rag.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('codebase_community',\n",
       "  '/home/data2/luzhan/projects/bird_bench/dev/dev_databases/codebase_community/codebase_community.sqlite'),\n",
       " ('formula_1',\n",
       "  '/home/data2/luzhan/projects/bird_bench/dev/dev_databases/formula_1/formula_1.sqlite'),\n",
       " ('card_games',\n",
       "  '/home/data2/luzhan/projects/bird_bench/dev/dev_databases/card_games/card_games.sqlite'),\n",
       " ('superhero',\n",
       "  '/home/data2/luzhan/projects/bird_bench/dev/dev_databases/superhero/superhero.sqlite'),\n",
       " ('student_club',\n",
       "  '/home/data2/luzhan/projects/bird_bench/dev/dev_databases/student_club/student_club.sqlite'),\n",
       " ('toxicology',\n",
       "  '/home/data2/luzhan/projects/bird_bench/dev/dev_databases/toxicology/toxicology.sqlite'),\n",
       " ('california_schools',\n",
       "  '/home/data2/luzhan/projects/bird_bench/dev/dev_databases/california_schools/california_schools.sqlite'),\n",
       " ('thrombosis_prediction',\n",
       "  '/home/data2/luzhan/projects/bird_bench/dev/dev_databases/thrombosis_prediction/thrombosis_prediction.sqlite'),\n",
       " ('financial',\n",
       "  '/home/data2/luzhan/projects/bird_bench/dev/dev_databases/financial/financial.sqlite'),\n",
       " ('european_football_2',\n",
       "  '/home/data2/luzhan/projects/bird_bench/dev/dev_databases/european_football_2/european_football_2.sqlite'),\n",
       " ('debit_card_specializing',\n",
       "  '/home/data2/luzhan/projects/bird_bench/dev/dev_databases/debit_card_specializing/debit_card_specializing.sqlite'),\n",
       " ('beer_factory',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/beer_factory/beer_factory.sqlite'),\n",
       " ('world',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/world/world.sqlite'),\n",
       " ('college_completion',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/college_completion/college_completion.sqlite'),\n",
       " ('synthea',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/synthea/synthea.sqlite'),\n",
       " ('disney',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/disney/disney.sqlite'),\n",
       " ('movielens',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/movielens/movielens.sqlite'),\n",
       " ('music_platform_2',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/music_platform_2/music_platform_2.sqlite'),\n",
       " ('books',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/books/books.sqlite'),\n",
       " ('food_inspection',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/food_inspection/food_inspection.sqlite'),\n",
       " ('retail_world',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/retail_world/retail_world.sqlite'),\n",
       " ('computer_student',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/computer_student/computer_student.sqlite'),\n",
       " ('movie_platform',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/movie_platform/movie_platform.sqlite'),\n",
       " ('car_retails',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/car_retails/car_retails.sqlite'),\n",
       " ('sales_in_weather',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/sales_in_weather/sales_in_weather.sqlite'),\n",
       " ('movies_4',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/movies_4/movies_4.sqlite'),\n",
       " ('shakespeare',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/shakespeare/shakespeare.sqlite'),\n",
       " ('sales',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/sales/sales.sqlite'),\n",
       " ('airline',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/airline/airline.sqlite'),\n",
       " ('olympics',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/olympics/olympics.sqlite'),\n",
       " ('talkingdata',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/talkingdata/talkingdata.sqlite'),\n",
       " ('chicago_crime',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/chicago_crime/chicago_crime.sqlite'),\n",
       " ('software_company',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/software_company/software_company.sqlite'),\n",
       " ('human_resources',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/human_resources/human_resources.sqlite'),\n",
       " ('retails',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/retails/retails.sqlite'),\n",
       " ('trains',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/trains/trains.sqlite'),\n",
       " ('language_corpus',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/language_corpus/language_corpus.sqlite'),\n",
       " ('address',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/address/address.sqlite'),\n",
       " ('law_episode',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/law_episode/law_episode.sqlite'),\n",
       " ('mental_health_survey',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/mental_health_survey/mental_health_survey.sqlite'),\n",
       " ('coinmarketcap',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/coinmarketcap/coinmarketcap.sqlite'),\n",
       " ('music_tracker',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/music_tracker/music_tracker.sqlite'),\n",
       " ('cars',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/cars/cars.sqlite'),\n",
       " ('works_cycles',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/works_cycles/works_cycles.sqlite'),\n",
       " ('student_loan',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/student_loan/student_loan.sqlite'),\n",
       " ('superstore',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/superstore/superstore.sqlite'),\n",
       " ('mondial_geo',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/mondial_geo/mondial_geo.sqlite'),\n",
       " ('legislator',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/legislator/legislator.sqlite'),\n",
       " ('cs_semester',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/cs_semester/cs_semester.sqlite'),\n",
       " ('food_inspection_2',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/food_inspection_2/food_inspection_2.sqlite'),\n",
       " ('video_games',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/video_games/video_games.sqlite'),\n",
       " ('menu',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/menu/menu.sqlite'),\n",
       " ('bike_share_1',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/bike_share_1/bike_share_1.sqlite'),\n",
       " ('codebase_comments',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/codebase_comments/codebase_comments.sqlite'),\n",
       " ('movie_3',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/movie_3/movie_3.sqlite'),\n",
       " ('regional_sales',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/regional_sales/regional_sales.sqlite'),\n",
       " ('european_football_1',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/european_football_1/european_football_1.sqlite'),\n",
       " ('shipping',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/shipping/shipping.sqlite'),\n",
       " ('soccer_2016',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/soccer_2016/soccer_2016.sqlite'),\n",
       " ('university',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/university/university.sqlite'),\n",
       " ('genes',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/genes/genes.sqlite'),\n",
       " ('simpson_episodes',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/simpson_episodes/simpson_episodes.sqlite'),\n",
       " ('authors',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/authors/authors.sqlite'),\n",
       " ('citeseer',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/citeseer/citeseer.sqlite'),\n",
       " ('cookbook',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/cookbook/cookbook.sqlite'),\n",
       " ('hockey',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/hockey/hockey.sqlite'),\n",
       " ('book_publishing_company',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/book_publishing_company/book_publishing_company.sqlite'),\n",
       " ('restaurant',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/restaurant/restaurant.sqlite'),\n",
       " ('professional_basketball',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/professional_basketball/professional_basketball.sqlite'),\n",
       " ('donor',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/donor/donor.sqlite'),\n",
       " ('app_store',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/app_store/app_store.sqlite'),\n",
       " ('social_media',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/social_media/social_media.sqlite'),\n",
       " ('public_review_platform',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/public_review_platform/public_review_platform.sqlite'),\n",
       " ('craftbeer',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/craftbeer/craftbeer.sqlite'),\n",
       " ('image_and_language',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/image_and_language/image_and_language.sqlite'),\n",
       " ('movie',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/movie/movie.sqlite'),\n",
       " ('retail_complains',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/retail_complains/retail_complains.sqlite'),\n",
       " ('world_development_indicators',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/world_development_indicators/world_development_indicators.sqlite'),\n",
       " ('ice_hockey_draft',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/ice_hockey_draft/ice_hockey_draft.sqlite'),\n",
       " ('shooting',\n",
       "  '/home/data2/luzhan/projects/bird_bench/train/train_databases/shooting/shooting.sqlite')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "\n",
    "def get_database_info(base_path: str):\n",
    "    output = []\n",
    "    for database_name in os.listdir(base_path):\n",
    "        if os.path.isdir(os.path.join(base_path, database_name)):\n",
    "            database_path = os.path.join(\n",
    "                base_path, database_name, f\"{database_name}.sqlite\"\n",
    "            )\n",
    "            output.append((database_name, database_path))\n",
    "    return output\n",
    "\n",
    "\n",
    "database_info = get_database_info(\n",
    "    \"/home/data2/luzhan/projects/bird_bench/dev/dev_databases\"\n",
    ") + get_database_info(\"/home/data2/luzhan/projects/bird_bench/train/train_databases\")\n",
    "database_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/data2/luzhan/miniconda3/envs/vllm/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "/home/data2/luzhan/miniconda3/envs/vllm/lib/python3.11/site-packages/transformers/utils/hub.py:124: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
      "  warnings.warn(\n",
      "/home/data2/luzhan/miniconda3/envs/vllm/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from pymilvus import DataType, MilvusClient, utility, connections, db, model\n",
    "import tool\n",
    "\n",
    "\n",
    "# from pymilvus.model.hybrid import BGEM3EmbeddingFunction\n",
    "# import pandas as pd\n",
    "\n",
    "# bge_m3_ef = BGEM3EmbeddingFunction(\n",
    "#     model_name=\"BAAI/bge-m3\",  # Specify the model name\n",
    "#     device=\"cpu\",  # Specify the device to use, e.g., 'cpu' or 'cuda:0'\n",
    "#     use_fp16=False,  # Specify whether to use fp16. Set to `False` if `device` is `cpu`.\n",
    "# )\n",
    "embedding_fn = model.dense.SentenceTransformerEmbeddingFunction(\n",
    "    model_name=\"all-MiniLM-L6-v2\",  # Specify the model name\n",
    "    device=\"cuda:0\",  # Specify the device to use, e.g., 'cpu' or 'cuda:0'\n",
    "    trust_remote_code=True,\n",
    "    local_files_only=True,\n",
    ")\n",
    "\n",
    "COLLECTION_NAME_FOR_TABLE_SCHEMA = \"{db_name}_table_schema\"\n",
    "COLLECTION_NAME_FOR_COLUMN_SCHEMA = \"{db_name}_column_schema\"\n",
    "COLLECTION_NAME_FOR_COLUMN_VALUE = \"{db_name}_column_value\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_table_schema(client: MilvusClient, engine, db_name: str):\n",
    "    collection_name = COLLECTION_NAME_FOR_TABLE_SCHEMA.format(db_name=db_name)\n",
    "    client.drop_collection(collection_name=collection_name)\n",
    "    schema = MilvusClient.create_schema(\n",
    "        auto_id=True,\n",
    "        enable_dynamic_field=True,\n",
    "    )\n",
    "    schema.add_field(field_name=\"id\", datatype=DataType.INT64, is_primary=True)\n",
    "    schema.add_field(field_name=\"table_name\", datatype=DataType.VARCHAR, max_length=128)\n",
    "    schema.add_field(\n",
    "        field_name=\"table_name_vector\", datatype=DataType.FLOAT_VECTOR, dim=768\n",
    "    )\n",
    "    client.create_collection(\n",
    "        collection_name=collection_name,\n",
    "        schema=schema,\n",
    "    )\n",
    "\n",
    "    index_params = MilvusClient.prepare_index_params()\n",
    "    index_params.add_index(\n",
    "        field_name=\"table_name_vector\",\n",
    "        metric_type=\"COSINE\",\n",
    "        index_type=\"IVF_FLAT\",\n",
    "        index_name=\"table_name_vector_index\",\n",
    "        params={\"nlist\": 128},\n",
    "    )\n",
    "    client.create_index(collection_name=collection_name, index_params=index_params)\n",
    "    res = client.list_indexes(collection_name=collection_name)\n",
    "\n",
    "    table_names = tool.get_tables_in_database(engine)\n",
    "    data = []\n",
    "    for table_name in table_names:\n",
    "        table_name_vector = embedding_fn.encode_documents(table_name)[0]\n",
    "        # print(table_name_vector)\n",
    "        data.append(\n",
    "            {\n",
    "                \"table_name\": table_name,\n",
    "                \"table_name_vector\": table_name_vector,\n",
    "            }\n",
    "        )\n",
    "    res = client.insert(collection_name=collection_name, data=data)\n",
    "    print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "def build_column_schema(client: MilvusClient, df: pd.DataFrame, db_name: str):\n",
    "    collection_name = COLLECTION_NAME_FOR_COLUMN_SCHEMA.format(db_name=db_name)\n",
    "    client.drop_collection(collection_name=collection_name)\n",
    "\n",
    "    schema = MilvusClient.create_schema(\n",
    "        auto_id=True,\n",
    "        enable_dynamic_field=True,\n",
    "    )\n",
    "    schema.add_field(field_name=\"id\", datatype=DataType.INT64, is_primary=True)\n",
    "    schema.add_field(field_name=\"table_name\", datatype=DataType.VARCHAR, max_length=128)\n",
    "    schema.add_field(\n",
    "        field_name=\"column_name\", datatype=DataType.VARCHAR, max_length=128\n",
    "    )\n",
    "    schema.add_field(\n",
    "        field_name=\"column_name_vector\", datatype=DataType.FLOAT_VECTOR, dim=768\n",
    "    )\n",
    "    schema.add_field(\n",
    "        field_name=\"column_description\", datatype=DataType.VARCHAR, max_length=4096\n",
    "    )\n",
    "    schema.add_field(\n",
    "        field_name=\"column_description_vector\", datatype=DataType.FLOAT_VECTOR, dim=768\n",
    "    )\n",
    "\n",
    "    client.create_collection(\n",
    "        collection_name=collection_name,\n",
    "        schema=schema,\n",
    "    )\n",
    "\n",
    "    index_params = MilvusClient.prepare_index_params()\n",
    "    index_params.add_index(\n",
    "        field_name=\"column_name_vector\",\n",
    "        metric_type=\"COSINE\",\n",
    "        index_type=\"IVF_FLAT\",\n",
    "        index_name=\"column_name_vector_index\",\n",
    "        params={\"nlist\": 128},\n",
    "    )\n",
    "    index_params.add_index(\n",
    "        field_name=\"column_description_vector\",\n",
    "        metric_type=\"COSINE\",\n",
    "        index_type=\"IVF_FLAT\",\n",
    "        index_name=\"column_description_vector_index\",\n",
    "        params={\"nlist\": 128},\n",
    "    )\n",
    "    client.create_index(collection_name=collection_name, index_params=index_params)\n",
    "    res = client.list_indexes(collection_name=collection_name)\n",
    "\n",
    "    data = []\n",
    "    for index, row in df.iterrows():\n",
    "        table_name = row[\"table_name\"]\n",
    "        column_name = row[\"original_column_name\"]\n",
    "        column_description = row[\"description\"]\n",
    "        column_name_vector, column_description_vector = embedding_fn.encode_documents(\n",
    "            [column_name, column_description]\n",
    "        )\n",
    "\n",
    "        data.append(\n",
    "            {\n",
    "                \"table_name\": table_name,\n",
    "                \"column_name\": column_name,\n",
    "                \"column_name_vector\": column_name_vector,\n",
    "                \"column_description\": column_description,\n",
    "                \"column_description_vector\": column_description_vector,\n",
    "            }\n",
    "        )\n",
    "    res = client.insert(collection_name=collection_name, data=data)\n",
    "    print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import select, text\n",
    "\n",
    "\n",
    "def build_column_value(client: MilvusClient, engine, db_name: str):\n",
    "    collection_name = COLLECTION_NAME_FOR_COLUMN_VALUE.format(db_name=db_name)\n",
    "    client.drop_collection(collection_name=collection_name)\n",
    "\n",
    "    schema = MilvusClient.create_schema(\n",
    "        auto_id=True,\n",
    "        enable_dynamic_field=True,\n",
    "    )\n",
    "    schema.add_field(field_name=\"id\", datatype=DataType.INT64, is_primary=True)\n",
    "    schema.add_field(field_name=\"table_name\", datatype=DataType.VARCHAR, max_length=128)\n",
    "    schema.add_field(\n",
    "        field_name=\"column_name\", datatype=DataType.VARCHAR, max_length=128\n",
    "    )\n",
    "    schema.add_field(\n",
    "        field_name=\"column_value\", datatype=DataType.VARCHAR, max_length=128\n",
    "    )\n",
    "    schema.add_field(\n",
    "        field_name=\"column_value_vector\", datatype=DataType.FLOAT_VECTOR, dim=768\n",
    "    )\n",
    "    client.create_collection(\n",
    "        collection_name=collection_name,\n",
    "        schema=schema,\n",
    "    )\n",
    "\n",
    "    index_params = MilvusClient.prepare_index_params()\n",
    "    index_params.add_index(\n",
    "        field_name=\"column_value_vector\",\n",
    "        metric_type=\"COSINE\",\n",
    "        index_type=\"IVF_FLAT\",\n",
    "        index_name=\"column_value_vector_index\",\n",
    "        params={\"nlist\": 128},\n",
    "    )\n",
    "    client.create_index(collection_name=collection_name, index_params=index_params)\n",
    "\n",
    "    for table_name in tool.get_tables_in_database(engine):\n",
    "        string_columns_names = tool.get_string_columns_in_database(engine, table_name)\n",
    "        print(string_columns_names)\n",
    "        for column_name in string_columns_names:\n",
    "            data = []\n",
    "            stmt = select(text(column_name)).distinct().select_from(text(table_name))\n",
    "\n",
    "            with engine.connect() as connection:\n",
    "                result = connection.execute(stmt)\n",
    "                unique_values = result.fetchall()\n",
    "\n",
    "            unique_values = [str(value[0]) for value in unique_values]\n",
    "            for column_value, column_value_vector in list(\n",
    "                zip(unique_values, embedding_fn.encode_documents(unique_values))\n",
    "            ):\n",
    "                data.append(\n",
    "                    {\n",
    "                        \"table_name\": table_name,\n",
    "                        \"column_name\": column_name,\n",
    "                        \"column_value\": column_value,\n",
    "                        \"column_value_vector\": column_value_vector,\n",
    "                    }\n",
    "                )\n",
    "\n",
    "            res = client.insert(collection_name=collection_name, data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'insert_count': 8, 'ids': [450680172451281395, 450680172451281396, 450680172451281397, 450680172451281398, 450680172451281399, 450680172451281400, 450680172451281401, 450680172451281402], 'cost': 0}\n",
      "{'insert_count': 71, 'ids': [450680172451281404, 450680172451281405, 450680172451281406, 450680172451281407, 450680172451281408, 450680172451281409, 450680172451281410, 450680172451281411, 450680172451281412, 450680172451281413, 450680172451281414, 450680172451281415, 450680172451281416, 450680172451281417, 450680172451281418, 450680172451281419, 450680172451281420, 450680172451281421, 450680172451281422, 450680172451281423, 450680172451281424, 450680172451281425, 450680172451281426, 450680172451281427, 450680172451281428, 450680172451281429, 450680172451281430, 450680172451281431, 450680172451281432, 450680172451281433, 450680172451281434, 450680172451281435, 450680172451281436, 450680172451281437, 450680172451281438, 450680172451281439, 450680172451281440, 450680172451281441, 450680172451281442, 450680172451281443, 450680172451281444, 450680172451281445, 450680172451281446, 450680172451281447, 450680172451281448, 450680172451281449, 450680172451281450, 450680172451281451, 450680172451281452, 450680172451281453, 450680172451281454, 450680172451281455, 450680172451281456, 450680172451281457, 450680172451281458, 450680172451281459, 450680172451281460, 450680172451281461, 450680172451281462, 450680172451281463, 450680172451281464, 450680172451281465, 450680172451281466, 450680172451281467, 450680172451281468, 450680172451281469, 450680172451281470, 450680172451281471, 450680172451281472, 450680172451281473, 450680172451281474], 'cost': 0}\n",
      "{'insert_count': 13, 'ids': [450680172451281476, 450680172451281477, 450680172451281478, 450680172451281479, 450680172451281480, 450680172451281481, 450680172451281482, 450680172451281483, 450680172451281484, 450680172451281485, 450680172451281486, 450680172451281487, 450680172451281488], 'cost': 0}\n",
      "{'insert_count': 95, 'ids': [450680172451281490, 450680172451281491, 450680172451281492, 450680172451281493, 450680172451281494, 450680172451281495, 450680172451281496, 450680172451281497, 450680172451281498, 450680172451281499, 450680172451281500, 450680172451281501, 450680172451281502, 450680172451281503, 450680172451281504, 450680172451281505, 450680172451281506, 450680172451281507, 450680172451281508, 450680172451281509, 450680172451281510, 450680172451281511, 450680172451281512, 450680172451281513, 450680172451281514, 450680172451281515, 450680172451281516, 450680172451281517, 450680172451281518, 450680172451281519, 450680172451281520, 450680172451281521, 450680172451281522, 450680172451281523, 450680172451281524, 450680172451281525, 450680172451281526, 450680172451281527, 450680172451281528, 450680172451281529, 450680172451281530, 450680172451281531, 450680172451281532, 450680172451281533, 450680172451281534, 450680172451281535, 450680172451281536, 450680172451281537, 450680172451281538, 450680172451281539, 450680172451281540, 450680172451281541, 450680172451281542, 450680172451281543, 450680172451281544, 450680172451281545, 450680172451281546, 450680172451281547, 450680172451281548, 450680172451281549, 450680172451281550, 450680172451281551, 450680172451281552, 450680172451281553, 450680172451281554, 450680172451281555, 450680172451281556, 450680172451281557, 450680172451281558, 450680172451281559, 450680172451281560, 450680172451281561, 450680172451281562, 450680172451281563, 450680172451281564, 450680172451281565, 450680172451281566, 450680172451281567, 450680172451281568, 450680172451281569, 450680172451281570, 450680172451281571, 450680172451281572, 450680172451281573, 450680172451281574, 450680172451281575, 450680172451281576, 450680172451281577, 450680172451281578, 450680172451281579, 450680172451281580, 450680172451281581, 450680172451281582, 450680172451281583, 450680172451281584], 'cost': 0}\n",
      "{'insert_count': 6, 'ids': [450680172451281586, 450680172451281587, 450680172451281588, 450680172451281589, 450680172451281590, 450680172451281591], 'cost': 0}\n",
      "{'insert_count': 115, 'ids': [450680172451281593, 450680172451281594, 450680172451281595, 450680172451281596, 450680172451281597, 450680172451281598, 450680172451281599, 450680172451281600, 450680172451281601, 450680172451281602, 450680172451281603, 450680172451281604, 450680172451281605, 450680172451281606, 450680172451281607, 450680172451281608, 450680172451281609, 450680172451281610, 450680172451281611, 450680172451281612, 450680172451281613, 450680172451281614, 450680172451281615, 450680172451281616, 450680172451281617, 450680172451281618, 450680172451281619, 450680172451281620, 450680172451281621, 450680172451281622, 450680172451281623, 450680172451281624, 450680172451281625, 450680172451281626, 450680172451281627, 450680172451281628, 450680172451281629, 450680172451281630, 450680172451281631, 450680172451281632, 450680172451281633, 450680172451281634, 450680172451281635, 450680172451281636, 450680172451281637, 450680172451281638, 450680172451281639, 450680172451281640, 450680172451281641, 450680172451281642, 450680172451281643, 450680172451281644, 450680172451281645, 450680172451281646, 450680172451281647, 450680172451281648, 450680172451281649, 450680172451281650, 450680172451281651, 450680172451281652, 450680172451281653, 450680172451281654, 450680172451281655, 450680172451281656, 450680172451281657, 450680172451281658, 450680172451281659, 450680172451281660, 450680172451281661, 450680172451281662, 450680172451281663, 450680172451281664, 450680172451281665, 450680172451281666, 450680172451281667, 450680172451281668, 450680172451281669, 450680172451281670, 450680172451281671, 450680172451281672, 450680172451281673, 450680172451281674, 450680172451281675, 450680172451281676, 450680172451281677, 450680172451281678, 450680172451281679, 450680172451281680, 450680172451281681, 450680172451281682, 450680172451281683, 450680172451281684, 450680172451281685, 450680172451281686, 450680172451281687, 450680172451281688, 450680172451281689, 450680172451281690, 450680172451281691, 450680172451281692, 450680172451281693, 450680172451281694, 450680172451281695, 450680172451281696, 450680172451281697, 450680172451281698, 450680172451281699, 450680172451281700, 450680172451281701, 450680172451281702, 450680172451281703, 450680172451281704, 450680172451281705, 450680172451281706, 450680172451281707], 'cost': 0}\n",
      "{'insert_count': 10, 'ids': [450680172451281709, 450680172451281710, 450680172451281711, 450680172451281712, 450680172451281713, 450680172451281714, 450680172451281715, 450680172451281716, 450680172451281717, 450680172451281718], 'cost': 0}\n",
      "{'insert_count': 31, 'ids': [450680172451281720, 450680172451281721, 450680172451281722, 450680172451281723, 450680172451281724, 450680172451281725, 450680172451281726, 450680172451281727, 450680172451281728, 450680172451281729, 450680172451281730, 450680172451281731, 450680172451281732, 450680172451281733, 450680172451281734, 450680172451281735, 450680172451281736, 450680172451281737, 450680172451281738, 450680172451281739, 450680172451281740, 450680172451281741, 450680172451281742, 450680172451281743, 450680172451281744, 450680172451281745, 450680172451281746, 450680172451281747, 450680172451281748, 450680172451281749, 450680172451281750], 'cost': 0}\n",
      "{'insert_count': 8, 'ids': [450680172451281752, 450680172451281753, 450680172451281754, 450680172451281755, 450680172451281756, 450680172451281757, 450680172451281758, 450680172451281759], 'cost': 0}\n",
      "{'insert_count': 48, 'ids': [450680172451281761, 450680172451281762, 450680172451281763, 450680172451281764, 450680172451281765, 450680172451281766, 450680172451281767, 450680172451281768, 450680172451281769, 450680172451281770, 450680172451281771, 450680172451281772, 450680172451281773, 450680172451281774, 450680172451281775, 450680172451281776, 450680172451281777, 450680172451281778, 450680172451281779, 450680172451281780, 450680172451281781, 450680172451281782, 450680172451281783, 450680172451281784, 450680172451281785, 450680172451281786, 450680172451281787, 450680172451281788, 450680172451281789, 450680172451281790, 450680172451281791, 450680172451281792, 450680172451281793, 450680172451281794, 450680172451281795, 450680172451281796, 450680172451281797, 450680172451281798, 450680172451281799, 450680172451281800, 450680172451281801, 450680172451281802, 450680172451281803, 450680172451281804, 450680172451281805, 450680172451281806, 450680172451281807, 450680172451281808], 'cost': 0}\n",
      "{'insert_count': 4, 'ids': [450680172451281810, 450680172451281811, 450680172451281812, 450680172451281813], 'cost': 0}\n",
      "{'insert_count': 11, 'ids': [450680172451281815, 450680172451281816, 450680172451281817, 450680172451281818, 450680172451281819, 450680172451281820, 450680172451281821, 450680172451281822, 450680172451281823, 450680172451281824, 450680172451281825], 'cost': 0}\n",
      "{'insert_count': 3, 'ids': [450680172451281827, 450680172451281828, 450680172451281829], 'cost': 0}\n",
      "{'insert_count': 89, 'ids': [450680172451281831, 450680172451281832, 450680172451281833, 450680172451281834, 450680172451281835, 450680172451281836, 450680172451281837, 450680172451281838, 450680172451281839, 450680172451281840, 450680172451281841, 450680172451281842, 450680172451281843, 450680172451281844, 450680172451281845, 450680172451281846, 450680172451281847, 450680172451281848, 450680172451281849, 450680172451281850, 450680172451281851, 450680172451281852, 450680172451281853, 450680172451281854, 450680172451281855, 450680172451281856, 450680172451281857, 450680172451281858, 450680172451281859, 450680172451281860, 450680172451281861, 450680172451281862, 450680172451281863, 450680172451281864, 450680172451281865, 450680172451281866, 450680172451281867, 450680172451281868, 450680172451281869, 450680172451281870, 450680172451281871, 450680172451281872, 450680172451281873, 450680172451281874, 450680172451281875, 450680172451281876, 450680172451281877, 450680172451281878, 450680172451281879, 450680172451281880, 450680172451281881, 450680172451281882, 450680172451281883, 450680172451281884, 450680172451281885, 450680172451281886, 450680172451281887, 450680172451281888, 450680172451281889, 450680172451281890, 450680172451281891, 450680172451281892, 450680172451281893, 450680172451281894, 450680172451281895, 450680172451281896, 450680172451281897, 450680172451281898, 450680172451281899, 450680172451281900, 450680172451281901, 450680172451281902, 450680172451281903, 450680172451281904, 450680172451281905, 450680172451281906, 450680172451281907, 450680172451281908, 450680172451281909, 450680172451281910, 450680172451281911, 450680172451281912, 450680172451281913, 450680172451281914, 450680172451281915, 450680172451281916, 450680172451281917, 450680172451281918, 450680172451281919], 'cost': 0}\n",
      "{'insert_count': 3, 'ids': [450680172451281921, 450680172451281922, 450680172451281923], 'cost': 0}\n",
      "{'insert_count': 64, 'ids': [450680172451281925, 450680172451281926, 450680172451281927, 450680172451281928, 450680172451281929, 450680172451281930, 450680172451281931, 450680172451281932, 450680172451281933, 450680172451281934, 450680172451281935, 450680172451281936, 450680172451281937, 450680172451281938, 450680172451281939, 450680172451281940, 450680172451281941, 450680172451281942, 450680172451281943, 450680172451281944, 450680172451281945, 450680172451281946, 450680172451281947, 450680172451281948, 450680172451281949, 450680172451281950, 450680172451281951, 450680172451281952, 450680172451281953, 450680172451281954, 450680172451281955, 450680172451281956, 450680172451281957, 450680172451281958, 450680172451281959, 450680172451281960, 450680172451281961, 450680172451281962, 450680172451281963, 450680172451281964, 450680172451281965, 450680172451281966, 450680172451281967, 450680172451281968, 450680172451281969, 450680172451281970, 450680172451281971, 450680172451281972, 450680172451281973, 450680172451281974, 450680172451281975, 450680172451281976, 450680172451281977, 450680172451281978, 450680172451281979, 450680172451281980, 450680172451281981, 450680172451281982, 450680172451281983, 450680172451281984, 450680172451281985, 450680172451281986, 450680172451281987, 450680172451281988], 'cost': 0}\n",
      "{'insert_count': 8, 'ids': [450680172451281990, 450680172451281991, 450680172451281992, 450680172451281993, 450680172451281994, 450680172451281995, 450680172451281996, 450680172451281997], 'cost': 0}\n",
      "{'insert_count': 55, 'ids': [450680172451281999, 450680172451282000, 450680172451282001, 450680172451282002, 450680172451282003, 450680172451282004, 450680172451282005, 450680172451282006, 450680172451282007, 450680172451282008, 450680172451282009, 450680172451282010, 450680172451282011, 450680172451282012, 450680172451282013, 450680172451282014, 450680172451282015, 450680172451282016, 450680172451282017, 450680172451282018, 450680172451282019, 450680172451282020, 450680172451282021, 450680172451282022, 450680172451282023, 450680172451282024, 450680172451282025, 450680172451282026, 450680172451282027, 450680172451282028, 450680172451282029, 450680172451282030, 450680172451282031, 450680172451282032, 450680172451282033, 450680172451282034, 450680172451282035, 450680172451282036, 450680172451282037, 450680172451282038, 450680172451282039, 450680172451282040, 450680172451282041, 450680172451282042, 450680172451282043, 450680172451282044, 450680172451282045, 450680172451282046, 450680172451282047, 450680172451282048, 450680172451282049, 450680172451282050, 450680172451282051, 450680172451282052, 450680172451282053], 'cost': 0}\n",
      "{'insert_count': 7, 'ids': [450680172451282055, 450680172451282056, 450680172451282057, 450680172451282058, 450680172451282059, 450680172451282060, 450680172451282061], 'cost': 0}\n",
      "{'insert_count': 199, 'ids': [450680172451282063, 450680172451282064, 450680172451282065, 450680172451282066, 450680172451282067, 450680172451282068, 450680172451282069, 450680172451282070, 450680172451282071, 450680172451282072, 450680172451282073, 450680172451282074, 450680172451282075, 450680172451282076, 450680172451282077, 450680172451282078, 450680172451282079, 450680172451282080, 450680172451282081, 450680172451282082, 450680172451282083, 450680172451282084, 450680172451282085, 450680172451282086, 450680172451282087, 450680172451282088, 450680172451282089, 450680172451282090, 450680172451282091, 450680172451282092, 450680172451282093, 450680172451282094, 450680172451282095, 450680172451282096, 450680172451282097, 450680172451282098, 450680172451282099, 450680172451282100, 450680172451282101, 450680172451282102, 450680172451282103, 450680172451282104, 450680172451282105, 450680172451282106, 450680172451282107, 450680172451282108, 450680172451282109, 450680172451282110, 450680172451282111, 450680172451282112, 450680172451282113, 450680172451282114, 450680172451282115, 450680172451282116, 450680172451282117, 450680172451282118, 450680172451282119, 450680172451282120, 450680172451282121, 450680172451282122, 450680172451282123, 450680172451282124, 450680172451282125, 450680172451282126, 450680172451282127, 450680172451282128, 450680172451282129, 450680172451282130, 450680172451282131, 450680172451282132, 450680172451282133, 450680172451282134, 450680172451282135, 450680172451282136, 450680172451282137, 450680172451282138, 450680172451282139, 450680172451282140, 450680172451282141, 450680172451282142, 450680172451282143, 450680172451282144, 450680172451282145, 450680172451282146, 450680172451282147, 450680172451282148, 450680172451282149, 450680172451282150, 450680172451282151, 450680172451282152, 450680172451282153, 450680172451282154, 450680172451282155, 450680172451282156, 450680172451282157, 450680172451282158, 450680172451282159, 450680172451282160, 450680172451282161, 450680172451282162, 450680172451282163, 450680172451282164, 450680172451282165, 450680172451282166, 450680172451282167, 450680172451282168, 450680172451282169, 450680172451282170, 450680172451282171, 450680172451282172, 450680172451282173, 450680172451282174, 450680172451282175, 450680172451282176, 450680172451282177, 450680172451282178, 450680172451282179, 450680172451282180, 450680172451282181, 450680172451282182, 450680172451282183, 450680172451282184, 450680172451282185, 450680172451282186, 450680172451282187, 450680172451282188, 450680172451282189, 450680172451282190, 450680172451282191, 450680172451282192, 450680172451282193, 450680172451282194, 450680172451282195, 450680172451282196, 450680172451282197, 450680172451282198, 450680172451282199, 450680172451282200, 450680172451282201, 450680172451282202, 450680172451282203, 450680172451282204, 450680172451282205, 450680172451282206, 450680172451282207, 450680172451282208, 450680172451282209, 450680172451282210, 450680172451282211, 450680172451282212, 450680172451282213, 450680172451282214, 450680172451282215, 450680172451282216, 450680172451282217, 450680172451282218, 450680172451282219, 450680172451282220, 450680172451282221, 450680172451282222, 450680172451282223, 450680172451282224, 450680172451282225, 450680172451282226, 450680172451282227, 450680172451282228, 450680172451282229, 450680172451282230, 450680172451282231, 450680172451282232, 450680172451282233, 450680172451282234, 450680172451282235, 450680172451282236, 450680172451282237, 450680172451282238, 450680172451282239, 450680172451282240, 450680172451282241, 450680172451282242, 450680172451282243, 450680172451282244, 450680172451282245, 450680172451282246, 450680172451282247, 450680172451282248, 450680172451282249, 450680172451282250, 450680172451282251, 450680172451282252, 450680172451282253, 450680172451282254, 450680172451282255, 450680172451282256, 450680172451282257, 450680172451282258, 450680172451282259, 450680172451282260, 450680172451282261], 'cost': 0}\n",
      "{'insert_count': 5, 'ids': [450680172451282263, 450680172451282264, 450680172451282265, 450680172451282266, 450680172451282267], 'cost': 0}\n",
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     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_train = pd.read_csv(\"./public_dataset/rag/bird_train.csv\")\n",
    "df_dev = pd.read_csv(\"./public_dataset/rag/bird_dev.csv\")\n",
    "df = pd.concat([df_train, df_dev], ignore_index=True)\n",
    "df = df.apply(lambda x: x.fillna(\"\"))\n",
    "\n",
    "# prepare milvus client and database\n",
    "conn = connections.connect(host=\"127.0.0.1\", port=19530)\n",
    "db_name_milvus = \"bird_bench\"\n",
    "if db_name_milvus not in db.list_database():\n",
    "    database = db.create_database(db_name_milvus)\n",
    "db.using_database(db_name_milvus)\n",
    "client = MilvusClient(uri=\"http://localhost:19530\", db_name=db_name_milvus)\n",
    "\n",
    "\n",
    "for db_name, db_path in database_info:\n",
    "    db_info = {\n",
    "        \"type\": \"sqlite\",\n",
    "        \"url\": db_path,\n",
    "    }\n",
    "    db_engine = tool.get_engine(db_info)\n",
    "\n",
    "    build_table_schema(client, db_engine, db_name)\n",
    "    build_column_schema(client, df[df[\"database_name\"] == db_name], db_name)\n",
    "    # build_column_value(client, db_engine, db_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: [\"[{'id': 450680172451291297, 'distance': 0.6023199558258057, 'entity': {'table_name': 'driver', 'column_name': 'last_name', 'column_description': 'Family name of the drivercommonsense evidence: full name = first_name + last_name'}}, {'id': 450680172451291296, 'distance': 0.5427758693695068, 'entity': {'table_name': 'driver', 'column_name': 'first_name', 'column_description': 'First given name of the driver'}}, {'id': 450680172451291272, 'distance': 0.45189914107322693, 'entity': {'table_name': 'customer', 'column_name': 'cust_name', 'column_description': 'Business name of the customer'}}]\", \"[{'id': 450680172451291295, 'distance': 0.673912763595581, 'entity': {'table_name': 'driver', 'column_name': 'driver_id', 'column_description': 'Unique identifier for the driver'}}, {'id': 450680172451291289, 'distance': 0.673912763595581, 'entity': {'table_name': 'shipment', 'column_name': 'driver_id', 'column_description': 'A reference to the driver table that indicates which driver transported the goods in the shipment'}}, {'id': 450680172451291279, 'distance': 0.3905678987503052, 'entity': {'table_name': 'customer', 'column_name': 'phone', 'column_description': 'Telephone number to reach the customer'}}]\", '[{\\'id\\': 450680172451291278, \\'distance\\': 0.4217195510864258, \\'entity\\': {\\'table_name\\': \\'customer\\', \\'column_name\\': \\'zip\\', \\'column_description\\': \"Postal code of the customer\\'s address\"}}, {\\'id\\': 450680172451291275, \\'distance\\': 0.42136746644973755, \\'entity\\': {\\'table_name\\': \\'customer\\', \\'column_name\\': \\'address\\', \\'column_description\\': \\'Physical street address of the customer\\'}}, {\\'id\\': 450680172451291298, \\'distance\\': 0.4213673770427704, \\'entity\\': {\\'table_name\\': \\'driver\\', \\'column_name\\': \\'address\\', \\'column_description\\': \"Street address of the driver\\'s home\"}}]', \"[{'id': 450680172451291291, 'distance': 0.415149450302124, 'entity': {'table_name': 'shipment', 'column_name': 'ship_date', 'column_description': 'the date the items were received by the driveryyyy-mm-dd'}}, {'id': 450680172451291285, 'distance': 0.36497002840042114, 'entity': {'table_name': 'shipment', 'column_name': 'ship_id', 'column_description': 'Unique identifier of the shipment'}}, {'id': 450680172451291282, 'distance': 0.27144333720207214, 'entity': {'table_name': 'city', 'column_name': 'state', 'column_description': 'state in which the city is'}}]\", '[{\\'id\\': 450680172451291276, \\'distance\\': 0.605014979839325, \\'entity\\': {\\'table_name\\': \\'customer\\', \\'column_name\\': \\'city\\', \\'column_description\\': \"City of the customer\\'s address\"}}, {\\'id\\': 450680172451291299, \\'distance\\': 0.6050149202346802, \\'entity\\': {\\'table_name\\': \\'driver\\', \\'column_name\\': \\'city\\', \\'column_description\\': \\'City the driver lives in\\'}}, {\\'id\\': 450680172451291281, \\'distance\\': 0.5212994813919067, \\'entity\\': {\\'table_name\\': \\'city\\', \\'column_name\\': \\'city_name\\', \\'column_description\\': \\'name of the city\\'}}]', \"[{'id': 450680172451291283, 'distance': 0.527606725692749, 'entity': {'table_name': 'city', 'column_name': 'population ', 'column_description': 'population of the city'}}, {'id': 450680172451291284, 'distance': 0.24310123920440674, 'entity': {'table_name': 'city', 'column_name': 'area', 'column_description': 'square miles the city coverscommonsense evidence: \\\\npopulation density (land area per capita) = area / population '}}, {'id': 450680172451291287, 'distance': 0.21688982844352722, 'entity': {'table_name': 'shipment', 'column_name': 'weight', 'column_description': 'The number of pounds being transported on the shipment'}}]\", \"[{'id': 450680172451291296, 'distance': 1.0, 'entity': {'table_name': 'driver', 'column_name': 'first_name', 'column_description': 'First given name of the driver'}}, {'id': 450680172451291297, 'distance': 0.6940487027168274, 'entity': {'table_name': 'driver', 'column_name': 'last_name', 'column_description': 'Family name of the drivercommonsense evidence: full name = first_name + last_name'}}, {'id': 450680172451291272, 'distance': 0.6145066022872925, 'entity': {'table_name': 'customer', 'column_name': 'cust_name', 'column_description': 'Business name of the customer'}}]\", \"[{'id': 450680172451291297, 'distance': 1.0, 'entity': {'table_name': 'driver', 'column_name': 'last_name', 'column_description': 'Family name of the drivercommonsense evidence: full name = first_name + last_name'}}, {'id': 450680172451291296, 'distance': 0.6940488219261169, 'entity': {'table_name': 'driver', 'column_name': 'first_name', 'column_description': 'First given name of the driver'}}, {'id': 450680172451291272, 'distance': 0.6279267072677612, 'entity': {'table_name': 'customer', 'column_name': 'cust_name', 'column_description': 'Business name of the customer'}}]\", \"[{'id': 450680172451291295, 'distance': 1.0, 'entity': {'table_name': 'driver', 'column_name': 'driver_id', 'column_description': 'Unique identifier for the driver'}}, {'id': 450680172451291289, 'distance': 1.0, 'entity': {'table_name': 'shipment', 'column_name': 'driver_id', 'column_description': 'A reference to the driver table that indicates which driver transported the goods in the shipment'}}, {'id': 450680172451291288, 'distance': 0.6039613485336304, 'entity': {'table_name': 'shipment', 'column_name': 'truck_id', 'column_description': 'A reference to the truck table that indicates which truck is used in the shipment'}}]\", \"[{'id': 450680172451291285, 'distance': 0.5779538154602051, 'entity': {'table_name': 'shipment', 'column_name': 'ship_id', 'column_description': 'Unique identifier of the shipment'}}, {'id': 450680172451291291, 'distance': 0.44563350081443787, 'entity': {'table_name': 'shipment', 'column_name': 'ship_date', 'column_description': 'the date the items were received by the driveryyyy-mm-dd'}}, {'id': 450680172451291286, 'distance': 0.3167955279350281, 'entity': {'table_name': 'shipment', 'column_name': 'cust_id', 'column_description': 'A reference to the customer table that indicates which customer the shipment is for'}}]\"] , extra_info: {'cost': 0}\n",
      "data: [\"[{'id': 450680172534975718, 'distance': 0.3805575966835022, 'entity': {'column_value': 'West Allis', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534975967, 'distance': 0.3773752450942993, 'entity': {'column_value': 'Killeen', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534975730, 'distance': 0.3750019371509552, 'entity': {'column_value': 'Elizabeth', 'table_name': 'city', 'column_name': 'city_name'}}]\", \"[{'id': 450680172534975842, 'distance': 0.41417229175567627, 'entity': {'column_value': 'Hamilton', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534975815, 'distance': 0.40137234330177307, 'entity': {'column_value': 'Pontiac', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534975916, 'distance': 0.3739142119884491, 'entity': {'column_value': 'Chico', 'table_name': 'city', 'column_name': 'city_name'}}]\", \"[{'id': 450680172534976000, 'distance': 0.3324459493160248, 'entity': {'column_value': 'Provo', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534976144, 'distance': 0.29342779517173767, 'entity': {'column_value': 'Dothan', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534976143, 'distance': 0.29182925820350647, 'entity': {'column_value': 'Edmond', 'table_name': 'city', 'column_name': 'city_name'}}]\", \"[{'id': 450680172534976141, 'distance': 0.26388224959373474, 'entity': {'column_value': 'Waco', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534976095, 'distance': 0.2621001601219177, 'entity': {'column_value': 'Washington', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534976056, 'distance': 0.2575581967830658, 'entity': {'column_value': 'Tacoma', 'table_name': 'city', 'column_name': 'city_name'}}]\", \"[{'id': 450680172534975673, 'distance': 0.5389645099639893, 'entity': {'column_value': 'National City', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534975925, 'distance': 0.45204871892929077, 'entity': {'column_value': 'Missouri City', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534976010, 'distance': 0.4470445513725281, 'entity': {'column_value': 'Bossier City', 'table_name': 'city', 'column_name': 'city_name'}}]\", \"[{'id': 450680172534975912, 'distance': 0.40951403975486755, 'entity': {'column_value': 'Minnetonka', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534976031, 'distance': 0.27786925435066223, 'entity': {'column_value': 'North Little Rock', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534975673, 'distance': 0.23406662046909332, 'entity': {'column_value': 'National City', 'table_name': 'city', 'column_name': 'city_name'}}]\", \"[{'id': 450680172534975730, 'distance': 0.3468960225582123, 'entity': {'column_value': 'Elizabeth', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534975731, 'distance': 0.3461073637008667, 'entity': {'column_value': 'Downey', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534976144, 'distance': 0.33893439173698425, 'entity': {'column_value': 'Dothan', 'table_name': 'city', 'column_name': 'city_name'}}]\", \"[{'id': 450680172534976122, 'distance': 0.36110401153564453, 'entity': {'column_value': 'Lawton', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534975714, 'distance': 0.3594874143600464, 'entity': {'column_value': 'Lynn', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534975730, 'distance': 0.3534557521343231, 'entity': {'column_value': 'Elizabeth', 'table_name': 'city', 'column_name': 'city_name'}}]\", \"[{'id': 450680172534976198, 'distance': 0.26312723755836487, 'entity': {'column_value': '2016-04-18', 'table_name': 'shipment', 'column_name': 'ship_date'}}, {'id': 450680172534976197, 'distance': 0.2529368996620178, 'entity': {'column_value': '2016-04-17', 'table_name': 'shipment', 'column_name': 'ship_date'}}, {'id': 450680172534976353, 'distance': 0.24574615061283112, 'entity': {'column_value': '2016-11-04', 'table_name': 'shipment', 'column_name': 'ship_date'}}]\", \"[{'id': 450680172534976040, 'distance': 0.19875875115394592, 'entity': {'column_value': 'Galveston', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534976083, 'distance': 0.19103150069713593, 'entity': {'column_value': 'Gulfport', 'table_name': 'city', 'column_name': 'city_name'}}, {'id': 450680172534975992, 'distance': 0.18021920323371887, 'entity': {'column_value': 'Roswell', 'table_name': 'city', 'column_name': 'city_name'}}]\"] , extra_info: {'cost': 0}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'driver': Table(table_name=None, columns={'last_name': Column(column_name=last_name, column_description=Family name of the drivercommonsense evidence: full name = first_name + last_name, sample_values=set()), 'first_name': Column(column_name=first_name, column_description=First given name of the driver, sample_values=set()), 'driver_id': Column(column_name=driver_id, column_description=Unique identifier for the driver, sample_values=set()), 'address': Column(column_name=address, column_description=Street address of the driver's home, sample_values=set()), 'city': Column(column_name=city, column_description=City the driver lives in, sample_values=set())}),\n",
       " 'customer': Table(table_name=None, columns={'cust_name': Column(column_name=cust_name, column_description=Business name of the customer, sample_values=set()), 'phone': Column(column_name=phone, column_description=Telephone number to reach the customer, sample_values=set()), 'zip': Column(column_name=zip, column_description=Postal code of the customer's address, sample_values=set()), 'address': Column(column_name=address, column_description=Physical street address of the customer, sample_values=set()), 'city': Column(column_name=city, column_description=City of the customer's address, sample_values=set())}),\n",
       " 'shipment': Table(table_name=None, columns={'driver_id': Column(column_name=driver_id, column_description=A reference to the driver table that indicates which driver transported the goods in the shipment, sample_values=set()), 'ship_date': Column(column_name=ship_date, column_description=the date the items were received by the driveryyyy-mm-dd, sample_values={'2016-04-17', '2016-11-04', '2016-04-18'}), 'ship_id': Column(column_name=ship_id, column_description=Unique identifier of the shipment, sample_values=set()), 'weight': Column(column_name=weight, column_description=The number of pounds being transported on the shipment, sample_values=set()), 'truck_id': Column(column_name=truck_id, column_description=A reference to the truck table that indicates which truck is used in the shipment, sample_values=set()), 'cust_id': Column(column_name=cust_id, column_description=A reference to the customer table that indicates which customer the shipment is for, sample_values=set())}),\n",
       " 'city': Table(table_name=None, columns={'state': Column(column_name=state, column_description=state in which the city is, sample_values=set()), 'city_name': Column(column_name=city_name, column_description=name of the city, sample_values={'Provo', 'Edmond', 'Minnetonka', 'Washington', 'National City', 'Lawton', 'Elizabeth', 'Missouri City', 'Hamilton', 'Gulfport', 'Waco', 'Dothan', 'North Little Rock', 'West Allis', 'Chico', 'Tacoma', 'Pontiac', 'Killeen', 'Bossier City', 'Lynn', 'Galveston', 'Downey', 'Roswell'}), 'population ': Column(column_name=population , column_description=population of the city, sample_values=set()), 'area': Column(column_name=area, column_description=square miles the city coverscommonsense evidence: \n",
       " population density (land area per capita) = area / population , sample_values=set())})}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from typing import List, Set, Tuple\n",
    "from tool import ColumnSchema, TableSchema\n",
    "from collections import defaultdict\n",
    "\n",
    "\n",
    "# prepare milvus client and database\n",
    "conn = connections.connect(host=\"127.0.0.1\", port=19530)\n",
    "db_name_milvus = \"bird_bench\"\n",
    "if db_name_milvus not in db.list_database():\n",
    "    database = db.create_database(db_name_milvus)\n",
    "db.using_database(db_name_milvus)\n",
    "client = MilvusClient(uri=\"http://localhost:19530\", db_name=db_name_milvus)\n",
    "query_embeddings = embedding_fn.encode_documents(\n",
    "    [\n",
    "        \"full name\",\n",
    "        \"driver\",\n",
    "        \"delivered\",\n",
    "        \"most shipments\",\n",
    "        \"least populated city\",\n",
    "        \"Min(population)\",\n",
    "        \"first_name\",\n",
    "        \"last_name\",\n",
    "        \"driver_id\",\n",
    "        \"Max(Count(ship_id))\",\n",
    "    ]\n",
    ")\n",
    "\n",
    "\n",
    "def search_column_schema(\n",
    "    client: MilvusClient,\n",
    "    collection_name: str,\n",
    "    query_emdbeddings: List[List[float]],\n",
    "    schema,\n",
    "):\n",
    "\n",
    "    res = client.load_collection(\n",
    "        collection_name=collection_name,\n",
    "    )\n",
    "    res = client.get_load_state(collection_name=collection_name)\n",
    "\n",
    "    res = client.search(\n",
    "        collection_name=collection_name,\n",
    "        data=query_emdbeddings,\n",
    "        limit=3,\n",
    "        output_fields=[\"table_name\", \"column_name\", \"column_description\"],\n",
    "        search_params={\"metric_type\": \"COSINE\", \"params\": {}},\n",
    "        anns_field=\"column_name_vector\",\n",
    "    )\n",
    "    # parse_table_column_from_result(res)\n",
    "    print(res)\n",
    "    for result_set in res:\n",
    "        for single_result in result_set:\n",
    "            table_name = single_result[\"entity\"][\"table_name\"]\n",
    "\n",
    "            schema[table_name].add_column(\n",
    "                ColumnSchema(\n",
    "                    single_result[\"entity\"][\"column_name\"],\n",
    "                    single_result[\"entity\"][\"column_description\"],\n",
    "                    None,\n",
    "                )\n",
    "            )\n",
    "\n",
    "\n",
    "def search_column_value(\n",
    "    client: MilvusClient,\n",
    "    collection_name: str,\n",
    "    query_emdbeddings: List[List[float]],\n",
    "    schema,\n",
    "):\n",
    "\n",
    "    res = client.load_collection(\n",
    "        collection_name=collection_name,\n",
    "    )\n",
    "    res = client.get_load_state(collection_name=collection_name)\n",
    "\n",
    "    res = client.search(\n",
    "        collection_name=collection_name,\n",
    "        data=query_emdbeddings,\n",
    "        limit=3,\n",
    "        output_fields=[\"table_name\", \"column_name\", \"column_value\"],\n",
    "        search_params={\"metric_type\": \"COSINE\", \"params\": {}},\n",
    "        anns_field=\"column_value_vector\",\n",
    "    )\n",
    "    print(res)\n",
    "    for result_set in res:\n",
    "        for single_result in result_set:\n",
    "            table_name = single_result[\"entity\"][\"table_name\"]\n",
    "\n",
    "            schema[table_name].add_column(\n",
    "                ColumnSchema(\n",
    "                    single_result[\"entity\"][\"column_name\"],\n",
    "                    None,\n",
    "                    single_result[\"entity\"][\"column_value\"],\n",
    "                )\n",
    "            )\n",
    "\n",
    "\n",
    "schema = defaultdict(TableSchema)\n",
    "search_column_schema(\n",
    "    client,\n",
    "    COLLECTION_NAME_FOR_COLUMN_SCHEMA.format(db_name=\"shipping\"),\n",
    "    query_embeddings,\n",
    "    schema,\n",
    ")\n",
    "search_column_value(\n",
    "    client,\n",
    "    COLLECTION_NAME_FOR_COLUMN_VALUE.format(db_name=\"shipping\"),\n",
    "    query_embeddings,\n",
    "    schema,\n",
    ")\n",
    "dict(schema)\n",
    "# SELECT T1.first_name, T1.last_name FROM driver AS T1 INNER JOIN shipment AS T2 ON T1.driver_id = T2.driver_id INNER JOIN city AS T3 ON T3.city_id = T2.city_id GROUP BY T1.first_name, T1.last_name, T3.population HAVING T3.population = MAX(T3.population) ORDER BY COUNT(*) DESC LIMIT 1\n",
    "# {('city', 'population '), ('customer', 'address'), ('customer', 'cust_name'), ('city', 'city_name'), ('driver', 'driver_id'), ('shipment', 'ship_date'), ('city', 'state'), ('driver', 'address'), ('city', 'area'), ('shipment', 'driver_id'), ('shipment', 'weight'), ('customer', 'phone'), ('shipment', 'cust_id'), ('customer', 'city'), ('driver', 'last_name'), ('driver', 'first_name'), ('shipment', 'truck_id'), ('customer', 'zip'), ('shipment', 'ship_id'), ('driver', 'city')}"
   ]
  }
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