add README.md

README.md

@@ -1,60 +1,47 @@
 # RAG Agent with Qdrant and Ollama
 
-## Project Overview
-This repository contains a minimal yet complete implementation of an AI agent that can **search** and **add** information to a local knowledge base powered by **Qdrant** (vector database) and **Ollama** (local LLM & embeddings). The agent is built using the LangChain framework.
+## Overview
+This repository contains a minimal implementation of an AI agent that uses **RAG (Retrieval‑Augmented Generation)** with a local vector store powered by **Qdrant** and embeddings from **Ollama**. The agent can:
 
-The main components are:
-- **Vector store** – Qdrant client with an initialized collection.
-- **Text splitter** – RecursiveCharacterTextSplitter for chunking documents.
-- **Embedding model** – OllamaEmbeddings (`nomic-embed-text`).
-- **LLM** – ChatOllama (`llama3`).
-- **Tools** – `search_knowledge_base` and `add_to_knowledge_base`.
-- **Agent** – created with `create_agent` from LangChain.
-- **CLI client** – simple interactive loop to demonstrate adding documents and searching the knowledge base.
+1. Add documents to the knowledge base.
+2. Search the knowledge base semantically.
+3. Answer arbitrary user queries using the stored information.
 
-## Directory Structure
-```
-├── README.md
-├── requirements.txt
-├── main.py          # CLI entry point
-├── agent.py         # Agent creation logic
-├── tools.py         # LangChain tool definitions
-├── utils.py         # Qdrant client, splitter, and helper functions
-└── docs/            # Directory with text files to load initially (optional)
-```
+The project is structured into three main files:
+
+- `main.py` – entry point with an interactive CLI and examples.
+- `tools.py` – LangChain tools for adding/searching documents.
+- `requirements.txt` – Python dependencies.
 
 ## Installation
 ```bash
-# Pull required Ollama models
+# Pull required Ollama models (run once)
 ollama pull llama3
 ollama pull nomic-embed-text
 
-# Install Python dependencies
+# Install Python packages
 pip install -r requirements.txt
 ```
 
 ## Usage
-1. **Load documents** – Place any `.txt` files in the `docs/` directory.
-2. **Run the CLI**:
-   ```bash
-   python main.py
-   ```
-3. In the interactive prompt you can use:
-   - `/add <file_path>` – Add a new document to the knowledge base.
-   - `/search <query>` – Search the knowledge base and display results.
-   - `/quit` – Exit the program.
-
-## Example
-```text
-> /search python data structures
-1. Python lists are ordered collections...
-2. Tuples are immutable sequences...
+Run the interactive client:
+```bash
+python main.py
 ```
+You can use the following commands:
+
+- `/add` – add a new document.
+- `/search <query>` – perform a semantic search.
+- `/quit` – exit.
+- Any other text is treated as a question for the agent.
 
 ## Architecture
-- The **agent** is a LangChain agent that uses two tools: `search_knowledge_base` and `add_to_knowledge_base`. It receives user messages, decides which tool to call, and returns the result.
-- The **vector store** is wrapped by `QdrantVectorStore`, which handles embedding generation via OllamaEmbeddings. Documents are split into chunks before insertion.
-- The **CLI** orchestrates loading documents at startup and provides a simple REPL for demonstration purposes.
+The agent uses LangChain’s `create_agent` with two custom tools:
 
-## License
-MIT © 2026
+1. **add_to_knowledge_base** – splits input into chunks, embeds them via Ollama, and stores in Qdrant.
+2. **search_knowledge_base** – performs a similarity search on the vector store.
+
+The LLM is an Ollama `llama3` model accessed through LangChain’s `ChatOllama`. The embeddings are provided by `OllamaEmbeddings` with the `nomic-embed-text` model.
+
+## Extending
+Feel free to add more tools or integrate a persistent Qdrant instance instead of an in‑memory one. The code is intentionally simple for educational purposes.