Implementation:Cohere ai Cohere python Text Preparation Pattern

Metadata	Value
Source	Cohere Embed Docs
Domains	NLP, Data_Preparation, Embeddings
Last Updated	2026-02-15 14:00 GMT
Implements	Principle:Cohere_ai_Cohere_python_Input_Text_Preparation

Overview

Interface specification for user-defined text preparation before embedding or chat API calls.

Description

This is a Pattern Doc — it documents the interface/pattern users implement themselves, not a library API. The Cohere SDK expects texts as List[str] for embedding and structured message objects for chat. Users are responsible for cleaning, chunking, and formatting their text data.

Usage

Implement text preparation logic before calling client.embed() or client.chat(). This pattern is user-defined and varies by use case.

Code Reference

Source Location

N/A (user-defined pattern)

Interface Specification

# Pattern: Text preparation for embedding
def prepare_texts_for_embedding(
    raw_texts: List[str],
    max_length: int = 512,  # Model-specific token limit
) -> List[str]:
    """
    Clean and chunk texts for embedding.
    Returns a list of clean text strings ready for client.embed().
    """
    prepared = []
    for text in raw_texts:
        # Remove excessive whitespace
        text = " ".join(text.split())
        # Skip empty texts
        if not text.strip():
            continue
        # Chunk if needed (simple character-based)
        if len(text) > max_length * 4:  # Rough char-to-token ratio
            chunks = [text[i:i + max_length * 4] for i in range(0, len(text), max_length * 4)]
            prepared.extend(chunks)
        else:
            prepared.append(text)
    return prepared

Import

N/A (user-defined)

I/O Contract

Direction	Description
Inputs	Raw text data (strings, documents, web pages, etc.)
Outputs	List[str] clean texts ready for client.embed() or structured messages for client.chat()

Usage Examples

from cohere import Client

client = Client()

# Prepare texts
raw_documents = ["  Document with   extra   spaces  ", "", "Normal document text"]
clean_texts = [" ".join(doc.split()) for doc in raw_documents if doc.strip()]

# Now embed
response = client.embed(
    texts=clean_texts,
    model="embed-english-v3.0",
    input_type="search_document",
)

Related Pages

• Principle:Cohere_ai_Cohere_python_Input_Text_Preparation