Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. Aggregator transformation
  7. B2B transformation
  8. Chunking transformation
  9. Cleanse transformation
  10. Data Masking transformation
  11. Data Services transformation
  12. Deduplicate transformation
  13. Expression transformation
  14. Filter transformation
  15. Hierarchy Builder transformation
  16. Hierarchy Parser transformation
  17. Hierarchy Processor transformation
  18. Input transformation
  19. Java transformation
  20. Java transformation API reference
  21. Joiner transformation
  22. Labeler transformation
  23. Lookup transformation
  24. Machine Learning transformation
  25. Mapplet transformation
  26. Normalizer transformation
  27. Output transformation
  28. Parse transformation
  29. Python transformation
  30. Rank transformation
  31. Router transformation
  32. Rule Specification transformation
  33. Sequence transformation
  34. Sorter transformation
  35. SQL transformation
  36. Structure Parser transformation
  37. Transaction Control transformation
  38. Union transformation
  39. Vector Embedding transformation
  40. Velocity transformation
  41. Verifier transformation
  42. Web Services transformation

Transformations

Transformations

Chunking transformation

Chunking transformation

In advanced mode, the Chunking transformation splits large pieces of text into smaller segments, or chunks. This helps to increase the content's relevance before the Target transformation writes the embeddings and metedata to a vector database.
Pass output from a Chunking transformation to a Vector Embedding transformation to create vector embeddings for the text. For more information, see Vector Embedding transformation.
When you configure a Chunking transformation, choose a chunking method based on the text you want to split.To select the method, consider the following factors:
  • Whether the text comes from a long document or short messages
  • The length and complexity of user queries to the large language model (LLM)
  • The use case of the application that uses the LLM, such as semantic search, question answering, or summarization
The Chunking transformation can't run in a serverless runtime environment or on GPUs. If the transformation runs on a GPU-enabled cluster, GPUs are disabled and the transformation consumes CPUs.

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