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deep-searcher-guide

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Open deep research alternative for private data with vector search

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# Deep Searcher Guide

## Overview

Deep Searcher is an open-source deep research tool developed by Zilliz with over 8,000 GitHub stars, designed to be an open alternative to proprietary deep research systems like OpenAI's Deep Research and Gemini Deep Research. What distinguishes Deep Searcher is its focus on private data -- it enables researchers to conduct deep, iterative research over their own document collections, databases, and institutional knowledge bases rather than being limited to public web content.

The system combines vector search via Milvus (or other vector databases) with agentic RAG (Retrieval-Augmented Generation) to decompose complex research questions, retrieve relevant passages from your document collection, reason over the retrieved content, and iteratively refine its search until it can produce a comprehensive answer. This makes it particularly valuable for researchers who work with proprietary datasets, unpublished manuscripts, internal reports, or specialized domain corpora that are not available through web search.

Deep Searcher supports multiple LLM providers and embedding models, and can be deployed entirely on-premises for organizations with strict data privacy requirements. It is built on top of Milvus, the high-performance open-source vector database also created by Zilliz, ensuring scalable and efficient similarity search across large document collections.

## Installation and Setup

```bash
# Install Deep Searcher
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