Api

Extract plain text from a PDF or image (base64-encoded). Use when you need raw text for downstream AI analysis (summarization, claim checking, structured extraction). For documents at a public URL, use extract_url instead (no base64 encoding needed). Returns: { pages: number, text: string } Example prompts: - "Extract the text from this scanned contract so I can search it." - "Give me the raw text from this PDF document." - "OCR this image and return the text content."

Extract tables and forms as Markdown from a PDF or image (base64-encoded). Use when the document contains structured tabular data such as financial statements, data sheets, or forms. For plain prose documents, use extract_text instead. Returns: { pages: number, text: string } — text contains Markdown-formatted tables. Example prompts: - "Extract the tables from this financial statement." - "Pull the data table from this PDF into Markdown format." - "Get the tabular data from this form document."

Parse a receipt or invoice document into structured fields. Uses a quality AI model for accuracy. Use when you need to extract line items, totals, and merchant info from financial documents. For general document text, use extract_text instead. Returns: { invoice: { merchant, date (YYYY-MM-DD), line_items[], subtotal, tax, total }, cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Parse this invoice and give me the line items and total." - "Extract the merchant, date, and amounts from this receipt." - "Read this scanned invoice and return structured data."

Verify a list of factual claims against document text. Uses a quality AI model with citation-level evidence. Use after extract_text or extract_url when you need to validate specific factual assertions. For open-ended questions about a document, use qa_url instead. For multi-document investigation, use ask_collection. Typical workflow: extract_text/extract_url → check_claims. Returns: { claims: [{ claim, status: "supported"|"contradicted"|"not_found", evidence: { quote, paragraphs[] }, confidence: "high"|"medium"|"low" }], truncated: boolean } Example prompts: - "Check whether this contract mentions a liability cap of $1M." - "Verify these claims against the document: [claims list]." - "Does the report actually say revenue grew 23%?"

Extract typed fields from document text using a caller-defined schema. Uses a quality AI model with retry logic. Use when you need specific data points from a document rather than full text. For invoices with known fields, parse_invoice (prebuilt schema) may be simpler. For general summarization, use summarize_document instead. Schema format: { "field_name": "type hint or description" } — e.g. { "contract_date": "ISO date", "party_a": "string", "penalty_usd": "number" }. Returns: { data: { <field>: value }, data_cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Extract the contract date, parties, and penalty amount from this agreement." - "Pull the vendor name, PO number, and total from this document." - "Get me all named fields from this form using my custom schema."

Summarize document text into a prose summary and key points with citations. Use after extract_text or extract_url when you need a condensed understanding of a long document. For single-sentence Q&A, use qa_url instead. For extracting specific fields, use extract_structured. Typical workflow: extract_text/extract_url → summarize_document. Returns: { summary: string, key_points: string[], summary_cited: { value, confidence, citations[] }, key_points_cited: [{ text, citations[] }], truncated: boolean, strategy: "full"|"truncated"|"chunked" } Example prompts: - "Summarize this financial report and give me the key points." - "What are the main takeaways from this document?" - "Give me a concise summary of this 50-page report."

Verify the cryptographic integrity of an evidence bundle (ev_...) owned by your API key. Checks manifest hash, EIP-191 signature, and R2 artifact hashes. Free — no credits consumed. Use when you need to confirm a bundle has not been tampered with. For quick metadata lookups (without full crypto verification), use get_bundle instead. Returns: { valid: boolean, bundle_id, manifest_sha256, checks: { status, manifest_hash, signature, artifacts: [{ name, ok }] }, tampered: string[], signer_address: string|null, attestation_tx: string|null, url: string, captured_at: string } Example prompts: - "Verify the cryptographic integrity of bundle ev_550e8400." - "Is this evidence bundle still valid and untampered?" - "Deep-check the manifest hash and signature of my bundle."

Create a named document collection for cross-document semantic search and RAG-based Q&A. Free — no credits consumed. Use when you want to group related evidence bundles for unified search (search_collection) or question answering (ask_collection). NOTE: Collections start empty. Add evidence bundles with add_document_to_collection. Indexing is async — once complete, use search_collection or ask_collection. Returns: { collection_id: string (col_...), name: string } Example prompts: - "Create a collection called Q4 Contracts for my quarterly reports." - "Set up a new document group named Due Diligence Docs." - "Make a collection to organize my vendor agreements."

Semantic (vector) search across documents in a collection. Returns ranked text chunks with relevance scores. Free — no credits consumed. Use when you need raw matching chunks from a collection. For a synthesized cited answer from the same context, use ask_collection instead. PREREQUISITE: Collection must be populated via add_document_to_collection and async indexing must complete (poll get_job_status) before results appear. Returns: { results: [{ bundle_id, chunk_id, text, score: number (0–1), title? }] } Example prompts: - "Search my Q4 Contracts collection for mentions of liability cap." - "Find the clause about data retention in my due diligence docs." - "Search for revenue numbers across my quarterly reports."

Answer a question using RAG over a document collection. Retrieves relevant chunks then synthesizes a cited answer with source attribution. Use when you need a direct answer grounded in your collection documents. For raw matching chunks (without synthesis), use search_collection instead. For single-document Q&A, use qa_url instead. PREREQUISITE: Collection must be populated via add_document_to_collection and indexed before results appear. Returns: { answer: string, sources: [{ bundle_id, chunk_id }], retrieval: [{ bundle_id, chunk_id, text, score }] } Example prompts: - "What are the key terms of the service agreement in my collection?" - "Based on my due diligence docs, what are the main risks?" - "Answer this question using all documents in the Q4 Contracts collection."

Fetch a public HTTPS URL and return extracted text and page metadata. Lean mode — no evidence bundle stored, no bundle_id returned. Use for raw text extraction from web pages and online documents. Use summarize_url for summaries, qa_url for Q&A, translate_url for translation, extract_text for base64 file uploads. Returns: { url, title, word_count, text, final_url (after redirects) } Example prompts: - "Extract the text from https://example.com/report.pdf for me." - "Get me the raw content of this web page: [URL]." - "Pull the text from this online article so I can analyze it."

Fetch a public HTTPS URL and return a prose summary with key points. Lean mode — no bundle stored. Use when you need a condensed understanding of a web page. For raw text, use extract_url. For asking a specific question about a page, use qa_url. Returns: { url, summary, key_points: string[], truncated: boolean, word_count } Example prompts: - "Summarize https://en.wikipedia.org/wiki/Artificial_intelligence for me." - "Give me the key points from this blog post: [URL]." - "What is this article about? Summarize [URL]."

Fetch a public HTTPS URL and answer a specific question about its content. Lean mode — no bundle stored. Use when you have a precise question about a web page. For a broad summary, use summarize_url. For multi-document Q&A, use ask_collection instead. Returns: { url, answer, answer_cited: { value, confidence, citations[] }, confidence: "high"|"medium"|"low", truncated } Example prompts: - "What is the refund policy at https://docs.example.com/policy?" - "Look at [URL] and tell me what the delivery terms are." - "Answer this question based on the content of [URL]: [question]."

Fetch a public HTTPS URL and return its content translated into a target language. Lean mode — no bundle stored. Use when you need to understand web content in a different language. For extracting raw untranslated text, use extract_url instead. Returns: { url, translated_text, target_lang, truncated } Example prompts: - "Translate https://example.de/artikel into English for me." - "Translate this German article into Spanish: [URL]." - "Fetch [URL] and give me the French translation."

Retrieve metadata for an evidence bundle (ev_...) owned by your API key. Free — no credits consumed. Use for quick status/metadata lookups such as checking if a bundle is complete, finding its notarization status, or viewing retention/legal hold info. For deep cryptographic integrity verification (hash + signature + artifact checks), use verify_bundle instead. Returns: { bundle_id, source_url, mode, status: "pending"|"complete"|"failed", manifest_sha256, manifest_signature, signer_address, attestation_tx, attestation_at, eas_uid, parent_bundle_id, superseded_by, legal_hold: boolean, retention_until, created_at } Example prompts: - "Show me the metadata for bundle ev_550e8400." - "Check the status and notarization info of my evidence bundle." - "Get me the details of bundle [ev_id] — is it complete?"

Notarize an evidence bundle on-chain by writing its manifest SHA-256 to the blockchain (Base/EVM). Creates a permanent, tamper-evident on-chain record of the document fingerprint. If the bundle is already notarized, returns the existing attestation immediately (idempotent). Use when you need an immutable on-chain timestamp proving a document existed at a point in time. For quick integrity checks without on-chain cost, use verify_bundle instead. PREREQUISITE: Bundle status must be "complete". Check status with get_bundle first. NOTE: Costs gas (ETH). The on-chain record is permanent and cannot be deleted even if the bundle is later purged. Returns: { bundle_id, attestation: { tx_hash, network, attested_at, key_id, eas_uid?, schema_uid? } } Example prompts: - "Notarize bundle ev_550e8400 on-chain so I have a permanent record." - "Put the fingerprint of my evidence bundle on the blockchain." - "Create an on-chain timestamp for this document bundle."

Independently verify a signed action receipt (rcpt_...) returned by get_bundle, verify_bundle, notarize_bundle, or add_document_to_collection. Free — no credits consumed. Proves both that the receipt signature is authentic AND that the manifest_sha256 it was bound to still matches the bundle's current manifest — i.e. that the action was not performed against a stale or since-superseded document. Use for third-party audit of an agent's prior actions. Returns: { receipt_id, valid: boolean, signature_valid: boolean, manifest_matches_current: boolean, bundle_id, agent_id, action, manifest_sha256, signer_address, signed_at, tampered: string[] } Example prompts: - "Verify action receipt rcpt_550e8400 is authentic and still current." - "Was this receipt signed against the real document, or a stale copy?"

Poll the status of an async job (extract, indexing, batch). Free — no credits consumed. Use after add_document_to_collection or async extract to check when processing completes. Poll this endpoint in a loop until status is "complete" or "failed". Completed jobs include the bundle_id or result_json in the response. Jobs are created when you POST /v1/extract with a webhook, or when add_document_to_collection triggers async indexing. Returns: { id, type: "extract"|"extract_batch"|"index_collection", status: "queued"|"processing"|"complete"|"failed"|"cancelled", progress_pct: number (0–100), progress_message, bundle_id (when complete), result_json (when complete), error (when failed), created_at, completed_at } Example prompts: - "Check the status of my indexing job job_550e8400." - "Is my async extract job done yet?" - "Poll job [job_id] — what is the current progress?"

List all document collections owned by your API key. Free — no credits consumed. Use before search_collection or ask_collection when you need the collection ID. Supports pagination with limit and offset. Returns: { collections: [{ id, name, created_at }] } Example prompts: - "List all my document collections." - "Show me the collections I have created." - "What collections do I own? List them."

Add an evidence bundle to a collection and trigger async vector indexing. Use after create_collection to populate a collection with documents. Once indexed, documents become searchable via search_collection and ask_collection. Indexing is async — poll get_job_status with the returned job_id until status is "complete". PREREQUISITE: Bundle must have status "complete" (check with get_bundle). Collection must be owned by your API key. Returns: { collection_id, bundle_id, job_id (poll for indexing completion) } Example prompts: - "Add my contract bundle ev_550e8400 to the Q4 Contracts collection." - "Put this evidence bundle into my Due Diligence Docs collection for search." - "Add document [bundle_id] to collection [col_id] with a title."

Get current credit balance and plan details for your API key. Free — no credits consumed. Check this before running credit-consuming operations (extract, summarize, etc.) to avoid QUOTA_EXCEEDED errors. Returns plan tier, billing period, and usage breakdown. Returns: { plan_id, billing_period (YYYY-MM), credits_used, credits_limit, credits_remaining, status: "active"|"suspended" } Example prompts: - "How many credits do I have left this month?" - "Check my current quota and plan status." - "Am I going to hit my credit limit soon?"

Takes no parameters.