Advanced RAG - Enhancing Embeddings with Parent Document Retriever

📚 A parent document retriever is useful in RAG and differs from LLMs in terms of embeddings for retrieval.

💡 RAG can extract relevant information from a set of documents by using a quality model and trimming out irrelevant bits.

🔍 In conventional RAG, documents are split into parts and embeddings are used to represent the semantic details of each part.

💰 When comparing different products or earnings, using multiple figures and descriptions leads to more general embeddings.

🔎 Parent document retrievers help create more specific embeddings by splitting the original document into parent and child chunks.

🧠 Using parent document retrievers allows for a comparison between different products and a larger context for analysis.

📚 Parent document retrievers pass back the parent documents instead of just the child documents.

💡 Using a larger context in the parent document allows the language model to take advantage of the extra information.

🔍 The parent document retriever can be used in two ways: returning full documents or returning bigger chunks from smaller chunks.

🔍 The video discusses the use of a parent document retriever in advanced RAG models.

💡 Different types of embeddings, such as BGE and OpenAI embeddings, can be used for retrieval.

📚 The retrieval process involves splitting documents into smaller chunks and storing them in memory.

The Advanced RAG 02 - Parent Document Retriever allows us to retrieve smaller chunks of a document and also their corresponding parent documents.

By using the vector store similarity search feature, we can find matching documents based on a specific query.

There are two methods for retrieving documents: retrieving the entire document in one shot or retrieving larger chunks to avoid processing extremely large documents.

🔑 The parent document retriever is introduced, allowing for multiple layers of documents that are split into big chunks.

🧩 The sub docs are smaller and more specific, providing relevant information when searching.

🔍 The big chunks retriever retrieves relevant documents, which are parts of the blog posts, based on the search query.

🔑 The parent document retriever is a useful tool for obtaining fine-grained information and providing a larger context for language models.

💡 By utilizing the retriever, developers can bridge the gap between prototype and production in their projects.

🎯 The retriever can be applied in various scenarios where specific embeddings and coherent answers are needed.