prebuilt.podcast_generator.interview¶

Interview - TODO: Add brief description.

TODO: Add detailed description of module functionality

Key Components:

  • Classes: InterviewState

Examples

Basic usage:

from haive.interview import InterviewState

instance = InterviewState()
# TODO: Complete example

Submodules¶

Classes¶

InterviewState

State schema for conversation management with LangChain integration.

Package Contents¶

class prebuilt.podcast_generator.interview.InterviewState(/, **data)¶

Bases: haive.core.schema.prebuilt.messages_state.MessagesState

State schema for conversation management with LangChain integration.

MessagesState is a specialized StateSchema that provides comprehensive message handling capabilities for conversational AI agents. It extends the base StateSchema with specific functionality for working with LangChain message types, message filtering, and conversation management.

This schema serves as the foundation for conversation-based agent states in the Haive framework, providing seamless integration with LangGraph for agent workflows. It includes built-in support for all standard message types (Human, AI, System, Tool) and handles message conversion, ordering, and serialization.

Key features include:

  • Automatic message conversion between different formats (dict/object)

  • System message handling with proper ordering enforcement

  • Message filtering by type, content, or custom criteria

  • Token counting and length estimation for context management

  • Conversation history manipulation (truncation, filtering, etc.)

  • LangGraph integration with proper message reducers

  • Conversion to formats required by different LLM providers

  • Conversation round tracking and analysis

  • Tool call deduplication and error handling

  • Message transformation utilities

Note: For token usage tracking, use MessagesStateWithTokenUsage instead.

The messages field is automatically shared with parent/child graphs and configured with the appropriate reducer function for merging message lists during state updates.

This class is commonly used as a base class for more specialized agent states that need conversation capabilities, and is the default base class used by SchemaComposer when message handling is detected in the components being composed.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)