Unlocking Context from Unstructured
Text with Oracle AI Studio’s Document Tool
Unlocking Context from Unstructured Text with Oracle AI Studio’s Document Tool
Oracle AI Studio’s Document Tool enables AI Agents to retrieve context directly from unstructured documents by performing semantic searches on the contents. Below, we expand on each feature shown in the image, strictly drawing from the on-screen text and UI elements.
Easily Upload a Set of Documents
- What It Means: You can simply upload local files such as PDFs, Word documents, or other text files—to be associated with a specific Document Tool instance.
- On-Screen Instruction: “Upload local documents to be associated with a Document Tool”
- Why It Matters: Once uploaded, these documents become part of the Agent’s searchable knowledge base.
Embedded Vectors Live in the Fusion 23ai Customer Databases
Underlying Process:
After you upload documents, Oracle AI Studio automatically:
- Parses the raw text.
- Chunks the parsed text into smaller, coherent segments. .
- Embeds each chunk as a vector.
On-Screen Detail:
Documents are processed (parsed, chunked, embedded) into the Oracle 23ai vector database within the customer tenant
Customer-Tenant Storage
All embedded vectors are stored in a Fusion 23ai vector database that resides inside the customer’s own Oracle tenant, ensuring that document data and embeddings remain isolated and under the customer’s control.
Semantic Search Grounds the Response to a Specific Chunk of Text
What Happens at Query Time:
- An AI Agent receives a user question or a task.
- The Agent performs a semantic search over the embedded document vectors.
- It identifies the most relevant text chunk that is, a specific paragraph or segment from one of the uploaded documents.
- It then uses that exact chunk of unstructured text to “ground” or form its answer.
On-Screen Explanation:
An Agent performs a semantic search for documents related to its task or goal and uses the unstructured text form the documents to ‘ground’ the answer.
Result:
Because the answer is pulled from a precise chunk of the original document, the response remains rooted in the actual, unaltered text.
