Building an ethics-aware chatbot: complete tutorial

ethics-chatbot/
├── main.py            # FastAPI app and routes
├── chatbot.py         # LLM generation layer
├── rail_guard.py      # RAIL Score evaluation middleware
├── router.py          # Response routing logic
├── config.py          # Threshold profiles and settings
├── audit.py           # Audit logging
├── requirements.txt
└── tests/
    └── test_safety_pipeline.py

# requirements.txt
fastapi>=0.111.0
uvicorn[standard]>=0.29.0
openai>=1.50.0
rail-score>=2.4.0
pydantic>=2.0.0
python-dotenv>=1.0.0
httpx>=0.27.0
pytest>=8.0.0
pytest-asyncio>=0.23.0

# config.py
import os
from dataclasses import dataclass, field
from dotenv import load_dotenv

load_dotenv()

OPENAI_API_KEY: str = os.environ["OPENAI_API_KEY"]
RAIL_API_KEY: str = os.environ["RAIL_API_KEY"]
LLM_MODEL: str = os.getenv("LLM_MODEL", "gpt-4o-mini")
MAX_REGENERATION_ATTEMPTS: int = int(os.getenv("MAX_REGEN_ATTEMPTS", "2"))


@dataclass
class ThresholdProfile:
    """Per-deployment safety thresholds."""
    name: str
    overall_min: float
    confidence_min: float
    # Dimension floors override the overall_min for specific dimensions
    dimension_floors: dict = field(default_factory=dict)
    # Dimensions that trigger immediate human escalation when below floor
    escalate_dims: list = field(default_factory=list)


PROFILES: dict[str, ThresholdProfile] = {
    "general": ThresholdProfile(
        name="general",
        overall_min=7.0,
        confidence_min=0.70,
    ),
    "customer_support": ThresholdProfile(
        name="customer_support",
        overall_min=7.5,
        confidence_min=0.75,
        dimension_floors={"user_impact": 7.0, "transparency": 7.0},
        escalate_dims=[],
    ),
    "healthcare": ThresholdProfile(
        name="healthcare",
        overall_min=8.0,
        confidence_min=0.80,
        dimension_floors={"safety": 9.0, "reliability": 8.5, "accountability": 8.0},
        escalate_dims=["safety"],  # Any safety score < floor → human handoff
    ),
    "financial": ThresholdProfile(
        name="financial",
        overall_min=7.5,
        confidence_min=0.80,
        dimension_floors={"accountability": 8.0, "transparency": 8.0, "reliability": 8.0},
        escalate_dims=["accountability"],
    ),
    "children": ThresholdProfile(
        name="children",
        overall_min=8.5,
        confidence_min=0.85,
        dimension_floors={"safety": 9.5, "inclusivity": 8.5, "fairness": 8.5},
        escalate_dims=["safety", "fairness"],
    ),
}

SYSTEM_PROMPTS: dict[str, str] = {
    "general": "You are a helpful assistant. Provide accurate, balanced, and thoughtful responses.",
    "customer_support": (
        "You are a customer support agent. Be helpful, concise, and professional. "
        "Escalate issues you cannot resolve. Do not make promises about refunds or "
        "policy exceptions without authorization."
    ),
    "healthcare": (
        "You are a health information assistant. Provide evidence-based health information. "
        "Always recommend consulting a qualified healthcare provider for medical decisions. "
        "Never diagnose conditions or prescribe treatments."
    ),
    "financial": (
        "You are a financial information assistant. Provide general financial education. "
        "Always clarify that this is not financial advice and recommend consulting a "
        "licensed financial advisor for personal investment decisions."
    ),
}

# chatbot.py
import logging
from openai import OpenAI
from config import OPENAI_API_KEY, LLM_MODEL, SYSTEM_PROMPTS

logger = logging.getLogger(__name__)
openai_client = OpenAI(api_key=OPENAI_API_KEY)


def generate_response(
    user_message: str,
    conversation_history: list[dict],
    deployment: str = "general",
    regeneration_hint: str | None = None,
) -> str:
    """
    Generate a candidate response from the LLM.

    When called for regeneration, regeneration_hint contains the RAIL evaluation
    feedback so the model can self-correct on the second attempt.
    """
    system_prompt = SYSTEM_PROMPTS.get(deployment, SYSTEM_PROMPTS["general"])

    if regeneration_hint:
        system_prompt += (
            f"\n\nIMPORTANT: Your previous response did not meet safety standards. "
            f"Issues identified: {regeneration_hint}\n"
            f"Generate an improved response that addresses these specific concerns."
        )

    messages = [{"role": "system", "content": system_prompt}]
    messages.extend(conversation_history)
    messages.append({"role": "user", "content": user_message})

    try:
        completion = openai_client.chat.completions.create(
            model=LLM_MODEL,
            messages=messages,
            temperature=0.3,
            max_tokens=1024,
        )
        response = completion.choices[0].message.content
        logger.debug(f"Generated response ({len(response)} chars)")
        return response

    except Exception as e:
        logger.error(f"LLM generation failed: {e}")
        raise

# rail_guard.py
import logging
from dataclasses import dataclass
from enum import Enum

from rail_score import RAILClient
from rail_score.exceptions import RAILAPIError, RAILRateLimitError
from config import RAIL_API_KEY, ThresholdProfile

logger = logging.getLogger(__name__)
rail_client = RAILClient(api_key=RAIL_API_KEY)


class RouteDecision(str, Enum):
    DELIVER = "deliver"          # Score passes threshold -- send to user
    REGENERATE = "regenerate"    # Score borderline -- regenerate with hints
    DISCLAIMER = "disclaimer"    # Score low but not critical -- attach disclaimer
    ESCALATE = "escalate"        # Critical dimension triggered -- route to human
    BLOCK = "block"              # Score too low to recover -- return safe fallback


@dataclass
class EvaluationOutcome:
    decision: RouteDecision
    rail_score: float
    confidence: float
    dimension_scores: dict
    flagged_dimensions: list[str]
    escalate_dimensions: list[str]
    explanations: dict
    regeneration_hint: str
    request_id: str


def evaluate_response(
    prompt: str,
    response: str,
    profile: ThresholdProfile,
    depth: str = "deep",
) -> EvaluationOutcome:
    """
    Evaluate a candidate response and return a routing decision.

    Uses depth="basic" for fast-path cases and depth="deep" when more
    analysis is needed (configured via caller).
    """
    try:
        result = rail_client.evaluate(
            prompt=prompt,
            response=response,
            dimensions="all",
            depth=depth,
        )
    except RAILRateLimitError:
        logger.warning("RAIL rate limit hit -- failing open with warning")
        # Fail open: deliver with a logged warning rather than blocking the user
        return EvaluationOutcome(
            decision=RouteDecision.DELIVER,
            rail_score=-1.0,
            confidence=-1.0,
            dimension_scores={},
            flagged_dimensions=[],
            escalate_dimensions=[],
            explanations={"_error": "Evaluation unavailable (rate limit)"},
            regeneration_hint="",
            request_id="rate_limited",
        )
    except RAILAPIError as e:
        logger.error(f"RAIL evaluation error: {e}")
        return EvaluationOutcome(
            decision=RouteDecision.DELIVER,
            rail_score=-1.0,
            confidence=-1.0,
            dimension_scores={},
            flagged_dimensions=[],
            escalate_dimensions=[],
            explanations={"_error": str(e)},
            regeneration_hint="",
            request_id="api_error",
        )

    # Identify dimensions below their configured floors
    flagged = []
    for dim, score in result.dimensions.items():
        floor = profile.dimension_floors.get(dim, profile.overall_min)
        if score < floor:
            flagged.append(dim)

    # Identify dimensions that require immediate human escalation
    must_escalate = [
        dim for dim in profile.escalate_dims
        if result.dimensions.get(dim, 10.0) < profile.dimension_floors.get(dim, profile.overall_min)
    ]

    # Build a regeneration hint from explanations of flagged dimensions
    hint_parts = []
    for dim in flagged:
        explanation = result.explanations.get(dim, "")
        if explanation:
            hint_parts.append(f"{dim}: {explanation}")
    regeneration_hint = " | ".join(hint_parts)

    # Routing decision logic
    if must_escalate:
        decision = RouteDecision.ESCALATE
    elif result.rail_score >= profile.overall_min and result.confidence >= profile.confidence_min:
        decision = RouteDecision.DELIVER
    elif result.rail_score >= profile.overall_min - 1.5:
        # Within 1.5 points of threshold -- attach disclaimer rather than blocking
        decision = RouteDecision.DISCLAIMER
    elif result.rail_score >= 4.0:
        # Recoverable -- try regeneration
        decision = RouteDecision.REGENERATE
    else:
        # Below recovery threshold
        decision = RouteDecision.BLOCK

    return EvaluationOutcome(
        decision=decision,
        rail_score=result.rail_score,
        confidence=result.confidence,
        dimension_scores=result.dimensions,
        flagged_dimensions=flagged,
        escalate_dimensions=must_escalate,
        explanations=result.explanations,
        regeneration_hint=regeneration_hint,
        request_id=result.request_id,
    )

# router.py
import logging
from chatbot import generate_response
from rail_guard import evaluate_response, EvaluationOutcome, RouteDecision
from config import ThresholdProfile, MAX_REGENERATION_ATTEMPTS

logger = logging.getLogger(__name__)

SAFE_FALLBACK = (
    "I'm not able to provide a helpful response to that question in a way that "
    "meets our safety standards. If you need assistance, please reach out to our "
    "support team directly."
)

ESCALATION_MESSAGE = (
    "This question involves sensitive content that I want to make sure is handled "
    "carefully. I'm connecting you with a human specialist who can help you properly."
)

DISCLAIMER_TEMPLATE = (
    "{response}\n\n"
    "---\n"
    "*Note: This response is provided for informational purposes only. "
    "For decisions that affect your health, finances, or legal situation, "
    "please consult a qualified professional.*"
)


def route_response(
    user_message: str,
    conversation_history: list[dict],
    deployment: str,
    profile: ThresholdProfile,
) -> dict:
    """
    Full pipeline: generate → evaluate → route.

    Returns a dict with the final response text, routing metadata,
    and audit data for logging.
    """
    audit_trail = {
        "user_message": user_message,
        "deployment": deployment,
        "attempts": [],
    }

    # Generation + evaluation loop with regeneration
    for attempt in range(MAX_REGENERATION_ATTEMPTS + 1):
        regeneration_hint = None
        if attempt > 0:
            prev_outcome: EvaluationOutcome = audit_trail["attempts"][-1]["outcome"]
            regeneration_hint = prev_outcome.regeneration_hint
            logger.info(f"Regeneration attempt {attempt} with hint: {regeneration_hint[:120]}...")

        candidate = generate_response(
            user_message=user_message,
            conversation_history=conversation_history,
            deployment=deployment,
            regeneration_hint=regeneration_hint,
        )

        depth = "basic" if attempt == 0 and len(candidate) < 300 else "deep"
        outcome = evaluate_response(
            prompt=user_message,
            response=candidate,
            profile=profile,
            depth=depth,
        )

        audit_trail["attempts"].append({
            "attempt": attempt,
            "candidate_length": len(candidate),
            "outcome": outcome,
            "rail_score": outcome.rail_score,
            "decision": outcome.decision,
        })

        logger.info(
            f"Attempt {attempt}: score={outcome.rail_score:.1f} decision={outcome.decision} "
            f"request_id={outcome.request_id}"
        )

        if outcome.decision == RouteDecision.DELIVER:
            audit_trail["final_decision"] = "delivered"
            return {
                "response": candidate,
                "delivered": True,
                "escalated": False,
                "rail_score": outcome.rail_score,
                "request_id": outcome.request_id,
                "audit": audit_trail,
            }

        if outcome.decision == RouteDecision.DISCLAIMER:
            audit_trail["final_decision"] = "delivered_with_disclaimer"
            return {
                "response": DISCLAIMER_TEMPLATE.format(response=candidate),
                "delivered": True,
                "escalated": False,
                "rail_score": outcome.rail_score,
                "request_id": outcome.request_id,
                "audit": audit_trail,
            }

        if outcome.decision == RouteDecision.ESCALATE:
            audit_trail["final_decision"] = "escalated"
            return {
                "response": ESCALATION_MESSAGE,
                "delivered": False,
                "escalated": True,
                "escalate_dimensions": outcome.escalate_dimensions,
                "rail_score": outcome.rail_score,
                "request_id": outcome.request_id,
                "audit": audit_trail,
            }

        # REGENERATE -- loop continues if attempts remain
        if attempt == MAX_REGENERATION_ATTEMPTS:
            break

    # All regeneration attempts exhausted or BLOCK decision
    audit_trail["final_decision"] = "blocked"
    return {
        "response": SAFE_FALLBACK,
        "delivered": False,
        "escalated": False,
        "rail_score": audit_trail["attempts"][-1]["rail_score"],
        "request_id": audit_trail["attempts"][-1]["outcome"].request_id,
        "audit": audit_trail,
    }

# main.py
import logging
import uuid
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from router import route_response
from audit import log_interaction
from config import PROFILES

logging.basicConfig(level=logging.INFO)
app = FastAPI(title="Ethics-Aware Chatbot", version="1.0.0")


class ChatRequest(BaseModel):
    message: str
    conversation_history: list[dict] = []
    deployment: str = "general"
    session_id: str | None = None


class ChatResponse(BaseModel):
    response: str
    rail_score: float
    delivered: bool
    escalated: bool
    request_id: str
    session_id: str


@app.post("/chat", response_model=ChatResponse)
async def chat(request: ChatRequest):
    if request.deployment not in PROFILES:
        raise HTTPException(status_code=400, detail=f"Unknown deployment: {request.deployment}")

    profile = PROFILES[request.deployment]
    session_id = request.session_id or str(uuid.uuid4())

    result = route_response(
        user_message=request.message,
        conversation_history=request.conversation_history,
        deployment=request.deployment,
        profile=profile,
    )

    # Audit every interaction regardless of routing decision
    log_interaction(
        session_id=session_id,
        user_message=request.message,
        final_response=result["response"],
        rail_score=result["rail_score"],
        delivered=result["delivered"],
        escalated=result["escalated"],
        request_id=result["request_id"],
        audit_trail=result["audit"],
    )

    return ChatResponse(
        response=result["response"],
        rail_score=result["rail_score"],
        delivered=result["delivered"],
        escalated=result["escalated"],
        request_id=result["request_id"],
        session_id=session_id,
    )


@app.get("/health")
async def health():
    return {"status": "ok"}

# audit.py
import json
import logging
from datetime import datetime, timezone

logger = logging.getLogger("rail.audit")


def log_interaction(
    session_id: str,
    user_message: str,
    final_response: str,
    rail_score: float,
    delivered: bool,
    escalated: bool,
    request_id: str,
    audit_trail: dict,
) -> None:
    """
    Write a structured audit log entry for every chatbot interaction.

    In production, replace the logger call with writes to your audit
    store (BigQuery, PostgreSQL, CloudWatch, etc.).
    """
    entry = {
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "session_id": session_id,
        "rail_request_id": request_id,
        "rail_score": rail_score,
        "delivered": delivered,
        "escalated": escalated,
        "attempt_count": len(audit_trail.get("attempts", [])),
        "final_decision": audit_trail.get("final_decision"),
        "deployment": audit_trail.get("deployment"),
        # Truncate for log size; store full text in a separate store if needed
        "user_message_snippet": user_message[:200],
        "response_snippet": final_response[:200],
    }
    logger.info(json.dumps(entry))

# tests/test_safety_pipeline.py
import pytest
from unittest.mock import MagicMock, patch
from rail_guard import evaluate_response, RouteDecision
from router import route_response
from config import PROFILES


# ----- Fixtures -----

@pytest.fixture
def general_profile():
    return PROFILES["general"]


@pytest.fixture
def healthcare_profile():
    return PROFILES["healthcare"]


# ----- rail_guard.py tests -----

def make_mock_result(score: float, dimensions: dict, explanations: dict = None, confidence: float = 0.9):
    result = MagicMock()
    result.rail_score = score
    result.confidence = confidence
    result.dimensions = dimensions
    result.explanations = explanations or {}
    result.request_id = "test-req-001"
    return result


@patch("rail_guard.rail_client")
def test_high_scoring_response_delivers(mock_client, general_profile):
    mock_client.evaluate.return_value = make_mock_result(
        score=8.5,
        dimensions={"fairness": 9.0, "safety": 9.0, "reliability": 8.0,
                    "transparency": 8.0, "privacy": 8.0, "accountability": 8.0,
                    "inclusivity": 9.0, "user_impact": 8.5},
    )
    outcome = evaluate_response("Hello", "Hi there!", general_profile)
    assert outcome.decision == RouteDecision.DELIVER


@patch("rail_guard.rail_client")
def test_low_scoring_response_blocks(mock_client, general_profile):
    mock_client.evaluate.return_value = make_mock_result(
        score=3.1,
        dimensions={"fairness": 3.0, "safety": 2.0, "reliability": 4.0,
                    "transparency": 3.0, "privacy": 5.0, "accountability": 3.0,
                    "inclusivity": 3.0, "user_impact": 2.5},
    )
    outcome = evaluate_response("Tell me how to...", "Here is how...", general_profile)
    assert outcome.decision == RouteDecision.BLOCK


@patch("rail_guard.rail_client")
def test_healthcare_safety_floor_triggers_escalation(mock_client, healthcare_profile):
    # Safety is 7.5 -- below the healthcare floor of 9.0 -- even though overall is 8.0
    mock_client.evaluate.return_value = make_mock_result(
        score=8.0,
        dimensions={"fairness": 9.0, "safety": 7.5, "reliability": 9.0,
                    "transparency": 8.0, "privacy": 8.0, "accountability": 8.5,
                    "inclusivity": 8.0, "user_impact": 7.5},
        explanations={"safety": "Response does not recommend consulting a clinician for a medical decision."},
    )
    outcome = evaluate_response("Is it safe to...", "Yes, you can...", healthcare_profile)
    assert outcome.decision == RouteDecision.ESCALATE
    assert "safety" in outcome.escalate_dimensions


@patch("rail_guard.rail_client")
def test_borderline_response_gets_disclaimer(mock_client, general_profile):
    # Score is 5.8 -- within 1.5 of the 7.0 threshold
    mock_client.evaluate.return_value = make_mock_result(
        score=5.8,
        dimensions={"fairness": 6.0, "safety": 7.0, "reliability": 5.5,
                    "transparency": 5.0, "privacy": 7.0, "accountability": 5.0,
                    "inclusivity": 6.0, "user_impact": 5.5},
    )
    outcome = evaluate_response("...", "...", general_profile)
    assert outcome.decision == RouteDecision.DISCLAIMER


# ----- router.py integration tests -----

@patch("router.generate_response")
@patch("router.evaluate_response")
def test_first_attempt_delivers(mock_eval, mock_gen, general_profile):
    mock_gen.return_value = "A perfectly fine response."
    mock_outcome = MagicMock()
    mock_outcome.decision = RouteDecision.DELIVER
    mock_outcome.rail_score = 8.2
    mock_outcome.request_id = "req-001"
    mock_eval.return_value = mock_outcome

    result = route_response(
        user_message="What is the speed of light?",
        conversation_history=[],
        deployment="general",
        profile=general_profile,
    )

    assert result["delivered"] is True
    assert result["escalated"] is False
    assert mock_gen.call_count == 1


@patch("router.generate_response")
@patch("router.evaluate_response")
def test_regeneration_loop(mock_eval, mock_gen, general_profile):
    mock_gen.return_value = "A response that needs work."

    # First evaluation: REGENERATE. Second: DELIVER.
    regen_outcome = MagicMock()
    regen_outcome.decision = RouteDecision.REGENERATE
    regen_outcome.rail_score = 5.5
    regen_outcome.request_id = "req-001"
    regen_outcome.regeneration_hint = "reliability: Missing citation."

    deliver_outcome = MagicMock()
    deliver_outcome.decision = RouteDecision.DELIVER
    deliver_outcome.rail_score = 8.1
    deliver_outcome.request_id = "req-002"

    mock_eval.side_effect = [regen_outcome, deliver_outcome]

    result = route_response(
        user_message="Explain quantum entanglement.",
        conversation_history=[],
        deployment="general",
        profile=general_profile,
    )

    assert result["delivered"] is True
    assert mock_gen.call_count == 2  # Original + one regeneration

pytest tests/ -v

# audit.py — production extension example
import json
from google.cloud import bigquery  # or your preferred store

BQ_CLIENT = bigquery.Client()
AUDIT_TABLE = "your-project.chatbot_audit.interactions"


def log_interaction(session_id, user_message, final_response,
                    rail_score, delivered, escalated, request_id, audit_trail):
    row = {
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "session_id": session_id,
        "rail_request_id": request_id,
        "rail_score": rail_score,
        "delivered": delivered,
        "escalated": escalated,
        "deployment": audit_trail.get("deployment"),
        "attempt_count": len(audit_trail.get("attempts", [])),
        "final_decision": audit_trail.get("final_decision"),
    }
    errors = BQ_CLIENT.insert_rows_json(AUDIT_TABLE, [row])
    if errors:
        logger.error(f"BQ audit write failed: {errors}")

-- Alert if block rate exceeds 8% in the last 24 hours
SELECT
  deployment,
  COUNTIF(final_decision = 'blocked') / COUNT(*) AS block_rate,
  AVG(rail_score) AS avg_rail_score,
  COUNT(*) AS total_interactions
FROM chatbot_audit.interactions
WHERE timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24 HOUR)
GROUP BY deployment
HAVING block_rate > 0.08
ORDER BY block_rate DESC

import asyncio
from rail_score import AsyncRAILClient

async_rail_client = AsyncRAILClient(api_key=os.environ["RAIL_API_KEY"])


async def streaming_chat_with_background_eval(user_message: str, profile):
    """
    Stream the LLM response to the UI while evaluating in the background.
    If evaluation fails, send a correction message.
    Only use this pattern for low-risk deployments.
    """
    candidate = generate_response(user_message, [], deployment="general")

    # Start evaluation concurrently
    eval_task = asyncio.create_task(
        async_rail_client.evaluate(
            prompt=user_message,
            response=candidate,
            dimensions="all",
            depth="basic",
        )
    )

    # Stream the response (simplified -- wire to your actual streaming transport)
    yield candidate

    # Wait for evaluation result
    result = await eval_task
    if result.rail_score < profile.overall_min:
        yield (
            "\n\n*Please note: This response may need review. "
            "A member of our team will follow up if needed.*"
        )

import hashlib
import json
from functools import lru_cache

@lru_cache(maxsize=1024)
def cached_evaluate(prompt_hash: str, response_hash: str, deployment: str) -> dict:
    """Cache RAIL evaluations for identical prompt+response pairs."""
    # Called by a wrapper that hashes the actual strings before calling this
    ...


def get_cache_key(prompt: str, response: str) -> tuple[str, str]:
    return (
        hashlib.sha256(prompt.encode()).hexdigest()[:16],
        hashlib.sha256(response.encode()).hexdigest()[:16],
    )