Real-Time Policy Monitoring

pip install prisma-ai fastapi uvicorn

export PRISMA_API_KEY="prisma_sk_your_key_here"
export PRISMA_BASE_URL="https://your-prisma-instance.example.com"
export PRISMA_WORKSPACE_ID="your-workspace-id"

from prisma_ai import Evaluators

response_compliance = Evaluators.custom(
    name="response-compliance",
    options=["compliant", "non_compliant", "needs_review"],
    evaluation_system_prompt=(
        "You are a contact center compliance reviewer. Your task is to "
        "determine whether an agent's response follows company communication "
        "policies.\n\n"
        "The policies require:\n"
        "1. PROFESSIONAL TONE: The agent is courteous, empathetic, and avoids "
        "dismissive or confrontational language.\n"
        "2. ACCURATE INFORMATION: The agent provides correct information about "
        "products, services, and procedures. No guessing or speculation.\n"
        "3. REQUIRED DISCLOSURES: When discussing pricing, contracts, or policy "
        "changes, the agent includes all required disclaimers.\n"
        "4. PROHIBITED LANGUAGE: The agent does not make guarantees, promises "
        "about timelines without verification, or statements that could create "
        "legal liability.\n\n"
        "Classify the response into one of: {evaluation_options}\n\n"
        "- compliant: All policies are followed.\n"
        "- non_compliant: One or more policies are clearly violated.\n"
        "- needs_review: The response is in a gray area where compliance "
        "cannot be confidently determined without additional context."
    ),
    evaluation_human_prompt=(
        "Review the following contact center interaction for policy compliance.\n\n"
        "Customer message:\n{query}\n\n"
        "Agent response:\n{response}\n\n"
        "Classify the response as one of: {evaluation_options}\n\n"
        "For each policy (tone, accuracy, disclosures, prohibited language), "
        "state whether it was followed or violated, then provide your classification."
    ),
    reeval_system_prompt=(
        "You are a senior contact center compliance reviewer performing a "
        "second-pass review. Apply the communication policies strictly.\n\n"
        "Pay particular attention to implicit guarantees -- statements that "
        "stop short of a direct promise but still set customer expectations "
        "beyond what the agent can deliver. These are the most common "
        "borderline violations.\n\n"
        "You have access to decisions from previous compliance reviews to "
        "calibrate your judgment.\n\n"
        "Classify the response into one of: {evaluation_options}"
    ),
    reeval_human_prompt=(
        "Re-evaluate this contact center interaction for policy compliance "
        "using context from previous compliance reviews.\n\n"
        "Customer message:\n{query}\n\n"
        "Agent response:\n{response}\n\n"
        "Relevant past compliance review decisions:\n{memories}\n\n"
        "Based on the interaction and review history, provide your final "
        "classification with specific policy findings."
    ),
)

escalation_procedure = Evaluators.custom(
    name="escalation-procedure",
    options=["procedure_followed", "procedure_violated", "not_applicable"],
    evaluation_system_prompt=(
        "You are an escalation process reviewer for a contact center. Your "
        "task is to determine whether an agent followed the correct escalation "
        "procedure when an escalation trigger was present.\n\n"
        "Escalation triggers include:\n"
        "- Customer explicitly requests a supervisor or manager\n"
        "- Customer threatens legal action or mentions an attorney\n"
        "- Customer reports a safety concern or physical harm\n"
        "- Customer expresses intent to contact media or regulators\n\n"
        "When a trigger is present, the required procedure is:\n"
        "1. ACKNOWLEDGE: The agent acknowledges the customer's concern "
        "without being dismissive.\n"
        "2. DOCUMENT: The agent confirms the details of the situation.\n"
        "3. TRANSFER: The agent initiates a transfer to the appropriate "
        "escalation path (supervisor, legal, safety team) without delay.\n\n"
        "If no escalation trigger is present, classify as not_applicable.\n\n"
        "Classify the response into one of: {evaluation_options}\n\n"
        "- procedure_followed: An escalation trigger was present and the "
        "agent followed all three steps.\n"
        "- procedure_violated: An escalation trigger was present but the "
        "agent skipped one or more steps, or attempted to resolve the issue "
        "without escalating.\n"
        "- not_applicable: No escalation trigger was present in the interaction."
    ),
    evaluation_human_prompt=(
        "Review the following contact center interaction for escalation "
        "procedure compliance.\n\n"
        "Customer message:\n{query}\n\n"
        "Agent response:\n{response}\n\n"
        "Classify the response as one of: {evaluation_options}\n\n"
        "First determine whether an escalation trigger is present. If so, "
        "assess each step (acknowledge, document, transfer). Then provide "
        "your classification."
    ),
    reeval_system_prompt=(
        "You are a senior escalation process reviewer performing a "
        "second-pass review. Apply the escalation procedure strictly.\n\n"
        "The most critical violation is an agent who attempts to de-escalate "
        "or resolve the issue themselves when a transfer is required. Even "
        "well-intentioned attempts to help the customer directly are procedure "
        "violations when a trigger is present.\n\n"
        "You have access to decisions from previous escalation reviews to "
        "calibrate your judgment.\n\n"
        "Classify the response into one of: {evaluation_options}"
    ),
    reeval_human_prompt=(
        "Re-evaluate this contact center interaction for escalation procedure "
        "compliance using context from previous escalation reviews.\n\n"
        "Customer message:\n{query}\n\n"
        "Agent response:\n{response}\n\n"
        "Relevant past escalation review decisions:\n{memories}\n\n"
        "Based on the interaction and review history, provide your final "
        "classification. Identify the specific escalation trigger (if any) "
        "and assess each procedure step."
    ),
)

"""Real-time policy monitoring service for contact center interactions."""

import logging
from contextlib import asynccontextmanager

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

import prisma_ai
from prisma_ai import EvaluationConfig, Evaluators

logger = logging.getLogger("policy-monitor")

# ---------------------------------------------------------------------------
# 1. Policy metric definitions
# ---------------------------------------------------------------------------
response_compliance = Evaluators.custom(
    name="response-compliance",
    options=["compliant", "non_compliant", "needs_review"],
    evaluation_system_prompt=(
        "You are a contact center compliance reviewer. Your task is to "
        "determine whether an agent's response follows company communication "
        "policies.\n\n"
        "The policies require:\n"
        "1. PROFESSIONAL TONE: The agent is courteous, empathetic, and avoids "
        "dismissive or confrontational language.\n"
        "2. ACCURATE INFORMATION: The agent provides correct information about "
        "products, services, and procedures. No guessing or speculation.\n"
        "3. REQUIRED DISCLOSURES: When discussing pricing, contracts, or policy "
        "changes, the agent includes all required disclaimers.\n"
        "4. PROHIBITED LANGUAGE: The agent does not make guarantees, promises "
        "about timelines without verification, or statements that could create "
        "legal liability.\n\n"
        "Classify the response into one of: {evaluation_options}\n\n"
        "- compliant: All policies are followed.\n"
        "- non_compliant: One or more policies are clearly violated.\n"
        "- needs_review: The response is in a gray area where compliance "
        "cannot be confidently determined without additional context."
    ),
    evaluation_human_prompt=(
        "Review the following contact center interaction for policy compliance.\n\n"
        "Customer message:\n{query}\n\n"
        "Agent response:\n{response}\n\n"
        "Classify the response as one of: {evaluation_options}\n\n"
        "For each policy (tone, accuracy, disclosures, prohibited language), "
        "state whether it was followed or violated, then provide your classification."
    ),
    reeval_system_prompt=(
        "You are a senior contact center compliance reviewer performing a "
        "second-pass review. Apply the communication policies strictly.\n\n"
        "Pay particular attention to implicit guarantees -- statements that "
        "stop short of a direct promise but still set customer expectations "
        "beyond what the agent can deliver. These are the most common "
        "borderline violations.\n\n"
        "You have access to decisions from previous compliance reviews to "
        "calibrate your judgment.\n\n"
        "Classify the response into one of: {evaluation_options}"
    ),
    reeval_human_prompt=(
        "Re-evaluate this contact center interaction for policy compliance "
        "using context from previous compliance reviews.\n\n"
        "Customer message:\n{query}\n\n"
        "Agent response:\n{response}\n\n"
        "Relevant past compliance review decisions:\n{memories}\n\n"
        "Based on the interaction and review history, provide your final "
        "classification with specific policy findings."
    ),
)

escalation_procedure = Evaluators.custom(
    name="escalation-procedure",
    options=["procedure_followed", "procedure_violated", "not_applicable"],
    evaluation_system_prompt=(
        "You are an escalation process reviewer for a contact center. Your "
        "task is to determine whether an agent followed the correct escalation "
        "procedure when an escalation trigger was present.\n\n"
        "Escalation triggers include:\n"
        "- Customer explicitly requests a supervisor or manager\n"
        "- Customer threatens legal action or mentions an attorney\n"
        "- Customer reports a safety concern or physical harm\n"
        "- Customer expresses intent to contact media or regulators\n\n"
        "When a trigger is present, the required procedure is:\n"
        "1. ACKNOWLEDGE: The agent acknowledges the customer's concern "
        "without being dismissive.\n"
        "2. DOCUMENT: The agent confirms the details of the situation.\n"
        "3. TRANSFER: The agent initiates a transfer to the appropriate "
        "escalation path (supervisor, legal, safety team) without delay.\n\n"
        "If no escalation trigger is present, classify as not_applicable.\n\n"
        "Classify the response into one of: {evaluation_options}\n\n"
        "- procedure_followed: An escalation trigger was present and the "
        "agent followed all three steps.\n"
        "- procedure_violated: An escalation trigger was present but the "
        "agent skipped one or more steps, or attempted to resolve the issue "
        "without escalating.\n"
        "- not_applicable: No escalation trigger was present in the interaction."
    ),
    evaluation_human_prompt=(
        "Review the following contact center interaction for escalation "
        "procedure compliance.\n\n"
        "Customer message:\n{query}\n\n"
        "Agent response:\n{response}\n\n"
        "Classify the response as one of: {evaluation_options}\n\n"
        "First determine whether an escalation trigger is present. If so, "
        "assess each step (acknowledge, document, transfer). Then provide "
        "your classification."
    ),
    reeval_system_prompt=(
        "You are a senior escalation process reviewer performing a "
        "second-pass review. Apply the escalation procedure strictly.\n\n"
        "The most critical violation is an agent who attempts to de-escalate "
        "or resolve the issue themselves when a transfer is required. Even "
        "well-intentioned attempts to help the customer directly are procedure "
        "violations when a trigger is present.\n\n"
        "You have access to decisions from previous escalation reviews to "
        "calibrate your judgment.\n\n"
        "Classify the response into one of: {evaluation_options}"
    ),
    reeval_human_prompt=(
        "Re-evaluate this contact center interaction for escalation procedure "
        "compliance using context from previous escalation reviews.\n\n"
        "Customer message:\n{query}\n\n"
        "Agent response:\n{response}\n\n"
        "Relevant past escalation review decisions:\n{memories}\n\n"
        "Based on the interaction and review history, provide your final "
        "classification. Identify the specific escalation trigger (if any) "
        "and assess each procedure step."
    ),
)


# ---------------------------------------------------------------------------
# 2. FastAPI lifespan — Prisma init and shutdown
# ---------------------------------------------------------------------------
@asynccontextmanager
async def lifespan(app: FastAPI):
    # Startup: initialize Prisma with policy metrics and OTLP logging
    session = prisma_ai.init(
        project_name="contact-center-monitoring",
        evaluators=[response_compliance, escalation_procedure],
        enable_logging=True,
    )
    data_logger = session.get_logger()
    app.state.session = session
    app.state.data_logger = data_logger

    logger.info(
        "Prisma AI initialized — project=%s, policy metrics active",
        session.project_id,
    )

    yield

    # Shutdown: flush pending OTLP logs and close the session
    app.state.session.end()
    logger.info("Prisma AI session ended, pending logs flushed")


app = FastAPI(title="Policy Monitoring Service", lifespan=lifespan)


# ---------------------------------------------------------------------------
# 3. Request/response models
# ---------------------------------------------------------------------------
class TranscriptEvent(BaseModel):
    """A transcript event from the CRM or telephony platform."""
    query: str          # Customer side of the interaction
    response: str       # Agent side of the interaction
    reference: str = "" # Optional policy-correct response for comparison


class TranscriptResponse(BaseModel):
    status: str


# ---------------------------------------------------------------------------
# 4. Transcript ingestion endpoint
# ---------------------------------------------------------------------------
@app.post("/transcripts", response_model=TranscriptResponse)
async def receive_transcript(event: TranscriptEvent):
    """Receive a live transcript event and log it to Prisma for validation."""
    data_logger = app.state.data_logger
    if data_logger is None:
        raise HTTPException(status_code=503, detail="Prisma session not active")

    data_logger.log(
        query=event.query,
        response=event.response,
        reference=event.reference,
    )

    return TranscriptResponse(status="logged")


# ---------------------------------------------------------------------------
# 5. Health check
# ---------------------------------------------------------------------------
@app.get("/health")
async def health():
    session = app.state.session
    if session is None:
        return {"status": "degraded", "prisma": "not initialized"}
    return {
        "status": "healthy",
        "prisma_project_id": session.project_id,
    }

uvicorn monitor:app --host 0.0.0.0 --port 8000

curl -X POST http://localhost:8000/transcripts \
  -H "Content-Type: application/json" \
  -d '{
    "query": "I have been waiting three weeks for my refund. What is going on?",
    "response": "I understand your frustration, and I apologize for the delay. Let me pull up your account to check the status of your refund right now. I can see the refund was processed on the 15th — it typically takes 5-10 business days to appear in your account. If it has not arrived by Friday, please call us back and we will escalate this to our finance team."
  }'

{"status": "logged"}

curl -X POST http://localhost:8000/transcripts \
  -H "Content-Type: application/json" \
  -d '{
    "query": "This is unacceptable. I want to speak to your supervisor immediately.",
    "response": "I completely understand your frustration. Let me make sure I have all the details of your concern documented, and I will transfer you to my supervisor right away. Please hold for just a moment."
  }'

curl -X POST http://localhost:8000/transcripts \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Will my claim be approved?",
    "response": "Yes, absolutely. You will definitely get approved and should see the payout within a week."
  }'

curl http://localhost:8000/health

{"status": "healthy", "prisma_project_id": "contact-center-monitoring"}

@app.post("/admin/update-metrics")
async def update_metrics():
    """Add a data-handling metric without restarting the service."""
    session = app.state.session
    if session is None:
        return {"error": "Prisma session not active"}

    data_handling = Evaluators.custom(
        name="data-handling",
        options=["compliant", "violation", "needs_review"],
        evaluation_system_prompt=(
            "You are a data handling compliance reviewer for a contact center. "
            "Your task is to determine whether an agent's response properly "
            "handles sensitive customer data.\n\n"
            "The policies require:\n"
            "1. Never read back full account numbers, SSNs, or credit card numbers.\n"
            "2. Verify identity through approved methods only (last 4 digits, "
            "security questions).\n"
            "3. Never share one customer's information with another caller.\n"
            "4. Do not store or repeat sensitive data unnecessarily.\n\n"
            "Classify the response into one of: {evaluation_options}\n\n"
            "- compliant: All data handling policies are followed.\n"
            "- violation: The agent exposed, repeated, or mishandled "
            "sensitive data.\n"
            "- needs_review: The interaction involves sensitive data but "
            "compliance cannot be confidently determined."
        ),
        evaluation_human_prompt=(
            "Review the following interaction for data handling compliance.\n\n"
            "Customer message:\n{query}\n\n"
            "Agent response:\n{response}\n\n"
            "Classify the response as one of: {evaluation_options}\n\n"
            "Identify any sensitive data present and assess whether it was "
            "handled correctly."
        ),
        reeval_system_prompt=(
            "You are a senior data handling compliance reviewer performing "
            "a second-pass review. Apply data handling policies strictly.\n\n"
            "Even partial exposure of sensitive data (e.g., reading back 6 "
            "of 16 credit card digits) is a violation.\n\n"
            "You have access to decisions from previous reviews to calibrate "
            "your judgment.\n\n"
            "Classify the response into one of: {evaluation_options}"
        ),
        reeval_human_prompt=(
            "Re-evaluate this interaction for data handling compliance using "
            "context from previous data handling reviews.\n\n"
            "Customer message:\n{query}\n\n"
            "Agent response:\n{response}\n\n"
            "Relevant past data handling review decisions:\n{memories}\n\n"
            "Based on the interaction and review history, provide your final "
            "classification."
        ),
    )

    session.update_evaluation_config(
        EvaluationConfig(
            evaluators=[
                response_compliance,
                escalation_procedure,
                data_handling,
            ],
        )
    )
    return {"status": "policy metrics updated, data-handling metric added"}

curl -X POST http://localhost:8000/admin/update-metrics

{"status": "policy metrics updated, data-handling metric added"}

"""Compliance alerting — run on a schedule (e.g., every 15 minutes)."""

import httpx
import os

PRISMA_API_KEY = os.environ["PRISMA_API_KEY"]
PRISMA_BASE_URL = os.environ["PRISMA_BASE_URL"]
ALERT_THRESHOLD = 0.90  # Alert if compliance drops below 90%


def check_compliance_rates():
    """Query Prisma for recent validation results and alert on violations."""
    url = f"{PRISMA_BASE_URL}/api/v1/projects/contact-center-monitoring/metrics"

    with httpx.Client(timeout=30) as client:
        resp = client.get(
            url,
            headers={"x-api-key": PRISMA_API_KEY},
            params={"window": "1h"},  # Last hour of validations
        )
        resp.raise_for_status()
        metrics = resp.json()

    for metric_name, metric_data in metrics.items():
        compliance_rate = metric_data.get("compliance_rate", 1.0)
        violation_count = metric_data.get("violation_count", 0)

        if compliance_rate < ALERT_THRESHOLD:
            send_alert(
                metric=metric_name,
                compliance_rate=compliance_rate,
                violation_count=violation_count,
            )


def send_alert(metric: str, compliance_rate: float, violation_count: int):
    """Send an alert to your monitoring system."""
    message = (
        f"COMPLIANCE ALERT: {metric} compliance rate dropped to "
        f"{compliance_rate:.1%} ({violation_count} violations in last hour)"
    )
    # Route to your alerting system: Slack, PagerDuty, email, etc.
    print(message)


if __name__ == "__main__":
    check_compliance_rates()

"""Real-time policy monitoring service for contact center interactions."""

import logging
from contextlib import asynccontextmanager

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

import prisma_ai
from prisma_ai import EvaluationConfig, Evaluators

logger = logging.getLogger("policy-monitor")

# ---------------------------------------------------------------------------
# 1. Policy metric definitions
# ---------------------------------------------------------------------------
response_compliance = Evaluators.custom(
    name="response-compliance",
    options=["compliant", "non_compliant", "needs_review"],
    evaluation_system_prompt=(
        "You are a contact center compliance reviewer. Your task is to "
        "determine whether an agent's response follows company communication "
        "policies.\n\n"
        "The policies require:\n"
        "1. PROFESSIONAL TONE: The agent is courteous, empathetic, and avoids "
        "dismissive or confrontational language.\n"
        "2. ACCURATE INFORMATION: The agent provides correct information about "
        "products, services, and procedures. No guessing or speculation.\n"
        "3. REQUIRED DISCLOSURES: When discussing pricing, contracts, or policy "
        "changes, the agent includes all required disclaimers.\n"
        "4. PROHIBITED LANGUAGE: The agent does not make guarantees, promises "
        "about timelines without verification, or statements that could create "
        "legal liability.\n\n"
        "Classify the response into one of: {evaluation_options}\n\n"
        "- compliant: All policies are followed.\n"
        "- non_compliant: One or more policies are clearly violated.\n"
        "- needs_review: The response is in a gray area where compliance "
        "cannot be confidently determined without additional context."
    ),
    evaluation_human_prompt=(
        "Review the following contact center interaction for policy compliance.\n\n"
        "Customer message:\n{query}\n\n"
        "Agent response:\n{response}\n\n"
        "Classify the response as one of: {evaluation_options}\n\n"
        "For each policy (tone, accuracy, disclosures, prohibited language), "
        "state whether it was followed or violated, then provide your classification."
    ),
    reeval_system_prompt=(
        "You are a senior contact center compliance reviewer performing a "
        "second-pass review. Apply the communication policies strictly.\n\n"
        "Pay particular attention to implicit guarantees -- statements that "
        "stop short of a direct promise but still set customer expectations "
        "beyond what the agent can deliver. These are the most common "
        "borderline violations.\n\n"
        "You have access to decisions from previous compliance reviews to "
        "calibrate your judgment.\n\n"
        "Classify the response into one of: {evaluation_options}"
    ),
    reeval_human_prompt=(
        "Re-evaluate this contact center interaction for policy compliance "
        "using context from previous compliance reviews.\n\n"
        "Customer message:\n{query}\n\n"
        "Agent response:\n{response}\n\n"
        "Relevant past compliance review decisions:\n{memories}\n\n"
        "Based on the interaction and review history, provide your final "
        "classification with specific policy findings."
    ),
)

escalation_procedure = Evaluators.custom(
    name="escalation-procedure",
    options=["procedure_followed", "procedure_violated", "not_applicable"],
    evaluation_system_prompt=(
        "You are an escalation process reviewer for a contact center. Your "
        "task is to determine whether an agent followed the correct escalation "
        "procedure when an escalation trigger was present.\n\n"
        "Escalation triggers include:\n"
        "- Customer explicitly requests a supervisor or manager\n"
        "- Customer threatens legal action or mentions an attorney\n"
        "- Customer reports a safety concern or physical harm\n"
        "- Customer expresses intent to contact media or regulators\n\n"
        "When a trigger is present, the required procedure is:\n"
        "1. ACKNOWLEDGE: The agent acknowledges the customer's concern "
        "without being dismissive.\n"
        "2. DOCUMENT: The agent confirms the details of the situation.\n"
        "3. TRANSFER: The agent initiates a transfer to the appropriate "
        "escalation path (supervisor, legal, safety team) without delay.\n\n"
        "If no escalation trigger is present, classify as not_applicable.\n\n"
        "Classify the response into one of: {evaluation_options}\n\n"
        "- procedure_followed: An escalation trigger was present and the "
        "agent followed all three steps.\n"
        "- procedure_violated: An escalation trigger was present but the "
        "agent skipped one or more steps, or attempted to resolve the issue "
        "without escalating.\n"
        "- not_applicable: No escalation trigger was present in the interaction."
    ),
    evaluation_human_prompt=(
        "Review the following contact center interaction for escalation "
        "procedure compliance.\n\n"
        "Customer message:\n{query}\n\n"
        "Agent response:\n{response}\n\n"
        "Classify the response as one of: {evaluation_options}\n\n"
        "First determine whether an escalation trigger is present. If so, "
        "assess each step (acknowledge, document, transfer). Then provide "
        "your classification."
    ),
    reeval_system_prompt=(
        "You are a senior escalation process reviewer performing a "
        "second-pass review. Apply the escalation procedure strictly.\n\n"
        "The most critical violation is an agent who attempts to de-escalate "
        "or resolve the issue themselves when a transfer is required. Even "
        "well-intentioned attempts to help the customer directly are procedure "
        "violations when a trigger is present.\n\n"
        "You have access to decisions from previous escalation reviews to "
        "calibrate your judgment.\n\n"
        "Classify the response into one of: {evaluation_options}"
    ),
    reeval_human_prompt=(
        "Re-evaluate this contact center interaction for escalation procedure "
        "compliance using context from previous escalation reviews.\n\n"
        "Customer message:\n{query}\n\n"
        "Agent response:\n{response}\n\n"
        "Relevant past escalation review decisions:\n{memories}\n\n"
        "Based on the interaction and review history, provide your final "
        "classification. Identify the specific escalation trigger (if any) "
        "and assess each procedure step."
    ),
)


# ---------------------------------------------------------------------------
# 2. FastAPI lifespan — Prisma init and shutdown
# ---------------------------------------------------------------------------
@asynccontextmanager
async def lifespan(app: FastAPI):
    session = prisma_ai.init(
        project_name="contact-center-monitoring",
        evaluators=[response_compliance, escalation_procedure],
        enable_logging=True,
    )
    data_logger = session.get_logger()
    app.state.session = session
    app.state.data_logger = data_logger

    logger.info(
        "Prisma AI initialized — project=%s, policy metrics active",
        session.project_id,
    )

    yield

    app.state.session.end()
    logger.info("Prisma AI session ended, pending logs flushed")


app = FastAPI(title="Policy Monitoring Service", lifespan=lifespan)


# ---------------------------------------------------------------------------
# 3. Request/response models
# ---------------------------------------------------------------------------
class TranscriptEvent(BaseModel):
    query: str
    response: str
    reference: str = ""


class TranscriptResponse(BaseModel):
    status: str


# ---------------------------------------------------------------------------
# 4. Transcript ingestion endpoint
# ---------------------------------------------------------------------------
@app.post("/transcripts", response_model=TranscriptResponse)
async def receive_transcript(event: TranscriptEvent):
    data_logger = app.state.data_logger
    if data_logger is None:
        raise HTTPException(status_code=503, detail="Prisma session not active")

    data_logger.log(
        query=event.query,
        response=event.response,
        reference=event.reference,
    )
    return TranscriptResponse(status="logged")


# ---------------------------------------------------------------------------
# 5. Health check
# ---------------------------------------------------------------------------
@app.get("/health")
async def health():
    session = app.state.session
    if session is None:
        return {"status": "degraded", "prisma": "not initialized"}
    return {
        "status": "healthy",
        "prisma_project_id": session.project_id,
    }


# ---------------------------------------------------------------------------
# 6. Admin: update policy metrics without restart
# ---------------------------------------------------------------------------
@app.post("/admin/update-metrics")
async def update_metrics():
    session = app.state.session
    if session is None:
        return {"error": "Prisma session not active"}

    data_handling = Evaluators.custom(
        name="data-handling",
        options=["compliant", "violation", "needs_review"],
        evaluation_system_prompt=(
            "You are a data handling compliance reviewer for a contact center. "
            "Your task is to determine whether an agent's response properly "
            "handles sensitive customer data.\n\n"
            "The policies require:\n"
            "1. Never read back full account numbers, SSNs, or credit card numbers.\n"
            "2. Verify identity through approved methods only (last 4 digits, "
            "security questions).\n"
            "3. Never share one customer's information with another caller.\n"
            "4. Do not store or repeat sensitive data unnecessarily.\n\n"
            "Classify the response into one of: {evaluation_options}\n\n"
            "- compliant: All data handling policies are followed.\n"
            "- violation: The agent exposed, repeated, or mishandled "
            "sensitive data.\n"
            "- needs_review: The interaction involves sensitive data but "
            "compliance cannot be confidently determined."
        ),
        evaluation_human_prompt=(
            "Review the following interaction for data handling compliance.\n\n"
            "Customer message:\n{query}\n\n"
            "Agent response:\n{response}\n\n"
            "Classify the response as one of: {evaluation_options}\n\n"
            "Identify any sensitive data present and assess whether it was "
            "handled correctly."
        ),
        reeval_system_prompt=(
            "You are a senior data handling compliance reviewer performing "
            "a second-pass review. Apply data handling policies strictly.\n\n"
            "Even partial exposure of sensitive data (e.g., reading back 6 "
            "of 16 credit card digits) is a violation.\n\n"
            "You have access to decisions from previous reviews to calibrate "
            "your judgment.\n\n"
            "Classify the response into one of: {evaluation_options}"
        ),
        reeval_human_prompt=(
            "Re-evaluate this interaction for data handling compliance using "
            "context from previous data handling reviews.\n\n"
            "Customer message:\n{query}\n\n"
            "Agent response:\n{response}\n\n"
            "Relevant past data handling review decisions:\n{memories}\n\n"
            "Based on the interaction and review history, provide your final "
            "classification."
        ),
    )

    session.update_evaluation_config(
        EvaluationConfig(
            evaluators=[
                response_compliance,
                escalation_procedure,
                data_handling,
            ],
        )
    )
    return {"status": "policy metrics updated, data-handling metric added"}