Getting Started with RAG in DSPy!

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Hey everyone! Thank you so much for watching this tutorial on getting started with RAG programming in DSPy! This video will take you through 4 major aspects of building DSPy programs (1) Installation, settings, and Datasets with dspy.Example, (2) LLM Metrics, (3) The DSPy programming model, and (4) Optimization!!

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Chapters
0:00 Intro!
1:35 Where to find the code
2:08 Community Notes
4:00 Getting Started with RAG
7:56 0. DSPy Settings and Installation
10:00 1. DSPy Datasets
11:56 2. LLM Metrics
19:22 3. The DSPy Programming Model
23:32 4. DSPy Optimization
30:45 Recap

Connor Shorten

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DSPartY is bumpin’

Love your tutorials and the engagement you’ve been putting in within the DSPy community.

kevon

Great one Connor! good to see your progress that, naturally, help us all...

joser

great tutorial Connor, looking forward to more advanced stuff like Agent application!

simingzhao

Brilliant Connor, thanks so much for this video and looking forward to more about this subject!

BradJonesus

Thank you Connor. This is exactly what the world needed.

ClintSearchEngineer

Thanks for the detailed video Connor, This is a great help. I am working on the lang graph and multi agent models. I had to optimise some of my prompts manually to reduce the number of agent hops to llm model. With BaysianSignature optimizer, I believe every prompt can be optimised and it'll reduce the hops made by agents

saivamsi

Great tutorial! I'm looking forward to building on this! Thank you

gumshoe

Connor - you can pan in and out as much as you want IMO, shows your excitement about the subject. The quality of the content is awesome. Also appreciate the shoutouts to the broader community. Thanks for sharing!

donb

Thank You. This particular vid motivated me to SUBSCRIBE !

davidtindell

Nice video, Connor. Could you do a more in-depth video on the optimization process? In particular, looking at the series of prompts/examples selected throughout the optimization (analogous to doing a small lin. regression/backprop example by hand for intuition) and the overall token cost of these optimizations.

robro

This was a fantastic walkthrough! Would love some insight into extracting structured data - I find this extremely useful, and being able to do this with a 7B/13B model (instead of GPT-4, for instance) would greatly decrease the cost of running my application. Thanks so much!

jakobkristensen

Thanks for the great content. One of the things I am missing is how to save the optimized program so I can use it after that without constantly re-training.

robboerman

Hay man, great video! I have a few questions tho. Can you use other vector DB as retriever like Milvus? Also, is it possible to use LLM that are less known like Baichuan, Kimi etc? Thank you!

runsenliu

You should also make a video on each GPT call cost. I believe there are hundreds (if not thousands) of calls happening every execution. DSPy is best paired with local model like mistral 7b. Otherwise, it will be impossible to scale such a tool on hundreds of docs.

bsihwwg

How can I load and use my own data to Weaviate and start implementing DSpy's implementation of RAG?

fox_trot

could we cover the creation of the schema from an empty database such that the notebook flow actually runs through

MandMatt

Any idea why the bootstrap with random search performed worse on the eval set? @ 29:00

codea

You tried with weaviem is there any way you could do with pinecone ?

stat_life

Is it possible to run DSPY on local windows environment, say with Mistral 7b model? It fails to for me because of default value of url param, which I do know how to avoid.

vitalybulgakov

So many haters wtf. Great video !! I been lazy in python bc of copy paste and langchain and llama-index. This video makes python more fun !

andrewdang

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