1. Implement retrieval augmented generation by using Oracle Integration
  2. Implement retrieval augmented generation by using Oracle Integration

Implement retrieval augmented generation by using Oracle Integration

Retrieval-augmented generation (RAG) is a powerful Generative AI implementation pattern that enhances generative models by incorporating corporate information by way of data retrieval mechanisms without additional model training. RAG lets you optimize the output of a large language model (LLM) with targeted information without modifying the underlying model itself; that targeted information can be more up-to-date than the LLM and can be specific to a particular organization and industry. This enables the GenAI system to provide more contextually appropriate answers to prompts and to base those answers on extremely current data.
RAG helps LLMs give better answers because:
  • It is a relatively new artificial intelligence technique that can improve the quality of GenAI by allowing LLMs to tap additional data resources without retraining.
  • RAG models build knowledge repositories based on the organization’s own data, and these repositories can be continually updated to help the GenAI provide timely, contextual answers.
  • Chatbots and other conversational systems that use natural language processing can benefit greatly from RAG and GenAI.
  • Implementing RAG requires technologies such as vector databases, which allow for the rapid coding of new data, and searches against that data to feed into the LLM.

Architecture

This reference architecture shows how you can implement a RAG framework using semantic search technique to answer a user query on a corporate data using low-code or no-code integration platform such as Oracle Integration (OIC) services.

In this architecture, Oracle Cloud Infrastructure Generative AI is used to create embeddings and generates optimized or helpful answers/responses based on the context specific corporate data. Oracle Autonomous Database 23ai is used to store the vector embeddings, create indexes and allows to do a semantic search based on the similarity or distance instead of keyword-based search. OCI Functions is used to perform a chunking of corporate documents or data using the standard LangChain python packages. OIC services handles the entire orchestration and automation process from receiving the corporate data to storing/querying those as vector embeddings and generate the optimized and creative context specific answers for the user queries in real or near-time fashion.

The following diagrams illustrate two processes supported by this reference architecture:
  • Retrieval process:


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    Description of the illustration rag-oic.png

    rag-oic-oracle.zip

    In this process, the following occurs:

    1. Corporate or company data is received to Oracle Integration Retriever service in various formats such as PDF, TXT, CSV, XML, JSON,and so on by way of REST, File or sFTP, or any other protocols.
    2. The Retriever service chunks the documents or data by using OCI Functions.
    3. The Retriever service then gets the vector embeddings for each chunk of data by calling the OCI Generative AI Embedding service by using embedding models such as Cohere or others.
    4. Finally, the Retriever service stores these embeddings in the Oracle Autonomous Database 23ai along with the chunked data.
  • Augmentation and Generation process:


    Description of rag-oic-aug-gen.png follows
    Description of the illustration rag-oic-aug-gen.png

    rag-oic-aug-gen-oracle.zip

    In this process, the following occurs:

    1. Corporate or company users through front end applications ask queries or questions about company data, such as polices, HR, sales, purchase history, financial reports, issues, and so on.
    2. OIC's Generate service receives the query data and invokes its local integration's Augment service to get the context for that query.
    3. OIC's Augment service, once invoked, calls OCI Generative AI's Embedding service to get the vector embeddings of the query data.
    4. OIC's Augment service gets the context stored in the Oracle Autonomous Database 23ai, based on the semantic search of the query data vector embeddings. Retrieved context is sent back as a response to the Generate service.
    5. The Generate service, with the received context and query, invokes the OCI Generative AI Generation service to generate the appropriate response.
    6. Finally, the Generate service replies with the generated response to the user.

OIC helps customers automate the end-to-end RAG process. Customers or companies can benefit from using a low-code, no-code integration platform to implement RAG on their corporate data. Building RAG by using a low-code, no-code platform enables development and go-to-market within hours or days rather than months.

The architecture has the following components:

  • Autonomous Database

    Oracle Autonomous Database is a fully managed, preconfigured database environments that you can use for transaction processing and data warehousing workloads. You do not need to configure or manage any hardware, or install any software. Oracle Cloud Infrastructure handles creating the database, as well as backing up, patching, upgrading, and tuning the database.

  • Autonomous Transaction Processing

    Oracle Autonomous Transaction Processing is a self-driving, self-securing, self-repairing database service that is optimized for transaction processing workloads. You do not need to configure or manage any hardware, or install any software. Oracle Cloud Infrastructure handles creating the database, as well as backing up, patching, upgrading, and tuning the database.

  • Functions

    Oracle Cloud Infrastructure Functions is a fully managed, multitenant, highly scalable, on-demand, Functions-as-a-Service (FaaS) platform. It is powered by the Fn Project open source engine. Functions enable you to deploy your code, and either call it directly or trigger it in response to events. Oracle Functions uses Docker containers hosted in Oracle Cloud Infrastructure Registry.

  • Integration

    Oracle Integration is a fully managed service that allows you to integrate your applications, automate processes, gain insight into your business processes, and create visual applications.

  • Generative AI

    Oracle Cloud Infrastructure Generative AI is a fully managed OCI service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases for text generation, summarization, semantic search, and more. Use the playground to try out the ready-to-use pretrained models, or create and host your own fine-tuned custom models based on your own data on dedicated AI clusters.

  • Oracle Database 23ai

    Oracle Database 23ai is the next long-term support release of Oracle Database. It includes over 300 new features with a focus on artificial intelligence (AI) and developer productivity. Features such as AI Vector Search enable you to leverage a new generation of AI models to generate and store vectors of documents, images, sound, and so on; index them and quickly look for similarity while leveraging the existing analytical capabilities of Oracle Database. This combined with the already extensive set of Machine Learning algorithms enables you to quickly create sophisticated AI-enabled applications. Oracle Database 23ai also uses AI to optimize many of the key database functions to make more accurate estimates on timings and resource costings.

Explore More

Learn more about implementing RAG by using Oracle Integration.

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Acknowledgments

Author: Pavan Rajalbandi