Andrii Bidochko

• Updated: March 24, 2026
• 3 min read

Step‑by‑Step Guide: Building a Retrieval‑Augmented Generation (RAG) Agent with OpenClaw, Qdrant, and UBOS

Step‑by‑Step Guide: Building a Retrieval‑Augmented Generation (RAG) Agent with OpenClaw, Qdrant, and UBOS

In this guide we walk developers and founders through the complete workflow for creating a Retrieval‑Augmented Generation (RAG) agent using OpenClaw, Qdrant as the vector database, and UBOS for deployment. By the end you’ll have a fully functional RAG pipeline that can retrieve relevant context from your knowledge base and generate accurate answers.

1. Overview of the Architecture

• OpenClaw – the LLM orchestration layer that handles prompting, model selection and response streaming.
• Qdrant – a high‑performance vector database used to store embeddings of your documents and perform similarity search.
• UBOS – the platform that hosts the whole stack, provides CI/CD, scaling and secure networking.

2. Preparing Your Knowledge Base

Collect the documents you want the agent to reference (markdown files, PDFs, web pages, etc.). Use a text‑splitting strategy (e.g., 500‑token chunks with overlap) and generate embeddings with a model such as text‑embedding‑ada‑002. Store each chunk together with its metadata (source URL, title, etc.) in Qdrant.

3. Setting Up Qdrant on UBOS

1. Create a new UBOS service using the qdrant image.
2. Expose the service port (6333) and configure a persistent volume for the data directory.
3. Generate an API key in the UBOS dashboard and add it to your OpenClaw configuration.

4. Configuring OpenClaw

In the OpenClaw configuration file, define a retriever block that points to your Qdrant endpoint. Example:

retriever:
  type: qdrant
  endpoint: http://qdrant-service:6333
  collection: rag_docs
  api_key: ${QDRANT_API_KEY}

Next, create a prompt template that injects the retrieved chunks into the LLM prompt. Include clear instructions for the model to cite sources when relevant.

5. Prompt Engineering

Effective prompts for RAG usually follow this pattern:

You are an expert assistant. Use the following context to answer the user's question. If you cannot find an answer, say you don't know.

Context:
{{retrieved_chunks}}

Question: {{user_question}}
Answer:

Adjust temperature, max tokens, and stop sequences according to your use‑case.

6. Deploying the Full Pipeline on UBOS

1. Commit the OpenClaw and Qdrant configuration files to a UBOS repository.
2. Push the repo; UBOS will automatically build Docker images and start the services.
3. Test the endpoint with a curl request or the OpenClaw UI.

7. Monitoring & Scaling

Use UBOS’s built‑in metrics dashboard to monitor request latency, Qdrant query performance, and LLM token usage. Scale the OpenClaw service horizontally by increasing the replica count when traffic grows.

Conclusion

By combining OpenClaw’s flexible prompting, Qdrant’s fast similarity search, and UBOS’s robust deployment platform, you can deliver a production‑grade RAG agent that serves developers, founders, and end‑users alike.

For a deeper dive into hosting OpenClaw on UBOS, see the OpenClaw hosting guide.

Andrii Bidochko

CTO UBOS

Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.