Practical overview
When it fits
Use it when a research or question-answering workflow needs to operate over local or organizational data and be configured around selected LLM and vector-database providers.
Who it is for
- Organizations managing internal knowledge
- Developers building private-data question-answering systems
- Teams creating self-hosted agentic RAG or information-retrieval workflows
Documented uses
- Search local private documents
- Generate answers and research reports from indexed data
- Connect LLM providers with vector-database retrieval
- Configure embeddings for private-data search
Requirements & limits
The project is open source, publicly hosted on GitHub and licensed under Apache-2.0. The documented quick start requires Python setup and an API key for the selected model provider.
- Python 3.10 is recommended for the documented installation workflow.
- Milvus and Zilliz Cloud are documented vector-database options.
- The repository documents local-file loading, while web crawling is described as under development.
- The workflow supports multiple LLM providers and configurable embedding models.
- The primary documented interface is a code-based Python workflow rather than a hosted consumer application.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install DeepSearcher and prepare a local research workflow
Set up DeepSearcher, configure an LLM and embedding provider, load local files, and submit a research question.
Before you start
- Python 3.10 is recommended.
- An API key for the selected LLM provider is required for the documented OpenAI example.
- A local file or directory to load as data.
Steps
Create and activate a Python virtual environment, then install DeepSearcher with pip; alternatively, clone the repository, run uv sync and activate the resulting environment.
Configure the LLM and embedding providers with the DeepSearcher Configuration object.
Load local files with load_from_local_files.
Submit a question with query, such as a request to write a report.
Expected outcome
A query result produced from the loaded local data through the configured LLM and embedding workflow.
Guide limitations
- The documented example requires a provider API key.
- Web crawling requires FIRECRAWL_API_KEY and is described in the repository as under development.
- The guide documents a quick-start workflow, not an evaluation of answer quality or production readiness.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 1 October 2026
- Original website title
- zilliztech/deep-searcher · Official homepage title · retained in its source language