docker / genai-stack

Loader Application for https://github.com/docker/genai-stack/

What is Loader Application?

The Loader Application is a component of the GenAI Stack (https://github.com/docker/genai-stack/) project. It is responsible for importing and processing data from various sources, such as Stack Overflow, and embedding questions and answers into a vector index for further analysis.

Why is Loader Application important?

The Loader Application plays a crucial role in the GenAI Stack by providing the foundation for data processing and indexing. It enables the system to access and understand large datasets, which is essential for generating accurate and relevant responses to user queries.

Features of Loader Application

Importing Stack Overflow Data

The Loader Application can import data from Stack Overflow using its API (Application Programming Interface). This data includes questions, answers, and metadata such as tags and timestamps.

# Importing Stack Overflow data using the loader
          from loader import Loader
          
          # Initialize the loader with the Stack Overflow API key
          loader = Loader(api_key="your_api_key")
          
          # Import questions and answers from a specific tag
          questions, answers = loader.import_data(tag="python")

Embedding Questions and Answers

The Loader Application uses a technique called “embedding” to represent questions and answers as numerical vectors. These vectors can be compared and analyzed using vector similarity measures, which is essential for generating relevant responses.

# Embedding questions and answers using the loader
          from loader import Loader, SentenceTransformer
          from transformers import AutoTokenizer
          
          # Initialize the loader and the sentence transformer model
          loader = Loader()
          model = SentenceTransformer("all-MiniLM-L6-v2")
          tokenizer = AutoTokenizer.from_pretrained("all-MiniLM-L6-v2")
          
          # Embed a question and an answer
          question_embedding = loader.embed_text(question="What is Python?", model=model, tokenizer=tokenizer)
          answer_embedding = loader.embed_text(answer="Python is a high-level, interpreted programming language.", model=model, tokenizer=tokenizer)

Storing Data in a Vector Index

The Loader Application stores the embedded questions and answers in a vector index, which can be queried using vector similarity measures. This allows the system to find the most relevant questions and answers to a user query.

# Storing questions and answers in a vector index using the loader
          from loader import Loader, AnnoyIndex
          
          # Initialize the loader and the Annoy index
          loader = Loader()
          index = AnnoyIndex(real_dim=768)
          
          # Store the embedded questions and answers in the index
          for question, answer in zip(questions, answers):
              index.add_item(id=len(questions), vector=question_embedding, metadata=answer)
          
          # Save the index to a file
          index.save("index.ann")

For more information, please refer to the following resources: