8.-samopisnyy-poiskovyy-agent-na-osnove-/README.md

Deep Agent Search with Virtual File System

This project demonstrates a simple deep agent search system that operates on virtual files stored entirely in memory. It uses PyTorch, scikit-learn, and NumPy to perform TF‑IDF vectorization and cosine similarity ranking.

Features

• Virtual File System: Create, read, write, unload, and delete virtual files.
• Search Agent: Rank lines from a virtual file based on a query using TF‑IDF and cosine similarity.
• Deep Learning Integration: Uses PyTorch tensors for similarity calculations.
• Easy to Extend: Replace the search logic with more sophisticated models (e.g., transformers) without changing the file system.

Installation

# Clone the repository
git clone https://git.brojs.ru/kuzakhmetovartur/8.-samopisnyy-poiskovyy-agent-na-osnove-
cd 8.-samopisnyy-poiskovyy-agent-na-osnove-

# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Usage

Run the example script:

python src/main.py

You should see output similar to:

Search results for query: 'neural networks'
1. Neural networks can approximate complex functions.
2. Deep learning has revolutionized many fields.
3. PyTorch provides dynamic computation graphs.
After unload: Cannot read from unloaded file 'sample.txt'.

Project Structure

├── src
│   ├── main.py                # Entry point and demo
│   └── virtual_file_system.py # Virtual file system implementation
├── requirements.txt           # Dependencies
└── README.md                  # Documentation

Extending the Search Agent

The SearchAgent class in src/main.py can be replaced with any model that accepts a query and returns ranked results. For example, you could:

• Load a pre‑trained transformer (e.g., BERT) and compute embeddings.
• Use a neural ranking model trained on relevance data.
• Integrate with external search APIs.

Just ensure that the agent receives a VirtualFileSystem instance and uses VirtualFile.read() to access data.

Testing

Unit tests are not included in this minimal example, but you can add tests using pytest to verify:

• Virtual file read/write/unload behavior.
• Search agent ranking correctness.
• Integration of the virtual file system with the agent.

License

MIT License