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{
"cells": [
{
"cell_type": "markdown",
"id": "b1a955e7",
"metadata": {},
"source": [
"# Update Blog Data\n",
"\n",
"This notebook demonstrates how to update the blog data and vector store when new blog posts are published. It uses the utility functions from `utils_data_loading.ipynb`."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6ec048b4",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"from pathlib import Path\n",
"from dotenv import load_dotenv\n",
"import importlib.util\n",
"\n",
"# Load environment variables\n",
"load_dotenv()\n",
"\n",
"# Import utility functions from utils_data_loading.ipynb\n",
"# We'll do this by first converting the notebook to a Python module"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f01d61f",
"metadata": {},
"outputs": [],
"source": [
"# Function to import the utility module\n",
"def import_notebook_as_module(notebook_path, module_name=\"utils_module\"):\n",
" \"\"\"\n",
" Import a Jupyter notebook as a Python module.\n",
" \n",
" Args:\n",
" notebook_path: Path to the notebook\n",
" module_name: Name to give the module\n",
" \n",
" Returns:\n",
" The imported module\n",
" \"\"\"\n",
" import nbformat\n",
" from importlib.util import spec_from_loader, module_from_spec\n",
" from IPython.core.interactiveshell import InteractiveShell\n",
" \n",
" shell = InteractiveShell.instance()\n",
" \n",
" with open(notebook_path) as f:\n",
" nb = nbformat.read(f, as_version=4)\n",
" \n",
" # Create a module\n",
" spec = spec_from_loader(module_name, loader=None)\n",
" module = module_from_spec(spec)\n",
" sys.modules[module_name] = module\n",
" \n",
" # Execute only the code cells in the notebook\n",
" for cell in nb.cells:\n",
" if cell.cell_type == 'code':\n",
" # Skip cells that start with certain keywords like \"if __name__ == \"__main__\":\"\n",
" if 'if __name__ == \"__main__\":' in cell.source:\n",
" continue\n",
" \n",
" # Execute the cell and store its content in the module\n",
" code = shell.input_transformer_manager.transform_cell(cell.source)\n",
" exec(code, module.__dict__)\n",
" \n",
" return module"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "774c1373",
"metadata": {},
"outputs": [],
"source": [
"# Import the utility functions\n",
"utils = import_notebook_as_module('utils_data_loading.ipynb')\n",
"\n",
"# Now you can access all the functions from the utils module\n",
"print(\"Successfully imported utility functions.\")"
]
},
{
"cell_type": "markdown",
"id": "85ae6617",
"metadata": {},
"source": [
"## Configuration\n",
"\n",
"Set up the configuration for data processing."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "54e9ca48",
"metadata": {},
"outputs": [],
"source": [
"# Configuration (can be overridden from .env file)\n",
"DATA_DIR = os.environ.get(\"DATA_DIR\", \"data/\")\n",
"VECTOR_STORAGE_PATH = os.environ.get(\"VECTOR_STORAGE_PATH\", \"./db/vectorstore_v3\")\n",
"BLOG_BASE_URL = os.environ.get(\"BLOG_BASE_URL\", \"https://thedataguy.pro/blog/\")\n",
"FORCE_RECREATE_EMBEDDINGS = os.environ.get(\"FORCE_RECREATE_EMBEDDINGS\", \"false\").lower() == \"true\"\n",
"\n",
"print(f\"Data Directory: {DATA_DIR}\")\n",
"print(f\"Vector Storage Path: {VECTOR_STORAGE_PATH}\")\n",
"print(f\"Blog Base URL: {BLOG_BASE_URL}\")\n",
"print(f\"Force Recreate Embeddings: {FORCE_RECREATE_EMBEDDINGS}\")"
]
},
{
"cell_type": "markdown",
"id": "cc19ab4c",
"metadata": {},
"source": [
"## Update Blog Data Process\n",
"\n",
"This process will:\n",
"1. Load existing blog posts\n",
"2. Process and update metadata\n",
"3. Create or update vector embeddings"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d56f688",
"metadata": {},
"outputs": [],
"source": [
"# Process blog posts and create/update embeddings\n",
"result = utils.process_blog_posts(\n",
" data_dir=DATA_DIR,\n",
" create_embeddings=True,\n",
" force_recreate_embeddings=FORCE_RECREATE_EMBEDDINGS\n",
")\n",
"\n",
"# Access the documents and vector store\n",
"documents = result[\"documents\"]\n",
"stats = result[\"stats\"]\n",
"vector_store = result[\"vector_store\"]\n",
"\n",
"print(f\"\\nProcessed {len(documents)} blog posts\")\n",
"print(f\"Vector store created/updated at: {VECTOR_STORAGE_PATH}\")"
]
},
{
"cell_type": "markdown",
"id": "ad3b2dca",
"metadata": {},
"source": [
"## Testing the Vector Store\n",
"\n",
"Let's test the vector store with a few queries to make sure it's working correctly."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b552e6b",
"metadata": {},
"outputs": [],
"source": [
"# Create a retriever from the vector store\n",
"retriever = vector_store.as_retriever(search_kwargs={\"k\": 2})\n",
"\n",
"# Test queries\n",
"test_queries = [\n",
" \"What is RAGAS?\",\n",
" \"How to build research agents?\",\n",
" \"What is metric driven development?\",\n",
" \"Who is TheDataGuy?\"\n",
"]\n",
"\n",
"for query in test_queries:\n",
" print(f\"\\nQuery: {query}\")\n",
" docs = retriever.invoke(query)\n",
" print(f\"Retrieved {len(docs)} documents:\")\n",
" for i, doc in enumerate(docs):\n",
" title = doc.metadata.get(\"post_title\", \"Unknown\")\n",
" url = doc.metadata.get(\"url\", \"No URL\")\n",
" print(f\"{i+1}. {title} ({url})\")"
]
},
{
"cell_type": "markdown",
"id": "ddbe9282",
"metadata": {},
"source": [
"## Schedule This Notebook\n",
"\n",
"To keep the blog data up-to-date, you can schedule this notebook to run periodically. \n",
"Here are some options:\n",
"\n",
"1. Use a cron job to run this notebook with papermill\n",
"2. Set up a GitHub Action to run this notebook on a schedule\n",
"3. Use Airflow or another workflow management system\n",
"\n",
"Example of running with papermill:\n",
"```bash\n",
"papermill update_blog_data.ipynb output_$(date +%Y%m%d).ipynb\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3634e064",
"metadata": {},
"outputs": [],
"source": [
"# Save stats to a file for tracking changes over time\n",
"import json\n",
"from datetime import datetime\n",
"\n",
"stats_dir = Path(\"stats\")\n",
"stats_dir.mkdir(exist_ok=True)\n",
"\n",
"# Add timestamp to stats\n",
"stats[\"timestamp\"] = datetime.now().isoformat()\n",
"\n",
"# Save stats\n",
"stats_path = stats_dir / f\"blog_stats_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json\"\n",
"with open(stats_path, \"w\") as f:\n",
" json.dump(stats, f, indent=2)\n",
"\n",
"print(f\"Saved stats to {stats_path}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.13.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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