Question Answering

The qa resource submits and manages AI question answering jobs. It is available via client.qa.

import asyncio
from biolevate import BiolevateClient, QuestionInput

async def main():
    async with BiolevateClient(
        base_url="https://<api-domain>",
        token="<your-pat>",
    ) as client:
        job = await client.qa.create_job(
            questions=[
                QuestionInput(
                    id="q1",
                    question="What is the main conclusion?",
                    answer_type={"dataType": "STRING", "multiValued": False},
                )
            ],
            file_ids=["a1b2c3d4-e5f6-7890-abcd-ef1234567890"],
        )
        print(f"Job created: {job.job_id} (status: {job.status})")

asyncio.run(main())

Methods

create_job()

Submit a new QA job. Target documents by file_ids, collection_ids, or both.

from biolevate import QuestionInput

job = await client.qa.create_job(
    questions=[
        QuestionInput(
            id="q1",
            question="What is the main conclusion of this document?",
            answer_type={"dataType": "STRING", "multiValued": False},
        ),
        QuestionInput(
            id="q2",
            question="What is the publication date?",
            answer_type={"dataType": "DATE", "multiValued": False},
        ),
    ],
    file_ids=["a1b2c3d4-e5f6-7890-abcd-ef1234567890"],
    collection_ids=None,
)
print(f"Job: {job.job_id}")

Parameter	Type	Default	Description
questions	list[QuestionInput]	—	Questions to ask
file_ids	list[str] | None	None	UUIDs of individual EliseFiles to process
collection_ids	list[str] | None	None	UUIDs of Collections to process

Returns Job

Raises AuthenticationError, APIError

---

get_job()

Get the current status of a QA job. Poll this until status is SUCCESS or FAILED.

job = await client.qa.get_job(job_id)
print(f"Status: {job.status}")

Parameter	Type	Description
job_id	str	UUID of the QA job

Returns Job

Raises NotFoundError, AuthenticationError, APIError

---

list_jobs()

List all QA jobs for the current user.

page = await client.qa.list_jobs(page=0, page_size=20)
for job in page.data:
    print(f"{job.job_id}: {job.status}")

Parameter	Type	Default	Description
page	int	0	Page number (0-based)
page_size	int	20	Number of jobs per page
sort_property	str | None	None	Field to sort by
sort_order	str | None	None	"ASC" or "DESC"

Returns JobPage

Raises AuthenticationError, APIError

---

get_job_outputs()

Retrieve the results of a completed QA job.

outputs = await client.qa.get_job_outputs(job_id)
for result in outputs.results:
    print(f"Q: {result.question}")
    print(f"A: {result.raw_value}")
    if result.explanation:
        print(f"   {result.explanation}")

Parameter	Type	Description
job_id	str	UUID of the QA job

Returns QAJobOutputs

Raises NotFoundError, AuthenticationError, APIError

---

get_job_inputs()

Retrieve the original inputs submitted to the job.

inputs = await client.qa.get_job_inputs(job_id)
print(f"Questions: {[q.question for q in inputs.questions]}")

Parameter	Type	Description
job_id	str	UUID of the QA job

Returns QAJobInputs

Raises NotFoundError, AuthenticationError, APIError

---

get_job_annotations()

Retrieve the source annotations for all results — document passages the AI used as evidence.

annotations = await client.qa.get_job_annotations(job_id)
for ann in annotations:
    if ann.data:
        print(f"[{ann.data.document_name}] {ann.data.content[:80]}")

Parameter	Type	Description
job_id	str	UUID of the QA job

Returns list[Annotation]

Raises NotFoundError, AuthenticationError, APIError

Full Example — Create, Poll, and Retrieve Results

import asyncio
from biolevate import BiolevateClient, QuestionInput

TERMINAL_STATUSES = {"SUCCESS", "FAILED", "ABORTED"}

async def main():
    async with BiolevateClient(
        base_url="https://<api-domain>",
        token="<your-pat>",
    ) as client:
        job = await client.qa.create_job(
            questions=[
                QuestionInput(
                    id="q1",
                    question="What is the main conclusion of this document?",
                    answer_type={"dataType": "STRING", "multiValued": False},
                ),
                QuestionInput(
                    id="q2",
                    question="What is the risk level?",
                    answer_type={
                        "dataType": "ENUM",
                        "multiValued": False,
                        "enumValues": ["LOW", "MEDIUM", "HIGH"],
                    },
                ),
            ],
            file_ids=["a1b2c3d4-e5f6-7890-abcd-ef1234567890"],
        )
        print(f"Job created: {job.job_id}")

        while True:
            job = await client.qa.get_job(job.job_id)
            print(f"Status: {job.status}")
            if job.status in TERMINAL_STATUSES:
                break
            await asyncio.sleep(3)

        if job.status == "SUCCESS":
            outputs = await client.qa.get_job_outputs(job.job_id)
            for result in outputs.results:
                print(f"Q: {result.question}")
                print(f"A: {result.raw_value}")

            annotations = await client.qa.get_job_annotations(job.job_id)
            annotation_map = {a.id.id: a for a in annotations}
            for result in outputs.results:
                for ref in result.reference_ids:
                    ann = annotation_map.get(ref.id)
                    if ann and ann.data:
                        print(f"  Source: [{ann.data.document_name}] {ann.data.content[:80]}")

asyncio.run(main())

Next Steps

• Annotations guide — full annotation data model and position types
• Extraction — extract structured metadata fields instead of free-form answers
• Question Answering guide — the same operations with cURL, Python (httpx), and R