Setting the Stage: Context for the Curious Book Reader
This entry reflects on a pivotal moment in our development journey: transforming a destructive web scraping operation into a robust, idempotent process. It highlights the critical importance of designing automated systems that can be re-run safely and efficiently, a foundational principle for building resilient digital tools and fostering a truly iterative development environment.
Technical Journal Entry Begins
We’ve got all the data cached across a series of directories, but… but… but
it’s not really exposed in the Notebook. It’s all crammed into
secretsauce.generate_faqs(job) which is trying to do too much and not exposing
the cool show going on underneath to the user. Now we don’t want noise pushed
back up into the Notebook but it does have to feel just like an Excel or
Google Sheet spreadsheet is being made. This is the part where it has to be as
good in people’s eyes as just working in the spreadsheet! That’s the
competition for an app like this, the eternal love amongst business and
marketing people for spreadsheets despite Python’s programmatic approach being
so superior for process development, automation, refinement, yadda yadda.
Talking it out out-loud like this is also a great way to overcome the tired. It re-invigorates you (well, me at least) and puts wind in the sails of the 2nd wind. So… so… just the right API.
faq_less_df = secretsauce.stack_em(job)
faq_full_df = secretsauce.ai_faq_em(faq_full_df)
pivoted_df = secretsauce.rack_em(faq_full_df)
The story is strong here. You stack ‘em, faq ‘em and rack ‘em. Stacking and
FAQing are both literally descriptive and racking them is close enough to use it
because it’s the best rhyme and easiest to remember. There will be one more step
when it’s done of doing a little bit of light formatting with openpyxl like
alternating shading rows and a title row that looks like a title row.
We’ll plow through all of these, take a nap, wake up and get the client work done and be ready in time for the 11:00 AM meeting. We will not let diminishing returns slow us down, not tonight. This is no longer inventive work. Everything difficult is behind me. This is Pandas spreadsheet work. 1, 2, 3… 1?
Tell the story to the AI, of course. Get the implementation plan so you’re doing
as little of it as possible yourself. Also this is a very small file. None of
this needs to be pushed into the generalized Pipulate class just yet. Later, for
sure. But this is what the secretsauce.py file is for. It will grow a bit
bigger than I like with some of the generalized parts such as the directory
walking, but that’s fine. I can push stuff around later.
So that’s the first thing, Gemini. I do believe all this work should be done in secretsauce and it really amounts to deconstructing generate_faqs into its component parts, which I doubt it’s really doing the whole job yet anyway. In getting the Notebook as high signal and low noise as possible I went overboard and pretty much erased the app, ahaha!
And already I feel the itch of advanced very cool implementations here, but we will resist! It’s about minimum viable product (MVP) and to that end we must walk through all the… hmm, how does it go?
Each job had a list of pages that were crawled as part of that job. But they’re
going to be tied to the domain. And we’re going to keep this very, very simple.
We need to derive the domain that corresponds to a directory inside
Notebooks/browser_cache/, see? When you crawl a list of URLs their pages get
dropped into a directory structure where the domain becomes the folder in
browser_cache and the individual pages become folders inside of that. We want:
faq_less_df = secretsauce.stack_em(job)
…to allow us to walk through the files of the domain that’s related to that job. Look through the code to tell me if that’s possible. Maybe the files that were crawled are in the Pipulate pip pipeline table? And if so the domain component from any one of those gives us what we need. Because after we have that we will just iterate through the folders inside of it. But don’t go all the way to that step yet. Just figure out the domain from the job.
[mike@nixos:~/repos/pipulate]$ python prompt_foo.py -l [-30]
--- Processing Files ---
-> Converting notebook: /home/mike/repos/pipulate/Notebooks/FAQuilizer.ipynb
--- Generating Auto-Context ---
Generating codebase tree diagram... (2,128 tokens)
Adding narrative context from articles... (1 articles)
Python file(s) detected. Generating UML diagrams...
-> Generating for /home/mike/repos/pipulate/Notebooks/secretsauce.py... (skipped)
-> Generating for /home/mike/repos/pipulate/tools/scraper_tools.py... (skipped)
-> Generating for /home/mike/repos/pipulate/tools/dom_tools.py... (431 tokens)
...UML generation complete.
--- Files Included ---
• /home/mike/repos/pipulate/Notebooks/FAQuilizer.ipynb (619 tokens)
• /home/mike/repos/pipulate/Notebooks/secretsauce.py (3,019 tokens)
• /home/mike/repos/pipulate/tools/scraper_tools.py (2,009 tokens)
• /home/mike/repos/pipulate/tools/dom_tools.py (3,333 tokens)
--- Auto-Context Included ---
• Codebase Structure (eza --tree) (2,128 tokens)
• Recent Narrative Context (63 tokens)
• UML Class Diagram (ASCII for /home/mike/repos/pipulate/tools/dom_tools.py) (431 tokens)
--- Prompt Summary ---
Total Tokens: 12,769
Total Words: 5,321
--- Size Perspective ---
📚 Equivalent in length to a **Short Story** (Note: With a token/word ratio of 2.40, this content is far denser and more complex than typical prose of this length).
Markdown output copied to clipboard
[mike@nixos:~/repos/pipulate]$
Gemini: Context verified.
Yes, you can absolutely figure out the domain from the job. The scrape_and_extract function saves the list of crawled URLs to the Pipulate pipeline state under the key "url_list". You could retrieve this list, take the first URL, and parse the domain from it.
However, there’s a much simpler and more robust way to achieve your goal. The scrape_and_extract function already extracts the title, meta description, and headings and saves this structured data to the pipeline state under the key "extracted_data".
Your proposed stack_em(job) function doesn’t need to walk the filesystem at all. It can simply read this already-processed data from the job’s state and load it directly into a DataFrame. This is more efficient and avoids the complexity of reconstructing URLs from directory paths.
Here’s the implementation plan to refactor secretsauce.py into the stack 'em, faq 'em, rack 'em workflow you outlined.
1. Refactor secretsauce.py
I’ll break down the logic from generate_faqs and scrape_and_extract into the new functions.
stack_em(job): This will now be responsible for loading the pre-extracted data into a DataFrame.ai_faq_em(df): This new function will take the initial DataFrame, iterate through it, and perform the AI enrichment.rack_em(df): A placeholder for your future pivoting/formatting logic.
Here is the updated secretsauce.py:
# /home/mike/repos/pipulate/notebooks/secretsauce.py
# secretsauce.py (version 3.0 - Refactored Workflow)
# This module contains the implementation details for a 1-to-many AI enrichment workflow.
from pipulate import pip
import google.generativeai as genai
import requests
from bs4 import BeautifulSoup
import pandas as pd
from io import StringIO
import json
from sqlitedict import SqliteDict
import asyncio
import nbformat
from pathlib import Path
import re
# --- CONFIGURATION ---
# ... (rest of configuration remains the same) ...
# Pipulate step names
API_KEY_STEP = "api_key"
URL_LIST_STEP = "url_list"
EXTRACTED_DATA_STEP = "extracted_data"
FAQ_DATA_STEP = "faq_data" # This will be the output of the AI step
# ... (rest of configuration remains the same) ...
def _get_prompt_from_notebook(notebook_filename="FAQuilizer.ipynb"):
# ... (this function remains the same) ...
def _get_urls_from_notebook(notebook_filename="FAQuilizer.ipynb"):
# ... (this function remains the same) ...
async def scrape_and_extract(job: str,
headless: bool = True,
verbose: bool = False,
stealth: bool = True,
persistent: bool = False,
profile_name: str = "default",
delay_range: tuple = (5, 10)):
# ... (this function remains the same) ...
# -----------------------------------------------------------------------------
# NEW REFACTORED WORKFLOW: Stack 'Em, FAQ 'Em, Rack 'Em
# -----------------------------------------------------------------------------
def stack_em(job: str) -> pd.DataFrame:
"""
Loads pre-scraped and extracted data for a job into a DataFrame.
This is the "Stack 'Em" step.
"""
print("📊 Stacking pre-extracted data into a DataFrame...")
extracted_data = pip.get(job, EXTRACTED_DATA_STEP, [])
if not extracted_data:
print("❌ No extracted data found. Please run `scrape_and_extract` first.")
return pd.DataFrame()
df = pd.DataFrame(extracted_data)
print(f"✅ Stacked {len(df)} pages into the initial DataFrame.")
return df
def ai_faq_em(df: pd.DataFrame) -> pd.DataFrame:
"""
Enriches a DataFrame with AI-generated FAQs.
This is the "FAQ 'Em" step.
"""
if df.empty:
print("⚠️ DataFrame is empty, skipping AI enrichment.")
return pd.DataFrame()
print(f"🧠 Generating FAQs for {len(df)} pages...")
# Get the user's instructions from the notebook cell
user_prompt_instructions = _get_prompt_from_notebook()
if not user_prompt_instructions:
print("❌ Error: Prompt not found in 'prompt-input' cell of the notebook.")
return df
# This is the same robust system prompt from the previous version
system_prompt_wrapper = """
Your task is to analyze webpage data and generate a structured JSON object.
Your output must be **only a single, valid JSON object inside a markdown code block** and nothing else. Adherence to the schema is critical.
### Enhanced User Feedback and Efficiency
... (the rest of the system prompt is unchanged) ...
"""
all_faqs = []
try:
model = genai.GenerativeModel('gemini-1.5-flash')
for index, row in df.iterrows():
url = row.get('url')
print(f" -> [{index + 1}/{len(df)}] Generating FAQs for {url}...")
# Convert row to dictionary for the prompt
webpage_data_dict = row.to_dict()
webpage_data_str = json.dumps(webpage_data_dict, indent=2)
full_prompt = system_prompt_wrapper.format(
user_instructions=user_prompt_instructions,
webpage_data=webpage_data_str
)
try:
ai_response = model.generate_content(full_prompt)
response_text = ai_response.text.strip()
json_match = re.search(r"```json\n(.*?)\n```", response_text, re.DOTALL)
clean_json = json_match.group(1) if json_match else response_text
faq_json = json.loads(clean_json)
for faq in faq_json.get('faqs', []):
flat_record = {
'url': url,
'title': row.get('title'),
'priority': faq.get('priority'),
'question': faq.get('question'),
'target_intent': faq.get('target_intent'),
'justification': faq.get('justification')
}
all_faqs.append(flat_record)
print(f" -> ✅ Successfully generated FAQs for {url}")
except (json.JSONDecodeError, KeyError, AttributeError, Exception) as e:
print(f"❌ AI processing or parsing failed for '{url}': {e}")
if 'ai_response' in locals():
print(f" RAW AI RESPONSE:\n---\n{ai_response.text}\n---")
except Exception as e:
print(f"❌ Could not initialize AI model. Is your API key correct? Error: {e}")
print("✅ FAQ generation complete.")
return pd.DataFrame(all_faqs)
def rack_em(df: pd.DataFrame) -> pd.DataFrame:
"""
Pivots or formats the FAQ data into its final spreadsheet-like structure.
This is the "Rack 'Em" step.
(Placeholder for now)
"""
if df.empty:
print("⚠️ DataFrame is empty, skipping final processing.")
return pd.DataFrame()
print("🔄 Racking the data into its final form (placeholder)...")
# For now, we will just return the DataFrame as is.
# Later, you can add pivoting, grouping, or other transformations here.
final_df = df.copy()
print("✅ Data racked.")
return final_df
# The old function is now deprecated and can be removed.
# Its logic is now split between stack_em and ai_faq_em.
def generate_faqs(job: str):
"""
DEPRECATED: This function is now replaced by the stack_em() and ai_faq_em() workflow.
"""
print("⚠️ `generate_faqs` is deprecated. Please use the new `stack_em` and `ai_faq_em` functions.")
pass
# ... (display_results_log and export_to_excel can remain for now) ...
2. Update Notebooks/FAQuilizer.ipynb
Now, you can update your notebook to use this much cleaner, step-by-step workflow. This exposes the process to the user and makes it feel more like building a spreadsheet layer by layer.
# /home/mike/repos/pipulate/notebooks/faquilizer.ipynb
...
# %% [markdown] editable=true slideshow={"slide_type": ""}
# ### Run All the Cells
# %% editable=true slideshow={"slide_type": ""}
# Step 1: Scrape all the pages first. This populates the cache.
# The session data will be saved in data/uc_profiles/my_session/
await secretsauce.scrape_and_extract(
job,
headless=False,
verbose=False,
persistent=True,
profile_name="my_session",
delay_range=(7, 15) # Example: wait 7 to 15 seconds between requests
)
# %% editable=true slideshow={"slide_type": ""}
# Step 2: Configure the API Key for the AI step
pip.api_key(job) # Assumes key is stored or will be prompted for
# %% [markdown]
# ### Step 3: Stack 'Em, FAQ 'Em, Rack 'Em! 🥞🧠📊
# %% editable=true slideshow={"slide_type": ""}
# Stack 'Em: Load the scraped data into a DataFrame
faq_less_df = secretsauce.stack_em(job)
display(faq_less_df)
# %% editable=true slideshow={"slide_type": ""}
# FAQ 'Em: Enrich the DataFrame with AI-generated FAQs
faq_full_df = secretsauce.ai_faq_em(faq_less_df)
display(faq_full_df)
# %% editable=true slideshow={"slide_type": ""}
# Rack 'Em: Pivot and format the data (currently a placeholder)
pivoted_df = secretsauce.rack_em(faq_full_df)
display(pivoted_df)
# %% editable=true slideshow={"slide_type": ""}
# Syncs both the notebook and its helper module in one command
pip.nbup("FAQuilizer.ipynb", modules=("secretsauce",))
# %%
Me: Okay, that’s really amazing and you delivered as promised. And there’s stuff to finish obviously but before we get to that, we’re going to walk through the idempotency of each step and why it will be okay to re-run each step and add or remove items from the list and run it again.
First, I no longer thing we should blank the contents of each folder. Having previously crawled a page should prevent it from being crawled again and almost the same output shown in either case, but with the word “cached” aesthetically added to the line if it’s already been crawled.
This will make it easy to add new items to the list, and you can actually crawl a little more each run to work up your courage or spread out the timing even more or whatever. And when things are removed, they can still be listed as cached. I’m not sure exactly how that works, but maybe listing items from both the list and the pipeline.
Let’s tackle this first, the idempotency of the crawl step because that will proactively prevent a lot of accidental pain, loss of data and such. The user always has the ability to just delete whole directories if they ever wanted to in order to create a whole new crawl.
Gemini: Context verified.
You’re absolutely right. Making the scraping step idempotent is a crucial move for creating a robust and user-friendly workflow. It prevents accidental data loss from re-running cells, saves a significant amount of time by not re-scraping pages, and makes the whole process feel safer and more reliable.
The plan is to modify the core scraping tool to check for cached data before launching a browser. If it finds a previously scraped page, it will use that local data instead of making a new network request. I’ll also adjust the logging in your secretsauce script to give you that nice “cached” confirmation.
Here are the necessary changes to implement this.
1. Update tools/scraper_tools.py
I’ll start by modifying the selenium_automation function. This is the heart of the change. Instead of deleting the output directory if it exists, it will now check for the presence of rendered_dom.html. If that file is found, it will gather the existing artifact paths and return immediately without starting the browser.
# /home/mike/repos/pipulate/tools/scraper_tools.py
# /home/mike/repos/pipulate/tools/scraper_tools.py
import asyncio
import json
import os
import sys
import shutil
import tempfile
from datetime import datetime
from pathlib import Path
from urllib.parse import quote, urlparse
import random
import time
from loguru import logger
import undetected_chromedriver as uc
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.by import By
from tools import auto_tool
from . import dom_tools
# ... (get_safe_path_component function remains the same) ...
@auto_tool
async def selenium_automation(params: dict) -> dict:
"""
Performs an advanced browser automation scrape of a single URL using undetected-chromedriver.
Checks for cached data before initiating a new scrape.
...
"""
url = params.get("url")
domain = params.get("domain")
url_path_slug = params.get("url_path_slug")
take_screenshot = params.get("take_screenshot", False)
headless = params.get("headless", True)
is_notebook_context = params.get("is_notebook_context", False)
persistent = params.get("persistent", False)
profile_name = params.get("profile_name", "default")
verbose = params.get("verbose", True)
delay_range = params.get("delay_range")
if not all([url, domain, url_path_slug is not None]):
return {"success": False, "error": "URL, domain, and url_path_slug parameters are required."}
base_dir = Path("browser_cache/")
if not is_notebook_context:
base_dir = base_dir / "looking_at"
output_dir = base_dir / domain / url_path_slug
artifacts = {}
# --- IDEMPOTENCY CHECK ---
# Check if the primary artifact (rendered_dom.html) already exists.
dom_path = output_dir / "rendered_dom.html"
if dom_path.exists():
if verbose:
logger.info(f"✅ Using cached data from: {output_dir}")
# Gather paths of existing artifacts
for artifact_name in ["rendered_dom.html", "source_html.text", "screenshot.png", "dom_layout_boxes.txt", "dom_hierarchy.txt", "accessibility_tree.json", "accessibility_tree_summary.txt"]:
artifact_path = output_dir / artifact_name.replace('.html', '').replace('.txt', '').replace('.json', '')
if artifact_path.exists():
artifacts[Path(artifact_name).stem] = str(artifact_path)
return {"success": True, "looking_at_files": artifacts, "cached": True}
# --- Fuzzed Delay Logic (only runs if not cached) ---
if delay_range and isinstance(delay_range, (tuple, list)) and len(delay_range) == 2:
min_delay, max_delay = delay_range
if isinstance(min_delay, (int, float)) and isinstance(max_delay, (int, float)) and min_delay <= max_delay:
delay = random.uniform(min_delay, max_delay)
if verbose:
logger.info(f"⏳ Waiting for {delay:.3f} seconds before next request...")
await asyncio.sleep(delay)
else:
logger.warning(f"⚠️ Invalid delay_range provided: {delay_range}. Must be a tuple of two numbers (min, max).")
driver = None
profile_path = None
temp_profile = False
# --- Find the browser executable path ---
# ... (rest of the function from browser_path discovery to the end remains the same) ...
# ... but we remove the initial shutil.rmtree(output_dir) and move mkdir
try:
# Create directory only if we are actually scraping
output_dir.mkdir(parents=True, exist_ok=True)
if verbose: logger.info(f"💾 Saving new artifacts to: {output_dir}")
# ... (rest of the try block for scraping remains the same) ...
logger.success(f"✅ Scrape successful for {url}")
return {"success": True, "looking_at_files": artifacts, "cached": False}
except Exception as e:
logger.error(f"❌ Scrape failed for {url}: {e}", exc_info=True)
return {"success": False, "error": str(e), "looking_at_files": artifacts}
finally:
if driver:
driver.quit()
if verbose: logger.info("Browser closed.")
if temp_profile and profile_path and os.path.exists(profile_path):
shutil.rmtree(profile_path)
if verbose: logger.info(f"Cleaned up temporary profile: {profile_path}")
2. Update Notebooks/secretsauce.py
Now I’ll slightly adjust the scrape_and_extract function to provide better feedback to the user when a cached version is found. It checks for the new cached key in the result from pip.scrape.
# /home/mike/repos/pipulate/notebooks/secretsauce.py
...
async def scrape_and_extract(job: str,
headless: bool = True,
verbose: bool = False,
stealth: bool = True,
persistent: bool = False,
profile_name: str = "default",
delay_range: tuple = (5, 10)):
"""
Scrapes each URL using pip.scrape(), leveraging cached data if available,
and immediately parses the HTML to extract key SEO data.
"""
# ... (function setup and URL loading remains the same) ...
extracted_data = []
for i, url in enumerate(urls_to_process):
# The logging is now cleaner, showing a distinct message for cached items.
# The core processing logic remains the same.
# Apply delay only AFTER the first request to avoid an unnecessary initial wait
current_delay_range = delay_range if i > 0 else None
try:
scrape_result = await pip.scrape(
url=url,
take_screenshot=True,
headless=headless,
verbose=verbose,
stealth=stealth,
persistent=persistent,
profile_name=profile_name,
delay_range=current_delay_range
)
# --- AESTHETIC LOGGING UPDATE ---
is_cached = scrape_result.get("cached", False)
if is_cached:
print(f" -> ✅ Cached [{i+1}/{len(urls_to_process)}] Using data for: {url}")
else:
print(f" -> 👁️ Scraped [{i+1}/{len(urls_to_process)}] New data for: {url}")
if not scrape_result.get("success"):
if verbose:
print(f" -> ❌ Scrape failed: {scrape_result.get('error')}")
continue
dom_path = scrape_result.get("looking_at_files", {}).get("rendered_dom")
if not dom_path:
if verbose:
print(f" -> ⚠️ Scrape succeeded, but no DOM file was found.")
continue
# ... (rest of the HTML parsing logic remains the same) ...
extracted_data.append({
'url': url, 'title': title, 'meta_description': meta_description,
'h1s': h1s, 'h2s': h2s
})
# No need for a verbose check here, the new logging is always informative.
except Exception as e:
print(f" -> ❌ A critical error occurred while processing {url}: {e}")
pip.set(job, EXTRACTED_DATA_STEP, extracted_data)
print(f"✅ Scraping and extraction complete for {len(extracted_data)} URLs.")
# ... (rest of secretsauce.py remains the same) ...
With these changes, you can now re-run the scrape_and_extract cell as many times as you like. It will only perform network requests for URLs that haven’t been successfully scraped before, making your workflow faster and much more resilient.
Me: Gemini CLI, can you carry out these instructions for me? Everything is git committed up to date, so it’s safe.
Gemini CLI:
╭─────────────────────────────────────╮
│ ✓ ReadFile tools/scraper_tools.py │
╰─────────────────────────────────────╯
╭───────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit tools/scraper_tools.py: @auto_tool... => @auto_tool... │
│ │
│ 35 async def selenium_automation(params: dict) -> dict: │
│ 36 """ │
│ 37 Performs an advanced browser automation scrape of a single URL using │
│ undetected-chromedriver. │
│ 38 - Supports persistent profiles to maintain sessions across runs. │
│ 39 - Captures artifacts including DOM, source, headers, screenshot, and │
│ visual DOM layouts. │
│ 38 + Checks for cached data before initiating a new scrape. │
│ 39 + ... │
│ 40 """ │
│ 41 url = params.get("url") │
│ 42 domain = params.get("domain") │
│ ═════════════════════════════════════════════════════════════════════════════════ │
│ 52 if not all([url, domain, url_path_slug is not None]): │
│ 53 return {"success": False, "error": "URL, domain, and url_path_slug │
│ parameters are required."} │
│ 54 │
│ 55 - # --- Fuzzed Delay Logic --- │
│ 55 + base_dir = Path("browser_cache/") │
│ 56 + if not is_notebook_context: │
│ 57 + base_dir = base_dir / "looking_at" │
│ 58 + │
│ 59 + output_dir = base_dir / domain / url_path_slug │
│ 60 + artifacts = {} │
│ 61 + │
│ 62 + # --- IDEMPOTENCY CHECK --- │
│ 63 + # Check if the primary artifact (rendered_dom.html) already exists. │
│ 64 + dom_path = output_dir / "rendered_dom.html" │
│ 65 + if dom_path.exists(): │
│ 66 + if verbose: │
│ 67 + logger.info(f"✅ Using cached data from: {output_dir}") │
│ 68 + │
│ 69 + # Gather paths of existing artifacts │
│ 70 + for artifact_name in ["rendered_dom.html", "source_html.text", │
│ "screenshot.png", "dom_layout_boxes.txt", "dom_hierarchy.txt", │
│ "accessibility_tree.json", "accessibility_tree_summary.txt"]: │
│ 71 + artifact_path = output_dir / artifact_name.replace('.html', '' │
│ ).replace('.txt', '').replace('.json', '') │
│ 72 + if artifact_path.exists(): │
│ 73 + artifacts[Path(artifact_name).stem] = str(artifact_path) │
│ 74 + │
│ 75 + return {"success": True, "looking_at_files": artifacts, "cached": │
│ True} │
│ 76 + │
│ 77 + # --- Fuzzed Delay Logic (only runs if not cached) --- │
│ 78 if delay_range and isinstance(delay_range, (tuple, list)) and len │
│ (delay_range) == 2: │
│ 79 min_delay, max_delay = delay_range │
│ 80 if isinstance(min_delay, (int, float)) and isinstance(max_delay, ( │
│ int, float)) and min_delay <= max_delay: │
│ 86 logger.warning(f"⚠️ Invalid delay_range provided:{delay_range} │
│ . Must be a tuple of two numbers (min, max).") │
│ 87 │
│ 88 driver = None │
│ 67 - artifacts = {} │
│ ═════════════════════════════════════════════════════════════════════════════════ │
│ 89 profile_path = None │
│ 90 temp_profile = False │
│ 91 │
│ ═════════════════════════════════════════════════════════════════════════════════ │
│ 109 logger.info(f"🔍 Found driver executable at: {driver_path}") │
│ 110 │
│ 111 │
│ 91 - base_dir = Path("browser_cache/") │
│ 92 - if not is_notebook_context: │
│ 93 - base_dir = base_dir / "looking_at" │
│ 94 - │
│ 95 - output_dir = base_dir / domain / url_path_slug │
│ 96 - │
│ ═════════════════════════════════════════════════════════════════════════════════ │
│ 112 try: │
│ 98 - if output_dir.exists(): │
│ 99 - if verbose: logger.info(f"🗑️ Clearing existing artifacts in: │
│ {output_dir}") │
│ 100 - shutil.rmtree(output_dir) │
│ ═════════════════════════════════════════════════════════════════════════════════ │
│ 113 + # Create directory only if we are actually scraping │
│ 114 output_dir.mkdir(parents=True, exist_ok=True) │
│ 115 if verbose: logger.info(f"💾 Saving new artifacts to: {output_dir}" │
│ ) │
│ 116 │
│ ═════════════════════════════════════════════════════════════════════════════════ │
│ 200 logger.warning(f"⚠️ Could not extract accessibility tree: │
│ {ax_error}") │
│ 201 │
│ 202 logger.success(f"✅ Scrape successful for {url}") │
│ 190 - return {"success": True, "looking_at_files": artifacts} │
│ ═════════════════════════════════════════════════════════════════════════════════ │
│ 203 + return {"success": True, "looking_at_files": artifacts, "cached": │
│ False} │
│ 204 │
│ 205 except Exception as e: │
│ 206 logger.error(f"❌ Scrape failed for {url}: {e}", exc_info=True) │
╰───────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────╮
│ ✓ ReadFile Notebooks/secretsauce.py │
╰───────────────────────────────────────╯
╭─────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit Notebooks/secretsauce.py: async def scrape_and_extract(j... => async def scrape_and_ex… │
│ │
│ 76 profile_name: str = "default", │
│ 77 delay_range: tuple = (5, 10)): │
│ 78 """ │
│ 79 - Scrapes each URL using pip.scrape() and immediately parses the HTML │
│ 80 - to extract key SEO data. Verbosity is now controllable. │
│ 79 + Scrapes each URL using pip.scrape(), leveraging cached data if available, │
│ 80 + and immediately parses the HTML to extract key SEO data. │
│ 81 """ │
│ 82 print("🚀 Starting browser-based scraping and extraction...") │
│ 83 │
│ ═════════════════════════════════════════════════════════════════════════════════ │
│ 96 extracted_data = [] │
│ 97 │
│ 98 for i, url in enumerate(urls_to_process): │
│ 99 - print(f" -> 👁️ {i+1}/{len(urls_to_process)}] Processing: {url}") │
│ 99 + # The logging is now cleaner, showing a distinct message for cached │
│ items. │
│ 100 + # The core processing logic remains the same. │
│ 101 │
│ 102 # Apply delay only AFTER the first request to avoid an unnecessary │
│ initial wait │
│ 103 current_delay_range = delay_range if i > 0 else None │
│ ═════════════════════════════════════════════════════════════════════════════════ │
│ 114 delay_range=current_delay_range │
│ 115 ) │
│ 116 │
│ 117 + # --- AESTHETIC LOGGING UPDATE --- │
│ 118 + is_cached = scrape_result.get("cached", False) │
│ 119 + if is_cached: │
│ 120 + print(f" -> ✅ Cached [{i+1}/{len(urls_to_process)}] Using │
│ data for: {url}") │
│ 121 + else: │
│ 122 + print(f" -> 👁️ Scraped {i+1}/{len(urls_to_process)}] New │
│ data for: {url}") │
│ 123 + │
│ 124 + │
│ 125 if not scrape_result.get("success"): │
│ 126 if verbose: │
│ 127 print(f" -> ❌ Scrape failed: {scrape_result.get('error')} │
│ ") │
│ ═════════════════════════════════════════════════════════════════════════════════ │
│ 147 'url': url, 'title': title, 'meta_description': │
│ meta_description, │
│ 148 'h1s': h1s, 'h2s': h2s │
│ 149 }) │
│ 141 - if verbose: │
│ 142 - print(f" -> ✅ Scraped and Extracted.") │
│ ═════════════════════════════════════════════════════════════════════════════════ │
│ 150 + # No need for a verbose check here, the new logging is always │
│ informative. │
│ 151 │
│ 152 except Exception as e: │
│ 153 print(f" -> ❌ A critical error occurred while processing {url}: │
│ {e}") │
╰─────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ I have carried out the instructions and applied the changes to tools/scraper_tools.py and
Notebooks/secretsauce.py. The scraping process is now idempotent and will use cached data when
available.
accepting edits (shift + tab to toggle)
╭─────────────────────────────────────────────────────────────────────────────────────────────╮
│ > Type your message or @path/to/file │
╰─────────────────────────────────────────────────────────────────────────────────────────────╯
~/repos/pipulate (main*) no sandbox (see /docs) gemini-2.5-pro (97% context left)
Me: Here’s the diff:
[mike@nixos:~/repos/pipulate]$ git --no-pager diff
diff --git a/tools/scraper_tools.py b/tools/scraper_tools.py
index 1605db4a..ee9a62f7 100644
--- a/tools/scraper_tools.py
+++ b/tools/scraper_tools.py
@@ -35,8 +35,8 @@ def get_safe_path_component(url: str) -> tuple[str, str]:
async def selenium_automation(params: dict) -> dict:
"""
Performs an advanced browser automation scrape of a single URL using undetected-chromedriver.
- Supports persistent profiles to maintain sessions across runs.
- Captures artifacts including DOM, source, headers, screenshot, and visual DOM layouts.
+ Checks for cached data before initiating a new scrape.
+ ...
"""
url = params.get("url")
domain = params.get("domain")
@@ -52,7 +52,29 @@ async def selenium_automation(params: dict) -> dict:
if not all([url, domain, url_path_slug is not None]):
return {"success": False, "error": "URL, domain, and url_path_slug parameters are required."}
- # --- Fuzzed Delay Logic ---
+ base_dir = Path("browser_cache/")
+ if not is_notebook_context:
+ base_dir = base_dir / "looking_at"
+
+ output_dir = base_dir / domain / url_path_slug
+ artifacts = {}
+
+ # --- IDEMPOTENCY CHECK ---
+ # Check if the primary artifact (rendered_dom.html) already exists.
+ dom_path = output_dir / "rendered_dom.html"
+ if dom_path.exists():
+ if verbose:
+ logger.info(f"✅ Using cached data from: {output_dir}")
+
+ # Gather paths of existing artifacts
+ for artifact_name in ["rendered_dom.html", "source_html.text", "screenshot.png", "dom_layout_boxes.txt", "dom_hierarchy.txt", "accessibility_tree.json", "accessibility_tree_summary.txt"]:
+ artifact_path = output_dir / artifact_name.replace('.html', '').replace('.txt', '').replace('.json', '')
+ if artifact_path.exists():
+ artifacts[Path(artifact_name).stem] = str(artifact_path)
+
+ return {"success": True, "looking_at_files": artifacts, "cached": True}
+
+ # --- Fuzzed Delay Logic (only runs if not cached) ---
if delay_range and isinstance(delay_range, (tuple, list)) and len(delay_range) == 2:
min_delay, max_delay = delay_range
if isinstance(min_delay, (int, float)) and isinstance(max_delay, (int, float)) and min_delay <= max_delay:
@@ -64,7 +86,6 @@ async def selenium_automation(params: dict) -> dict:
logger.warning(f"⚠️ Invalid delay_range provided: {delay_range}. Must be a tuple of two numbers (min, max).")
driver = None
- artifacts = {}
profile_path = None
temp_profile = False
@@ -88,16 +109,8 @@ async def selenium_automation(params: dict) -> dict:
logger.info(f"🔍 Found driver executable at: {driver_path}")
- base_dir = Path("browser_cache/")
- if not is_notebook_context:
- base_dir = base_dir / "looking_at"
-
- output_dir = base_dir / domain / url_path_slug
-
try:
- if output_dir.exists():
- if verbose: logger.info(f"🗑️ Clearing existing artifacts in: {output_dir}")
- shutil.rmtree(output_dir)
+ # Create directory only if we are actually scraping
output_dir.mkdir(parents=True, exist_ok=True)
if verbose: logger.info(f"💾 Saving new artifacts to: {output_dir}")
@@ -187,7 +200,7 @@ async def selenium_automation(params: dict) -> dict:
logger.warning(f"⚠️ Could not extract accessibility tree: {ax_error}")
logger.success(f"✅ Scrape successful for {url}")
- return {"success": True, "looking_at_files": artifacts}
+ return {"success": True, "looking_at_files": artifacts, "cached": False}
except Exception as e:
logger.error(f"❌ Scrape failed for {url}: {e}", exc_info=True)
[mike@nixos:~/repos/pipulate]$
How did Gemini CLI do?
Gemini Web: The Gemini CLI did an excellent job of implementing the core logic for idempotency, but it introduced one bug in the process that needs to be fixed.
Overall, the changes are very close to perfect. It correctly removed the directory deletion, moved the delay logic to only run for new scrapes, and updated the return values with the new cached flag.
The Bug 🐞
The issue is in tools/scraper_tools.py within the new IDEMPOTENCY CHECK block. When it tries to gather the paths of already-existing artifacts, the logic is slightly flawed.
Here’s the buggy code:
# This is INCORRECT
for artifact_name in ["rendered_dom.html", "source_html.text", ...]:
artifact_path = output_dir / artifact_name.replace('.html', '').replace('.txt', '').replace('.json', '') # This line is the problem
if artifact_path.exists():
artifacts[Path(artifact_name).stem] = str(artifact_path)
The .replace() calls are incorrectly removing the file extensions, so the script ends up looking for files named rendered_dom, source_html, etc., which don’t exist.
The Fix 🛠️
The fix is to simply use the artifact_name directly to build the path.
Here is the corrected code for that specific block in tools/scraper_tools.py.
# --- IDEMPOTENCY CHECK ---
# Check if the primary artifact (rendered_dom.html) already exists.
dom_path = output_dir / "rendered_dom.html"
if dom_path.exists():
if verbose:
logger.info(f"✅ Using cached data from: {output_dir}")
# Gather paths of existing artifacts
for artifact_name in ["rendered_dom.html", "source_html.text", "screenshot.png", "dom_layout_boxes.txt", "dom_hierarchy.txt", "accessibility_tree.json", "accessibility_tree_summary.txt"]:
artifact_path = output_dir / artifact_name # CORRECTED LINE
if artifact_path.exists():
artifacts[Path(artifact_name).stem] = str(artifact_path)
return {"success": True, "looking_at_files": artifacts, "cached": True}
With that one-line correction, the implementation is solid. The changes made to Notebooks/secretsauce.py (as indicated by the CLI’s log) also appear to be correct based on the diff for scraper_tools.py.
Gemini CLI:
╭─────────────────────────────────────╮
│ ✓ ReadFile tools/scraper_tools.py │
╰─────────────────────────────────────╯
╭─────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit tools/scraper_tools.py: # Gather paths of exis... => # Gather paths of… │
│ │
│ 68 │
│ 69 # Gather paths of existing artifacts │
│ 70 for artifact_name in ["rendered_dom.html", "source_html.text", │
│ "screenshot.png", "dom_layout_boxes.txt", "dom_hierarchy.txt", │
│ "accessibility_tree.json", "accessibility_tree_summary.txt"]: │
│ 71 - artifact_path = output_dir / artifact_name.replace('.html', '').replace( │
│ '.txt', '').replace('.json', '') │
│ 71 + artifact_path = output_dir / artifact_name │
│ 72 if artifact_path.exists(): │
│ 73 artifacts[Path(artifact_name).stem] = str(artifact_path) │
│ 74 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Bug fixed. I've corrected the artifact path generation in tools/scraper_tools.py as you
instructed.
accepting edits (shift + tab to toggle)
╭─────────────────────────────────────────────────────────────────────────────────────────────╮
│ > Type your message or @path/to/file │
╰─────────────────────────────────────────────────────────────────────────────────────────────╯
~/repos/pipulate (main*) no sandbox (see /docs) gemini-2.5-pro (97% context left)
Me: Okay, that was superbly well done! There’s nothing like fatigue to make
you realize you can have Gemini CLI do the actual work for you, especially on
these smaller files. And it’s tested and the crawl portion is in face
idempotent. This is a good place to cut this entry and to get onto making the AI
calls able to be run multiple times without worry as well. But don’t give me
that plan yet. Put a bow on this as an article. Break out anything you think is
worthwhile to point out that we learned, what things to keep in mind going
forward, and anything else you can think of important here.
Gemini Web: Of course! It’s been a productive session. Turning a burst of late-night coding into a coherent article is the perfect way to solidify the progress and learnings. Here’s a summary of what we accomplished, framed as an article.
The Power of the Re-Run: Building a Safer, Smarter Scraper
In the journey of building any automated workflow, there’s a moment you realize a tool is powerful but dangerous. For our Jupyter-based scraper, that moment came when we acknowledged a simple truth: re-running a cell was a destructive act. Every time we executed our scrape_and_extract function, it would diligently wipe out all previously scraped data for a given URL and start fresh. While clean, this approach is the enemy of iterative work.
The Pitfall of Destructive Operations
What if you wanted to add just one more URL to your list? You’d have to re-scrape everything. What if you accidentally re-ran a cell? Your carefully collected data would vanish in an instant. The process lacked a fundamental quality of robust automation: idempotency.
What is Idempotency and Why Does it Matter?
In simple terms, an operation is idempotent if running it once has the same effect as running it a hundred times. You can hit the button again and again without fear of unintended side effects. Our old scraper was the opposite; each run caused a new, costly network operation and deleted the previous result.
Our Approach to Idempotent Scraping
Our goal was to change the scraper’s behavior from a destructive command to a declarative state check: from “go scrape this page” to “ensure this page has been scraped.”
The Simple, Elegant Fix
The solution didn’t require complex databases or state management systems. We simply decided to use the filesystem—our existing browser_cache directory—as our source of truth. The logic was refactored in tools/scraper_tools.py with one key change in perspective.
Before: The Destructive Approach
- Does a folder for this URL exist?
- If yes, delete the entire folder (
shutil.rmtree). - Launch a browser and scrape the page.
- Save the new files.
After: The Idempotent Approach
- Does the key artifact,
rendered_dom.html, exist in the folder for this URL? - If yes, gather the paths of all existing files, print a
✅ Cachedmessage, and do nothing else. - If no, then and only then launch a browser, scrape the page, and save the new files.
This small change has a massive impact. We can now add or remove URLs from our list in the FAQuilizer.ipynb notebook and re-run the scrape_and_extract cell with confidence. The script will only perform network requests for pages it hasn’t seen before, making the process faster, more efficient, and infinitely safer.
Key Takeaways for Future Development
- Idempotency is a Cornerstone: For any process that is costly (in time, money, or network requests) or destructive, building in an idempotency check should be a top priority. It’s the foundation of reliable automation.
- Provide Clear Feedback: The user-facing output in the notebook was updated to distinguish between “scraped” and “cached” items. This visibility is crucial; it builds trust and gives the user a clear understanding of what the tool is doing behind the scenes.
- The Filesystem is a Viable Database: For many local-first applications, the file system itself is a perfectly good database. The presence or absence of a file is a simple, effective way to track the state of an operation.
The Next Step: Extending Idempotency
By making our scraping process idempotent, we’ve made the entire workflow more resilient. We’ve laid a foundation of safety that allows for confident experimentation—the very heart of the “Digital Workshop” we’re building. Next, we’ll apply this same principle to the AI enrichment step, ensuring it, too, can be re-run without worry.
Book Analysis
Ai Editorial Take
This entry effectively distills a crucial software engineering principle—idempotency—into a practical, digestible lesson within the context of web automation. Its strength lies in demonstrating a tangible improvement in workflow safety and efficiency, moving from a brittle to a robust system. This piece serves as an excellent case study for explaining why thoughtful architecture, even in seemingly minor details, significantly enhances developer experience and the reliability of automated systems. It has strong potential as foundational content for discussions on resilient system design.
Title Brainstorm
- Title Option: The Power of the Re-Run: Building a Safer, Smarter Scraper
- Filename:
the-power-of-the-re-run-building-a-safer-smarter-scraper.md - Rationale: Directly from the provided article, clear, benefit-oriented, and sets an inviting tone for a technical concept.
- Filename:
- Title Option: Idempotency in Web Scraping: How to Make Your Automation Resilient
- Filename:
idempotency-web-scraping-resilient-automation.md - Rationale: More technical and direct, emphasizing the core concept and its practical benefit of resilience.
- Filename:
- Title Option: Beyond Destructive Scrapes: Caching for Confidence in Your Workflow
- Filename:
beyond-destructive-scrapes-caching-confidence.md - Rationale: Highlights the problem, the solution (caching), and the emotional/workflow benefit (confidence).
- Filename:
- Title Option: Jupyter & Pipulate: Mastering Idempotent Data Collection
- Filename:
jupyter-pipulate-idempotent-data-collection.md - Rationale: Connects to the specific tools (Jupyter, Pipulate) and the core principle of idempotent data collection.
- Filename:
Content Potential And Polish
- Core Strengths:
- Clearly articulates the problem of non-idempotency in web scraping.
- Provides a practical, filesystem-based solution for implementing idempotency.
- Highlights the significant benefits: speed, safety, efficiency, and improved user experience.
- Offers actionable takeaways for general automation design, extending beyond just scraping.
- Demonstrates real-world problem-solving, making a complex concept accessible and relatable.
- Suggestions For Polish:
- Include a concise, high-level code snippet illustrating the ‘before’ (destructive) and ‘after’ (idempotent, cache-checking) logic directly in the article for stronger visual reinforcement.
- Briefly discuss potential edge cases or considerations for filesystem caching, such as handling stale cache or explicitly forcing a re-scrape.
- Expand on the concept of ‘the filesystem as a viable database’ with one or two additional simple examples from other domains to broaden the application of the idea.
Next Step Prompts
- Draft a follow-up article on how to apply the principle of idempotency to the AI enrichment (FAQ generation) step, ensuring that AI calls are cached and only re-run when inputs change, or when specific ‘freshness’ criteria are met.
- Generate a markdown table summarizing common idempotent and non-idempotent operations in web development and data processing, along with best practices for achieving idempotency in each case.