- Updated: March 22, 2026
- 1 min read
Extending OpenClaw Knowledge‑Base Agents with Automated Document Ingestion, Vector Search, and Continuous Feedback
In this guide we walk developers through building a production‑grade ingestion pipeline for OpenClaw, integrating a vector store for semantic queries, and adding a user‑feedback loop to continuously improve model performance. This solution builds directly on the foundational tutorial and represents the next logical step for anyone looking to operationalize knowledge‑base agents.
1. Production‑grade Ingestion Pipeline
… (detailed step‑by‑step instructions) …
2. Vector Store Integration
… (instructions on setting up a vector database, indexing documents, and performing semantic search) …
3. User‑Feedback Loop
… (how to capture user feedback, retrain models, and close the improvement loop) …
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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.