Custom LLM using huggingface(transformers). To build it locally, follow the given steps.

• docker build -t rag-chat-test .
• docker run rag-chat-test

Custom wrapper to extract important features from PDFs.

• extract data into text files
• extract based on topics in index
• extract based on page numbers
• create sep folder for each book
• create seperate text files for each topic

Embeds the extarcted text into numerical vectors for model to understand. Uses langchain to create the custom embedder, with an In-Memory Vectore Store.

Rag chat app deployment to AWS,

aws ecr get-login-password --region {region} | docker login --username AWS --password-stdin {username}.dkr.ecr.{region}.amazonaws.com

aws ecr create-repository --repository-name rag-chat-cohere --region {region}

docker tag rag-chat-test:latest {username}.dkr.ecr.{region}.amazonaws.com/rag-chat-cohere

docker push {username}.dkr.ecr.{region}.amazonaws.com/rag-chat-cohere