Texts and Files Classification

Quick start

• Define classification rules:

from llama_cloud.types import ClassifierRule

# Define classification rules (natural language descriptions)
rules = [
    ClassifierRule(
        type="invoice",
        description="Documents that are invoices for goods or services, containing line items, prices, and payment terms",
    ),
    ClassifierRule(
        type="contract",
        description="Legal agreements between parties, containing terms, conditions, and signatures",
    ),
    ClassifierRule(
        type="receipt",
        description="Proof of payment documents, typically shorter than invoices, showing items purchased and amount paid",
    ),
]

• Create the classification client and run the job:

from llama_cloud_services.beta.classifier.client import ClassifyClient

# Initialize client
# Note: the beta client differs in usage slightly compared to other clients in llama-cloud-services
classifier = ClassifyClient.from_api_key(api_key)

# Classify a PDF directly (parsing happens implicitly)
result = await classifier.aclassify_file_path(
    rules=rules,
    file_input_path="document.pdf",
)

# Access classification results
classification = result.items[0].result
print(f"Predicted Type: {classification.type}")
print(f"Confidence: {classification.confidence:.2%}")
print(f"Reasoning: {classification.reasoning}")

For more detailed code implementations, see [REFERENCE.md](REFERENCE.md).

Requirements

The llamacloudservices package must be installed in your environment (with it come the pydantic and llama_cloud packages):

pip install llama_cloud_services

And the LLAMACLOUDAPI_KEY must be available as an environment variable:

export LLAMA_CLOUD_API_KEY="..."

For more detailed code implementations, see [REFERENCE.md](REFERENCE.md).

Requirements

The llamacloudservices package must be installed in your environment:

pip install llama_cloud_services

And the LLAMACLOUDAPI_KEY must be available as an environment variable:

export LLAMA_CLOUD_API_KEY="..."