Workflow:Groq Groq python Text Embedding
| Knowledge Sources | |
|---|---|
| Domains | Embeddings, NLP, Inference |
| Last Updated | 2026-02-15 16:00 GMT |
Overview
End-to-end process for generating vector embeddings from text using Groq-hosted embedding models.
Description
This workflow covers the procedure for converting text strings into dense vector representations (embeddings) using Groq's embeddings API. Embeddings are numerical representations that capture semantic meaning, enabling downstream tasks like similarity search, clustering, classification, and retrieval-augmented generation (RAG). The API accepts single strings or arrays of strings and returns embedding vectors in the requested encoding format.
Usage
Execute this workflow when you need to convert text into vector embeddings for semantic search, document similarity, clustering, classification, or as input to a RAG pipeline. This is appropriate for building search indices, computing text similarity scores, or preparing embeddings for downstream machine learning tasks.
Execution Steps
Step 1: Client Initialization
Instantiate the Groq client with authentication credentials. The embeddings API shares the same client as other Groq endpoints.
Key considerations:
- Same Groq() or AsyncGroq() client used for all API endpoints
- Embedding requests are typically fast; default timeout is usually sufficient
Step 2: Input Preparation
Prepare the text input for embedding. Input can be a single string or an array of strings for batch embedding. Each string is independently embedded into a vector. Consider preprocessing steps like trimming whitespace or splitting long documents into chunks that fit within the model's context window.
Key considerations:
- Input accepts a single string or list of strings
- Each input string produces one embedding vector
- Long texts should be chunked to fit model context limits
- Input quality directly affects embedding quality
Step 3: Embedding Request
Call the embeddings create endpoint with the input text, model identifier, and optional parameters. The API processes each input string and returns a CreateEmbeddingResponse containing the embedding vectors. Optional parameters include encoding_format and a user identifier for tracking.
Key considerations:
- Model must be a valid embedding model (e.g., nomic-embed-text-v1.5)
- encoding_format controls the vector representation (float is standard)
- User parameter is optional and used for abuse monitoring
Step 4: Vector Extraction
Parse the CreateEmbeddingResponse to extract the embedding vectors. The response contains a data array where each element is an Embedding object with an embedding field (the vector as a list of floats) and an index indicating which input it corresponds to. Usage statistics report the total tokens processed.
Key considerations:
- Each embedding in response.data corresponds to one input string by index
- The embedding field is a list of floats representing the dense vector
- Usage.total_tokens reports the token count for billing
- Store or index the vectors for downstream similarity/search operations