llm-search MCP Server
snexus/llm-search
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Querying local documents, powered by LLM
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snexus/llm-search is a Jupyter Notebook tool licensed under MIT. Querying local documents, powered by LLM Topics: chroma, langchain-python, large-language-models, openai-chatgpt, splade, chatbot, llm, streamlit, hyde, rag, reranking, retrieval-augmented-generation, mcp.
Ranked #10818 out of 24846 indexed tools.
Ecosystem
Jupyter Notebook MIT
chromalangchain-pythonlarge-language-modelsopenai-chatgptspladechatbotllmstreamlithyderagrerankingretrieval-augmented-generationmcp
Score Breakdown
Stars 15% 662
662 stars → growing community
Freshness 25% 9mo ago
Last commit 9mo ago → stale
Issue Health 25% 88%
44/50 issues closed → responsive maintainer
Contributors 10% 6
6 contributors → small team
Dependents 25% 2
2 dependents → few downstream users
npm Downloads N/A
PyPI Downloads N/A
Forks 71
Description Brief
License MIT
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
How to Improve
Description low impact
Freshness high impact
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From the README
# pyLLMSearch - Advanced RAG [Documentation](https://llm-search.readthedocs.io/en/latest/) The purpose of this package is to offer an advanced question-answering (RAG) system with a simple YAML-based configuration that enables interaction with a collection of local documents. Special attention is given to improvements in various components of the system **in addition to basic LLM-based RAGs** - better document parsing, hybrid search, HyDE, chat history, deep linking, re-ranking, the ability to customize embeddings, and more. The package is designed to work with custom Large Language Models (LLMs) – whether from OpenAI or installed locally. Interaction with the package is supported through the built-in frontend, or by exposing an MCP server, allowing clients like Cursor, Windsurf or VSCode GH Copilot to interact with the RAG system. ## Features * Fast parsing and embedding of medium size document bases (tested on up to few gigabytes of markdown and pdfs) * Incremental updates for nRead full README on GitHub →
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