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File Search Assistant with LLM Integration MCP Server

@Code-Trees

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Code-Trees

About File Search Assistant with LLM Integration MCP Server

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 c

#mathgpt#math-solver#math-assistant

File Search Assistant with LLM Integration MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "Agentic_search": {
      "command": "python",
      "args": [
        "main.py"
      ]
    }
  }
}

File Search Assistant with LLM Integration MCP Server tools

Tool list not available yet

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Overview

What is File Search Assistant with LLM Integration?

File Search Assistant with LLM Integration combines semantic search (BERT embeddings) with Google’s Gemini large language model to enable natural‑language file queries on Linux systems. It is built as an MCP server for file‑system integration.

How to use File Search Assistant with LLM Integration?

Install Python 3.8+ and dependencies (pip install -r requirements.txt), set the GEMINI_API_KEY in a .env file, then run python main.py. Enter a natural‑language query when prompted; the system extracts a filename via the LLM, performs semantic search, and displays matching file locations.

Key features of File Search Assistant with LLM Integration

  • Natural language file search queries
  • Semantic search using BERT embeddings
  • Gemini LLM integration for query understanding
  • Automatic file extension inference
  • MCP server for file system operations
  • Extensible architecture

Use cases of File Search Assistant with LLM Integration

  • Find a Python script that handles file operations by describing its purpose
  • Locate a document by describing its content or context
  • Search for files when the exact filename is forgotten

Frequently asked questions

1

What are the prerequisites?

Python 3.8 or higher, a Linux operating system, and a Gemini API key. A CUDA‑compatible GPU is optional for faster processing.

2

How does the semantic search work?

It uses BERT embeddings for context‑aware file matching, combined with the LLM to convert natural language into search terms.

3

Where are the embeddings stored?

Embeddings are stored in the file `OSData_store.pth` inside the project directory.

4

What should I do if I get “Embeddings file not found”?

Run the command `cd ~ && find / -type f 2>/dev/null >> Desktop/LLm_To_agent` to rebuild the file index.

5

What transport or authentication does it use?

The README does not specify transport details. Authentication is handled via the Gemini API key set in the `.env` file.

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