01_baseline.ipynb

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   "source": [
    "%%capture --no-stderr\n",
    "!pip install -U langchain langchain_community pypdf sentence_transformers chromadb"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import langchain, langchain_community, pypdf, sentence_transformers, chromadb\n",
    "\n",
    "for module in (langchain, langchain_community, pypdf, sentence_transformers, chromadb):\n",
    "    print(f\"{module.__name__:<30}{module.__version__}\")"
   ],
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    {
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     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm, trange\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "langchain                     0.2.10\n",
      "langchain_community           0.2.9\n",
      "pypdf                         4.3.1\n",
      "sentence_transformers         3.0.1\n",
      "chromadb                      0.5.5\n"
     ]
    }
   ]
  },
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    "execution": {
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   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "from langchain_community.vectorstores import Chroma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "841d2b02-ad06-40d2-b11f-c7adccec6ca2",
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    "execution": {
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   "source": [
    "# 如果已经下载到本地，可以替换为本地路径\n",
    "EMBEDDING_MODEL_PATH = 'BAAI/bge-large-zh-v1.5'\n",
    "dt = '20240713'\n",
    "version = 'v1'\n",
    "\n",
    "output_dir = os.path.join('outputs', f'{version}_{dt}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7e81e3-4c82-4842-aef5-7592caaf1d39",
   "metadata": {
    "id": "cf7e81e3-4c82-4842-aef5-7592caaf1d39"
   },
   "source": [
    "# 文档处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da15f02e-3131-43fb-81c5-f4da615c449b",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "from langchain_community.document_loaders import PyPDFLoader"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 文档加载"
   ],
   "metadata": {
    "id": "0PNdWJAALMni"
   },
   "id": "0PNdWJAALMni"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6920e29-bc7d-4635-be06-d151eaf0e100",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-07-21T13:22:04.081006Z"
    },
    "id": "e6920e29-bc7d-4635-be06-d151eaf0e100"
   },
   "outputs": [],
   "source": [
    "loader = PyPDFLoader(\"data/2024全球经济金融展望报告.pdf\")\n",
    "documents = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03f6f2a8-d1d9-42ad-9eee-cd5fe74c312f",
   "metadata": {
    "execution": {
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    "id": "03f6f2a8-d1d9-42ad-9eee-cd5fe74c312f"
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   "outputs": [],
   "source": [
    "qa_df = pd.read_excel(os.path.join(output_dir, 'question_answer.xlsx'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "841ec659-4ad7-4e1f-b1ea-3477bf97fde3",
   "metadata": {
    "id": "841ec659-4ad7-4e1f-b1ea-3477bf97fde3"
   },
   "source": [
    "## 文档切分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74fe856a-7c19-4c3c-bb30-7abfa6298f74",
   "metadata": {
    "execution": {
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    },
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   "outputs": [],
   "source": [
    "from uuid import uuid4\n",
    "import os\n",
    "import pickle\n",
    "\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "\n",
    "def split_docs(documents, filepath, chunk_size=400, chunk_overlap=40, seperators=['\\n\\n\\n', '\\n\\n'], force_split=False):\n",
    "    if os.path.exists(filepath) and not force_split:\n",
    "        print('found cache, restoring...')\n",
    "        return pickle.load(open(filepath, 'rb'))\n",
    "\n",
    "    splitter = RecursiveCharacterTextSplitter(\n",
    "        chunk_size=chunk_size,\n",
    "        chunk_overlap=chunk_overlap,\n",
    "        separators=seperators\n",
    "    )\n",
    "    split_docs = splitter.split_documents(documents)\n",
    "    for chunk in split_docs:\n",
    "        chunk.metadata['uuid'] = str(uuid4())\n",
    "\n",
    "    pickle.dump(split_docs, open(filepath, 'wb'))\n",
    "\n",
    "    return split_docs"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "splitted_docs = split_docs(documents, os.path.join(output_dir, 'split_docs.pkl'), chunk_size=500, chunk_overlap=50)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fh0N6CwXe-Ho",
    "outputId": "1f1d5b00-f1cc-451b-827c-f834a59191b3"
   },
   "id": "fh0N6CwXe-Ho",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "found cache, restoring...\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "id": "220dbc3a-fceb-4e49-a3f1-01e16660b2a6",
   "metadata": {
    "id": "220dbc3a-fceb-4e49-a3f1-01e16660b2a6"
   },
   "source": [
    "## 向量化"
   ]
  },
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   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "device: cuda\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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"
     ]
    },
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     "metadata": {}
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       "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
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     "metadata": {}
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    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "1_Pooling/config.json:   0%|          | 0.00/191 [00:00<?, ?B/s]"
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       "model_id": "9c6d6ecd27ec456f8fe894c8ab31fab1"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "from langchain.embeddings import HuggingFaceBgeEmbeddings\n",
    "import torch\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(f'device: {device}')\n",
    "\n",
    "embeddings = HuggingFaceBgeEmbeddings(\n",
    "    model_name=EMBEDDING_MODEL_PATH,\n",
    "    model_kwargs={'device': device},\n",
    "    encode_kwargs={'normalize_embeddings': True}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "663ef1a4-5866-4f6b-8d9d-4724f62142cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-21T13:22:07.755864Z",
     "iopub.status.busy": "2024-07-21T13:22:07.755656Z",
     "iopub.status.idle": "2024-07-21T13:22:07.758822Z",
     "shell.execute_reply": "2024-07-21T13:22:07.758495Z",
     "shell.execute_reply.started": "2024-07-21T13:22:07.755850Z"
    },
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   },
   "outputs": [],
   "source": [
    "from tqdm.auto import tqdm\n",
    "\n",
    "def get_vector_db(docs, store_path, force_rebuild=False):\n",
    "    if not os.path.exists(store_path):\n",
    "        force_rebuild = True\n",
    "\n",
    "    if force_rebuild:\n",
    "        vector_db = Chroma.from_documents(\n",
    "            docs,\n",
    "            embedding=embeddings,\n",
    "            persist_directory=store_path\n",
    "        )\n",
    "    else:\n",
    "        vector_db = Chroma(\n",
    "            persist_directory=store_path,\n",
    "            embedding_function=embeddings\n",
    "        )\n",
    "    return vector_db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41821b04-afc4-4a9b-98d2-f95f7bf83f74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-21T13:22:07.773482Z",
     "iopub.status.busy": "2024-07-21T13:22:07.773355Z",
     "iopub.status.idle": "2024-07-21T13:22:08.327108Z",
     "shell.execute_reply": "2024-07-21T13:22:08.326541Z",
     "shell.execute_reply.started": "2024-07-21T13:22:07.773470Z"
    },
    "id": "41821b04-afc4-4a9b-98d2-f95f7bf83f74"
   },
   "outputs": [],
   "source": [
    "vector_db = get_vector_db(splitted_docs, store_path=os.path.join(output_dir, 'chromadb', 'bge_large_v1.5'))"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 检索"
   ],
   "metadata": {
    "id": "CKF0J7duLRMw"
   },
   "id": "CKF0J7duLRMw"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9ba0818-d608-4e87-aa9e-aced270a113c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-21T13:22:07.759389Z",
     "iopub.status.busy": "2024-07-21T13:22:07.759238Z",
     "iopub.status.idle": "2024-07-21T13:22:07.772903Z",
     "shell.execute_reply": "2024-07-21T13:22:07.772468Z",
     "shell.execute_reply.started": "2024-07-21T13:22:07.759377Z"
    },
    "id": "f9ba0818-d608-4e87-aa9e-aced270a113c"
   },
   "outputs": [],
   "source": [
    "def retrieve(vector_db, query: str, k=5):\n",
    "    return vector_db.similarity_search(query, k=k)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "566c6f3c-5777-4aa9-bc60-a3ee23050506",
   "metadata": {
    "id": "566c6f3c-5777-4aa9-bc60-a3ee23050506"
   },
   "source": [
    "## 计算检索准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b03e3382-39e9-4932-a265-69b811041629",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-21T13:22:08.327815Z",
     "iopub.status.busy": "2024-07-21T13:22:08.327571Z",
     "iopub.status.idle": "2024-07-21T13:22:08.330973Z",
     "shell.execute_reply": "2024-07-21T13:22:08.330592Z",
     "shell.execute_reply.started": "2024-07-21T13:22:08.327802Z"
    },
    "id": "b03e3382-39e9-4932-a265-69b811041629"
   },
   "outputs": [],
   "source": [
    "test_df = qa_df[(qa_df['dataset'] == 'test') & (qa_df['qa_type'] == 'detailed')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ad650ce-43c4-4bbc-b4b3-809a5269b545",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-21T13:22:08.332774Z",
     "iopub.status.busy": "2024-07-21T13:22:08.332444Z",
     "iopub.status.idle": "2024-07-21T13:22:08.344726Z",
     "shell.execute_reply": "2024-07-21T13:22:08.344334Z",
     "shell.execute_reply.started": "2024-07-21T13:22:08.332758Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3ad650ce-43c4-4bbc-b4b3-809a5269b545",
    "outputId": "93832afb-ae63-4b3d-d5fe-bbab4bb7b264"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "len(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32c3ad14-b217-44aa-bdb9-909b9d559668",
   "metadata": {
    "execution": {
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    "outputId": "c152f51e-4faa-42ef-ba26-ce03b9ffa261"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "  0%|          | 0/93 [00:00<?, ?it/s]"
      ],
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   ],
   "source": [
    "top_k_arr = list(range(1, 9))\n",
    "hit_stat_data = []\n",
    "\n",
    "for idx, row in tqdm(test_df.iterrows(), total=len(test_df)):\n",
    "    question = row['question']\n",
    "    true_uuid = row['uuid']\n",
    "    chunks = retrieve(vector_db, question, k=max(top_k_arr))\n",
    "    retrieved_uuids = [doc.metadata['uuid'] for doc in chunks]\n",
    "\n",
    "    for k in top_k_arr:\n",
    "        hit_stat_data.append({\n",
    "            'question': question,\n",
    "            'top_k': k,\n",
    "            'hit': int(true_uuid in retrieved_uuids[:k])\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcaa74a3-3873-4c9e-ae8a-47f4e940b724",
   "metadata": {
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     "iopub.execute_input": "2024-07-21T13:22:11.555548Z",
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     "shell.execute_reply.started": "2024-07-21T13:22:11.555535Z"
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   },
   "outputs": [],
   "source": [
    "hit_stat_df = pd.DataFrame(hit_stat_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f85cef68-c3ef-49d0-b9ff-9009953d5685",
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    "outputId": "47fd9b04-2653-46ad-fa7a-e55b400f4ade"
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       "                      question  top_k  hit\n",
       "340     2023年10月欧元区经济景气指数是多少点?      5    0\n",
       "450       欧洲央行在3月16日的利率是多少百分比?      3    1\n",
       "743  商业房地产贷款在中小型银行资产中的占比大约是多少？      8    1\n",
       "88               全球经济复苏呈现什么特点？      1    0\n",
       "367      2023年9月南非的CPI同比上涨了多少？      8    1"
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       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-58f74e18-6125-4608-b74a-672fa90e6365 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "summary": "{\n  \"name\": \"hit_stat_df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"question\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"\\u6b27\\u6d32\\u592e\\u884c\\u57283\\u670816\\u65e5\\u7684\\u5229\\u7387\\u662f\\u591a\\u5c11\\u767e\\u5206\\u6bd4?\",\n          \"2023\\u5e749\\u6708\\u5357\\u975e\\u7684CPI\\u540c\\u6bd4\\u4e0a\\u6da8\\u4e86\\u591a\\u5c11\\uff1f\",\n          \"\\u5546\\u4e1a\\u623f\\u5730\\u4ea7\\u8d37\\u6b3e\\u5728\\u4e2d\\u5c0f\\u578b\\u94f6\\u884c\\u8d44\\u4ea7\\u4e2d\\u7684\\u5360\\u6bd4\\u5927\\u7ea6\\u662f\\u591a\\u5c11\\uff1f\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top_k\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 8,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "hit_stat_df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48438b40-6e03-4459-81ce-31a41867bad8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-21T13:22:11.575377Z",
     "iopub.status.busy": "2024-07-21T13:22:11.575255Z",
     "iopub.status.idle": "2024-07-21T13:22:11.920473Z",
     "shell.execute_reply": "2024-07-21T13:22:11.919973Z",
     "shell.execute_reply.started": "2024-07-21T13:22:11.575364Z"
    },
    "id": "48438b40-6e03-4459-81ce-31a41867bad8"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a63797c7-4151-4f55-8e5d-080c34265393",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-21T13:22:11.921495Z",
     "iopub.status.busy": "2024-07-21T13:22:11.920978Z",
     "iopub.status.idle": "2024-07-21T13:22:11.927266Z",
     "shell.execute_reply": "2024-07-21T13:22:11.926849Z",
     "shell.execute_reply.started": "2024-07-21T13:22:11.921481Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "a63797c7-4151-4f55-8e5d-080c34265393",
    "outputId": "7687dcf9-54ac-4ed8-f2ba-b15cd9198aef"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   top_k       hit\n",
       "0      1  0.462366\n",
       "1      2  0.591398\n",
       "2      3  0.688172\n",
       "3      4  0.774194\n",
       "4      5  0.806452\n",
       "5      6  0.817204\n",
       "6      7  0.838710\n",
       "7      8  0.849462"
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-23b6378b-f5da-4bd4-9560-224151edc8b1\" class=\"colab-df-container\">\n",
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       "        vertical-align: middle;\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>0.774194</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.806452</td>\n",
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       "      <td>6</td>\n",
       "      <td>0.817204</td>\n",
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.838710</td>\n",
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       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.849462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23b6378b-f5da-4bd4-9560-224151edc8b1')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
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       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-23b6378b-f5da-4bd4-9560-224151edc8b1 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-23b6378b-f5da-4bd4-9560-224151edc8b1');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-a826a0c6-c750-4873-9bec-34d087fa706e\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a826a0c6-c750-4873-9bec-34d087fa706e')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-a826a0c6-c750-4873-9bec-34d087fa706e button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "summary": "{\n  \"name\": \"hit_stat_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"top_k\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 8,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2,\n          6,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13838958530269438,\n        \"min\": 0.46236559139784944,\n        \"max\": 0.8494623655913979,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5913978494623656,\n          0.8172043010752689,\n          0.46236559139784944\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "hit_stat_df.groupby('top_k')['hit'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0b086d1-6cec-4743-8df6-2ab3b1593689",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-21T13:22:11.929189Z",
     "iopub.status.busy": "2024-07-21T13:22:11.929035Z",
     "iopub.status.idle": "2024-07-21T13:22:12.047934Z",
     "shell.execute_reply": "2024-07-21T13:22:12.047474Z",
     "shell.execute_reply.started": "2024-07-21T13:22:11.929177Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 468
    },
    "id": "b0b086d1-6cec-4743-8df6-2ab3b1593689",
    "outputId": "add763c5-3aee-4724-c9ce-8ffc51994a5e"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Axes: xlabel='top_k', ylabel='hit'>"
      ]
     },
     "metadata": {},
     "execution_count": 23
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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9n3/+uX77299+46EAAADO1zm9LPWHP/wh+Od33nlHHo8n+Njn86m2tlYpKSk9Nx0AAMA5Oqe4ycjIkM1mk81m6/Llp0GDBumRRx7pseEAAADO1TnFzQcffKBAIKAxY8aooaFBI0aMCP4sOjpaiYmJstvtPT4kAADA2TqnuBk9erQkye/398owAAAA39RZx80LL7yg2bNna+DAgXrhhRfOuPaf/umfvvFgAAAA5+Os42bevHnyeDxKTEzUvHnzul1ns9nk8/l6YjYAAIBzdtZx87cvRfGyFAAAiFTn/QnFbrdbbrdbJ06cCIkdm82mDRs29MhwAAAA5+q84mbFihW69957lZWVpZEjR8pms/X0XAAAAOflvOKmurpaGzdu1E033dTT8wAAAHwj5/X1C52dnZo+fXpPzwIAAPCNnVfc3Hrrrdq0aVNPzwIAAPCNnfXLUi6XK/hnv9+vdevW6bXXXtOUKVM0cODAkLUVFRU9NyEAAMA5OOu4efvtt0MeZ2RkSJL27dsXsp+biwEAgJXOOm5ef/313pwDAACgR5zXPTcAAACRirgBAABGIW4AAIBRIiJuqqqqlJaWptjYWOXk5KihoeGsjtu8ebNsNtsZv8gTAAD0L5bHzZYtW+RyuVRWVqbdu3dr6tSpys/P14kTJ8543OHDh3XXXXfpyiuvDNOkAACgL7A8bioqKlRcXCyHw6H09HRVV1crLi5ONTU13R7j8/k0f/58rVixQmPGjAnjtAAAINJZGjednZ1qbGxUXl5ecF9UVJTy8vJUX1/f7XH33nuvEhMTtWDBgnCMCQAA+pDz+uLMntLa2iqfz6ekpKSQ/UlJSdq/f3+Xx+zYsUMbNmzQnj17zuo5vF6vvF5v8HF7e/t5zwsAACKf5S9LnYtTp07ppptu0vr165WQkHBWx5SXlys+Pj64paam9vKUAADASpZeuUlISJDdbldLS0vI/paWFiUnJ5+2/k9/+pMOHz6suXPnBvf5/X5J0oABA3TgwAGNHTs25JiSkpKQ78Vqb28ncAAAMJilcRMdHa3MzEy53e7g27n9fr/cbrecTudp6ydMmKC9e/eG7Fu+fLlOnTqlNWvWdBktMTExiomJ6ZX5AQBA5LE0bqQvv228qKhIWVlZys7OVmVlpTo6OuRwOCRJhYWFSklJUXl5uWJjY3X55ZeHHD9s2DBJOm0/AADonyyPm4KCAp08eVKlpaXyeDzKyMhQbW1t8CbjpqYmRUX1qVuDAACAhSyPG0lyOp1dvgwlSXV1dWc8duPGjT0/EAAA6LO4JAIAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKBERN1VVVUpLS1NsbKxycnLU0NDQ7dqtW7cqKytLw4YN0wUXXKCMjAw99dRTYZwWAABEMsvjZsuWLXK5XCorK9Pu3bs1depU5efn68SJE12uHz58uJYtW6b6+nr94Q9/kMPhkMPh0MsvvxzmyQEAQCSyPG4qKipUXFwsh8Oh9PR0VVdXKy4uTjU1NV2unzVrlv75n/9ZEydO1NixY7Vo0SJNmTJFO3bsCPPkAAAgElkaN52dnWpsbFReXl5wX1RUlPLy8lRfX/+1xwcCAbndbh04cEDf+973ulzj9XrV3t4esgEAAHNZGjetra3y+XxKSkoK2Z+UlCSPx9PtcW1tbRo8eLCio6M1Z84cPfLII7r66qu7XFteXq74+Pjglpqa2qPnAAAAIovlL0udjyFDhmjPnj3atWuXVq5cKZfLpbq6ui7XlpSUqK2tLbg1NzeHd1gAABBWA6x88oSEBNntdrW0tITsb2lpUXJycrfHRUVFady4cZKkjIwMvfvuuyovL9esWbNOWxsTE6OYmJgenRsAAEQuS6/cREdHKzMzU263O7jP7/fL7XYrNzf3rH+P3++X1+vtjREBAEAfY+mVG0lyuVwqKipSVlaWsrOzVVlZqY6ODjkcDklSYWGhUlJSVF5eLunLe2iysrI0duxYeb1ebdu2TU899ZTWrl1r5WkAAIAIYXncFBQU6OTJkyotLZXH41FGRoZqa2uDNxk3NTUpKuqrC0wdHR267bbb9OGHH2rQoEGaMGGCnn76aRUUFFh1CgAAIIJYHjeS5HQ65XQ6u/zZ398ofP/99+v+++8Pw1QAAKAv6pPvlgIAAOgOcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjBIRcVNVVaW0tDTFxsYqJydHDQ0N3a5dv369rrzySl144YW68MILlZeXd8b1AACgf7E8brZs2SKXy6WysjLt3r1bU6dOVX5+vk6cONHl+rq6Ot144416/fXXVV9fr9TUVF1zzTU6evRomCcHAACRyPK4qaioUHFxsRwOh9LT01VdXa24uDjV1NR0uf6Xv/ylbrvtNmVkZGjChAl6/PHH5ff75Xa7wzw5AACIRJbGTWdnpxobG5WXlxfcFxUVpby8PNXX15/V7/j000/1l7/8RcOHD++tMQEAQB8ywMonb21tlc/nU1JSUsj+pKQk7d+//6x+x5IlSzRq1KiQQPpbXq9XXq83+Li9vf38BwYAABHP8pelvonVq1dr8+bNevbZZxUbG9vlmvLycsXHxwe31NTUME8JAADCydK4SUhIkN1uV0tLS8j+lpYWJScnn/HYhx56SKtXr9Yrr7yiKVOmdLuupKREbW1twa25ublHZgcAAJHJ0riJjo5WZmZmyM3Af705ODc3t9vjfvazn+m+++5TbW2tsrKyzvgcMTExGjp0aMgGAADMZek9N5LkcrlUVFSkrKwsZWdnq7KyUh0dHXI4HJKkwsJCpaSkqLy8XJL0wAMPqLS0VJs2bVJaWpo8Ho8kafDgwRo8eLBl5wEAACKD5XFTUFCgkydPqrS0VB6PRxkZGaqtrQ3eZNzU1KSoqK8uMK1du1adnZ36t3/7t5DfU1ZWpp/+9KfhHB0AAEQgy+NGkpxOp5xOZ5c/q6urC3l8+PDh3h8IAAD0WX363VIAAAB/j7gBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFEsj5uqqiqlpaUpNjZWOTk5amho6HbtH//4R/3rv/6r0tLSZLPZVFlZGb5BAQBAn2Bp3GzZskUul0tlZWXavXu3pk6dqvz8fJ04caLL9Z9++qnGjBmj1atXKzk5OczTAgCAvsDSuKmoqFBxcbEcDofS09NVXV2tuLg41dTUdLn+iiuu0IMPPqgbbrhBMTExYZ4WAAD0BZbFTWdnpxobG5WXl/fVMFFRysvLU319fY89j9frVXt7e8gGAADMZVnctLa2yufzKSkpKWR/UlKSPB5Pjz1PeXm54uPjg1tqamqP/W4AABB5LL+huLeVlJSora0tuDU3N1s9EgAA6EUDrHrihIQE2e12tbS0hOxvaWnp0ZuFY2JiuD8HAIB+xLIrN9HR0crMzJTb7Q7u8/v9crvdys3NtWosAADQx1l25UaSXC6XioqKlJWVpezsbFVWVqqjo0MOh0OSVFhYqJSUFJWXl0v68ibkd955J/jno0ePas+ePRo8eLDGjRtn2XkAAIDIYWncFBQU6OTJkyotLZXH41FGRoZqa2uDNxk3NTUpKuqri0vHjh3TtGnTgo8feughPfTQQ5o5c6bq6urCPT4AAIhAlsaNJDmdTjmdzi5/9vfBkpaWpkAgEIapAABAX2X8u6UAAED/QtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKNERNxUVVUpLS1NsbGxysnJUUNDwxnX//rXv9aECRMUGxuryZMna9u2bWGaFAAARDrL42bLli1yuVwqKyvT7t27NXXqVOXn5+vEiRNdrt+5c6duvPFGLViwQG+//bbmzZunefPmad++fWGeHAAARCLL46aiokLFxcVyOBxKT09XdXW14uLiVFNT0+X6NWvW6B//8R+1ePFiTZw4Uffdd5++/e1v69FHHw3z5AAAIBJZGjednZ1qbGxUXl5ecF9UVJTy8vJUX1/f5TH19fUh6yUpPz+/2/UAAKB/GWDlk7e2tsrn8ykpKSlkf1JSkvbv39/lMR6Pp8v1Ho+ny/Ver1derzf4uK2tTZLU3t5+2lqf97Nzmj9SdXVuZ8J5922c99nhvPs2zvvsmHzef90XCAS+/hcELHT06NGApMDOnTtD9i9evDiQnZ3d5TEDBw4MbNq0KWRfVVVVIDExscv1ZWVlAUlsbGxsbGxsBmzNzc1f2xeWXrlJSEiQ3W5XS0tLyP6WlhYlJyd3eUxycvI5rS8pKZHL5Qo+9vv9+uijj3TRRRfJZrN9wzM4N+3t7UpNTVVzc7OGDh0a1ue2EufNefcHnDfn3R9Yed6BQECnTp3SqFGjvnatpXETHR2tzMxMud1uzZs3T9KX8eF2u+V0Ors8Jjc3V263W3fccUdw36uvvqrc3Nwu18fExCgmJiZk37Bhw3pi/PM2dOjQfvUvw19x3v0L592/cN79i1XnHR8ff1brLI0bSXK5XCoqKlJWVpays7NVWVmpjo4OORwOSVJhYaFSUlJUXl4uSVq0aJFmzpypn//855ozZ442b96st956S+vWrbPyNAAAQISwPG4KCgp08uRJlZaWyuPxKCMjQ7W1tcGbhpuamhQV9dWbuqZPn65NmzZp+fLluvvuu3XppZfqueee0+WXX27VKQAAgAhiedxIktPp7PZlqLq6utP2XX/99br++ut7eaqeFxMTo7KystNeJjMd58159wecN+fdH/SV87YFAmfznioAAIC+wfJPKAYAAOhJxA0AADAKcQMAAIxC3ITBG2+8oblz52rUqFGy2Wx67rnnrB4pLMrLy3XFFVdoyJAhSkxM1Lx583TgwAGrx+p1a9eu1ZQpU4KfA5Gbm6uXXnrJ6rHCbvXq1bLZbCGfSWWin/70p7LZbCHbhAkTrB4rLI4ePaof/vCHuuiiizRo0CBNnjxZb731ltVj9aq0tLTT/nnbbDYtXLjQ6tF6lc/n0z333KNvfetbGjRokMaOHav77rvv7L4KwQIR8W4p03V0dGjq1Km65ZZb9C//8i9WjxM227dv18KFC3XFFVfoiy++0N13361rrrlG77zzji644AKrx+s1F198sVavXq1LL71UgUBATz75pK677jq9/fbbmjRpktXjhcWuXbv02GOPacqUKVaPEhaTJk3Sa6+9Fnw8YID5/2n985//rBkzZugf/uEf9NJLL2nEiBF67733dOGFF1o9Wq/atWuXfD5f8PG+fft09dVX98l38J6LBx54QGvXrtWTTz6pSZMm6a233pLD4VB8fLx+8pOfWD3eacz/NzACzJ49W7Nnz7Z6jLCrra0Nebxx40YlJiaqsbFR3/ve9yyaqvfNnTs35PHKlSu1du1avfnmm/0ibj755BPNnz9f69ev1/3332/1OGExYMCAbr8CxlQPPPCAUlNT9cQTTwT3fetb37JwovAYMWJEyOPVq1dr7NixmjlzpkUThcfOnTt13XXXac6cOZK+vIL1q1/9Sg0NDRZP1jVelkLY/PUb2YcPH27xJOHj8/m0efNmdXR0dPsVIaZZuHCh5syZo7y8PKtHCZv33ntPo0aN0pgxYzR//nw1NTVZPVKve+GFF5SVlaXrr79eiYmJmjZtmtavX2/1WGHV2dmpp59+WrfcckvYv6sw3KZPny63262DBw9Kkn7/+99rx44dEfsXd67cICz8fr/uuOMOzZgxo198mvTevXuVm5urzz//XIMHD9azzz6r9PR0q8fqdZs3b9bu3bu1a9cuq0cJm5ycHG3cuFHjx4/X8ePHtWLFCl155ZXat2+fhgwZYvV4veb999/X2rVr5XK5dPfdd2vXrl36yU9+oujoaBUVFVk9Xlg899xz+vjjj3XzzTdbPUqvW7p0qdrb2zVhwgTZ7Xb5fD6tXLlS8+fPt3q0LhE3CIuFCxdq37592rFjh9WjhMX48eO1Z88etbW16Te/+Y2Kioq0fft2owOnublZixYt0quvvqrY2Firxwmbv/2b65QpU5STk6PRo0frmWee0YIFCyycrHf5/X5lZWVp1apVkqRp06Zp3759qq6u7jdxs2HDBs2ePfusvqW6r3vmmWf0y1/+Ups2bdKkSZO0Z88e3XHHHRo1alRE/vMmbtDrnE6n/ud//kdvvPGGLr74YqvHCYvo6GiNGzdOkpSZmaldu3ZpzZo1euyxxyyerPc0NjbqxIkT+va3vx3c5/P59MYbb+jRRx+V1+uV3W63cMLwGDZsmC677DIdOnTI6lF61ciRI0+L9YkTJ+q//uu/LJoovI4cOaLXXntNW7dutXqUsFi8eLGWLl2qG264QZI0efJkHTlyROXl5cQN+pdAIKDbb79dzz77rOrq6vrFzYbd8fv98nq9Vo/Rq6666irt3bs3ZJ/D4dCECRO0ZMmSfhE20pc3VP/pT3/STTfdZPUovWrGjBmnfbTDwYMHNXr0aIsmCq8nnnhCiYmJwRtsTffpp5+GfIm1JNntdvn9fosmOjPiJgw++eSTkL/FffDBB9qzZ4+GDx+uSy65xMLJetfChQu1adMmPf/88xoyZIg8Ho8kKT4+XoMGDbJ4ut5TUlKi2bNn65JLLtGpU6e0adMm1dXV6eWXX7Z6tF41ZMiQ0+6nuuCCC3TRRRcZfZ/VXXfdpblz52r06NE6duyYysrKZLfbdeONN1o9Wq+68847NX36dK1atUr//u//roaGBq1bt07r1q2zerRe5/f79cQTT6ioqKhfvO1f+vJdoCtXrtQll1yiSZMm6e2331ZFRYVuueUWq0frWgC97vXXXw9IOm0rKiqyerRe1dU5Swo88cQTVo/Wq2655ZbA6NGjA9HR0YERI0YErrrqqsArr7xi9ViWmDlzZmDRokVWj9GrCgoKAiNHjgxER0cHUlJSAgUFBYFDhw5ZPVZY/Pd//3fg8ssvD8TExAQmTJgQWLdundUjhcXLL78ckBQ4cOCA1aOETXt7e2DRokWBSy65JBAbGxsYM2ZMYNmyZQGv12v1aF3iW8EBAIBR+JwbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwD9WlpamiorK60eA0APIm4ARIxZs2bpjjvusHoMAH0ccQMAAIxC3ACICDfffLO2b9+uNWvWyGazyWaz6fDhw9q+fbuys7MVExOjkSNHaunSpfriiy+Cx82aNUtOp1NOp1Px8fFKSEjQPffco/P92rzHH39cw4YNk9vt7qlTAxBmxA2AiLBmzRrl5uaquLhYx48f1/HjxzVw4EBde+21uuKKK/T73/9ea9eu1YYNG3T//feHHPvkk09qwIABamho0Jo1a1RRUaHHH3/8nGf42c9+pqVLl+qVV17RVVdd1VOnBiDMBlg9AABIUnx8vKKjoxUXF6fk5GRJ0rJly5SamqpHH31UNptNEyZM0LFjx7RkyRKVlpYqKurLv5+lpqbq4Ycfls1m0/jx47V37149/PDDKi4uPuvnX7JkiZ566ilt375dkyZN6pVzBBAeXLkBELHeffdd5ebmymazBffNmDFDn3zyiT788MPgvu985zsha3Jzc/Xee+/J5/Od1fP8/Oc/1/r167Vjxw7CBjAAcQOg37vyyivl8/n0zDPPWD0KgB5A3ACIGNHR0SFXWyZOnKj6+vqQm4P/7//+T0OGDNHFF18c3Pe73/0u5Pe8+eabuvTSS2W328/qebOzs/XSSy9p1apVeuihh77hWQCwGnEDIGKkpaXpd7/7nQ4fPqzW1lbddtttam5u1u233679+/fr+eefV1lZmVwuV/B+G0lqamqSy+XSgQMH9Ktf/UqPPPKIFi1adE7PPX36dG3btk0rVqzgQ/2APo4bigFEjLvuuktFRUVKT0/XZ599pg8++EDbtm3T4sWLNXXqVA0fPlwLFizQ8uXLQ44rLCzUZ599puzsbNntdi1atEg/+tGPzvn5v/vd7+rFF1/UtddeK7vdrttvv72nTg1AGNkC5/thEAAQAWbNmqWMjAyutgAI4mUpAABgFOIGgLF++9vfavDgwd1uAMzEy1IAjPXZZ5/p6NGj3f583LhxYZwGQLgQNwAAwCi8LAUAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwyv8D5QDeQ1ZVaNcAAAAASUVORK5CYII=\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "sns.barplot(x='top_k', y='hit', data=hit_stat_df.groupby('top_k')['hit'].mean().reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7925564a-7d30-4914-baaf-4a00abb7686d",
   "metadata": {
    "id": "7925564a-7d30-4914-baaf-4a00abb7686d"
   },
   "source": [
    "# 问答"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-23T15:08:15.067283Z",
     "start_time": "2024-07-23T15:08:15.065512Z"
    }
   },
   "cell_type": "code",
   "source": "## 使用LCEL",
   "id": "f289897a4f3bf47b",
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "source": "### 流式处理",
   "metadata": {
    "id": "_o62oZuZ-6qi"
   },
   "id": "_o62oZuZ-6qi"
  },
  {
   "cell_type": "code",
   "source": [
    "from langchain.llms import Ollama\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain_core.runnables import RunnablePassthrough\n",
    "from langchain_core.prompts import PromptTemplate\n",
    "\n",
    "def format_docs(docs):\n",
    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
    "\n",
    "llm = Ollama(\n",
    "    model='qwen2:7b-instruct',\n",
    "    base_url=\"http://localhost:11434\"\n",
    ")\n",
    "\n",
    "prompt_tmpl = \"\"\"\n",
    "你是一个金融分析师，擅长根据所获取的信息片段，对问题进行分析和推理。\n",
    "你的任务是根据所获取的信息片段（<<<<context>>><<<</context>>>之间的内容）回答问题。\n",
    "回答保持简洁，不必重复问题，不要要添加描述性解释和与答案无关的任何内容。\n",
    "已知信息：\n",
    "<<<<context>>>\n",
    "{context}\n",
    "<<<</context>>>\n",
    "\n",
    "问题：{question}\n",
    "请回答：\n",
    "\"\"\"\n",
    "prompt = PromptTemplate.from_template(prompt_tmpl)\n",
    "retriever = vector_db.as_retriever(search_kwargs={'k': 4})\n",
    "\n",
    "rag_chain = (\n",
    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
    "    | prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")\n",
    "\n",
    "for chunk in rag_chain.stream(\"2023年10月美国ISM制造业PMI指数较上月有何变化？\"):\n",
    "    print(chunk, end=\"\", flush=True)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jl36b7QsQA0t",
    "outputId": "77c06e77-e633-40e2-e587-c41cee2a3d79"
   },
   "id": "jl36b7QsQA0t",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "2023年10月美国ISM制造业PMI指数较上个月大幅下降2.3个百分点。"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### 非流式输出",
   "metadata": {
    "id": "HbUjsGaF_B4g"
   },
   "id": "HbUjsGaF_B4g"
  },
  {
   "cell_type": "code",
   "source": [
    "print(rag_chain.invoke('2023年10月美国ISM制造业PMI指数较上月有何变化？'))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FhM128X6-v0V",
    "outputId": "856bfab3-a015-41b9-c859-c145b8c630bb"
   },
   "id": "FhM128X6-v0V",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "2023年10月美国ISM制造业PMI指数较上个月大幅下降了2.3个百分点。\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 流程拆解"
   ],
   "metadata": {
    "id": "6iEKt5prCrW8"
   },
   "id": "6iEKt5prCrW8"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "llm = Ollama(\n",
    "    model='qwen2:7b-instruct',\n",
    "    base_url=\"http://localhost:11434\"\n",
    ")"
   ],
   "id": "26f8a2678a7d45a3"
  },
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    "outputId": "697d71d2-62a1-48d1-e6c7-02b9dda7467c"
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    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'我是阿里云开发的一款超大规模语言模型，我叫通义千问。作为一个AI助手，我的目标是帮助用户获得准确、有用的信息，解决他们的问题和疑惑。我可以回答各种领域的问题、提供信息查询、解释概念、辅助学习等。请随时向我提问或寻求帮助，我会尽力提供支持。如果您有任何问题或需要解答，请尽管告诉我！'"
      ],
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       "type": "string"
      }
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     "metadata": {},
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    }
   ],
   "source": "llm.invoke('你是谁')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cf52672-6e47-458d-b050-51cb45178184",
   "metadata": {
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     "iopub.execute_input": "2024-07-21T14:29:36.744056Z",
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   "outputs": [],
   "source": [
    "def rag(query, n_chunks=5):\n",
    "    prompt_tmpl = \"\"\"\n",
    "你是一个金融分析师，擅长根据所获取的信息片段，对问题进行分析和推理。\n",
    "你的任务是根据所获取的信息片段（<<<<context>>><<<</context>>>之间的内容）回答问题。\n",
    "回答保持简洁，不必重复问题，不要要添加描述性解释和与答案无关的任何内容。\n",
    "已知信息：\n",
    "<<<<context>>>\n",
    "{{knowledge}}\n",
    "<<<</context>>>\n",
    "\n",
    "问题：{{query}}\n",
    "请回答：\n",
    "\"\"\".strip()\n",
    "\n",
    "    chunks = retrieve(vector_db, question, k=n_chunks)\n",
    "    prompt = prompt_tmpl.replace('{{knowledge}}', '\\n\\n'.join([doc.page_content for doc in chunks])).replace('{{query}}', query)\n",
    "\n",
    "    return llm(prompt), [doc.page_content for doc in chunks]"
   ]
  },
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   "cell_type": "code",
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   "id": "258a51b8-e303-444d-9304-700abf3deef8",
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    },
    "id": "258a51b8-e303-444d-9304-700abf3deef8"
   },
   "outputs": [],
   "source": [
    "prediction_df = qa_df[qa_df['dataset'] == 'test'][['uuid', 'question', 'qa_type', 'answer']]\n",
    "\n",
    "answer_dict = {}"
   ]
  },
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     "shell.execute_reply.started": "2024-07-21T14:29:55.595235Z"
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     "base_uri": "https://localhost:8080/",
     "height": 105,
     "referenced_widgets": [
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    "outputId": "916ac0e0-fd69-4330-fe5e-bd4d9847cdcf"
   },
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    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ],
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       "version_major": 2,
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     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:139: LangChainDeprecationWarning: The method `BaseLLM.__call__` was deprecated in langchain-core 0.1.7 and will be removed in 0.3.0. Use invoke instead.\n",
      "  warn_deprecated(\n"
     ]
    }
   ],
   "source": [
    "for idx, row in tqdm(prediction_df.iterrows(), total=len(prediction_df)):\n",
    "    uuid = row['uuid']\n",
    "    question = row['question']\n",
    "    answer, context = rag(question, n_chunks=4)\n",
    "    answer_dict[question] = {\n",
    "        'uuid': uuid,\n",
    "        'ref_answer': row['answer'],\n",
    "        'gen_answer': answer,\n",
    "        'context': context\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "prediction_df.loc[:, 'gen_answer'] = prediction_df['question'].apply(lambda q: answer_dict[q]['gen_answer'])\n",
    "prediction_df.loc[:, 'context'] = prediction_df['question'].apply(lambda q: answer_dict[q]['context'])"
   ],
   "metadata": {
    "id": "1Tq4E-QmizCF"
   },
   "id": "1Tq4E-QmizCF",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "prediction_df.sample(5)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 553
    },
    "id": "RJdE6AFRizr_",
    "outputId": "dd08120b-ffcc-4c2f-b2b1-3313f466660b"
   },
   "id": "RJdE6AFRizr_",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "                                     uuid                       question  \\\n",
       "136  3c0da094-8444-428b-9729-f1caa33e4b06        2023年二季度沙特GDP的同比增长率是多少？   \n",
       "168  9d41e5b3-2a8c-4584-b7b2-a6b7489758c9         日本央行的基准利率在10月31日维持在多少？   \n",
       "5    e73a0c9d-d42b-4350-a4c3-b38bf67c68a5           展望2024年，全球经济复苏的预期如何？   \n",
       "51   0a58414c-bf99-493b-b13b-b83c96af2687  世贸组织对2023年全球货物贸易增速的预测值是多少百分点?   \n",
       "265  28c7d4f0-8cea-48b3-9cfa-6683446fd425              海湾六国经济结构的主要问题是什么？   \n",
       "\n",
       "      qa_type          answer  \\\n",
       "136  detailed            1.2%   \n",
       "168  detailed           -0.1%   \n",
       "5    detailed  全球经济复苏预计将依旧疲软。   \n",
       "51   detailed         0.9个百分点   \n",
       "265  detailed        经济结构相对单一   \n",
       "\n",
       "                                            gen_answer  \\\n",
       "136                            2023年二季度沙特GDP同比仅增长1.2%。   \n",
       "168                       日本央银在10月31日将基准利率维持在-0.1%的水平。   \n",
       "5    2024年全球经济复苏预期将较为疲软，并可能出现进一步分化。发达经济体将面临“遏通胀和稳增长...   \n",
       "51              世贸组织将2023年全球货物贸易增速预测值下调至0.8%，为近几年最低水平。   \n",
       "265  海湾六国经济结构的主要问题是过于依赖传统能源，尤其是石油和天然气，这在一定程度上限制了工业发...   \n",
       "\n",
       "                                               context  \n",
       "136  [全球经济金融展望报告\\n中国银行研究院 40 2024年\\n注重绿色经济发展，为中海经贸合...  \n",
       "168  [全球经济金融展望报告\\n中国银行研究院 17 2024年\\n图11：日本GDP环比增长折年...  \n",
       "5    [全球经济金融展望报告\\n中国银行研究院 2 2024年\\n图1：全球GDP增速（%）\\n资...  \n",
       "51   [全球经济金融展望报告\\n中国银行研究院 7 2024年\\n（图7）。10月，世贸组织将20...  \n",
       "265  [全球经济金融展望报告\\n中国银行研究院 41 2024年\\n湾六国经济增长提供一定支撑，但...  "
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       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uuid</th>\n",
       "      <th>question</th>\n",
       "      <th>qa_type</th>\n",
       "      <th>answer</th>\n",
       "      <th>gen_answer</th>\n",
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       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>3c0da094-8444-428b-9729-f1caa33e4b06</td>\n",
       "      <td>2023年二季度沙特GDP的同比增长率是多少？</td>\n",
       "      <td>detailed</td>\n",
       "      <td>1.2%</td>\n",
       "      <td>2023年二季度沙特GDP同比仅增长1.2%。</td>\n",
       "      <td>[全球经济金融展望报告\\n中国银行研究院 40 2024年\\n注重绿色经济发展，为中海经贸合...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>9d41e5b3-2a8c-4584-b7b2-a6b7489758c9</td>\n",
       "      <td>日本央行的基准利率在10月31日维持在多少？</td>\n",
       "      <td>detailed</td>\n",
       "      <td>-0.1%</td>\n",
       "      <td>日本央银在10月31日将基准利率维持在-0.1%的水平。</td>\n",
       "      <td>[全球经济金融展望报告\\n中国银行研究院 17 2024年\\n图11：日本GDP环比增长折年...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>e73a0c9d-d42b-4350-a4c3-b38bf67c68a5</td>\n",
       "      <td>展望2024年，全球经济复苏的预期如何？</td>\n",
       "      <td>detailed</td>\n",
       "      <td>全球经济复苏预计将依旧疲软。</td>\n",
       "      <td>2024年全球经济复苏预期将较为疲软，并可能出现进一步分化。发达经济体将面临“遏通胀和稳增长...</td>\n",
       "      <td>[全球经济金融展望报告\\n中国银行研究院 2 2024年\\n图1：全球GDP增速（%）\\n资...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0a58414c-bf99-493b-b13b-b83c96af2687</td>\n",
       "      <td>世贸组织对2023年全球货物贸易增速的预测值是多少百分点?</td>\n",
       "      <td>detailed</td>\n",
       "      <td>0.9个百分点</td>\n",
       "      <td>世贸组织将2023年全球货物贸易增速预测值下调至0.8%，为近几年最低水平。</td>\n",
       "      <td>[全球经济金融展望报告\\n中国银行研究院 7 2024年\\n（图7）。10月，世贸组织将20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>28c7d4f0-8cea-48b3-9cfa-6683446fd425</td>\n",
       "      <td>海湾六国经济结构的主要问题是什么？</td>\n",
       "      <td>detailed</td>\n",
       "      <td>经济结构相对单一</td>\n",
       "      <td>海湾六国经济结构的主要问题是过于依赖传统能源，尤其是石油和天然气，这在一定程度上限制了工业发...</td>\n",
       "      <td>[全球经济金融展望报告\\n中国银行研究院 41 2024年\\n湾六国经济增长提供一定支撑，但...</td>\n",
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       "  </tbody>\n",
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       "\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
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       "\n",
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       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "        element.appendChild(docLink);\n",
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       "\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
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       "\n",
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       "    background-color: var(--bg-color);\n",
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       "    border-radius: 50%;\n",
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