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

# Pull required Ollama models
ollama pull llama3
ollama pull nomic-embed-text

# Install Python dependencies
pip install -r requirements.txt

Usage

Load documents – Place any .txt files in the docs/ directory.
Run the CLI:
```
python main.py
```
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

> /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.

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