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

README.md

@@ -1,82 +1,61 @@
 # RAG Agent with ChromaDB and Web Search
 
-This project implements a Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** as the vector store and performs web search to ingest documents. The agent answers user questions by retrieving relevant passages from the stored documents and generating responses with OpenAI’s GPT models.
+This project demonstrates a simple Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** for vector storage and similarity search, and performs web search using DuckDuckGo.
 
 ## Features
 
-- **ChromaDB** vector store (no Qdrant usage)
-- Web content ingestion via HTTP fetch
-- OpenAI embeddings for vector representation
-- GPT-4o-mini for answer generation
-- Simple CLI usage
-
-## Prerequisites
-
-- Node.js 20+ (ESM support)
-- Docker (optional, for running ChromaDB locally)
-- OpenAI API key
+- **Vector Store**: Stores embeddings in a local ChromaDB collection.
+- **RAG Agent**: Retrieves relevant documents and constructs an answer.
+- **Web Search**: Fetches top results from DuckDuckGo.
 
 ## Setup
 
-1. **Clone the repository**
+```bash
+# Clone the repository
+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
 
-   ```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
-   ```
+# Install dependencies
+npm install
 
-2. **Install dependencies**
+# Run the example
+npm start
+```
 
-   ```bash
-   npm install
-   ```
+## Running Tests
 
-3. **Configure environment variables**
+```bash
+npm test
+```
 
-   Create a `.env` file in the project root:
+## Configuration
 
-   ```dotenv
-   CHROMA_HOST=localhost
-   CHROMA_PORT=8000
-   OPENAI_API_KEY=YOUR_OPENAI_API_KEY
-   ```
+The project uses a local ChromaDB instance by default. If you need to connect to a remote instance, set the following environment variables in a `.env` file:
 
-4. **Run ChromaDB**
-
-   The simplest way is to use Docker:
-
-   ```bash
-   docker run -d --name chromadb -p 8000:8000 chromadb/chroma
-   ```
-
-   Or install ChromaDB locally following the official docs.
-
-5. **Run the agent**
-
-   ```bash
-   npm start
-   ```
-
-   The script will ingest a sample document from GitHub and answer a question about the OpenAI Node.js library.
+```dotenv
+CHROMA_HOST=localhost
+CHROMA_PORT=8000
+```
 
 ## Project Structure
 
 ```
 src/
-├── agent.js        # RAG agent logic
-├── index.js        # Entry point
-├── vectorStore.js  # ChromaDB wrapper
-└── webSearch.js    # Simple web fetch helper
+  index.js        # Entry point
+  agent.js        # RAG agent logic
+  vectorStore.js  # ChromaDB wrapper
+  search.js       # Web search helper
+  utils.js        # Embedding helper
+tests/
+  vectorStore.test.js
+  agent.test.js
 ```
 
 ## Notes
 
-- The project **does not** use Qdrant. All references to Qdrant have been removed.
-- Only ChromaDB is used for vector storage.
-- The agent can be extended to ingest multiple URLs or local files by calling `agent.ingestFromUrl(url)`.
+- The embedding function in `utils.js` is a deterministic placeholder. Replace it with a real embedding model (e.g., OpenAI embeddings) for production use.
+- The agent currently returns concatenated context as the answer. Integrate a language model for richer responses.
 
 ## License
 
-MIT License
---- 
-Feel free to contribute or open issues for enhancements.
+MIT License