Update README.md

README.md

@@ -1,36 +1,23 @@
-# RAG Agent with Qdrant & Ollama
+# RAG Agent with Local Qdrant and Ollama
 
-This project implements a simple **RAG (Retrieval‑Augmented Generation) agent** that uses:
+This repository implements a simple AI agent that can store, search, and retrieve information from a local vector store using Qdrant and Ollama embeddings. The agent is built with LangChain v1 and supports an interactive CLI with the following commands:
 
-* **Qdrant** – a vector database for storing embeddings.
+* `/add` – add a new document to the knowledge base.
-* **Ollama** – local LLM and embedding model (`llama3` and `nomic‑embed‑text`).
+* `/search` – perform a semantic search in the knowledge base.
-* **LangChain** – framework for building the agent and tools.
+* `/quit` – exit the program.
 
-The agent can:
+## Features
 
-* **Add** documents to the knowledge base.
+* **RAG** – Retrieval-Augmented Generation using a local vector store.
-* **Search** the knowledge base for relevant chunks.
+* **Qdrant** – Vector similarity search engine.
-* Answer user queries using the stored knowledge.
+* **Ollama** – Local LLM (`llama3`) and embeddings (`nomic-embed-text`).
+* **LangChain v1** – Modern agent framework.
+* **Recursive text splitter** – Chunk documents before embedding.
 
-## Project structure
+## Setup
-
-```
-workspace/
-├── src/
-│   ├── __init__.py
-│   ├── vector_store.py   # Qdrant wrapper
-│   ├── tools.py          # LangChain tools
-│   ├── agent.py          # Agent definition
-│   ├── loader.py         # Load all .txt files from a directory
-│   └── cli.py            # Interactive command‑line client
-├── requirements.txt
-└── README.md
-```
-
-## Installation
 
 ```bash
-# Pull the required Ollama models
+# Install Ollama models
 ollama pull llama3
 ollama pull nomic-embed-text
 
@@ -40,60 +27,9 @@ pip install -r requirements.txt
 
 ## Usage
 
-### 1. Load documents into the knowledge base
-
 ```bash
-python -m src.loader /path/to/text/files
+# Load documents from the `docs` folder and start the CLI
+python -m src.cli --docs docs
 ```
 
-All `.txt` files in the directory (recursively) are added to the vector store.
+You can then interact with the agent using the commands described above.
-
-### 2. Start the interactive CLI
-
-```bash
-python -m src.cli
-```
-
-Once started you can use the following commands:
-
-| Command | Description |
-|---------|-------------|
-| `/add <title> <file_path>` | Add a single file to the knowledge base. |
-| `/search <query>` | Search the knowledge base and display top results. |
-| `/quit` | Exit the program. |
-| `/help` | Show help. |
-| Any other text | Sent to the agent as a user query. |
-
-### 3. Example session
-
-```
-RAG Agent CLI. Type /help for commands.
-> /add example docs/example.txt
-Document 'example' added.
-> /search quantum
-Results:
-1. [example - chunk 0] Quantum mechanics is the branch of physics that deals with...
-> Tell me more about quantum.
-Sure! Here is what I found in the knowledge base: ...
-> /quit
-Goodbye.
-```
-
-## How it works
-
-* **Vector Store** – `KnowledgeBase` wraps a `QdrantVectorStore`.  It creates the collection only if it does not exist, preventing accidental data loss.
-* **Chunking** – Documents are split into 500‑character chunks with 50‑character overlap using `RecursiveCharacterTextSplitter`.
-* **Tools** – Two LangChain tools are exposed:
-  * `search_knowledge_base(query, max_results)` – returns a list of relevant chunks.
-  * `add_to_knowledge_base(content, title)` – adds a document.
-* **Agent** – Built with `create_agent` from `langchain.agents`.  It uses the local `ChatOllama` model (`llama3`).
-
-## Extending
-
-* Replace the embedding model by editing `KnowledgeBase.__init__`.
-* Add more tools (e.g., delete from knowledge base) following the same pattern.
-* Deploy the agent as a web service by wrapping `run_query` in a FastAPI endpoint.
-
-## License
-
-MIT License.