Пікірлер

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  @UjjayanroyКүн бұрын

  @3:46, how can low reach to high with search action?

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  @NarotamDhaliwal3 күн бұрын

  🎯 Key points for quick navigation: Exciting new framework Programming model optimization Graph computation programs 09:55 *Signature, dock string* 10:10 *Prompt optimization, syntax* 10:38 *Input, output fields* 10:52 *Control flow, loops* 11:20 *UAPI, web queries* 12:26 *DSPy Assertions, suggestions* 13:49 *Citation attribution suggestions* 14:16 *Optimization, instructions, examples* 14:59 *DpY as PyTorch* 16:31 *Inductive biases, depth* 17:43 *Intermediate supervision, DpY compiler* 19:22 *Testing with programs* 19:35 *Optimizing instructions and examples* 20:03 *Automatic data labeling* 20:46 *Ending manual prompts* 21:14 *Adapting to new models* 22:49 *Structured output with prompts* 25:33 *Fine-tuning neural networks* 26:26 *Using few-shot examples* 27:51 *Bootstrapping rationales* 28:19 *Evaluating synthetic examples* Overlapping keywords metrics LM judge prompt LM produce metric 37:58 *Deep learning paradigm shift* 38:41 *Data set formatting* 40:03 *Inspect intermediate outputs* 40:59 *Add Chain of Thought* 43:35 *Define optimization metric* 45:13 *Value of reasoning* 45:28 *Inspect parameters* 46:25 *Multi-hop search integration* Queries connected to final answer Introduction to multi-hop search Supervision on intermediate hops Made with HARPA AI

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  @RichardHamnett6 күн бұрын

  The recipe is gone

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  @connorshorten63116 күн бұрын

  Hey Richard! Sorry we refactored recipes! The links are now fixed!

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  @B_knows_A_R_D-xh5lo11 күн бұрын

  😊😊😊😊

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  @B_knows_A_R_D-xh5lo11 күн бұрын

  😊😊😊😊😊

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  @B_knows_A_R_D-xh5lo11 күн бұрын

  👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿😁😁👏🏿💯💯💯💯💯

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  @Meow-xm5kv11 күн бұрын

  short and sweet, gets to the point. marvelous video

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  @thirukarthikanadar61511 күн бұрын

  Hi Connor, thanks for the awesome content. I have one small suggestion - Instead of covering maximum information, if it was topic by topic it would be more better. Example: In depth Information on 1 topic "Optimizers (formerly Teleprompters)". Thank you🙂

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  @B_knows_A_R_D-xh5lo15 күн бұрын

  😁😁😁😁😁😁 0:10 0:11 0:11 👀 0:13 0:13 0:13 👀👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿Educational

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  @ThaLiquidEdit17 күн бұрын

  Is this notebook shared somewhere?

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  @B_knows_A_R_D-xh5lo18 күн бұрын

  🎉🎉🎉🎉🎉🎉🎉🎉😊

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  @B_knows_A_R_D-xh5lo18 күн бұрын

  😊😊😊😊😊🎉🎉🎉🎉

• 
  @ecardenas30020 күн бұрын

  Back on it again

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  @connorshorten631120 күн бұрын

  Keeping up with the LLMs!

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  @explorer94522 күн бұрын

  Do you laugh when you go in small tangents?

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  @B_knows_A_R_D-xh5lo23 күн бұрын

  lets gooo 🎉🎉🎉

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  @connorshorten631122 күн бұрын

  Haha, indeed! Thanks for watching!

• 
  @djbracken298723 күн бұрын

  Hey Connor, this is great content. Thanks for posting it.

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  @connorshorten631122 күн бұрын

  Thanks so much DJ!

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  @virtuous824 күн бұрын

  zooming in and out is distracting

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  @charismaowojoameh768124 күн бұрын

  I tried the implementation but i keep getting the error "model not found"

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  @zhouyangbo449825 күн бұрын

  great intro, and any GitHub repository for it verification?if so ,will be greatly appreciated.

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  @actorjohanmatsfredkarlsson229329 күн бұрын

  Super interesting. But boy you move quickly through all this. It's really hard to follow at times.

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  @prashlovessamosaАй бұрын

  Thanks

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  @dianaaytАй бұрын

  What metrics should i keep an eye on to know what to change to get better and better results? I'm trying to create the fake images to use for data augmentation, and obviously I want them as realistic as possible, but I honestly don't know which parameters to change for it to get better. Plus, i have 10 classes and I dont know if i should just change the same for all of them or just see what works for each. But, again, i dont even know how to make it better for even one

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  @tobkinАй бұрын

  Two questions: - Why use gpt-4 instead of gpt-4-turbo for the teleprompter? - What are you using to make your pointer act like that?

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  @respair1385Ай бұрын

  I always thought it was pronounced as D ES Pie. thanks for the deep dive btw!

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  @shawndeggansАй бұрын

  great presentation!

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  @robboerman9378Ай бұрын

  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.

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  @MrjbushMАй бұрын

  Thank you.

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  @NicolasAudioBooksАй бұрын

  i'm getting a headache by the zooming-in and then skipping across the page.

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  @lakshaysagarrana3965Ай бұрын

  Connor be experimenting with video formats.

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  @stefanbuys1927Ай бұрын

  This is almost exactly the same video as the one by Qdrant. Weird.

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  @ecardenas300Ай бұрын

  Guess which one came out first... 🫣 It's super weird indeed

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  @PeterWilliams97Ай бұрын

  I ran your notebook and got the following error. print(RAG()("What is binary quantization?").answer) AttributeError Traceback (most recent call last) Cell In[7], line 1 ----> 1 print(RAG()("What is binary quantization?").answer) File ~/code/vector_search/weaviate/recipes/.wenv/lib/python3.11/site-packages/dspy/primitives/program.py:26, in Module.__call__(self, *args, **kwargs) 25 def __call__(self, *args, **kwargs): ---> 26 return self.forward(*args, **kwargs) Cell In[6], line 16 15 def forward(self, question): ---> 16 context = self.retrieve(question).passages 17 pred = self.generate_answer(context=context, question=question).answer 18 return dspy.Prediction(context=context, answer=pred, question=question) File ~/code/vector_search/weaviate/recipes/.wenv/lib/python3.11/site-packages/dspy/retrieve/retrieve.py:30, in Retrieve.__call__(self, *args, **kwargs) 29 def __call__(self, *args, **kwargs): ---> 30 return self.forward(*args, **kwargs) File ~/code/vector_search/weaviate/recipes/.wenv/lib/python3.11/site-packages/dspy/retrieve/retrieve.py:39, in Retrieve.forward(self, query_or_queries, k) 36 # print(queries) 37 # TODO: Consider removing any quote-like markers that surround the query too. 38 k = k if k is not None else self.k ---> 39 passages = dsp.retrieveEnsemble(queries, k=k) 40 return Prediction(passages=passages) ... 79 .do() 81 results = results["data"]["Get"][self._weaviate_collection_name] 82 parsed_results = [result[self._weaviate_collection_text_key] for result in results] AttributeError: 'WeaviateClient' object has no attribute 'query'

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  @connorshorten6311Ай бұрын

  Hey Peter! Apologies we have upgraded the WeaviateRM to use the Weaviate v4 client, can you please try upgrading dspy with `!pip install dspy-ai --upgrade` ?

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  @connorshorten6311Ай бұрын

  Can you please share any error messages as an Issue on Weaviate recipes? It might be easier to help debug there instead of KZread comments.

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  @LyuboslavPetrovАй бұрын

  @connorshorten6311 Please do update the video with accurate setup instructions. I have been fighting to get this running (DSPY + Weaviate + OLLAMA) for the past 2-3 hours to no avail. Tried multiple weaviate-client/server combinations, ran trough docker and standalone, configured, updated/downgraded dspy-ai. Went through so many help pages, cannot count now. I am tired, but still would like to play with this set of technologies. Thanks

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  @koljanosАй бұрын

  Hey, what version of Weaviate-client you are using????

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  @connorshorten6311Ай бұрын

  Hey! I am using v4 and the latest version of dspy-ai, can you please share any error messages as an Issue on Weaviate recipes? It might be easier to help debug there instead of KZread comments.

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  @koljanosАй бұрын

  Allright, will check the issues!

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  @PrathameshSarafАй бұрын

  How can we get metadata that is associated with any chunk of docs

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  @cipritomАй бұрын

  Is it me, or at least the last part is a digital avatar?

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  @catchychazzАй бұрын

  Great video, Connor. Have you tested out if SAMMO is better than DSPy for production?

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  @larsbell1569Ай бұрын

  I thought SAMMO was primarily prompt templating? Does it have some new features we are not aware of?

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  @catchychazzАй бұрын

  @@larsbell1569 No, you're right. I was working on the assumption that in production, you'd be using the most capable language models. At the risk of running token-heavy pre-compiled DSPy prompts, having a simpler prompt that automatically augments few-shot prompting only on specific user inputs/triggered events might be a more cost-efficient solution.

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  @dianaliu7543Ай бұрын

  This is so different from RAG using GPT. Lots to learn

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  @leeme179Ай бұрын

  maybe just me but the blur/smooth filter to the face cam makes me suspect that face cam is AI generated 🤣🤣🤣🤣🤣🤣

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  @dixon1eАй бұрын

  Uncanny valley feel.

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  @Tarun_MamidiАй бұрын

  Is llama really OSS if we don’t know how or what it is trained on?

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  @human_agiАй бұрын

  how to install ollama

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  @420_gunnaАй бұрын

  this mf cookin

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  @VincentFulcoАй бұрын

  Would love to see an interface to groq please!

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  @LaHoraMakerАй бұрын

  Dear Connor, that was the fastest release ever!

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  @emirhanbilgic2475Ай бұрын

  man, that's an amazing overview, thank you

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  @connorshorten631112 күн бұрын

  Means a lot! Thank you so much!

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  @AliA17736Ай бұрын

  Thank you for great content, but the zoom in/zoom out effects on video are really annoying! Why not using simple highlighting or just leave the slide as simple as they are?

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  @thewimo8298Ай бұрын

  I love your energy throughout this video Connor!

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  @d4qatoaАй бұрын

  If I can give some feedback, too much discussion of regular RAG not enough of getting to the point of the benefits of this model. 128k context windows is what everyone has, if not better....I gave up waiting for the point.

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  @awsrftgyhuokjoАй бұрын

  Hey conor can we connect to ollama local cohere commad r model to implement function calling or tool use using dspy

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  @sirnovikovАй бұрын

  Constant zoom-in and zoom-out is quite too much - so distracting. Didn’t finish the video 😘

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  @3MandMattАй бұрын

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

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  @8eckАй бұрын

  Is there video about optimization with gradient descent?