
How to Chunk Documents for Vector Search in Oracle
Turn PDF, Word, and HTML files into searchable chunks in Oracle AI Database 26ai with UTL_TO_TEXT, UTL_TO_CHUNKS, and VECTOR_CHUNKS.
Oracle Database 26ai brings advanced automation, AI-driven optimization, and modern data architecture to the forefront. This category explores its capabilities, new features, performance benefits, and practical guidance to help developers and DBAs adopt the next generation of Oracle innovation.

Turn PDF, Word, and HTML files into searchable chunks in Oracle AI Database 26ai with UTL_TO_TEXT, UTL_TO_CHUNKS, and VECTOR_CHUNKS.

Get the best of semantic and keyword search in Oracle AI Database 26ai with reciprocal rank fusion in SQL or a hybrid vector index.

Find exact codes, versions, and product names that semantic search misses, with an Oracle Text index, CONTAINS, and SCORE in Oracle Database.

Find answers by meaning in Oracle AI Database 26ai, learn how near is near enough, flag unanswerable questions, and keep filters accurate.

Get millisecond vector search with an in-memory HNSW index in Oracle AI Database 26ai, sized, measured, and tuned on 50,000 rows.

Speed up similarity search on large tables with an IVF vector index in Oracle AI Database 26ai, and measure what it gains and gives up.

Test embedding models on your own questions in Oracle AI Database 26ai and learn when a small in-database model is enough and when Gemini pays off.

Get Gemini embeddings from SQL and PL/SQL, embed whole tables in batches, call the REST API for extra options, and survive a busy provider.

AI Applications 26ai Oracle AI Database and APEX The book Contents Examples Look inside FAQ Look insideReal pages from the book Why this book How an entry works Examples and screens Look inside What the book covers AI in the…

Embed text inside Oracle AI Database 26ai with VECTOR_EMBEDDING, keep embeddings current with a trigger, and search tickets by meaning.

Run embeddings inside Oracle AI Database 26ai by loading a prebuilt ONNX model from a file or a BLOB, checking it, and managing it safely.

Compare vectors with VECTOR_DISTANCE in Oracle AI Database 26ai, choose the right metric, and find the nearest rows with ORDER BY and FETCH FIRST.

Store embeddings in Oracle AI Database 26ai with the right VECTOR declaration, format, and storage, and convert and inspect them in SQL and PL/SQL.
The few ideas behind every AI feature, from language models and tokens to embeddings, vectors, RAG, and agents, explained in database terms.

Store a Gemini API key safely as an Oracle credential, open the network to Google, and send your first prompt from SQL in Oracle AI Database 26ai.

Oracle Database 26ai SQL and PL/SQL The book Contents Examples Look inside FAQ Look insideReal pages from the book Why this book How an entry works Examples and output Look inside What the book covers New since 19c About the…

You might have noticed your database queries slowing down over time, even though you regularly delete old records. You run a massive cleanup operation, but your full table scans still take forever to complete. This frustrating scenario happens because of…

If you work with Oracle Cloud Infrastructure and need to move files between your database and object storage, the DBMS_CLOUD package is the tool you will reach for most often. I have used it across several projects, and once you…