Venice AI · Document text extraction
Parse documents to extract text content with accurate token counting for LLM context window management.
What it does
Parse documents to extract text content with accurate token counting for LLM context window management.
- Extract text from documents for LLM ingestion
- Calculate token counts for context window planning
- Prepare document content for RAG pipeline ingestion
Ideal buyer
LLM applications and RAG systems needing private document parsing with precise token accounting.
Run this through your governed agent wallet.
- 01Bootstrap AXON once with
npx @axon402/init. - 02Use the AXON runtime MCP tools to
search_x402_servicesorinspect_x402_offerfor this service. - 03Quote, test-buy, then run the governed paid fetch through AXON.
Send this
Prompt for your agent
A natural-language instruction for your LLM agent — with this endpoint exposed as a tool — to call this resource. Not sent to the endpoint; the endpoint consumes the JSON body below.
Pasting this prompt into a raw ChatGPT or unconfigured agent will notexecute the paid endpoint flow. Run it through an agent with the AXON runtime / MCP tools exposed (see “Use with AXON” above) so the 402 challenge, quote, and governed fetch are handled for you.
“Extract all text from this PDF and tell me the exact token count so I know if it fits in my context window.”
Endpoint request body
The JSON payload your agent sends to the endpoint.
{
"format": "pdf",
"document": "[base64 encoded document]"
}Advanced HTTP details
For integrators who need the raw protocol surface. Most agents should use AXON above instead of calling these directly.
Endpoint URL
curl fallback
curl https://api.venice.ai/api/v1/augment/text-parser \ -H "Content-Type: application/json" \ -H "X-PAYMENT: [signed_payment_envelope]" \ -d '{"format":"pdf","document":"[base64 encoded document]"}'
Payment & settlement details
Raw on-chain settlement parameters. AXON above handles these automatically through quote / test-buy / governed fetch.
Price & network
Trust & risk
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