Unlocking the Power of Retrieval Augmented Generation: A Comprehensive Guide
Retrieval Augmented Generation (RAG) is a technology that has garnered significant attention recently, yet remains misunderstood by many. Thanks to Pinecone for sponsoring and partnering on this insightful journey into RAG. Pinecone’s advanced vector database product plays a crucial role in how RAG functions, and this article aims to demystify the concept.
One of the most common misconceptions about RAG and large language models is the method used to imbue these models with additional knowledge. Many people assume that fine-tuning is the way to go. Fine-tuning involves feeding additional information into a model and expecting it to retain this knowledge. However, fine-tuning is more about instructing the model on how to respond with the right tone. In reality, when you think you need fine-tuning, you often need RAG instead. RAG simplifies the process of providing additional knowledge to large language models.
What is Retrieval Augmented Generation?
RAG stands for Retrieval Augmented Generation. It involves enhancing large language models by providing them with an external source of information. Think of RAG as a method that offers two significant benefits: it swiftly and efficiently equips large language models with additional knowledge, and it provides them with long-term memory, which they inherently lack. Once large language models finish their training, they become static and do not acquire new information unless it is provided externally.
There are several ways to provide additional knowledge to large language models. You can include it directly in the prompt, but this approach quickly becomes impractical at scale due to the limited context window. The context window refers to the number of words or tokens you can include in your prompt combined with the model’s response. For instance, Llama 3 has an 8,000 token context window, while advanced models like GPT-4 have a 128,000 token context window. Although these numbers may seem substantial, they get used up quickly, especially when providing extensive additional knowledge or memory storage.
Practical Examples of RAG
Consider a customer service chatbot that needs to store conversations with customers. Without RAG, the entire conversation history would need to be fed back into the prompt each time the customer interacts with the bot. This would rapidly exhaust the context window, and most of that history might be irrelevant to the current query.
Another example is handling internal company documents. A large language model like GPT-4 is unaware of specific internal documents unless they were part of its training data, which they ideally shouldn’t be. Including all these documents in the prompt every time would quickly deplete the prompt window. RAG addresses this by enabling the model to query or ask questions of the documents stored externally, retrieving only the relevant information.
So, what exactly is RAG? It involves taking information, such as a document, and storing it externally. The large language model can then query this external storage and combine the retrieved information with the prompt. For example, if Tesla releases a new earnings report and you want the model to have that knowledge, you can store the report in a RAG database and retrieve relevant information when needed.
The Role of Pinecone in RAG
Pinecone simplifies this process by offering a fast and scalable vector storage solution. Embeddings are used to convert text into a format that can be stored in a vector database. Each word or phrase is represented as a series of numbers plotted on a multi-dimensional graph. Similar words and phrases cluster together in this vector space. When a query is made, it is also converted to embeddings and matched against the stored data to find relevant information.
For instance, if you ask a large language model, “How do I turn off the automatic reverse braking on the Volvo XC60?” without RAG, the model might hallucinate and provide incorrect instructions. Using RAG, the Volvo user manual can be converted to embeddings, stored in a vector database, and relevant information can be retrieved to provide an accurate response.
Why RAG Matters
RAG is a powerful tool for supplying large language models with additional knowledge and long-term memory. It is efficient and scalable, making it ideal for applications like customer service chatbots and querying internal documents. Pinecone’s robust vector storage solution makes it easy to implement RAG in various projects.
If you’re interested in a full tutorial on setting up RAG with Pinecone, let us know. It’s simpler than you might think. Thank you to Pinecone for sponsoring and partnering on this channel. Links to Pinecone and more information about RAG are included below.
By understanding and leveraging RAG, users can vastly improve the capabilities of large language models, making them more responsive, accurate, and versatile in a wide range of applications.