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Citiview Document Chunker with Embeddings
x402
Citiview Document Chunker with Embeddings is a paid API for AI agents from agents.citiviewgroup.com, paid per call via x402, $0.015/call, status unknown (last checked 2026-10-14).
Converts a URL (web page, PDF, Office file) or raw text into clean overlapping chunks with character offsets and bge-m3 embeddings (1024 dims), ready for vector store ingestion.
Price / call$0.015
Success—
StatusUnknown
How this service works
Ready-to-search chunks: turns a URL (page, PDF, Office file) or text into clean overlapping chunks with character offsets and bge-m3 embeddings (1024 dims) in one call, ready for any vector store. Up to 300 chunks. Also callable as GET with the same fields as query parameters.
Output
Returns up to 300 text chunks, each with its text content, start and end character offsets within the original document, and a 1024-dimensional bge-m3 embedding vector. The embeddings are immediately usable with any vector store (Pinecone, Weaviate, Qdrant, etc.) for semantic similarity search.
Example use cases
RAG pipeline document ingestion
“Take this research paper at https://arxiv.org/pdf/2310.01513.pdf, split it into 800-character chunks with 200-character overlap, and give me the bge-m3 embeddings so I can push them into my Pinecone index.”
Semantic search over company policy
“Chunk our employee handbook at https://acme.com/handbook.pdf into 1000-character pieces with 150-character overlap and embed them with bge-m3 — I want to build a search tool over it.”
Ad-hoc text embedding for a knowledge base
“I have a long article I'll paste as text — split it into overlapping 500-character chunks with 100-character overlap and return the embeddings so I can add it to our knowledge base.”
Technical details
Endpoint
GEThttps://agents.citiviewgroup.com/v1/chunk?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both chunking and embedding in a single call, specifically with bge-m3 (1024-dim) embeddings suitable for multilingual semantic search. It is ideal for RAG pipeline ingestion where you want overlapping chunks with character offsets from a URL or raw text, without managing a separate embedding model. Prefer alternatives if you need a different embedding model, more than 300 chunks, or only chunking without embeddings.
Side effects
- URL is unreachable or returns a non-200 response — chunking fails with an error
- Unsupported file format at the provided URL — only web pages, PDFs, and Office files are supported
- chunk_size out of range (must be 200–4000) — request rejected with validation error
- Document exceeds the 300-chunk limit — output is capped at 300 chunks
- embed=false requested — embeddings are omitted from the response
- Oversized document may time out or be truncated before 300 chunks are produced
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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