Build an
AI Chatbot Engine with 23ai and OCI GenAI

Building an AI chatbot engine with Oracle Database 23ai and OCI Generative AI allows you to create a highly intelligent system capable of answering questions from documents, knowledge bases, and structured data. By combining 23ai’s vector search capabilities with OCI GenAI’s large language models, you can implement retrieval-augmented generation (RAG) for accurate and context-aware responses. This architecture leverages embeddings to retrieve relevant document chunks and uses GenAI to generate natural and informative answers in real-time, making it ideal for customer support, internal knowledge bases, and automated assistants.

Understanding 23ai and OCI GenAI

23ai in Oracle Database provides vector storage and search for embeddings, enabling semantic similarity queries that retrieve relevant content quickly and efficiently. OCI Generative AI provides large language models that can generate text, summarize content, and answer questions based on input data. By combining these technologies, you can create a chatbot engine that retrieves contextually relevant information from large datasets and generates human-like responses.

Benefits of Using 23ai and OCI GenAI Together

This combination ensures fast and precise retrieval of information, the ability to handle complex questions, scalability for multiple users, and integration with cloud applications. It allows chatbots to answer questions accurately using the knowledge stored in your documents while maintaining conversational flow and context awareness.

High-Level Architecture

The architecture of a chatbot engine using 23ai and OCI GenAI includes document ingestion, embedding generation, vector storage, query handling, generative answering, API layer, deployment, and monitoring. Documents are ingested and split into chunks, embeddings are generated and stored in 23ai, and user queries are converted to embeddings for similarity search. The top relevant document chunks are then passed to OCI GenAI to generate the final response, which can be delivered via web chat, messaging platforms, or mobile apps.

Document Ingestion

Collect your documents such as manuals, PDFs, Word files, knowledge bases, or FAQs. Preprocess them by cleaning the text, removing irrelevant sections, and converting scanned PDFs to text if necessary. Split large documents into manageable chunks and ensure clear headings and structure to improve AI comprehension.

Generating Embeddings

Use an embedding model to convert each text chunk into vector representations. OCI GenAI provides embedding models, or you can use another embedding service compatible with 23ai. Generate embeddings for each chunk and associate them with the original text for retrieval purposes.

Storing Embeddings in 23ai

Create a table in Oracle Database 23ai to store chunks and their embeddings. Use a vector column type for embeddings and build a vector index to support fast similarity searches. Insert all chunk embeddings and their corresponding text into this table to prepare for query retrieval.

Query Handling with RAG

When a user asks a question, convert the query into an embedding using the same model. Perform a similarity search against the 23ai vector index to retrieve the top relevant document chunks. These chunks provide context for the generative model to answer accurately.

Generative Answering with OCI GenAI

Take the retrieved chunks and build a prompt that includes the user question and the context from the documents. Pass this prompt to the OCI GenAI model and receive a generated response. Configure parameters such as temperature and maximum tokens to control the response style and length.

API and Agent Layer

Wrap the retrieval and generative process in an API that handles user queries. Accept user messages, retrieve relevant context, call the GenAI model, and return the response. Optionally, integrate the API with a chat interface, web widget, or Oracle Digital Assistant for conversational deployment.

Deployment

Deploy the chatbot engine on OCI using Compute instances, serverless functions, or containerized solutions. Configure networking, security, and access permissions to ensure reliable and secure communication between the database, GenAI, and clients.

Monitoring and Optimization

Monitor queries, responses, and system usage to improve performance and control costs. Update document embeddings as the knowledge base changes. Tune retrieval parameters and refine prompt instructions to maximize accuracy and relevance. Collect user feedback to improve conversational quality and expand the chatbot’s capabilities over time.

Advanced Features

Add memory capabilities to maintain conversation context across multiple interactions. Implement tool usage for retrieving additional data from databases or APIs. Enable fallback options or human escalation for unrecognized queries. Integrate multi-language support and analytics to measure engagement and performance.

Summary

Building an AI chatbot engine with 23ai and OCI GenAI provides a powerful solution for creating context-aware, intelligent, and scalable chatbots. By ingesting documents, generating embeddings, storing them in 23ai, retrieving relevant chunks, and using OCI GenAI for response generation, you can develop a chatbot that delivers accurate and human-like answers. With proper setup, deployment, and ongoing optimization, this architecture enables businesses to automate customer support, provide internal knowledge assistance, and enhance user experiences without complex coding.

Author

He is a SaaS-focused writer and the author of Xsone Consultants, sharing insights on digital transformation, cloud solutions, and the evolving SaaS landscape.