{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d591916b",
   "metadata": {},
   "source": [
    "## Customized LLM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d70ce91",
   "metadata": {},
   "source": [
    "## Chroma self-querying"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555e7796",
   "metadata": {},
   "source": [
    "## Similarity search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca838ff5",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "def get_txt_files(path):\n",
    "    txt_files = []\n",
    "    for root, dirs, files in os.walk(path):\n",
    "        for file in files:\n",
    "            if file.endswith(\".txt\"):\n",
    "                txt_files.append(file)\n",
    "    return txt_files\n",
    " # 指定路径\n",
    "folder_path = \"data/\"\n",
    "txt_files_list = get_txt_files(folder_path)\n",
    "print(txt_files_list)\n",
    "str = '\\n'.join(txt_files_list)\n",
    "# print(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac7ee0da-2f12-48b9-abf4-1af3356323e2",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2023-08-04T12:26:04.617995Z",
     "iopub.status.busy": "2023-08-04T12:26:04.617570Z",
     "iopub.status.idle": "2023-08-04T12:26:09.041333Z",
     "shell.execute_reply": "2023-08-04T12:26:09.040686Z",
     "shell.execute_reply.started": "2023-08-04T12:26:04.617970Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirrors.cloud.aliyuncs.com/pypi/simple\n",
      "Requirement already satisfied: langchain in /opt/conda/lib/python3.8/site-packages (0.0.251)\n",
      "Collecting sentence_transformers\n",
      "  Downloading https://mirrors.cloud.aliyuncs.com/pypi/packages/20/9c/f07bd70d128fdb107bc02a0c702b9058b4fe147d0ba67b5a0f4c3cf15a54/sentence-transformers-2.2.2.tar.gz (85 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: PyYAML>=5.4.1 in /opt/conda/lib/python3.8/site-packages (from langchain) (6.0.1)\n",
      "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /opt/conda/lib/python3.8/site-packages (from langchain) (2.0.19)\n",
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      "Requirement already satisfied: async-timeout<5.0.0,>=4.0.0 in /opt/conda/lib/python3.8/site-packages (from langchain) (4.0.2)\n",
      "Requirement already satisfied: dataclasses-json<0.6.0,>=0.5.7 in /opt/conda/lib/python3.8/site-packages (from langchain) (0.5.14)\n",
      "Requirement already satisfied: langsmith<0.1.0,>=0.0.11 in /opt/conda/lib/python3.8/site-packages (from langchain) (0.0.18)\n",
      "Requirement already satisfied: numexpr<3.0.0,>=2.8.4 in /opt/conda/lib/python3.8/site-packages (from langchain) (2.8.4)\n",
      "Requirement already satisfied: numpy<2,>=1 in /opt/conda/lib/python3.8/site-packages (from langchain) (1.22.0)\n",
      "Requirement already satisfied: openapi-schema-pydantic<2.0,>=1.2 in /opt/conda/lib/python3.8/site-packages (from langchain) (1.2.4)\n",
      "Requirement already satisfied: pydantic<2,>=1 in /opt/conda/lib/python3.8/site-packages (from langchain) (1.10.12)\n",
      "Requirement already satisfied: requests<3,>=2 in /opt/conda/lib/python3.8/site-packages (from langchain) (2.29.0)\n",
      "Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in /opt/conda/lib/python3.8/site-packages (from langchain) (8.2.2)\n",
      "Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /opt/conda/lib/python3.8/site-packages (from sentence_transformers) (4.30.2)\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.8/site-packages (from sentence_transformers) (4.65.0)\n",
      "Requirement already satisfied: torch>=1.6.0 in /opt/conda/lib/python3.8/site-packages (from sentence_transformers) (2.0.1+cpu)\n",
      "Requirement already satisfied: torchvision in /opt/conda/lib/python3.8/site-packages (from sentence_transformers) (0.15.2+cpu)\n",
      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.8/site-packages (from sentence_transformers) (1.3.0)\n",
      "Requirement already satisfied: scipy in /opt/conda/lib/python3.8/site-packages (from sentence_transformers) (1.10.1)\n",
      "Requirement already satisfied: nltk in /opt/conda/lib/python3.8/site-packages (from sentence_transformers) (3.8.1)\n",
      "Requirement already satisfied: sentencepiece in /opt/conda/lib/python3.8/site-packages (from sentence_transformers) (0.1.99)\n",
      "Requirement already satisfied: huggingface-hub>=0.4.0 in /opt/conda/lib/python3.8/site-packages (from sentence_transformers) (0.16.4)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.8/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (23.1.0)\n",
      "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /opt/conda/lib/python3.8/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (2.0.4)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.8/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (6.0.4)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.8/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.9.2)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.8/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.4.0)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.8/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\n",
      "Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /opt/conda/lib/python3.8/site-packages (from dataclasses-json<0.6.0,>=0.5.7->langchain) (3.20.1)\n",
      "Requirement already satisfied: typing-inspect<1,>=0.4.0 in /opt/conda/lib/python3.8/site-packages (from dataclasses-json<0.6.0,>=0.5.7->langchain) (0.9.0)\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.8/site-packages (from huggingface-hub>=0.4.0->sentence_transformers) (3.12.2)\n",
      "Requirement already satisfied: fsspec in /opt/conda/lib/python3.8/site-packages (from huggingface-hub>=0.4.0->sentence_transformers) (2023.6.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.8/site-packages (from huggingface-hub>=0.4.0->sentence_transformers) (4.7.1)\n",
      "Requirement already satisfied: packaging>=20.9 in /opt/conda/lib/python3.8/site-packages (from huggingface-hub>=0.4.0->sentence_transformers) (23.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests<3,>=2->langchain) (3.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests<3,>=2->langchain) (1.26.16)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests<3,>=2->langchain) (2023.5.7)\n",
      "Requirement already satisfied: greenlet!=0.4.17 in /opt/conda/lib/python3.8/site-packages (from SQLAlchemy<3,>=1.4->langchain) (2.0.2)\n",
      "Requirement already satisfied: sympy in /opt/conda/lib/python3.8/site-packages (from torch>=1.6.0->sentence_transformers) (1.12)\n",
      "Requirement already satisfied: networkx in /opt/conda/lib/python3.8/site-packages (from torch>=1.6.0->sentence_transformers) (2.8.4)\n",
      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.8/site-packages (from torch>=1.6.0->sentence_transformers) (3.1.2)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.8/site-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (2023.6.3)\n",
      "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /opt/conda/lib/python3.8/site-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (0.13.3)\n",
      "Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.8/site-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (0.3.1)\n",
      "Requirement already satisfied: click in /opt/conda/lib/python3.8/site-packages (from nltk->sentence_transformers) (8.0.4)\n",
      "Requirement already satisfied: joblib in /opt/conda/lib/python3.8/site-packages (from nltk->sentence_transformers) (1.3.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from scikit-learn->sentence_transformers) (3.2.0)\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.8/site-packages (from torchvision->sentence_transformers) (10.0.0)\n",
      "Requirement already satisfied: mypy-extensions>=0.3.0 in /opt/conda/lib/python3.8/site-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.6.0,>=0.5.7->langchain) (1.0.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.8/site-packages (from jinja2->torch>=1.6.0->sentence_transformers) (2.1.3)\n",
      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.8/site-packages (from sympy->torch>=1.6.0->sentence_transformers) (1.3.0)\n",
      "Building wheels for collected packages: sentence_transformers\n",
      "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for sentence_transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125942 sha256=f051c7604ba725248597b8c38e928dd8412097fab545607792b8b4a8977badf6\n",
      "  Stored in directory: /root/.cache/pip/wheels/28/da/61/66f7ea6452cf9c524d0e4512c2b83ee95cb932b784d9bd02e4\n",
      "Successfully built sentence_transformers\n",
      "\u001b[33mDEPRECATION: pytorch-lightning 1.7.7 has a non-standard dependency specifier torch>=1.9.*. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling collected packages: sentence_transformers\n",
      "Successfully installed sentence_transformers-2.2.2\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install langchain sentence_transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "63a05c63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-04T12:26:10.009230Z",
     "iopub.status.busy": "2023-08-04T12:26:10.008894Z",
     "iopub.status.idle": "2023-08-04T12:30:31.733686Z",
     "shell.execute_reply": "2023-08-04T12:30:31.733084Z",
     "shell.execute_reply.started": "2023-08-04T12:26:10.009211Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5adc0727a5b473a9f45c8e32ec885ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)6e0c5/.gitattributes:   0%|          | 0.00/1.48k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc758212c4b04eb4a0e6c3b943a5b4d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)d6cda6e0c5/README.md:   0%|          | 0.00/362 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5bcdf3ada70457a8e5bc8eeca951e0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "cf47407d34e1472a8cc43a3f128ae95c",
       "version_major": 2,
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      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1bead784381f4f1db53628b735cf6fbc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading model.safetensors:   0%|          | 0.00/1.30G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "68cc1f6358124a1a866c817c2df0924e",
       "version_major": 2,
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      },
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/1.30G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3283ee429e8d49888abcdc673df490b0",
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "75ba378d2cec4b3a907322710248c828",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)6e0c5/tokenizer.json:   0%|          | 0.00/439k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3e8bcecd4878439c97a3e99c0ff939ac",
       "version_major": 2,
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      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/514 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c452706ba7a94c6aa676b557ca2db0e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)d6cda6e0c5/vocab.txt:   0%|          | 0.00/110k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No sentence-transformers model found with name /root/.cache/torch/sentence_transformers/GanymedeNil_text2vec-large-chinese. Creating a new one with MEAN pooling.\n",
      "/opt/conda/lib/python3.8/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.\n",
      "  warn(\"The installed version of bitsandbytes was compiled without GPU support. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32\n",
      "[2023-08-04 20:30:27,805] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
     ]
    }
   ],
   "source": [
    "embedding_model_dict = {\n",
    "    \"ernie-tiny\": \"nghuyong/ernie-3.0-nano-zh\",\n",
    "    \"ernie-base\": \"nghuyong/ernie-3.0-base-zh\",\n",
    "    \"text2vec-base\": \"shibing624/text2vec-base-chinese\",\n",
    "    \"text2vec\": \"GanymedeNil/text2vec-large-chinese\",\n",
    "    \"m3e-small\": \"moka-ai/m3e-small\",\n",
    "    \"m3e-base\": \"moka-ai/m3e-base\",\n",
    "}\n",
    "\n",
    "# Embedding model name\n",
    "EMBEDDING_MODEL = \"text2vec\"\n",
    "import torch.cuda\n",
    "import torch.backends\n",
    "# Embedding running device\n",
    "EMBEDDING_DEVICE = \"cuda\" if torch.cuda.is_available() else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
    "\n",
    "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
    "embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[EMBEDDING_MODEL],\n",
    "                                       model_kwargs={'device': EMBEDDING_DEVICE})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f9da661",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-04T12:30:58.393981Z",
     "iopub.status.busy": "2023-08-04T12:30:58.393651Z",
     "iopub.status.idle": "2023-08-04T12:30:58.407837Z",
     "shell.execute_reply": "2023-08-04T12:30:58.407308Z",
     "shell.execute_reply.started": "2023-08-04T12:30:58.393964Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS\n",
    "\n",
    " \n",
    "\n",
    "text_splitter = CharacterTextSplitter(\n",
    "    separator = \"\\n\",\n",
    "    chunk_size = 60,\n",
    "    chunk_overlap  = 5,\n",
    "    length_function = len,\n",
    ")\n",
    "texts = text_splitter.split_text(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "65147c30",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-04T12:30:59.821226Z",
     "iopub.status.busy": "2023-08-04T12:30:59.820883Z",
     "iopub.status.idle": "2023-08-04T12:30:59.824514Z",
     "shell.execute_reply": "2023-08-04T12:30:59.824054Z",
     "shell.execute_reply.started": "2023-08-04T12:30:59.821207Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11579"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "48a30d2b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-04T12:31:01.598309Z",
     "iopub.status.busy": "2023-08-04T12:31:01.597962Z",
     "iopub.status.idle": "2023-08-04T12:31:01.600916Z",
     "shell.execute_reply": "2023-08-04T12:31:01.600441Z",
     "shell.execute_reply.started": "2023-08-04T12:31:01.598291Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "query = '2021年博世科货币资金增长率是多少?保留2位小数。'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4ab14e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-04T12:31:02.623952Z",
     "iopub.status.busy": "2023-08-04T12:31:02.623614Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "docsearch = FAISS.from_texts(texts, embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2834b2f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = docsearch.as_retriever(search_kwargs={\"k\": 1})\n",
    "\n",
    "docs = retriever.get_relevant_documents(query)\n",
    "docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "212b21aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# docs = docsearch.similarity_search(query,k=1)\n",
    "# docs[0].page_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7b5801d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019年英洛华科技股份有限公司\n",
      "药明康德2019年\n"
     ]
    }
   ],
   "source": [
    "def keep(string, word_list):\n",
    "    for item in word_list:\n",
    "        if item in string:\n",
    "            index = string.index(item)\n",
    "            string =  string[:index]\n",
    "    return string\n",
    "word_list = [\"外文\", \"研发\",\"对\", \"三费\",\"销售\", \"电子信箱\",\"年报\", \"营业\",\"衍生\", \"投资\",\"总资产\",\"综合收益\",\"衍生\",\"利息\",\"负债\",\"博士\",\"财务\",\"应付\",\"公允\",\"现金\",\"办公地址\",\"证券代码\",\"注册地址\",\"企业名称\",\"应收款\",\"无形资产\",\"硕士\",\"每股收益\",\"法定代表\",\"其他\",\"速动比\",\"固定资产\"]\n",
    "string = \"2019年英洛华科技股份有限公司投资收益占营业收入比率为多少\"\n",
    "结果 = keep(string, word_list)\n",
    "print(结果)\n",
    "def abstract_year(string):\n",
    "    index = string.find(\"年\")\n",
    "    if index != -1:\n",
    "        year = string[index-4:index + 1]\n",
    " \n",
    "        return string[:index-4] + string[index + 1 :] + year \n",
    "    return string\n",
    "string = \"2019年药明康德\"\n",
    "结果 = abstract_year(string)\n",
    "print(结果)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71ac6a2a",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    " # 打开文件\n",
    "path = 'submit_example.jsonl'\n",
    "\n",
    "# 打开文件\n",
    "with open(path, 'r', encoding='utf-8') as f:\n",
    "    content = f.read()\n",
    " # 打印文件内容\n",
    "lis = content.split('\\n')[:-1]\n",
    "print(lis)\n",
    "ques_list = []\n",
    "for item in lis:\n",
    "    json_data = json.loads(item)\n",
    "    filename = ''\n",
    "    for txt in txt_files_list:\n",
    "        key = txt.split('__')[2]\n",
    "        if '股份' in key:\n",
    "            key = key.replace('股份','')\n",
    "        year = txt.split('__')[3]\n",
    "        if key in json_data['question'] and year in json_data['question']:\n",
    "            filename = txt\n",
    "            json_data['filename'] = txt\n",
    "            ques_list.append(json_data) \n",
    "            print(txt)\n",
    "            break\n",
    "        # elif key in json_data['question']:\n",
    "        #     filename = 'no year'\n",
    "        #     json_data['filename'] = filename\n",
    "        #     ques_list.append(json_data) \n",
    "        #     print('no year' + txt + ':' + key + year + \":\" + json_data['question'])\n",
    "        #     break        \n",
    "\n",
    "    if filename != '':\n",
    "        continue\n",
    "\n",
    "    res = keep(json_data['question'], word_list)\n",
    "    res = abstract_year(res)\n",
    "    docs = retriever.get_relevant_documents(res)\n",
    "\n",
    "    filename = docs[0].page_content\n",
    "    print('embeding' + filename)\n",
    "    json_data['filename'] = filename\n",
    "    ques_list.append(json_data)\n",
    "\n",
    "\n",
    "print(ques_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "efdf01ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JSON数据已成功写入文件。\n"
     ]
    }
   ],
   "source": [
    "\n",
    " # 打开文件并将JSON数据写入\n",
    "with open('output.json', 'w', encoding='utf-8') as f:\n",
    "    json.dump(ques_list, f, ensure_ascii=False, indent=4)\n",
    "print(\"JSON数据已成功写入文件。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9c96fd7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d931e25c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(query):\n",
    "    for txt in txt_files_list:\n",
    "        key = txt.split('__')[2]\n",
    "        print(key)\n",
    "        if '股份' in key:\n",
    "            key =key.replace('股份','')\n",
    "        year = txt.split('__')[3]\n",
    "        if key in query and year in query:\n",
    "            filename = txt\n",
    "            json_data['filename'] = txt\n",
    "            ques_list.append(json_data) \n",
    "            print(txt)\n",
    "            break\n",
    "        # elif key in query:\n",
    "        #     filename = 'no year'\n",
    "        #     json_data['filename'] = filename\n",
    "        #     ques_list.append(json_data) \n",
    "        #     print(txt)\n",
    "        #     break  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "207d34f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = '2021年利群商业集团股份有限公司固定资产增长率为多少?保留2位小数'\n",
    "test(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dc76255",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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