Track and Monitor RAG Pipelines using Weights & Biases (wandb)

In this video, I demonstrate how to effectively track and monitor RAG (Retrieval Augmented Generation) pipelines using the powerful tool, Weights & Biases (wandb). By integrating LangChain and LlamaIndex, we can seamlessly record trace information and visualize the processing flow, ensuring optimal model performance.
🎥 What You'll Learn:
Tracing: Automatically log experiment parameters, metrics, and output for easy comparison.
Visualization: Real-time visualization of the learning process for intuitive understanding of model performance.
Hyperparameter Optimization: Experiment with different hyperparameter sets to find the best combination.
Team Collaboration: Share experiment data and collaborate efficiently with team members.
Integration: Seamless integration with major ML frameworks, including LangChain and LlamaIndex.
👩‍💻 Follow Along:
Set up wandb for RAG pipelines.
Integrate LangChain for automatic trace recording.
Visualize and analyse the processing flow in real time.
👍 Like this video if you found it helpful, and comment below with any questions or topics you'd like me to cover in future tutorials.
GitHub Code: github.com/AIA...
W&B here: wandb.ai/site
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#openai #langchain #rag

[@giridharreddy7011]

Sir we need a video on advance RAG techniques like the Hierarchial search, Hybrid search, Reranking using open source etc. Basically show us how to make a production level grade RAG

[@andydataguy]

For sure!

[@andydataguy]

Awesome video! 🙌🏾

[@sneharoy3566]

Thanks for the video 👏

[@sibaprasadnaikbehera3442]

in 13:23 index & retriver i.e decoding RAG video watch it out .Thank sir for creating this kind of video

[@CreativityCourse]

Thank you so much.. amazing

[@AIAnytime]

You're welcome 😊

[@PaulSchwarzer-ou9sw]

Thanks!

[@nirmesh44]

can you please provide that insurance pdf file as well. i couldn't find in repo