"""
Kateryna FastAPI Integration Example
====================================

Shows how to add epistemic uncertainty to a FastAPI RAG endpoint.
"""

from typing import Optional
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

# Uncomment when running:
# from kateryna import EpistemicDetector, TernaryState
# from kateryna.adapters.ollama import OllamaSyncEpistemicAdapter

app = FastAPI(title="Kateryna RAG API")


# === Request/Response Models ===

class AskRequest(BaseModel):
    question: str
    workspace: Optional[str] = None


class AskResponse(BaseModel):
    answer: Optional[str]
    abstained: bool
    state: str  # "CONFIDENT", "UNCERTAIN", "OVERCONFIDENT"
    confidence: float
    grounded: bool
    reason: str
    danger_zone: bool


# === Simulated Dependencies (replace with your actual implementations) ===

def get_detector():
    """Get epistemic detector instance."""
    from kateryna import EpistemicDetector
    return EpistemicDetector()


def search_knowledge_base(question: str, workspace: str = None) -> tuple[list, float]:
    """
    Search your vector DB. Returns (chunks, confidence).

    Replace this with your actual RAG retrieval:
    - ChromaDB
    - Pinecone
    - Weaviate
    - pgvector
    - etc.
    """
    # Simulated - replace with real implementation
    return [
        {"content": "Example documentation...", "distance": 0.2}
    ], 0.75


def generate_response(question: str, chunks: list) -> str:
    """
    Generate LLM response.

    Replace this with your actual LLM:
    - OpenAI
    - Anthropic
    - Ollama
    - etc.
    """
    # Simulated - replace with real implementation
    context = "\n".join(c.get("content", "") for c in chunks)
    return f"Based on the documentation: {context[:100]}..."


# === API Endpoints ===

@app.post("/ask", response_model=AskResponse)
async def ask(request: AskRequest):
    """
    RAG endpoint with epistemic uncertainty detection.

    Returns abstention message instead of hallucination when:
    - No relevant chunks found
    - Low retrieval confidence
    - Model shows overconfidence without grounding (DANGER ZONE)
    """
    detector = get_detector()

    # Pre-check: Is this question even answerable?
    should_abstain, reason = detector.should_abstain_on_question(request.question)
    if should_abstain:
        return AskResponse(
            answer=None,
            abstained=True,
            state="UNCERTAIN",
            confidence=0.0,
            grounded=False,
            reason=reason,
            danger_zone=False
        )

    # RAG retrieval
    chunks, rag_confidence = search_knowledge_base(
        request.question,
        request.workspace
    )

    # Early abstention if no chunks
    if not chunks:
        return AskResponse(
            answer=None,
            abstained=True,
            state="UNCERTAIN",
            confidence=0.0,
            grounded=False,
            reason="No relevant documentation found",
            danger_zone=False
        )

    # Generate response
    response = generate_response(request.question, chunks)

    # Epistemic analysis
    state = detector.analyze(
        text=response,
        question=request.question,
        retrieval_confidence=rag_confidence,
        chunks_found=len(chunks)
    )

    # Check for danger zone (confident hallucination)
    if state.is_danger_zone:
        return AskResponse(
            answer=None,
            abstained=True,
            state="OVERCONFIDENT",
            confidence=state.confidence,
            grounded=False,
            reason="DANGER: Confident response without adequate grounding",
            danger_zone=True
        )

    # Check for general abstention
    if state.should_abstain:
        return AskResponse(
            answer=None,
            abstained=True,
            state=state.state.name,
            confidence=state.confidence,
            grounded=state.grounded,
            reason=state.reason,
            danger_zone=False
        )

    # Return grounded response
    return AskResponse(
        answer=response,
        abstained=False,
        state=state.state.name,
        confidence=state.confidence,
        grounded=state.grounded,
        reason=state.reason,
        danger_zone=False
    )


@app.get("/health")
async def health():
    return {"status": "healthy", "epistemic": "enabled"}


# === Run with: uvicorn fastapi_integration:app --reload ===

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)
