Llm-Search

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llm-search is an AI Agents & Automation tool that provides an advanced question-answering system for local documents. It supports custom LLMs and offers features like hybrid search, deep linking, and re-ranking.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is llm-search?

llm-search offers an advanced Retrieval-Augmented Generation (RAG) system designed for querying local documents using Large Language Models. It features a simple YAML-based configuration for easy setup and interaction. The tool focuses on enhancing various RAG components, including improved document parsing for formats like Markdown, PDF, and DOCX, hybrid search capabilities, and re-ranking strategies. It supports custom LLMs, including OpenAI compatible models, HuggingFace models, and interoperability with LiteLLM + Ollama. Users can interact with the system via a built-in frontend or an MCP server, allowing integration with clients like Cursor, Windsurf, or VSCode GH Copilot. Additional features include incremental updates for document bases, chat history, and optional HyDE and multi-querying for improved result quality.

Best used for

Ideal for developers and data scientists who need to build advanced question-answering systems over local document collections, integrate custom LLMs, and enhance search capabilities with features like hybrid search and re-ranking. Especially valuable for creating intelligent knowledge retrieval systems within development environments.

Common actions

query local documents

build RAG systems

integrate LLMs

enhance document search

manage knowledge bases

open-sourceworkflowsgithub copilotcollaborationface swappingdeepfakeautomated workflowlow-code/no-code"AI Agents"

Capabilities

Key features

Advanced RAG system
YAML-based configuration
Hybrid search
Deep linking documents
Document re-ranking
Custom LLM support
Incremental document updates

Target Audience

developerdata scientist

Integrations

openaihugging-facelitellmollamachromadbcursorvscode

Pricing & Plans

Open Source

Free

FAQs

What document formats does llm-search support for parsing?

llm-search includes built-in parsers for .md, .pdf, and .docx files, supporting features like logical component division and nested tables. It also integrates with Unstructured pre-processor to support a wide range of other common document formats.

Can I use my own Large Language Models with llm-search?

Yes, llm-search is designed to work with custom LLMs. It supports OpenAI compatible models and APIs, HuggingFace models, and offers interoperability with LiteLLM and Ollama, allowing access to hundreds of different models.

How does llm-search improve search quality beyond basic RAG?

llm-search enhances search quality through features like hybrid search (sparse + dense embeddings), re-ranking with modern cross-encoders, HyDE (Hypothetical Document Embeddings) for better query formulation, and multi-querying to generate diverse perspectives on a user's question.

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Also listed in

This tool also appears in

Productivity & Business › Document Management Research & Education › Knowledge Management

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