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MCP.so

File Search Assistant with LLM Integration

Created By
Code-Trees7 months ago
Learn how to: ✅ Build a file-search AI using natural language queries ✅ Create embeddings from local Linux files using Hugging Face models ✅ Integrate Gemini API (Google AI Studio) into your local apps ✅ Use MCP to control multiple agents with server-client architecture ✅ Apply cosine similarity, asyncio Python, and more!
# mathgpt
# math-solver
OverviewContentToolsComments
Overview

Agentic_search is a file search assistant that integrates Large Language Models (LLM) to enable intelligent file searching using natural language queries in Linux systems.

To use Agentic_search, clone the repository, install the dependencies, configure your environment variables with the Gemini API key, and run the main application to start searching for files using natural language queries.

  • Natural language file search queries
  • Semantic search using BERT embeddings
  • Integration with Gemini LLM for enhanced query understanding
  • MCP server for efficient file system operations
  • Automatic inference of file extensions
  1. Finding specific files based on natural language descriptions.
  2. Searching for documents or scripts in a Linux environment.
  3. Enhancing productivity by quickly locating files without remembering exact names.
  • What programming language is used for Agentic_search?

The project is developed in Python.

  • Do I need a GPU to run Agentic_search?

A CUDA-compatible GPU is optional but recommended for faster processing.

  • How does the semantic search work?

It uses BERT embeddings to understand the context of the search queries.

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