feat: solution for 'Агент с RAG-памятью'

README.md

@@ -1,70 +1,94 @@
-# RAG Agent with Ollama Embeddings and Qdrant
+# RAG Agent with ChromaDB
 
-This project implements a Retrieval-Augmented Generation (RAG) agent that uses:
+This project implements a simple Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** as the vector store.  
+The agent loads text documents, indexes them with embeddings, and answers user questions by retrieving relevant passages and generating a response with an OpenAI LLM.
 
-- **OllamaEmbeddings** from `langchain-community` for local embeddings.
-- **Qdrant** as the vector store for efficient similarity search.
-- **OpenAI LLM** for generating responses.
+## Features
 
-## Prerequisites
+- **ChromaDB** persistence for fast similarity search.
+- OpenAI embeddings (`text-embedding-3-small`) for vector representation.
+- OpenAI LLM (`gpt-4o-mini` by default) for answer generation.
+- Simple command‑line interface to index documents and ask questions.
+- Backward‑compatible API: `RAGAgent` exposes `add_documents`, `ask`, `get_document_count`, and `clear_store`.
 
-- Python 3.10+
-- 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.
+## Requirements
+
+```text
+chromadb==0.4.24
+langchain==0.1.13
+openai==1.12.0
+tqdm==4.66.1
+pydantic==2.6.3
+python-dotenv==1.0.1
+```
+
+Install them with:
+
+```bash
+pip install -r requirements.txt
+```
 
 ## Setup
 
-```bash
-# Clone the repository
-git clone https://git.brojs.ru/kuzakhmetovartur/agent-s-rag-pamyatyu.git
-cd agent-s-rag-pamyatyu
+1. **OpenAI API Key**  
+   The agent uses OpenAI services for embeddings and LLM.  
+   Set your key in an environment variable:
 
-# Create a virtual environment
-python -m venv .venv
-source .venv/bin/activate  # On Windows use .venv\\Scripts\\activate
+   ```bash
+   export OPENAI_API_KEY="sk-..."
+   ```
 
-# Install dependencies
-pip install -r requirements.txt
-# or using Poetry
-# poetry install
-```
+2. **Prepare Documents**  
+   Place all `.txt` files you want to index in a directory, e.g., `data/`.
 
-Create a `.env` file in the project root with your OpenAI key:
-
-```
-OPENAI_API_KEY=sk-...
-```
-
-## Running the Agent
+## Usage
 
 ```bash
-python src/main.py
+python -m src.main --docs data/ --question "What is the capital of France?"
 ```
 
-You can then interact with the agent in the console. Type `exit` or `quit` to stop.
+### Arguments
 
-## Adding Documents
+| Argument | Description | Default |
+|----------|-------------|---------|
+| `--docs` | Path to directory with `.txt` files. | **Required** |
+| `--question` | The question to ask the agent. | **Required** |
+| `--persist` | Directory where ChromaDB stores its data. | `./chromadb` |
+| `--model` | OpenAI LLM model to use. | `gpt-4o-mini` |
+| `--k` | Number of documents to retrieve for RAG. | `4` |
 
-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:
+The first run will index all documents. Subsequent runs reuse the persisted index.
+
+## API
 
 ```python
-from vector_store import get_vector_store
+from src.vector_store import ChromaDBVectorStore
+from src.agent import RAGAgent
+from langchain.schema import Document
 
-vs = get_vector_store()
-vs.add_texts(["Hello world", "Another document"])
+# Create vector store
+store = ChromaDBVectorStore(persist_directory="./chromadb")
+
+# Add documents
+docs = [Document(page_content="Hello world", metadata={"source": "greeting.txt"})]
+store.add_documents(docs)
+
+# Create agent
+agent = RAGAgent(vector_store=store)
+
+# Ask a question
+answer = agent.ask("What is this?")
+print(answer)
 ```
 
 ## Testing
 
-The project includes a minimal test suite (not shown here). To run tests:
+The project includes no automated tests, but you can manually verify:
 
-```bash
-pytest
-```
-
-Ensure that your local Ollama and Qdrant instances are running before executing tests.
+1. Run the CLI with a small set of documents.
+2. Ask a question that should be answered using the indexed content.
+3. Verify that the answer references the correct context.
 
 ## License
 
-MIT License
+MIT License.