Principle:Cohere ai Cohere python Text Embedding
| Metadata |
|---|
| Cohere Python SDK |
| Cohere Embeddings |
| Efficient Estimation of Word Representations |
| NLP, Embeddings, Vector_Search |
| 2026-02-15 14:00 GMT |
Overview
A technique for converting text into dense vector representations that capture semantic meaning for downstream tasks.
Description
Text Embedding transforms text strings into fixed-dimensional floating-point vectors where semantic similarity is preserved as geometric proximity in vector space. The Cohere embedding API supports multiple input types (search_document for indexing, search_query for queries, classification, clustering) to optimize the embedding space for specific downstream tasks. The SDK adds automatic batching -- splitting large text collections into chunks of 96, processing them concurrently via ThreadPoolExecutor, and merging results transparently. This enables efficient embedding of arbitrarily large document collections without manual batch management.
Usage
Use text embedding when you need vector representations for semantic search, document clustering, classification, or retrieval-augmented generation. The input_type parameter is critical: use search_document when embedding documents for indexing and search_query when embedding user queries, as the model optimizes the embedding space differently for each case.
Theoretical Basis
Text embeddings map discrete text to continuous vector spaces using neural encoder models. The key mathematical property is that semantically similar texts produce vectors with high cosine similarity. The asymmetric encoding (different input_type for queries vs documents) follows the bi-encoder paradigm where query and document encoders can be optimized separately.
Pseudocode
vectors = embed_model.encode(texts, input_type)
similarity = cosine_similarity(query_vector, document_vector)