- File Search Assistant with LLM Integration
File Search Assistant with LLM Integration
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!
Overview
what is Agentic_search?
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.
how to use Agentic_search?
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.
key features of Agentic_search?
- 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
use cases of Agentic_search?
- Finding specific files based on natural language descriptions.
- Searching for documents or scripts in a Linux environment.
- Enhancing productivity by quickly locating files without remembering exact names.
FAQ from Agentic_search?
- 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.
Project Info
Created At
a year agoUpdated At
a year agoAuthor Name
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research-and-data
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