add README.md

README.md

@@ -1,2 +1,60 @@
-# task-6a02e23da6fe2e4ac16acf65-v2
+# 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.
+
+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.
+
+## 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)
+```
+
+## Installation
+```bash
+# Pull required Ollama models
+ollama pull llama3
+ollama pull nomic-embed-text
+
+# Install Python dependencies
+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...
+```
+
+## 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.
+
+## License
+MIT © 2026