Update README.md

README.md

@@ -1,79 +1,86 @@
-# RAG Agent with Qdrant and Tavily
+# RAG Agent with ChromaDB and Tavily Web Search
 
-This repository implements an AI agent that can answer questions using a local knowledge base stored in **Qdrant** and up‑to‑date information fetched from the web via **Tavily**.  The agent is built with **LangChain 1.x** and **Ollama** for local LLM and embeddings.
+## Overview
 
-## Features
+This repository contains a simple RAG (Retrieval‑Augmented Generation) agent that can answer user questions by searching a local knowledge base stored in **ChromaDB** and by performing live web searches via **Tavily**.  The agent automatically selects the appropriate source and reports it in the answer.
 
-* **Local RAG** – Semantic search in Qdrant using Ollama embeddings.
+The project uses the following stack:
-* **Web search** – Tavily integration for real‑time information.
-* **Automatic source selection** – The LLM decides whether to use the local KB or the web.
-* **Persistent vector store** – Data is saved in `./qdrant_db` and reused across runs.
-* **Interactive CLI** – Add documents, ask questions, and see the source.
 
-## Installation
+- **Python 3.10+**
+- **LangChain 1.x** – modern agent framework
+- **ChromaDB** – local vector store
+- **Ollama** – LLM (`llama3`) and embeddings (`nomic-embed-text`)
+- **Tavily** – web search API
+- **LangGraph** – (not used directly, but required by LangChain 1.x)
 
-```bash
+## Folder structure
-# 1. Pull required Ollama models
-ollama pull llama3
-ollama pull nomic-embed-text
-
-# 2. Install Python dependencies
-pip install -r requirements.txt
-
-# 3. Run Qdrant (Docker recommended)
-#    If you prefer a local binary, download from https://qdrant.tech
-#    Docker command:
-#    docker run -p 6333:6333 qdrant/qdrant
-```
-
-## Usage
-
-```bash
-# Start the CLI
-python -m workspace.task-6a1864f78a94f887e50d46da.cli
-```
-
-Commands:
-
-* `/add <directory>` – Load all `.txt` and `.md` files from the directory into Qdrant.
-* `/search <question>` – Ask the agent a question.
-* `/quit` – Exit.
-
-Example:
-
-```
-> /add ./documents
-Loaded 12 chunks into Qdrant.
-Documents added.
-> /search What is LangGraph?
-Answer:
-LangGraph is a framework for building ...
-Source: chromadb
-```
-
-## Environment Variables
-
-* `TAVILY_API_KEY` – Your Tavily API key.
-
-Create a `.env` file in the project root:
-
-```
-TAVILY_API_KEY=your_api_key_here
-```
-
-## Project Structure
 
 ```
 workspace/
-├─ task-6a1864f78a94f887e50d46da/
+├─ documents/          # .txt/.md files that will be loaded into Chroma
-│  ├─ vector_store.py          # Qdrant vector store helpers
+├─ chroma_db/          # persistent Chroma data (created on first run)
-│  ├─ tools.py                 # Local KB and web search tools
+├─ main.py             # CLI entry point
-│  ├─ agent.py                 # Agent definition
+├─ vectorstore.py      # Chroma store helpers
-│  ├─ cli.py                   # Interactive command line
+├─ tools.py            # Agent tools
-│  ├─ requirements.txt
+├─ README.md
-│  └─ README.md
+└─ requirements.txt
 ```
 
+## Setup
+
+1. **Install Ollama** and pull the required models:
+   ```bash
+   ollama pull llama3
+   ollama pull nomic-embed-text
+   ```
+
+2. **Set the Tavily API key** (obtain a free key from https://tavily.com):
+   ```bash
+   export TAVILY_API_KEY=your_api_key_here
+   ```
+   On Windows use `set` instead of `export`.
+
+3. **Install Python dependencies**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+4. **Add documents** you want the agent to know about into the `documents/` folder.  Any `.txt` or `.md` files will be automatically loaded.
+
+## Running the Agent
+
+```bash
+python main.py
+```
+
+You will see a prompt where you can type questions.  Type `exit`, `quit`, or `q` to end the session.
+
+Example interaction:
+
+```
+User: What are the latest developments in AI agents?
+Assistant: [Web Search] - ...
+Source: tavily
+
+User: What does our lab say about LangGraph?
+Assistant: [Local KB] - ...
+Source: chromadb
+```
+
+## How It Works
+
+1. **Vector Store** – `vectorstore.py` creates a persistent Chroma collection using `OllamaEmbeddings`.  Documents from `documents/` are split with `RecursiveCharacterTextSplitter` and added to the store.
+2. **Tools** – `tools.py` defines two tools:
+   * `search_local_kb` – semantic search in the local vector store.
+   * `web_search` – live web search via Tavily.
+3. **Agent** – In `main.py` we create a `ChatOllama` LLM and pass the two tools to `create_agent`.  A system prompt instructs the LLM to choose the correct tool.  The agent returns the answer along with a source tag.
+
+## Extending
+
+- Add more documents to `documents/` and restart the CLI – the store will be updated automatically.
+- Replace the LLM or embedding model by changing the `ChatOllama` and `OllamaEmbeddings` parameters.
+- Add additional tools (e.g., file system access, calculator) following the same pattern.
+
 ## License
 
-MIT
+MIT License.