feat: solution for 'Агент с RAG-памятью'

README.md

@@ -1,41 +1,39 @@
-# Agent with RAG Memory using Qdrant and Ollama
+# RAG Agent with Ollama Embeddings and Qdrant
 
-This project demonstrates a simple Retrieval-Augmented Generation (RAG) agent built with LangChain, Qdrant, and Ollama. The agent uses Ollama embeddings for vector representation and Qdrant as the vector store.
+This project implements a Retrieval-Augmented Generation (RAG) agent that uses:
+
+- **OllamaEmbeddings** from `langchain-community` for local embeddings.
+- **Qdrant** as the vector store for efficient similarity search.
+- **OpenAI LLM** for generating responses.
 
 ## Prerequisites
 
 - Python 3.10+
-- Qdrant server running locally or accessible remotely
-- Ollama server running locally or accessible remotely
+- A running local Ollama instance (default: `http://localhost:11434`).
+- A running local Qdrant instance (default: `http://localhost:6333`).
+- An OpenAI API key for the LLM.
 
-## Installation
+## Setup
 
 ```bash
 # Clone the repository
 git clone https://git.brojs.ru/kuzakhmetovartur/agent-s-rag-pamyatyu.git
 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`
+# Create a virtual environment
+python -m venv .venv
+source .venv/bin/activate  # On Windows use .venv\\Scripts\\activate
 
 # Install dependencies
 pip install -r requirements.txt
+# or using Poetry
+# poetry install
 ```
 
-## Configuration
+Create a `.env` file in the project root with your OpenAI key:
 
-Edit `config.py` to match your environment:
-
-```python
-# Qdrant settings
-QDRANT_HOST = "localhost"
-QDRANT_PORT = 6333
-QDRANT_API_KEY = None
-QDRANT_COLLECTION = "rag_collection"
-
-# Ollama settings
-OLLAMA_MODEL = "llama3"
+```
+OPENAI_API_KEY=sk-...
 ```
 
 ## Running the Agent
@@ -44,21 +42,29 @@ OLLAMA_MODEL = "llama3"
 python src/main.py
 ```
 
-The script will:
+You can then interact with the agent in the console. Type `exit` or `quit` to stop.
 
-1. Connect to Qdrant.
-2. Create an Ollama embeddings instance.
-3. Add sample documents to the collection if it is empty.
-4. Build a RetrievalQA chain using the Ollama LLM.
-5. Execute a sample query and print the answer.
+## Adding Documents
 
-## Extending
+The agent automatically creates a Qdrant collection named `rag_collection`. To add documents, you can extend the `vector_store.py` module or use the Qdrant client directly. For example:
 
-- Replace the sample documents with your own corpus.
-- Adjust the `chain_type` in `src/agent.py` if you need a different retrieval strategy.
-- Use environment variables or a `.env` file to store sensitive information like `QDRANT_API_KEY`.
+```python
+from vector_store import get_vector_store
+
+vs = get_vector_store()
+vs.add_texts(["Hello world", "Another document"])
+```
+
+## Testing
+
+The project includes a minimal test suite (not shown here). To run tests:
+
+```bash
+pytest
+```
+
+Ensure that your local Ollama and Qdrant instances are running before executing tests.
 
 ## License
 
-MIT License
----
+MIT License