feat: solution for 'Агент с RAG-памятью'

README.md

@@ -1,12 +1,14 @@
-# Agent with RAG Memory
+# RAG Agent with LangChain, Qdrant, and Ollama
 
-This project demonstrates a simple RAG (Retrieval-Augmented Generation) agent that uses LangChain tools to perform basic operations via an interactive command line interface (CLI).
+This repository contains a minimal example of a Retrieval-Augmented Generation (RAG) agent built with **LangChain**, **Qdrant**, and **Ollama**. The agent retrieves relevant documents from a local Qdrant vector store and generates answers using an Ollama language model.
 
-## Features
+## Prerequisites
 
-- **Add Numbers** – Add two integers using the `add_numbers` tool.
-- **Search Items** – Search a predefined list of strings for a query using the `search_item` tool.
-- **Interactive CLI** – Use `/add`, `/search`, and `/quit` commands to interact with the agent.
+- **Python 3.10+**  
+- **Qdrant** server running locally (default port `6333`).  
+  - Create a collection named `rag_collection` and populate it with embeddings.  
+- **Ollama** server running locally (default port `11434`).  
+  - Ensure the model `llama3.1` (or any other supported model) is available.
 
 ## Installation
 
@@ -17,7 +19,7 @@ cd agent-s-rag-pamyatyu
 
 # Create a virtual environment (optional but recommended)
 python -m venv .venv
-source .venv/bin/activate  # On Windows use `.venv\\Scripts\\activate`
+source .venv/bin/activate  # On Windows: .venv\\Scripts\\activate
 
 # Install dependencies
 pip install -r requirements.txt
@@ -25,72 +27,34 @@ pip install -r requirements.txt
 
 ## Usage
 
-Run the CLI:
-
 ```bash
 python -m src.main
 ```
 
-You will see a prompt:
-
-```
-Welcome to the RAG Agent CLI!
-Available commands:
-  /add <int> <int>   - Add two numbers.
-  /search <query>   - Search items in memory.
-  /quit             - Exit the program.
-```
-
-### Commands
-
-- **/add**  
-  Add two integers.
-
-  ```text
-  >> /add 5 7
-  Result: 12
-  ```
-
-- **/search**  
-  Search the internal memory for a query string.
-
-  ```text
-  >> /search python
-  Matches found:
-    1. Python programming
-  ```
-
-- **/quit**  
-  Exit the program.
-
-  ```text
-  >> /quit
-  Goodbye!
-  ```
+You will be prompted to enter a question. The agent will retrieve relevant documents from Qdrant and generate an answer using Ollama. Type `exit` or `quit` to terminate the program.
 
 ## Project Structure
 
 ```
 agent-s-rag-pamyatyu/
-├── src/
-│   ├── __init__.py
-│   ├── cli.py
-│   ├── main.py
-│   └── tools.py
-├── README.md
 ├── requirements.txt
-└── pyproject.toml
+├── src/
+│   └── main.py
+└── README.md
 ```
 
-## Dependencies
+- `requirements.txt` – lists all Python dependencies, including `langchain-qdrant` and `langchain-ollama`.
+- `src/main.py` – contains the RAG agent implementation.
+- `README.md` – this documentation file.
 
-- `langchain` – The core library for building language model agents.
-- `python-dotenv` – (Optional) For loading environment variables if needed.
+## Troubleshooting
+
+- **Missing dependencies**: Ensure you ran `pip install -r requirements.txt`.  
+- **Qdrant connection errors**: Verify Qdrant is running and the collection name matches `rag_collection`.  
+- **Ollama connection errors**: Verify Ollama is running and the model name is correct.
 
 ## License
 
-MIT License
+This project is provided as-is for educational purposes. Feel free to modify and extend it.
 
---- 
-
-Feel free to extend the tools or the CLI to suit your needs!
+---