Update README.md

README.md

@@ -1,83 +1,77 @@
-# RAG Agent with ChromaDB and Web Search
+# RAG‑Agent with ChromaDB and Web Search
 
-This repository implements a simple RAG (Retrieval-Augmented Generation) agent that can:
+This repository implements a simple RAG (Retrieval‑Augmented Generation) agent that can answer questions using a local knowledge base stored in **ChromaDB** or by searching the web via **Tavily**.  The agent automatically chooses the appropriate source and reports it in the answer.
 
-1. Search a local knowledge base stored in **ChromaDB** using semantic embeddings from **Ollama**.
+## Features
-2. Perform real‑time web search via **Tavily**.
-3. Decide automatically which source to use and indicate the source in the final answer.
 
-## Prerequisites
+* **Local semantic search** – Uses a ChromaDB vector store backed by Ollama embeddings.
+* **Web search** – Uses Tavily to fetch up‑to‑date information.
+* **Automatic source selection** – The agent decides whether to query the local KB or the web.
+* **Persisted vector store** – Data is stored on disk and reused across runs.
+* **Simple CLI** – Chat loop with `exit` to quit.
 
-- Python 3.10+ (recommended via `pyenv` or `conda`).
+## Setup
-- 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
-# Optional: create a virtual environment
+# 1. Clone the repo
+git clone <repo-url>
+cd <repo-dir>
+
+# 2. (Optional) Create a virtual environment
 python -m venv venv
 source venv/bin/activate   # Windows: venv\Scripts\activate
 
-# Install dependencies
+# 3. Install dependencies
 pip install -r requirements.txt
-```
 
-## Preparing the Knowledge Base
+# 4. Pull required Ollama models
+ollama pull llama3
+ollama pull nomic-embed-text
 
-Place any `.txt` or `.md` files you want the agent to know about in the `documents/` folder.
+# 5. Set Tavily API key
-Run the following command once to load them into ChromaDB:
+export TAVILY_API_KEY=your_api_key   # Windows: set TAVILY_API_KEY=your_api_key
 
-```bash
+# 6. Prepare documents
-python -c "from vectorstore import create_vectorstore, load_documents; store=create_vectorstore(); load_documents('./documents', store)"
+# Place any .txt or .md files you want to index in the ./documents folder.
-```
+# They will be automatically loaded into ChromaDB on first run.
 
-The vector store is persisted in the `chroma_db/` directory, so the data will be available for subsequent runs.
+# 7. Run the agent
-
-## Running the Agent
-
-```bash
 python main.py
 ```
 
-You will see a simple chat loop. Type your questions and the agent will answer.
+## Usage
 
-```
+```text
-Welcome to the RAG agent. Type 'exit' to quit.
+Запрос: Какие последние новости про AI-агентов?
+[Web Search]
+1. AI Agents: The Future of Automation: ...
+2. ...
+Source: tavily
 
-User: What is LangGraph?
+Запрос: Что в наших конспектах про LangGraph?
-
+[Local KB]
-Assistant: LangGraph is a framework for building ...
+1. LangGraph is a ...
+2. ...
 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
 
 ```
-├── agent.py          # Core agent logic
+├── agent.py          # Core agent logic and tools
-├── rag_tools.py      # Tool implementations
+├── vectorstore.py    # ChromaDB creation and document loading
-├── vectorstore.py    # ChromaDB utilities
+├── rag_tools.py      # Web search tool
-├── main.py           # Entry point
+├── main.py           # CLI entry point
 ├── requirements.txt
-├── .gitignore
 └── README.md
 ```
 
 ## Extending
 
-- Add more tools by creating new functions decorated with `@tool`.
+* **Add more tools** – Define new functions decorated with `@tool` and add them to the `tools` list.
-- Replace the LLM or embeddings with other Ollama models.
+* **Change LLM** – Swap `ChatOllama` for another provider (e.g., OpenAI) by adjusting the import and model name.
-- Switch to a different vector store (e.g., Qdrant) by updating `vectorstore.py`.
+* **Custom prompt** – Edit `agent_prompt` in `agent.py` to modify the agent’s instruction.
 
-## License
+---
 
-MIT License.
+Happy querying!