The vectors help to recover information by meaning. The agents coordinate tools for a goal. Oracle AI Database allows both capabilities to be brought closer to the business data.
Objectives: following the vector workflow, To distinguish exact and approximate searches. select Private Agent Factory or Select AI Agent as applicable. and explain what MCP provides without an automatic permissions.
Key concepts
A embedding It's a numerical representation produced by a model. AI Vector Search allows saving with text and business attributes in a column VECTOR. A consultation can combine similarities and conditions as headquarters, category or availability. It doesn't require you to copies all information to a separate vector engine.
Select AI helps to consult with a natural language. Select AI Agent coordinates agents, tasks and tools. Private Agent Factory offers a visual environment to build agents. A MCP server Expand tools to supported customers. Those capabilities are complementary but they are not interchangeable names.
AI Vector Search Workflow
- Extract text or prepare the modality compatible with your model.
- Divide long documents into consistent fragments preserves an adequate background and context.
- Generates embeddings with a supported model, local with ONNX or external as set up.
- Stores vectors, text and metadata. It evaluates indexes as required by the scale.
- Generates consultation representation, applying filters and ordering by distance.
- If you need a RAG response, pass the relevant fragments to the LLM and check the citations.
VECTOR_EMBEDDING generates representations with an established model; VECTOR_DISTANCE allows to compare vectors. DBMS_VECTOR_CHAIN includes utilities as UTL_TO_TEXT, UTL_TO_CHUNKS y UTL_TO_EMBEDDINGS. The fragment size depends on the model and the task, not a universal limit of 512 tokens.
HNSW means Hierarchical Navigable Small World and uses a graph structure; IVF organize partitions vectors. The rough search interchanges velocity and recovery of neighbours. an exact search serves as a reference. The target accuracy does not promise a fixed number of success points at each result. Use the recommended metric for model and index.
Agent Factory, Select AI Agent and MCP
Knowledge Agent it's focused on questions about documentary content. Data Analysis Agentstructured data and analysis. Private Agent Factory offers a flow builder with models and tools, playground testing and publication of a endpoint. Sources, credentials and permissions continue to require configuration. The set of prepared agents develops with the version.
Select AI Agent related tools, tasks, agents and teams using interfaces as DBMS_CLOUD_AI_AGENT. You can combine recovery, NL2SQL and registered PL/SQL functions. A dedicated task. a tool exposes an operation; A team co-ordinates agents. Being close to data reduces some movements but calling an external LLM or API remains a data flow that you have to review.
MCP's managed Autonomous AI Database server allows access to tools registered from a supported client using HTTPS and Streamable HTTP. Authentication, privileges, line policies, network and audit have limited access. Check what tools are exposed and with what identity. Uncover a tool doesn't allow any use.
Step-by-step example
An employee wants to "Find the manual for this bug and tell me the team's open orders."
- It recovers fragments of the manual by semantic search, restricted to authorized documents.
- Query commands by exact computer identifier. Don't use similarity to decide what equipment they are from.
- Deliver relevant fragments and rows to the agent and preserve provenance.
- The agent explains the recommendation and order status with references.
- If an order is requested to be closed, use a different tool with operation validation and policy confirmation.
A semantic search can get back an wrong manual from a similar model. Check metadata and compatibility of equipment before converting the result into an operational instruction.
Common errors
- Fusing Vector Search with text generation or with a complete agent.
- To change the embedding model of the consultations without updating the documents.
- To assume that an approximate index always guarantees exact neighbours.
- Treat MCP as an administrator's access concession.
- To assume that proximity to the base prevents any data output to outside models.
Architecture exercise
Your organization already has an authorized PL/SQL feature to check inventory and wants to offer it to an MCP application. Do you have to give free access to all your tables?
Solution
No. You can register and display a cut-down tool for that function as per the server's configuration. The client should be authenticated and the execution respect the privileges, range and validation of parameters. The tool contract allows to offer a concrete operation without turning the agent into a base manager.
Sources and review
Query el AI Vector Search workflow, Private Agent Factory, Select AI Agent and MCP architecture by Autonomous. Check compatibility with your deployment before applying the demonstrations.