Split large text documents into smaller, manageable chunks using different chunking strategies optimized for RAG (Retrieval-Augmented Generation) workflows. This endpoint supports multiple chunking algorithms including token-based, sentence-based, recursive, semantic, and specialized strategies.
from orq_ai_sdk import Orq
import os
with Orq(
api_key=os.getenv("ORQ_API_KEY", ""),
) as orq:
res = orq.chunking.parse(request={
"text": "The quick brown fox jumps over the lazy dog. This is a sample text that will be chunked into smaller pieces. Each chunk will maintain context while respecting the maximum chunk size.",
"metadata": True,
"strategy": "semantic",
"chunk_size": 256,
"threshold": 0.8,
"embedding_model": "openai/text-embedding-3-small",
"dimensions": 512,
"mode": "window",
"similarity_window": 1,
})
# Handle response
print(res)models.ParseResponse
| Error Type |
Status Code |
Content Type |
| models.APIDefaultError |
4XX, 5XX |
*/* |