Perform Chunking With Embedding

In these examples, you can see how to split text data (such as text strings and full documents) into meaningful chunks and then represent each chunk as a vector embedding.

Chunking prepares your unstructured data to ensure that it is in a format that can be processed by vector embedding models.

• Convert Text to Chunks With Custom Chunking Specifications
  A chunked output, especially for long and complex documents, sometimes loses contextual meaning or coherence with its parent content. In this example, you can see how to refine your chunks by applying custom chunking specifications.
• Convert File to Text to Chunks to Embeddings Within Oracle Database
  First convert a PDF file to text, split the text into chunks, and then create vector embeddings on each chunk by accessing a vector embedding model stored in the database.
• Convert File to Embeddings Within Oracle Database
  Directly extract vector embeddings from a PDF document, using a single-step statement, by accessing a vector embedding model stored in the database.
• Generate and Use Embeddings for an End-to-End Search
  First generate vector embeddings from textual content by using a vector embedding model stored in the database, and then populate and query a vector index. At query time, you also vectorize the query criteria on the fly.