llm_config,\n", + " prompts_config=prompts_config,\n", + " execution_config=execution_config,\n", + " toolkit=tools_list,\n", + " )\n", + " \n", + " await agent.execute()\n", + " \n", + " print(\"AGENTS LOG:\")\n", + " for msg in agent.conversation:\n", + " role = msg.get(\"role\")\n", + " content = msg.get(\"content\")\n", + " if role == \"assistant\" and content:\n", + " print(f\"[{role}]: {content}\")\n", + " elif role == \"tool\":\n", + " print(f\"[{role}]: {content}\")\n", + "\n", + "# Запуск\n", + "if __name__ == \"__main__\":\n", + " # Сценарий 1: Вопрос по каталогу\n", + " await run_agent_demo(\"Сколько стоит конструктор звездный крейсер и есть ли бесплатная доставка?\")\n", + " \n", + " # Сценарий 2: Покупка\n", + " await run_agent_demo(\"Хочу купить плюшевого медведя. Меня зовут Саша, телефон 89990000000. Оформите заказ.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86cc5c8f", + "metadata": {}, + "outputs": [], + "source": [ + "giga_settings.verify_ssl_certs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b4c5a6a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e51588eb-1e1e-4f34-b9c2-5c7e6d34a301", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/sgr_deep_research/core/agents/sgr_agent.py b/sgr_deep_research/core/agents/sgr_agent.py index f2f5cf4b..84186c85 100644 --- a/sgr_deep_research/core/agents/sgr_agent.py +++ b/sgr_deep_research/core/agents/sgr_agent.py @@ -1,6 +1,6 @@ from typing import Type -from openai import AsyncOpenAI +from openai import AsyncOpenAI, pydantic_function_tool from sgr_deep_research.core.agent_definition import ExecutionConfig, LLMConfig, PromptsConfig from sgr_deep_research.core.base_agent import BaseAgent @@ -58,19 +58,46 @@ async def _prepare_tools(self) -> Type[NextStepToolStub]: return NextStepToolsBuilder.build_NextStepTools(list(tools)) async def _reasoning_phase(self) -> NextStepToolStub: - async with self.openai_client.chat.completions.stream( + # GigaChat/Legacy path using functions to simulate Structured Outputs + next_step_cls = await self._prepare_tools() + # Create a function definition from the model + tool_def = pydantic_function_tool(next_step_cls, name="plan_next_step", description="Plan the next step and select a tool") + functions = [tool_def["function"]] + + reasoning = None + + messages = await self._prepare_context() + + completion = await self.openai_client.chat.completions.create( model=self.llm_config.model, - response_format=await self._prepare_tools(), - messages=await self._prepare_context(), + messages=messages, max_tokens=self.llm_config.max_tokens, temperature=self.llm_config.temperature, - ) as stream: - async for event in stream: - if event.type == "chunk": - self.streaming_generator.add_chunk(event.chunk) - reasoning: NextStepToolStub = (await stream.get_final_completion()).choices[0].message.parsed # type: ignore - # we are not fully sure if it should be in conversation or not. Looks like not necessary data - # self.conversation.append({"role": "assistant", "content": reasoning.model_dump_json(exclude={"function"})}) + functions=functions, + function_call={"name": "plan_next_step"}, + stream=False + ) + + message = completion.choices[0].message + tool_args_str = None + + if message.function_call and message.function_call.name == "plan_next_step": + tool_args_str = message.function_call.arguments + elif message.tool_calls: + for tc in message.tool_calls: + if tc.function.name == "plan_next_step": + tool_args_str = tc.function.arguments + break + + if tool_args_str: + reasoning = next_step_cls.model_validate_json(tool_args_str) + else: + error_msg = f"Model did not call plan_next_step. Content: {message.content}" + raise ValueError(f"Model failed to generate structured output. Error: {error_msg}") + + # We do NOT append reasoning to conversation for SGRAgent as it is an internal thought process + # that resolves to a tool call in the next step, or it might be appended if desired. + # The original SGRAgent didn't seem to append it. self._log_reasoning(reasoning) return reasoning @@ -78,20 +105,16 @@ async def _select_action_phase(self, reasoning: NextStepToolStub) -> BaseTool: tool = reasoning.function if not isinstance(tool, BaseTool): raise ValueError("Selected tool is not a valid BaseTool instance") + + # Use legacy function_call format for history self.conversation.append( { "role": "assistant", "content": reasoning.remaining_steps[0] if reasoning.remaining_steps else "Completing", - "tool_calls": [ - { - "type": "function", - "id": f"{self._context.iteration}-action", - "function": { - "name": tool.tool_name, - "arguments": tool.model_dump_json(), - }, - } - ], + "function_call": { + "name": tool.tool_name, + "arguments": tool.model_dump(), + } } ) self.streaming_generator.add_tool_call( @@ -101,8 +124,13 @@ async def _select_action_phase(self, reasoning: NextStepToolStub) -> BaseTool: async def _action_phase(self, tool: BaseTool) -> str: result = await tool(self._context) + # Use legacy function role for history self.conversation.append( - {"role": "tool", "content": result, "tool_call_id": f"{self._context.iteration}-action"} + { + "role": "function", + "name": tool.tool_name, + "content": result + } ) self.streaming_generator.add_chunk_from_str(f"{result}\n") self._log_tool_execution(tool, result) diff --git a/sgr_deep_research/core/agents/sgr_so_tool_calling_agent.py b/sgr_deep_research/core/agents/sgr_so_tool_calling_agent.py index 98ba702c..3d81e035 100644 --- a/sgr_deep_research/core/agents/sgr_so_tool_calling_agent.py +++ b/sgr_deep_research/core/agents/sgr_so_tool_calling_agent.py @@ -1,12 +1,11 @@ from typing import Type from warnings import warn -from openai import AsyncOpenAI +from openai import AsyncOpenAI, pydantic_function_tool from sgr_deep_research.core.agent_definition import ExecutionConfig, LLMConfig, PromptsConfig -from sgr_deep_research.core.agents.sgr_tool_calling_agent import SGRToolCallingAgent -from sgr_deep_research.core.base_tool import BaseTool -from sgr_deep_research.core.tools import ReasoningTool +from .sgr_tool_calling_agent import SGRToolCallingAgent +from sgr_deep_research.core.tools import BaseTool, ReasoningTool class SGRSOToolCallingAgent(SGRToolCallingAgent): @@ -39,50 +38,64 @@ def __init__( ) async def _reasoning_phase(self) -> ReasoningTool: - async with self.openai_client.chat.completions.stream( - model=self.llm_config.model, - messages=await self._prepare_context(), - max_tokens=self.llm_config.max_tokens, - temperature=self.llm_config.temperature, - tools=await self._prepare_tools(), - tool_choice={"type": "function", "function": {"name": ReasoningTool.tool_name}}, - ) as stream: - async for event in stream: - if event.type == "chunk": - self.streaming_generator.add_chunk(event.chunk) - reasoning: ReasoningTool = ( # noqa - (await stream.get_final_completion()).choices[0].message.tool_calls[0].function.parsed_arguments # - ) - async with self.openai_client.chat.completions.stream( + # GigaChat/Legacy implementation + # Since GigaChat doesn't support response_format with Pydantic models, + # we simulate Structured Output by forcing a function call to ReasoningTool. + + # Create function definition for ReasoningTool + tool_def = pydantic_function_tool(ReasoningTool, name=ReasoningTool.tool_name, description=ReasoningTool.description) + functions = [tool_def["function"]] + + reasoning = None + + messages = await self._prepare_context() + + completion = await self.openai_client.chat.completions.create( model=self.llm_config.model, - response_format=ReasoningTool, - messages=await self._prepare_context(), + messages=messages, max_tokens=self.llm_config.max_tokens, temperature=self.llm_config.temperature, - ) as stream: - async for event in stream: - if event.type == "chunk": - self.streaming_generator.add_chunk(event.chunk) - reasoning: ReasoningTool = (await stream.get_final_completion()).choices[0].message.parsed + functions=functions, + function_call={"name": ReasoningTool.tool_name}, # Force specific function + stream=False + ) + + message = completion.choices[0].message + tool_args_str = None + + if message.function_call and message.function_call.name == ReasoningTool.tool_name: + tool_args_str = message.function_call.arguments + elif message.tool_calls: + for tc in message.tool_calls: + if tc.function.name == ReasoningTool.tool_name: + tool_args_str = tc.function.arguments + break + + if tool_args_str: + reasoning = ReasoningTool.model_validate_json(tool_args_str) + else: + error_msg = f"Model did not call {ReasoningTool.tool_name}. Content: {message.content}" + raise ValueError(f"Model failed to select ReasoningTool. Error: {error_msg}") + tool_call_result = await reasoning(self._context) + + # Use legacy function_call format for history self.conversation.append( { "role": "assistant", - "content": None, - "tool_calls": [ - { - "type": "function", - "id": f"{self._context.iteration}-reasoning", - "function": { - "name": reasoning.tool_name, - "arguments": "{}", - }, - } - ], + "content": "", + "function_call": { + "name": reasoning.tool_name, + "arguments": {}, # Match original which passed empty dict + } } ) self.conversation.append( - {"role": "tool", "content": tool_call_result, "tool_call_id": f"{self._context.iteration}-reasoning"} + { + "role": "function", + "name": reasoning.tool_name, + "content": tool_call_result + } ) self._log_reasoning(reasoning) return reasoning diff --git a/sgr_deep_research/core/agents/sgr_tool_calling_agent.py b/sgr_deep_research/core/agents/sgr_tool_calling_agent.py index 84027538..d88c9d51 100644 --- a/sgr_deep_research/core/agents/sgr_tool_calling_agent.py +++ b/sgr_deep_research/core/agents/sgr_tool_calling_agent.py @@ -1,7 +1,7 @@ from typing import Literal, Type from openai import AsyncOpenAI, pydantic_function_tool -from openai.types.chat import ChatCompletionFunctionToolParam +from openai.types.chat import ChatCompletionToolParam from sgr_deep_research.core.agent_definition import ExecutionConfig, LLMConfig, PromptsConfig from sgr_deep_research.core.agents.sgr_agent import SGRAgent @@ -42,7 +42,7 @@ def __init__( self.toolkit.append(ReasoningTool) self.tool_choice: Literal["required"] = "required" - async def _prepare_tools(self) -> list[ChatCompletionFunctionToolParam]: + async def _prepare_tools(self) -> list[ChatCompletionToolParam]: """Prepare available tools for current agent state and progress.""" tools = set(self.toolkit) if self._context.iteration >= self.max_iterations: @@ -62,84 +62,135 @@ async def _prepare_tools(self) -> list[ChatCompletionFunctionToolParam]: return [pydantic_function_tool(tool, name=tool.tool_name, description="") for tool in tools] async def _reasoning_phase(self) -> ReasoningTool: - async with self.openai_client.chat.completions.stream( + # GigaChat/Legacy function calling path + tools_params = await self._prepare_tools() + functions = [t["function"] for t in tools_params if "function" in t] + + reasoning = None + + messages = await self._prepare_context() + + completion = await self.openai_client.chat.completions.create( model=self.llm_config.model, - messages=await self._prepare_context(), + messages=messages, max_tokens=self.llm_config.max_tokens, temperature=self.llm_config.temperature, - tools=await self._prepare_tools(), - tool_choice={"type": "function", "function": {"name": ReasoningTool.tool_name}}, - ) as stream: - async for event in stream: - if event.type == "chunk": - self.streaming_generator.add_chunk(event.chunk) - reasoning: ReasoningTool = ( - (await stream.get_final_completion()).choices[0].message.tool_calls[0].function.parsed_arguments - ) + functions=functions, + function_call={"name": ReasoningTool.tool_name}, + stream=False + ) + + message = completion.choices[0].message + + tool_args_str = None + if message.function_call and message.function_call.name == ReasoningTool.tool_name: + tool_args_str = message.function_call.arguments + elif message.tool_calls: + # Try to find ReasoningTool in tool_calls + for tc in message.tool_calls: + if tc.function.name == ReasoningTool.tool_name: + tool_args_str = tc.function.arguments + break + + if tool_args_str: + reasoning = ReasoningTool.model_validate_json(tool_args_str) + else: + error_msg = f"Model did not call {ReasoningTool.tool_name}. Content: {message.content}" + raise ValueError(f"Model failed to select ReasoningTool. Error: {error_msg}") + + # Use legacy function_call format for history self.conversation.append( { "role": "assistant", - "content": None, - "tool_calls": [ - { - "type": "function", - "id": f"{self._context.iteration}-reasoning", - "function": { - "name": reasoning.tool_name, - "arguments": reasoning.model_dump_json(), - }, - } - ], + "content": "", + "function_call": { + "name": reasoning.tool_name, + "arguments": reasoning.model_dump(), + } } ) tool_call_result = await reasoning(self._context) self.conversation.append( - {"role": "tool", "content": tool_call_result, "tool_call_id": f"{self._context.iteration}-reasoning"} + { + "role": "function", + "name": reasoning.tool_name, + "content": tool_call_result + } ) self._log_reasoning(reasoning) return reasoning async def _select_action_phase(self, reasoning: ReasoningTool) -> BaseTool: - async with self.openai_client.chat.completions.stream( + # GigaChat/Legacy path + tools_params = await self._prepare_tools() + functions = [t["function"] for t in tools_params if "function" in t] + + tool = None + + messages = await self._prepare_context() + + completion = await self.openai_client.chat.completions.create( model=self.llm_config.model, - messages=await self._prepare_context(), + messages=messages, max_tokens=self.llm_config.max_tokens, temperature=self.llm_config.temperature, - tools=await self._prepare_tools(), - tool_choice=self.tool_choice, - ) as stream: - async for event in stream: - if event.type == "chunk": - self.streaming_generator.add_chunk(event.chunk) - - completion = await stream.get_final_completion() - - try: - tool = completion.choices[0].message.tool_calls[0].function.parsed_arguments - except (IndexError, AttributeError, TypeError): - # LLM returned a text response instead of a tool call - treat as completion - final_content = completion.choices[0].message.content or "Task completed successfully" - tool = FinalAnswerTool( - reasoning="Agent decided to complete the task", - completed_steps=[final_content], - status=AgentStatesEnum.COMPLETED, - ) + functions=functions, + function_call="auto", + stream=False + ) + + message = completion.choices[0].message + tool_name = None + tool_args_str = None + + if message.function_call: + tool_name = message.function_call.name + tool_args_str = message.function_call.arguments + elif message.tool_calls: + tool_call = message.tool_calls[0] + tool_name = tool_call.function.name + tool_args_str = tool_call.function.arguments + else: + # Fallback: check if content implies completion + pass + + if tool_name: + # Find tool class + candidate_tools = set(self.toolkit) + candidate_tools.update({ClarificationTool, CreateReportTool, FinalAnswerTool, WebSearchTool, ReasoningTool}) + + tool_cls = next((t for t in candidate_tools if t.tool_name == tool_name), None) + if tool_cls: + tool = tool_cls.model_validate_json(tool_args_str) + else: + error_msg = f"Tool {tool_name} not found in toolkit" + raise ValueError(error_msg) + else: + # No tool called. + # Fallback to FinalAnswer if content exists + if 'completion' in locals() and completion.choices[0].message.content: + final_content = completion.choices[0].message.content + tool = FinalAnswerTool( + reasoning="Agent decided to complete the task (Fallback)", + completed_steps=[final_content], + status=AgentStatesEnum.COMPLETED, + ) + else: + error_msg = f"No function called and no content. Content: {message.content}" + raise ValueError(error_msg) + if not isinstance(tool, BaseTool): raise ValueError("Selected tool is not a valid BaseTool instance") + + # Use legacy function_call format for history self.conversation.append( { "role": "assistant", - "content": reasoning.remaining_steps[0] if reasoning.remaining_steps else "Completing", - "tool_calls": [ - { - "type": "function", - "id": f"{self._context.iteration}-action", - "function": { - "name": tool.tool_name, - "arguments": tool.model_dump_json(), - }, - } - ], + "content": "", + "function_call": { + "name": tool.tool_name, + "arguments": tool.model_dump(), + } } ) self.streaming_generator.add_tool_call( diff --git a/sgr_deep_research/core/agents/tool_calling_agent.py b/sgr_deep_research/core/agents/tool_calling_agent.py index cf132aa7..7c695042 100644 --- a/sgr_deep_research/core/agents/tool_calling_agent.py +++ b/sgr_deep_research/core/agents/tool_calling_agent.py @@ -1,7 +1,7 @@ from typing import Literal, Type from openai import AsyncOpenAI, pydantic_function_tool -from openai.types.chat import ChatCompletionFunctionToolParam +from openai.types.chat import ChatCompletionToolParam from sgr_deep_research.core.agent_definition import ExecutionConfig, LLMConfig, PromptsConfig from sgr_deep_research.core.base_agent import BaseAgent @@ -40,7 +40,7 @@ def __init__( self.max_searches = execution_config.max_searches self.tool_choice: Literal["required"] = "required" - async def _prepare_tools(self) -> list[ChatCompletionFunctionToolParam]: + async def _prepare_tools(self) -> list[ChatCompletionToolParam]: """Prepare tool classes with current context limits.""" tools = set(self.toolkit) if self._context.iteration >= self.max_iterations: @@ -63,35 +63,70 @@ async def _reasoning_phase(self) -> None: return None async def _select_action_phase(self, reasoning=None) -> BaseTool: - async with self.openai_client.chat.completions.stream( + # GigaChat specific: Use legacy 'functions' parameter instead of 'tools' + # This often works better for models with older OpenAI API compatibility + tools_params = await self._prepare_tools() + + # Extract 'function' definitions from the tools parameters + functions = [] + for t in tools_params: + # t is a ChatCompletionToolParam (dict) with keys 'type' and 'function' + if "function" in t: + functions.append(t["function"]) + + messages = await self._prepare_context() + # print(f"{messages=}") + completion = await self.openai_client.chat.completions.create( model=self.llm_config.model, - messages=await self._prepare_context(), + messages=messages, max_tokens=self.llm_config.max_tokens, temperature=self.llm_config.temperature, - tools=await self._prepare_tools(), - tool_choice=self.tool_choice, - ) as stream: - async for event in stream: - if event.type == "chunk": - self.streaming_generator.add_chunk(event.chunk) - tool = (await stream.get_final_completion()).choices[0].message.tool_calls[0].function.parsed_arguments + functions=functions, + function_call="auto", # Use 'auto' for function calling + stream=False + ) + + message = completion.choices[0].message + + # Check for legacy function_call response + tool_name = None + tool_args_str = None + + if message.function_call: + tool_name = message.function_call.name + tool_args_str = message.function_call.arguments + # Fallback check for tool_calls (just in case) + elif message.tool_calls: + tool_call = message.tool_calls[0] + tool_name = tool_call.function.name + tool_args_str = tool_call.function.arguments + else: + error_msg = f"Model returned no function call. Content: {message.content}" + raise ValueError(f"Model failed to select a tool. Error: {error_msg}") + + # Find the tool class + candidate_tools = set(self.toolkit) + candidate_tools.update({ClarificationTool, CreateReportTool, FinalAnswerTool, WebSearchTool}) + + tool_cls = next((t for t in candidate_tools if t.tool_name == tool_name), None) + + if not tool_cls: + raise ValueError(f"Tool {tool_name} not found in toolkit") + # print(f"{tool_args_str=}") + + tool = tool_cls.model_validate(tool_args_str) if not isinstance(tool, BaseTool): + raise ValueError("Selected tool is not a valid BaseTool instance") self.conversation.append( { "role": "assistant", - "content": None, - "tool_calls": [ - { - "type": "function", - "id": f"{self._context.iteration}-action", - "function": { - "name": tool.tool_name, - "arguments": tool.model_dump_json(), - }, - } - ], + "content": "", + "function_call": { + "name": tool.tool_name, + "arguments": tool.model_dump(), + } } ) self.streaming_generator.add_tool_call( @@ -102,8 +137,12 @@ async def _select_action_phase(self, reasoning=None) -> BaseTool: async def _action_phase(self, tool: BaseTool) -> str: result = await tool(self._context) self.conversation.append( - {"role": "tool", "content": result, "tool_call_id": f"{self._context.iteration}-action"} + { + "role": "function", + "name": tool.tool_name, + "content": result + } ) self.streaming_generator.add_chunk_from_str(f"{result}\n") self._log_tool_execution(tool, result) - return result + return result \ No newline at end of file From 20cb14396341391c1f28386c552c813a7dfff7ba Mon Sep 17 00:00:00 2001 From: xFtagn Date: Wed, 26 Nov 2025 10:26:18 +0300 Subject: [PATCH 2/2] Enhance token management in agents - Added token accumulation functionality to track total tokens used during agent operations. - Updated the `ResearchContext` model to include a `tokens_used` field. - Integrated token tracking in various agent classes, ensuring accurate logging of token usage during tool calls and reasoning steps. - Adjusted logging to display total tokens used in agent outputs for better monitoring and analysis. --- demo_agent_Gigachat.ipynb | 358 ++++++++++++++---- sgr_deep_research/core/agents/sgr_agent.py | 1 + .../core/agents/sgr_so_tool_calling_agent.py | 1 + .../core/agents/sgr_tool_calling_agent.py | 2 + .../core/agents/tool_calling_agent.py | 1 + sgr_deep_research/core/base_agent.py | 16 + sgr_deep_research/core/models.py | 1 + 7 files changed, 311 insertions(+), 69 deletions(-) diff --git a/demo_agent_Gigachat.ipynb b/demo_agent_Gigachat.ipynb index afa38502..0bfe82c7 100644 --- a/demo_agent_Gigachat.ipynb +++ b/demo_agent_Gigachat.ipynb @@ -2,10 +2,142 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "ac38ba1c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Obtaining file:///home/owner/Documents/DEV/sgr-agent-core\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Checking if build backend supports build_editable ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build editable ... \u001b[?25ldone\n", + "\u001b[?25h Preparing editable metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: openai>=1.0.0 in ./.venv/lib/python3.13/site-packages (from sgr-deep-research==0.4.0) (2.8.1)\n", + "Requirement already satisfied: pydantic>=2.0.0 in ./.venv/lib/python3.13/site-packages (from sgr-deep-research==0.4.0) (2.12.4)\n", + "Requirement already satisfied: rich>=13.0.0 in ./.venv/lib/python3.13/site-packages (from sgr-deep-research==0.4.0) (14.2.0)\n", + "Requirement already satisfied: httpx>=0.25.0 in ./.venv/lib/python3.13/site-packages (from sgr-deep-research==0.4.0) (0.28.1)\n", + "Requirement already satisfied: socksio>=1.0.0 in ./.venv/lib/python3.13/site-packages (from sgr-deep-research==0.4.0) (1.0.0)\n", + "Requirement already satisfied: tavily-python>=0.3.0 in ./.venv/lib/python3.13/site-packages (from 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./.venv/lib/python3.13/site-packages (from cffi>=2.0.0->cryptography->authlib>=1.6.5->fastmcp>=2.12.4->sgr-deep-research==0.4.0) (2.23)\n", + "Requirement already satisfied: more-itertools in ./.venv/lib/python3.13/site-packages (from jaraco.classes->keyring>=25.6.0->py-key-value-aio[disk,keyring,memory]<0.3.0,>=0.2.8->fastmcp>=2.12.4->sgr-deep-research==0.4.0) (10.8.0)\n", + "Building wheels for collected packages: sgr-deep-research\n", + " Building editable for sgr-deep-research (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for sgr-deep-research: filename=sgr_deep_research-0.4.0-0.editable-py3-none-any.whl size=4544 sha256=c32934ac0d42bb59b88126f85f428102afd81985eeafefc56346d9c1829d7fae\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-nprer88f/wheels/e8/4e/71/d3c441f32821ae3230ad7689e08f9bc3a1a2f9ec93942bcb68\n", + "Successfully built sgr-deep-research\n", + "Installing collected packages: sgr-deep-research\n", + " Attempting uninstall: sgr-deep-research\n", + " Found existing installation: sgr-deep-research 0.4.0\n", + " Uninstalling sgr-deep-research-0.4.0:\n", + " Successfully uninstalled sgr-deep-research-0.4.0\n", + "Successfully installed sgr-deep-research-0.4.0\n" + ] + } + ], "source": [ "!pip install -e \".[dev]\"" ] @@ -16,14 +148,6 @@ "id": "d56662b9", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:--- envs've been successfully loaded ---\n", - "INFO:__main__:--- GIGA CONFIG: {'base_url': 'https://gigachat.devices.sberbank.ru/api/v1', 'verify_ssl_certs': False, 'main_model': 'GigaChat', 'temperature': 0.0} ---\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -80,30 +204,104 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/embeddings \"HTTP/1.1 200 OK\"\n" + "INFO:httpx:HTTP Request: GET https://gigachat.devices.sberbank.ru/api/v1/models \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_f17cf3db-2f44-42f3-8031-617e867f4922:🚀 Starting for task: 'Сколько стоит конструктор звездный крейсер и есть ли бесплатная доставка?'\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_f17cf3db-2f44-42f3-8031-617e867f4922:Step 1 started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Токен успешно получен: eyJjdHkiOi...\n" + "\n", + "Доступные модели GigaChat:\n", + "- GigaChat\n", + "- GigaChat-2\n", + "- GigaChat-2-Max\n", + "- GigaChat-2-Pro\n", + "- GigaChat-Max\n", + "- GigaChat-Max-preview\n", + "- GigaChat-Plus\n", + "- GigaChat-Pro\n", + "- GigaChat-Pro-preview\n", + "- GigaChat-preview\n", + "- Embeddings\n", + "- Embeddings-2\n", + "- EmbeddingsGigaR\n", + "- GigaEmbeddings-3B-2025-09\n", + "\n", + ">>> ПОКУПАТЕЛЬ: Сколько стоит конструктор звездный крейсер и есть ли бесплатная доставка?\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:sgr_deep_research.agents.tool_calling_agent_28a9634c-f00e-4ae4-8819-f67c87a19a4b:🚀 Starting for task: 'Сколько стоит конструктор звездный крейсер и есть ли бесплатная доставка?'\n", - "INFO:sgr_deep_research.agents.tool_calling_agent_28a9634c-f00e-4ae4-8819-f67c87a19a4b:Step 1 started\n" + "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_f17cf3db-2f44-42f3-8031-617e867f4922:\n", + "###############################################\n", + "🛠️ TOOL EXECUTION DEBUG:\n", + " 🔧 Tool Name: search_catalog\n", + " 📋 Tool Model: {\n", + " \"query\": \"конструктор звездный крейсер цена и бесплатная доставка\"\n", + "}\n", + " 🔍 Result: '\"Найдено в базе:\\n- Конструктор 'Звездный крейсер' стоит 5000 рублей. Подходит для детей от 10 лет.\\n- Доставка осуществляется бесплатно при заказе от 3000 рублей. Срок доставки 2-3 дня.\"...'\n", + " 🔢 Tokens Used Total: 558\n", + "###############################################\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_f17cf3db-2f44-42f3-8031-617e867f4922:Step 2 started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + " [DEBUG] Поиск в каталоге: конструктор звездный крейсер цена и бесплатная доставка\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_f17cf3db-2f44-42f3-8031-617e867f4922:\n", + "###############################################\n", + "🛠️ TOOL EXECUTION DEBUG:\n", + " 🔧 Tool Name: finalanswertool\n", + " 📋 Tool Model: {\n", + " \"reasoning\": \"Я поискал информацию о конструкторе 'Звездный крейсер' в нашем каталоге. Там указано, что его стоимость 5000 рублей, и бесплатная доставка доступна при заказе от 3000 рублей. Также указано, что срок доставки составляет 2-3 дня.\",\n", + " \"completed_steps\": [\n", + " \"search_catalog\"\n", + " ],\n", + " \"answer\": \"Конструктор 'Звездный крейсер' стоит 5000 рублей. Бесплатная доставка доступна при заказе от 3000 рублей. Срок доставки 2-3 дня.\",\n", + " \"status\": \"completed\"\n", + "}\n", + " 🔍 Result: '{\n", + " \"reasoning\": \"Я поискал информацию о конструкторе 'Звездный крейсер' в нашем каталоге. Там указано, что его стоимость 5000 рублей, и бесплатная доставка доступна при заказе от 3000 рублей. Также указано, что срок доставки составляет 2-3 дня.\",\n", + " \"completed_steps\": [\n", + " \"search_catalog\"\n", + " ],\n", + " \"answer\": \"Конструктор 'Звездный крейсер' стоит 5000 рублей. Бесплатная доставка доступна при заказе ...'\n", + " 🔢 Tokens Used Total: 1153\n", + "###############################################\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_29dc8ecb-2352-4531-b4da-2898119074d0:🚀 Starting for task: 'Хочу купить плюшевого медведя. Меня зовут Саша, телефон 89990000000. Оформите заказ.'\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_29dc8ecb-2352-4531-b4da-2898119074d0:Step 1 started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AGENTS LOG:\n", "\n", - ">>> ПОКУПАТЕЛЬ: Сколько стоит конструктор звездный крейсер и есть ли бесплатная доставка?\n" + ">>> ПОКУПАТЕЛЬ: Хочу купить плюшевого медведя. Меня зовут Саша, телефон 89990000000. Оформите заказ.\n" ] }, { @@ -112,23 +310,24 @@ "text": [ "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n", "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/embeddings \"HTTP/1.1 200 OK\"\n", - "INFO:sgr_deep_research.agents.tool_calling_agent_28a9634c-f00e-4ae4-8819-f67c87a19a4b:\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_29dc8ecb-2352-4531-b4da-2898119074d0:\n", "###############################################\n", "🛠️ TOOL EXECUTION DEBUG:\n", " 🔧 Tool Name: search_catalog\n", " 📋 Tool Model: {\n", - " \"query\": \"конструктор звездный крейсер цена\"\n", + " \"query\": \"плюшевый мишка\"\n", "}\n", - " 🔍 Result: '\"Найдено в базе:\\n- Конструктор 'Звездный крейсер' стоит 5000 рублей. Подходит для детей от 10 лет.\\n- Настольная игра 'Монополия' стоит 2500 рублей.\"...'\n", + " 🔍 Result: '\"Найдено в базе:\\n- Плюшевый медведь 'Мишутка' (50 см) стоит 1200 рублей.\\n- Доставка осуществляется бесплатно при заказе от 3000 рублей. Срок доставки 2-3 дня.\"...'\n", + " 🔢 Tokens Used Total: 572\n", "###############################################\n", - "INFO:sgr_deep_research.agents.tool_calling_agent_28a9634c-f00e-4ae4-8819-f67c87a19a4b:Step 2 started\n" + "INFO:sgr_deep_research.agents.tool_calling_agent_29dc8ecb-2352-4531-b4da-2898119074d0:Step 2 started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " [DEBUG] Поиск в каталоге: конструктор звездный крейсер цена\n" + " [DEBUG] Поиск в каталоге: плюшевый мишка\n" ] }, { @@ -136,24 +335,25 @@ "output_type": "stream", "text": [ "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/embeddings \"HTTP/1.1 200 OK\"\n", - "INFO:sgr_deep_research.agents.tool_calling_agent_28a9634c-f00e-4ae4-8819-f67c87a19a4b:\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_29dc8ecb-2352-4531-b4da-2898119074d0:\n", "###############################################\n", "🛠️ TOOL EXECUTION DEBUG:\n", - " 🔧 Tool Name: search_catalog\n", + " 🔧 Tool Name: create_order\n", " 📋 Tool Model: {\n", - " \"query\": \"бесплатная доставка\"\n", + " \"items\": \"Плюшевый медведь 'Мишутка' (50 см)\",\n", + " \"customer_info\": \"Саша, 89990000000\"\n", "}\n", - " 🔍 Result: '\"Найдено в базе:\\n- Доставка осуществляется бесплатно при заказе от 3000 рублей. Срок доставки 2-3 дня.\\n- Возврат товара возможен в течение 14 дней при сохранении упаковки.\"...'\n", + " 🔍 Result: '{\"status\": \"success\", \"order_id\": \"ORD-999\", \"message\": \"Order created successfully\"}...'\n", + " 🔢 Tokens Used Total: 1089\n", "###############################################\n", - "INFO:sgr_deep_research.agents.tool_calling_agent_28a9634c-f00e-4ae4-8819-f67c87a19a4b:Step 3 started\n" + "INFO:sgr_deep_research.agents.tool_calling_agent_29dc8ecb-2352-4531-b4da-2898119074d0:Step 3 started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " [DEBUG] Поиск в каталоге: бесплатная доставка\n" + " [DEBUG] Создание заказа: Плюшевый медведь 'Мишутка' (50 см) для Саша, 89990000000\n" ] }, { @@ -161,28 +361,30 @@ "output_type": "stream", "text": [ "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "INFO:sgr_deep_research.agents.tool_calling_agent_28a9634c-f00e-4ae4-8819-f67c87a19a4b:\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_29dc8ecb-2352-4531-b4da-2898119074d0:\n", "###############################################\n", "🛠️ TOOL EXECUTION DEBUG:\n", " 🔧 Tool Name: finalanswertool\n", " 📋 Tool Model: {\n", - " \"reasoning\": \"Обе части запроса (цена конструктора и условия бесплатной доставки) были успешно найдены в каталоге магазина. Ответ содержит точные данные о цене и условиях доставки. 5000 рублей >= 3000 рублей, следовательно, доставка будет бесплатной.😊\",\n", + " \"reasoning\": \"Я поискал информацию о товаре в каталоге, создал заказ с Вашими данными и указанным товаром, и проверил статус заказа в системе. Всё сделано правильно.\",\n", " \"completed_steps\": [\n", - " \"Поиск цены конструктора 'Звездный крейсер' в каталоге\",\n", - " \"Поиск информации о бесплатной доставке в каталоге\"\n", + " \"Поиск товара в каталоге\",\n", + " \"Создание заказа с Вашими данными и выбранным товаром\"\n", " ],\n", - " \"answer\": \"Конструктор 'Звездный крейсер' стоит 5000 рублей. Бесплатная доставка предоставляется при заказе от 3000 рублей, срок доставки 2-3 дня.🚗🚀\",\n", + " \"answer\": \"Вы заказали плюшевого мишку 'Мишутку' (50 см) в нашем магазине. Номер Вашего заказа: ORD-999. Ожидаемый срок доставки 2-3 дня. Спасибо, что выбрали \\\"Детский Мир\\\"! :)', \\n \",\n", " \"status\": \"completed\"\n", "}\n", " 🔍 Result: '{\n", - " \"reasoning\": \"Обе части запроса (цена конструктора и условия бесплатной доставки) были успешно найдены в каталоге магазина. Ответ содержит точные данные о цене и условиях доставки. 5000 рублей >= 3000 рублей, следовательно, доставка будет бесплатной.😊\",\n", + " \"reasoning\": \"Я поискал информацию о товаре в каталоге, создал заказ с Вашими данными и указанным товаром, и проверил статус заказа в системе. Всё сделано правильно.\",\n", " \"completed_steps\": [\n", - " \"Поиск цены конструктора 'Звездный крейсер' в каталоге\",\n", - " \"Поиск информации о бесплатной доставке в каталоге\"\n", - " ...'\n", + " \"Поиск товара в каталоге\",\n", + " \"Создание заказа с Вашими данными и выбранным товаром\"\n", + " ],\n", + " \"answer\": \"Вы заказали плюшевого мишку 'Мишутку' (50 см) в нашем магазине. Номер Вашего заказа: ORD-999. Ожи...'\n", + " 🔢 Tokens Used Total: 1801\n", "###############################################\n", - "INFO:sgr_deep_research.agents.tool_calling_agent_d4bd554b-a05e-4f7c-a5e5-9fa224c42e30:🚀 Starting for task: 'Хочу купить плюшевого медведя. Меня зовут Саша, телефон 89990000000. Оформите заказ.'\n", - "INFO:sgr_deep_research.agents.tool_calling_agent_d4bd554b-a05e-4f7c-a5e5-9fa224c42e30:Step 1 started\n" + "INFO:sgr_deep_research.agents.tool_calling_agent_5b889ed3-e4ec-47b4-b296-182199e2124b:🚀 Starting for task: 'Привет, снова Саша. Какой статус у заказа ORD-999?'\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_5b889ed3-e4ec-47b4-b296-182199e2124b:Step 1 started\n" ] }, { @@ -191,7 +393,7 @@ "text": [ "AGENTS LOG:\n", "\n", - ">>> ПОКУПАТЕЛЬ: Хочу купить плюшевого медведя. Меня зовут Саша, телефон 89990000000. Оформите заказ.\n" + ">>> ПОКУПАТЕЛЬ: Привет, снова Саша. Какой статус у заказа ORD-999?\n" ] }, { @@ -199,24 +401,24 @@ "output_type": "stream", "text": [ "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "INFO:sgr_deep_research.agents.tool_calling_agent_d4bd554b-a05e-4f7c-a5e5-9fa224c42e30:\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_5b889ed3-e4ec-47b4-b296-182199e2124b:\n", "###############################################\n", "🛠️ TOOL EXECUTION DEBUG:\n", - " 🔧 Tool Name: create_order\n", + " 🔧 Tool Name: check_order_status\n", " 📋 Tool Model: {\n", - " \"items\": \"плюшевый медведь 1\",\n", - " \"customer_info\": \"Саша, 89990000000\"\n", + " \"order_id\": \"ORD-999\"\n", "}\n", - " 🔍 Result: '{\"status\": \"success\", \"order_id\": \"ORD-999\", \"message\": \"Order created successfully\"}...'\n", + " 🔍 Result: '\"Статус: Передан в курьерскую службу. Ожидайте доставку завтра.\"...'\n", + " 🔢 Tokens Used Total: 104\n", "###############################################\n", - "INFO:sgr_deep_research.agents.tool_calling_agent_d4bd554b-a05e-4f7c-a5e5-9fa224c42e30:Step 2 started\n" + "INFO:sgr_deep_research.agents.tool_calling_agent_5b889ed3-e4ec-47b4-b296-182199e2124b:Step 2 started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " [DEBUG] Создание заказа: плюшевый медведь 1 для Саша, 89990000000\n" + " [DEBUG] Проверка статуса заказа: ORD-999\n" ] }, { @@ -224,24 +426,27 @@ "output_type": "stream", "text": [ "INFO:httpx:HTTP Request: POST https://gigachat.devices.sberbank.ru/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "INFO:sgr_deep_research.agents.tool_calling_agent_d4bd554b-a05e-4f7c-a5e5-9fa224c42e30:\n", + "INFO:sgr_deep_research.agents.tool_calling_agent_5b889ed3-e4ec-47b4-b296-182199e2124b:\n", "###############################################\n", "🛠️ TOOL EXECUTION DEBUG:\n", " 🔧 Tool Name: finalanswertool\n", " 📋 Tool Model: {\n", - " \"reasoning\": \"Заказ на плюшевого медведя оформлен успешно и был присвоен номер заказа ORD-999. Ответ включает имя клиента и информацию о статусе заказа. Ответ позитивный и включает эмодзи для улучшения пользовательского опыта. 00000\",\n", + " \"reasoning\": \"Я использовал инструмент check_order_status для проверки статуса заказа ORD-999. Результат показывает, что заказ уже передан в курьерскую службу и ожидается доставка завтра.\",\n", " \"completed_steps\": [\n", - " \"Оформлен заказ на плюшевого медведя на имя Саши с контактным телефоном 89990000000.00000\"\n", + " \"check_order_status\"\n", " ],\n", - " \"answer\": \"Саша, Ваш заказ № ORD-999 на плюшевого медведя оформлен успешно. Пожалуйста, сохраняйте свой номер заказа. 🥰🧸\",\n", + " \"answer\": \"Статус заказа ORD-999: Передан в курьерскую службу. Ожидайте доставку завтра.\",\n", " \"status\": \"completed\"\n", "}\n", " 🔍 Result: '{\n", - " \"reasoning\": \"Заказ на плюшевого медведя оформлен успешно и был присвоен номер заказа ORD-999. Ответ включает имя клиента и информацию о статусе заказа. Ответ позитивный и включает эмодзи для улучшения пользовательского опыта. 00000\",\n", + " \"reasoning\": \"Я использовал инструмент check_order_status для проверки статуса заказа ORD-999. Результат показывает, что заказ уже передан в курьерскую службу и ожидается доставка завтра.\",\n", " \"completed_steps\": [\n", - " \"Оформлен заказ на плюшевого медведя на имя Саши с контактным телефоном 89990000000.00000\"\n", + " \"check_order_status\"\n", " ],\n", - " \"answer\": \"Саша, Ваш заказ № ORD-999...'\n", + " \"answer\": \"Статус заказа ORD-999: Передан в курьерскую службу. Ожидайте доставку завтра.\",\n", + " \"status\": \"completed\"\n", + "}...'\n", + " 🔢 Tokens Used Total: 593\n", "###############################################\n" ] }, @@ -327,13 +532,29 @@ "\n", "# Получаем актуальный токен\n", "ACCESS_TOKEN = get_gigachat_token(auth_key)\n", - "print(f\"Токен успешно получен: {ACCESS_TOKEN[:10]}...\")\n", + "# print(f\"Токен успешно получен: {ACCESS_TOKEN}\")\n", + "\n", + "from openai import OpenAI # Импортируем синхронный клиент OpenAI\n", + "import httpx # httpx нужен для http_client, даже в синхронном режиме\n", + "\n", + "def print_giga_models_sync():\n", + " client = OpenAI( # Используем синхронный клиент\n", + " api_key=ACCESS_TOKEN,\n", + " base_url=giga_settings.base_url,\n", + " http_client=httpx.Client(verify=giga_settings.verify_ssl_certs) # Используем синхронный httpx.Client\n", + " )\n", + " models = client.models.list() # Синхронный вызов\n", + " print(\"\\nДоступные модели GigaChat:\")\n", + " for model in models.data:\n", + " print(f\"- {model.id}\")\n", + "\n", + "print_giga_models_sync() \n", "\n", "# Настройки для GigaChat (предполагаем наличие giga_settings как в оригинале, или задаем тут)\n", "class GigaSettings:\n", " base_url = \"https://gigachat.devices.sberbank.ru/api/v1\"\n", " verify_ssl_certs = False\n", - " main_model = \"GigaChat-Pro\"\n", + " main_model = \"GigaChat\"\n", " temperature = 0.0\n", "\n", "giga_settings = GigaSettings()\n", @@ -418,12 +639,15 @@ "prompts_config = PromptsConfig(\n", " system_prompt_str=(\n", " \"Ты консультант магазина игрушек 'Детский Мир'. Отвечаешь на вопросы о товарах и помогаешь с заказами.\"\n", - " \"Список твоих инструментов = [CatalogSearchTool, OrderCreationTool, OrderStatusTool, ClarificationTool, GeneratePlanTool, AdaptPlanTool, ReasoningTool, FinalAnswerTool]\"\n", + " \"Список твоих инструментов = [CatalogSearchTool, OrderCreationTool, OrderStatusTool, FinalAnswerTool]\"\n", " \"Когда ответ готов - вызови FinalAnswerTool.\"\n", " \"ПРАВИЛА: \"\n", " \"0. Будь вежлив и дружелюбен, используй эмодзи (🧸, 🚗).\"\n", " \"1. Сначала ищи информацию в каталоге (CatalogSearchTool), не выдумывай цены.\"\n", - " \"2. Если клиент хочет купить, сначала оформи заказ (OrderCreationTool), потом скажи номер заказа.\"\n", + " \"2. Если клиент хочет купить товар: \"\n", + " \" 2.1) оформи заказ (OrderCreationTool), \"\n", + " \" 2.2) потом проверь что его статус уже отобразился в системе (ВАЖНО!) (OrderStatusTool)\"\n", + " \" 2.3) потом скажи номер заказа и срок доставки.\"\n", " \"3. Если спрашивают про условия доставки, тоже смотри в CatalogSearchTool.\"\n", " ),\n", " initial_user_request_str=None,\n", @@ -437,7 +661,11 @@ "\n", "tools_list = [\n", " CatalogSearchTool, OrderCreationTool, OrderStatusTool, \n", - " ClarificationTool, GeneratePlanTool, AdaptPlanTool, ReasoningTool, FinalAnswerTool\n", + " # ClarificationTool, \n", + " # GeneratePlanTool, \n", + " # AdaptPlanTool, \n", + " # ReasoningTool, \n", + " FinalAnswerTool\n", "]\n", "\n", "\n", @@ -471,17 +699,9 @@ " await run_agent_demo(\"Сколько стоит конструктор звездный крейсер и есть ли бесплатная доставка?\")\n", " \n", " # Сценарий 2: Покупка\n", - " await run_agent_demo(\"Хочу купить плюшевого медведя. Меня зовут Саша, телефон 89990000000. Оформите заказ.\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "86cc5c8f", - "metadata": {}, - "outputs": [], - "source": [ - "giga_settings.verify_ssl_certs" + " await run_agent_demo(\"Хочу купить плюшевого медведя. Меня зовут Саша, телефон 89990000000. Оформите заказ.\")\n", + " \n", + " await run_agent_demo(\"Привет, снова Саша. Какой статус у заказа ORD-999?\")" ] }, { diff --git a/sgr_deep_research/core/agents/sgr_agent.py b/sgr_deep_research/core/agents/sgr_agent.py index 84186c85..3f17467a 100644 --- a/sgr_deep_research/core/agents/sgr_agent.py +++ b/sgr_deep_research/core/agents/sgr_agent.py @@ -77,6 +77,7 @@ async def _reasoning_phase(self) -> NextStepToolStub: function_call={"name": "plan_next_step"}, stream=False ) + self._accumulate_tokens(getattr(completion, "usage", None)) message = completion.choices[0].message tool_args_str = None diff --git a/sgr_deep_research/core/agents/sgr_so_tool_calling_agent.py b/sgr_deep_research/core/agents/sgr_so_tool_calling_agent.py index 3d81e035..278f7f66 100644 --- a/sgr_deep_research/core/agents/sgr_so_tool_calling_agent.py +++ b/sgr_deep_research/core/agents/sgr_so_tool_calling_agent.py @@ -59,6 +59,7 @@ async def _reasoning_phase(self) -> ReasoningTool: function_call={"name": ReasoningTool.tool_name}, # Force specific function stream=False ) + self._accumulate_tokens(getattr(completion, "usage", None)) message = completion.choices[0].message tool_args_str = None diff --git a/sgr_deep_research/core/agents/sgr_tool_calling_agent.py b/sgr_deep_research/core/agents/sgr_tool_calling_agent.py index d88c9d51..c373ada5 100644 --- a/sgr_deep_research/core/agents/sgr_tool_calling_agent.py +++ b/sgr_deep_research/core/agents/sgr_tool_calling_agent.py @@ -79,6 +79,7 @@ async def _reasoning_phase(self) -> ReasoningTool: function_call={"name": ReasoningTool.tool_name}, stream=False ) + self._accumulate_tokens(getattr(completion, "usage", None)) message = completion.choices[0].message @@ -138,6 +139,7 @@ async def _select_action_phase(self, reasoning: ReasoningTool) -> BaseTool: function_call="auto", stream=False ) + self._accumulate_tokens(getattr(completion, "usage", None)) message = completion.choices[0].message tool_name = None diff --git a/sgr_deep_research/core/agents/tool_calling_agent.py b/sgr_deep_research/core/agents/tool_calling_agent.py index 7c695042..94615aaf 100644 --- a/sgr_deep_research/core/agents/tool_calling_agent.py +++ b/sgr_deep_research/core/agents/tool_calling_agent.py @@ -85,6 +85,7 @@ async def _select_action_phase(self, reasoning=None) -> BaseTool: function_call="auto", # Use 'auto' for function calling stream=False ) + self._accumulate_tokens(getattr(completion, "usage", None)) message = completion.choices[0].message diff --git a/sgr_deep_research/core/base_agent.py b/sgr_deep_research/core/base_agent.py index 33b38e41..de3e92e8 100644 --- a/sgr_deep_research/core/base_agent.py +++ b/sgr_deep_research/core/base_agent.py @@ -71,6 +71,17 @@ async def provide_clarification(self, clarifications: str): self._context.state = AgentStatesEnum.RESEARCHING self.logger.info(f"✅ Clarification received: {clarifications[:2000]}...") + def _accumulate_tokens(self, usage) -> None: + """Store token usage reported by the LLM response.""" + if usage is None: + return + total_tokens = getattr(usage, "total_tokens", None) + if total_tokens is None and isinstance(usage, dict): + total_tokens = usage.get("total_tokens") + if total_tokens is None: + return + self._context.tokens_used += total_tokens + def _log_reasoning(self, result: ReasoningTool) -> None: next_step = result.remaining_steps[0] if result.remaining_steps else "Completing" self.logger.info( @@ -86,6 +97,7 @@ def _log_reasoning(self, result: ReasoningTool) -> None: 📝 Remaining Steps: {result.remaining_steps} 🏁 Task Completed: {result.task_completed} ➡️ Next Step: {next_step} + 🔢 Tokens Used Total: {self._context.tokens_used} ###############################################""" ) self.log.append( @@ -94,6 +106,7 @@ def _log_reasoning(self, result: ReasoningTool) -> None: "timestamp": datetime.now().isoformat(), "step_type": "reasoning", "agent_reasoning": result.model_dump(), + "tokens_used": self._context.tokens_used, } ) @@ -105,6 +118,7 @@ def _log_tool_execution(self, tool: BaseTool, result: str): 🔧 Tool Name: {tool.tool_name} 📋 Tool Model: {tool.model_dump_json(indent=2)} 🔍 Result: '{result[:400]}...' + 🔢 Tokens Used Total: {self._context.tokens_used} ###############################################""" ) self.log.append( @@ -115,6 +129,7 @@ def _log_tool_execution(self, tool: BaseTool, result: str): "tool_name": tool.tool_name, "agent_tool_context": tool.model_dump(), "agent_tool_execution_result": result, + "tokens_used": self._context.tokens_used, } ) @@ -166,6 +181,7 @@ async def _action_phase(self, tool: BaseTool) -> str: async def execute( self, ): + self._context.tokens_used = 0 self.logger.info(f"🚀 Starting for task: '{self.task}'") self.conversation.extend( [ diff --git a/sgr_deep_research/core/models.py b/sgr_deep_research/core/models.py index 7b5d844d..2ddde527 100644 --- a/sgr_deep_research/core/models.py +++ b/sgr_deep_research/core/models.py @@ -48,6 +48,7 @@ class ResearchContext(BaseModel): current_step_reasoning: Any = None execution_result: str | None = None + tokens_used: int = Field(default=0, description="Total tokens consumed by the agent") state: AgentStatesEnum = Field(default=AgentStatesEnum.INITED, description="Current research state") iteration: int = Field(default=0, description="Current iteration number")