Thank you! 🙂 Are you asking how you can create a Weaviate schema and connect to it? There is a `Weaviate-import` notebook in the demo URL. I can't share links in my response, sorry!
@@ecardenas300 thanks for the response !, but no we have already that my question is if in the retrieval function can we use like a where in sql to filter by one field in the schema, i dont know if i was clear enaugh :)
@@ivanarozamena Oh, yes! Weaviate supports filtering and can even couple multiple filters together. I can't share a link here, but you can find it by going to --> Weaviate --> Developers --> Weaviate docs --> How-to: Search --> Filters
Hi ! Thanks for your video There is something i still do not understand with DSPy : If i want to create a programme similar like you, a bit different. Do i have to create a testing and eval dataset ? Do I have to do it for each application i have to do ? It makes the processus heavy...
Hi, thanks for your question! If you want to build a different DSPy program, you have to: 1. Create your Weaviate cluster and upload data 2. Connect to the Weaviate retriever model in DSPy 3. Build out your program with a signature(s)
Thanks for watching! Feel free to let me know if you have any questions. 🥳
you links need to be updated.
Amazing! This is an incredible example of how much creativity DSPy unlocks for LLM developers! Questions to blog posts, wow!!
Thank you, Connor! The possibilities are endless 🚀
Going to explore DSPy. Thank you for the example!
Awesome 🥳
You produce done of the best content on YT for AI! Great job and keep going. You are a rockstar.
Thanks for the support, George!
Great walkthrough, looking forward to trying this.
Thank you!
Amazing tutorial Erika, very clear...
Thanks so much! Happy you enjoyed it 🙂
Great video, Erika!
Thank you!!
great video, and even better follow up support from Erika and the Weaviate team to resolve an issue I was having the code example.
Thank you!
Hey Don,
Just responded to you on Slack! Hope we're able to resolve this quickly
🙂
Great video, very clear explanations, and the example is great. Thanks Erika!
Thank you! Glad you enjoyed the video!
Super interesting!
I agree!
Great video!! I can’t wait to do this for myself!!! 🎉
Thanks so much, David! Happy building 🚀
Hi ! great content ! how you can add a filter where in your Weaviate database after to connect the retrieval ?
Thank you! 🙂
Are you asking how you can create a Weaviate schema and connect to it? There is a `Weaviate-import` notebook in the demo URL. I can't share links in my response, sorry!
@@ecardenas300 thanks for the response !, but no we have already that my question is if in the retrieval function can we use like a where in sql to filter by one field in the schema, i dont know if i was clear enaugh :)
@@ivanarozamena Oh, yes! Weaviate supports filtering and can even couple multiple filters together. I can't share a link here, but you can find it by going to --> Weaviate --> Developers --> Weaviate docs --> How-to: Search --> Filters
Great!!!
Interesting. I may have missed it, but did you have training/test data before hand, or was this data generated by the pipeline (?) you just built out?
Hi ! Thanks for your video
There is something i still do not understand with DSPy :
If i want to create a programme similar like you, a bit different. Do i have to create a testing and eval dataset ? Do I have to do it for each application i have to do ? It makes the processus heavy...
Hi, thanks for your question!
If you want to build a different DSPy program, you have to:
1. Create your Weaviate cluster and upload data
2. Connect to the Weaviate retriever model in DSPy
3. Build out your program with a signature(s)
@@ecardenas300 hum I understand
Then, having training data is always mandatory ?
(Requires effort to have it sometimes)
thanks for sharing! at the end of the video we can see how LLMS are not good at math yet 😅
We'll get there 🤣
can we use dspy with weaviate-client latest versions ?
Not yet (keyword is "yet")
great application idea! I'd just add a small suggestion: please, use dark background, eyes bleed of this much brightness -.-
Thank you for the feedback! I'll keep that in mind next time. 🙂