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Mikiko Bazeley
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Added additional env vars & Airbnb data
Browse files- HW.md +53 -0
- data/fdb60f7d-e616-43dc-86ef-e33d3a9bdd05.pdf +0 -0
HW.md
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# HW 7: Solutions
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## Deliverables
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* Completed Notebook
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* Chainlit Application in a Hugging Face Space Powered by Hugging Face Endpoints
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* Screenshot of endpoint usage
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### Completed notebook
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[Located here](./Completed_BazeleyMikiko_Open_Source_RAG_Leveraging_Hugging_Face_Endpoints_through_LangChain.ipynb)
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[Also on HF space](https://huggingface.co/spaces/mmbazel/AIE3-Demo-Wk4Day1/blob/main/%5BCompleted%5D%20BazeleyMikiko_Open_Source_RAG_Leveraging_Hugging_Face_Endpoints_through_LangChain.ipynb)
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### Chainlit Application
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[Link to Chainlit App in HuggingFace Space](https://huggingface.co/spaces/mmbazel/AIE3-Demo-Wk4Day1)
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### Screenshots
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#### The chat
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#### The trace
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#### The endpoints
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#### The LLM model endpoint
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#### The embeddings model endpoint
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### The Loom video
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https://www.loom.com/share/162d71e4d445442faa40dba76f4cbf13
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### Lessons Learned & Open Questions
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#### Lessons
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1. Learning how to translate notebook code into scripts.
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2. Learning/reminder that HF spaces can be used in dev mode and connected to VSCode.
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3. Learning how to setup LCEL RAG Chain.
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- Understand how to deploy open-source LLMs & embedding models to scalable endpoints for production-grade LLM & RAG applications
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- Build a RAG application with LCEL
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- Build a front-end UI for RAG applications with Chainlit
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#### Questions
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1. What are the challenges using LangChain in production - see lots of folks complaining about it on LinkedIn and Twitter.
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2. What complex RAG looks like.
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3. Have a basic understanding of the metrics used to monitor performance but still a novice with regards to LLM evals.
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data/fdb60f7d-e616-43dc-86ef-e33d3a9bdd05.pdf
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Binary file (596 kB). View file
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