Retrieval_Augmented_QA

A Question-Answering API on a specific topic using RAG.

##1. Usage

There are three services in total, each in separate containers.

• weaviate : Local setup of vector store
• embedding_generator : To add any topic to knowledge base in its chunked + embedded format.
• qa_service : Main end-user facing service that takes user question and answers it based on context retrieved from vector store.

To test, run the following commands from app directory

docker compose up -d 

After four minutes of waiting ( poor , but conscious design choices..still poor overall ), try the following:

• POST request to localhost:8081/generate_and_save_embeddings
  -- Parses StreamingLLM.pdf, chunks it and stores text + embeddings in weaviate
• GET request to localhost:8082/answer
  -- Default question "What is a KV cache" is used

Clean up after testing as follows:

docker compose down

• *Note : Make sure the bloated images are removed after testing.