haive.core.engine.output_parser¶

Output parser engine module for the Haive framework.

This module provides engine implementations for LangChain output parsers, enabling structured output parsing from LLM responses within the Haive framework.

Output parsers transform raw LLM text outputs into structured data formats, including JSON, Pydantic models, enums, lists, and custom formats. This module wraps LangChain’s output parser functionality in Haive’s engine system for consistent usage across the framework.

Key Components:

OutputParserEngine: Engine wrapper for LangChain output parsers OutputParserType: Enumeration of supported parser types

Supported Parser Types:

JSON: Parse output as JSON objects
PydanticOutputParser: Parse into Pydantic model instances
EnumOutputParser: Parse into enum values
DatetimeOutputParser: Parse datetime strings
BooleanOutputParser: Parse boolean values
CommaSeparatedListOutputParser: Parse comma-separated lists
MarkdownListOutputParser: Parse markdown formatted lists
NumberedListOutputParser: Parse numbered lists
XMLOutputParser: Parse XML formatted output

Examples

JSON output parsing:

from haive.core.engine.output_parser import OutputParserEngine, OutputParserType

parser = OutputParserEngine(
    parser_type=OutputParserType.JSON,
    name="json_parser"
)

result = parser.invoke('{"name": "John", "age": 30}')
# Returns: {"name": "John", "age": 30}

Pydantic model parsing:

from pydantic import BaseModel, Field
from haive.core.engine.output_parser import OutputParserEngine, OutputParserType

class Person(BaseModel):
    name: str = Field(description="Person's name")
    age: int = Field(description="Person's age")

parser = OutputParserEngine(
    parser_type=OutputParserType.PYDANTIC,
    pydantic_object=Person,
    name="person_parser"
)

result = parser.invoke("name: John\\nage: 30")
# Returns: Person(name="John", age=30)