Intro to RAG for AI (Retrieval Augmented Generation)

Introduction to Retrieval Augmented Generation (RAG)

Overview of RAG

• The course aims to clarify the often misunderstood concept of Retrieval Augmented Generation (RAG), emphasizing its importance in enhancing large language models.

• Sponsored by Pinecone, which provides a vector database product essential for RAG functionality.

Misconceptions about Fine-Tuning

• Many believe fine-tuning is necessary for adding knowledge to large language models; however, it is primarily used for adjusting model responses rather than knowledge enhancement.

• In most cases, retrieval augmented generation is more effective than fine-tuning when additional information is needed.

Understanding Context Windows

Limitations of Large Language Models

• Large language models lack long-term memory and are "frozen in time" post-training, requiring external methods to update their knowledge.

• A context window defines the limit on the number of tokens that can be processed in a prompt and response. For instance, Llama 3 has an 8,000 token limit while GPT-4 has a 128,000 token limit.

Challenges with Context Windows

• As context windows increase, they become inefficient and costly for continuously updating model knowledge.

Practical Applications of RAG

Example: Customer Service Chatbot

• Without RAG, chatbots would need to include entire conversation histories in prompts after each interaction, quickly exhausting context limits.

Example: Internal Company Documents

• When using internal documents with a model like GPT-4 that lacks prior training on them, including all document content in prompts can lead to rapid depletion of the context window.

How RAG Works

Mechanism of RAG

• RAG involves storing information externally (e.g., documents), allowing large language models to query this data as needed alongside user prompts.

Real-world Application Example

Retrieval-Augmented Generation: Enhancing AI Responses

Understanding Retrieval-Augmented Generation (RAG)

• The process of using relevant information from large documents, like 10K filings, is streamlined by only including necessary data for specific queries rather than the entire document.

• Different companies' financial data (e.g., Tesla vs. Apple) are unrelated; thus, prompts should focus solely on the relevant company’s information to improve accuracy.

Workflow Without RAG

• A typical query example involves asking a generative AI model about turning off automatic reverse braking in a Volvo XC60.

• When the model lacks specific information, it may generate inaccurate responses or "hallucinate" details that aren't correct.

Implementing RAG for Accurate Responses

• To prepare for accurate responses, all relevant user manuals (like Volvo's) are sent to an embedding model to convert text into numerical embeddings.

• Embeddings represent words as numbers and position them in a multi-dimensional space where similar terms cluster together.

Vector Space and Query Processing

• In this vector space, related words and phrases are located near each other, facilitating better understanding of context during queries.

• The user's query is converted into an embedding which is then used to search the vector database for similar relevant data before sending it back to the language model.

Achieving Non-Hallucinated Information

• By combining original queries with additional context from the vector database, accurate answers can be generated without hallucination.

• This method allows models to access external knowledge sources effectively, enhancing their response quality significantly.

Advanced Applications of RAG with Agents

• An example question about devices controlled by an Apple remote illustrates how agents can leverage RAG for deeper insights beyond basic answers.

• Agents iteratively research and incorporate external knowledge sources to provide comprehensive answers through structured thought processes.

The Power of Pinecone in Vector Storage

• Pinecone excels at managing vast amounts of points in high-dimensional spaces efficiently, making it suitable for advanced retrieval tasks.

How Does Search Work in Vector Space?

Understanding Search Mechanisms

• Search is conducted using natural language queries, which are then represented in a vector space to find relevant results based on proximity within that space.

• Retrieval Augmented Generation (RAG) operates similarly, allowing developers to build applications without needing deep technical knowledge about the underlying processes or vector databases.

• The process involves sending data to an embedding model, which converts it into a format suitable for storage and retrieval in Pine Cone's vector database.

• Pine Cone is highlighted for its scalability and speed, making it efficient for handling large datasets and quick searches.