Implementation:Groq Groq python Embedding Input Pattern

Knowledge Sources	Groq API Docs
Domains	NLP, Embeddings
Last Updated	2026-02-15 16:00 GMT

Overview

User-defined pattern for preparing text inputs for the Groq embedding API.

Description

This is a Pattern Doc — there is no library API for this step. Users prepare input text as a str or List[str] using standard Python. The input is passed directly to client.embeddings.create(input=...).

Constraints:

• Input cannot be an empty string
• Arrays must be 2048 dimensions or less
• Total tokens per input limited by model context window

Usage

Construct the input value before calling embeddings.create(). Use a single string for one embedding or a list for batch.

Interface Specification

# Type: Union[str, List[str]]

# Single input
input = "Text to embed"

# Batch input
input = ["First text", "Second text", "Third text"]

I/O Contract

Inputs

Name	Type	Required	Description
text	str or List[str]	Yes	Text to embed; single string or array of strings

Outputs

Name	Type	Description
(value)	Union[str, List[str]]	Value ready for embeddings.create(input=...)

Usage Examples

Single Text

# Embed a single text
input_text = "What is machine learning?"
response = client.embeddings.create(
    input=input_text,
    model="nomic-embed-text-v1_5",
)

Batch Texts

# Embed multiple texts at once
documents = [
    "Machine learning is a subset of AI.",
    "Deep learning uses neural networks.",
    "Natural language processing handles text.",
]
response = client.embeddings.create(
    input=documents,
    model="nomic-embed-text-v1_5",
)
# response.data[0].embedding -> first document vector
# response.data[1].embedding -> second document vector

Related Pages

Implements Principle

• Principle:Groq_Groq_python_Embedding_Input_Preparation