from langgraph.graph import StateGraph, END

def route_based_on_quality(state: WorkflowState) -> str:
    """Decide next step based on quality score."""
    if state["quality_score"] >= 0.8:
        return "publish"
    elif state["retry_count"] < 3:
        return "retry"
    else:
        return "manual_review"

workflow.add_conditional_edges(
    "quality_check",
    route_based_on_quality,
    {
        "publish": "publish_node",
        "retry": "generator",
        "manual_review": "review_queue"
    }
)

def route_after_quality_gate(state: AnalysisState) -> str:
    """Route based on quality gate result."""
    if state["quality_passed"]:
        return "compress_findings"
    elif state["retry_count"] < 2:
        return "supervisor"  # Retry
    else:
        return END  # Return partial results

workflow.add_conditional_edges(
    "quality_gate",
    route_after_quality_gate,
    {
        "compress_findings": "compress_findings",
        "supervisor": "supervisor",
        END: END
    }
)

def llm_call_with_retry(state):
    """Retry failed LLM calls."""
    try:
        result = call_llm(state["input"])
        state["output"] = result
        state["retry_count"] = 0
        return state
    except Exception as e:
        state["retry_count"] += 1
        state["error"] = str(e)
        return state

def should_retry(state) -> str:
    if state.get("output"):
        return "success"
    elif state["retry_count"] < 3:
        return "retry"
    else:
        return "failed"

workflow.add_conditional_edges(
    "llm_call",
    should_retry,
    {
        "success": "next_step",
        "retry": "llm_call",  # Loop back
        "failed": "error_handler"
    }
)

Sequential:    A → B → C              (simple edges)
Branching:     A → (B or C)           (conditional edges)
Looping:       A → B → A              (retry logic)
Convergence:   (A or B) → C           (multiple inputs)
Diamond:       A → (B, C) → D         (parallel then merge)

def dynamic_router(state: WorkflowState) -> str:
    """Route based on multiple state conditions."""
    if state.get("error"):
        return "error_handler"
    if not state.get("validated"):
        return "validator"
    if state["confidence"] < 0.5:
        return "enhance"
    return "finalize"

from langgraph.types import Command
from typing import Literal

undefined

from sentence_transformers import SentenceTransformer
import numpy as np

embedder = SentenceTransformer("all-MiniLM-L6-v2")

# Calculate cosine similarities
similarities = {}
for route, route_embedding in ROUTE_EMBEDDINGS.items():
    similarity = np.dot(query_embedding, route_embedding) / (
        np.linalg.norm(query_embedding) * np.linalg.norm(route_embedding)
    )
    similarities[route] = similarity

# Return highest similarity route
best_route = max(similarities, key=similarities.get)

# Optional: threshold check
if similarities[best_route] < 0.3:
    return "general"  # Fallback

return best_route

undefined