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MediGuard AI — Agentic RAG State
Enhanced LangGraph state for the guardrail → retrieve → grade → generate
pipeline that wraps the existing 6-agent clinical workflow.
"""
from __future__ import annotations
import operator
from typing import Annotated, Any
from typing_extensions import TypedDict
class AgenticRAGState(TypedDict, total=False):
"""State flowing through the agentic RAG graph."""
# ── Input ────────────────────────────────────────────────────────────
query: str
biomarkers: dict[str, float] | None
patient_context: dict[str, Any] | None
# ── Guardrail ────────────────────────────────────────────────────────
guardrail_score: float # 0-100 medical-relevance score
is_in_scope: bool # passed guardrail?
# ── Retrieval ────────────────────────────────────────────────────────
retrieved_documents: list[dict[str, Any]]
retrieval_attempts: int
max_retrieval_attempts: int
# ── Grading ──────────────────────────────────────────────────────────
grading_results: list[dict[str, Any]]
relevant_documents: list[dict[str, Any]]
needs_rewrite: bool
# ── Rewriting ────────────────────────────────────────────────────────
rewritten_query: str | None
# ── Generation / routing ─────────────────────────────────────────────
routing_decision: str # "analyze" | "rag_answer" | "out_of_scope"
final_answer: str | None
analysis_result: dict[str, Any] | None
# ── Metadata ─────────────────────────────────────────────────────────
trace_id: str | None
errors: Annotated[list[str], operator.add]
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