RAG‑Agent with Qdrant & Ollama

This repository contains a minimal but functional implementation of a RAG (Retrieval‑Augmented Generation) agent that:

• Stores embeddings in a Qdrant vector database.
• Generates embeddings with Ollama (nomic-embed-text).
• Uses LangChain (v1+) for the agent, tools and prompt‑engineering.

The agent can:

• Search the knowledge base (/search).
• Add new documents (/add).
• Interact through a simple CLI.

Installation

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

# 2. Install Python dependencies
pip install -r requirements.txt

# 3. Run the CLI
python -m src.cli

Note

: Qdrant must be running locally on port 6333. You can start it using Docker:

docker run -p 6333:6333 qdrant/qdrant

Directory structure

workspace/task-6a02e23da6fe2e4ac16acf65/
├─ src/
│  ├─ vector_store.py   # Qdrant wrapper
│  ├─ tools.py          # LangChain tools
│  ├─ agent.py          # Agent implementation
│  ├─ loader.py         # Utility for bulk loading
│  └─ cli.py            # Interactive CLI
├─ requirements.txt
└─ README.md

Usage

Load documents from a folder

python -m src.loader /path/to/text/files

Start the interactive CLI

python -m src.cli

• /add – add a new document.
• /search – perform a semantic search.
• /quit – exit.

Any other input is treated as a user message and processed by the agent.

How it works

1. Vector store – KnowledgeBase wraps QdrantVectorStore. It splits documents into chunks using RecursiveCharacterTextSplitter, embeds them with OllamaEmbeddings, and stores the vectors.
2. Tools – Two tools (search_knowledge_base, add_to_knowledge_base) are exposed to the agent via LangChain's @tool decorator.
3. Agent – Built with create_tool_calling_agent and AgentExecutor. The system prompt encourages the assistant to use the tools.
4. CLI – Provides a simple REPL for adding documents, searching, and chatting with the agent.

Extending

• Replace the embedding model with any Ollama model.
• Swap Qdrant for another vector store supported by LangChain.
• Add more tools (e.g., delete, update) following the same pattern.

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Happy experimenting!