Background requirements
Unstructured AI output requires additional processing to access enterprise systems, which increases the cost of data processing by more than 301 TP3T.
Functional realization
- Defining Output Templates: Use the StructuredOutputAgent class to specify the JSON/XML format.
- Field Mapping Configuration: Associate natural language with structured fields via schema parameter
- Data validation rules: integrating mathematical intelligences for numerical range validation
- Conversion Adapter: Built-in support for conversion to CSV/Excel and other common formats
typical case
Automation of market research reports:
- Research Intelligence Collects Raw Comments
- Structured Intelligence to Extract "Product Features - Emotional Tendencies - Keywords" Triad
- Automatic generation of data sheets that can be directly imported into BI tools
best practice
It is recommended to test the validity of the output templates through small samples first, and then combine it with LangChain integration to realize seamless connection with the enterprise ETL pipeline.
This answer comes from the articlePraisonAI: A Low-Code Multi-Intelligent Body Framework to Simplify Automation Solutions for Complex TasksThe
May not be reproduced without permission:AI productivity tools " How to improve data processing processes with PraisonAI's structured output intelligences?
