knowhere-python-sdk 0.3.0

Official Python SDK for the Knowhere document parsing API

Knowhere Python SDK

Official Python SDK for the Knowhere document parsing API.

Installation

pip install knowhere-python-sdk

Or with uv:

uv add knowhere-python-sdk

Usage

import knowhere

client = knowhere.Knowhere(api_key="sk_...")

result = client.parse(url="https://example.com/report.pdf")

print(result.statistics.total_chunks)
print(result.full_markdown[:200])

for chunk in result.text_chunks:
    print(chunk.content[:80])

Retrieval and document lifecycle

New documents are published into a retrieval namespace. The server returns a stable document_id when you create a job; persist that value if you need to update or archive the same document later.

job = client.jobs.create(
    source_type="url",
    source_url="https://example.com/manual.pdf",
    namespace="support-center",
)

print(job.document_id)  # "doc_..."

After the job is done and published, query the canonical document content:

response = client.retrieval.query(
    namespace="support-center",
    query="How do I reset Bluetooth pairing?",
    top_k=5,
)

for result in response.results:
    print(result.content)
    print(result.score)
    print(result.source.source_file_name, result.source.section_path)

Use document_id to update or archive a document:

update_job = client.jobs.create(
    source_type="url",
    source_url="https://example.com/manual-v2.pdf",
    document_id=job.document_id,
)

document = client.documents.get(job.document_id)
print(document.status)

client.documents.archive(job.document_id)

You can also list documents in a namespace:

documents = client.documents.list(namespace="support-center")
for document in documents.documents:
    print(document.document_id, document.status)

Retrieval supports exclusions when clients want follow-up results that avoid previously used documents or sections:

response = client.retrieval.query(
    namespace="support-center",
    query="battery charging",
    exclude_document_ids=["doc_old"],
    exclude_sections=[
        {"document_id": "doc_123", "section_path": "Appendix / Legal"}
    ],
)

While you can provide an api_key keyword argument, we recommend using python-dotenv to add KNOWHERE_API_KEY="sk_..." to your .env file so that your API key is not stored in source control.

Parse a local file

from pathlib import Path

result = client.parse(
    file=Path("report.pdf"),
    parsing_params={"model": "advanced", "ocr_enabled": True},
)

print(result.manifest.source_file_name)  # "report.pdf"
print(len(result.chunks))                # 152

Access different chunk types

result = client.parse(url="https://example.com/report.pdf")

# Text chunks
for chunk in result.text_chunks:
    print(chunk.keywords)
    print(chunk.summary)

# Image chunks (raw bytes loaded from ZIP)
for chunk in result.image_chunks:
    print(chunk.file_path)
    print(len(chunk.data))       # bytes
    chunk.save("./output/")      # writes image to disk

# Table chunks (HTML loaded from ZIP)
for chunk in result.table_chunks:
    print(chunk.file_path)
    print(chunk.html[:100])

Save all results to disk

result = client.parse(file=Path("report.pdf"))
result.save("./output/report/")

Async usage

import asyncio
import knowhere

async def main():
    async with knowhere.AsyncKnowhere(api_key="sk_...") as client:
        result = await client.parse(url="https://example.com/report.pdf")
        print(result.statistics.total_chunks)

        for chunk in result.text_chunks:
            print(chunk.summary)

asyncio.run(main())

Step-by-step control

For granular control over the parsing workflow, use the jobs resource directly:

from pathlib import Path

# Step 1: Create a parsing job
job = client.jobs.create(
    source_type="file",
    file_name="report.pdf",
    namespace="support-center",
    parsing_params={"model": "advanced", "ocr_enabled": True},
)

print(job.document_id)  # Persist this to update/archive the document later.

# Step 2: Upload file to presigned URL
client.jobs.upload(job, file=Path("report.pdf"))

# Step 3: Poll until done (adaptive backoff)
job_result = client.jobs.wait(job.job_id, poll_interval=10.0, poll_timeout=1800.0)

# Step 4: Download and parse results
result = client.jobs.load(job_result)
print(result.statistics)

Handling errors

All errors inherit from knowhere.KnowhereError.

import knowhere

try:
    result = client.parse(url="https://example.com/report.pdf")
except knowhere.AuthenticationError:
    print("Invalid API key")
except knowhere.APIStatusError as e:
    print(f"{e.status_code}: {e.message}")

Configuration

The SDK reads configuration from constructor arguments, environment variables, or defaults (in that priority order):

Variable	Description	Default
KNOWHERE_API_KEY	API key (required)	—
KNOWHERE_BASE_URL	API base URL	https://api.knowhereto.ai
KNOWHERE_LOG_LEVEL	Log level	WARNING

# Uses environment variables automatically
client = knowhere.Knowhere()

# Or configure explicitly
client = knowhere.Knowhere(
    api_key="sk_...",
    base_url="https://api.knowhereto.ai",
    timeout=30.0,           # HTTP request timeout (default: 60s)
    upload_timeout=300.0,   # File upload timeout (default: 600s)
    max_retries=3,          # Max retry attempts (default: 5)
)

Retries

Connection errors, 429 Rate Limit, and >=500 Internal errors are automatically retried with exponential backoff.

client = knowhere.Knowhere(
    api_key="sk_...",
    max_retries=3,  # default is 5
)

Determining the installed version

import knowhere
print(knowhere.__version__)

Versioning

This package follows Semantic Versioning.

We publish stable releases to PyPI. To install the latest unreleased changes directly from the repository: https://github.com/Ontos-AI/knowhere-python-sdk

Requirements

• Python 3.9+
• httpx >=0.25.0,<1.0
• pydantic >=2.0.0,<3.0
• typing-extensions >=4.7.0

License

MIT