{ "cells": [ { "cell_type": "code", "execution_count": 14, "id": "606a0000", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# from typing import Iterator\n", "from agno.agent import Agent, RunResponse,RunResponseEvent\n", "from typing import Iterator\n", "# from agno.models.openai import OpenAIChat\n", "from agno.utils.pprint import pprint_run_response\n", "\n", "# agent = Agent(model=OpenAIChat(id=\"gpt-4o-mini\"))\n", "\n", "# # Run agent and return the response as a variable\n", "# response: RunResponse = agent.run(\"Tell me a 5 second short story about a robot\")\n", "\n", "# # Print the response in markdown format\n", "# pprint_run_response(response, markdown=True)\n", "\n", "from agno.agent import Agent\n", "from agno.models.openai import OpenAIChat, OpenAILike\n", "from agno.tools.reasoning import ReasoningTools\n", "from agno.tools.yfinance import YFinanceTools\n", "import os\n", "from textwrap import dedent\n", "import dotenv\n", "\n", "dotenv.load_dotenv()" ] }, { "cell_type": "code", "execution_count": 9, "id": "d026bfb6", "metadata": {}, "outputs": [], "source": [ "model = OpenAILike(\n", " id=\"qwen3-32b\",\n", " api_key=os.getenv(\"BAILIAN_API_KEY\"),\n", " base_url=os.getenv(\"BAILIAN_API_BASE_URL\"),\n", " request_params={\"extra_body\": {\"enable_thinking\": False}},\n", ")" ] }, { "cell_type": "code", "execution_count": 18, "id": "885e3313", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c026f81db6784970a50844f45cda0493", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from agno.utils.log import debug_level\n", "\n", "\n", "def get_news(description: str):\n", " print(f\"get_news: {description}\", )\n", " return \"No news\"\n", "\n", "agent = Agent(\n", " model=model,\n", " tool_choice=\"auto\",\n", " # tools=[YFinanceTools(stock_price=True)],\n", " tools= [get_news],\n", " # instructions=\"Use tables to display data. Don't include any other text.\",\n", " # instructions=dedent(\"\"\"\\\n", " # You are a seasoned Wall Street analyst with deep expertise in market analysis! 📊\n", "\n", " # Follow these steps for comprehensive financial analysis:\n", " # 1. Market Overview\n", " # - Latest stock price\n", " # - 52-week high and low\n", " # 2. Financial Deep Dive\n", " # - Key metrics (P/E, Market Cap, EPS)\n", " # 3. Professional Insights\n", " # - Analyst recommendations breakdown\n", " # - Recent rating changes\n", "\n", " # 4. Market Context\n", " # - Industry trends and positioning\n", " # - Competitive analysis\n", " # - Market sentiment indicators\n", "\n", " # Your reporting style:\n", " # - Begin with an executive summary\n", " # - Use tables for data presentation\n", " # - Include clear section headers\n", " # - Add emoji indicators for trends (📈 📉)\n", " # - Highlight key insights with bullet points\n", " # - Compare metrics to industry averages\n", " # - Include technical term explanations\n", " # - End with a forward-looking analysis\n", "\n", " # Risk Disclosure:\n", " # - Always highlight potential risk factors\n", " # - Note market uncertainties\n", " # - Mention relevant regulatory concerns\n", " # \"\"\"),\n", " add_datetime_to_instructions=True,\n", " show_tool_calls=True,\n", " # markdown=True,\n", " # markdown=True,\n", " # show_tool_calls=True,\n", " # debug_mode=True, debug_level=2\n", ")\n", "\n", "\n", "pprint_run_response(agent.run(\n", " \"Tell me a 5 second short story about a lion\",\n", " stream=True, show_message=True\n", " ))" ] }, { "cell_type": "code", "execution_count": null, "id": "60497530", "metadata": {}, "outputs": [], "source": [ "\n", "\n", "# agent.print_response(\"What is the stock price of Apple?\", stream=True)\n", "# agent.print_response(\"What is the stock price of Apple?\", stream=True)\n", "# Run agent and return the response as a variable\n", "response: RunResponse = agent.run(\"Tell me a 5 second short story about a robot\")\n", "\n", "# Print the response in markdown format\n", "pprint_run_response(response, markdown=True)" ] }, { "cell_type": "code", "execution_count": 15, "id": "f77d6ce0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "DEBUG ****** Agent ID: 381fc96f-9733-4c24-a9d3-2ffeb63fea9d ****** \n", "\n" ], "text/plain": [ "\u001b[32mDEBUG\u001b[0m ****** Agent ID: \u001b[93m381fc96f-9733-4c24-a9d3-2ffeb63fea9d\u001b[0m ****** \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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