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Date: Thu, 28 May 2026 16:55:35 +0000
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-# task-6a186500-ekzamen-rag-agent-s-chrom
+# RAG‑Agent with ChromaDB and Web Search
 
-Решения домашних заданий
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+A lightweight Retrieval‑Augmented Generation (RAG) agent that scrapes web pages, stores their embeddings in a local **Chroma** vector store, and answers user questions using OpenAI’s GPT‑3.5 Turbo.  
+The project demonstrates how to combine:
+
+* **BeautifulSoup** – for HTML parsing  
+* **OpenAI embeddings** – to convert text into vectors  
+* **ChromaDB** – fast local vector search  
+* **LangChain** – orchestration of LLM, embeddings and QA chain  
+
+---
+
+## 📦 Installation
+
+```bash
+# 1. Clone the repo (or copy solution.py)
+git clone https://github.com/your‑repo/rag-agent.git
+cd rag-agent
+
+# 2. Create a virtual environment (recommended)
+python -m venv .venv
+source .venv/bin/activate   # Windows: .venv\Scripts\activate
+
+# 3. Install dependencies
+pip install -r requirements.txt
+```
+
+`requirements.txt`:
+
+```text
+beautifulsoup4==4.12.3
+langchain==0.2.0
+openai==1.30.0
+chromadb==0.5.6
+rich==13.7.1
+requests==2.32.3
+```
+
+> **Note**  
+> You must have an OpenAI API key set in the environment:
+
+```bash
+export OPENAI_API_KEY="sk-..."
+# Windows: set OPENAI_API_KEY=sk-...
+```
+
+---
+
+## 🚀 Running the Agent
+
+The project contains a single script `solution.py`. It performs three actions depending on the command‑line argument:
+
+| Argument | Action |
+|----------|--------|
+| `scrape` | Scrapes URLs from a file, stores embeddings in ChromaDB. |
+| `ask`    | Loads the vector store and asks a question via the console. |
+| `clean`  | Deletes the local Chroma database (optional). |
+
+### 1. Prepare a list of URLs
+
+Create a plain text file (`urls.txt`) with one URL per line, e.g.:
+
+```text
+https://en.wikipedia.org/wiki/Artificial_intelligence
+https://www.python.org/
+```
+
+### 2. Scrape and index the pages
+
+```bash
+python solution.py scrape urls.txt
+```
+
+The script will:
+
+1. Download each page (respecting `robots.txt` is **not** enforced).  
+2. Extract visible text with BeautifulSoup.  
+3. Generate embeddings via OpenAI.  
+4. Store them in `./chroma_db`.
+
+### 3. Ask a question
+
+```bash
+python solution.py ask
+```
+
+You will be prompted to type your question. The agent retrieves the most relevant chunks from ChromaDB and feeds them into GPT‑3.5 Turbo, printing the answer.
+
+> **Tip** – If you want to see the raw retrieved snippets, set `console.print(chunks)` in the script before the QA chain.
+
+### 4. Clean up
+
+```bash
+python solution.py clean
+```
+
+Deletes the local Chroma database (`./chroma_db`).
+
+---
+
+## 📄 Example Session
+
+```text
+$ python solution.py scrape urls.txt
+[green]Scraping and indexing 2 URLs...[/green]
+[green]Done! 3 chunks stored in chroma_db.[/green]
+
+$ python solution.py ask
+Enter your question: What is the main goal of artificial intelligence?
+[cyan]Retrieving relevant documents...[/cyan]
+Answer:
+Artificial Intelligence (AI) aims to create systems that can perform tasks requiring human-like intelligence, such as learning, reasoning, problem‑solving, perception, and language understanding. The primary goal is to develop machines that can adapt, learn from experience, and make decisions in complex environments.
+
+```
+
+---
+
+## 🛠️ Customization
+
+| Setting | Where to change |
+|---------|-----------------|
+| Embedding model | `EMBEDDINGS_MODEL` in `solution.py` |
+| LLM model | `LLM_MODEL` in `solution.py` |
+| Chroma directory | `CHROMA_DIR` in `solution.py` |
+
+Feel free to replace the OpenAI models with local alternatives (e.g., HuggingFace) by adjusting the LangChain wrappers.
+
+---
+
+## 🤝 Contributing
+
+Pull requests are welcome!  
+Please open an issue first to discuss major changes or new features.
+
+---
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