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"""LangGraph state definitions for the research workflow."""
import operator
from typing import Annotated, Literal, TypedDict
from langchain_core.messages import BaseMessage
from pydantic import BaseModel, Field
# --- Domain Models (Inner Objects) ---
# We use Pydantic for strict validation of the data objects
class Hypothesis(BaseModel):
"""A research hypothesis with evidence tracking."""
id: str = Field(description="Unique identifier for the hypothesis")
statement: str = Field(description="The hypothesis statement")
status: Literal["proposed", "validating", "confirmed", "refuted"] = Field(
default="proposed", description="Current validation status"
)
confidence: float = Field(default=0.0, ge=0.0, le=1.0, description="Confidence score (0.0-1.0)")
supporting_evidence_ids: list[str] = Field(default_factory=list)
contradicting_evidence_ids: list[str] = Field(default_factory=list)
reasoning: str | None = Field(default=None, description="Reasoning for current status")
class Conflict(BaseModel):
"""A detected contradiction between sources."""
id: str = Field(description="Unique identifier for the conflict")
description: str = Field(description="Description of the contradiction")
source_a_id: str = Field(description="ID of the first conflicting source")
source_b_id: str = Field(description="ID of the second conflicting source")
status: Literal["open", "resolved"] = Field(default="open")
resolution: str | None = Field(default=None, description="Resolution explanation if resolved")
# --- Graph State (The Blackboard) ---
# LangGraph requires TypedDict for the main state object to support
# partial updates and reducers (operator.add).
class ResearchState(TypedDict):
"""The cognitive state shared across all graph nodes.
Fields with 'Annotated[..., operator.add]' are reducers:
returning a dict with these keys from a node will APPEND to the list
instead of overwriting it.
"""
# Immutable context
query: str
# Cognitive state (The "Blackboard")
# Note: We store these as lists of Pydantic models.
# Nodes should be careful to update existing items by ID if needed,
# or we might need a custom reducer for merging by ID.
# For now, we'll append and let the synthesizer filter the latest.
hypotheses: Annotated[list[Hypothesis], operator.add]
conflicts: Annotated[list[Conflict], operator.add]
# Evidence links (actual content stored in ChromaDB)
evidence_ids: Annotated[list[str], operator.add]
# Chat history (for LLM context)
messages: Annotated[list[BaseMessage], operator.add]
# Control flow
next_step: Literal["search", "judge", "resolve", "synthesize", "finish"]
iteration_count: int
max_iterations: int
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