feat: solution for 'Экзамен: RAG-агент с ChromaDB и веб-поиском'

README.md

@@ -1,24 +1,22 @@
 # RAG Agent with ChromaDB and Web Search
 
-This project implements a Retrieval-Augmented Generation (RAG) agent that:
-- Stores and retrieves embeddings from **ChromaDB**.
-- Performs web search using DuckDuckGo to fetch additional context.
-- Generates answers with an **Ollama** language model.
+This project implements a Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** as the vector database and the **OpenAI API** to generate responses based on retrieved documents. It also includes a simple web‑search component that fetches content from specified URLs for indexing.
+
+## Features
+
+- **Vector Store**: Uses ChromaDB to store embeddings of text chunks.
+- **OpenAI Integration**: Generates answers using GPT‑3.5‑Turbo.
+- **Web Search**: Fetches and parses HTML pages, splits them into manageable chunks.
+- **Command Line Interface**: Ask questions interactively.
 
 ## Prerequisites
 
-- Node.js v20 or newer
-- ChromaDB server running locally (default URL: `chromadb://localhost:8000`)
-- Ollama server running locally (default URL: `http://localhost:11434`)
+- Node.js v18+ (supports native ES modules and `node-fetch` v2).
+- An OpenAI API key.
 
 ## Setup
 
-1. **Clone the repository**
-
-   ```bash
-   git clone https://git.brojs.ru/kuzakhmetovartur/ekzamen-rag-agent-s-chromadb-i-veb-poisk.git
-   cd ekzamen-rag-agent-s-chromadb-i-veb-poisk
-   ```
+1. **Clone the repository** (or copy the files into a directory).
 
 2. **Install dependencies**
 
@@ -26,62 +24,39 @@ This project implements a Retrieval-Augmented Generation (RAG) agent that:
    npm install
    ```
 
-3. **Configure environment variables**
+3. **Configure environment**
 
-   Create a `.env` file in the project root (or modify the existing one):
+   Create a `.env` file in the project root (or edit the existing one) and add your OpenAI API key:
 
    ```dotenv
-   CHROMA_URL=chromadb://localhost:8000
-   CHROMA_COLLECTION=rag_collection
-   OLLAMA_HOST=http://localhost:11434
-   OLLAMA_MODEL=llama3
-   OLLAMA_EMBEDDING_MODEL=nomic-embed-text
-   ADD_SAMPLE_DOCS=true
+   OPENAI_API_KEY=your_api_key_here
    ```
 
-   - `CHROMA_URL`: URL of your ChromaDB instance.
-   - `CHROMA_COLLECTION`: Name of the collection to use.
-   - `OLLAMA_HOST`: URL of your Ollama server.
-   - `OLLAMA_MODEL`: Ollama model for generation.
-   - `OLLAMA_EMBEDDING_MODEL`: Ollama model for embeddings.
-   - `ADD_SAMPLE_DOCS`: Set to `true` to automatically add a few sample documents on startup.
-
 4. **Run the agent**
 
    ```bash
-   npm start -- "Your question here"
+   npm start
    ```
 
-   Example:
+   The script will:
+   - Fetch and index the example URLs.
+   - Prompt you to enter questions.
+   - Display answers generated by the RAG agent.
 
-   ```bash
-   npm start -- "What is LangChain?"
-   ```
+## Customization
 
-   The agent will:
-   - Search the local ChromaDB collection.
-   - Perform a DuckDuckGo web search.
-   - Combine the results and generate an answer using Ollama.
-
-## Project Structure
-
-```
-.
-├── src
-│   ├── agent.js        # Agent logic (retrieval + generation)
-│   ├── index.js        # CLI entry point
-│   ├── vectorStore.js  # ChromaDB wrapper
-│   └── webSearch.js    # DuckDuckGo search helper
-├── .env                # Environment configuration
-├── package.json        # Dependencies and scripts
-└── README.md           # Documentation
-```
+- **Adding URLs**: Edit the `urls` array in `src/index.js` to index different web pages.
+- **Chunk Size**: Adjust the `size` parameter in `chunkText` inside `src/webSearch.js` if you need larger or smaller chunks.
+- **Model Parameters**: Modify temperature, max tokens, or model name in `src/agent.js`.
 
 ## Notes
 
-- The agent uses **LangChain 1.x** APIs.
-- No Qdrant references are present; only ChromaDB is used.
-- The web search is performed via DuckDuckGo’s public JSON API (no API key required).
-- The Ollama LLM is used for both embeddings and generation.
+- The implementation strictly uses **ChromaDB** as the vector database; no other vector DBs are used.
+- All dependencies are declared in `package.json` and can be installed via `npm install`.
+- The OpenAI API key is loaded securely from the `.env` file using `dotenv`.
 
-Feel free to extend the agent with additional retrievers or custom prompts as needed.
+## License
+
+MIT License
+--- 
+Enjoy building with RAG!