deep-research

Automating multi-source research involves a three-phase workflow: parallel collection with deduplication, structured storage with metadata, and semantic clustering for markdown report synthesis. This approach ensures traceable, reproducible outputs across academic, market, and technology research tasks.

Semantic clustering synthesizes research documents by grouping similar findings from collected sources into structured clusters. It relies on scikit-learn and numpy to process the collected text, organizing findings into a clusters.json file before generating the final markdown report.

Yes, generating a markdown research report with traceable citations is the core output of this workflow. It produces a final-report.md file alongside metadata.json and clusters.json, ensuring all sourced information remains auditable and reproducible.

Yes, the research workflow supports parallel web sourcing and deduplication during its initial collection phase. It utilizes duckduckgo-search and httpx to fetch information concurrently, while beautifulsoup4 helps process and filter the collected documents.

To ensure reproducibility when generating market research reports, store all artifacts under a timestamped workspace directory like research-output. This structured storage with metadata allows you to audit the exact collection and clustering steps that produced the final report.

You should not use an automated multi-source collection workflow if your academic research requires interacting with login-walled databases or real-time API data, as the collection phase currently relies on public search via duckduckgo-search and static parsing.