This video demonstrates a practical application of agentic coding using the newly released Quen 3 Coder Plus model to build a customer service chatbot. The core focus is on the process of development, including debugging dependency conflicts, correctly integrating external APIs (specifically Olama for the language model backend), and handling environment setup challenges. The chatbot is designed to provide RAG (Retrieval Augmented Generation) capabilities for a product knowledge base and dynamically adapt its personality to mirror the user's tone. Despite facing issues like Python dependency mismatches, browser auto-closing, and an IDE freeze, the model successfully constructs a functional web-based application, showcasing its robust troubleshooting and code generation abilities for a real-world business use case.
Summarizing the Quen 3 Coder Agentic Coding Experience: Building a Dynamic Customer Service Chatbot
0:00 - 0:44: Introduction to Agentic Coding and Quen 3 Coder: The video introduces a hands-on test of the new Quen 3 coder in an agentic coding scenario, emphasizing the importance of observing the process of code generation, including debugging dependencies and API calls, rather than just the final product.
0:45 - 1:24: Project Goal - Customer Service Chatbot with RAG: The objective is to build a simple, web-based customer service chatbot for the presenter's R1 AI companion robot. This chatbot will feature a basic RAG setup to answer queries and dynamically adapt its personality based on user tone. It uses Olama for the LLM backend and is temporarily publicly hosted on RunPod.
2:50 - 4:19: Detailed Prompt for the Chatbot: The prompt specified a clean React frontend, a Flask backend, RAG implementation (loading/chunking product brief into a vector DB), Olama integration with an uncensored Quen model (to mirror rude user tones), and all functionality contained within a single web page.
5:08 - 5:51: Initial Project Scaffolding: After receiving the prompt, the Quen 3 Coder quickly generated a project outline proposing a React frontend and Flask backend, which the presenter approved, noting the rapid token generation speed.
6:47 - 8:00: Proactive Error Handling: The model began building the chat function, including RAG and personality mirroring. It also proactively added error handling for when Olama is unavailable, providing a mock response, which the presenter appreciated as a quality improvement.
8:01 - 9:43: Early Dependency & Environment Setup Issues: The model's first attempt to run the app encountered a dependency issue. It initially suggested a single HTML file solution, which the presenter rejected, instructing it to provide only scripts and a requirements.txt file without environment setup. The model complied and outlined the next steps.
9:44 - 12:54: Debugging Olama API Integration and Dependencies: The presenter challenged the model to strip direct Olama imports and use the Olama API directly. The model proposed the correct API endpoint (11434 API/generate). Further debugging was required for huggingface_hub incompatibility (fixed by installing an older transformers version) and the application not staying open in the browser, which the model surprisingly fixed by launching it in Firefox and keeping it running.
14:17 - 17:09: Persistent Olama Loading Bug: Although the model correctly advised ollama pull for the desired Quen 3 4B Uncensored model, the application still failed to load it. The model suggested making error handling more robust and created a sanity check script (olama_test.sh) to verify Olama connectivity, which successfully ran the model.
18:10 - 19:13: Agentic Coding Tool Freeze: While attempting to fix the persistent issue of the environment variable not sticking, the agentic coding program froze, forcing the presenter to quit.
19:14 - 20:23: Post-Freeze Success and Cost Analysis: After restarting, the model usage showed 1.2 million input tokens and 30,000 output tokens. Miraculously, the Olama integration was now working, and the chatbot successfully responded to queries.
20:24 - 23:34: Functional RAG and Personality Mirroring: The code review showed a simple, yet effective RAG implementation and personality mirroring logic based on "friendly" and "rude" keywords. When the presenter injected custom product information and asked a rude question, the uncensored Quen model correctly referenced the new RAG data and responded with a matching tone.
23:35 - 25:56: UI Enhancement and Unexpected Persona Shift: The model successfully polished the UI to a "software as a service startup" style, adding branding and improving chat bubble aesthetics. However, it unexpectedly changed the AI's persona from a customer service rep to the robot itself, which the presenter noted as unacceptable.
25:57 - 27:44: Conclusion and Key Takeaways: The presenter expressed high satisfaction with the functional outcome, having built a local, production-ready customer service chatbot with a local AI backend, RAG, and adaptive personality in about an hour. The video successfully demonstrated the depth and typical experience of using an agentic coding model for practical applications, moving beyond mere "party tricks."