Take Your AI Applications to the Next Level with RAG using Databricks and Pinecone

Take Your AI Applications to the Next Level with RAG using Databricks and Pinecone

Table of Contents:

• Introduction
• What Are Embeddings?
• Understanding Vector Databases
• Retrieval Augmented Generation (RAG)
• The Role of Databricks in RAG Applications
• Data Preparation for RAG Applications
• Creating a Vector Search Index with Pinecone
• Building a RAG Application with Databricks
• Deploying a RAG Application in Production
• Conclusion

Introduction

Welcome to this session on retrieval augmented generation (RAG). In this session, we will explore the concept of RAG, understand the role of embeddings and vector databases, and learn how to build RAG applications using Databricks and Pinecone. We'll cover topics such as data preparation, creating a vector search index, building a RAG application, and deploying it in production. By the end of this session, You'll have a clear understanding of RAG and be equipped with the knowledge to Create your own RAG applications.

What Are Embeddings?

AI models that mimic the behaviors of the human brain often use neural networks, which consist of interconnected artificial neurons arranged in consecutive layers. These networks learn from large amounts of data and extract high-level features from the input. These features are represented as vectors, also known as embeddings. Embeddings are numerical representations of relationships between discrete objects like words or sentences. They allow us to Apply mathematical utilities to these objects and enable applications like semantic search, image search, and long-term memory for conversational agents.

Understanding Vector Databases

Vector databases are designed to manage vectors or embeddings at Scale. They provide efficient storage and retrieval of vectors, and allow the application of similarity metrics to retrieve semantically Relevant content. Vector databases can also associate metadata with each vector, enriching the Context and making queries more efficient. Pinecone is an example of a high-performance, distributed vector database that is optimized for large-scale applications with low latency.

Retrieval Augmented Generation (RAG)

Retrieval augmented generation is a technique used to mitigate the limitations of Generative AI models. Generative models, like language models, can generate plausible but potentially incorrect responses. RAG combines retrieval-Based methods with generative models to provide more contextually relevant and accurate responses. By leveraging a vector database and embeddings, RAG applications can retrieve semantically relevant content that is then used to augment the generation process. This approach allows for more accurate and reliable responses in conversational AI applications.

The Role of Databricks in RAG Applications

Databricks provides a unified lake house platform for managing data and AI. With Databricks, you can Collect and prepare data, access state-of-the-art models, fine-tune models, and deploy generative AI applications in production. Databricks offers tools like MLflow for model management, AI Gateway for connecting to external SAS providers, and Lakehouse Monitoring for monitoring and evaluating models in production.Databricks also supports Pinecone, a high-performance vector database, for managing vector embeddings at scale.

Data Preparation for RAG Applications

Data preparation is a crucial step in building RAG applications. This involves collecting and cleaning data, chunking large documents into contextually relevant chunks, and creating embeddings for each chunk. Databricks provides tools for data preparation, such as Spark and MLflow, which make it easy to clean, preprocess, and transform data for RAG applications.

Creating a Vector Search Index with Pinecone

Pinecone is a fully managed, distributed vector database that is optimized for storing and querying vector embeddings. To create a vector search index, you can use Pinecone's API to ingest the embeddings along with associated metadata, such as URLs or document IDs. Pinecone allows for efficient storage and retrieval of embeddings, and provides powerful search capabilities using similarity metrics like Cosine similarity or Euclidean distance.

Building a RAG Application with Databricks

With Databricks, you can build RAG applications by integrating state-of-the-art models, vector databases, and data processing tools. Databricks provides MLflow for managing models, AI Gateway for connecting to external SAS providers, and Spark for large-scale data processing. By combining these tools, you can create a pipeline that takes user queries, retrieves relevant embeddings from the vector database, and generates responses using generative models.

Deploying a RAG Application in Production

Once you have built a RAG application, you can deploy it in a production environment. Databricks offers features like model serving, API gateways, and custom endpoints to enable the deployment and scaling of RAG applications. Monitoring and evaluation tools, like Lakehouse Monitoring, help ensure that your RAG application is performing optimally and meeting your requirements.

Conclusion

Retrieval augmented generation is a powerful technique for improving the accuracy and relevance of generative AI applications. By combining retrieval-based methods with generative models and vector databases, RAG applications can provide more contextually relevant responses. Databricks and Pinecone offer tools and services that simplify the development, deployment, and management of RAG applications. With these tools, you can build and deploy RAG applications that deliver accurate and contextually relevant results in a production environment.