> ## Documentation Index > Fetch the complete documentation index at: https://docs.orq.ai/llms.txt > Use this file to discover all available pages before exploring further. # Parse text > 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. ## OpenAPI ````yaml post /v2/chunking openapi: 3.1.0 info: title: orq.ai API version: '2.0' description: orq.ai API documentation servers: - url: https://api.orq.ai security: - ApiKey: [] tags: - name: Guardrail Rules - name: Policies - name: Routing Rules - name: Files description: File upload and retrieval operations. - name: FilesService - name: Projects description: Projects organize resources within a workspace - name: ProjectsService - name: Skills description: >- Skills are modular instructions you can use to codify processes and conventions - name: SkillsService - name: Responses - description: >- Run agents on a cadence — cron, interval, or one-off. Minimum firing interval is 1 hour. name: Agent Schedules - name: Reporting description: >- GenAI reporting API over canonical analytics rollups. Accepts a metric name, time range, grain, group-by, and filters; returns a typed time series and optional totals. - name: ReportingService description: |- ReportingService exposes a single QueryReport RPC that maps allowlisted analytics payloads onto safe rollup queries. Callers never send SQL; the backend picks the rollup family and grain from the metric catalogue, the requested range, and the requested grouping. externalDocs: url: https://docs.orq.ai description: orq.ai Documentation paths: /v2/chunking: post: tags: - Chunking summary: Parse text description: >- 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. operationId: parse requestBody: required: true content: application/json: schema: oneOf: - type: object properties: text: type: string description: The text content to be chunked metadata: type: boolean default: true description: Whether to include metadata for each chunk return_type: type: string enum: - chunks - texts default: chunks description: >- Return format: chunks (with metadata) or texts (plain strings) strategy: type: string enum: - token title: Token Chunker chunk_size: type: integer exclusiveMinimum: 0 default: 512 description: Maximum tokens per chunk chunk_overlap: type: integer minimum: 0 default: 0 description: Number of tokens to overlap between chunks required: - text - strategy title: Token Chunker Strategy description: >- Splits text based on token count. Best for ensuring chunks fit within LLM context windows and maintaining consistent chunk sizes for embedding models. - type: object properties: text: type: string description: The text content to be chunked metadata: type: boolean default: true description: Whether to include metadata for each chunk return_type: type: string enum: - chunks - texts default: chunks description: >- Return format: chunks (with metadata) or texts (plain strings) strategy: type: string enum: - sentence title: Sentence Chunker chunk_size: type: integer exclusiveMinimum: 0 default: 512 description: Maximum tokens per chunk chunk_overlap: type: integer minimum: 0 default: 0 description: Number of overlapping tokens between chunks min_sentences_per_chunk: type: integer exclusiveMinimum: 0 default: 1 description: Minimum number of sentences per chunk required: - text - strategy title: Sentence Chunker Strategy description: >- Splits text at sentence boundaries while respecting token limits. Ideal for maintaining semantic coherence and readability. - type: object properties: text: type: string description: The text content to be chunked metadata: type: boolean default: true description: Whether to include metadata for each chunk return_type: type: string enum: - chunks - texts default: chunks description: >- Return format: chunks (with metadata) or texts (plain strings) strategy: type: string enum: - recursive title: Recursive Chunker chunk_size: type: integer exclusiveMinimum: 0 default: 512 description: Maximum tokens per chunk separators: type: array items: type: string default: - |+ - |+ - ' ' - '' description: Hierarchy of separators to use for splitting min_characters_per_chunk: type: integer exclusiveMinimum: 0 default: 24 description: Minimum characters allowed per chunk required: - text - strategy title: Recursive Chunker Strategy description: >- Recursively splits text using a hierarchy of separators (paragraphs, sentences, words). Versatile general-purpose chunker that preserves document structure. - type: object properties: text: type: string description: The text content to be chunked metadata: type: boolean default: true description: Whether to include metadata for each chunk return_type: type: string enum: - chunks - texts default: chunks description: >- Return format: chunks (with metadata) or texts (plain strings) strategy: type: string enum: - semantic title: Semantic Chunker chunk_size: type: integer exclusiveMinimum: 0 default: 512 description: Maximum tokens per chunk threshold: anyOf: - type: number minimum: 0 maximum: 1 - type: string enum: - auto default: auto description: >- Similarity threshold for grouping (0-1) or "auto" for automatic detection embedding_model: type: string description: >- Embedding model to use for semantic similarity. (Available embedding models)[https://docs.orq.ai/docs/proxy/supported-models#embedding-models] dimensions: type: integer exclusiveMinimum: 0 description: >- Number of dimensions for the embedding output. Required for text-embedding-3 models. Supported range: 256-3072 for text-embedding-3-large, 256-1536 for text-embedding-3-small. max_tokens: type: integer exclusiveMinimum: 0 description: >- Maximum number of tokens per embedding request. Default is 8191 for text-embedding-3 models. mode: type: string enum: - window - sentence default: window description: 'Chunking mode: window-based or sentence-based similarity' similarity_window: type: integer exclusiveMinimum: 0 default: 1 description: Window size for similarity comparison required: - text - strategy - embedding_model title: Semantic Chunker Strategy description: >- Groups semantically similar sentences using embeddings. Excellent for maintaining topic coherence and context within chunks. - type: object properties: text: type: string description: The text content to be chunked metadata: type: boolean default: true description: Whether to include metadata for each chunk return_type: type: string enum: - chunks - texts default: chunks description: >- Return format: chunks (with metadata) or texts (plain strings) strategy: type: string enum: - agentic title: Agentic Chunker model: type: string description: >- Model to use for chunking. (Available models)[https://docs.orq.ai/docs/proxy/supported-models#chat-models] example: openai/gpt-4.1 chunk_size: type: integer exclusiveMinimum: 0 default: 1024 description: Maximum tokens per chunk candidate_size: type: integer exclusiveMinimum: 0 default: 128 description: Size of candidate splits for LLM evaluation min_characters_per_chunk: type: integer exclusiveMinimum: 0 default: 24 description: Minimum characters allowed per chunk system_prompt: type: string description: >- Custom system prompt for the agentic chunker LLM. Overrides the default prompt that instructs the model how to identify chunk boundaries. Maximum 20,000 tokens. required: - text - strategy - model title: Agentic Chunker Strategy description: >- Agentic LLM-powered chunker that uses AI to determine optimal split points. Best for complex documents requiring intelligent segmentation. - type: object properties: text: type: string description: The text content to be chunked metadata: type: boolean default: true description: Whether to include metadata for each chunk return_type: type: string enum: - chunks - texts default: chunks description: >- Return format: chunks (with metadata) or texts (plain strings) strategy: type: string enum: - fast title: Fast Chunker target_size: type: integer exclusiveMinimum: 0 default: 4096 description: Target chunk size in bytes delimiters: type: string default: |- .? description: >- Single-byte delimiter characters. Each character is treated as a separate delimiter (e.g., ".?!" splits on period, question mark, or exclamation). Use escaped sequences for special chars. pattern: type: string description: >- Multi-byte pattern for splitting (e.g., "▁" for SentencePiece tokenizers). Takes precedence over delimiters if set. prefix: type: boolean default: false description: >- Attach delimiter to start of next chunk instead of end of current chunk consecutive: type: boolean default: false description: >- When true, splits at the START of consecutive delimiter runs, keeping the run with the following chunk (e.g., splits before "\n\n\n" not in the middle) forward_fallback: type: boolean default: false description: >- Search forward if no delimiter found in backward search window required: - text - strategy title: Fast Chunker Strategy description: >- High-performance SIMD-optimized byte-level chunking. Best for large files (>1MB) where speed and memory efficiency are critical. 2x faster and 3x less memory than token-based chunking. title: Chunking Request description: >- Request payload for text chunking with strategy-specific configuration example: 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. strategy: semantic chunk_size: 256 threshold: 0.8 embedding_model: openai/text-embedding-3-small dimensions: 512 mode: window similarity_window: 1 metadata: true responses: '200': description: Text successfully chunked content: application/json: schema: type: object properties: chunks: type: array items: type: object properties: text: type: string description: The text content of the chunk index: type: number description: The position index of this chunk in the sequence metadata: type: object properties: start_index: type: - number - 'null' end_index: type: - number - 'null' token_count: type: - number - 'null' required: - start_index - end_index - token_count required: - text - index required: - chunks example: chunks: - id: 01HQ3K4M5N6P7Q8R9SATBVCWDX text: The quick brown fox jumps over the lazy dog. index: 0 metadata: start_index: 0 end_index: 44 token_count: 10 - id: 01HQ3K4M5N6P7Q8R9SATBVCWDY text: >- This is a sample text that will be chunked into smaller pieces. index: 1 metadata: start_index: 45 end_index: 108 token_count: 12 x-code-samples: - lang: typescript label: Node.js source: |- import { Orq } from "@orq-ai/node"; const orq = new Orq({ apiKey: process.env["ORQ_API_KEY"] }); const result = await orq.chunking.parse({ text: "Your long text content here...", strategy: "semantic", chunk_size: 256, threshold: 0.8, embedding_model: "openai/text-embedding-3-small", dimensions: 512 }); console.log(result.chunks); - lang: python label: Python source: |- from orq_ai_sdk import Orq orq = Orq(api_key=os.getenv("ORQ_API_KEY")) result = orq.chunking.parse( text="Your long text content here...", strategy="semantic", chunk_size=256, threshold=0.8, embedding_model="openai/text-embedding-3-small", dimensions=512 ) for chunk in result.chunks: print(f"Chunk {chunk.index}: {chunk.text[:50]}...") components: securitySchemes: ApiKey: type: http scheme: bearer bearerFormat: JWT ````