Deep Searcher
A Python project that combines language models and vector search to investigate questions over a document collection.
About Deep Searcher
Answering questions across private documents requires more than passing a single document to a model.
Who it’s for
- Developers prototyping document research
- Teams evaluating retrieval over an internal corpus
When to consider it
Consider DeepSearcher for a research prototype with a defined corpus and answer-quality test set. Start with questions whose evidence you can verify. Treat ingestion, retrieval and answer synthesis as separate things to evaluate.
Tradeoffs & limitations
- Private source data does not automatically mean local inference; inspect the configured model and embedding providers.
- Model, embedding and storage choices affect cost and data handling.
A useful first evaluation
Use a small corpus with known answers and conflicting documents. Check whether each conclusion can be traced back to the correct passage before expanding the dataset.
Suggested evaluation, not a report of tests we ran. Download the worksheet.
SOTA overview · Documentation-based assessment · Sources & review method
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Sources & review method
Documentation-based assessment · Sep 28, 2026 · Prepared with AI assistance; not a hands-on benchmark.
Checked by SOTA · AI-assisted documentation review. Selection advice is our assessment; verify current requirements for your deployment.
Import history & original evidence
Imported source
Open Source Deep Research alternative with Jev search-stopping evaluation.
Author and repository text is preserved where clear; missing languages are enriched automatically. Checks establish source-level Jev integration, not runtime, safety, or performance validation.