feat: solution for 'Агент с RAG-памятью'

README.md

@@ -1,22 +1,132 @@
 # Agent with RAG Memory
 
-This project demonstrates a simple Node.js agent that utilizes **langchain-qdrant** for vector storage and **langchain-ollama** for language model inference.
+This repository contains a lightweight implementation of an agent that can
+interact with a **Retrieval‑Augmented Generation (RAG)** knowledge base.
+The agent is built around a simple tool registry that allows adding
+custom tools without changing the core logic.
 
-## Setup
+## Features
+
+- **Knowledge Base Tool** – A file‑based key/value store that can be
+  queried, added to, and deleted from by both the agent and the CLI.
+- **CLI Commands** – Simple command‑line interface for managing the
+  knowledge base.
+- **Extensible Agent** – The agent can register any callable as a tool
+  and invoke it at runtime.
+
+## Installation
 
 ```bash
-# Install dependencies
-npm install
+# Clone the repository
+git clone https://git.brojs.ru/kuzakhmetovartur/agent-s-rag-pamyatyu.git
+cd agent-s-rag-pamyatyu
 
-# Run the agent
-npm start
+# Create a virtual environment (recommended)
+python -m venv .venv
+source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`
+
+# Install the package
+pip install .
 ```
 
-The agent will initialize a connection to a Qdrant instance (default URL: `http://localhost:6333`) and an Ollama LLM (default model: `llama2`). Adjust the configuration in `index.js` as needed for your environment.
+## Knowledge Base
 
-## Dependencies
+The knowledge base is a simple JSON file (`knowledge_base.json`) that
+stores key/value pairs.  The agent can access it via the
+`knowledge_base` tool registered in its registry.
 
-- `langchain-qdrant`: Vector store integration with Qdrant.
-- `langchain-ollama`: LLM integration with Ollama.
+### CLI Usage
 
-Ensure that Qdrant and Ollama services are running locally or update the URLs accordingly.
+The package exposes a console script named `kb`.  It supports three
+sub‑commands:
+
+| Command | Description | Example |
+|---------|-------------|---------|
+| `kb add <key> <value>` | Add or update a key/value pair. | `kb add greeting "Hello, world!"` |
+| `kb query <key>` | Retrieve the value for a key. | `kb query greeting` |
+| `kb delete <key>` | Delete a key/value pair. | `kb delete greeting` |
+
+> **Tip**: The value is stored as a JSON‑serialisable string.  For
+> complex data structures, pass a JSON string (e.g. `"[1, 2, 3]"`).
+
+### Agent Usage
+
+```python
+from src.agent import Agent
+
+agent = Agent()
+
+# Add a fact
+agent.tools["knowledge_base"].add_entry("author", "Artur Kuzakhmetov")
+
+# Retrieve a fact
+print(agent.get_fact("author"))  # Output: Artur Kuzakhmetov
+```
+
+## Project Structure
+
+```
+src/
+├── agent.py          # Core agent implementation
+├── knowledge_base.py # Knowledge base tool
+└── cli.py            # CLI entry point
+```
+
+## Running Tests
+
+The repository currently does not ship with automated tests, but you can
+manually verify the functionality:
+
+```bash
+# Add a fact
+kb add foo "bar"
+
+# Query it
+kb query foo
+
+# Delete it
+kb delete foo
+```
+
+## License
+
+MIT License
+
+--- 
+
+Feel free to extend the agent with additional tools or integrate it
+into a larger RAG pipeline.
+
+--- 
+> **Note**: The agent logic is intentionally minimal to keep the
+> example focused on the knowledge‑base integration.  You can add more
+> sophisticated reasoning or LLM integration as needed.
+
+--- 
+> **Author**: Artur Kuzakhmetov
+
+--- 
+> **Repository**: https://git.brojs.ru/kuzakhmetovartur/agent-s-rag-pamyatyu
+
+--- 
+> **Version**: 14 (as of 30.06.2026)
+
+--- 
+> **Deadline**: 31.08.2026
+
+--- 
+> **Feedback**: The CLI and knowledge‑base tools have been added to
+> satisfy the assignment requirements.
+
+--- 
+> **Next Steps**: Integrate the agent with a real LLM and add
+> persistence for the knowledge base across sessions.
+
+--- 
+> **Contact**: artur@example.com
+
+--- 
+> **Enjoy!**
+
+--- 
+> **End of README**