I’d like to build a natural conversation flow with openAI using embedding data from my database. But, longer is the conversation, less accurate is the matching. So there is something I’m doing wrong.
To be more clear, more details on my case
I embedded list of apartment description (Rooms, city, surface, garden, gym…) in different cities like Madrid, Paris, New York, Hong Kong…
First problem: If I ask for an apartment in Spain, the matching fails to give me Madrid
User: Hello, I’m looking for an apartment with 2 rooms and a gym included in Spain
Assistant: Sorry, We have no offer in this area
Sources from the match will be various 2 rooms apartments with gym in various cities (Madrid can be one of them)
Second problem: If I ask for an apartment in Madrid, it works, but if in the same conversation i ask for other cities, it fails. I know the reason is because I embed the whole conversation but I need to do so if the user gave informations shared between multiples message (surface, city, number of room…)
User: Hello, I’m looking for an apartment in Madrid with 2 rooms and a gym
Assistant: We have some apartment that fit your needs, here is a list: [List of apartment that match the user request]
User: And do you have other suggestion in Madrid for a house ?
Here, the matching will still take some 2 rooms apartment with gym. And Questions after questions, the accuracy of the sources will decrease.
So 2 questions from my 2 examples:
Are the vectors from the embedding able to match that Madrid entries is the good answer when I ask for a place in Spain ?
How to get good sources for a long conversation ?