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:
```python
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.
Requirements
The llama_cloud_services package must be installed in your environment (with it come the pydantic and llama_cloud packages):
pip install llama_cloud_services
And the LLAMA_CLOUD_API_KEY must be available as an environment variable:
export LLAMA_CLOUD_API_KEY="..."
For more detailed code implementations, see REFERENCE.md.
Requirements
The llama_cloud_services package must be installed in your environment:
pip install llama_cloud_services
And the LLAMA_CLOUD_API_KEY must be available as an environment variable:
export LLAMA_CLOUD_API_KEY="..."