Sarvam Vision extracts text from documents with exceptional accuracy, preserving the original structure and reading order across 23 languages (22 Indian + English).
**Features:** * High-accuracy text extraction from PDFs and scanned documents * Preserves document layout and reading order * Native support for all Indian scripts * Outputs clean HTML or Markdown #### TablesFinancial reports and scientific papers are notorious for complex tables—merged cells, multi-level headers, and invisible borders. Where traditional tools scramble this data into a jumbled mess, Sarvam Vision understands the spatial relationships.
**Features:** * Preserves row/column structure perfectly * Handles merged cells and multi-level headers * Supports invisible borders and complex layouts * Outputs clean HTML or Markdown tables #### MultilingualUnlike other models that force translation to English, Sarvam Vision supports both input and output in all 23 languages (22 Indian + English).
**Examples:** * Marathi financial report → Structured Marathi content * Tamil official document → Tamil structured output * Bengali textbook → Full Bengali structured output --- ## Quick Start Get started with Document AI for high-fidelity text extraction across all supported languages. Create the job in one call, poll until it reaches a terminal state, then fetch the output. #### Python ```python import os, time from sarvamai import SarvamAI client = SarvamAI(api_subscription_key=os.environ["SARVAM_API_KEY"]) # Create and submit a digitise job in one call with open("document.pdf", "rb") as f: job = client.doc_ai.digitise( file=[("document.pdf", f, "application/pdf")], language="hi-IN", output_format="md", # "html" (default) or "md" ) print("job created:", job.job_id, "| status:", job.status) # Poll until the job reaches a terminal state TERMINAL = {"completed", "partially_completed", "failed", "rejected"} while True: st = client.doc_ai.get_status(job_id=job.job_id) print(f"status={st.status} pages={st.usage.pages_processed}/{st.usage.pages_total}") if st.status.lower() in TERMINAL: break time.sleep(5) # Fetch the output (a ZIP holding the .md plus page-level JSON) if st.status.lower() in ("completed", "partially_completed"): dl = client.doc_ai.get_download_url(job_id=job.job_id) print("download:", dl.method, dl.url) ``` #### JavaScript ```javascript import fs from "fs"; import { SarvamAIClient } from "sarvamai"; const client = new SarvamAIClient({ apiSubscriptionKey: process.env.SARVAM_API_KEY }); async function main() { // Create and submit a digitise job in one call const job = await client.docAi.digitise({ file: [fs.createReadStream("document.pdf")], language: "hi-IN", output_format: "md", // "html" (default) or "md" }); console.log("job created:", job.job_id, "| status:", job.status); // Poll until the job reaches a terminal state const TERMINAL = new Set(["completed", "partially_completed", "failed", "rejected"]); let st; while (true) { st = await client.docAi.getStatus(job.job_id); console.log(`status=${st.status} pages=${st.usage.pages_processed}/${st.usage.pages_total}`); if (TERMINAL.has(st.status.toLowerCase())) break; await new Promise((resolve) => setTimeout(resolve, 5000)); } // Fetch the output (a ZIP holding the .md plus page-level JSON) if (["completed", "partially_completed"].includes(st.status.toLowerCase())) { const dl = await client.docAi.getDownloadUrl(job.job_id); console.log("download:", dl.method, dl.url); } } main(); ``` See the [Document AI overview](/api/api-guides-tutorials/document-intelligence/overview) for schema-based **Extract**, output formats, and the full job lifecycle. #### Legacy: the document\_intelligence job API Superseded by `doc_ai` above. The older Document Digitization API used a multi-step job flow (create → upload → start → poll → download) and is still supported for existing integrations. New integrations should use `doc_ai`. #### Python ```python from sarvamai import SarvamAI client = SarvamAI( api_subscription_key="YOUR_SARVAM_API_KEY" ) # Create a document intelligence job job = client.document_intelligence.create_job( language="hi-IN", output_format="md" ) print(f"Job created: {job.job_id}") # Upload document job.upload_file("document.pdf") print("File uploaded") # Start processing job.start() print("Job started") # Wait for completion status = job.wait_until_complete() print(f"Job completed with state: {status.job_state}") # Get processing metrics metrics = job.get_page_metrics() print(f"Page metrics: {metrics}") # Download output (ZIP file containing the processed document) job.download_output("./output.zip") print("Output saved to ./output.zip") ``` #### JavaScript ```javascript import { SarvamAIClient } from "sarvamai"; const client = new SarvamAIClient({ apiSubscriptionKey: "YOUR_SARVAM_API_KEY" }); async function main() { // Create a document intelligence job const job = await client.documentIntelligence.createJob({ language: "hi-IN", outputFormat: "md" }); console.log(`Job created: ${job.jobId}`); // Upload document await job.uploadFile("document.pdf"); console.log("File uploaded"); // Start processing await job.start(); console.log("Job started"); // Wait for completion const status = await job.waitUntilComplete(); console.log(`Job completed with state: ${status.job_state}`); // Get processing metrics const metrics = job.getPageMetrics(); console.log("Page metrics:", metrics); // Download output (ZIP file containing the processed document) await job.downloadOutput("./output.zip"); console.log("Output saved to ./output.zip"); } main(); ``` --- ## Model Specifications #### Technical Specifications