9536395468
Reverts topoteretes/cognee#690 I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin.
536 lines
21 KiB
Text
536 lines
21 KiB
Text
{
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"cells": [
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-04T11:58:00.193158Z",
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"start_time": "2025-03-04T11:58:00.190238Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import nest_asyncio\n",
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"nest_asyncio.apply()"
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],
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"id": "2efba278d106bb5f",
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"outputs": [],
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"execution_count": 2
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"### Environment Configuration\n",
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"#### Setup required directories and environment variables.\n"
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],
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"id": "ccbb2bc23aa456ee"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-04T11:59:33.879188Z",
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"start_time": "2025-03-04T11:59:33.873682Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import pathlib\n",
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"import os\n",
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"import cognee\n",
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"\n",
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"notebook_dir = pathlib.Path().resolve()\n",
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"data_directory_path = str(notebook_dir / \".data_storage\")\n",
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"cognee_directory_path = str(notebook_dir / \".cognee_system\")\n",
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"\n",
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"cognee.config.data_root_directory(data_directory_path)\n",
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"cognee.config.system_root_directory(cognee_directory_path)\n",
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"\n",
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"BASE_URL = \"https://pokeapi.co/api/v2/\"\n",
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"os.environ[\"BUCKET_URL\"] = data_directory_path\n",
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"os.environ[\"DATA_WRITER__DISABLE_COMPRESSION\"] = \"true\"\n"
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],
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"id": "662d554f96f211d9",
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"outputs": [],
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"execution_count": 8
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## Initialize DLT Pipeline\n",
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"### Create the DLT pipeline to fetch Pokémon data.\n"
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],
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"id": "36ae0be71f6e9167"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-04T11:58:03.982939Z",
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"start_time": "2025-03-04T11:58:03.819676Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import dlt\n",
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"from pathlib import Path\n",
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"\n",
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"pipeline = dlt.pipeline(\n",
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" pipeline_name=\"pokemon_pipeline\",\n",
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" destination=\"filesystem\",\n",
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" dataset_name=\"pokemon_data\",\n",
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")\n"
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],
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"id": "25101ae5f016ce0c",
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"outputs": [],
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"execution_count": 4
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## Fetch Pokémon List\n",
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"### Retrieve a list of Pokémon from the API.\n"
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],
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"id": "9a87ce05a072c48b"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-04T11:58:03.990076Z",
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"start_time": "2025-03-04T11:58:03.987199Z"
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}
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},
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"cell_type": "code",
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"source": [
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"@dlt.resource(write_disposition=\"replace\")\n",
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"def pokemon_list(limit: int = 50):\n",
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" import requests\n",
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" response = requests.get(f\"{BASE_URL}pokemon\", params={\"limit\": limit})\n",
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" response.raise_for_status()\n",
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" yield response.json()[\"results\"]\n"
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],
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"id": "3b6e60778c61e24a",
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"outputs": [],
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"execution_count": 5
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## Fetch Pokémon Details\n",
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"### Fetch detailed information about each Pokémon.\n"
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],
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"id": "9952767846194e97"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-04T11:58:03.996394Z",
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"start_time": "2025-03-04T11:58:03.994122Z"
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}
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},
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"cell_type": "code",
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"source": [
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"@dlt.transformer(data_from=pokemon_list)\n",
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"def pokemon_details(pokemons):\n",
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" \"\"\"Fetches detailed info for each Pokémon\"\"\"\n",
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" import requests\n",
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" for pokemon in pokemons:\n",
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" response = requests.get(pokemon[\"url\"])\n",
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" response.raise_for_status()\n",
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" yield response.json()\n"
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],
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"id": "79ec9fef12267485",
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"outputs": [],
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"execution_count": 6
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## Run Data Pipeline\n",
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"### Execute the pipeline and store Pokémon data.\n"
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],
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"id": "41e05f660bf9e9d2"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-04T11:59:41.571015Z",
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"start_time": "2025-03-04T11:59:36.840744Z"
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}
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},
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"cell_type": "code",
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"source": [
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"info = pipeline.run([pokemon_list, pokemon_details])\n",
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"print(info)\n"
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],
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"id": "20a3b2c7f404677f",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Pipeline pokemon_pipeline load step completed in 0.06 seconds\n",
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"1 load package(s) were loaded to destination filesystem and into dataset pokemon_data\n",
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"The filesystem destination used file:///Users/lazar/PycharmProjects/cognee/.data_storage location to store data\n",
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"Load package 1741089576.860229 is LOADED and contains no failed jobs\n"
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]
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}
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],
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"execution_count": 9
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## Load Pokémon Abilities\n",
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"### Load Pokémon ability data from stored files.\n"
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],
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"id": "937f10b8d1037743"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-04T11:59:44.377719Z",
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"start_time": "2025-03-04T11:59:44.363718Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import json\n",
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"from cognee.low_level import DataPoint\n",
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"from uuid import uuid5, NAMESPACE_OID\n",
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"\n",
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"class Abilities(DataPoint):\n",
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" name: str = \"Abilities\"\n",
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" metadata: dict = {\"index_fields\": [\"name\"]}\n",
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"\n",
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"def load_abilities_data(jsonl_abilities):\n",
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" abilities_root = Abilities()\n",
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" pokemon_abilities = []\n",
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"\n",
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" for jsonl_ability in jsonl_abilities:\n",
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" with open(jsonl_ability, \"r\") as f:\n",
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" for line in f:\n",
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" ability = json.loads(line)\n",
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" ability[\"id\"] = uuid5(NAMESPACE_OID, ability[\"_dlt_id\"])\n",
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" ability[\"name\"] = ability[\"ability__name\"]\n",
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" ability[\"is_type\"] = abilities_root\n",
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" pokemon_abilities.append(ability)\n",
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"\n",
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" return abilities_root, pokemon_abilities\n"
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],
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"id": "be73050036439ea1",
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"outputs": [],
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"execution_count": 10
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## Load Pokémon Data\n",
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"### Load Pokémon details and associate them with abilities.\n"
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],
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"id": "98c97f799f73df77"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-04T11:59:46.251306Z",
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"start_time": "2025-03-04T11:59:46.238283Z"
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}
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},
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"cell_type": "code",
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"source": [
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"from typing import List, Optional\n",
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"\n",
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"class Pokemons(DataPoint):\n",
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" name: str = \"Pokemons\"\n",
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" have: Abilities\n",
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" metadata: dict = {\"index_fields\": [\"name\"]}\n",
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"\n",
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"class PokemonAbility(DataPoint):\n",
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" name: str\n",
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" ability__name: str\n",
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" ability__url: str\n",
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" is_hidden: bool\n",
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" slot: int\n",
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" _dlt_load_id: str\n",
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" _dlt_id: str\n",
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" _dlt_parent_id: str\n",
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" _dlt_list_idx: str\n",
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" is_type: Abilities\n",
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" metadata: dict = {\"index_fields\": [\"ability__name\"]}\n",
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"\n",
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"class Pokemon(DataPoint):\n",
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" name: str\n",
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" base_experience: int\n",
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" height: int\n",
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" weight: int\n",
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" is_default: bool\n",
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" order: int\n",
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" location_area_encounters: str\n",
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" species__name: str\n",
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" species__url: str\n",
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" cries__latest: str\n",
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" cries__legacy: str\n",
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" sprites__front_default: str\n",
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" sprites__front_shiny: str\n",
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" sprites__back_default: Optional[str]\n",
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" sprites__back_shiny: Optional[str]\n",
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" _dlt_load_id: str\n",
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" _dlt_id: str\n",
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" is_type: Pokemons\n",
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" abilities: List[PokemonAbility]\n",
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" metadata: dict = {\"index_fields\": [\"name\"]}\n",
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"\n",
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"def load_pokemon_data(jsonl_pokemons, pokemon_abilities, pokemon_root):\n",
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" pokemons = []\n",
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"\n",
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" for jsonl_pokemon in jsonl_pokemons:\n",
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" with open(jsonl_pokemon, \"r\") as f:\n",
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" for line in f:\n",
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" pokemon_data = json.loads(line)\n",
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" abilities = [\n",
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" ability for ability in pokemon_abilities\n",
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" if ability[\"_dlt_parent_id\"] == pokemon_data[\"_dlt_id\"]\n",
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" ]\n",
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" pokemon_data[\"external_id\"] = pokemon_data[\"id\"]\n",
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" pokemon_data[\"id\"] = uuid5(NAMESPACE_OID, str(pokemon_data[\"id\"]))\n",
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" pokemon_data[\"abilities\"] = [PokemonAbility(**ability) for ability in abilities]\n",
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" pokemon_data[\"is_type\"] = pokemon_root\n",
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" pokemons.append(Pokemon(**pokemon_data))\n",
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"\n",
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" return pokemons\n"
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],
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"id": "7862951248df0bf5",
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"outputs": [],
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"execution_count": 11
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## Process Pokémon Data\n",
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"### Load and associate Pokémon abilities.\n"
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],
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"id": "676fa5a2b61c2107"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-04T11:59:47.365226Z",
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"start_time": "2025-03-04T11:59:47.356722Z"
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}
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},
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"cell_type": "code",
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"source": [
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"STORAGE_PATH = Path(\".data_storage/pokemon_data/pokemon_details\")\n",
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"jsonl_pokemons = sorted(STORAGE_PATH.glob(\"*.jsonl\"))\n",
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"\n",
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"ABILITIES_PATH = Path(\".data_storage/pokemon_data/pokemon_details__abilities\")\n",
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"jsonl_abilities = sorted(ABILITIES_PATH.glob(\"*.jsonl\"))\n",
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"\n",
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"abilities_root, pokemon_abilities = load_abilities_data(jsonl_abilities)\n",
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"pokemon_root = Pokemons(have=abilities_root)\n",
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"pokemons = load_pokemon_data(jsonl_pokemons, pokemon_abilities, pokemon_root)\n"
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],
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"id": "ad14cdecdccd71bb",
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"outputs": [],
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"execution_count": 12
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## Initialize Cognee\n",
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"### Setup Cognee for data processing.\n"
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],
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"id": "59dec67b2ae50f0f"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-04T11:59:49.244577Z",
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"start_time": "2025-03-04T11:59:48.618261Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import asyncio\n",
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"from cognee.low_level import setup as cognee_setup\n",
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"\n",
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"async def initialize_cognee():\n",
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" await cognee.prune.prune_data()\n",
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" await cognee.prune.prune_system(metadata=True)\n",
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" await cognee_setup()\n",
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"\n",
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"await initialize_cognee()\n"
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],
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"id": "d2e095ae576a02c1",
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:cognee.infrastructure.databases.relational.sqlalchemy.SqlAlchemyAdapter:Database deleted successfully.INFO:cognee.infrastructure.databases.relational.sqlalchemy.SqlAlchemyAdapter:Database deleted successfully."
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]
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}
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],
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"execution_count": 13
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## Process Pokémon Data\n",
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"### Add Pokémon data points to Cognee.\n"
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],
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"id": "5f0b8090bc7b1fe6"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-04T11:59:57.744035Z",
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"start_time": "2025-03-04T11:59:50.574033Z"
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}
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},
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"cell_type": "code",
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"source": [
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"from cognee.modules.pipelines.tasks.Task import Task\n",
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"from cognee.tasks.storage import add_data_points\n",
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"from cognee.modules.pipelines import run_tasks\n",
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"\n",
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"tasks = [Task(add_data_points, task_config={\"batch_size\": 50})]\n",
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"results = run_tasks(\n",
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" tasks=tasks,\n",
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" data=pokemons,\n",
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" dataset_id=uuid5(NAMESPACE_OID, \"Pokemon\"),\n",
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" pipeline_name='pokemon_pipeline',\n",
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")\n",
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"\n",
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"async for result in results:\n",
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" print(result)\n",
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"print(\"Done\")\n"
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],
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"id": "ffa12fc1f5350d95",
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:run_tasks(tasks: [Task], data):Pipeline run started: `fd2ed59d-b550-5b05-bbe6-7b708fe12483`INFO:run_tasks(tasks: [Task], data):Coroutine task started: `add_data_points`"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<cognee.modules.pipelines.models.PipelineRun.PipelineRun object at 0x300bb3950>\n",
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"User d347ea85-e512-4cae-b9d7-496fe1745424 has registered.\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/lazar/PycharmProjects/cognee/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py:79: SAWarning: This declarative base already contains a class with the same class name and module name as cognee.infrastructure.databases.vector.pgvector.PGVectorAdapter.PGVectorDataPoint, and will be replaced in the string-lookup table.\n",
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" class PGVectorDataPoint(Base):\n",
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"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"/Users/lazar/PycharmProjects/cognee/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py:113: SAWarning: This declarative base already contains a class with the same class name and module name as cognee.infrastructure.databases.vector.pgvector.PGVectorAdapter.PGVectorDataPoint, and will be replaced in the string-lookup table.\n",
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" class PGVectorDataPoint(Base):\n",
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"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"WARNING:neo4j.notifications:Received notification from DBMS server: {severity: WARNING} {code: Neo.ClientNotification.Statement.FeatureDeprecationWarning} {category: DEPRECATION} {title: This feature is deprecated and will be removed in future versions.} {description: The query used a deprecated function: `id`.} {position: line: 8, column: 16, offset: 335} for query: '\\n UNWIND $nodes AS node\\n MERGE (n {id: node.node_id})\\n ON CREATE SET n += node.properties, n.updated_at = timestamp()\\n ON MATCH SET n += node.properties, n.updated_at = timestamp()\\n WITH n, node.node_id AS label\\n CALL apoc.create.addLabels(n, [label]) YIELD node AS labeledNode\\n RETURN ID(labeledNode) AS internal_id, labeledNode.id AS nodeId\\n 'WARNING:neo4j.notifications:Received notification from DBMS server: {severity: WARNING} {code: Neo.ClientNotification.Statement.FeatureDeprecationWarning} {category: DEPRECATION} {title: This feature is deprecated and will be removed in future versions.} {description: The query used a deprecated function: `id`.} {position: line: 1, column: 18, offset: 17} for query: 'MATCH (n) RETURN ID(n) AS id, labels(n) AS labels, properties(n) AS properties'WARNING:neo4j.notifications:Received notification from DBMS server: {severity: WARNING} {code: Neo.ClientNotification.Statement.FeatureDeprecationWarning} {category: DEPRECATION} {title: This feature is deprecated and will be removed in future versions.} {description: The query used a deprecated function: `id`.} {position: line: 3, column: 16, offset: 43} for query: '\\n MATCH (n)-[r]->(m)\\n RETURN ID(n) AS source, ID(m) AS target, TYPE(r) AS type, properties(r) AS properties\\n 'WARNING:neo4j.notifications:Received notification from DBMS server: {severity: WARNING} {code: Neo.ClientNotification.Statement.FeatureDeprecationWarning} {category: DEPRECATION} {title: This feature is deprecated and will be removed in future versions.} {description: The query used a deprecated function: `id`.} {position: line: 3, column: 33, offset: 60} for query: '\\n MATCH (n)-[r]->(m)\\n RETURN ID(n) AS source, ID(m) AS target, TYPE(r) AS type, properties(r) AS properties\\n 'INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:run_tasks(tasks: [Task], data):Coroutine task completed: `add_data_points`INFO:run_tasks(tasks: [Task], data):Pipeline run completed: `fd2ed59d-b550-5b05-bbe6-7b708fe12483`"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<cognee.modules.pipelines.models.PipelineRun.PipelineRun object at 0x30016fd40>\n",
|
|
"Done\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 14
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"## Search Pokémon Data\n",
|
|
"### Execute a search query using Cognee.\n"
|
|
],
|
|
"id": "e0d98d9832a2797a"
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-03-04T12:00:02.878871Z",
|
|
"start_time": "2025-03-04T11:59:59.571965Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"from cognee.api.v1.search import SearchType\n",
|
|
"\n",
|
|
"search_results = await cognee.search(\n",
|
|
" query_type=SearchType.GRAPH_COMPLETION,\n",
|
|
" query_text=\"pokemons?\"\n",
|
|
")\n",
|
|
"\n",
|
|
"print(\"Search results:\")\n",
|
|
"for result_text in search_results:\n",
|
|
" print(result_text)"
|
|
],
|
|
"id": "bb2476b6b0c2aff",
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"WARNING:neo4j.notifications:Received notification from DBMS server: {severity: WARNING} {code: Neo.ClientNotification.Statement.FeatureDeprecationWarning} {category: DEPRECATION} {title: This feature is deprecated and will be removed in future versions.} {description: The query used a deprecated function: `id`.} {position: line: 1, column: 18, offset: 17} for query: 'MATCH (n) RETURN ID(n) AS id, labels(n) AS labels, properties(n) AS properties'WARNING:neo4j.notifications:Received notification from DBMS server: {severity: WARNING} {code: Neo.ClientNotification.Statement.FeatureDeprecationWarning} {category: DEPRECATION} {title: This feature is deprecated and will be removed in future versions.} {description: The query used a deprecated function: `id`.} {position: line: 3, column: 16, offset: 43} for query: '\\n MATCH (n)-[r]->(m)\\n RETURN ID(n) AS source, ID(m) AS target, TYPE(r) AS type, properties(r) AS properties\\n 'WARNING:neo4j.notifications:Received notification from DBMS server: {severity: WARNING} {code: Neo.ClientNotification.Statement.FeatureDeprecationWarning} {category: DEPRECATION} {title: This feature is deprecated and will be removed in future versions.} {description: The query used a deprecated function: `id`.} {position: line: 3, column: 33, offset: 60} for query: '\\n MATCH (n)-[r]->(m)\\n RETURN ID(n) AS source, ID(m) AS target, TYPE(r) AS type, properties(r) AS properties\\n 'INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\u001B[92m13:00:02 - LiteLLM:INFO\u001B[0m: utils.py:2784 - \n",
|
|
"LiteLLM completion() model= gpt-4o-mini; provider = openaiINFO:LiteLLM:\n",
|
|
"LiteLLM completion() model= gpt-4o-mini; provider = openai"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Search results:\n",
|
|
"The Pokemons mentioned are: golbat, jigglypuff, raichu, vulpix, and pikachu.\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 15
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"execution_count": null,
|
|
"source": "",
|
|
"id": "a4c2d3e9c15b017"
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"version": "3.8.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|