> ## Documentation Index > Fetch the complete documentation index at: https://docs.unsiloed.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Claude Integration > Drop-in Anthropic tool-use schemas to give Claude structured access to Unsiloed's document processing API ## Overview This page provides ready-to-use [Anthropic tool-use](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/overview) JSON schemas that let Claude interact with Unsiloed's document processing API. Copy the tool definitions into your `messages.create()` call and Claude can parse, extract, classify, and split documents on your behalf. All Unsiloed API operations are **asynchronous**. Each tool submits a job and returns a `job_id`. Use the `unsiloed_get_job_result` tool to poll for results. Since Claude cannot upload binary files, all tools accept a **publicly accessible URL** to the document. ## Prerequisites Before using these tool schemas, you'll need: * An **Unsiloed API key** — [sign up at unsiloed.ai](https://www.unsiloed.ai) to get one * An **Anthropic API key** — [get one from the Anthropic console](https://console.anthropic.com) * **Python 3.8+** or **Node.js 18+** ```bash Python theme={null} pip install anthropic requests ``` ```bash TypeScript theme={null} npm install @anthropic-ai/sdk ``` ## Key Features Copy-paste JSON tool definitions directly into your Anthropic API calls Built-in polling tool to retrieve results from asynchronous operations Parse documents, extract structured data, classify, and split PDFs Full working example of an autonomous document processing agent ## Tool Definitions Each tool follows [Anthropic's tool format](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/overview) with `name`, `description`, and `input_schema`. Expand each tool below to see its full schema. Parse documents (PDF, images, Office files) into structured chunks with element detection, OCR, and reading order analysis. ```json theme={null} { "name": "unsiloed_parse_document", "description": "Parse a document into structured chunks with element detection, text extraction, and reading order analysis. Use this tool when the user wants to break a document into its structural components (text blocks, tables, images, headers, footers) or convert a document to markdown. You must provide a publicly accessible URL to the document. Supported formats include PDF, PNG, JPEG, TIFF, BMP, DOCX, XLSX, and PPTX. The operation is asynchronous — it returns a job_id that you must poll using unsiloed_get_job_result to retrieve the parsed content.", "input_schema": { "type": "object", "properties": { "url": { "type": "string", "description": "Publicly accessible URL to the document to parse." }, "use_high_resolution": { "type": "boolean", "description": "Use high-resolution image processing for better OCR accuracy. Defaults to false." }, "segmentation_method": { "type": "string", "enum": ["smart_layout_detection", "page_by_page"], "description": "Document segmentation strategy. 'smart_layout_detection' (default) groups related elements into semantic chunks. 'page_by_page' creates one chunk per page." }, "ocr_mode": { "type": "string", "enum": ["auto_ocr", "full_ocr"], "description": "OCR strategy. 'auto_ocr' (default) only runs OCR when needed. 'full_ocr' forces OCR on all pages." }, "merge_tables": { "type": "boolean", "description": "Merge adjacent table segments into a single table. Defaults to false." }, "segment_filter": { "type": "string", "description": "Filter output to include only specific segment types. Accepts comma-separated values (e.g., 'table', 'picture', 'table,picture') or 'all' for everything. Defaults to 'all'." }, "xml_citation": { "type": "boolean", "description": "Enable citation extraction from PDF documents. Extracts structured bibliography and in-text citations in markdown output. Defaults to false." }, "output_fields": { "type": "string", "description": "JSON object controlling which fields are included in the response. Set fields to false to exclude them and reduce response size. Available fields: html, markdown, ocr, image, content, bbox, confidence, embed. All default to true. Example: {\"html\":true,\"markdown\":true,\"ocr\":false,\"image\":false}" }, "segment_analysis": { "type": "string", "description": "JSON object controlling HTML/Markdown generation strategy and AI model per segment type. Example: {\"Table\":{\"html\":\"VLM\",\"markdown\":\"VLM\"},\"Picture\":{\"html\":\"VLM\",\"markdown\":\"VLM\"}}" }, "page_range": { "type": "string", "description": "Specify which pages to process. Formats: '1-5', '2,4,6', or '[1,3,5]'. Defaults to all pages." }, "segment_type_naming": { "type": "string", "enum": ["Unsiloed", "Other"], "description": "Segment type naming convention. 'Unsiloed' (default) uses names like PageHeader, ListItem, Picture. 'Other' uses alternative names like Header, List Item, Figure." }, "extract_colors": { "type": "boolean", "description": "Transfer text color from the PDF text layer to OCR results. Defaults to false." }, "extract_links": { "type": "boolean", "description": "Attach hyperlink URLs from PDF annotations to OCR results. Defaults to false." }, "export_format": { "type": "string", "description": "JSON array of export format(s) to generate after parsing. The exported files are available as presigned URLs in the exports field of the response. Currently supported: [\"docx\"]. Example: [\"docx\"]" }, "error_handling": { "type": "string", "enum": ["Continue", "Fail"], "description": "How to handle per-page errors. 'Continue' (default) skips failed pages and continues. 'Fail' aborts the entire job on the first error." }, "expires_in": { "type": "integer", "description": "Seconds until the task and its output are automatically deleted." }, "chunk_processing": { "type": "string", "description": "JSON object for chunk processing configuration." }, "llm_processing": { "type": "string", "description": "JSON object for LLM processing configuration." } }, "required": ["url"] } } ``` Extract structured data from PDF documents using a custom JSON schema you define. ```json theme={null} { "name": "unsiloed_extract_data", "description": "Extract structured data from a PDF document using a custom JSON schema. Use this tool when the user wants to pull specific fields (such as invoice numbers, names, dates, or line items) out of a PDF. You must provide a publicly accessible URL to the PDF and a JSON schema defining the fields to extract. The operation is asynchronous — it returns a job_id that you must poll using unsiloed_get_job_result to retrieve the extracted data. Do not use this tool for document classification or splitting.", "input_schema": { "type": "object", "properties": { "file_url": { "type": "string", "description": "Publicly accessible URL to the PDF file to process." }, "schema_data": { "type": "string", "description": "A JSON-stringified schema defining the fields to extract. Example: {\"type\":\"object\",\"properties\":{\"invoice_number\":{\"type\":\"string\",\"description\":\"The invoice number\"},\"total\":{\"type\":\"number\",\"description\":\"Total amount\"}},\"required\":[\"invoice_number\"],\"additionalProperties\":false}" }, "model": { "type": "string", "enum": ["alpha", "beta", "gamma", "delta"], "description": "Model tier for extraction. Default is 'gamma', recommended for most use cases." }, "enable_citations": { "type": "boolean", "description": "When true, returns bounding box coordinates for each extracted value, enabling you to trace data back to its location in the PDF. Defaults to false." } }, "required": ["file_url", "schema_data"] } } ``` Classify a PDF document into one of several predefined categories with confidence scoring. ```json theme={null} { "name": "unsiloed_classify_document", "description": "Classify a PDF document into one of several predefined categories. Use this tool when the user wants to determine what type of document a PDF is (for example, invoice, receipt, contract, or medical record). You must provide a publicly accessible URL to the PDF and a list of candidate categories. The operation is asynchronous — it returns a job_id that you must poll using unsiloed_get_job_result to retrieve the classification result with confidence scores. Do not use this for data extraction or document splitting.", "input_schema": { "type": "object", "properties": { "file_url": { "type": "string", "description": "Publicly accessible URL to the PDF file to classify." }, "categories": { "type": "string", "description": "A JSON-stringified array of category objects. Each object must have a 'name' field and may have an optional 'description' field for better accuracy. Example: [{\"name\":\"Invoice\",\"description\":\"Financial invoices with itemized charges\"},{\"name\":\"Receipt\"},{\"name\":\"Contract\",\"description\":\"Legal agreements\"}]" } }, "required": ["file_url", "categories"] } } ``` Split a multi-document PDF into separate files by classifying each page into categories. ```json theme={null} { "name": "unsiloed_split_document", "description": "Split a multi-document PDF into separate files by classifying each page into predefined categories. Use this tool when the user has a single PDF containing multiple document types (for example, a scanned batch of invoices, receipts, and contracts) and wants them separated into individual files. You must provide a publicly accessible URL to the PDF and a list of candidate categories. The operation is asynchronous — it returns a job_id that you must poll using unsiloed_get_job_result to retrieve download links for the split files. Do not use this for single-document classification or data extraction.", "input_schema": { "type": "object", "properties": { "file_url": { "type": "string", "description": "Publicly accessible URL to the PDF file to split." }, "categories": { "type": "string", "description": "A JSON-stringified array of category objects. Each object must have a 'name' field and may have an optional 'description' field for better accuracy. Example: [{\"name\":\"Invoice\",\"description\":\"Financial invoices\"},{\"name\":\"Contract\"},{\"name\":\"Receipt\"}]" } }, "required": ["file_url", "categories"] } } ``` Poll for the result of any asynchronous Unsiloed job. ```json theme={null} { "name": "unsiloed_get_job_result", "description": "Poll for the result of an asynchronous Unsiloed job. Use this tool after calling unsiloed_parse_document, unsiloed_extract_data, unsiloed_classify_document, or unsiloed_split_document to check whether the job has completed and retrieve its results. If the status indicates the job is still processing, wait a few seconds and call this tool again. Once the job is complete, the response contains the output data. If the job failed, the response includes an error message explaining what went wrong.", "input_schema": { "type": "object", "properties": { "job_id": { "type": "string", "description": "The job_id returned by a previous Unsiloed tool call." }, "job_type": { "type": "string", "enum": ["parse", "extract", "classify", "splitter"], "description": "The type of job to check. Use 'parse' for parsing jobs, 'extract' for extraction jobs, 'classify' for classification jobs, and 'splitter' for splitting jobs." } }, "required": ["job_id", "job_type"] } } ``` ## All Tools (Copy & Paste) Copy this complete `tools` array and pass it directly to your Anthropic API call. The SDK examples below refer back to it as the `tools` array. ```json theme={null} [ { "name": "unsiloed_parse_document", "description": "Parse a document into structured chunks with element detection, text extraction, and reading order analysis. Use this tool when the user wants to break a document into its structural components (text blocks, tables, images, headers, footers) or convert a document to markdown. You must provide a publicly accessible URL to the document. Supported formats include PDF, PNG, JPEG, TIFF, BMP, DOCX, XLSX, and PPTX. The operation is asynchronous — it returns a job_id that you must poll using unsiloed_get_job_result to retrieve the parsed content.", "input_schema": { "type": "object", "properties": { "url": { "type": "string", "description": "Publicly accessible URL to the document to parse." }, "use_high_resolution": { "type": "boolean", "description": "Use high-resolution image processing for better OCR accuracy. Defaults to false." }, "segmentation_method": { "type": "string", "enum": ["smart_layout_detection", "page_by_page"], "description": "Document segmentation strategy. 'smart_layout_detection' (default) groups related elements into semantic chunks. 'page_by_page' creates one chunk per page." }, "ocr_mode": { "type": "string", "enum": ["auto_ocr", "full_ocr"], "description": "OCR strategy. 'auto_ocr' (default) only runs OCR when needed. 'full_ocr' forces OCR on all pages." }, "merge_tables": { "type": "boolean", "description": "Merge adjacent table segments into a single table. Defaults to false." }, "segment_filter": { "type": "string", "description": "Filter output to include only specific segment types. Accepts comma-separated values (e.g., 'table', 'picture', 'table,picture') or 'all' for everything. Defaults to 'all'." }, "xml_citation": { "type": "boolean", "description": "Enable citation extraction from PDF documents. Extracts structured bibliography and in-text citations in markdown output. Defaults to false." }, "output_fields": { "type": "string", "description": "JSON object controlling which fields are included in the response. Set fields to false to exclude them and reduce response size. Available fields: html, markdown, ocr, image, content, bbox, confidence, embed. All default to true. Example: {\"html\":true,\"markdown\":true,\"ocr\":false,\"image\":false}" }, "segment_analysis": { "type": "string", "description": "JSON object controlling HTML/Markdown generation strategy and AI model per segment type. Example: {\"Table\":{\"html\":\"VLM\",\"markdown\":\"VLM\"},\"Picture\":{\"html\":\"VLM\",\"markdown\":\"VLM\"}}" }, "page_range": { "type": "string", "description": "Specify which pages to process. Formats: '1-5', '2,4,6', or '[1,3,5]'. Defaults to all pages." }, "segment_type_naming": { "type": "string", "enum": ["Unsiloed", "Other"], "description": "Segment type naming convention. 'Unsiloed' (default) uses names like PageHeader, ListItem, Picture. 'Other' uses alternative names like Header, List Item, Figure." }, "extract_colors": { "type": "boolean", "description": "Transfer text color from the PDF text layer to OCR results. Defaults to false." }, "extract_links": { "type": "boolean", "description": "Attach hyperlink URLs from PDF annotations to OCR results. Defaults to false." }, "export_format": { "type": "string", "description": "JSON array of export format(s) to generate after parsing. The exported files are available as presigned URLs in the exports field of the response. Currently supported: [\"docx\"]. Example: [\"docx\"]" }, "error_handling": { "type": "string", "enum": ["Continue", "Fail"], "description": "How to handle per-page errors. 'Continue' (default) skips failed pages and continues. 'Fail' aborts the entire job on the first error." }, "expires_in": { "type": "integer", "description": "Seconds until the task and its output are automatically deleted." }, "chunk_processing": { "type": "string", "description": "JSON object for chunk processing configuration." }, "llm_processing": { "type": "string", "description": "JSON object for LLM processing configuration." } }, "required": ["url"] } }, { "name": "unsiloed_extract_data", "description": "Extract structured data from a PDF document using a custom JSON schema. Use this tool when the user wants to pull specific fields (such as invoice numbers, names, dates, or line items) out of a PDF. You must provide a publicly accessible URL to the PDF and a JSON schema defining the fields to extract. The operation is asynchronous — it returns a job_id that you must poll using unsiloed_get_job_result to retrieve the extracted data. Do not use this tool for document classification or splitting.", "input_schema": { "type": "object", "properties": { "file_url": { "type": "string", "description": "Publicly accessible URL to the PDF file to process." }, "schema_data": { "type": "string", "description": "A JSON-stringified schema defining the fields to extract. Example: {\"type\":\"object\",\"properties\":{\"invoice_number\":{\"type\":\"string\",\"description\":\"The invoice number\"},\"total\":{\"type\":\"number\",\"description\":\"Total amount\"}},\"required\":[\"invoice_number\"],\"additionalProperties\":false}" }, "model": { "type": "string", "enum": ["alpha", "beta", "gamma", "delta"], "description": "Model tier for extraction. Default is 'gamma', recommended for most use cases." }, "enable_citations": { "type": "boolean", "description": "When true, returns bounding box coordinates for each extracted value, enabling you to trace data back to its location in the PDF. Defaults to false." } }, "required": ["file_url", "schema_data"] } }, { "name": "unsiloed_classify_document", "description": "Classify a PDF document into one of several predefined categories. Use this tool when the user wants to determine what type of document a PDF is (for example, invoice, receipt, contract, or medical record). You must provide a publicly accessible URL to the PDF and a list of candidate categories. The operation is asynchronous — it returns a job_id that you must poll using unsiloed_get_job_result to retrieve the classification result with confidence scores. Do not use this for data extraction or document splitting.", "input_schema": { "type": "object", "properties": { "file_url": { "type": "string", "description": "Publicly accessible URL to the PDF file to classify." }, "categories": { "type": "string", "description": "A JSON-stringified array of category objects. Each object must have a 'name' field and may have an optional 'description' field for better accuracy. Example: [{\"name\":\"Invoice\",\"description\":\"Financial invoices with itemized charges\"},{\"name\":\"Receipt\"},{\"name\":\"Contract\",\"description\":\"Legal agreements\"}]" } }, "required": ["file_url", "categories"] } }, { "name": "unsiloed_split_document", "description": "Split a multi-document PDF into separate files by classifying each page into predefined categories. Use this tool when the user has a single PDF containing multiple document types (for example, a scanned batch of invoices, receipts, and contracts) and wants them separated into individual files. You must provide a publicly accessible URL to the PDF and a list of candidate categories. The operation is asynchronous — it returns a job_id that you must poll using unsiloed_get_job_result to retrieve download links for the split files. Do not use this for single-document classification or data extraction.", "input_schema": { "type": "object", "properties": { "file_url": { "type": "string", "description": "Publicly accessible URL to the PDF file to split." }, "categories": { "type": "string", "description": "A JSON-stringified array of category objects. Each object must have a 'name' field and may have an optional 'description' field for better accuracy. Example: [{\"name\":\"Invoice\",\"description\":\"Financial invoices\"},{\"name\":\"Contract\"},{\"name\":\"Receipt\"}]" } }, "required": ["file_url", "categories"] } }, { "name": "unsiloed_get_job_result", "description": "Poll for the result of an asynchronous Unsiloed job. Use this tool after calling unsiloed_parse_document, unsiloed_extract_data, unsiloed_classify_document, or unsiloed_split_document to check whether the job has completed and retrieve its results. If the status indicates the job is still processing, wait a few seconds and call this tool again. Once the job is complete, the response contains the output data. If the job failed, the response includes an error message explaining what went wrong.", "input_schema": { "type": "object", "properties": { "job_id": { "type": "string", "description": "The job_id returned by a previous Unsiloed tool call." }, "job_type": { "type": "string", "enum": ["parse", "extract", "classify", "splitter"], "description": "The type of job to check. Use 'parse' for parsing jobs, 'extract' for extraction jobs, 'classify' for classification jobs, and 'splitter' for splitting jobs." } }, "required": ["job_id", "job_type"] } } ] ``` ## Usage with the Anthropic SDK Here's how to register these tools and handle Claude's tool calls: ```python Python theme={null} import anthropic import requests import json import time UNSILOED_API_KEY = "your-unsiloed-api-key" UNSILOED_BASE_URL = "https://prod.visionapi.unsiloed.ai" UNSILOED_HEADERS = {"api-key": UNSILOED_API_KEY} # Paste the tools array from the "All Tools" section above tools = [...] client = anthropic.Anthropic(api_key="your-anthropic-api-key") def process_tool_call(tool_name: str, tool_input: dict) -> str: """Execute an Unsiloed API tool call and return the result as a string.""" if tool_name == "unsiloed_parse_document": data = {"url": tool_input["url"]} if "use_high_resolution" in tool_input: data["use_high_resolution"] = str(tool_input["use_high_resolution"]).lower() if "segmentation_method" in tool_input: data["segmentation_method"] = tool_input["segmentation_method"] if "ocr_mode" in tool_input: data["ocr_mode"] = tool_input["ocr_mode"] if "merge_tables" in tool_input: data["merge_tables"] = str(tool_input["merge_tables"]).lower() if "segment_filter" in tool_input: data["segment_filter"] = tool_input["segment_filter"] if "xml_citation" in tool_input: data["xml_citation"] = str(tool_input["xml_citation"]).lower() resp = requests.post(f"{UNSILOED_BASE_URL}/parse", headers=UNSILOED_HEADERS, data=data) elif tool_name == "unsiloed_extract_data": data = { "file_url": tool_input["file_url"], "schema_data": tool_input["schema_data"], } if "model" in tool_input: data["model"] = tool_input["model"] if "enable_citations" in tool_input: data["enable_citations"] = str(tool_input["enable_citations"]).lower() resp = requests.post(f"{UNSILOED_BASE_URL}/v2/extract", headers=UNSILOED_HEADERS, data=data) elif tool_name == "unsiloed_classify_document": data = { "file_url": tool_input["file_url"], "categories": tool_input["categories"], } resp = requests.post(f"{UNSILOED_BASE_URL}/classify", headers=UNSILOED_HEADERS, data=data) elif tool_name == "unsiloed_split_document": data = { "file_url": tool_input["file_url"], "categories": tool_input["categories"], } resp = requests.post(f"{UNSILOED_BASE_URL}/splitter", headers=UNSILOED_HEADERS, data=data) elif tool_name == "unsiloed_get_job_result": job_type = tool_input["job_type"] job_id = tool_input["job_id"] time.sleep(5) # Brief pause before polling resp = requests.get(f"{UNSILOED_BASE_URL}/{job_type}/{job_id}", headers=UNSILOED_HEADERS) else: return json.dumps({"error": f"Unknown tool: {tool_name}"}) return json.dumps(resp.json()) ``` ```typescript TypeScript theme={null} import Anthropic from "@anthropic-ai/sdk"; const UNSILOED_API_KEY = "your-unsiloed-api-key"; const UNSILOED_BASE_URL = "https://prod.visionapi.unsiloed.ai"; // Paste the tools array from the "All Tools" section above const tools: Anthropic.Tool[] = []; const client = new Anthropic({ apiKey: "your-anthropic-api-key" }); async function processToolCall( toolName: string, toolInput: Record ): Promise { const headers: Record = { "api-key": UNSILOED_API_KEY }; let resp: Response; if (toolName === "unsiloed_parse_document") { const formData = new FormData(); formData.append("url", toolInput.url as string); if (toolInput.use_high_resolution !== undefined) formData.append("use_high_resolution", String(toolInput.use_high_resolution)); if (toolInput.segmentation_method) formData.append("segmentation_method", toolInput.segmentation_method as string); if (toolInput.ocr_mode) formData.append("ocr_mode", toolInput.ocr_mode as string); if (toolInput.merge_tables !== undefined) formData.append("merge_tables", String(toolInput.merge_tables)); if (toolInput.segment_filter) formData.append("segment_filter", toolInput.segment_filter as string); if (toolInput.xml_citation !== undefined) formData.append("xml_citation", String(toolInput.xml_citation)); resp = await fetch(`${UNSILOED_BASE_URL}/parse`, { method: "POST", headers, body: formData, }); } else if (toolName === "unsiloed_extract_data") { const formData = new FormData(); formData.append("file_url", toolInput.file_url as string); formData.append("schema_data", toolInput.schema_data as string); if (toolInput.model) formData.append("model", toolInput.model as string); if (toolInput.enable_citations !== undefined) formData.append("enable_citations", String(toolInput.enable_citations)); resp = await fetch(`${UNSILOED_BASE_URL}/v2/extract`, { method: "POST", headers, body: formData, }); } else if (toolName === "unsiloed_classify_document") { const formData = new FormData(); formData.append("file_url", toolInput.file_url as string); formData.append("categories", toolInput.categories as string); resp = await fetch(`${UNSILOED_BASE_URL}/classify`, { method: "POST", headers, body: formData, }); } else if (toolName === "unsiloed_split_document") { const formData = new FormData(); formData.append("file_url", toolInput.file_url as string); formData.append("categories", toolInput.categories as string); resp = await fetch(`${UNSILOED_BASE_URL}/splitter`, { method: "POST", headers, body: formData, }); } else if (toolName === "unsiloed_get_job_result") { const jobType = toolInput.job_type as string; const jobId = toolInput.job_id as string; await new Promise((r) => setTimeout(r, 5000)); // Brief pause before polling resp = await fetch(`${UNSILOED_BASE_URL}/${jobType}/${jobId}`, { headers }); } else { return JSON.stringify({ error: `Unknown tool: ${toolName}` }); } return JSON.stringify(await resp.json()); } ``` ## Complete Agentic Loop Example This standalone example shows a full autonomous loop where Claude processes a document end-to-end: ```python Python theme={null} import anthropic import requests import json import time # --- Configuration --- ANTHROPIC_API_KEY = "your-anthropic-api-key" UNSILOED_API_KEY = "your-unsiloed-api-key" UNSILOED_BASE_URL = "https://prod.visionapi.unsiloed.ai" UNSILOED_HEADERS = {"api-key": UNSILOED_API_KEY} # --- Load tools (paste the array from the "All Tools" section above) --- tools = [...] # --- Tool executor --- def process_tool_call(tool_name: str, tool_input: dict) -> str: if tool_name == "unsiloed_extract_data": resp = requests.post( f"{UNSILOED_BASE_URL}/v2/extract", headers=UNSILOED_HEADERS, data={ "file_url": tool_input["file_url"], "schema_data": tool_input["schema_data"], **({"model": tool_input["model"]} if "model" in tool_input else {}), }, ) elif tool_name == "unsiloed_get_job_result": time.sleep(5) resp = requests.get( f"{UNSILOED_BASE_URL}/{tool_input['job_type']}/{tool_input['job_id']}", headers=UNSILOED_HEADERS, ) # Add other tool handlers (parse, classify, split) as needed else: return json.dumps({"error": f"Unhandled tool: {tool_name}"}) return json.dumps(resp.json()) # --- Agentic loop --- client = anthropic.Anthropic(api_key=ANTHROPIC_API_KEY) messages = [ { "role": "user", "content": "Extract the invoice number, date, and total amount from this PDF: https://example.com/invoice.pdf", } ] print("Starting agentic loop...\n") while True: response = client.messages.create( model="claude-sonnet-4-20250514", max_tokens=4096, tools=tools, messages=messages, ) # Collect assistant response messages.append({"role": "assistant", "content": response.content}) # If Claude is done, print the final text and exit if response.stop_reason == "end_turn": for block in response.content: if hasattr(block, "text"): print(f"Claude: {block.text}") break # If Claude wants to use tools, execute them if response.stop_reason == "tool_use": tool_results = [] for block in response.content: if block.type == "tool_use": print(f" -> Calling {block.name}({json.dumps(block.input, indent=2)[:100]}...)") result = process_tool_call(block.name, block.input) tool_results.append( { "type": "tool_result", "tool_use_id": block.id, "content": result, } ) messages.append({"role": "user", "content": tool_results}) print("\nDone.") ``` ## Error Handling When integrating with the Unsiloed API, handle these common scenarios: If a job's status is `"Failed"` or `"failed"`, the response includes an error message. Parse jobs use capitalized statuses (`Succeeded`, `Failed`), while extraction, classification, and splitting jobs use lowercase (`completed`, `failed`). ```json theme={null} { "status": "failed", "error": "Error processing document: unsupported file format" } ``` A `401` response means the API key is missing or invalid. Ensure you pass the key in the `api-key` header. ```json theme={null} { "detail": "API key is required. Please provide 'api-key' in the request header." } ``` A `402` response means your organization has run out of credits. Check the `quota_remaining` field in successful responses to monitor usage proactively. ```json theme={null} { "detail": { "message": "Insufficient quota", "status": "QUOTA_EXCEEDED" } } ``` If a job hasn't completed after 5 minutes of polling, treat it as a timeout. Jobs rarely take longer than 2 minutes for standard documents. ## Best Practices 1. **Use descriptive categories** — When classifying or splitting, add `description` fields to your category objects. This significantly improves accuracy. 2. **Poll with backoff** — Wait 5-10 seconds between `unsiloed_get_job_result` calls. Tight polling wastes quota and adds no benefit. 3. **Use publicly accessible URLs** — Claude cannot upload binary files. Use presigned URLs from your cloud storage or any publicly accessible link. 4. **Keep extraction schemas focused** — Smaller, targeted extraction schemas produce better results than large catch-all schemas. Extract what you need. 5. **Handle errors gracefully** — Always check job status before processing results. Return clear error messages so Claude can inform the user. 6. **Monitor your quota** — Check the `quota_remaining` field in API responses and alert when running low. ## Next Steps Learn about document parsing and structure analysis Explore document classification with confidence scoring Split multi-document PDFs into separate files Full API reference with all endpoints and parameters