Update README.md

README.md

@@ -1,77 +1,79 @@
-# RAG‑Agent with ChromaDB and Web Search
+# RAG Agent with ChromaDB and Tavily
 
-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.
+This repository contains a lightweight RAG (Retrieval‑Augmented Generation) agent that:
 
-## Features
+1. Stores local knowledge in **ChromaDB** using **Ollama** embeddings.
+2. Performs semantic search over the local store.
+3. Falls back to **Tavily** web search for up‑to‑date information.
+4. Decides automatically which source to use and indicates the source in the answer.
 
-* **Local semantic search** – Uses a ChromaDB vector store backed by Ollama embeddings.
+## Prerequisites
-* **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.
 
-## Setup
+* Python 3.10+ (recommended via `pyenv` or `conda`).
+* [Ollama](https://ollama.ai/) installed locally.
+* A Tavily API key – set it in a `.env` file.
 
 ```bash
-# 1. Clone the repo
+# Pull the required models
-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
-
-# 3. Install dependencies
-pip install -r requirements.txt
-
-# 4. Pull required Ollama models
 ollama pull llama3
 ollama pull nomic-embed-text
+```
 
-# 5. Set Tavily API key
+## Installation
-export TAVILY_API_KEY=your_api_key   # Windows: set TAVILY_API_KEY=your_api_key
 
-# 6. Prepare documents
+```bash
-# Place any .txt or .md files you want to index in the ./documents folder.
+pip install -r requirements.txt
-# They will be automatically loaded into ChromaDB on first run.
-
-# 7. Run the agent
-python main.py
 ```
 
 ## Usage
 
-```text
+```bash
-Запрос: Какие последние новости про AI-агентов?
+# Create a .env file with your Tavily key
-[Web Search]
+# TAVILY_API_KEY=YOUR_KEY
-1. AI Agents: The Future of Automation: ...
-2. ...
-Source: tavily
 
-Запрос: Что в наших конспектах про LangGraph?
+# Populate the vector store from the documents folder
-[Local KB]
+python main.py
-1. LangGraph is a ...
-2. ...
-Source: chromadb
 ```
 
+You will be presented with a prompt.  Type your question and press **Enter**.
+Type `exit` to quit.
+
 ## Project Structure
 
 ```
-├── agent.py          # Core agent logic and tools
+├── agent.py          # Agent definition
-├── vectorstore.py    # ChromaDB creation and document loading
-├── rag_tools.py      # Web search tool
 ├── main.py           # CLI entry point
+├── tools.py          # Local KB and web search tools
+├── vectorstore.py    # ChromaDB helpers
 ├── requirements.txt
-└── README.md
+├── README.md
+└── documents/        # Folder with .txt/.md files to ingest
 ```
 
+## How It Works
+
+1. **Vector Store** – `vectorstore.py` creates a ChromaDB instance backed by
+   `OllamaEmbeddings`.  Documents from `documents/` are chunked with
+   `RecursiveCharacterTextSplitter` and added to the store.
+
+2. **Tools** – `tools.py` exposes two LangChain tools:
+   * `search_local_kb` – semantic search in ChromaDB.
+   * `web_search` – web search via Tavily.
+
+3. **Agent** – `agent.py` builds an OpenAI‑functions‑style agent that
+   chooses between the two tools based on the user’s query.  The system prompt
+   instructs the LLM to use `search_local_kb` for knowledge‑base queries and
+   `web_search` for recent facts.  The answer always contains a source tag.
+
+4. **CLI** – `main.py` ties everything together: it loads the vector store,
+   creates the agent and runs an interactive chat loop.
+
 ## Extending
 
-* **Add more tools** – Define new functions decorated with `@tool` and add them to the `tools` list.
+* Replace the LLM with any other LangChain‑compatible model.
-* **Change LLM** – Swap `ChatOllama` for another provider (e.g., OpenAI) by adjusting the import and model name.
+* Add more tools (e.g., database queries, file system access).
-* **Custom prompt** – Edit `agent_prompt` in `agent.py` to modify the agent’s instruction.
+* Persist the vector store across runs – it already does this via `persist_directory`.
 
 ---
 
-Happy querying!
+Happy experimenting!