prebuilt.perplexity.base.state

Base state schemas for the Perplexity multi-agent system.

This module defines the core state schemas that are shared across all Perplexity agents, including search results, citations, and performance metrics.

Classes

BasicSearchState	State for basic search mode.
Citation	Represents a citation for a piece of information.
LabsState	State for Labs mode with project-based workflows.
ModelChoice	Available model choices for different tasks.
PerformanceMetrics	Tracks performance metrics for the system.
PerplexityBaseState	Base state schema for all Perplexity agents.
ProSearchState	State for Pro search mode with enhanced reasoning.
QueryAnalysis	Results of query analysis.
QueryType	Types of queries that can be processed.
ResearchState	State for deep research mode.
SearchMode	Search execution modes.
SearchResult	Represents a search result from web search or retrieval.
SourceTrustLevel	Trust levels for information sources.

Module Contents

class prebuilt.perplexity.base.state.BasicSearchState(/, **data: Any)

Bases: PerplexityBaseState

State for basic search mode.

class prebuilt.perplexity.base.state.Citation(/, **data: Any)

Bases: pydantic.BaseModel

Represents a citation for a piece of information.

relevance_score: float = None

sentence_indices: list[int] = None

snippet: str = None

source_id: str = None

timestamp: datetime.datetime = None

title: str = None

trust_level: SourceTrustLevel = None

url: str | None = None

class prebuilt.perplexity.base.state.LabsState(/, **data: Any)

Bases: PerplexityBaseState

State for Labs mode with project-based workflows.

actual_completion_time: float | None = None

asset_dependencies: dict[str, list[str]] = None

deliverables: list[dict[str, Any]] = None

deployment_ready: bool = None

estimated_completion_time: float | None = None

generated_assets: dict[str, Any] = None

integration_status: dict[str, bool] = None

project_requirements: list[str] = None

project_type: str = None

required_tools: list[str] = None

tool_execution_log: list[dict[str, Any]] = None

class prebuilt.perplexity.base.state.ModelChoice

Bases: str, enum.Enum

Available model choices for different tasks.

CLAUDE_35_SONNET = 'claude-3.5-sonnet'

GPT_4O = 'gpt-4o'

MIXTRAL_8X22B = 'mixtral-8x22b'

SONAR_7B = 'sonar-7b'

class prebuilt.perplexity.base.state.PerformanceMetrics(/, **data: Any)

Bases: pydantic.BaseModel

Tracks performance metrics for the system.

calculate_latency() → float | None

Calculate latency if start and end times are available.

documents_processed: int = None

end_time: datetime.datetime | None = None

latency_ms: float | None = None

model_calls: dict[str, int] = None

start_time: datetime.datetime = None

tokens_used: int = None

total_searches: int = None

class prebuilt.perplexity.base.state.PerplexityBaseState(/, **data: Any)

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

Base state schema for all Perplexity agents.

This state extends MessagesState to provide conversation management while adding Perplexity-specific fields for search, retrieval, and quality assurance.

add_citation(citation: Citation) → None

Add a citation, avoiding duplicates.

add_search_result(result: SearchResult) → None

Add a search result to the state.

get_high_confidence_citations() → list[Citation]

Get citations above the confidence threshold.

should_continue() → bool

Determine if searching should continue.

citations: list[Citation] = None

confidence_threshold: float = None

current_search_query: str | None = None

draft_response: str | None = None

final_response: str | None = None

max_iterations: int = None

metadata: dict[str, Any] = None

needs_clarification: bool = None

performance_metrics: PerformanceMetrics = None

query: str = None

query_analysis: QueryAnalysis | None = None

search_iteration: int = None

search_mode: SearchMode = None

search_results: list[SearchResult] = None

should_continue_searching: bool = None

verified_facts: list[dict[str, Any]] = None

class prebuilt.perplexity.base.state.ProSearchState(/, **data: Any)

Bases: PerplexityBaseState

State for Pro search mode with enhanced reasoning.

code_results: list[dict[str, Any]] = None

code_snippets: list[dict[str, Any]] = None

cross_references: list[dict[str, Any]] = None

current_step: int = None

execution_plan: list[dict[str, Any]] = None

follow_up_queries: list[str] = None

model_selection_rationale: str | None = None

reasoning_traces: list[str] = None

selected_model: ModelChoice | None = None

class prebuilt.perplexity.base.state.QueryAnalysis(/, **data: Any)

Bases: pydantic.BaseModel

Results of query analysis.

clarifying_questions: list[str] = None

complexity_score: float = None

decomposed_steps: list[str] = None

original_query: str = None

query_type: QueryType = None

requires_real_time: bool = None

requires_reasoning: bool = None

requires_tools: bool = None

suggested_mode: SearchMode = None

class prebuilt.perplexity.base.state.QueryType

Bases: str, enum.Enum

Types of queries that can be processed.

CODE_RELATED = 'code_related'

COMPLEX_REASONING = 'complex_reasoning'

CONVERSATIONAL = 'conversational'

MATHEMATICAL = 'mathematical'

MULTI_STEP = 'multi_step'

PROJECT = 'project'

REAL_TIME = 'real_time'

RESEARCH = 'research'

SIMPLE_FACTUAL = 'simple_factual'

class prebuilt.perplexity.base.state.ResearchState(/, **data: Any)

Bases: PerplexityBaseState

State for deep research mode.

contradictions: list[dict[str, Any]] = None

coverage_completeness: float = None

patterns: list[dict[str, Any]] = None

report_outline: dict[str, Any] | None = None

report_sections: dict[str, str] = None

research_roadmap: list[dict[str, Any]] = None

research_topic: str = None

source_coverage: dict[str, list[str]] = None

source_diversity_score: float = None

subtopics: list[str] = None

visual_elements: list[dict[str, Any]] = None

class prebuilt.perplexity.base.state.SearchMode

Bases: str, enum.Enum

Search execution modes.

BASIC = 'basic'

DEEP_RESEARCH = 'deep_research'

LABS = 'labs'

PRO = 'pro'

class prebuilt.perplexity.base.state.SearchResult(/, **data: Any)

Bases: pydantic.BaseModel

Represents a search result from web search or retrieval.

documents: list[langchain_core.documents.Document] = None

metadata: dict[str, Any] = None

query: str = None

raw_results: list[dict[str, Any]] = None

search_type: str = None

timestamp: datetime.datetime = None

class prebuilt.perplexity.base.state.SourceTrustLevel

Bases: str, enum.Enum

Trust levels for information sources.

STANDARD = 'standard'

TRUSTED = 'trusted'

UNVERIFIED = 'unverified'

VERIFIED = 'verified'