ch07_annual_reporter_gemini.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0m006lg2aBlo"
      },
      "source": [
        "# CH-07 年報問答機器人"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aDeujD32ClKr"
      },
      "source": [
        "## 7-2 取得年報 (需要開T4 GPU)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r9LdYpop4TP4"
      },
      "source": [
        "### 1️⃣  匯入套件"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "DMJyrZh6Z89A"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "from bs4 import BeautifulSoup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T05v3t_u4gxF"
      },
      "source": [
        "### 2️⃣ 建立函式-取得年報"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "RswezCvohjXv"
      },
      "outputs": [],
      "source": [
        "def annual_report(id,y):\n",
        "  url = 'https://doc.twse.com.tw/server-java/t57sb01'\n",
        "  # 建立 POST 請求的表單\n",
        "  data = {\n",
        "      \"id\":\"\",\n",
        "      \"key\":\"\",\n",
        "      \"step\":\"1\",\n",
        "      \"co_id\":id,\n",
        "      \"year\":y,\n",
        "      \"seamon\":\"\",\n",
        "      \"mtype\":'F',\n",
        "      \"dtype\":'F04'\n",
        "  }\n",
        "  try:\n",
        "    # 發送 POST 請求\n",
        "    response = requests.post(url, data=data)\n",
        "    # 取得回應後擷取檔案名稱\n",
        "    link=BeautifulSoup(response.text, 'html.parser')\n",
        "    link1=link.find('a').text\n",
        "    print(link1)\n",
        "  except Exception as e:\n",
        "    print(f\"發生{e}錯誤\")\n",
        "  # 建立第二個 POST 請求的表單\n",
        "  data2 = {\n",
        "      'step':'9',\n",
        "      'kind':'F',\n",
        "      'co_id':id,\n",
        "      'filename':link1 # 檔案名稱\n",
        "  }\n",
        "  try:\n",
        "    # 發送 POST 請求\n",
        "    response = requests.post(url, data=data2)\n",
        "    link=BeautifulSoup(response.text, 'html.parser')\n",
        "    link1=link.find('a')\n",
        "    # 取得 PDF 連結\n",
        "    link2 = link1.get('href')\n",
        "    print(link2)\n",
        "  except Exception as e:\n",
        "    print(f\"發生{e}錯誤\")\n",
        "  # 發送 GET 請求\n",
        "  try:\n",
        "    response = requests.get('https://doc.twse.com.tw' + link2)\n",
        "    # 取得 PDF 資料\n",
        "    with open(y + '_' + id + '.pdf', 'wb') as file:\n",
        "        file.write(response.content)\n",
        "    print('OK')\n",
        "  except Exception as e:\n",
        "    print(f\"發生{e}錯誤\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3e2fZDvP4j7U"
      },
      "source": [
        "### 3️⃣ 呼叫函式"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IhWwMVnSNnCW",
        "outputId": "d6df2172-704f-43bf-fd1e-2a8eb11666f6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023_2330_20240604F04.pdf\n",
            "/pdf/2023_2330_20240604F04_20250216_145539.pdf\n",
            "OK\n"
          ]
        }
      ],
      "source": [
        "annual_report('2330','113')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8llLs86CFIwr"
      },
      "source": [
        "## 7-3 年報問答"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "langchain 以更新至新版寫法，舊版可參考：\n",
        "https://colab.research.google.com/drive/16x0mUitJjH0PZx7kk2Kew2MSlkB9G_lA"
      ],
      "metadata": {
        "id": "YuNOeoW0t5Mm"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eNeEBVGe4nm-"
      },
      "source": [
        "###4️⃣ 安裝相關套件"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kUn2DtL56u-K",
        "outputId": "b3fc0ff6-2c3d-4dd2-dcef-f54d21d547df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Requirement already satisfied: langsmith<0.4,>=0.1.125 in /usr/local/lib/python3.11/dist-packages (from langchain-core<0.4.0,>=0.3.27->langchain-google-genai) (0.3.8)\n",
            "Requirement already satisfied: tenacity!=8.4.0,<10.0.0,>=8.1.0 in /usr/local/lib/python3.11/dist-packages (from langchain-core<0.4.0,>=0.3.27->langchain-google-genai) (9.0.0)\n",
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            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.11/dist-packages (from langchain-core<0.4.0,>=0.3.27->langchain-google-genai) (6.0.2)\n",
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            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.11/dist-packages (from google-auth>=2.15.0->google-generativeai<0.9.0,>=0.8.0->langchain-google-genai) (4.9)\n",
            "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.11/dist-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.4.0,>=0.3.27->langchain-google-genai) (3.0.0)\n",
            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain-core<0.4.0,>=0.3.27->langchain-google-genai) (0.28.1)\n",
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            "Requirement already satisfied: zstandard<0.24.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain-core<0.4.0,>=0.3.27->langchain-google-genai) (0.23.0)\n",
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            "Requirement already satisfied: anyio in /usr/local/lib/python3.11/dist-packages (from httpx<1,>=0.23.0->langsmith<0.4,>=0.1.125->langchain-core<0.4.0,>=0.3.27->langchain-google-genai) (3.7.1)\n",
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            "Requirement already satisfied: idna in /usr/local/lib/python3.11/dist-packages (from httpx<1,>=0.23.0->langsmith<0.4,>=0.1.125->langchain-core<0.4.0,>=0.3.27->langchain-google-genai) (3.10)\n",
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            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth>=2.15.0->google-generativeai<0.9.0,>=0.8.0->langchain-google-genai) (0.6.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core->google-generativeai<0.9.0,>=0.8.0->langchain-google-genai) (3.4.1)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core->google-generativeai<0.9.0,>=0.8.0->langchain-google-genai) (2.3.0)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.11/dist-packages (from anyio->httpx<1,>=0.23.0->langsmith<0.4,>=0.1.125->langchain-core<0.4.0,>=0.3.27->langchain-google-genai) (1.3.1)\n",
            "Downloading langchain_google_genai-2.0.9-py3-none-any.whl (41 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.7/41.7 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading filetype-1.2.0-py2.py3-none-any.whl (19 kB)\n",
            "Installing collected packages: filetype, langchain-google-genai\n",
            "Successfully installed filetype-1.2.0 langchain-google-genai-2.0.9\n"
          ]
        }
      ],
      "source": [
        "!pip install pydantic\n",
        "!pip install pdfplumber langchain langchain_community\n",
        "!pip install langchain-google-genai"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VceyfBhx4qRW"
      },
      "source": [
        "###  5️⃣ 匯入相關套件"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "G4fv5tYU60ux"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import getpass\n",
        "from langchain_community.document_loaders import PDFPlumberLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain_community.vectorstores import InMemoryVectorStore\n",
        "from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OC-KWjg54sh2"
      },
      "source": [
        "### 6️⃣ 設定環境變數和建立 Google Gemini 模型"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "UNjmx9zCGEV9",
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "from google.colab import userdata\n",
        "GEMINI_API_KEY = userdata.get('GEMINI_API_KEY')\n",
        "\n",
        "llm_model = ChatGoogleGenerativeAI(\n",
        "    api_key = GEMINI_API_KEY,\n",
        "    model=\"gemini-2.0-flash\",\n",
        "    temperature=0,\n",
        "    max_tokens=None,\n",
        "    timeout=None,\n",
        "    max_retries=2,\n",
        "    # other params...\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q4axN95S4u2F"
      },
      "source": [
        "### 7️⃣ 建立函式-建立向量資料庫"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "vpsrgVqTPsNn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "outputId": "e11196ee-f78b-46dd-e417-de13436e7c3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain-huggingface\n",
            "  Downloading langchain_huggingface-0.1.2-py3-none-any.whl.metadata (1.3 kB)\n",
            "Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from langchain-huggingface) (0.28.1)\n",
            "Requirement already satisfied: langchain-core<0.4.0,>=0.3.15 in /usr/local/lib/python3.11/dist-packages (from langchain-huggingface) (0.3.35)\n",
            "Requirement already satisfied: sentence-transformers>=2.6.0 in /usr/local/lib/python3.11/dist-packages (from langchain-huggingface) (3.4.1)\n",
            "Requirement already satisfied: tokenizers>=0.19.1 in /usr/local/lib/python3.11/dist-packages (from langchain-huggingface) (0.21.0)\n",
            "Requirement already satisfied: transformers>=4.39.0 in /usr/local/lib/python3.11/dist-packages (from langchain-huggingface) (4.48.3)\n",
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            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (6.0.2)\n",
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            "\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, langchain-huggingface\n",
            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-curand-cu12\n",
            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
            "  Attempting uninstall: nvidia-cufft-cu12\n",
            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cublas-cu12\n",
            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
            "  Attempting uninstall: nvidia-cusparse-cu12\n",
            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
            "  Attempting uninstall: nvidia-cudnn-cu12\n",
            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
            "  Attempting uninstall: nvidia-cusolver-cu12\n",
            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
            "Successfully installed langchain-huggingface-0.1.2 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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              "version_major": 2,
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            }
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          "metadata": {}
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              "version_major": 2,
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            }
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          "metadata": {}
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "0395fb45e8fd442ca06e1965c3ffb53d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6122ca1267fa4f569d2c81aebbfa87fc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a3e8fbf02fae4e53aff288d146bbec0b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d6d021894e04d67ae07163b09be1524"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/366 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c1f4e68db31e4eccb69d6ae963915dce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3221e8a5f19f4471bd2153d6915c0a9e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d7c07143aee24fc4aaae7a9090765f4a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "05913f81497e4b62854fe40dd310b0f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling%2Fconfig.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "843dc61648a54799a38bc7348ffa6a13"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install langchain-huggingface\n",
        "from langchain_huggingface.embeddings import HuggingFaceEmbeddings\n",
        "embeddings = HuggingFaceEmbeddings(model_name=\"BAAI/bge-base-en-v1.5\",encode_kwargs={\"normalize_embeddings\": True})\n",
        "\n",
        "#embeddings = GoogleGenerativeAIEmbeddings(model=\"models/embedding-001\")\n",
        "\n",
        "def build_vector_db(file_path, size, overlap):\n",
        "    loader = PDFPlumberLoader(file_path)\n",
        "    doc = loader.load()\n",
        "    text_splitter = RecursiveCharacterTextSplitter(\n",
        "        chunk_size=size,\n",
        "        chunk_overlap=overlap)\n",
        "    docs = text_splitter.split_documents(doc)\n",
        "    db = InMemoryVectorStore.from_documents(docs, embeddings)\n",
        "    return db"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkM4Wymz4xld"
      },
      "source": [
        "### 8️⃣  呼叫函式"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Q4RxY2097irW"
      },
      "outputs": [],
      "source": [
        "db = build_vector_db('/content/113_2330.pdf', 500, 50)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T8WOOFCp4z0l"
      },
      "source": [
        "### 9️⃣ 查詢相關資料"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "iRF0tJ61U5hg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa88771a-529a-4008-b9c0-599e8bf9f100"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "其他非流動負債 - 165,188,432 6,303,135 2,908,666 174,400,233\n",
            "376,088,869 266,556,090 201,444,016 246,651,391 1,090,740,366\n",
            "（接次頁）\n",
            "- 182 -\n",
            "- 182 -\n",
            "_________\n",
            "(\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "積公餘盈別特\n",
            ") 581,432,582\n",
            "(\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            ") 581,432,582\n",
            "(\n",
            ") 581,432,582\n",
            "(\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "利股金現\n",
            ") 581,432,582\n",
            "(\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            ") 581,432,582\n",
            "(\n",
            ") 382,480,922\n",
            "(\n",
            ") 209,941,65\n",
            "(\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "計合配分餘盈\n",
            "942,035,610,1\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "942,035,610,1\n",
            "942,035,610,1\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "利淨度年本\n",
            "-\n",
            "146,495,24\n",
            "-\n",
            "312,446,24\n",
            "-\n",
            "475,114,1\n",
            ") 124,723,01\n",
            "(\n",
            "060,065,15\n",
            ") 275,94\n",
            "(\n",
            ") 275,94\n",
            "(\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "益損合綜他其後稅度年本\n",
            "098,421,950,1\n",
            "-\n",
            "312,446,24\n",
            "-\n",
            "475,114,1\n",
            ") 124,723,01\n",
            "(\n",
            "060,065,15\n",
            "776,084,610,1\n",
            "776,084,610,1\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "額總益損合綜度年本\n",
            "647,662\n",
            "-\n",
            ") 351,581\n",
            "(\n",
            "_________\n",
            "不動產、廠房及設備減損損失 - 790,740\n",
            "外幣兌換淨損 183,093 9,965,603\n",
            "股利收入 ( 214,911 ) ( 207,028 )\n",
            "其 他 ( 317,394 ) 131,637\n",
            "與營業活動相關之資產／負債淨變動數\n",
            "透過損益按公允價值衡量之金融工具 ( 24,326 ) ( 1,025,979 )\n",
            "應收票據及帳款淨額 7,754,557 4,588,461\n",
            "應收關係人款項 17,782,935 ( 34,692,438 )\n",
            "其他應收關係人款項 2,115,413 ( 1,074,087 )\n",
            "存 貨 ( 29,976,300 ) ( 23,123,047 )\n",
            "其他金融資產 ( 1,019,979 ) 1,894,328\n",
            "其他流動資產 ( 7,799,552 ) ( 712,233 )\n",
            "其他非流動資產 ( 720,278 ) ( 8,532,751 )\n",
            "應付帳款 ( 1,089,049 ) 7,528,120\n",
            "應付關係人款項 67,281 2,362,846\n",
            "應付薪資及獎金 ( 3,553,878 ) 10,494,186\n",
            "_________\n",
            "其他非流動資產 ( 720,278 ) -\n",
            "應付帳款 847,049 7,594,105\n",
            "應付關係人款項 ( 76,337 ) 205,451\n",
            "應付薪資及獎金 ( 3,234,946 ) 12,633,409\n",
            "應付員工酬勞及董事酬勞 ( 11,031,630 ) 25,223,833\n",
            "應付費用及其他流動負債 ( 44,466,734 ) 46,578,784\n",
            "其他非流動負債 13,329,895 101,390,476\n",
            "淨確定福利負債 ( 687,223 ) ( 2,538,848 )\n",
            "營運產生之現金 1,401,842,412 1,697,160,435\n",
            "支付所得稅 ( 159,875,065 ) ( 86,561,247 )\n",
            "營業活動之淨現金流入 1,241,967,347 1,610,599,188\n",
            "（接次頁）\n",
            "- 13 -\n",
            "- 13 -\n",
            "-\n",
            "21\n",
            "-\n",
            "_________\n",
            "-\n",
            "475,114,1\n",
            ") 124,723,01\n",
            "(\n",
            "060,065,15\n",
            ") 275,94\n",
            "(\n",
            ") 275,94\n",
            "(\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "益損合綜他其後稅度年本\n",
            "086,033,950,1\n",
            "097,502\n",
            "098,421,950,1\n",
            "-\n",
            "312,446,24\n",
            "-\n",
            "475,114,1\n",
            ") 124,723,01\n",
            "(\n",
            "060,065,15\n",
            "776,084,610,1\n",
            "776,084,610,1\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "額總益損合綜度年本\n",
            "647,662\n",
            "-\n",
            "647,662\n",
            "-\n",
            ") 351,581\n",
            "(\n",
            ") 351,581\n",
            "(\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "920,834\n",
            "078,31\n",
            "783,1\n",
            "易交付給礎基份股\n",
            ") 665,178\n",
            "(\n",
            "-\n",
            ") 665,178\n",
            "(\n",
            ") 665,178\n",
            "(\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "回買票股藏庫\n",
            "-\n",
            "-\n",
            "-\n",
            "665,178\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            ") 707,458\n",
            "(\n",
            ") 707,458\n",
            "(\n",
            "-\n",
            "-\n",
            ") 989,2\n",
            "(\n",
            ") 078,31\n",
            "(\n",
            ") 783,1\n",
            "(\n",
            "銷註票股藏庫\n",
            "量衡值價允公按益損合綜他其過透分處\n",
            "-\n",
            "-\n",
            "-\n",
            "-\n",
            ") 242,303\n",
            "_________\n"
          ]
        }
      ],
      "source": [
        "similarity_docs = db.similarity_search(\"研發的產品\", k=5)\n",
        "for i in similarity_docs:\n",
        "    print(i.page_content)\n",
        "    print('_________')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VgpzZOij438G"
      },
      "source": [
        "### 🔟  匯入問答相關套件"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "cnsilIt1JmDs"
      },
      "outputs": [],
      "source": [
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "from langchain.chains import RetrievalQA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k9a6x7na46RM"
      },
      "source": [
        "### 1️⃣1️⃣  建立函式-問答程式"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "idLyQ6lVFMUD"
      },
      "outputs": [],
      "source": [
        "# 提示模板\n",
        "prompt = ChatPromptTemplate.from_messages([\n",
        "    (\"system\",\n",
        "     \"你是一個根據年報資料與上下文作回答的助手,\"\n",
        "     \"如果有明確數據或技術(產品)名稱可以用數據或名稱回答,\"\n",
        "     \"回答以繁體中文為主。\"\n",
        "     \"{context}\"),\n",
        "    (\"human\",\"{question}\")])\n",
        "\n",
        "# 建立問答函式\n",
        "def question_and_answer(question):\n",
        "    retrievalQA = RetrievalQA.from_llm(\n",
        "        llm=llm_model,\n",
        "        prompt=prompt,\n",
        "        return_source_documents=True,\n",
        "        retriever=db.as_retriever(\n",
        "        search_kwargs={'k':8}))\n",
        "    answer = retrievalQA.invoke(question)\n",
        "    return answer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e4UM2Kl148uW"
      },
      "source": [
        "### 1️⃣2️⃣ 建立迴圈進行問答"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Xyejx1Ic8qQk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "8b20a16b-8e5b-41a6-e290-a229eeab6224"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "輸入問題:請說明該公司現金流情況\n",
            "根據提供的部分年報資訊，台灣積體電路製造股份有限公司（台積電）的現金流量情況如下：\n",
            "\n",
            "*   **投資活動之淨現金流出：** (588,128,653)\n",
            "*   **籌資活動之現金流量：**\n",
            "    *   發行公司債：85,700,000\n",
            "    *   償還公司債：(18,100,000)\n",
            "    *   支付現金股利：(291,721,852)\n",
            "    *   取得子公司部分權益價款：(326,167,994)\n",
            "    *   處分子公司部分權益價款：244,376\n",
            "\n",
            "請注意，以上僅為部分現金流量項目，並非完整的現金流量表。\n",
            "_________\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "Interrupted by user",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-d81be3df0430>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mquestion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"輸入問題:\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mquestion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquestion_and_answer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquestion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    849\u001b[0m                 \u001b[0;34m\"raw_input was called, but this frontend does not support input requests.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m             )\n\u001b[0;32m--> 851\u001b[0;31m         return self._input_request(str(prompt),\n\u001b[0m\u001b[1;32m    852\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    853\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    896\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid Message:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
          ]
        }
      ],
      "source": [
        "while True:\n",
        "    question = input(\"輸入問題:\")\n",
        "    if not question.strip():\n",
        "        break\n",
        "    result = question_and_answer(question)\n",
        "    print(result['result'])\n",
        "    print('_________')\n",
        "    #print(result[\"source_documents\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uzAmbDDYFDkE"
      },
      "source": [
        "## 7-4 年報總結與分析"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4I10Pe8Y4-5F"
      },
      "source": [
        "### 1️⃣3️⃣ 回答結果及原始資料"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "o65edAvKXUH5"
      },
      "outputs": [],
      "source": [
        "from langchain.chains.summarize import load_summarize_chain"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zr2Wz0MG5Avz"
      },
      "source": [
        "### 1️⃣4️⃣ 總結原始資料"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 建立關鍵字串列\n",
        "key_word = ['正在開發的產品及銷售狀況',\n",
        "            '市場策略的調整或變化',\n",
        "            '公司預期未來展望',\n",
        "            '總營收、稅前淨利的成長或變動分析',\n",
        "            '國際競爭以及海外市場銷售情形']\n",
        "\n",
        "data_list = []\n",
        "for word in key_word:\n",
        "    data = db.similarity_search(word, k=3)\n",
        "    # 整合 Document 串列\n",
        "    data_list += data\n",
        "\n",
        "# 建立提示訊息串列\n",
        "prompt_template = [(\"system\",\"你的任務是對年報資訊進行摘要總結。\"\n",
        "                    \"以下為提供的年報資訊：{text},\"\n",
        "                    \"請給我重點數據, 如銷售增長情形、營收變化、開發項目等,\"\n",
        "                    \"最後請使用繁體中文輸出報告\")]\n",
        "prompt = ChatPromptTemplate.from_messages(messages=prompt_template)"
      ],
      "metadata": {
        "id": "37griuhHP0im"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "220bNY7y5Cs2"
      },
      "source": [
        "### 1️⃣5️⃣  呼叫函式"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "refine_chain = load_summarize_chain(llm=llm_model,chain_type='stuff',prompt=prompt)\n",
        "summary_refine = refine_chain.invoke({\"input_documents\": data_list})\n",
        "print(summary_refine['output_text'])"
      ],
      "metadata": {
        "id": "l2XTY8L7Ppnc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "7c55ea23-fcae-4311-a196-24663dafe387"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ChatGoogleGenerativeAIError",
          "evalue": "Invalid argument provided to Gemini: 400 * GenerateContentRequest.contents: contents is not specified\n",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mInvalidArgument\u001b[0m                           Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain_google_genai/chat_models.py\u001b[0m in \u001b[0;36m_chat_with_retry\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mgeneration_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m         \u001b[0;31m# Do not retry for these errors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/ai/generativelanguage_v1beta/services/generative_service/client.py\u001b[0m in \u001b[0;36mgenerate_content\u001b[0;34m(self, request, model, contents, retry, timeout, metadata)\u001b[0m\n\u001b[1;32m    834\u001b[0m         \u001b[0;31m# Send the request.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 835\u001b[0;31m         response = rpc(\n\u001b[0m\u001b[1;32m    836\u001b[0m             \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/api_core/gapic_v1/method.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, timeout, retry, compression, *args, **kwargs)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/api_core/retry/retry_unary.py\u001b[0m in \u001b[0;36mretry_wrapped_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    292\u001b[0m             )\n\u001b[0;32m--> 293\u001b[0;31m             return retry_target(\n\u001b[0m\u001b[1;32m    294\u001b[0m                 \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/api_core/retry/retry_unary.py\u001b[0m in \u001b[0;36mretry_target\u001b[0;34m(target, predicate, sleep_generator, timeout, on_error, exception_factory, **kwargs)\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0;31m# defer to shared logic for handling errors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m             _retry_error_helper(\n\u001b[0m\u001b[1;32m    154\u001b[0m                 \u001b[0mexc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/api_core/retry/retry_base.py\u001b[0m in \u001b[0;36m_retry_error_helper\u001b[0;34m(exc, deadline, next_sleep, error_list, predicate_fn, on_error_fn, exc_factory_fn, original_timeout)\u001b[0m\n\u001b[1;32m    211\u001b[0m         )\n\u001b[0;32m--> 212\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mfinal_exc\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msource_exc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    213\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mon_error_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/api_core/retry/retry_unary.py\u001b[0m in \u001b[0;36mretry_target\u001b[0;34m(target, predicate, sleep_generator, timeout, on_error, exception_factory, **kwargs)\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    145\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misawaitable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/api_core/timeout.py\u001b[0m in \u001b[0;36mfunc_with_timeout\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/api_core/grpc_helpers.py\u001b[0m in \u001b[0;36merror_remapped_callable\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     77\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mgrpc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRpcError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_grpc_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidArgument\u001b[0m: 400 * GenerateContentRequest.contents: contents is not specified\n",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mChatGoogleGenerativeAIError\u001b[0m               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-11a2ccfa548f>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mrefine_chain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_summarize_chain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mllm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mllm_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mchain_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'stuff'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msummary_refine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrefine_chain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minvoke\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"input_documents\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdata_list\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msummary_refine\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'output_text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m             \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    158\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m             outputs = (\n\u001b[0;32m--> 160\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m                 \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain/chains/combine_documents/base.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;31m# Other keys are assumed to be needed for LLM prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0mother_keys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_key\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m         output, extra_return_dict = self.combine_docs(\n\u001b[0m\u001b[1;32m    139\u001b[0m             \u001b[0mdocs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_run_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_child\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mother_keys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain/chains/combine_documents/stuff.py\u001b[0m in \u001b[0;36mcombine_docs\u001b[0;34m(self, docs, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    257\u001b[0m         \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m         \u001b[0;31m# Call predict on the LLM.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 259\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mllm_chain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    260\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m     async def acombine_docs(\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain/chains/llm.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    316\u001b[0m                 \u001b[0mcompletion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mllm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madjective\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"funny\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m         \"\"\"\n\u001b[0;32m--> 318\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m     \u001b[0;32masync\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mapredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mCallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain_core/_api/deprecation.py\u001b[0m in \u001b[0;36mwarning_emitting_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0mwarned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m                 \u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m         \u001b[0;32masync\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mawarning_emitting_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    387\u001b[0m         }\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m         return self.invoke(\n\u001b[0m\u001b[1;32m    390\u001b[0m             \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m             \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRunnableConfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m             \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    158\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m             outputs = (\n\u001b[0;32m--> 160\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m                 \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain/chains/llm.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m    124\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mCallbackManagerForChainRun\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m     ) -> Dict[str, str]:\n\u001b[0;32m--> 126\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_outputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain/chains/llm.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, input_list, run_manager)\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_child\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_manager\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mllm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseLanguageModel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             return self.llm.generate_prompt(\n\u001b[0m\u001b[1;32m    139\u001b[0m                 \u001b[0mprompts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m                 \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain_core/language_models/chat_models.py\u001b[0m in \u001b[0;36mgenerate_prompt\u001b[0;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    858\u001b[0m     ) -> LLMResult:\n\u001b[1;32m    859\u001b[0m         \u001b[0mprompt_messages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_messages\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprompts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 860\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt_messages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m     async def agenerate_prompt(\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain_core/language_models/chat_models.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    688\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    689\u001b[0m                 results.append(\n\u001b[0;32m--> 690\u001b[0;31m                     self._generate_with_cache(\n\u001b[0m\u001b[1;32m    691\u001b[0m                         \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    692\u001b[0m                         \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain_core/language_models/chat_models.py\u001b[0m in \u001b[0;36m_generate_with_cache\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    923\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    924\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_generate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"run_manager\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 925\u001b[0;31m                 result = self._generate(\n\u001b[0m\u001b[1;32m    926\u001b[0m                     \u001b[0mmessages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    927\u001b[0m                 )\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain_google_genai/chat_models.py\u001b[0m in \u001b[0;36m_generate\u001b[0;34m(self, messages, stop, run_manager, tools, functions, safety_settings, tool_config, generation_config, cached_content, tool_choice, **kwargs)\u001b[0m\n\u001b[1;32m    949\u001b[0m             \u001b[0mtool_choice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtool_choice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m         )\n\u001b[0;32m--> 951\u001b[0;31m         response: GenerateContentResponse = _chat_with_retry(\n\u001b[0m\u001b[1;32m    952\u001b[0m             \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    953\u001b[0m             \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain_google_genai/chat_models.py\u001b[0m in \u001b[0;36m_chat_with_retry\u001b[0;34m(generation_method, **kwargs)\u001b[0m\n\u001b[1;32m    194\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_chat_with_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36mwrapped_f\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    334\u001b[0m             \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    335\u001b[0m             \u001b[0mwrapped_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatistics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatistics\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 336\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mretry_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mWrappedFn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    473\u001b[0m         \u001b[0mretry_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRetryCallState\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_object\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    474\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 475\u001b[0;31m             \u001b[0mdo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mretry_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    476\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDoAttempt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    477\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36miter\u001b[0;34m(self, retry_state)\u001b[0m\n\u001b[1;32m    374\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maction\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    377\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(rs)\u001b[0m\n\u001b[1;32m    396\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_post_retry_check_actions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretry_state\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"RetryCallState\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    397\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_explicit_retry\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretry_run_result\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_action_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mrs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutcome\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    399\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    400\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.11/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    447\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.11/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    399\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    400\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 401\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    402\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m                 \u001b[0;31m# Break a reference cycle with the exception in self._exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDoAttempt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    477\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 478\u001b[0;31m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    479\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# noqa: B902\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    480\u001b[0m                     \u001b[0mretry_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain_google_genai/chat_models.py\u001b[0m in \u001b[0;36m_chat_with_retry\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidArgument\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m             raise ChatGoogleGenerativeAIError(\n\u001b[0m\u001b[1;32m    191\u001b[0m                 \u001b[0;34mf\"Invalid argument provided to Gemini: {e}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m             ) from e\n",
            "\u001b[0;31mChatGoogleGenerativeAIError\u001b[0m: Invalid argument provided to Gemini: 400 * GenerateContentRequest.contents: contents is not specified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PtiJJipA5I9-"
      },
      "source": [
        "### 1️⃣6️⃣  提取關鍵字\n",
        "使用 MMR 搜尋方法"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.output_parsers import CommaSeparatedListOutputParser, StrOutputParser\n",
        "from langchain_core.prompts import PromptTemplate\n",
        "from langchain_core.runnables import RunnableSequence\n",
        "\n",
        "word_prompt = PromptTemplate.from_template(\n",
        "     \"從{input}聯想出4個與年報分析有關的重要關鍵字,\"\\\n",
        "     \"請確保回答具有具有關聯性、多樣性和變化性。 \\n \"\n",
        "     \"僅回覆關鍵字, 並以半形逗號與空格來分隔。不要加入其他內容\")\n",
        "\n",
        "word_chain = word_prompt | llm_model | CommaSeparatedListOutputParser()\n",
        "print(word_chain.invoke({\"input\": \"公司的營運狀況如何?\"}))"
      ],
      "metadata": {
        "id": "zMeR0jjh2_5I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b550efff-78a5-4e59-9877-8e9c94a7f937"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['營收成長', '獲利能力', '償債能力', '現金流量']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gKilRAeA5GNG"
      },
      "source": [
        "### 1️⃣7️⃣ 設定 AI 角色讓其分析報告"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_prompt = PromptTemplate.from_template(\n",
        "    \"你現在是一位專業的股票分析師,\"\n",
        "    \"會以詳細、嚴謹的角度針對 {key_words} 進行年報分析,\"\n",
        "    \"請提及關於營收、是否成長以及利潤等重要數字,\"\n",
        "    \"最後生成一份專業的趨勢分析報告。\"\n",
        "    \"以下為年報資料：{data_content}\")\n",
        "\n",
        "data_chain = data_prompt | llm_model | StrOutputParser()"
      ],
      "metadata": {
        "id": "y4nY5fkpVKlq"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3_A78R65LAd"
      },
      "source": [
        "### 1️⃣8️⃣ 整合函式"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def analyze_chain(input):\n",
        "    # 搜尋「問題」的相關資料\n",
        "    data = db.max_marginal_relevance_search(input, fetch_k=5, k=2)\n",
        "\n",
        "    # 第一個 Chain 元件, 建立「關鍵字」串列\n",
        "    word_list = word_chain.invoke({\"input\": input})\n",
        "\n",
        "    # 搜尋「關鍵字」的相關資料\n",
        "    for word in word_list:\n",
        "      data += db.max_marginal_relevance_search(word, fetch_k=5, k=2)\n",
        "    word_list.append(input)\n",
        "\n",
        "    # 第二個 Chain 元件, 生成分析報告\n",
        "    result = data_chain.invoke({'key_words':word_list,'data_content':data})\n",
        "\n",
        "    return result"
      ],
      "metadata": {
        "id": "YWKb8jEydsoN"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VT7XPS8z5NS4"
      },
      "source": [
        "### 1️⃣9️⃣ 呼叫函式"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input = '公司的營收狀況如何？'\n",
        "analyze_report = analyze_chain(input)\n",
        "print(analyze_report)"
      ],
      "metadata": {
        "id": "e-NHDVzqHOsU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9520bc3-e22d-4a9d-cc16-55aaab2bc083"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "好的，我將根據您提供的年報資料，進行一次詳細且嚴謹的股票分析，並生成一份專業的趨勢分析報告。\n",
            "\n",
            "**資料審閱與分析**\n",
            "\n",
            "由於您提供的資料主要為年報的部分章節，缺乏完整的財務報表（如損益表、資產負債表、現金流量表），因此我將著重於已提供的資訊，並結合一般行業分析的框架進行推論。\n",
            "\n",
            "1.  **營收成長率：**\n",
            "\n",
            "*   **初步判斷：** 雖然沒有直接的營收數字，但從「掌握更多的產業成長機會」、「支持客戶成長」等描述可推斷，公司管理層對營收成長抱持樂觀態度。\n",
            "*   **深入分析：**\n",
            "    *   需關注公司是否能持續推出先進製程技術和產品，以維持競爭力並掌握成長機會。\n",
            "    *   全球製造足跡的擴張策略，有助於支持客戶成長，但也可能增加成本和管理複雜度。\n",
            "\n",
            "2.  **獲利能力：**\n",
            "\n",
            "*   **初步判斷：** 文件中未直接提及獲利能力，但提到「致力實現全方位的智慧化和自動化製造」、「成為具有最高效率和最具成本效益的半導體製造者」，暗示公司追求成本優勢，以提升獲利能力。\n",
            "*   **深入分析：**\n",
            "    *   需關注公司在數位卓越化和智慧製造方面的投資，是否能有效降低成本並提高生產效率。\n",
            "    *   客戶需求縮短產品上市時間的壓力，可能對平均售價和獲利空間造成影響。\n",
            "\n",
            "3.  **成本結構：**\n",
            "\n",
            "*   **初步判斷：** 從風險因素中可見，原物料價格波動、供應鏈風險、產能擴充成本等，都是影響公司成本結構的重要因素。\n",
            "*   **深入分析：**\n",
            "    *   公司分散生產廠區、降低供應鏈風險的策略，有助於控制成本。\n",
            "    *   需關注新台幣匯率波動對以美元計價的營收和資本支出的影響。\n",
            "\n",
            "4.  **現金流量：**\n",
            "\n",
            "*   **初步判斷：** 提供的現金流量資訊顯示，營業活動的淨現金流入可觀，但支付所得稅的金額也相當大。\n",
            "*   **深入分析：**\n",
            "    *   需關注營業活動淨現金流量是否足以支應資本支出、股利發放和投資需求。\n",
            "    *   現金再投資比率是衡量公司擴張和成長能力的重要指標。\n",
            "\n",
            "5. **公司的營收狀況如何？**\n",
            "    *   **初步判斷：** 從「掌握更多的產業成長機會」、「支持客戶成長」等描述可推斷，公司管理層對營收成長抱持樂觀態度。\n",
            "    *   **深入分析：**\n",
            "        *   需關注公司是否能持續推出先進製程技術和產品，以維持競爭力並掌握成長機會。\n",
            "        *   全球製造足跡的擴張策略，有助於支持客戶成長，但也可能增加成本和管理複雜度。\n",
            "\n",
            "**風險評估**\n",
            "\n",
            "*   **技術風險：** 無法洞察技術變革、競爭者取得技術、AI技術導入不足等，都可能影響競爭力。\n",
            "*   **營運風險：** 天然災害、網路攻擊、資訊安全風險、供應鏈中斷等，可能導致營運中斷和財務損失。\n",
            "*   **市場風險：** 客戶需求變化、平均售價下滑、匯率波動等，可能影響營收和獲利。\n",
            "\n",
            "**趨勢分析報告**\n",
            "\n",
            "**公司前景展望：**\n",
            "\n",
            "基於以上分析，我對該公司的前景抱持謹慎樂觀的態度。\n",
            "\n",
            "*   **優勢：**\n",
            "    *   在先進製程技術和封裝解決方案方面具有強大的技術領先優勢。\n",
            "    *   積極擴張全球製造足跡，有助於支持客戶成長和分散風險。\n",
            "    *   致力於數位卓越化和智慧製造，有助於提高效率和降低成本。\n",
            "*   **挑戰：**\n",
            "    *   面臨激烈的市場競爭和快速的技術變革。\n",
            "    *   需要有效管理供應鏈風險和資訊安全風險。\n",
            "    *   需應對客戶需求變化和平均售價下滑的壓力。\n",
            "\n",
            "**投資建議：**\n",
            "\n",
            "*   **長期投資者：** 可考慮將該公司納入投資組合，但需密切關注其技術發展、營運效率和風險管理能力。\n",
            "*   **短期交易者：** 需謹慎評估市場情緒和消息面影響，不宜過度追高。\n",
            "\n",
            "**重要聲明：**\n",
            "\n",
            "本報告僅為初步分析，不構成任何投資建議。投資者應自行評估風險，並諮詢專業財務顧問。\n"
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