extract-structured-data-from-unstructured-files-pdf-pptx-docx
Invoke this skill BEFORE implementing any structured data extraction from documents to learn the correct llama_cloud_services API usage. Required reading before writing extraction code. Requires llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable.
Installation
Details
Usage
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skills listSkill Instructions
name: Extract structured data from unstructured files (PDF, PPTX, DOCX...) description: Invoke this skill BEFORE implementing any structured data extraction from documents to learn the correct llama_cloud_services API usage. Required reading before writing extraction code. Requires llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable.
Structured Data Extraction
Quick start
- Define a schema for the for the data you would like to extract:
from pydantic import BaseModel, Field
class Resume(BaseModel):
name: str = Field(description="Full name of candidate")
email: str = Field(description="Email address")
skills: list[str] = Field(description="Technical skills and technologies")
NOTE: Use basic types when possible. Avoid nested dictionaries. Lists are ok.
- Create a LlamaExtract instance:
from llama_cloud_services import LlamaExtract
# Initialize client
extractor = LlamaExtract(
show_progress=True,
check_interval=5,
# Optional API key, else reads from env
# api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),
)
- Define the extraction configuration:
from llama_cloud import ExtractConfig, ExtractMode
# Configure extraction settings
extract_config = ExtractConfig(
# Basic options
extraction_mode=ExtractMode.MULTIMODAL, # FAST, BALANCED, MULTIMODAL, PREMIUM
extraction_target=ExtractTarget.PER_DOC, # PER_DOC, PER_PAGE
system_prompt="<Insert relevant context for extraction>", # set system prompt - can leave blank
# Advanced options
high_resolution_mode=True, # Enable for better OCR
nvalidate_cache=False, # Set to True to bypass cache
# Extensions
cite_sources=True, # Enable citations
use_reasoning=True, # Enable reasoning (not available in FAST mode)
confidence_scores=True, # Enable confidence scores (MULTIMODAL/PREMIUM only)
)
- Extract the data from the document:
result = extractor.extract(Resume, config, "resume.pdf")
# result.data has our model as a python dict
print(Resume.model_validate(result.data))
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="..."
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