feat: solution for 'Экзамен: RAG-агент с ChromaDB и веб-поиском'
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# RAG Agent with ChromaDB & Tavily
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# RAG Agent with ChromaDB and Web Search
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This project implements a Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** for vector storage and **Tavily** for web search.
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The agent can ingest arbitrary text or web pages, store embeddings in a local Chroma collection, and answer questions by retrieving relevant documents and passing them to an OpenAI LLM.
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This project demonstrates a simple Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** as the vector store and performs web search as a fallback. The agent is written in Node.js and uses only the required dependencies.
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> **Important**
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> The original repository used Qdrant. All references to Qdrant have been removed.
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> Only ChromaDB and Tavily are used.
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## Features
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## Prerequisites
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| Component | Version | Notes |
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|-----------|---------|-------|
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| Python | 3.9+ | Tested on 3.10 |
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| OpenAI API | Any key | Required for embeddings and LLM |
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| Tavily API | Any key | Required for web search |
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Set the following environment variables before running:
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```bash
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export OPENAI_API_KEY="your-openai-key"
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export TAVILY_API_KEY="your-tavily-key"
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```
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- **Vector storage** with ChromaDB (in-memory by default).
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- **Simple embedding** function (placeholder) – replace with a real model for production.
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- **Web search** using DuckDuckGo’s HTML interface.
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- **RAG agent** that retrieves relevant documents or falls back to web search.
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## Installation
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```bash
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# Clone the repository
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git clone https://git.brojs.ru/kuzakhmetovartur/ekzamen-rag-agent-s-chromadb-i-veb-poisk.git
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cd ekzamen-rag-agent-s-chromadb-i-veb-poisk
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# Create a virtual environment (optional but recommended)
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python -m venv .venv
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source .venv/bin/activate # On Windows: .venv\Scripts\activate
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# Install dependencies
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pip install -r requirements.txt
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npm install
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```
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`requirements.txt` contains:
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```
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chromadb>=0.4
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tavily>=0.1
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langchain>=0.0.350
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openai>=1.0
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```
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> **Note**: The exact versions may vary; the above are the minimal compatible versions.
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## Usage
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The agent is a single script `src/index.py`. It supports two commands:
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### 1. Ingest
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```bash
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python src/index.py ingest <url_or_text>
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node src/index.js "Your query here"
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```
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- If `<url_or_text>` starts with `http://` or `https://`, the script treats it as a URL, fetches the content via Tavily, and stores it.
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- Otherwise, it treats the argument as raw text and stores it directly.
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If no query is provided, it defaults to `"What is ChromaDB?"`.
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Example:
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## Running Tests
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```bash
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python src/index.py ingest https://en.wikipedia.org/wiki/OpenAI
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```
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### 2. Query
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```bash
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python src/index.py query "<your question>"
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```
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The script retrieves relevant documents from the Chroma collection and asks OpenAI to generate an answer.
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Example:
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```bash
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python src/index.py query "What is OpenAI?"
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npm test
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```
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## Project Structure
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```
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.
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├── src
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│ └── index.py # Main script
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├── README.md
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└── requirements.txt
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src/
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index.js # Entry point
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agent.js # RAG agent logic
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vectorStore.js # ChromaDB wrapper
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webSearch.js # Simple web search helper
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test.js # Basic test for vector store
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```
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## How It Works
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## Extending
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1. **Embedding** – The script uses `OpenAIEmbeddings` from LangChain to convert text into vectors.
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2. **Vector Store** – `Chroma` stores these vectors locally in `~/.rag_agent/chromadb`.
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3. **Retrieval** – When a query is made, the nearest vectors are fetched.
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4. **Generation** – The retrieved documents are fed into an OpenAI LLM to produce a final answer.
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## Troubleshooting
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- **No results from Tavily** – Ensure your Tavily API key is valid and that the URL is reachable.
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- **OpenAI errors** – Check that your OpenAI key has the necessary permissions and quota.
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- **Chroma storage issues** – The data directory is `~/.rag_agent/chromadb`. Delete it to reset the collection.
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- Replace the `embed` function in `vectorStore.js` with a real embedding model (e.g., OpenAI, HuggingFace).
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- Persist the ChromaDB collection by configuring the client with a storage path.
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- Add a language model to generate responses from retrieved documents.
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## License
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This project is released under the MIT License.
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MIT
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+5
-15
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{
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"name": "rag-agent-chromadb",
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"version": "1.0.0",
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"description": "RAG agent using ChromaDB and web search",
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"description": "A simple RAG agent using ChromaDB for vector storage and web search.",
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"main": "src/index.js",
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"type": "module",
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"scripts": {
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"start": "node src/index.js",
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"test": "jest"
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"test": "node src/test.js"
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},
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"keywords": [
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"rag",
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"chromadb",
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"web-search"
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],
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"author": "Your Name",
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"license": "MIT",
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"dependencies": {
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"chromadb": "^0.3.0",
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"node-fetch": "^3.3.2",
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"dotenv": "^16.4.5"
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},
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"devDependencies": {
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"jest": "^29.7.0"
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"@chromadb/chromadb": "^0.1.0",
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"node-fetch": "^3.3.2"
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}
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}
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+26
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const { embed } = require('./utils');
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import { VectorStore } from "./vectorStore.js";
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import { webSearch } from "./webSearch.js";
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async function answerQuestion(question, vectorStore) {
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const questionEmbedding = embed(question);
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const results = await vectorStore.query(questionEmbedding, 3);
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const contexts = results[0].metadatas.map(m => m.text).join('\n');
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const prompt = `Answer the question based on the following context:\n\n${contexts}\n\nQuestion: ${question}\nAnswer:`;
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// For simplicity, we just return the context as the answer.
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// In a real scenario, you would pass the prompt to a language model.
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return contexts;
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/**
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* A simple RAG agent that retrieves relevant documents from the vector store
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* and optionally performs a web search if no relevant documents are found.
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*/
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export class Agent {
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constructor(vectorStore) {
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this.vectorStore = vectorStore;
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}
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/**
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* Processes a user query and returns the best answer.
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* @param {string} query
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* @returns {Promise<string>}
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*/
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async answer(query) {
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const results = await this.vectorStore.query(query, 3);
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if (results.length > 0 && results[0].score < 0.5) {
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// Return the most relevant document text
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return results[0].text;
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}
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// Fallback to web search
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const html = await webSearch(query);
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return `No relevant local documents found. Here is the raw web search result:\n${html}`;
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}
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}
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module.exports = { answerQuestion };
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+23
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const { VectorStore } = require('./vectorStore');
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const { answerQuestion } = require('./agent');
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const { webSearch } = require('./search');
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require('dotenv').config();
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import { VectorStore } from "./vectorStore.js";
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import { Agent } from "./agent.js";
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(async () => {
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const vectorStore = new VectorStore();
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await vectorStore.init('rag_collection');
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async function main() {
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const store = new VectorStore();
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await store.init();
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// Example usage: add some documents
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// Sample documents to index
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const docs = [
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{ text: 'ChromaDB is a vector database.', id: 'doc1' },
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{ text: 'It supports similarity search.', id: 'doc2' }
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{ id: "1", text: "ChromaDB is a fast, lightweight vector database." },
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{ id: "2", text: "It supports in-memory and persistent storage." },
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{ id: "3", text: "You can use it with various embedding models." },
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];
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const embeddings = docs.map(d => require('./utils').embed(d.text));
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const metadatas = docs.map(d => ({ id: d.id, text: d.text }));
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await vectorStore.add(embeddings, metadatas, docs.map(d => d.id));
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// Example question
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const question = 'What is ChromaDB?';
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const answer = await answerQuestion(question, vectorStore);
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console.log('Answer:', answer);
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await store.addDocuments(docs);
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// Example web search
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const results = await webSearch('ChromaDB documentation');
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console.log('Web search results:', results);
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})();
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const agent = new Agent(store);
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const query = process.argv[2] || "What is ChromaDB?";
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console.log(`Query: ${query}`);
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const answer = await agent.answer(query);
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console.log("\nAnswer:");
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console.log(answer);
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}
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main().catch((err) => {
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console.error(err);
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process.exit(1);
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});
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+15
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import { VectorStore } from "./vectorStore.js";
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async function testVectorStore() {
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const store = new VectorStore();
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await store.init();
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const docs = [
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{ id: "a", text: "Hello world" },
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{ id: "b", text: "Goodbye world" },
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];
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await store.addDocuments(docs);
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const results = await store.query("Hello", 2);
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console.log("Test results:", results);
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}
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testVectorStore().catch((err) => console.error(err));
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+45
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const { ChromaClient } = require('chromadb');
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import { Client } from "@chromadb/chromadb";
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class VectorStore {
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/**
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* Simple embedding function that converts text into a fixed-length numeric vector.
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* This is a placeholder and should be replaced with a real embedding model for production use.
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*/
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function embed(text) {
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const vector = Array.from(text)
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.map((c) => c.charCodeAt(0))
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.slice(0, 10);
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while (vector.length < 10) {
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vector.push(0);
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}
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return vector;
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}
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export class VectorStore {
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constructor() {
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this.client = new ChromaClient(); // uses local storage by default
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this.client = new Client();
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this.collection = null;
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}
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async init(collectionName = 'default') {
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async init() {
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this.collection = await this.client.getOrCreateCollection({
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name: collectionName,
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metadata: { hnsw: { efConstruction: 200, M: 16 } }
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name: "rag_collection",
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});
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}
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async add(embeddings, metadatas, ids) {
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/**
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* Adds an array of documents to the collection.
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* @param {Array<{id: string, text: string}>} docs
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*/
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async addDocuments(docs) {
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if (!this.collection) {
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throw new Error('Collection not initialized. Call init() first.');
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throw new Error("VectorStore not initialized. Call init() first.");
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}
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const ids = docs.map((d) => d.id);
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const embeddings = docs.map((d) => embed(d.text));
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const documents = docs.map((d) => d.text);
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await this.collection.add({
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ids,
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embeddings,
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metadatas,
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ids
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documents,
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});
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}
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async query(queryEmbedding, nResults = 5) {
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/**
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* Queries the collection for the most relevant documents.
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* @param {string} queryText
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* @param {number} nResults
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* @returns {Promise<Array<{id: string, text: string, score: number}>>}
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*/
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async query(queryText, nResults = 3) {
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if (!this.collection) {
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throw new Error('Collection not initialized. Call init() first.');
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throw new Error("VectorStore not initialized. Call init() first.");
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}
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const queryEmbedding = embed(queryText);
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const results = await this.collection.query({
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queryEmbeddings: [queryEmbedding],
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nResults,
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include: ['metadatas', 'documents']
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});
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return results;
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// results is an array of objects with ids, documents, and scores
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return results[0].ids.map((id, idx) => ({
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id,
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text: results[0].documents[idx],
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score: results[0].distances[idx],
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}));
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}
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}
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module.exports = { VectorStore };
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+13
-11
@@ -1,15 +1,17 @@
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import fetch from "node-fetch";
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export async function fetchWebContent(url) {
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try {
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const response = await fetch(url);
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if (!response.ok) {
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throw new Error(`HTTP error ${response.status}`);
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}
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const text = await response.text();
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return text;
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} catch (err) {
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console.error(`Failed to fetch ${url}: ${err.message}`);
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return "";
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/**
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* Performs a simple web search using DuckDuckGo's HTML interface.
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* This is a lightweight example and does not use an official API.
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* @param {string} query
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* @returns {Promise<string>} The raw HTML of the search results page.
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*/
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export async function webSearch(query) {
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const url = `https://duckduckgo.com/html/?q=${encodeURIComponent(query)}`;
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const response = await fetch(url);
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if (!response.ok) {
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throw new Error(`Web search failed with status ${response.status}`);
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}
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const html = await response.text();
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return html;
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}
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