From 27c8d2294f17bfe9932e4734666d8a0237776633 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=9A=D0=B8=D1=80=D0=B8=D0=BB=D0=BB=20=D0=A0=D0=BE=D0=BC?=
 =?UTF-8?q?=D0=B0=D0=BD=D0=BE=D0=B2?= <kirilinka320@gmail.com>
Date: Mon, 1 Jun 2026 17:40:41 +0000
Subject: [PATCH] Update README.md

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-# task-6a1864f78a94f887e50d46da
+# RAG Agent with ChromaDB and Tavily
 
-Экзамен: RAG-агент с ChromaDB и веб-поиском
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+## Overview
+
+This repository implements a **RAG (Retrieval‑Augmented Generation) agent** that can answer questions by searching a local knowledge base stored in **ChromaDB** or by fetching up‑to‑date information from the web using **Tavily**. The agent automatically chooses the most appropriate source based on the query and returns the answer together with the source identifier.
+
+## Features
+
+- **Local Knowledge Base** – Vector store backed by ChromaDB with embeddings from Ollama (`nomic-embed-text`).
+- **Web Search** – Uses Tavily API for real‑time web queries.
+- **Automatic Routing** – The agent decides whether to use the local KB or the web search.
+- **CLI** – Simple command‑line interface for interactive queries.
+- **Persistence** – The ChromaDB store is persisted between runs.
+
+## Setup
+
+1. **Install Ollama** and pull the required models:
+   ```bash
+   ollama pull llama3
+   ollama pull nomic-embed-text
+   ```
+
+2. **Install Python dependencies**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Set up the Tavily API key**. Create a `.env` file in the project root with:
+   ```env
+   TAVILY_API_KEY=your_api_key_here
+   ```
+
+4. **Add documents** you want to index into the `documents/` folder. The script will automatically load `.txt` and `.md` files.
+
+## Usage
+
+```bash
+python main.py
+```
+
+You will be prompted for a query. Type `exit` to quit.
+
+Example:
+```
+Query: Какие последние новости про AI-агентов?
+Answer:
+[Web Search]
+1. AI Agents are ...
+   https://example.com
+   ...
+Source: tavily
+```
+
+## Project Structure
+
+- `vectorstore.py` – Helper functions for creating and populating the ChromaDB vector store.
+- `agent.py` – Defines the tools and initializes the LangChain agent.
+- `main.py` – CLI entry point.
+- `requirements.txt` – Python dependencies.
+- `README.md` – Documentation.
+
+## Notes
+
+- The agent uses the **Zero‑Shot React** strategy. It may call both tools if the query is ambiguous. You can tweak the prompt or the routing logic if needed.
+- The ChromaDB store is persisted in `./chroma_db`. Delete this folder to re‑index.
+- Ensure the Ollama server is running locally when executing the agent.
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