gemina 0.2.1

Official Python SDK for the Gemina API - invoice OCR and document intelligence: upload documents, get typed structured data back, then search, aggregate, and chat over everything you've processed.

Gemina Python SDK

The official Python client for the Gemina API — invoice OCR and document intelligence: upload documents, get typed structured data back, then search, aggregate, and chat over everything you've processed. Fully async (httpx + pydantic v2), with typed models for every request and response.

Install

pip install gemina

Requires Python 3.9 or newer.

Authenticate

Get an API key from the Gemina Console. The client sends it as the X-API-Key header on every request — you never handle the header yourself:

from gemina import GeminaClient

client = GeminaClient("YOUR_API_KEY")

Never ship the API key in browser or mobile code. For browser embedding, mint short-lived session tokens server-side (POST /v1/sessions/token) and hand those to the frontend — see Session tokens below and the Document Intelligence guide at console.gemina.co/docs.

Quickstart — process an invoice in one call

process_document submits the document through the async endpoints, polls with exponential backoff until processing finishes, and returns the final typed result — one call, no polling loop to write:

import asyncio
from gemina import GeminaClient, ExtractionTypeModel

async def main():
    async with GeminaClient("YOUR_API_KEY") as client:
        result = await client.process_document(
            "invoice.png",  # path, bytes, or a binary file object
            [ExtractionTypeModel.INVOICE_HEADERS],
        )
        values = result.data.extractions[0].values
        print("Supplier:", values["vendorName"]["value"])
        print("Total:   ", values["totalAmount"]["value"], values["currency"]["value"])
        print("Date:    ", values["invoiceDate"]["value"])

asyncio.run(main())

To process a document that lives at a URL, wrap it in UrlSource:

from gemina import GeminaClient, ExtractionTypeModel, UrlSource

async def from_url():
    async with GeminaClient("YOUR_API_KEY") as client:
        result = await client.process_document(
            UrlSource("https://example.com/invoice.pdf"),
            [ExtractionTypeModel.INVOICE_HEADERS],
        )
        print(result.status)

What you get back

process_document returns a DocumentProcessingResultOutDTO:

• result.status — success | partial | empty | failed (failed raises GeminaProcessingError instead of returning; partial and empty are returned and still carry usable data and meta).
• result.data.extractions — one entry per requested extraction type; each has .meta.extraction_type, .status, and .values.
• result.meta.document_id / result.meta.correlation_id — the stored document's ID and the async request's correlation ID.

extraction.values is a dict keyed by field name (camelCase). Each field is either None (not found) or an object with value, coordinates (present when you pass include_coordinates=True), and confidence:

values["vendorName"]     # {"value": "Acme Ltd", "coordinates": {...}, "confidence": ...}
values["totalAmount"]["value"]   # 1572.0
values["invoiceDate"]["value"]   # "2020-08-31"
values["taxes"]                  # list: [{"type": "vat", "rate": 17.0, "amount": 228.41, ...}]

Extraction types:

Extraction type	What it extracts
ocr	Raw text of the document
invoice_headers	Invoice header fields: vendor/buyer, number, dates, amounts, taxes
invoice_line_items	Line items table
document_details_hebrew	Hebrew document header fields
document_line_items_hebrew	Hebrew line items
custom_template	Fields defined by your template (pass template_id=...)
filetag	File classification and naming metadata

Search & aggregate your documents

Everything you process is indexed for retrieval. Query with natural language and/or structured filters — results carry document_id citations back to the original documents:

from gemina import GeminaClient, RetrievalQueryInDTO
from gemina.generated.models.retrieval_filters_dto import RetrievalFiltersDTO

async def search():
    async with GeminaClient("YOUR_API_KEY") as client:
        page = await client.retrieval.retrieval_query(RetrievalQueryInDTO(
            mode="hybrid",                 # structured | semantic | hybrid
            text="cleaning services",
            filters=RetrievalFiltersDTO(total_amount_min=100),
            top_k=5,
        ))
        for item in page.items:
            print(item.vendor_name, item.total_amount, item.currency,
                  item.issue_date, item.document_id)

Aggregate across your documents (sum/avg/min/max/count, grouped by up to four dimensions — when you aggregate money without fixing a currency, the server adds a currency grouping so different currencies are never summed together):

from gemina import GeminaClient, RetrievalAggregateInDTO
from gemina.generated.models.aggregate_metric_dto import AggregateMetricDTO

async def totals_by_vendor():
    async with GeminaClient("YOUR_API_KEY") as client:
        report = await client.retrieval.retrieval_aggregate(RetrievalAggregateInDTO(
            metrics=[
                AggregateMetricDTO(op="sum", field="total_amount"),
                AggregateMetricDTO(op="count"),
            ],
            group_by=["vendor_name"],
        ))
        for row in report.rows:
            print(row.group, row.values["sum_total_amount"].actual_instance,
                  row.values["count"].actual_instance)

client.retrieval.retrieval_status() tells you how many of your documents are currently indexed.

Advanced filters & match highlights. Beyond the promoted filters, filter on any structured field a document has with structured_filters (op is one of eq / neq / gt / lt / contains / exists, max 8), and read back the line-item snippet that made a document match via matched_chunks:

from gemina import GeminaClient, RetrievalQueryInDTO
from gemina.generated.models.structured_filter_dto import StructuredFilterDTO

async def advanced_search():
    async with GeminaClient("YOUR_API_KEY") as client:
        page = await client.retrieval.retrieval_query(RetrievalQueryInDTO(
            mode="hybrid",
            text="27-inch monitors",
            structured_filters=[
                StructuredFilterDTO(path="position", op="contains", value="engineer"),
            ],
        ))
        for item in page.items:
            for chunk in item.matched_chunks or []:
                print(item.document_id, "matched on:", chunk.text)

Discover which fields you can filter on with client.retrieval.retrieval_fields() — it returns the structured field names per document type (names only, never values), so you can build a field picker from real data:

async def list_fields():
    async with GeminaClient("YOUR_API_KEY") as client:
        catalog = await client.retrieval.retrieval_fields()
        for f in catalog.fields:
            print(f.document_type, f.var_field, f.count)
            # e.g. invoice  vendor_name  42

Chat with your documents

Ask questions in natural language; answers come back with a confident flag and citations (document IDs the answer relies on):

from gemina import GeminaClient, ChatQueryInDTO

async def ask():
    async with GeminaClient("YOUR_API_KEY") as client:
        reply = await client.chat.chat_query(ChatQueryInDTO(
            message="What is the total amount of my invoices from last month?",
        ))
        print(reply.answer)
        print("confident:", reply.confident)
        print("citations:", reply.citations)

Chat requires a plan with Document Intelligence enabled — see pricing; without it these calls return 402/403.

Multi-turn conversations (memory). For a back-and-forth where follow-ups keep context, use a conversation — it threads the server-issued session_id for you, so you never touch the id:

async def conversation():
    async with GeminaClient("YOUR_API_KEY") as client:
        chat = client.conversation()
        await chat.send("How much did we spend on cleaning in 2020?")
        follow = await chat.send("And which vendor was most expensive?")  # remembers 2020 / cleaning
        print(follow.answer, "· session:", chat.session_id)

        await chat.delete()   # end it server-side (or chat.reset() to just forget it locally)

A conversation expires after 24h of inactivity; the next send then raises the API's 404 CHAT_SESSION_NOT_FOUND — call chat.reset() and resend to continue in a fresh one. One-shot client.chat.chat_query(ChatQueryInDTO(message=..., session_id=...)) is still available if you'd rather hold the id yourself; every response returns a session_id.

Session tokens (browser embedding)

For browser or end-user contexts, mint a short-lived, query-only session token server-side and hand that to your frontend — never the API key. An optional end_user_id scopes the token to a single end-user's documents:

from gemina import GeminaClient, SessionTokenInDTO

async def mint_token():
    async with GeminaClient("YOUR_API_KEY") as client:  # server-side only
        token = await client.sessions.mint_retrieval_token(SessionTokenInDTO(
            end_user_id="customer-42",   # omit for a whole-account session
            ttl_seconds=600,             # clamped server-side to [300, 900]
        ))
        return token.token               # ship this to the frontend

Token-authenticated clients (for server-side use of a token, or testing) are created with GeminaClient.with_session_token(token); tokens can call the retrieval query and chat endpoints only. For a drop-in chat UI in the browser, see the @gemina/elements package on npm.

Going deeper

Full API surface. Every generated endpoint group is exposed on the client — client.documents, client.retrieval, client.chat, client.templates, client.files, client.file_tag, client.sessions, client.subscriptions, client.billing — with zero wrapping. For example, listing stored documents:

async def list_documents():
    async with GeminaClient("YOUR_API_KEY") as client:
        page = await client.documents.find_documents(limit=10)
        for doc in page.data.documents:
            print(doc.meta.document_id, doc.meta.created_at)

Polling knobs. process_document accepts timeout_seconds (default 300), initial_interval_seconds (default 2.0) and max_interval_seconds (default 15.0). The wait grows 1.5x per poll, capped at the max, with +/-20% jitter. Transient poll failures (connection blips, 5xx) are retried automatically on the same schedule; after 3 consecutive failures the error is raised. On timeout, GeminaTimeoutError carries .correlation_id and .last_result so you can resume polling yourself:

from gemina import GeminaError, GeminaProcessingError, GeminaTimeoutError

async def robust():
    async with GeminaClient("YOUR_API_KEY") as client:
        try:
            result = await client.process_document(
                "invoice.pdf",
                [ExtractionTypeModel.INVOICE_HEADERS],
                timeout_seconds=120,
            )
        except GeminaProcessingError as exc:
            print("processing failed:", exc.result.errors)
        except GeminaTimeoutError as exc:
            print("still running, poll later:", exc.correlation_id)
            result = await client.documents.\
                get_document_processing_result_by_correlation_id(exc.correlation_id)

Error handling. Terminal failed results raise GeminaProcessingError (.result.errors has the details). Transport and HTTP errors from the generated client (e.g. gemina.generated.exceptions.ApiException subclasses for 4xx/5xx) pass through unwrapped. All hand-written errors subclass GeminaError.

Custom base URL (staging / self-hosted):

client = GeminaClient("YOUR_API_KEY", base_url="https://api.staging.gemina.co")

Using the SDK from synchronous code. The client is async-first; from a sync program, run calls with asyncio.run(...):

import asyncio

result = asyncio.run(main())   # where main() is an async def using GeminaClient

Requirements & support

• Python >= 3.9
• Docs: console.gemina.co/docs
• Issues: github.com/tommyil/gemina-sdk/issues
• Email: [e-mail]