Classify Real-Time Agent Assist Intent Matches with Python SDK
What You Will Build
A production-grade Python module that constructs, validates, and executes atomic classification requests against the Genesys Cloud Agent Assist Knowledge Base Search API. The code filters results by confidence thresholds and domain restrictions, tracks latency and success rates, generates structured audit logs, and synchronizes classification events to external QA webhooks. This tutorial uses the official genesyscloud Python SDK and httpx for webhook delivery.
Prerequisites
- OAuth Client Type: Service Account or User Impersonation
- Required Scopes:
agentassist:knowledgebase:read,agentassist:search:read - SDK Version:
genesyscloud>=1.2.0 - Runtime: Python 3.9 or higher
- External Dependencies:
pip install genesyscloud httpx pydantic typing_extensions
Authentication Setup
Genesys Cloud requires OAuth 2.0 client credentials or user password grant. The Python SDK handles token acquisition and refresh automatically when initialized with PlatformClient. You must cache the client instance to avoid redundant authentication calls.
import os
from genesyscloud.platform.client import PlatformClient
from genesyscloud.platform.auth import OAuthClientCredentialsConfig
def initialize_genesys_client() -> PlatformClient:
"""Initialize and return a cached Genesys Cloud PlatformClient."""
org_id = os.getenv("GENESYS_ORG_ID")
client_id = os.getenv("GENESYS_CLIENT_ID")
client_secret = os.getenv("GENESYS_CLIENT_SECRET")
if not all([org_id, client_id, client_secret]):
raise ValueError("GENESYS_ORG_ID, GENESYS_CLIENT_ID, and GENESYS_CLIENT_SECRET must be set.")
oauth_config = OAuthClientCredentialsConfig(
client_id=client_id,
client_secret=client_secret,
org_id=org_id,
scopes=["agentassist:knowledgebase:read", "agentassist:search:read"]
)
client = PlatformClient(config=oauth_config)
client.login()
return client
The SDK stores the access token and automatically refreshes it before expiration. You will use this PlatformClient instance to instantiate the AgentAssistApi module in subsequent steps.
Implementation
Step 1: SDK Initialization & Knowledge Base Fetch Trigger
Before executing classification, you must verify the target Knowledge Base exists and is active. This automatic fetch trigger prevents routing requests to deprecated or misconfigured KBs. The SDK method get_knowledge_base performs a GET request to /api/v2/agentassist/knowledgebases/{knowledgeBaseId}.
from genesyscloud.api import AgentAssistApi
from genesyscloud.models import KnowledgeBase
def fetch_knowledge_base(client: PlatformClient, kb_id: str) -> KnowledgeBase:
"""Fetch KB metadata to verify routing configuration before classification."""
agent_assist_api = AgentAssistApi(client)
try:
response = agent_assist_api.get_knowledge_base(knowledge_base_id=kb_id)
if response.status_code not in (200, 201):
raise RuntimeError(f"KB fetch failed with status {response.status_code}: {response.body}")
return response.body
except Exception as e:
raise ConnectionError(f"Knowledge Base fetch trigger failed: {str(e)}")
HTTP Equivalent:
GET /api/v2/agentassist/knowledgebases/{knowledgeBaseId} HTTP/1.1
Host: api.mypurecloud.com
Authorization: Bearer <access_token>
Accept: application/json
HTTP/1.1 200 OK
Content-Type: application/json
{
"id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
"name": "Billing & Support KB",
"description": "Primary agent assist knowledge base",
"status": "published",
"language": "en-US",
"vectorEmbeddingEnabled": true
}
This step confirms vectorEmbeddingEnabled is true, which is required for synonym expansion and vector routing logic downstream.
Step 2: Payload Construction & Schema Validation
The classification payload requires an intent reference, transcript matrix (input text context), and score directive (confidence threshold). You will use Pydantic to enforce schema constraints before sending data to the API. This prevents classification failures caused by malformed requests or out-of-bounds thresholds.
from pydantic import BaseModel, Field, validator
from typing import List, Optional
class SearchOptions(BaseModel):
synonymExpansion: bool = Field(default=True, description="Enable synonym expansion logic")
vectorRouting: bool = Field(default=True, description="Enable vector embedding routing")
domainRestriction: Optional[str] = Field(default=None, description="Restrict search to specific domain")
class ClassifyPayload(BaseModel):
intent_reference: str = Field(..., min_length=3, max_length=100)
transcript_matrix: str = Field(..., min_length=1, max_length=2000)
score_directive: float = Field(..., ge=0.0, le=1.0)
language: str = Field(default="en-US")
limit: int = Field(default=5, ge=1, le=20)
search_options: SearchOptions = Field(default_factory=SearchOptions)
@validator("score_directive")
def validate_confidence_threshold(cls, v):
if v < 0.05:
raise ValueError("Score directive must be at least 0.05 to prevent noise classification.")
return v
The ClassifyPayload model enforces AI constraints. The score_directive acts as your maximum confidence threshold filter. The transcript_matrix holds the real-time conversation snippet. The search_options dictionary maps directly to the Genesys Cloud API body parameters.
Step 3: Atomic POST Execution & Confidence Threshold Enforcement
You will execute the classification via an atomic POST operation to /api/v2/agentassist/knowledgebases/{knowledgeBaseId}/search. The SDK method search_knowledge_base handles serialization, retry logic for 429 responses, and deserialization. You must wrap the call in a retry decorator to handle rate limits gracefully.
import time
import httpx
from genesyscloud.models import SearchRequest, SearchOptions as SDKSearchOptions
from functools import wraps
def retry_on_rate_limit(max_retries: int = 3, base_delay: float = 1.0):
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
for attempt in range(max_retries):
try:
return func(*args, **kwargs)
except Exception as e:
error_msg = str(e).lower()
if "429" in error_msg or "rate limit" in error_msg:
delay = base_delay * (2 ** attempt)
time.sleep(delay)
continue
raise
raise RuntimeError("Max retries exceeded due to rate limiting.")
return wrapper
return decorator
@retry_on_rate_limit(max_retries=3)
def execute_classification(client: PlatformClient, kb_id: str, payload: ClassifyPayload):
"""Execute atomic POST classification against Agent Assist API."""
agent_assist_api = AgentAssistApi(client)
# Map Pydantic model to SDK models
sdk_search_options = SDKSearchOptions(
synonym_expansion=payload.search_options.synonymExpansion,
vector_routing=payload.search_options.vectorRouting,
domain_restriction=payload.search_options.domainRestriction
)
search_request = SearchRequest(
text=payload.transcript_matrix,
language=payload.language,
limit=payload.limit,
search_options=sdk_search_options
)
response = agent_assist_api.search_knowledge_base(
knowledge_base_id=kb_id,
body=search_request
)
if response.status_code != 200:
raise RuntimeError(f"Classification POST failed: {response.status_code} {response.body}")
return response.body
HTTP Request/Response Cycle:
POST /api/v2/agentassist/knowledgebases/a1b2c3d4-e5f6-7890-abcd-ef1234567890/search HTTP/1.1
Host: api.mypurecloud.com
Authorization: Bearer <access_token>
Content-Type: application/json
Accept: application/json
{
"text": "I want to dispute a charge on my statement",
"language": "en-US",
"limit": 5,
"searchOptions": {
"synonymExpansion": true,
"vectorRouting": true,
"domainRestriction": "billing"
}
}
HTTP/1.1 200 OK
Content-Type: application/json
{
"results": [
{
"id": "article-001",
"title": "How to Dispute a Billing Charge",
"score": 0.94,
"domain": "billing",
"summary": "Step-by-step guide to filing a dispute..."
},
{
"id": "article-002",
"title": "Understanding Statement Cycles",
"score": 0.78,
"domain": "billing",
"summary": "Overview of monthly billing periods..."
}
],
"totalResults": 2
}
The SDK handles the atomic POST. You must enforce the score_directive threshold immediately after receiving the response to prevent low-confidence results from reaching the agent.
Step 4: Ambiguity Filtering & Domain Restriction Pipeline
Raw API results may contain ambiguous matches or cross-domain suggestions. You will implement a validation pipeline that filters results by the score_directive threshold and verifies domain alignment. This ensures precise agent recommendations during Genesys Cloud scaling events when traffic spikes increase noise.
from typing import Dict, Any
def filter_classification_results(
raw_results: List[Dict[str, Any]],
payload: ClassifyPayload
) -> List[Dict[str, Any]]:
"""Apply ambiguity filtering and domain restriction verification."""
filtered = []
for item in raw_results:
score = item.get("score", 0.0)
domain = item.get("domain", "")
# Enforce maximum confidence threshold limit
if score < payload.score_directive:
continue
# Domain restriction verification pipeline
if payload.search_options.domainRestriction:
if domain.lower() != payload.search_options.domainRestriction.lower():
continue
# Ambiguity filtering: discard results with near-identical scores
if filtered:
top_score = filtered[-1].get("score", 0.0)
if abs(score - top_score) < 0.02:
continue
filtered.append(item)
return filtered
This pipeline runs after the SDK deserializes the response. It removes results below the threshold, blocks cross-domain leakage, and collapses ambiguous clusters where multiple articles return nearly identical confidence scores. Agents only receive high-signal recommendations.
Step 5: Webhook Synchronization, Latency Tracking & Audit Logging
You must synchronize classification events with external QA systems, track latency, and generate audit logs for governance. The following function handles webhook delivery, metrics calculation, and structured logging. It uses httpx for reliable HTTP POST delivery to your QA endpoint.
import json
import logging
from datetime import datetime, timezone
logger = logging.getLogger("agentassist.classifier")
def sync_and_log(
payload: ClassifyPayload,
filtered_results: List[Dict[str, Any]],
latency_ms: float,
qa_webhook_url: str
) -> Dict[str, Any]:
"""Synchronize with external QA, track latency, and generate audit logs."""
timestamp = datetime.now(timezone.utc).isoformat()
audit_log = {
"timestamp": timestamp,
"intent_reference": payload.intent_reference,
"transcript_length": len(payload.transcript_matrix),
"score_directive": payload.score_directive,
"results_count": len(filtered_results),
"latency_ms": latency_ms,
"success_rate": 1.0 if filtered_results else 0.0,
"top_recommendation": filtered_results[0] if filtered_results else None
}
# Generate audit log for assist governance
logger.info(json.dumps(audit_log))
# Synchronize with external QA system via webhook
if qa_webhook_url:
try:
with httpx.Client(timeout=5.0) as client:
resp = client.post(
qa_webhook_url,
json={"event": "intent_classified", "data": audit_log},
headers={"Content-Type": "application/json"}
)
if resp.status_code >= 400:
logger.warning(f"QA webhook sync failed: {resp.status_code} {resp.text}")
except Exception as e:
logger.error(f"QA webhook delivery error: {str(e)}")
return audit_log
The latency_ms parameter is calculated by measuring the time delta between payload construction and API response receipt. The success_rate metric tracks how often classifications return valid results above the threshold. The webhook payload aligns with standard QA alignment schemas.
Complete Working Example
The following script integrates all components into a single, runnable module. Replace the environment variables and webhook URL before execution.
import os
import time
import logging
from typing import List, Dict, Any
import httpx
from pydantic import BaseModel, Field
from genesyscloud.platform.client import PlatformClient
from genesyscloud.platform.auth import OAuthClientCredentialsConfig
from genesyscloud.api import AgentAssistApi
from genesyscloud.models import SearchRequest, SearchOptions as SDKSearchOptions
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger("agentassist.classifier")
# --- Models ---
class SearchOptionsPayload(BaseModel):
synonymExpansion: bool = True
vectorRouting: bool = True
domainRestriction: str | None = None
class ClassifyPayload(BaseModel):
intent_reference: str = Field(..., min_length=3, max_length=100)
transcript_matrix: str = Field(..., min_length=1, max_length=2000)
score_directive: float = Field(..., ge=0.05, le=1.0)
language: str = "en-US"
limit: int = 5
search_options: SearchOptionsPayload = Field(default_factory=SearchOptionsPayload)
# --- Core Logic ---
def run_intent_classifier(
org_id: str,
client_id: str,
client_secret: str,
kb_id: str,
qa_webhook_url: str,
payload: ClassifyPayload
) -> Dict[str, Any]:
# 1. Authentication Setup
oauth_config = OAuthClientCredentialsConfig(
client_id=client_id,
client_secret=client_secret,
org_id=org_id,
scopes=["agentassist:knowledgebase:read", "agentassist:search:read"]
)
client = PlatformClient(config=oauth_config)
client.login()
# 2. Knowledge Base Fetch Trigger
agent_assist_api = AgentAssistApi(client)
kb_resp = agent_assist_api.get_knowledge_base(knowledge_base_id=kb_id)
if kb_resp.status_code != 200:
raise RuntimeError(f"KB fetch failed: {kb_resp.body}")
# 3. Atomic POST Execution
start_time = time.perf_counter()
sdk_opts = SDKSearchOptions(
synonym_expansion=payload.search_options.synonymExpansion,
vector_routing=payload.search_options.vectorRouting,
domain_restriction=payload.search_options.domainRestriction
)
search_req = SearchRequest(
text=payload.transcript_matrix,
language=payload.language,
limit=payload.limit,
search_options=sdk_opts
)
search_resp = agent_assist_api.search_knowledge_base(
knowledge_base_id=kb_id,
body=search_req
)
if search_resp.status_code != 200:
raise RuntimeError(f"Classification failed: {search_resp.status_code} {search_resp.body}")
latency_ms = (time.perf_counter() - start_time) * 1000
raw_results = search_resp.body.get("results", [])
# 4. Ambiguity Filtering & Domain Restriction Pipeline
filtered_results = []
for item in raw_results:
score = item.get("score", 0.0)
domain = item.get("domain", "")
if score < payload.score_directive:
continue
if payload.search_options.domainRestriction and domain.lower() != payload.search_options.domainRestriction.lower():
continue
if filtered_results and abs(score - filtered_results[-1].get("score", 0.0)) < 0.02:
continue
filtered_results.append(item)
# 5. Webhook Sync, Latency Tracking & Audit Logging
audit_log = {
"timestamp": time.strftime("%Y-%m-%dT%H:%M:%S", time.gmtime()),
"intent_reference": payload.intent_reference,
"transcript_length": len(payload.transcript_matrix),
"score_directive": payload.score_directive,
"results_count": len(filtered_results),
"latency_ms": round(latency_ms, 2),
"success_rate": 1.0 if filtered_results else 0.0,
"top_recommendation": filtered_results[0] if filtered_results else None
}
logger.info(f"AUDIT: {audit_log}")
if qa_webhook_url:
try:
with httpx.Client(timeout=5.0) as http_client:
resp = http_client.post(
qa_webhook_url,
json={"event": "intent_classified", "data": audit_log},
headers={"Content-Type": "application/json"}
)
if resp.status_code >= 400:
logger.warning(f"QA webhook sync failed: {resp.status_code}")
except Exception as e:
logger.error(f"QA webhook delivery error: {e}")
return {
"filtered_results": filtered_results,
"latency_ms": round(latency_ms, 2),
"audit_log": audit_log
}
if __name__ == "__main__":
test_payload = ClassifyPayload(
intent_reference="billing_dispute",
transcript_matrix="I want to dispute a charge on my statement",
score_directive=0.80,
search_options=SearchOptionsPayload(domainRestriction="billing")
)
result = run_intent_classifier(
org_id=os.getenv("GENESYS_ORG_ID"),
client_id=os.getenv("GENESYS_CLIENT_ID"),
client_secret=os.getenv("GENESYS_CLIENT_SECRET"),
kb_id="a1b2c3d4-e5f6-7890-abcd-ef1234567890",
qa_webhook_url=os.getenv("QA_WEBHOOK_URL", ""),
payload=test_payload
)
print(f"Classification complete. Latency: {result['latency_ms']}ms. Results: {len(result['filtered_results'])}")
Common Errors & Debugging
Error: 401 Unauthorized or 403 Forbidden
Cause: The OAuth client lacks the agentassist:knowledgebase:read or agentassist:search:read scopes, or the token has expired.
Fix: Verify the scopes in your OAuthClientCredentialsConfig. Ensure the service account has the Agent Assist Administrator or Agent Assist Viewer role assigned in the Genesys Cloud admin console. The SDK automatically refreshes tokens, but stale cached sessions may require a fresh client.login() call.
Error: 429 Too Many Requests
Cause: Exceeding the Genesys Cloud rate limit for the /search endpoint (typically 10-20 requests per second per org).
Fix: The retry_on_rate_limit decorator implements exponential backoff. For sustained high-volume classification, implement a token bucket rate limiter before invoking execute_classification. Reduce the limit parameter if batching multiple transcripts per call.
Error: Pydantic ValidationError on score_directive
Cause: The confidence threshold falls below the enforced minimum of 0.05 or exceeds 1.0.
Fix: Adjust the score_directive in your payload. Values below 0.05 typically return noise. Values above 0.95 may return zero results during scaling events. Maintain a dynamic threshold based on historical success rates.
Error: Domain Restriction Mismatch Returns Empty List
Cause: The KB articles lack the domain metadata tag, or the tag casing differs from your domainRestriction parameter.
Fix: Verify article metadata in the Genesys Cloud Admin Console under Agent Assist > Knowledge Base > Articles. Normalize casing in the filtering pipeline or switch to case-insensitive comparison.