File Search Assistant with LLM Integration

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

FAQ from File Search Assistant with LLM Integration

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.

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.

Where are the embeddings stored?

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

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.

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.