Principle:Cohere ai Cohere python Input Text Preparation
| Metadata | Value |
|---|---|
| Source | Cohere Embed Docs |
| Domains | NLP, Data_Preparation, Embeddings |
| Last Updated | 2026-02-15 14:00 GMT |
| Implemented By | Implementation:Cohere_ai_Cohere_python_Text_Preparation_Pattern |
Overview
A data preparation pattern for formatting and validating text inputs before submitting them to embedding or chat APIs.
Description
Input Text Preparation is the client-side process of cleaning, formatting, and organizing text data before sending it to Cohere APIs. For embedding, texts must be provided as a list of strings. The SDK auto-batches at 96 items, but users should be aware of per-text length limits (model-specific, typically 512 tokens for embed models). For best results: remove excessive whitespace, handle encoding issues, and ensure texts are meaningful (not empty strings). For chat messages, content should be well-structured and within token limits.
Usage
Prepare input texts before any embed() or chat() call. Ensure texts are clean, non-empty strings. For large document collections, the SDK handles batching automatically — just pass the full list. For structured documents, consider chunking strategies to stay within token limits.
Theoretical Basis
Data quality directly impacts embedding quality. The garbage-in-garbage-out principle applies: noisy, poorly formatted text produces lower-quality embeddings. Text chunking strategies (fixed-size, sentence-based, semantic) trade off between context preservation and token limit compliance.
Practical Guide
- Remove HTML tags, excessive whitespace, and special characters
- Handle encoding (ensure UTF-8)
- Chunk long documents to stay within model token limits
- Don't embed empty strings (they produce meaningless vectors)
- Use consistent preprocessing for documents and queries