The Secret Sauce of RAG: Vector Search and Embeddings

Have you ever felt a large language model's (LLM) response was lacking in specific details or missed the mark entirely? Retrieval-Augmented Generation (RAG) is an architecture designed to address these shortcomings. But how exactly does it work? Check out my previous blog on how RAG works.

Let's break down the core components of a basic RAG architecture:

1. The Powerhouse: Pre-Trained LLM

Imagine a super-powered language whiz. That's essentially the LLM. It's a large language model, pre-trained on massive datasets, allowing it to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. However, LLMs rely solely on the knowledge they were trained on, which can sometimes be limited.

2. Digging Up the Gems: Vector Search and Embeddings

This is where things get interesting. Here's where RAG shines. It incorporates a retrieval system – a super-powered librarian that finds relevant information from a vast knowledge base stored outside the LLM. This knowledge base could be anything from a company's product manuals to scientific research papers.

But how does the retrieval system find the right information? Enter vector search and embeddings. Imagine each piece of information in the knowledge base as a unique fingerprint. Vector embeddings are mathematical representations that capture the essence of this information, like a digital fingerprint. The retrieval system uses vector search to find information in the knowledge base with fingerprints most similar to the user's query.

For example, if a user asks an LLM "What are the symptoms of the common cold?", the LLM might respond with a general answer based on its training data. But with RAG, the retrieval system can use vector search to find documents in a medical knowledge base that discuss the common cold. These documents would have vector embeddings most similar to the user's query.

3. Putting it All Together: Orchestration

The final piece is the fusion mechanism, also called orchestration. Think of it as a maestro, combining the outputs from the LLM and the retrieval system. The LLM generates its initial response based on its knowledge, and the retrieval system finds relevant information from the knowledge base. The orchestration layer then blends these two elements to create the final response.

So, instead of just a generic answer, the LLM, guided by the retrieved information, might provide a more comprehensive response like "Symptoms of the common cold include a runny or stuffy nose, sore throat, cough, and mild fatigue. If your symptoms worsen, consult a doctor."

By working together, these components allow RAG to leverage the strengths of LLMs while incorporating specific knowledge from external sources. This leads to more informative, accurate, and trustworthy outputs for tasks like question answering, chatbot interactions, and more.

RAG: Vector Search and Embeddings explained.

Deep Dive into Vector Search and Embeddings

Retrieval-Augmented Generation (RAG) leverages the strengths of Large Language Models (LLMs) and external knowledge bases to deliver more informative and accurate outputs. Here's a breakdown of the key components focusing on data chunking, embeddings, vector databases, and their interaction:

1. Data Chunking:

• Large datasets can be overwhelming for both LLMs and retrieval systems. RAG employs data chunking, a process of dividing the knowledge base into smaller, manageable segments. This improves processing efficiency and allows for more targeted searches.

• Chunking strategies can vary depending on the data type. Text data might be chunked by paragraphs or documents, while code could be chunked by functions or modules.

2. Embeddings and Vectorization:

• Each chunk of data is then converted into a vector representation using embedding techniques like word2vec or sentence transformers. These techniques capture the semantic meaning of the data in a high-dimensional space. Similar chunks will have vector representations closer to each other in this space.

• This vectorization process allows the system to compare the meaning of the query with the meaning of the chunked data, rather than relying on exact keyword matches.

3. Vector Database Storage:

• The generated vector representations, also known as embeddings, are stored in a specialized vector database. These databases are optimized for efficient storage and retrieval of high-dimensional vectors. Popular options include Pinecone, Milvus, and Faiss.

• Storing embeddings in a dedicated database allows for faster retrieval compared to traditional relational databases, which are not optimized for vector data.

4. Query Processing with RAG:

• When a user submits a query, the LLM first generates an initial response based on its internal knowledge.

• Simultaneously, the query is also transformed into a vector representation using the same embedding technique used for the chunked data.

• The vector representing the query is then compared with the embeddings stored in the vector database using vector search algorithms like Approximate Nearest Neighbors (ANN).

• These algorithms efficiently retrieve the chunks from the database that have vector representations most similar to the query vector.

5. Orchestration and Response Generation:

• The retrieved chunks are considered the most relevant information from the knowledge base based on their semantic similarity to the query.

• A fusion mechanism, also called orchestration, then combines the initial LLM response with the retrieved information from the knowledge base. This might involve selecting specific parts of the retrieved chunks or summarizing them.

• Finally, the LLM leverages this combined information to generate the final response, ensuring it's not only comprehensive based on the LLM's knowledge but also accurate and enriched by the retrieved information from the knowledge base.