Oracle Cloud Infrastructure Documentation

Select AI with Retrieval Augmented Generation (RAG)

Select AI with RAG augments your natural language prompt by retrieving content from your specified vector store using semantic similarity search. This reduces hallucinations by using your specific and up-to-date content and provides more relevant natural language responses to your prompts.

Select AI offers fully automated implementation of Retrieval Augmented Generation (RAG). This technique retrieves data from enterprise sources using AI vector search and augments user prompts for your specified large language model (LLM). By leveraging information from enterprise data stores, RAG reduces hallucinations and generates grounded responses.

RAG uses AI vector search on a vector index to find semantically similar data for the specified question. Vector store processes vector embeddings, which are mathematical representations of various data points like text, images, and sounds. These embeddings capture the meaning of the data, enabling efficient processing and analysis. For more details on vector embeddings and AI vector search, see Overview of AI Vector Search.

Select AI integrates with AI vector search available in Oracle Autonomous Database 23ai for similarity search using vector embeddings.

Topics

  • Build your Vector Store
    Select AI automates the creation and population of vector store from text files such as, txt and html, on your object store.
  • Create and Manage Vector Indexes
    Use the DBMS_CLOUD_AI package to create and manage vector indexes and configure vector database JSON parameters.
  • Benefits of Select AI RAG
    Simplify querying, enhance response accuracy with current data and gain transparency by reviewing sources used by the LLM.

Parent topic: Use Select AI for Natural Language Interaction with your Database

Build your Vector Store

Select AI automates the creation and population of vector store from text files such as, txt and html, on your object store.

Select AI automatically processes documents to chunks, generates embeddings, stores them in the specified vector store, and updates the vector index as new data arrives.

Here is how the input from Object Storage is used with Select AI RAG:
Description of adb-obj-storage-select-ai-rag.eps follows

  1. Input: Data is initially stored in an Object Storage.
  2. Oracle Autonomous Database retrieves the input data or the document, chunks it and sends the chunks to an embedding model.
  3. The embedding model processes the chunk data and returns vector embeddings.
  4. The vector embeddings are then stored in a vector store for use with RAG. As content is added, the vector index is automatically updated.

RAG retrieves relevant pieces of information from the enterprise database to answer a user's question. This information is provided to the specified large language model along with the user prompt. Select AI uses this additional enterprise information to enhance the prompt, improving the LLM's response. RAG can enhance response quality with update-to-date enterprise information from the vector store.


Description of adb-diagram-third-party-vector-db-select-ai-rag.eps follows

Select AI implements RAG as follows:

  1. Input: User asks a question (specifies a prompt) using Select AI narrate action.

  2. Select AI generates vector embeddings of the prompt using the embedding model specified in the AI profile.

  3. The vector search index uses the vector embedding of the question to find matching content from the customer's enterprise data (searching the vector store) which has been indexed.

  4. The vector search returns top K texts similar to the input to your Autonomous Database instance.
  5. Autonomous Database then sends these top K query results with user question to the LLM.
  6. The LLM returns its response to your Autonomous Database instance.
  7. Autonomous Database Select AI provides the response to the user.

Create and Manage Vector Indexes

Use the DBMS_CLOUD_AI package to create and manage vector indexes and configure vector database JSON parameters.

After you create credentials and provide network access to the vector database and the AI provider, your Autonomous Database instance uses AI profiles to configure access to LLMs. See Example: Set Up and Use Select AI with RAG for a complete example on setting it up and using it in Select AI statements.

You can configure AI profiles for providers listed in Select your AI Provider and LLMs through the DBMS_CLOUD_AI package.

See Also:

Parent topic: Select AI with Retrieval Augmented Generation (RAG)

Benefits of Select AI RAG

Simplify querying, enhance response accuracy with current data and gain transparency by reviewing sources used by the LLM.

Select AI RAG offers the following benefits:

  • Simplify data querying: Enable users to query enterprise data using natural language instead of depending on SQL experts.

  • Increase response accuracy: Provide LLMs with detailed context from enterprise data to generate more accurate and relevant responses, reducing instances of LLM hallucinations.

  • Up-to-date information: Leverage vector stores to keep data current, avoiding issues with outdated training datasets and eliminating the need for expensive or time-consuming model fine-tuning.

  • Understandable contextual results: Access and review the sources used by the LLM from vector stores, ensuring transparency and confidence in results. View and extract data in natural language text or JSON format for easier integration and application development.