📸 SnapIR
Capture web search over time. SnapIR is an open-source toolkit for creating snapshots of live web search: one time snapshots or repeated (longitudinal) ones. It links information needs, topics, queries, SERPs, captured web pages and relevance judgments across collection dates, so you can study how rankings, AI overviews and page content change, and evaluate retrieval systems on judgments made at the time of the search.
Each step is one CLI command. Steps 4 and 5 are repeated for every snapshot. Steps can be run on their own (for example, bring your own topics and start at step 4), are resumable (existing records are skipped) and can be scheduled with the included Prefect workflows.
Install
pip install snapir
playwright install chromium # browser for capturing pages
Optional extras: snapir[gemini] for Gemini models, snapir[prefect] for the
scheduled workflows.
The LLM steps (topics generate, queries generate, judgments generate)
also need topic-gen:
pip install "topic-gen @ git+https://github.com/irgroup/topic-gen.git"
Quickstart: two snapshots of one query
This needs only a SerpApi key, no LLM. Use your own topic and query. IDs can be any unique strings.
export SERPAPI_API_KEY=...
echo '{"topic_id": "t1", "title": "EU AI Act enforcement", "description": "How is the EU AI Act being enforced?"}' > topics.jsonl
echo '{"topic_id": "t1", "query_id": "q1", "query": "eu ai act enforcement news"}' > queries.jsonl
snapir serps fetch --queries queries.jsonl --output data/serps
snapir documents scrape --serps data/serps/$(date +%F)_queries --output data/docs
snapir documents extract-text --documents data/docs/$(date +%F)
Run the same three commands again tomorrow (or later). Then package every day into a collection:
snapir export snapshot --serps data/serps --documents data/docs \
--topics topics.jsonl --queries queries.jsonl --output collection
collection/<YYYY-MM-DD>/ now contains, for each day, the topics, queries,
raw SERPs, the organic ranking as a TREC run, and the extracted page text.
To generate topics and queries from news debates, judge relevance and export qrels, follow the tutorial.
Commands
snapir
├── topics
│ ├── fetch-debates Collect source debates from euro|topics
│ ├── generate Generate topics from debates (LLM)
│ └── select Add a topic and its queries to a curated set
├── queries
│ └── generate Generate queries for topics (LLM)
├── serps
│ └── fetch Fetch search results for queries (SerpApi)
├── documents
│ ├── scrape Capture pages referenced by SERPs (MHTML, screenshot)
│ ├── extract-text Extract page text, including PDFs
│ ├── review Inspect screenshots interactively
│ └── clear Remove flagged captures so the next scrape retries them
├── judgments
│ ├── generate LLM relevance judgments (-DNA- prompt by default)
│ ├── create-questions Generate the SoSci question pool
│ └── questionnaire Build a SoSci questionnaire for human assessors
└── export
├── rankings Rankings as a table
├── answers Human assessments from SoSci
├── qrels Aggregate assessments into TREC qrels
├── snapshot Assemble a distributable collection, one directory per day
└── verify Check queries → SERPs → captures → text for gaps
snapir <group> <command> --help documents every option.
Configuration
All settings are environment variables:
| Variable | Used by |
|---|---|
SNAPIR_LLM_MODEL, SNAPIR_LLM_BASE_URL, SNAPIR_LLM_API_KEY |
LLM steps, using any OpenAI-compatible endpoint. Models named gemini* use Google GenAI (pip install "snapir[gemini]") |
SNAPIR_LLM_TEMPERATURE (default 1), SNAPIR_LLM_CONCURRENCY (default 1) |
LLM steps |
SERPAPI_API_KEY |
serps fetch |
BROWSER_PROFILE_DIR (default data/.browser_profile), EXTENSION_DIR (default data/extensions/idcac) |
documents scrape |
SNAPIR_ROBOTS_USER_AGENT (default SnapIR); bypass with --ignore-robots |
documents scrape |
SOSCI_EXPORT_URL |
default for export answers --sosci |
Documentation
- Tutorial: build a dynamic collection for one news topic across two days
- Concepts: snapshots, IDs, on-disk layout, resuming
- Guides: your own topics · capture quality · human assessments · LLM assessments · responsible use
- Scheduled collection with Prefect and Docker (setup)
- Agent skill: lets a coding agent run, resume and diagnose collection builds
- Example data (prompts, topics, queries) and analyses behind the paper's figures
Responsible use
SnapIR obeys robots.txt by default and does not hide its automation unless
you pass --stealth. You are responsible for following the terms of service
of the search and scraping providers and for any copyright and data-protection
law that applies to the pages you capture. Laws on redistributing captured
pages differ by country.
License
Metadata
Release files for snapir 0.1.2
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| snapir-0.1.2-py3-none-any.whl | Python 3 | none | any | Details for snapir-0.1.2-py3-none-any.whl |
Total release size: 499.9 kB
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