Workflow:Cohere ai Cohere python Text Embedding
| Knowledge Sources | |
|---|---|
| Domains | Embeddings, Vector_Search, API_Client |
| Last Updated | 2026-02-15 14:00 GMT |
Overview
End-to-end process for generating vector embeddings from text inputs using the Cohere Embed API, with automatic batching for large-scale embedding operations.
Description
This workflow covers generating dense vector embeddings from text using the Cohere Python SDK. The SDK provides automatic batching (splitting inputs into chunks of 96 items), concurrent execution via ThreadPoolExecutor (sync) or asyncio.gather (async), and transparent result merging. The workflow supports multiple embedding types (float, int8, uint8, binary, ubinary) and input types (search_document, search_query, classification, clustering) for different use cases.
Usage
Execute this workflow when you need to convert text into dense vector representations for semantic search, clustering, classification, or retrieval-augmented generation (RAG). The auto-batching feature makes this suitable for both small (single query) and large (thousands of documents) embedding operations without manual batch management.
Execution Steps
Step 1: Initialize the Client
Create a Client or ClientV2 instance with API credentials. The embed method is available on both client types. A ThreadPoolExecutor with 64 threads is created by default for concurrent batch processing.
Key considerations:
- The sync Client uses ThreadPoolExecutor for parallel batch processing
- The async AsyncClient uses asyncio.gather for concurrent batches
- The thread pool size (64) can be customized via the thread_pool_executor parameter
Step 2: Prepare the Input Texts
Assemble the list of text strings to embed. Each text should be concise and representative of the content being indexed or queried.
Key considerations:
- The input_type parameter should match the use case: search_document for indexing, search_query for queries
- Mismatched input types between indexing and query time degrade retrieval quality
- The truncate parameter controls behavior when texts exceed the model's context length
Step 3: Select the Embedding Model and Type
Choose an embedding model (e.g., embed-english-v3.0, embed-multilingual-v3.0) and specify the desired embedding format. The model determines the output dimensionality, and the embedding_types parameter selects the numeric representation.
Key considerations:
- embed-english-v3.0 and embed-multilingual-v3.0 produce 1024-dimensional vectors
- Light variants (embed-english-light-v3.0) produce 384-dimensional vectors for reduced storage
- Embedding types include float (default), int8, uint8, binary, and ubinary for quantized representations
- Multiple embedding types can be requested simultaneously
Step 4: Call the Embed Method
Invoke the embed() method with the texts, model, input_type, and embedding_types. The SDK automatically splits the input into batches of 96 items, sends them concurrently, and merges the results into a single EmbedResponse.
Key considerations:
- The batch size is fixed at 96 (configured in config.py as embed_batch_size)
- Batching is enabled by default; set batching=False to send all texts in a single request
- Image embeddings bypass batching and are sent in a single request
- Each batch is an independent API call, enabling parallel processing
Step 5: Process the Embedding Response
Extract the embeddings from the response. The response type depends on the requested embedding_types: EmbeddingsFloatsEmbedResponse for float-only requests, or EmbeddingsByTypeEmbedResponse when specific types are requested. The response also includes metadata with token usage and billing information.
Key considerations:
- The response_type field discriminates between float and by-type response formats
- For by-type responses, access embeddings via response.embeddings.float_, response.embeddings.int8, etc.
- The merge logic concatenates embeddings from all batches in order, preserving input alignment
- The meta field aggregates billed_units across all batches
Step 6: Batch Embedding Jobs (Optional)
For very large datasets, use the asynchronous embed jobs API (embed_jobs.create) which processes a pre-uploaded Dataset asynchronously. Use client.wait() to poll for job completion.
Key considerations:
- Embed jobs require a pre-uploaded Dataset of type embed-input
- The result is a new Dataset of type embed-output containing text-embedding pairs
- The wait() utility polls the job status with configurable timeout and interval
- Job statuses progress through processing, complete, or failed states