I’m having trouble with my n8n semantic search setup. I’ve got embeddings from company data stored in Qdrant, but when I ask a question, the AI Agent doesn’t seem to use this info.
My workflow looks like this:
Make embeddings from company sheets
Put them in Qdrant
AI Agent asks Qdrant for relevant stuff when there’s a question
The weird part is, Qdrant gets the right data, but the AI Agent ignores it and gives generic answers. I’ve checked that:
Embeddings are made and stored okay
Qdrant searches work fine
AI Agent is set up to use Qdrant
I’m thinking maybe it’s how I’m making the embeddings, or how the AI Agent is supposed to use them. Maybe I messed up some settings?
Has anyone run into this before? How do I make sure the AI Agent actually uses what’s in Qdrant?
I’ve encountered this issue before, and it can be frustrating. One thing that worked for me was fine-tuning the relevance scoring in Qdrant. Sometimes, the default settings don’t quite capture the nuances of your specific data.
Try adjusting the cosine similarity threshold or experimenting with different distance metrics. This can help ensure that only the most relevant information is being passed to the AI Agent.
Another approach that yielded good results was implementing a pre-processing step between Qdrant and the AI Agent. I created a simple script that reformatted the Qdrant output, highlighting key information and structuring it in a way that was easier for the AI to parse.
Don’t forget to monitor your AI Agent’s token usage. If it’s consistently hitting the maximum, it might be truncating the Qdrant data before even considering it. Adjusting the max tokens or splitting the task into multiple API calls could help here.
Lastly, consider fine-tuning your AI model on a dataset that includes examples of how to properly utilize vector search results. This can significantly improve its ability to incorporate the Qdrant data into its responses.
yo sophia, i had the same problem! check ur AI Agent’s context injection. sometimes it doesnt know how to use the qdrant stuff properly. try tweaking the prompt to say smthing like ‘use this qdrant data to answer’. also, make sure the data format from qdrant matches what the agent expects. good luck!
hey sophia, sounds like a tricky one! have u checked if the ai agent’s prompt includes instructions to use the qdrant results? sometimes they need explicit directions. also, maybe try increasing the ‘temperature’ setting if its too low - could make the agent more creative with the data. good luck!
Have you verified the context window size of your AI Agent? If it’s too small, it might not be able to process all the Qdrant results effectively. Try increasing the context window or reducing the amount of data retrieved from Qdrant per query.
Another aspect to consider is the relevance threshold for Qdrant results. You might want to implement a filtering mechanism that only passes highly relevant information to the AI Agent. This could help ensure that the agent focuses on the most pertinent data.
Lastly, it’s worth examining the integration between Qdrant and the AI Agent. Ensure that the data format and structure align perfectly between the two components. Sometimes, minor discrepancies can cause the AI to overlook valuable information.
I’ve dealt with a similar issue in my n8n setup. What worked for me was tweaking the AI Agent’s system message. Make sure it explicitly instructs the agent to prioritize and incorporate the Qdrant results. Something like: ‘Your primary knowledge source is the data provided from Qdrant. Use this information to formulate your responses.’
Also, double-check the format of the data you’re passing from Qdrant to the AI Agent. If it’s not structured properly, the agent might struggle to parse and use it effectively. I found that presenting the Qdrant results in a clear, easy-to-read format helped the AI Agent utilize the information better.
Lastly, consider adjusting the ‘top-k’ parameter in your Qdrant query. If it’s set too high, it might be retrieving less relevant information, confusing the AI Agent. Start with a lower value and gradually increase it until you find the sweet spot.