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jhamonjhamon
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Verify links when notebooks are changed (#426)
## Problem Some notebooks contain broken links ## Solution When someone is working on a notebook, we want to ensure all links are valid. Parse out the links, then do a `HEAD` request to see if they are valid. ## Type of Change - [x] Infrastructure change (CI configs, etc)
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‎.github/actions/check-links/action.yml

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name: "Check Links in Notebook"
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description: "Check Links in Notebook"
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inputs:
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notebook:
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description: "The notebook to check for broken links"
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required: true
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runs:
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using: 'composite'
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steps:
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: '3.11'
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- name: Install dependencies
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shell: bash
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run: |
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pip install --upgrade pip
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pip install nbformat requests
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- id: convert
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shell: bash
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name: Check links in notebook
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run: |
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python .github/actions/check-links/check-links.py ${{ inputs.notebook }}

‎.github/actions/check-links/check-links.py

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#! /usr/bin/env python
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# Check links in a notebook
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import re
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import os
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import sys
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import json
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import nbformat
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import requests
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# Get the notebook filename from the command line
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filename = "../../../" + sys.argv[1]
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print(f"Processing notebook: {filename}")
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nb_source_path = os.path.join(os.path.dirname(__file__), filename)
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known_good = [
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"https://www.pinecone.io",
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"https://app.pinecone.io",
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]
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known_good_links = set(known_good)
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for link in known_good:
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known_good_links.add(f"{link}/")
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# Read the notebook
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with open(nb_source_path, "r", encoding="utf-8") as f:
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nb = nbformat.read(f, as_version=4)
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try:
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good_links = set()
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failed_links = set()
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links = set() # Use set to avoid duplicates
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# URL regex pattern - updated to handle markdown links better
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url_pattern = r'https?://[^\s<>"\)]+|www\.[^\s<>"\)]+'
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# Search through all cells
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for cell in nb['cells']:
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if 'source' in cell:
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# Join multi-line source into single string
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content = ''.join(cell['source'])
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# Find all URLs
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found_links = re.findall(url_pattern, content)
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links.update(found_links)
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if links:
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print(f"\nFile: {filename}")
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for link in sorted(links):
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if link in known_good_links:
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good_links.add(link)
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print(f" ✅ {link}")
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continue
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elif link in good_links:
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continue
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elif link in failed_links:
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continue
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else:
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try:
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response = requests.head(link, timeout=10)
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if response.status_code == 405:
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# Not all links can be checked with HEAD, so we fall back to GET
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response = requests.get(link, timeout=10)
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if response.status_code == 200:
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good_links.add(link)
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print(f" ✅ {link}")
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else:
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failed_links.add(link)
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print(f" ❌ {response.status_code} {link}")
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except Exception as e:
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failed_links.add(link)
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print(f" ❌ {link}")
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print(f"Found {len(links)} links")
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print(f"Good links: {len(good_links)}")
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if len(failed_links) > 0:
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print("Failed links:")
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for link in sorted(failed_links):
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print(f" ❌ {link}")
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sys.exit(1)
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else:
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print("No bad links found")
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sys.exit(0)
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except json.JSONDecodeError:
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print(f"Error: Could not parse {filename} as JSON")
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except KeyError:
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print(f"Error: Unexpected notebook format in {filename}")

‎.github/workflows/test-notebooks-changed.yaml

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notebook: ${{ matrix.notebook }}
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PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }}
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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check-links:
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needs:
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- validate-notebooks
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- detect-changes
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if: needs.detect-changes.outputs.has_changes == 'true'
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix: ${{ fromJSON(needs.detect-changes.outputs.matrix) }}
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steps:
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- uses: actions/checkout@v4
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- uses: ./.github/actions/check-links
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with:
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notebook: ${{ matrix.notebook }}

‎docs/langchain-retrieval-agent.ipynb

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"id": "bhWwrfbbVGOA"
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},
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"source": [
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"#### [LangChain Handbook](https://pinecone.io/learn/langchain)\n",
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"#### [LangChain Handbook](https://www.pinecone.io/learn/series/langchain/)\n",
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"\n",
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"# Retrieval Agents\n",
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"\n",
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"We've seen in previous chapters how powerful [retrieval augmentation](https://www.pinecone.io/learn/langchain-retrieval-augmentation/) and [conversational agents](https://www.pinecone.io/learn/langchain-agents/) can be. They become even more impressive when we begin using them together.\n",
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"We've seen in previous chapters how powerful [retrieval augmentation](https://www.pinecone.io/learn/series/langchain/langchain-retrieval-augmentation/) and [conversational agents](https://www.pinecone.io/learn/series/langchain/langchain-agents/) can be. They become even more impressive when we begin using them together.\n",
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"\n",
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"Conversational agents can struggle with data freshness, knowledge about specific domains, or accessing internal documentation. By coupling agents with retrieval augmentation tools we no longer have these problems.\n",
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"\n",
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"One the other side, using \"naive\" retrieval augmentation without the use of an agent means we will retrieve contexts with *every* query. Again, this isn't always ideal as not every query requires access to external knowledge.\n",
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"\n",
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"Merging these methods gives us the best of both worlds. In this notebook we'll learn how to do this.\n",
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"\n",
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"[![Open full notebook](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/full-link.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/generation/langchain/handbook/08-langchain-retrieval-agent.ipynb)\n",
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"[![Open full notebook](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/full-link.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/generation/langchain/handbook/08-langchain-retrieval-agent.ipynb)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Prerequisites\n",
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"\n",
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"To begin, we must install the prerequisite libraries that we will be using in this notebook."
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"To begin, we must install several libraries that we will be using in this notebook."
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]
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},
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{
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"- `name` can be anything we like. The name is used as an identifier for the index when performing other operations such as `describe_index`, `delete_index`, and so on. \n",
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"- `metric` specifies the similarity metric that will be used later when you make queries to the index.\n",
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"- `dimension` should correspond to the dimension of the dense vectors produced by your embedding model. In this quick start, we are using made-up data so a small value is simplest.\n",
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"- `spec` holds a specification which tells Pinecone how you would like to deploy our index. You can find a list of all [available providers and regions here](https://docs.pinecone.io/docs/projects).\n",
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"- `spec` holds a specification which tells Pinecone how you would like to deploy our index. You can find a list of all [available providers and regions here](https://docs.pinecone.io/troubleshooting/available-cloud-regions).\n",
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"\n",
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"There are more configurations available, but this minimal set will get us started."
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]

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