Update README.md

README.md

@@ -1,61 +1,82 @@
 # RAG Agent with ChromaDB and Web Search
 
-This repository implements a simple RAG (Retrieval‑Augmented Generation) agent that can answer questions using a local knowledge base stored in **ChromaDB** and also perform real‑time web search via **Tavily**. The agent automatically decides which source to use and reports the chosen source in the answer.
+This repository implements a simple RAG (Retrieval-Augmented Generation) agent that can:
 
-## Features
+1. Search a local knowledge base stored in **ChromaDB** using semantic embeddings from **Ollama**.
+2. Perform real‑time web search via **Tavily**.
+3. Decide automatically which source to use and indicate the source in the final answer.
 
-- **Local knowledge base** – Text files (.txt, .md) are loaded, chunked, and stored in a persistent ChromaDB collection.
-- **Semantic search** – Uses Ollama embeddings (`nomic-embed-text`).
-- **Web search** – Powered by Tavily.
-- **Automatic source selection** – The agent chooses between local and web search based on the query.
-- **CLI** – Simple chat loop with `exit` to quit.
+## Prerequisites
 
-## Setup
+- Python 3.10+ (recommended via `pyenv` or `conda`).
+- Ollama installed locally and the following models pulled:
+  ```bash
+  ollama pull llama3
+  ollama pull nomic-embed-text
+  ```
+- A Tavily API key. Create a `.env` file in the project root with:
+  ```text
+  TAVILY_API_KEY=YOUR_KEY_HERE
+  ```
+
+## Installation
 
 ```bash
-# 1. Create a virtual environment (optional but recommended)
+# Optional: create a virtual environment
 python -m venv venv
 source venv/bin/activate   # Windows: venv\Scripts\activate
 
-# 2. Install dependencies
+# Install dependencies
 pip install -r requirements.txt
-
-# 3. Pull required Ollama models
-ollama pull llama3
-ollama pull nomic-embed-text
-
-# 4. Set your Tavily API key
-export TAVILY_API_KEY=YOUR_KEY   # Windows: set TAVILY_API_KEY=YOUR_KEY
 ```
 
-## Usage
+## Preparing the Knowledge Base
+
+Place any `.txt` or `.md` files you want the agent to know about in the `documents/` folder.
+Run the following command once to load them into ChromaDB:
 
-1. **Load documents** – Place your `.txt` or `.md` files in the `documents/` folder.
-2. **Run the agent**
 ```bash
-   python agent.py
+python -c "from vectorstore import create_vectorstore, load_documents; store=create_vectorstore(); load_documents('./documents', store)"
 ```
-3. **Chat** – Type your question. Type `exit` to quit.
 
-## Example
+The vector store is persisted in the `chroma_db/` directory, so the data will be available for subsequent runs.
+
+## Running the Agent
+
+```bash
+python main.py
+```
+
+You will see a simple chat loop. Type your questions and the agent will answer.
 
 ```
-Query: Какие последние новости про AI-агентов?
-[Web Search] ...
-Источник: tavily
+Welcome to the RAG agent. Type 'exit' to quit.
 
-Query: Что в наших конспектах про LangGraph?
-[Local KB] ...
-Источник: chromadb
+User: What is LangGraph?
+
+Assistant: LangGraph is a framework for building ...
+Source: chromadb
 ```
 
+If the information is not present locally, the agent will automatically perform a web search and label the answer with `Source: tavily`.
+
 ## Project Structure
 
-- `vectorstore.py` – Functions to create and load the ChromaDB vector store.
-- `rag_tools.py` – Two LangChain tools: `search_local_kb` and `web_search`.
-- `agent.py` – Main script that sets up the agent and runs the chat loop.
-- `requirements.txt` – Python dependencies.
-- `README.md` – This file.
+```
+├── agent.py          # Core agent logic
+├── rag_tools.py      # Tool implementations
+├── vectorstore.py    # ChromaDB utilities
+├── main.py           # Entry point
+├── requirements.txt
+├── .gitignore
+└── README.md
+```
+
+## Extending
+
+- Add more tools by creating new functions decorated with `@tool`.
+- Replace the LLM or embeddings with other Ollama models.
+- Switch to a different vector store (e.g., Qdrant) by updating `vectorstore.py`.
 
 ## License