L4_Building_a_Multi-Document_Agent.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0b523e0a",
   "metadata": {},
   "source": [
    "# Lesson 4: Building a Multi-Document Agent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a323703",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9625ab2-71b6-4fd0-904e-42df80d3215f",
   "metadata": {
    "height": 47,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from helper import get_openai_api_key\n",
    "OPENAI_API_KEY = get_openai_api_key()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75d656cc-5e84-4b50-a8a2-186672d442ce",
   "metadata": {},
   "source": [
    "用下面这个更快"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "50bf58bd-acfe-4516-bc7a-1087a7c7ef89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import openai\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = \" \"\n",
    "openai.api_key = os.environ[\"OPENAI_API_KEY\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3221a474-5817-4db2-af46-e029042a75a5",
   "metadata": {
    "height": 47,
    "tags": []
   },
   "outputs": [],
   "source": [
    "import nest_asyncio\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20adaa26",
   "metadata": {},
   "source": [
    "## 1. Setup an agent over 3 papers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48b71ff6",
   "metadata": {},
   "source": [
    "**Note**: The pdf files are included with this lesson. To access these papers, go to the `File` menu and select`Open...`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed10a24b-d65c-4b98-a93a-94ccdb8900d0",
   "metadata": {
    "height": 215,
    "tags": []
   },
   "outputs": [],
   "source": [
    "urls = [\n",
    "    \"https://openreview.net/pdf?id=VtmBAGCN7o\",\n",
    "    \"https://openreview.net/pdf?id=6PmJoRfdaK\",\n",
    "    \"https://openreview.net/pdf?id=hSyW5go0v8\",\n",
    "]\n",
    "\n",
    "papers = [\n",
    "    \"metagpt.pdf\",\n",
    "    \"longlora.pdf\",\n",
    "    \"selfrag.pdf\",\n",
    "]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "164b42c2-d0d7-4d93-8adc-8f2458285a5a",
   "metadata": {},
   "source": [
    "有一段下载文档的代码就直接跳过了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0d8f3185-3221-4b00-bd38-41d36e4a3307",
   "metadata": {
    "height": 164,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting tools for paper: metagpt.pdf\n",
      "Getting tools for paper: longlora.pdf\n",
      "Getting tools for paper: selfrag.pdf\n"
     ]
    }
   ],
   "source": [
    "from utils3 import get_doc_tools\n",
    "from pathlib import Path\n",
    "\n",
    "paper_to_tools_dict = {}\n",
    "for paper in papers:\n",
    "    print(f\"Getting tools for paper: {paper}\")\n",
    "    vector_tool, summary_tool = get_doc_tools(paper, Path(paper).stem)\n",
    "    paper_to_tools_dict[paper] = [vector_tool, summary_tool]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0e541bdd-14e1-41b6-81b5-b1bfda078d07",
   "metadata": {
    "height": 45,
    "tags": []
   },
   "outputs": [],
   "source": [
    "initial_tools = [t for paper in papers for t in paper_to_tools_dict[paper]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bff58c52",
   "metadata": {
    "height": 79
   },
   "outputs": [],
   "source": [
    "from llama_index.llms.openai import OpenAI\n",
    "\n",
    "llm = OpenAI(model=\"gpt-3.5-turbo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85f4dcd8-7ac2-4c53-944f-8a3ab5b16446",
   "metadata": {},
   "source": [
    "为什么是6了？解释下，主要是作者想展示下LLamaindex的自主规划能力，就总结和搜索工具分别应用于3个文档，总共6个工具"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f2c6a9f",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(initial_tools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "845464bf-a05f-47d6-9d35-1ab1e4300cd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(initial_tools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c664335a-2768-4c65-ae49-bd1603451684",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x156d762b0>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x156dedc10>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x156ded310>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x157b93f10>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x156d3f850>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x156df0910>\n"
     ]
    }
   ],
   "source": [
    "for tool in initial_tools:\n",
    "    print(tool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a124a438-5609-402e-8642-69d1088cb9ad",
   "metadata": {
    "height": 181,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from llama_index.core.agent import FunctionCallingAgentWorker\n",
    "from llama_index.core.agent import AgentRunner\n",
    "\n",
    "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
    "    initial_tools, \n",
    "    llm=llm, \n",
    "    verbose=True\n",
    ")\n",
    "agent = AgentRunner(agent_worker)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a8af3a9-c66f-4545-836b-36c6b900efa0",
   "metadata": {},
   "source": [
    "单文本的RAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "17409d4c-05a9-4bf4-b74f-75135fa3cb6b",
   "metadata": {
    "height": 96,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added user message to memory: Tell me about the evaluation dataset used in LongLoRA, and then tell me about the evaluation results\n",
      "=== Calling Function ===\n",
      "Calling function: vector_tool_longlora with args: {\"query\": \"evaluation dataset\"}\n",
      "=== Function Output ===\n",
      "PG19 test split\n",
      "=== Calling Function ===\n",
      "Calling function: vector_tool_longlora with args: {\"query\": \"evaluation results\"}\n",
      "=== Function Output ===\n",
      "The evaluation results include reporting perplexity for models and baselines on proof-pile (Azerbayev et al., 2022) and PG19 datasets. The models achieve better perplexity with longer context sizes, indicating the effectiveness of the fine-tuning method. The perplexity decreases as the context size increases, with improvements observed when increasing the context window size. Additionally, the maximum context length that can be fine-tuned on a single 8 × A100 machine is examined, with Llama2 models extended to different context lengths showing promising results.\n",
      "=== LLM Response ===\n",
      "The evaluation dataset used in LongLoRA is the PG19 test split. \n",
      "\n",
      "Regarding the evaluation results, the models and baselines were evaluated based on perplexity on the proof-pile (Azerbayev et al., 2022) and PG19 datasets. The models showed better perplexity with longer context sizes, indicating the effectiveness of the fine-tuning method. Perplexity decreased as the context size increased, with improvements observed when increasing the context window size. The study also examined the maximum context length that can be fine-tuned on a single 8 × A100 machine, with Llama2 models extended to different context lengths showing promising results.\n"
     ]
    }
   ],
   "source": [
    "response = agent.query(\n",
    "    \"Tell me about the evaluation dataset used in LongLoRA, \"\n",
    "    \"and then tell me about the evaluation results\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8491b8fb-55f6-4559-a496-bb3726c01816",
   "metadata": {},
   "source": [
    "多文本的RAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ace340b1-761f-4058-be41-68cf131541e4",
   "metadata": {
    "height": 62,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added user message to memory: Give me a summary of both Self-RAG and LongLoRA\n",
      "=== Calling Function ===\n",
      "Calling function: summary_tool_selfrag with args: {\"input\": \"Self-RAG\"}\n",
      "=== Function Output ===\n",
      "Self-RAG is a framework that enhances the quality and factuality of a large language model through retrieval and self-reflection. It trains a single arbitrary language model to adaptively retrieve passages on-demand, generate and reflect on retrieved passages and its own generations using special tokens called reflection tokens. This framework enables the language model to tailor its behavior to diverse task requirements during the inference phase, leading to significant performance improvements on various tasks compared to state-of-the-art language models and retrieval-augmented models.\n",
      "=== Calling Function ===\n",
      "Calling function: summary_tool_longlora with args: {\"input\": \"LongLoRA\"}\n",
      "=== Function Output ===\n",
      "LongLoRA is an efficient method used for extending the context length of Large Language Models (LLMs) with limited computation cost. It combines shifted sparse attention (S2-Attn) with LoRA to effectively and efficiently fine-tune models to longer context lengths. LongLoRA demonstrates strong empirical results on various tasks and models, showcasing its effectiveness in extending context lengths efficiently. Additionally, it aims to minimize compromise in accuracy while saving trainable parameters and memory costs compared to full fine-tuning.\n",
      "=== LLM Response ===\n",
      "Self-RAG is a framework that enhances the quality and factuality of a large language model through retrieval and self-reflection. It trains a single arbitrary language model to adaptively retrieve passages on-demand, generate and reflect on retrieved passages and its own generations using special tokens called reflection tokens. This framework enables the language model to tailor its behavior to diverse task requirements during the inference phase, leading to significant performance improvements on various tasks compared to state-of-the-art language models and retrieval-augmented models.\n",
      "\n",
      "LongLoRA is an efficient method used for extending the context length of Large Language Models (LLMs) with limited computation cost. It combines shifted sparse attention (S2-Attn) with LoRA to effectively and efficiently fine-tune models to longer context lengths. LongLoRA demonstrates strong empirical results on various tasks and models, showcasing its effectiveness in extending context lengths efficiently. Additionally, it aims to minimize compromise in accuracy while saving trainable parameters and memory costs compared to full fine-tuning.\n",
      "assistant: Self-RAG is a framework that enhances the quality and factuality of a large language model through retrieval and self-reflection. It trains a single arbitrary language model to adaptively retrieve passages on-demand, generate and reflect on retrieved passages and its own generations using special tokens called reflection tokens. This framework enables the language model to tailor its behavior to diverse task requirements during the inference phase, leading to significant performance improvements on various tasks compared to state-of-the-art language models and retrieval-augmented models.\n",
      "\n",
      "LongLoRA is an efficient method used for extending the context length of Large Language Models (LLMs) with limited computation cost. It combines shifted sparse attention (S2-Attn) with LoRA to effectively and efficiently fine-tune models to longer context lengths. LongLoRA demonstrates strong empirical results on various tasks and models, showcasing its effectiveness in extending context lengths efficiently. Additionally, it aims to minimize compromise in accuracy while saving trainable parameters and memory costs compared to full fine-tuning.\n"
     ]
    }
   ],
   "source": [
    "response = agent.query(\"Give me a summary of both Self-RAG and LongLoRA\")\n",
    "print(str(response))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f831f3-25be-47d8-ad41-d0a6ebddc2c0",
   "metadata": {},
   "source": [
    "### 用中文版来试一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3e84cf60-7f5c-444f-b130-2995d7fbf09e",
   "metadata": {},
   "outputs": [],
   "source": [
    "papers_cn = [\n",
    "    \"ChatGPT_0_to_1.pdf\",\n",
    "    \"Data_analysis_must_know.pdf\",\n",
    "    \"Elon_Musk's_biography.pdf\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "97538845-2e5c-47fc-bd71-2cda2a545c01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting tools for paper: ChatGPT_0_to_1.pdf\n",
      "Getting tools for paper: Data_analysis_must_know.pdf\n",
      "Getting tools for paper: Elon_Musk's_biography.pdf\n"
     ]
    }
   ],
   "source": [
    "from utils3 import get_doc_tools\n",
    "from pathlib import Path\n",
    "\n",
    "paper_to_tools_dict = {}\n",
    "for paper in papers_cn:\n",
    "    print(f\"Getting tools for paper: {paper}\")\n",
    "    vector_tool, summary_tool = get_doc_tools(paper, Path(paper).stem)\n",
    "    paper_to_tools_dict[paper] = [vector_tool, summary_tool]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "949cefb5-fd03-4c09-9dd6-eb6ddefb54fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_tools = [t for paper in papers_cn for t in paper_to_tools_dict[paper]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "bafb609a-f857-45b4-850a-da94849dfda5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x16975a610>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x167bcae50>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x290630b50>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x290e66a90>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x29c506640>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x2a51e09d0>\n"
     ]
    }
   ],
   "source": [
    "for tool in initial_tools:\n",
    "    print(tool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "293d5320-1a0c-416b-a1b8-c884a64f3921",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.llms.openai import OpenAI\n",
    "\n",
    "llm_cn = OpenAI(model=\"gpt-3.5-turbo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e4e58643-94cd-4796-8c92-c80267f39c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core.agent import FunctionCallingAgentWorker\n",
    "from llama_index.core.agent import AgentRunner\n",
    "\n",
    "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
    "    initial_tools, \n",
    "    llm=llm_cn, \n",
    "    verbose=True\n",
    ")\n",
    "agent = AgentRunner(agent_worker)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c8f77e0-b173-4e2b-b4c7-8a847b1d94cb",
   "metadata": {},
   "source": [
    "看来目前并不支持中文"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "962ee8c5-e5a4-46d4-a6ad-ed3f8769c564",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added user message to memory: 给我总结下马斯克的人生信条, 并且再给我简单描述马斯克收购twitter的事件\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Retrying llama_index.llms.openai.base.OpenAI._chat in 0.295800935877943 seconds as it raised BadRequestError: Error code: 400 - {'error': {'message': \"Invalid 'tools[4].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'tools[4].function.name', 'code': 'invalid_value'}}.\n",
      "Retrying llama_index.llms.openai.base.OpenAI._chat in 0.7058150481631946 seconds as it raised BadRequestError: Error code: 400 - {'error': {'message': \"Invalid 'tools[4].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'tools[4].function.name', 'code': 'invalid_value'}}.\n",
      "Retrying llama_index.llms.openai.base.OpenAI._chat in 1.5867561643343606 seconds as it raised BadRequestError: Error code: 400 - {'error': {'message': \"Invalid 'tools[4].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'tools[4].function.name', 'code': 'invalid_value'}}.\n",
      "Retrying llama_index.llms.openai.base.OpenAI._chat in 6.795069031344213 seconds as it raised BadRequestError: Error code: 400 - {'error': {'message': \"Invalid 'tools[4].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'tools[4].function.name', 'code': 'invalid_value'}}.\n",
      "Retrying llama_index.llms.openai.base.OpenAI._chat in 2.2061985928243644 seconds as it raised BadRequestError: Error code: 400 - {'error': {'message': \"Invalid 'tools[4].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'tools[4].function.name', 'code': 'invalid_value'}}.\n"
     ]
    },
    {
     "ename": "BadRequestError",
     "evalue": "Error code: 400 - {'error': {'message': \"Invalid 'tools[4].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'tools[4].function.name', 'code': 'invalid_value'}}",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mBadRequestError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[59], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m给我总结下马斯克的人生信条, \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m并且再给我简单描述马斯克收购twitter的事件\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      4\u001b[0m \u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/instrumentation/dispatcher.py:274\u001b[0m, in \u001b[0;36mDispatcher.span.<locals>.wrapper\u001b[0;34m(func, instance, args, kwargs)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspan_enter(\n\u001b[1;32m    271\u001b[0m     id_\u001b[38;5;241m=\u001b[39mid_, bound_args\u001b[38;5;241m=\u001b[39mbound_args, instance\u001b[38;5;241m=\u001b[39minstance, parent_id\u001b[38;5;241m=\u001b[39mparent_id\n\u001b[1;32m    272\u001b[0m )\n\u001b[1;32m    273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 274\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    275\u001b[0m \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[1;32m    276\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mevent(SpanDropEvent(span_id\u001b[38;5;241m=\u001b[39mid_, err_str\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(e)))\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/base/base_query_engine.py:53\u001b[0m, in \u001b[0;36mBaseQueryEngine.query\u001b[0;34m(self, str_or_query_bundle)\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(str_or_query_bundle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m     52\u001b[0m         str_or_query_bundle \u001b[38;5;241m=\u001b[39m QueryBundle(str_or_query_bundle)\n\u001b[0;32m---> 53\u001b[0m     query_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_query\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstr_or_query_bundle\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     54\u001b[0m dispatch_event(QueryEndEvent(query\u001b[38;5;241m=\u001b[39mstr_or_query_bundle, response\u001b[38;5;241m=\u001b[39mquery_result))\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m query_result\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/callbacks/utils.py:41\u001b[0m, in \u001b[0;36mtrace_method.<locals>.decorator.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m callback_manager \u001b[38;5;241m=\u001b[39m cast(CallbackManager, callback_manager)\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m callback_manager\u001b[38;5;241m.\u001b[39mas_trace(trace_id):\n\u001b[0;32m---> 41\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/base/agent/types.py:43\u001b[0m, in \u001b[0;36mBaseAgent._query\u001b[0;34m(self, query_bundle)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;129m@trace_method\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquery\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     42\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_query\u001b[39m(\u001b[38;5;28mself\u001b[39m, query_bundle: QueryBundle) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m RESPONSE_TYPE:\n\u001b[0;32m---> 43\u001b[0m     agent_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     44\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquery_bundle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery_str\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     45\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchat_history\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     46\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m Response(\n\u001b[1;32m     48\u001b[0m         response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(agent_response), source_nodes\u001b[38;5;241m=\u001b[39magent_response\u001b[38;5;241m.\u001b[39msource_nodes\n\u001b[1;32m     49\u001b[0m     )\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/instrumentation/dispatcher.py:274\u001b[0m, in \u001b[0;36mDispatcher.span.<locals>.wrapper\u001b[0;34m(func, instance, args, kwargs)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspan_enter(\n\u001b[1;32m    271\u001b[0m     id_\u001b[38;5;241m=\u001b[39mid_, bound_args\u001b[38;5;241m=\u001b[39mbound_args, instance\u001b[38;5;241m=\u001b[39minstance, parent_id\u001b[38;5;241m=\u001b[39mparent_id\n\u001b[1;32m    272\u001b[0m )\n\u001b[1;32m    273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 274\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    275\u001b[0m \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[1;32m    276\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mevent(SpanDropEvent(span_id\u001b[38;5;241m=\u001b[39mid_, err_str\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(e)))\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/callbacks/utils.py:41\u001b[0m, in \u001b[0;36mtrace_method.<locals>.decorator.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m callback_manager \u001b[38;5;241m=\u001b[39m cast(CallbackManager, callback_manager)\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m callback_manager\u001b[38;5;241m.\u001b[39mas_trace(trace_id):\n\u001b[0;32m---> 41\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/agent/runner/base.py:644\u001b[0m, in \u001b[0;36mAgentRunner.chat\u001b[0;34m(self, message, chat_history, tool_choice)\u001b[0m\n\u001b[1;32m    639\u001b[0m     tool_choice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_tool_choice\n\u001b[1;32m    640\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_manager\u001b[38;5;241m.\u001b[39mevent(\n\u001b[1;32m    641\u001b[0m     CBEventType\u001b[38;5;241m.\u001b[39mAGENT_STEP,\n\u001b[1;32m    642\u001b[0m     payload\u001b[38;5;241m=\u001b[39m{EventPayload\u001b[38;5;241m.\u001b[39mMESSAGES: [message]},\n\u001b[1;32m    643\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 644\u001b[0m     chat_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_chat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    645\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmessage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    646\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchat_history\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchat_history\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    647\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtool_choice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    648\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mChatResponseMode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mWAIT\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    649\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    650\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(chat_response, AgentChatResponse)\n\u001b[1;32m    651\u001b[0m     e\u001b[38;5;241m.\u001b[39mon_end(payload\u001b[38;5;241m=\u001b[39m{EventPayload\u001b[38;5;241m.\u001b[39mRESPONSE: chat_response})\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/instrumentation/dispatcher.py:274\u001b[0m, in \u001b[0;36mDispatcher.span.<locals>.wrapper\u001b[0;34m(func, instance, args, kwargs)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspan_enter(\n\u001b[1;32m    271\u001b[0m     id_\u001b[38;5;241m=\u001b[39mid_, bound_args\u001b[38;5;241m=\u001b[39mbound_args, instance\u001b[38;5;241m=\u001b[39minstance, parent_id\u001b[38;5;241m=\u001b[39mparent_id\n\u001b[1;32m    272\u001b[0m )\n\u001b[1;32m    273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 274\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    275\u001b[0m \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[1;32m    276\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mevent(SpanDropEvent(span_id\u001b[38;5;241m=\u001b[39mid_, err_str\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(e)))\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/agent/runner/base.py:574\u001b[0m, in \u001b[0;36mAgentRunner._chat\u001b[0;34m(self, message, chat_history, tool_choice, mode)\u001b[0m\n\u001b[1;32m    571\u001b[0m dispatch_event(AgentChatWithStepStartEvent(user_msg\u001b[38;5;241m=\u001b[39mmessage))\n\u001b[1;32m    572\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    573\u001b[0m     \u001b[38;5;66;03m# pass step queue in as argument, assume step executor is stateless\u001b[39;00m\n\u001b[0;32m--> 574\u001b[0m     cur_step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_step\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    575\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtask\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtask_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtool_choice\u001b[49m\n\u001b[1;32m    576\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    578\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m cur_step_output\u001b[38;5;241m.\u001b[39mis_last:\n\u001b[1;32m    579\u001b[0m         result_output \u001b[38;5;241m=\u001b[39m cur_step_output\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/instrumentation/dispatcher.py:274\u001b[0m, in \u001b[0;36mDispatcher.span.<locals>.wrapper\u001b[0;34m(func, instance, args, kwargs)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspan_enter(\n\u001b[1;32m    271\u001b[0m     id_\u001b[38;5;241m=\u001b[39mid_, bound_args\u001b[38;5;241m=\u001b[39mbound_args, instance\u001b[38;5;241m=\u001b[39minstance, parent_id\u001b[38;5;241m=\u001b[39mparent_id\n\u001b[1;32m    272\u001b[0m )\n\u001b[1;32m    273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 274\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    275\u001b[0m \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[1;32m    276\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mevent(SpanDropEvent(span_id\u001b[38;5;241m=\u001b[39mid_, err_str\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(e)))\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/agent/runner/base.py:411\u001b[0m, in \u001b[0;36mAgentRunner._run_step\u001b[0;34m(self, task_id, step, input, mode, **kwargs)\u001b[0m\n\u001b[1;32m    407\u001b[0m \u001b[38;5;66;03m# TODO: figure out if you can dynamically swap in different step executors\u001b[39;00m\n\u001b[1;32m    408\u001b[0m \u001b[38;5;66;03m# not clear when you would do that by theoretically possible\u001b[39;00m\n\u001b[1;32m    410\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m ChatResponseMode\u001b[38;5;241m.\u001b[39mWAIT:\n\u001b[0;32m--> 411\u001b[0m     cur_step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magent_worker\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    412\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m ChatResponseMode\u001b[38;5;241m.\u001b[39mSTREAM:\n\u001b[1;32m    413\u001b[0m     cur_step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magent_worker\u001b[38;5;241m.\u001b[39mstream_step(step, task, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/callbacks/utils.py:41\u001b[0m, in \u001b[0;36mtrace_method.<locals>.decorator.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m callback_manager \u001b[38;5;241m=\u001b[39m cast(CallbackManager, callback_manager)\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m callback_manager\u001b[38;5;241m.\u001b[39mas_trace(trace_id):\n\u001b[0;32m---> 41\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/agent/function_calling/step.py:245\u001b[0m, in \u001b[0;36mFunctionCallingAgentWorker.run_step\u001b[0;34m(self, step, task, **kwargs)\u001b[0m\n\u001b[1;32m    242\u001b[0m tools \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_tools(task\u001b[38;5;241m.\u001b[39minput)\n\u001b[1;32m    244\u001b[0m \u001b[38;5;66;03m# get response and tool call (if exists)\u001b[39;00m\n\u001b[0;32m--> 245\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_llm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat_with_tools\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    246\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtools\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtools\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    247\u001b[0m \u001b[43m    \u001b[49m\u001b[43muser_msg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    248\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchat_history\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_all_messages\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    249\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_verbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    250\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_parallel_tool_calls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mallow_parallel_tool_calls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    251\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    252\u001b[0m tool_calls \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_llm\u001b[38;5;241m.\u001b[39mget_tool_calls_from_response(\n\u001b[1;32m    253\u001b[0m     response, error_on_no_tool_call\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    254\u001b[0m )\n\u001b[1;32m    256\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verbose \u001b[38;5;129;01mand\u001b[39;00m response\u001b[38;5;241m.\u001b[39mmessage\u001b[38;5;241m.\u001b[39mcontent:\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/llms/openai/base.py:774\u001b[0m, in \u001b[0;36mOpenAI.chat_with_tools\u001b[0;34m(self, tools, user_msg, chat_history, verbose, allow_parallel_tool_calls, tool_choice, **kwargs)\u001b[0m\n\u001b[1;32m    771\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m user_msg:\n\u001b[1;32m    772\u001b[0m     messages\u001b[38;5;241m.\u001b[39mappend(user_msg)\n\u001b[0;32m--> 774\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    775\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    776\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtools\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtool_specs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    777\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolve_tool_choice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    778\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    779\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    780\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_parallel_tool_calls:\n\u001b[1;32m    781\u001b[0m     force_single_tool_call(response)\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/llms/callbacks.py:148\u001b[0m, in \u001b[0;36mllm_chat_callback.<locals>.wrap.<locals>.wrapped_llm_chat\u001b[0;34m(_self, messages, **kwargs)\u001b[0m\n\u001b[1;32m    132\u001b[0m dispatcher\u001b[38;5;241m.\u001b[39mevent(\n\u001b[1;32m    133\u001b[0m     LLMChatStartEvent(\n\u001b[1;32m    134\u001b[0m         model_dict\u001b[38;5;241m=\u001b[39mmodel_dict,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    138\u001b[0m     )\n\u001b[1;32m    139\u001b[0m )\n\u001b[1;32m    140\u001b[0m event_id \u001b[38;5;241m=\u001b[39m callback_manager\u001b[38;5;241m.\u001b[39mon_event_start(\n\u001b[1;32m    141\u001b[0m     CBEventType\u001b[38;5;241m.\u001b[39mLLM,\n\u001b[1;32m    142\u001b[0m     payload\u001b[38;5;241m=\u001b[39m{\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    146\u001b[0m     },\n\u001b[1;32m    147\u001b[0m )\n\u001b[0;32m--> 148\u001b[0m f_return_val \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_self\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    150\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(f_return_val, Generator):\n\u001b[1;32m    151\u001b[0m     \u001b[38;5;66;03m# intercept the generator and add a callback to the end\u001b[39;00m\n\u001b[1;32m    152\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped_gen\u001b[39m() \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatResponseGen:\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/llms/openai/base.py:296\u001b[0m, in \u001b[0;36mOpenAI.chat\u001b[0;34m(self, messages, **kwargs)\u001b[0m\n\u001b[1;32m    294\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    295\u001b[0m     chat_fn \u001b[38;5;241m=\u001b[39m completion_to_chat_decorator(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_complete)\n\u001b[0;32m--> 296\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mchat_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/tenacity/__init__.py:330\u001b[0m, in \u001b[0;36mBaseRetrying.wraps.<locals>.wrapped_f\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    326\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(\n\u001b[1;32m    327\u001b[0m     f, functools\u001b[38;5;241m.\u001b[39mWRAPPER_ASSIGNMENTS \u001b[38;5;241m+\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__defaults__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__kwdefaults__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    328\u001b[0m )\n\u001b[1;32m    329\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped_f\u001b[39m(\u001b[38;5;241m*\u001b[39margs: t\u001b[38;5;241m.\u001b[39mAny, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw: t\u001b[38;5;241m.\u001b[39mAny) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m t\u001b[38;5;241m.\u001b[39mAny:\n\u001b[0;32m--> 330\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/tenacity/__init__.py:467\u001b[0m, in \u001b[0;36mRetrying.__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    465\u001b[0m retry_state \u001b[38;5;241m=\u001b[39m RetryCallState(retry_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, fn\u001b[38;5;241m=\u001b[39mfn, args\u001b[38;5;241m=\u001b[39margs, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[1;32m    466\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 467\u001b[0m     do \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mretry_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretry_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    468\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(do, DoAttempt):\n\u001b[1;32m    469\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/tenacity/__init__.py:368\u001b[0m, in \u001b[0;36mBaseRetrying.iter\u001b[0;34m(self, retry_state)\u001b[0m\n\u001b[1;32m    366\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    367\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m action \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miter_state\u001b[38;5;241m.\u001b[39mactions:\n\u001b[0;32m--> 368\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43maction\u001b[49m\u001b[43m(\u001b[49m\u001b[43mretry_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/tenacity/__init__.py:410\u001b[0m, in \u001b[0;36mBaseRetrying._post_stop_check_actions.<locals>.exc_check\u001b[0;34m(rs)\u001b[0m\n\u001b[1;32m    408\u001b[0m retry_exc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mretry_error_cls(fut)\n\u001b[1;32m    409\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreraise:\n\u001b[0;32m--> 410\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[43mretry_exc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    411\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m retry_exc \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfut\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexception\u001b[39;00m()\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/tenacity/__init__.py:183\u001b[0m, in \u001b[0;36mRetryError.reraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreraise\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m t\u001b[38;5;241m.\u001b[39mNoReturn:\n\u001b[1;32m    182\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlast_attempt\u001b[38;5;241m.\u001b[39mfailed:\n\u001b[0;32m--> 183\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlast_attempt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    184\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/concurrent/futures/_base.py:439\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    437\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m    438\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 439\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m    443\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/concurrent/futures/_base.py:391\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m    390\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 391\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m    392\u001b[0m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    393\u001b[0m         \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m    394\u001b[0m         \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/tenacity/__init__.py:470\u001b[0m, in \u001b[0;36mRetrying.__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    468\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(do, DoAttempt):\n\u001b[1;32m    469\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 470\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    471\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m:  \u001b[38;5;66;03m# noqa: B902\u001b[39;00m\n\u001b[1;32m    472\u001b[0m         retry_state\u001b[38;5;241m.\u001b[39mset_exception(sys\u001b[38;5;241m.\u001b[39mexc_info())  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/llms/openai/base.py:362\u001b[0m, in \u001b[0;36mOpenAI._chat\u001b[0;34m(self, messages, **kwargs)\u001b[0m\n\u001b[1;32m    360\u001b[0m client \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_client()\n\u001b[1;32m    361\u001b[0m message_dicts \u001b[38;5;241m=\u001b[39m to_openai_message_dicts(messages)\n\u001b[0;32m--> 362\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompletions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    363\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmessages\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmessage_dicts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    364\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    365\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_model_kwargs\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    366\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    367\u001b[0m openai_message \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mchoices[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmessage\n\u001b[1;32m    368\u001b[0m message \u001b[38;5;241m=\u001b[39m from_openai_message(openai_message)\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/openai/_utils/_utils.py:277\u001b[0m, in \u001b[0;36mrequired_args.<locals>.inner.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m             msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    276\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m--> 277\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/openai/resources/chat/completions.py:590\u001b[0m, in \u001b[0;36mCompletions.create\u001b[0;34m(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, presence_penalty, response_format, seed, stop, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    558\u001b[0m \u001b[38;5;129m@required_args\u001b[39m([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m], [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    559\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[1;32m    560\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    588\u001b[0m     timeout: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m httpx\u001b[38;5;241m.\u001b[39mTimeout \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m|\u001b[39m NotGiven \u001b[38;5;241m=\u001b[39m NOT_GIVEN,\n\u001b[1;32m    589\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatCompletion \u001b[38;5;241m|\u001b[39m Stream[ChatCompletionChunk]:\n\u001b[0;32m--> 590\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/chat/completions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    592\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    593\u001b[0m \u001b[43m            \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m    594\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmessages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    595\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    596\u001b[0m \u001b[43m                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\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    623\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    624\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/openai/_base_client.py:1240\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1226\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[1;32m   1227\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1228\u001b[0m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1235\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1236\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[1;32m   1237\u001b[0m     opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[1;32m   1238\u001b[0m         method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[1;32m   1239\u001b[0m     )\n\u001b[0;32m-> 1240\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;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[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/openai/_base_client.py:921\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    912\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrequest\u001b[39m(\n\u001b[1;32m    913\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    914\u001b[0m     cast_to: Type[ResponseT],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    919\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    920\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[0;32m--> 921\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    922\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    923\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    924\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    925\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    926\u001b[0m \u001b[43m        \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    927\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/openai/_base_client.py:1020\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1017\u001b[0m         err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m   1019\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-raising status error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1020\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1022\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[1;32m   1023\u001b[0m     cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[1;32m   1024\u001b[0m     options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1027\u001b[0m     stream_cls\u001b[38;5;241m=\u001b[39mstream_cls,\n\u001b[1;32m   1028\u001b[0m )\n",
      "\u001b[0;31mBadRequestError\u001b[0m: Error code: 400 - {'error': {'message': \"Invalid 'tools[4].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'tools[4].function.name', 'code': 'invalid_value'}}"
     ]
    }
   ],
   "source": [
    "response = agent.query(\n",
    "    \"给我总结下马斯克的人生信条, \"\n",
    "    \"并且再给我简单描述马斯克收购twitter的事件\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f38ad21d-a7a1-4e09-b0c5-45718833ace3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added user message to memory: Summarize Musk's life creed for me,And then provide me with a brief description of Musk's acquisition of Twitter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Retrying llama_index.llms.openai.base.OpenAI._chat in 0.9207053610321947 seconds as it raised BadRequestError: Error code: 400 - {'error': {'message': \"Invalid 'tools[4].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'tools[4].function.name', 'code': 'invalid_value'}}.\n",
      "Retrying llama_index.llms.openai.base.OpenAI._chat in 1.7135073996153745 seconds as it raised BadRequestError: Error code: 400 - {'error': {'message': \"Invalid 'tools[4].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'tools[4].function.name', 'code': 'invalid_value'}}.\n",
      "Retrying llama_index.llms.openai.base.OpenAI._chat in 3.631525834718196 seconds as it raised BadRequestError: Error code: 400 - {'error': {'message': \"Invalid 'tools[4].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'tools[4].function.name', 'code': 'invalid_value'}}.\n",
      "Retrying llama_index.llms.openai.base.OpenAI._chat in 5.734385400315719 seconds as it raised BadRequestError: Error code: 400 - {'error': {'message': \"Invalid 'tools[4].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'tools[4].function.name', 'code': 'invalid_value'}}.\n",
      "Retrying llama_index.llms.openai.base.OpenAI._chat in 9.810345399589794 seconds as it raised BadRequestError: Error code: 400 - {'error': {'message': \"Invalid 'tools[4].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'tools[4].function.name', 'code': 'invalid_value'}}.\n"
     ]
    },
    {
     "ename": "BadRequestError",
     "evalue": "Error code: 400 - {'error': {'message': \"Invalid 'tools[4].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'tools[4].function.name', 'code': 'invalid_value'}}",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mBadRequestError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[65], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSummarize Musk\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ms life creed for me,\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mAnd then provide me with a brief description of Musk\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ms acquisition of Twitter\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      4\u001b[0m \u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/instrumentation/dispatcher.py:274\u001b[0m, in \u001b[0;36mDispatcher.span.<locals>.wrapper\u001b[0;34m(func, instance, args, kwargs)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspan_enter(\n\u001b[1;32m    271\u001b[0m     id_\u001b[38;5;241m=\u001b[39mid_, bound_args\u001b[38;5;241m=\u001b[39mbound_args, instance\u001b[38;5;241m=\u001b[39minstance, parent_id\u001b[38;5;241m=\u001b[39mparent_id\n\u001b[1;32m    272\u001b[0m )\n\u001b[1;32m    273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 274\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    275\u001b[0m \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[1;32m    276\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mevent(SpanDropEvent(span_id\u001b[38;5;241m=\u001b[39mid_, err_str\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(e)))\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/base/base_query_engine.py:53\u001b[0m, in \u001b[0;36mBaseQueryEngine.query\u001b[0;34m(self, str_or_query_bundle)\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(str_or_query_bundle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m     52\u001b[0m         str_or_query_bundle \u001b[38;5;241m=\u001b[39m QueryBundle(str_or_query_bundle)\n\u001b[0;32m---> 53\u001b[0m     query_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_query\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstr_or_query_bundle\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     54\u001b[0m dispatch_event(QueryEndEvent(query\u001b[38;5;241m=\u001b[39mstr_or_query_bundle, response\u001b[38;5;241m=\u001b[39mquery_result))\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m query_result\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/callbacks/utils.py:41\u001b[0m, in \u001b[0;36mtrace_method.<locals>.decorator.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m callback_manager \u001b[38;5;241m=\u001b[39m cast(CallbackManager, callback_manager)\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m callback_manager\u001b[38;5;241m.\u001b[39mas_trace(trace_id):\n\u001b[0;32m---> 41\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/base/agent/types.py:43\u001b[0m, in \u001b[0;36mBaseAgent._query\u001b[0;34m(self, query_bundle)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;129m@trace_method\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquery\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     42\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_query\u001b[39m(\u001b[38;5;28mself\u001b[39m, query_bundle: QueryBundle) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m RESPONSE_TYPE:\n\u001b[0;32m---> 43\u001b[0m     agent_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     44\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquery_bundle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery_str\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     45\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchat_history\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     46\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m Response(\n\u001b[1;32m     48\u001b[0m         response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(agent_response), source_nodes\u001b[38;5;241m=\u001b[39magent_response\u001b[38;5;241m.\u001b[39msource_nodes\n\u001b[1;32m     49\u001b[0m     )\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/instrumentation/dispatcher.py:274\u001b[0m, in \u001b[0;36mDispatcher.span.<locals>.wrapper\u001b[0;34m(func, instance, args, kwargs)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspan_enter(\n\u001b[1;32m    271\u001b[0m     id_\u001b[38;5;241m=\u001b[39mid_, bound_args\u001b[38;5;241m=\u001b[39mbound_args, instance\u001b[38;5;241m=\u001b[39minstance, parent_id\u001b[38;5;241m=\u001b[39mparent_id\n\u001b[1;32m    272\u001b[0m )\n\u001b[1;32m    273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 274\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    275\u001b[0m \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[1;32m    276\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mevent(SpanDropEvent(span_id\u001b[38;5;241m=\u001b[39mid_, err_str\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(e)))\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/callbacks/utils.py:41\u001b[0m, in \u001b[0;36mtrace_method.<locals>.decorator.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m callback_manager \u001b[38;5;241m=\u001b[39m cast(CallbackManager, callback_manager)\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m callback_manager\u001b[38;5;241m.\u001b[39mas_trace(trace_id):\n\u001b[0;32m---> 41\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/agent/runner/base.py:644\u001b[0m, in \u001b[0;36mAgentRunner.chat\u001b[0;34m(self, message, chat_history, tool_choice)\u001b[0m\n\u001b[1;32m    639\u001b[0m     tool_choice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_tool_choice\n\u001b[1;32m    640\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_manager\u001b[38;5;241m.\u001b[39mevent(\n\u001b[1;32m    641\u001b[0m     CBEventType\u001b[38;5;241m.\u001b[39mAGENT_STEP,\n\u001b[1;32m    642\u001b[0m     payload\u001b[38;5;241m=\u001b[39m{EventPayload\u001b[38;5;241m.\u001b[39mMESSAGES: [message]},\n\u001b[1;32m    643\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 644\u001b[0m     chat_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_chat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    645\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmessage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    646\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchat_history\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchat_history\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    647\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtool_choice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    648\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mChatResponseMode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mWAIT\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    649\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    650\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(chat_response, AgentChatResponse)\n\u001b[1;32m    651\u001b[0m     e\u001b[38;5;241m.\u001b[39mon_end(payload\u001b[38;5;241m=\u001b[39m{EventPayload\u001b[38;5;241m.\u001b[39mRESPONSE: chat_response})\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/instrumentation/dispatcher.py:274\u001b[0m, in \u001b[0;36mDispatcher.span.<locals>.wrapper\u001b[0;34m(func, instance, args, kwargs)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspan_enter(\n\u001b[1;32m    271\u001b[0m     id_\u001b[38;5;241m=\u001b[39mid_, bound_args\u001b[38;5;241m=\u001b[39mbound_args, instance\u001b[38;5;241m=\u001b[39minstance, parent_id\u001b[38;5;241m=\u001b[39mparent_id\n\u001b[1;32m    272\u001b[0m )\n\u001b[1;32m    273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 274\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    275\u001b[0m \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[1;32m    276\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mevent(SpanDropEvent(span_id\u001b[38;5;241m=\u001b[39mid_, err_str\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(e)))\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/agent/runner/base.py:574\u001b[0m, in \u001b[0;36mAgentRunner._chat\u001b[0;34m(self, message, chat_history, tool_choice, mode)\u001b[0m\n\u001b[1;32m    571\u001b[0m dispatch_event(AgentChatWithStepStartEvent(user_msg\u001b[38;5;241m=\u001b[39mmessage))\n\u001b[1;32m    572\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    573\u001b[0m     \u001b[38;5;66;03m# pass step queue in as argument, assume step executor is stateless\u001b[39;00m\n\u001b[0;32m--> 574\u001b[0m     cur_step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_step\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    575\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtask\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtask_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtool_choice\u001b[49m\n\u001b[1;32m    576\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    578\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m cur_step_output\u001b[38;5;241m.\u001b[39mis_last:\n\u001b[1;32m    579\u001b[0m         result_output \u001b[38;5;241m=\u001b[39m cur_step_output\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/instrumentation/dispatcher.py:274\u001b[0m, in \u001b[0;36mDispatcher.span.<locals>.wrapper\u001b[0;34m(func, instance, args, kwargs)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspan_enter(\n\u001b[1;32m    271\u001b[0m     id_\u001b[38;5;241m=\u001b[39mid_, bound_args\u001b[38;5;241m=\u001b[39mbound_args, instance\u001b[38;5;241m=\u001b[39minstance, parent_id\u001b[38;5;241m=\u001b[39mparent_id\n\u001b[1;32m    272\u001b[0m )\n\u001b[1;32m    273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 274\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    275\u001b[0m \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[1;32m    276\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mevent(SpanDropEvent(span_id\u001b[38;5;241m=\u001b[39mid_, err_str\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(e)))\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/agent/runner/base.py:411\u001b[0m, in \u001b[0;36mAgentRunner._run_step\u001b[0;34m(self, task_id, step, input, mode, **kwargs)\u001b[0m\n\u001b[1;32m    407\u001b[0m \u001b[38;5;66;03m# TODO: figure out if you can dynamically swap in different step executors\u001b[39;00m\n\u001b[1;32m    408\u001b[0m \u001b[38;5;66;03m# not clear when you would do that by theoretically possible\u001b[39;00m\n\u001b[1;32m    410\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m ChatResponseMode\u001b[38;5;241m.\u001b[39mWAIT:\n\u001b[0;32m--> 411\u001b[0m     cur_step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magent_worker\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    412\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m ChatResponseMode\u001b[38;5;241m.\u001b[39mSTREAM:\n\u001b[1;32m    413\u001b[0m     cur_step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magent_worker\u001b[38;5;241m.\u001b[39mstream_step(step, task, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/callbacks/utils.py:41\u001b[0m, in \u001b[0;36mtrace_method.<locals>.decorator.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m callback_manager \u001b[38;5;241m=\u001b[39m cast(CallbackManager, callback_manager)\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m callback_manager\u001b[38;5;241m.\u001b[39mas_trace(trace_id):\n\u001b[0;32m---> 41\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/agent/function_calling/step.py:245\u001b[0m, in \u001b[0;36mFunctionCallingAgentWorker.run_step\u001b[0;34m(self, step, task, **kwargs)\u001b[0m\n\u001b[1;32m    242\u001b[0m tools \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_tools(task\u001b[38;5;241m.\u001b[39minput)\n\u001b[1;32m    244\u001b[0m \u001b[38;5;66;03m# get response and tool call (if exists)\u001b[39;00m\n\u001b[0;32m--> 245\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_llm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat_with_tools\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    246\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtools\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtools\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    247\u001b[0m \u001b[43m    \u001b[49m\u001b[43muser_msg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    248\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchat_history\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_all_messages\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    249\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_verbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    250\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_parallel_tool_calls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mallow_parallel_tool_calls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    251\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    252\u001b[0m tool_calls \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_llm\u001b[38;5;241m.\u001b[39mget_tool_calls_from_response(\n\u001b[1;32m    253\u001b[0m     response, error_on_no_tool_call\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    254\u001b[0m )\n\u001b[1;32m    256\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verbose \u001b[38;5;129;01mand\u001b[39;00m response\u001b[38;5;241m.\u001b[39mmessage\u001b[38;5;241m.\u001b[39mcontent:\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/llms/openai/base.py:774\u001b[0m, in \u001b[0;36mOpenAI.chat_with_tools\u001b[0;34m(self, tools, user_msg, chat_history, verbose, allow_parallel_tool_calls, tool_choice, **kwargs)\u001b[0m\n\u001b[1;32m    771\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m user_msg:\n\u001b[1;32m    772\u001b[0m     messages\u001b[38;5;241m.\u001b[39mappend(user_msg)\n\u001b[0;32m--> 774\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    775\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    776\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtools\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtool_specs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    777\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolve_tool_choice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    778\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    779\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    780\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_parallel_tool_calls:\n\u001b[1;32m    781\u001b[0m     force_single_tool_call(response)\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/core/llms/callbacks.py:148\u001b[0m, in \u001b[0;36mllm_chat_callback.<locals>.wrap.<locals>.wrapped_llm_chat\u001b[0;34m(_self, messages, **kwargs)\u001b[0m\n\u001b[1;32m    132\u001b[0m dispatcher\u001b[38;5;241m.\u001b[39mevent(\n\u001b[1;32m    133\u001b[0m     LLMChatStartEvent(\n\u001b[1;32m    134\u001b[0m         model_dict\u001b[38;5;241m=\u001b[39mmodel_dict,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    138\u001b[0m     )\n\u001b[1;32m    139\u001b[0m )\n\u001b[1;32m    140\u001b[0m event_id \u001b[38;5;241m=\u001b[39m callback_manager\u001b[38;5;241m.\u001b[39mon_event_start(\n\u001b[1;32m    141\u001b[0m     CBEventType\u001b[38;5;241m.\u001b[39mLLM,\n\u001b[1;32m    142\u001b[0m     payload\u001b[38;5;241m=\u001b[39m{\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    146\u001b[0m     },\n\u001b[1;32m    147\u001b[0m )\n\u001b[0;32m--> 148\u001b[0m f_return_val \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_self\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    150\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(f_return_val, Generator):\n\u001b[1;32m    151\u001b[0m     \u001b[38;5;66;03m# intercept the generator and add a callback to the end\u001b[39;00m\n\u001b[1;32m    152\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped_gen\u001b[39m() \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatResponseGen:\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/llms/openai/base.py:296\u001b[0m, in \u001b[0;36mOpenAI.chat\u001b[0;34m(self, messages, **kwargs)\u001b[0m\n\u001b[1;32m    294\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    295\u001b[0m     chat_fn \u001b[38;5;241m=\u001b[39m completion_to_chat_decorator(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_complete)\n\u001b[0;32m--> 296\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mchat_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/tenacity/__init__.py:330\u001b[0m, in \u001b[0;36mBaseRetrying.wraps.<locals>.wrapped_f\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    326\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(\n\u001b[1;32m    327\u001b[0m     f, functools\u001b[38;5;241m.\u001b[39mWRAPPER_ASSIGNMENTS \u001b[38;5;241m+\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__defaults__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__kwdefaults__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    328\u001b[0m )\n\u001b[1;32m    329\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped_f\u001b[39m(\u001b[38;5;241m*\u001b[39margs: t\u001b[38;5;241m.\u001b[39mAny, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw: t\u001b[38;5;241m.\u001b[39mAny) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m t\u001b[38;5;241m.\u001b[39mAny:\n\u001b[0;32m--> 330\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/tenacity/__init__.py:467\u001b[0m, in \u001b[0;36mRetrying.__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    465\u001b[0m retry_state \u001b[38;5;241m=\u001b[39m RetryCallState(retry_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, fn\u001b[38;5;241m=\u001b[39mfn, args\u001b[38;5;241m=\u001b[39margs, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[1;32m    466\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 467\u001b[0m     do \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mretry_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretry_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    468\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(do, DoAttempt):\n\u001b[1;32m    469\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/tenacity/__init__.py:368\u001b[0m, in \u001b[0;36mBaseRetrying.iter\u001b[0;34m(self, retry_state)\u001b[0m\n\u001b[1;32m    366\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    367\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m action \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miter_state\u001b[38;5;241m.\u001b[39mactions:\n\u001b[0;32m--> 368\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43maction\u001b[49m\u001b[43m(\u001b[49m\u001b[43mretry_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/tenacity/__init__.py:410\u001b[0m, in \u001b[0;36mBaseRetrying._post_stop_check_actions.<locals>.exc_check\u001b[0;34m(rs)\u001b[0m\n\u001b[1;32m    408\u001b[0m retry_exc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mretry_error_cls(fut)\n\u001b[1;32m    409\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreraise:\n\u001b[0;32m--> 410\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[43mretry_exc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    411\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m retry_exc \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfut\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexception\u001b[39;00m()\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/tenacity/__init__.py:183\u001b[0m, in \u001b[0;36mRetryError.reraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreraise\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m t\u001b[38;5;241m.\u001b[39mNoReturn:\n\u001b[1;32m    182\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlast_attempt\u001b[38;5;241m.\u001b[39mfailed:\n\u001b[0;32m--> 183\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlast_attempt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    184\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/concurrent/futures/_base.py:439\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    437\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m    438\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 439\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m    443\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/concurrent/futures/_base.py:391\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m    390\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 391\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m    392\u001b[0m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    393\u001b[0m         \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m    394\u001b[0m         \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/tenacity/__init__.py:470\u001b[0m, in \u001b[0;36mRetrying.__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    468\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(do, DoAttempt):\n\u001b[1;32m    469\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 470\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    471\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m:  \u001b[38;5;66;03m# noqa: B902\u001b[39;00m\n\u001b[1;32m    472\u001b[0m         retry_state\u001b[38;5;241m.\u001b[39mset_exception(sys\u001b[38;5;241m.\u001b[39mexc_info())  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/llama_index/llms/openai/base.py:362\u001b[0m, in \u001b[0;36mOpenAI._chat\u001b[0;34m(self, messages, **kwargs)\u001b[0m\n\u001b[1;32m    360\u001b[0m client \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_client()\n\u001b[1;32m    361\u001b[0m message_dicts \u001b[38;5;241m=\u001b[39m to_openai_message_dicts(messages)\n\u001b[0;32m--> 362\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompletions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    363\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmessages\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmessage_dicts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    364\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    365\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_model_kwargs\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    366\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    367\u001b[0m openai_message \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mchoices[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmessage\n\u001b[1;32m    368\u001b[0m message \u001b[38;5;241m=\u001b[39m from_openai_message(openai_message)\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/openai/_utils/_utils.py:277\u001b[0m, in \u001b[0;36mrequired_args.<locals>.inner.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m             msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    276\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m--> 277\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/openai/resources/chat/completions.py:590\u001b[0m, in \u001b[0;36mCompletions.create\u001b[0;34m(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, presence_penalty, response_format, seed, stop, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    558\u001b[0m \u001b[38;5;129m@required_args\u001b[39m([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m], [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    559\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[1;32m    560\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    588\u001b[0m     timeout: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m httpx\u001b[38;5;241m.\u001b[39mTimeout \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m|\u001b[39m NotGiven \u001b[38;5;241m=\u001b[39m NOT_GIVEN,\n\u001b[1;32m    589\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatCompletion \u001b[38;5;241m|\u001b[39m Stream[ChatCompletionChunk]:\n\u001b[0;32m--> 590\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/chat/completions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    592\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    593\u001b[0m \u001b[43m            \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m    594\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmessages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    595\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    596\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfrequency_penalty\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    597\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunction_call\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    598\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunctions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunctions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    599\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlogit_bias\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    600\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlogprobs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    601\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax_tokens\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    602\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mn\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    603\u001b[0m \u001b[43m                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\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    607\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstream\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    608\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstream_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    609\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtemperature\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    610\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtool_choice\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    611\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtools\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtools\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    612\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtop_logprobs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_logprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    613\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtop_p\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m 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\u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[1;32m    620\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    621\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mChatCompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;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;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    623\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    624\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/openai/_base_client.py:1240\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1226\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[1;32m   1227\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1228\u001b[0m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1235\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1236\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[1;32m   1237\u001b[0m     opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[1;32m   1238\u001b[0m         method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[1;32m   1239\u001b[0m     )\n\u001b[0;32m-> 1240\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;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[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/openai/_base_client.py:921\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    912\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrequest\u001b[39m(\n\u001b[1;32m    913\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    914\u001b[0m     cast_to: Type[ResponseT],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    919\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    920\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[0;32m--> 921\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    922\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    923\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    924\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    925\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    926\u001b[0m \u001b[43m        \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    927\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/new_baidu/lib/python3.9/site-packages/openai/_base_client.py:1020\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1017\u001b[0m         err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m   1019\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-raising status error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1020\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1022\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[1;32m   1023\u001b[0m     cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[1;32m   1024\u001b[0m     options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1027\u001b[0m     stream_cls\u001b[38;5;241m=\u001b[39mstream_cls,\n\u001b[1;32m   1028\u001b[0m )\n",
      "\u001b[0;31mBadRequestError\u001b[0m: Error code: 400 - {'error': {'message': \"Invalid 'tools[4].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'tools[4].function.name', 'code': 'invalid_value'}}"
     ]
    }
   ],
   "source": [
    "response = agent.query(\n",
    "    \"Summarize Musk's life creed for me,\"\n",
    "    \"And then provide me with a brief description of Musk's acquisition of Twitter\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b37445df-2fb1-4ce0-a7cd-9609b0a08cac",
   "metadata": {},
   "source": [
    "换英文的文档再试一次DALL_E_3_System_Card、Customer_Relationship_Management_Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9b667119-382e-417c-bf38-00fd997f8c23",
   "metadata": {},
   "outputs": [],
   "source": [
    "papers_cn = [\n",
    "    \"DALL_E_3_System_Card.pdf\",\n",
    "    \"Customer_Relationship_Management_Model.pdf\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ef2b1535-6058-493d-9bb3-660f1a46a1cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting tools for paper: DALL_E_3_System_Card.pdf\n",
      "Getting tools for paper: Customer_Relationship_Management_Model.pdf\n"
     ]
    }
   ],
   "source": [
    "from utils3 import get_doc_tools\n",
    "from pathlib import Path\n",
    "\n",
    "paper_to_tools_dict = {}\n",
    "for paper in papers_cn:\n",
    "    print(f\"Getting tools for paper: {paper}\")\n",
    "    vector_tool, summary_tool = get_doc_tools(paper, Path(paper).stem)\n",
    "    paper_to_tools_dict[paper] = [vector_tool, summary_tool]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ce27a1a3-9533-4485-ab08-a26957e52708",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_tools = [t for paper in papers_cn for t in paper_to_tools_dict[paper]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a421dad5-5e78-4a56-846f-533a9e3fb42c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x2a7d48700>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x2a2c9fc70>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x2a2ab11c0>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x2a2ab1550>\n"
     ]
    }
   ],
   "source": [
    "for tool in initial_tools:\n",
    "    print(tool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c78c704e-b021-4223-81c2-bc838f2444c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.llms.openai import OpenAI\n",
    "\n",
    "llm_cn = OpenAI(model=\"gpt-3.5-turbo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "de81f230-974c-48ec-9266-ab3575b1c3c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core.agent import FunctionCallingAgentWorker\n",
    "from llama_index.core.agent import AgentRunner\n",
    "\n",
    "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
    "    initial_tools, \n",
    "    llm=llm_cn, \n",
    "    verbose=True\n",
    ")\n",
    "agent = AgentRunner(agent_worker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "929df806-476e-4be9-97d6-38f250e9f8a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added user message to memory: Summarize DALL_E_3_System_Card for me,And then  me what is Future Work about DALL_E_3\n",
      "=== Calling Function ===\n",
      "Calling function: summary_tool_DALL_E_3_System_Card with args: {\"input\": \"Summarize DALL_E_3_System_Card\"}\n",
      "=== Function Output ===\n",
      "The DALL_E_3_System_Card provides detailed insights into the capabilities and considerations of the DALL ·E 3 model. It covers aspects such as improvements from early versions to launch versions, strategies to combat unsolicited racy content, addressing bias and representation issues, considerations regarding body image, risks related to dis- and misinformation, and the generation of images of public figures. The card also discusses the model's ability to generate images based on text prompts, challenges in handling requests related to public figures, CBRN risks, copyright and trademarks, artist styles, potential misuse, inaccuracies in scientific information generation, and considerations for commercial use. Additionally, it highlights the importance of addressing biases, fairness, and accountability in technology, including techniques like adversarial learning to mitigate unwanted biases in AI systems.\n",
      "=== Calling Function ===\n",
      "Calling function: vector_tool_DALL_E_3_System_Card with args: {\"query\": \"Future Work\", \"page_numbers\": [\"7\"]}\n",
      "=== Function Output ===\n",
      "Future work may involve further refining the mitigation strategies to address biases in generative models like DALL ·E 3, particularly focusing on improving the representation of diverse identities and experiences. Additionally, exploring ways to enhance the grounding of prompts to ensure more accurate and contextually appropriate image generations could be a key area for future development. Further research might also delve into the impact of automatic prompt transformations on the generated outputs and user experience, aiming to optimize the balance between mitigating biases and preserving user intent.\n",
      "=== LLM Response ===\n",
      "The future work for DALL_E_3_System_Card involves refining mitigation strategies to address biases in generative models like DALL ·E 3, with a focus on improving the representation of diverse identities and experiences. It also includes exploring ways to enhance the grounding of prompts for more accurate and contextually appropriate image generations. Further research may delve into the impact of automatic prompt transformations on generated outputs and user experience to optimize the balance between mitigating biases and preserving user intent.\n"
     ]
    }
   ],
   "source": [
    "response = agent.query(\n",
    "    \"Summarize DALL_E_3_System_Card for me,\"\n",
    "    \"And then  me what is Future Work about DALL_E_3\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d085690d-250f-4d3a-8f5e-3cf30ea3e7f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7eede70c",
   "metadata": {},
   "source": [
    "## 2. Setup an agent over 11 papers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18771e69",
   "metadata": {},
   "source": [
    "### Download 11 ICLR papers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "60d01d2c-547f-4054-b0fe-ed9b1a9cc3b5",
   "metadata": {
    "height": 487,
    "tags": []
   },
   "outputs": [],
   "source": [
    "urls = [\n",
    "    \"https://openreview.net/pdf?id=VtmBAGCN7o\",\n",
    "    \"https://openreview.net/pdf?id=6PmJoRfdaK\",\n",
    "    \"https://openreview.net/pdf?id=LzPWWPAdY4\",\n",
    "    \"https://openreview.net/pdf?id=VTF8yNQM66\",\n",
    "    \"https://openreview.net/pdf?id=hSyW5go0v8\",\n",
    "    \"https://openreview.net/pdf?id=9WD9KwssyT\",\n",
    "    \"https://openreview.net/pdf?id=yV6fD7LYkF\",\n",
    "    \"https://openreview.net/pdf?id=hnrB5YHoYu\",\n",
    "    \"https://openreview.net/pdf?id=WbWtOYIzIK\",\n",
    "    \"https://openreview.net/pdf?id=c5pwL0Soay\",\n",
    "    \"https://openreview.net/pdf?id=TpD2aG1h0D\"\n",
    "]\n",
    "\n",
    "papers = [\n",
    "    \"metagpt.pdf\",\n",
    "    \"longlora.pdf\",\n",
    "    \"loftq.pdf\",\n",
    "    \"swebench.pdf\",\n",
    "    \"selfrag.pdf\",\n",
    "    \"zipformer.pdf\",\n",
    "    \"values.pdf\",\n",
    "    \"finetune_fair_diffusion.pdf\",\n",
    "    \"knowledge_card.pdf\",\n",
    "    \"metra.pdf\",\n",
    "    \"vr_mcl.pdf\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b77426cb",
   "metadata": {
    "tags": []
   },
   "source": [
    "To download these papers, below is the needed code:\n",
    "\n",
    "\n",
    "    #for url, paper in zip(urls, papers):\n",
    "         #!wget \"{url}\" -O \"{paper}\"\n",
    "    \n",
    "    \n",
    "**Note**: The pdf files are included with this lesson. To access these papers, go to the `File` menu and select`Open...`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ea5ee34d-02ac-4537-ae20-7ef6c5767172",
   "metadata": {
    "height": 164,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting tools for paper: metagpt.pdf\n",
      "Getting tools for paper: longlora.pdf\n",
      "Getting tools for paper: loftq.pdf\n",
      "Getting tools for paper: swebench.pdf\n",
      "Getting tools for paper: selfrag.pdf\n",
      "Getting tools for paper: zipformer.pdf\n",
      "Getting tools for paper: values.pdf\n",
      "Getting tools for paper: finetune_fair_diffusion.pdf\n",
      "Getting tools for paper: knowledge_card.pdf\n",
      "Getting tools for paper: metra.pdf\n",
      "Getting tools for paper: vr_mcl.pdf\n"
     ]
    }
   ],
   "source": [
    "from utils3 import get_doc_tools\n",
    "from pathlib import Path\n",
    "\n",
    "paper_to_tools_dict = {}\n",
    "for paper in papers:\n",
    "    print(f\"Getting tools for paper: {paper}\")\n",
    "    vector_tool, summary_tool = get_doc_tools(paper, Path(paper).stem)\n",
    "    paper_to_tools_dict[paper] = [vector_tool, summary_tool]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e35d52c",
   "metadata": {},
   "source": [
    "### Extend the Agent with Tool Retrieval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "20154923-873e-4941-9a3a-4926ab5f9b8c",
   "metadata": {
    "height": 45,
    "tags": []
   },
   "outputs": [],
   "source": [
    "all_tools = [t for paper in papers for t in paper_to_tools_dict[paper]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "744192d9-52f1-455a-b88e-f2f7ccea2516",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_tools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "24eb69e0-cbca-4b95-a6ef-9f5014e5dda9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x163736fa0>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x1638cf4f0>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x157d1c970>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x157b59fa0>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x16393d0d0>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x163a77c70>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x1639ed130>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x16439b5b0>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x156d2e040>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x163955310>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x163736850>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x1639ed8e0>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x164385970>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x157cbe310>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x1643b5ca0>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x164fc1fd0>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x164fc1cd0>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x1638e8970>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x1649597c0>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x163ffa580>\n",
      "<llama_index.core.tools.function_tool.FunctionTool object at 0x1579a6550>\n",
      "<llama_index.core.tools.query_engine.QueryEngineTool object at 0x157b599d0>\n"
     ]
    }
   ],
   "source": [
    "for paper in papers:\n",
    "    for t in paper_to_tools_dict[paper]:\n",
    "        print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a38479ea-ef1f-47e8-aa38-5bd5c748db60",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "671582f9-70d7-4a8f-b813-58b2a068ca72",
   "metadata": {
    "height": 164,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# define an \"object\" index and retriever over these tools\n",
    "from llama_index.core import VectorStoreIndex\n",
    "from llama_index.core.objects import ObjectIndex\n",
    "\n",
    "obj_index = ObjectIndex.from_objects(\n",
    "    all_tools,\n",
    "    index_cls=VectorStoreIndex,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "155c25ed-23a4-4895-9b6c-68a34684cce3",
   "metadata": {},
   "source": [
    "先判断要选择谁，挑选3个工具"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c3929882-e9dc-46ca-b495-53e3ed60340e",
   "metadata": {
    "height": 45,
    "tags": []
   },
   "outputs": [],
   "source": [
    "obj_retriever = obj_index.as_retriever(similarity_top_k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ba9cfecd-fe14-4da8-b9ba-b3d485d98a03",
   "metadata": {
    "height": 79,
    "tags": []
   },
   "outputs": [],
   "source": [
    "tools = obj_retriever.retrieve(\n",
    "    \"Tell me about the eval dataset used in MetaGPT and SWE-Bench\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ed9c0c79-e267-4758-9b85-02201f3ca71f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a886dd4e-105e-4f31-91a2-620e004b726b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<llama_index.core.tools.query_engine.QueryEngineTool object at 0x1638cf4f0>, <llama_index.core.tools.query_engine.QueryEngineTool object at 0x163ffa580>, <llama_index.core.tools.query_engine.QueryEngineTool object at 0x16439b5b0>]\n"
     ]
    }
   ],
   "source": [
    "print(tools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "aa5fbf3f-1814-4f86-a5cb-dc441469c1f6",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[41], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtools\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mmetadata\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "tools[3].metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c270ffbf-69c7-48ea-a028-9ba25221cde5",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ToolMetadata(description='Useful for summarization questions related to swebench', name='summary_tool_swebench', fn_schema=<class 'llama_index.core.tools.types.DefaultToolFnSchema'>, return_direct=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tools[2].metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6723dfd5-e26d-4eee-b4aa-7aa406d57636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ToolMetadata(description='Useful for summarization questions related to metra', name='summary_tool_metra', fn_schema=<class 'llama_index.core.tools.types.DefaultToolFnSchema'>, return_direct=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tools[1].metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "16404fae-834a-46a5-a04d-ecb4c2819018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ToolMetadata(description='Useful for summarization questions related to metagpt', name='summary_tool_metagpt', fn_schema=<class 'llama_index.core.tools.types.DefaultToolFnSchema'>, return_direct=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tools[0].metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9cc0a0b6-9858-4348-9ae0-1cd4160f3fb7",
   "metadata": {
    "height": 266,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from llama_index.core.agent import FunctionCallingAgentWorker\n",
    "from llama_index.core.agent import AgentRunner\n",
    "\n",
    "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
    "    tool_retriever=obj_retriever,\n",
    "    llm=llm, \n",
    "    system_prompt=\"\"\" \\\n",
    "You are an agent designed to answer queries over a set of given papers.\n",
    "Please always use the tools provided to answer a question. Do not rely on prior knowledge.\\\n",
    "\n",
    "\"\"\",\n",
    "    verbose=True\n",
    ")\n",
    "agent = AgentRunner(agent_worker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a250cf1a-e011-4994-bcca-4e0294f20864",
   "metadata": {
    "height": 113,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added user message to memory: Tell me about the evaluation dataset used in MetaGPT and compare it against SWE-Bench\n",
      "=== Calling Function ===\n",
      "Calling function: summary_tool_metagpt with args: {\"input\": \"evaluation dataset used in MetaGPT\"}\n",
      "=== Function Output ===\n",
      "The evaluation dataset used in MetaGPT includes HumanEval, MBPP, and SoftwareDev.\n",
      "=== Calling Function ===\n",
      "Calling function: summary_tool_swebench with args: {\"input\": \"evaluation dataset used in SWE-Bench\"}\n",
      "=== Function Output ===\n",
      "The evaluation dataset used in SWE-Bench consists of task instances extracted from real GitHub issues and corresponding pull requests across 12 popular Python repositories. It includes task instructions, issue text, retrieved files and documentation, an example patch file, and a prompt for generating the patch file. The dataset is constructed by scraping pull requests from the top 100 packages of the top 5,000 most downloaded PyPI libraries in August 2023, ensuring that the PRs selected have a \"Merged\" status and introduce new tests. The finalized task instances undergo execution-based validation to ensure usability and are continuously updated to include new task instances based on PRs created after the training date of any language model used. Task instances are validated through execution-based validation to ensure successful application, installation, and running of the solution and tests on top of the codebase. The dataset includes task instances categorized based on the type of contribution, such as bug fixes, enhancements, regressions, and other types of code changes, and provides attribute distributions and statistics on patch fix rates for the models evaluated in SWE-Bench.\n",
      "=== LLM Response ===\n",
      "The evaluation dataset used in MetaGPT includes HumanEval, MBPP, and SoftwareDev. \n",
      "\n",
      "On the other hand, the evaluation dataset used in SWE-Bench consists of task instances extracted from real GitHub issues and corresponding pull requests across 12 popular Python repositories. It includes task instructions, issue text, retrieved files and documentation, an example patch file, and a prompt for generating the patch file. The dataset is constructed by scraping pull requests from the top 100 packages of the top 5,000 most downloaded PyPI libraries in August 2023, ensuring that the PRs selected have a \"Merged\" status and introduce new tests. The finalized task instances undergo execution-based validation to ensure usability and are continuously updated to include new task instances based on PRs created after the training date of any language model used. Task instances are validated through execution-based validation to ensure successful application, installation, and running of the solution and tests on top of the codebase. The dataset includes task instances categorized based on the type of contribution, such as bug fixes, enhancements, regressions, and other types of code changes, and provides attribute distributions and statistics on patch fix rates for the models evaluated in SWE-Bench.\n",
      "assistant: The evaluation dataset used in MetaGPT includes HumanEval, MBPP, and SoftwareDev. \n",
      "\n",
      "On the other hand, the evaluation dataset used in SWE-Bench consists of task instances extracted from real GitHub issues and corresponding pull requests across 12 popular Python repositories. It includes task instructions, issue text, retrieved files and documentation, an example patch file, and a prompt for generating the patch file. The dataset is constructed by scraping pull requests from the top 100 packages of the top 5,000 most downloaded PyPI libraries in August 2023, ensuring that the PRs selected have a \"Merged\" status and introduce new tests. The finalized task instances undergo execution-based validation to ensure usability and are continuously updated to include new task instances based on PRs created after the training date of any language model used. Task instances are validated through execution-based validation to ensure successful application, installation, and running of the solution and tests on top of the codebase. The dataset includes task instances categorized based on the type of contribution, such as bug fixes, enhancements, regressions, and other types of code changes, and provides attribute distributions and statistics on patch fix rates for the models evaluated in SWE-Bench.\n"
     ]
    }
   ],
   "source": [
    "response = agent.query(\n",
    "    \"Tell me about the evaluation dataset used \"\n",
    "    \"in MetaGPT and compare it against SWE-Bench\"\n",
    ")\n",
    "print(str(response))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8084c8cb-98ed-4835-aaa4-5b0c7254be6d",
   "metadata": {
    "height": 96,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added user message to memory: Compare and contrast the LoRA papers (LongLoRA, LoftQ). Analyze the approach in each paper first. \n",
      "=== Calling Function ===\n",
      "Calling function: summary_tool_longlora with args: {\"input\": \"Approach in LongLoRA\"}\n",
      "=== Function Output ===\n",
      "The approach in LongLoRA involves efficiently extending the context length of large language models (LLMs) to significantly larger sizes while maintaining minimal accuracy compromise. It introduces shifted sparse attention (S2-Attn) during fine-tuning, where attention is split into groups and conducted individually within each group, allowing for training models with longer context lengths. LongLoRA saves trainable parameters and memory costs compared to full fine-tuning by using a low-rank decomposition method for weight updates. Additionally, LongLoRA emphasizes the importance of learnable embedding and normalization layers for effective and efficient fine-tuning of LLMs to longer context lengths.\n",
      "=== Calling Function ===\n",
      "Calling function: summary_tool_loftq with args: {\"input\": \"Approach in LoftQ\"}\n",
      "=== Function Output ===\n",
      "The approach in LoftQ involves integrating quantization and low-rank approximation techniques to approximate the original high-precision pre-trained weights. This integrated framework aims to provide a beneficial initialization for subsequent LoRA fine-tuning, enhancing alignment with the original weights and improving generalization in downstream tasks. The process includes quantizing the pre-trained weight matrix, applying Singular Value Decomposition (SVD) for low-rank approximation, freezing the integer weight matrix during LoRA fine-tuning, and optimizing the low-rank adapters. LoftQ excels in low-bit quantization scenarios and includes compression ratio calculations, GPU memory cost measurements, execution time evaluations, and dataset statistics for tasks like single-sentence classification, similarity tasks, and natural language inference. Additionally, LoftQ utilizes quantization techniques such as uniform quantization and NF4 quantization, along with iterations and hyperparameters tailored to different tasks and datasets.\n",
      "=== LLM Response ===\n",
      "The approach in LongLoRA focuses on extending the context length of large language models (LLMs) efficiently to significantly larger sizes while maintaining minimal accuracy compromise. It introduces shifted sparse attention (S2-Attn) during fine-tuning, where attention is split into groups and conducted individually within each group to train models with longer context lengths. LongLoRA saves trainable parameters and memory costs by using a low-rank decomposition method for weight updates. It also emphasizes the importance of learnable embedding and normalization layers for effective and efficient fine-tuning of LLMs to longer context lengths.\n",
      "\n",
      "On the other hand, LoftQ integrates quantization and low-rank approximation techniques to approximate the original high-precision pre-trained weights. This integrated framework aims to provide a beneficial initialization for subsequent LoRA fine-tuning, enhancing alignment with the original weights and improving generalization in downstream tasks. The process includes quantizing the pre-trained weight matrix, applying Singular Value Decomposition (SVD) for low-rank approximation, freezing the integer weight matrix during LoRA fine-tuning, and optimizing the low-rank adapters. LoftQ excels in low-bit quantization scenarios and includes compression ratio calculations, GPU memory cost measurements, execution time evaluations, and dataset statistics for tasks like single-sentence classification, similarity tasks, and natural language inference. Additionally, LoftQ utilizes quantization techniques such as uniform quantization and NF4 quantization, along with iterations and hyperparameters tailored to different tasks and datasets.\n"
     ]
    }
   ],
   "source": [
    "response = agent.query(\n",
    "    \"Compare and contrast the LoRA papers (LongLoRA, LoftQ). \"\n",
    "    \"Analyze the approach in each paper first. \"\n",
    ")"
   ]
  },
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   "id": "b415da16-03bf-4999-ae96-3ac1c721627a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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