Supercharge Your RAG with DeepSeek's Reasoning Model

In this chapter, the video introduces building a simple rack pipeline without using external frameworks like L-chain. It covers the knowledge-based creation step and generation step, detailing the process of handling PDF files and user queries.

This chapter explores the document indexing process, including loading PDF files, metadata inclusion, and chunking strategies. It also mentions the importance of using a reasoning model for advanced treatment of the topic.

The chapter discusses retrieving relevant chunks for a user query, computing embeddings, and using a vector store. It emphasizes the role of a reasoning model in determining chunk relevance and generating responses.

This chapter delves into using a reasoning model for retrieval and ranking based on similarity. It highlights the advanced capabilities of the reasoning model in determining relevant chunks and providing responses.

Details on signing up for the reasoning model, configuring the API key, and running retrieval code are covered in this chapter. It also mentions the involvement of a Python client for communication.

The video presents a demonstration of the reasoning model's thinking process in generating responses based on provided context and documents. It showcases the model's ability to understand complex queries and provide relevant information.