At EDB, we believe the AI generation is a world in which Postgres is meant to thrive. That's why we built EDB Postgres AI, the first intelligent data platform for transactional, analytical, and new AI workloads powered by an enhanced Postgres engine. It can be deployed as a cloud-managed service, self-managed software, or as a physical appliance. It delivers built-in observability, AI-driven assistance, migration tooling, and a single pane of glass for managing hybrid data estates.
In our recent video and blog series, EDB Chief Architect for Analytics and AI, Torsten Steinbach, has explored important topics around AI applications for the enterprise of the future. In the last blog article in our Unleashing AI series, we discussed customizing generative AI models to incorporate private or domain-specific data. Retrieval-augmented generation (RAG) is the most popular way to boost the accuracy and reliability of generative AI models.
What is RAG?
RAG is a technique for building your own generative AI applications. Organizations use RAG to retrieve and utilize their private data to augment a generative AI model’s knowledge.
The process begins with collecting private data, which can include unstructured data such as images, videos, documents, PDFs and other binary files. Once collected, this data is prepared to ensure its usability within the generative AI application workflow.
Data preparation may involve filtering out unwanted or low-quality data (e.g., profanity, noise, etc.), cleansing the data to remove irrelevant information, and condensing large volumes of data into more digestible fragments by summarizing it. If the data consists of lengthy documents, it may be chunked into smaller, self-contained nuggets of information to make it more manageable.
After preparing the private data, it’s stored for further processing. Vector embeddings are then computed for these prepared documents. This process converts each data chunk into a numerical vector representation, capturing its semantic meaning as arrays of floating-point numbers.
Once computed, these vector embeddings are stored in a database or data store. Just storing them is not enough, though. To enable rapid access, vector indexing techniques are employed to create an index specifically designed for efficiently searching and retrieving relevant vector embeddings.
EDB Chief Architect for Analytics and AI, Torsten Steinbach explains how RAG works. Watch the video.
Data flow in generative AI applications
Here’s how generative AI applications consume and interact with the data:
1. The application sends a text prompt (query) for data generation.
2. The prompt is encoded into a vector embedding, representing its semantics.
3. This query embedding is used to perform a similarity search in the vector store (database) containing the application’s private data embeddings.
4. The similarity search, accelerated by vector indexes, returns the most relevant data embeddings matching the query.
5. The actual data (documents, images, etc.) corresponding to the retrieved embeddings is fetched from the store.
6. The retrieved data is used to augment the original prompt.
7. The augmented prompt is sent to a large language model (LLM) to generate the final output, which is returned to the application.
This RAG process has led to the development of integrated systems like AI databases and vector databases which store, index and enable similarity searches on vector embeddings.
RAG powers scalable AI applications
AI databases expand on vector databases by storing the actual data (documents, images, etc.), computing embeddings, and automating the entire RAG process for applications. An AI data platform encapsulates all these capabilities into a unified solution, allowing developers to build generative AI applications by treating the entire RAG workflow as a database workload.
This integration of RAG capabilities into database systems highlights the critical role databases play in enabling efficient and scalable generative AI solutions.
EDB Postgres AI: The ultimate AI data platform
As an integrated platform designed for modern analytical and AI workloads, EDB Postgres AI, makes it possible to leverage AI-driven insights and build enterprise-grade AI applications quickly and efficiently. To discuss the ways this platform can drive your specific AI initiatives, just reach out.
Watch the video to learn more about Retrieval Augmented Generation (RAG)
Read the white paper: Intelligent Data: Unleashing AI with PostgreSQL