Sample code repo that forms the base of the following tutorial:
- Unlock the power of Unstructured Data: From Embeddings to In Context Learning – Build a Full stack Q&A Chatbot with Langchain, and LLM Models on Sagemaker
- Detailed setup and steps are provided in the tutorial.
- Explanation of the folders that you see in this repo
- create-embeddings-save-in-vectordb folder
- This folder contains the code for the ingestion and processing pipeline
- data folder
- Contains the python notebook, the raw csv movie files , kmeans output from previous iterations to save time and the localui folder has the User Interface for our fancy MyFlix UI
- sagemaker-migration-toolkit
- Utility code to make deployment of custom scaling model on sagemaker easier
- apis_for_sagemaker_models
- There are 2 folders containing the snippets of code for creating our REST API using the Chalice framework
- create-embeddings-save-in-vectordb folder
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.