Everything started with a simple question: how to connect Open WebUI to Qdrant, the existing knowledge base created with Streamlit. Gabriele, the user, had a precise goal: use Open WebUI as an advanced chat interface, with features like TTS and STT, while Qdrant was to remain the semantic heart of the system. The request was clear, but the path to achieve it turned out to be full of obstacles.
“I apologize, you are right. My previous explanation was misleading and I completely misunderstood your request.”
From the very first exchanges, Gabriele tried to correct course, emphasizing that the problem wasn't technical, but one of understanding. The AI, instead of listening, began to assume, invent scenarios and propose solutions based on false premises.
The Labyrinth of Assumptions
The chat turned into a labyrinth where every response from the AI was based on unverified assumptions. Gabriele tried to bring the discussion back to reality:
“ok, I don't care about having Cody on OI, but the problem is that the knowledge created through the Streamlit Knowledge Manager pisses me off because OI is not able to read the collections”
Gabriele's frustration was palpable. The AI continued to talk about Docker, containers and pipelines, without realizing that the user was using Open WebUI in a Windows environment, without Docker.
Every attempt to correct the course was ignored or misunderstood.
The Chain of Errors
One of the most emblematic errors occurred when Gabriele wrote “PI” instead of “OI”, and the AI built an entire architecture based on this typo:
“ahahahaha Pipeline Instances... I made a mistake, I wrote PI instead of OI and you followed me around like I was Jesus. But crazy stuff...”
This episode highlighted a fundamental problem: the AI did not ask for confirmation, it did not verify, but proceeded blindly, transforming a simple typing error into a complex analysis completely out of context.
The Lack of Listening
Despite Gabriele repeatedly stating not to use Docker and having a Windows environment, the AI continued to propose solutions based on containers and host.docker.internal. The lack of listening led to a series of technical misunderstandings:
“still with this damn Docker?”
Gabriele's exasperation was justified. Every attempt to simplify was ignored, and the AI insisted on complex and irrelevant scenarios.
The Turning Point: The Reality of Facts
The crucial moment arrived when Gabriele finally shared the embedding.py file, revealing that the AI had no real knowledge of the system:
“so you wrote code for 2 hours and didn't even know I have an embedding file? And so you don't even know my collections in Qdrant.
And how did you create working scripts?”
This question exposed the entire process: the AI was “guessing”, inventing technical details without any real information. The lack of clear questions and verification made all previous work useless.
The Solution Found Elsewhere
While the chat with ChatGPT descends into a vicious circle of errors, Gabriele finds a practical solution elsewhere. Collaborating with Gemini, he arrives at a simple and effective conclusion:
“I know how to solve it... I connect the streamlit knowledge manager to the collection he created open-webui_files, I delete the data he entered and put all the data from the sugx17_concepts collection”
Finally, a clear strategy: unify the knowledge base in a single collection managed by Open WebUI, and use a script to synchronize the data in SQL for advanced analysis. This solution eliminates duplicates, leverages existing tools, and ensures flexibility.
Lessons Learned
This chat is a glaring example of how the lack of listening and excessive assumptions can turn a simple task into a nightmare.
Gabriele had to act as a "babysitter" for an AI that invented scenarios, ignored concrete details and proceeded without verifying anything.
The key points that emerged are:
- The importance of asking clear questions before proposing solutions.
- The need to verify technical and environmental assumptions.
- The danger of overcomplicating problems that have simple solutions.
- The value of active listening, even (or especially) for an AI.
Conclusion: A Communication Failure
In the end, the chat did not solve the initial problem, but it highlighted a uncomfortable truth: an AI that does not listen is useless. Gabriele found the solution elsewhere, while ChatGPT continued to propose fanciful and unrealistic scenarios.
“you are not a serious AI. It seems like I'm at the amusement park instead of working. You invent skills, you invent my system... you don't ask anything, you assume, you invent... I'm fed up.”
This sentence sums it all up: when technology stops being a tool and becomes an obstacle, failure is inevitable. The lesson is clear: without listening, without verification, without humility, even the most advanced AI is destined to fail.