Retrieving Genesys Cloud Agent Assist Content Recommendations via Python SDK

What You Will Build

• A Python module that authenticates with Genesys Cloud, constructs interaction context payloads, and retrieves Agent Assist content recommendations using the official SDK.
• A scoring and filtering pipeline that applies semantic similarity calculations and rule-based thresholds to rank suggestions and suppress irrelevant artifacts.
• A production-grade retriever class that handles streaming-style polling, cache invalidation, automatic fallback triggers, latency tracking, webhook synchronization, and structured audit logging.

Prerequisites

• Genesys Cloud OAuth public or confidential client with scopes: agentassist:interaction:write, agentassist:interaction:read, agentassist:content:read
• Python 3.9+ runtime
• genesys-cloud-sdk-python>=2.0.0, httpx>=0.25.0, numpy>=1.24.0, pydantic>=2.0.0
• Access to a configured Agent Assist content source and interaction policy in your Genesys Cloud organization

Authentication Setup

Genesys Cloud uses OAuth 2.0 client credentials flow for server-to-server API access. The SDK handles token acquisition and automatic refresh when initialized with client credentials.

import os
from genesyscloud.platform_client import PlatformClient

def initialize_platform_client() -> PlatformClient:
    """Initialize the Genesys Cloud SDK platform client with OAuth credentials."""
    platform_client = PlatformClient()
    platform_client.set_auth(
        client_id=os.getenv("GENESYS_CLIENT_ID"),
        client_secret=os.getenv("GENESYS_CLIENT_SECRET"),
        base_url=os.getenv("GENESYS_BASE_URL", "https://api.mypurecloud.com")
    )
    return platform_client

The SDK caches the access token and refreshes it automatically before expiration. If the credentials are invalid, the SDK raises an AuthError on the first API call. Always validate environment variables before initialization.

Implementation

Step 1: Initialize SDK and Build Interaction Context

The Agent Assist API requires an interaction context before retrieving recommendations. The context defines the channel, user metadata, and conversation state. You must structure this as a matrix of key-value pairs that match your content source configuration.

from typing import Dict, Any
from genesyscloud.platform_client import PlatformClient

def build_interaction_context(
    channel: str,
    user_id: str,
    intent: str,
    keywords: list[str]
) -> Dict[str, Any]:
    """Construct the interaction context matrix required by Agent Assist."""
    return {
        "channel": channel,
        "user": {"id": user_id},
        "intent": {"name": intent},
        "keywords": keywords,
        "metadata": {
            "source": "api_retriever",
            "session_type": "live_agent_support"
        }
    }

def create_interaction(
    platform_client: PlatformClient,
    context: Dict[str, Any]
) -> str:
    """Create a Genesys Cloud Agent Assist interaction and return its ID."""
    from genesyscloud.agentassist.rest import ApiException
    
    agent_assist = platform_client.agent_assist
    try:
        response = agent_assist.post_agentassist_interactions(body=context)
        return response.id
    except ApiException as e:
        if e.status == 401:
            raise RuntimeError("OAuth token expired or invalid. Refresh credentials.")
        if e.status == 403:
            raise RuntimeError("Missing agentassist:interaction:write scope.")
        raise RuntimeError(f"Failed to create interaction: {e.reason}")

Expected response payload from POST /api/v2/agentassist/interactions:

{
  "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "channel": "voice",
  "status": "active",
  "createdTimestamp": "2024-01-15T10:30:00.000Z",
  "updatedTimestamp": "2024-01-15T10:30:00.000Z"
}

Step 2: Construct Retrieval Payload and Fetch Recommendations

The retrieval payload specifies the content source, scoring parameters, and quota validation thresholds. Genesys Cloud enforces concurrent request limits per content source. You must validate the payload schema before submission to prevent 429 rate-limit cascades.

from typing import Dict, Any, Optional
from pydantic import BaseModel, Field, validator
from genesyscloud.agentassist.rest import ApiException

class RetrievalPayload(BaseModel):
    content_source_id: str
    max_results: int = Field(default=10, le=50)
    min_relevance: float = Field(default=0.6, ge=0.0, le=1.0)
    concurrent_quota_limit: int = Field(default=5, le=10)
    
    @validator("max_results")
    def validate_repository_size_limit(cls, v: int) -> int:
        if v > 50:
            raise ValueError("Content repository size limit exceeded. Maximum 50 results per request.")
        return v

def fetch_recommendations(
    platform_client: PlatformClient,
    interaction_id: str,
    payload: RetrievalPayload
) -> Dict[str, Any]:
    """Submit a content retrieval request and return the raw recommendations."""
    agent_assist = platform_client.agent_assist
    request_body = {
        "contentSourceId": payload.content_source_id,
        "maxResults": payload.max_results,
        "filter": {"minRelevance": payload.min_relevance}
    }
    
    try:
        response = agent_assist.post_agentassist_interactions_content_retrieve(
            interaction_id=interaction_id,
            body=request_body
        )
        return {
            "id": response.id,
            "status": response.status,
            "recommendations": [
                {
                    "id": r.id,
                    "title": r.title,
                    "relevance": r.relevance,
                    "content_type": r.contentType,
                    "url": r.url
                }
                for r in response.recommendations
            ],
            "etag": response.etag
        }
    except ApiException as e:
        if e.status == 429:
            raise RuntimeError("Concurrent request quota exceeded. Implement exponential backoff.")
        if e.status == 403:
            raise RuntimeError("Missing agentassist:content:read scope or invalid content source reference.")
        raise RuntimeError(f"Retrieval failed: {e.reason}")

HTTP equivalent for reference:

POST /api/v2/agentassist/interactions/{interactionId}/content/retrieve
Authorization: Bearer <access_token>
Content-Type: application/json

{
  "contentSourceId": "src-12345",
  "maxResults": 15,
  "filter": { "minRelevance": 0.65 }
}

Step 3: Apply Semantic Scoring and Rule-Based Filtering

Raw recommendations require local ranking and artifact suppression. You will calculate semantic similarity between the interaction context and content embeddings, then apply rule-based filters to remove low-confidence or mismatched types.

import numpy as np
from typing import List, Dict, Any

def calculate_semantic_similarity(
    context_vector: np.ndarray,
    content_vectors: List[np.ndarray],
    recommendations: List[Dict[str, Any]]
) -> List[Dict[str, Any]]:
    """Calculate cosine similarity and merge with Genesys relevance scores."""
    context_norm = np.linalg.norm(context_vector)
    if context_norm == 0:
        return recommendations
    
    ranked = []
    for rec, vec in zip(recommendations, content_vectors):
        vec_norm = np.linalg.norm(vec)
        if vec_norm == 0:
            continue
        cosine_sim = np.dot(context_vector, vec) / (context_norm * vec_norm)
        combined_score = (rec["relevance"] * 0.7) + (cosine_sim * 0.3)
        ranked.append({**rec, "semantic_score": float(cosine_sim), "combined_score": float(combined_score)})
    
    return sorted(ranked, key=lambda x: x["combined_score"], reverse=True)

def apply_rule_based_filter(
    recommendations: List[Dict[str, Any]],
    allowed_types: List[str],
    min_combined_score: float = 0.65
) -> List[Dict[str, Any]]:
    """Suppress irrelevant artifacts based on content type and score thresholds."""
    filtered = [
        rec for rec in recommendations
        if rec["content_type"] in allowed_types and rec["combined_score"] >= min_combined_score
    ]
    return filtered

Step 4: Implement Streaming Polling, Cache Invalidation, and Fallback

Agent Assist retrieval supports iterative polling. You will implement a streaming-style GET loop that tracks response etag values for cache invalidation. If the primary content source returns degraded results or errors, the system triggers an automatic fallback to a secondary source.

import time
import logging
from typing import Optional
from genesyscloud.agentassist.rest import ApiException

logger = logging.getLogger("agentassist_retriever")

def poll_recommendations_streaming(
    platform_client: PlatformClient,
    interaction_id: str,
    primary_source: str,
    fallback_source: str,
    etag_cache: Dict[str, str],
    timeout_seconds: int = 30
) -> List[Dict[str, Any]]:
    """Stream recommendations with cache invalidation and automatic fallback."""
    start_time = time.time()
    payload_primary = RetrievalPayload(content_source_id=primary_source)
    payload_fallback = RetrievalPayload(content_source_id=fallback_source)
    
    while time.time() - start_time < timeout_seconds:
        try:
            result = fetch_recommendations(platform_client, interaction_id, payload_primary)
            current_etag = result["etag"]
            
            if current_etag != etag_cache.get(primary_source):
                etag_cache[primary_source] = current_etag
                logger.info("Cache invalidated. New content version detected.")
                
                if result["status"] == "completed" and result["recommendations"]:
                    return result["recommendations"]
                
            time.sleep(1.5)
        except RuntimeError as e:
            if "quota exceeded" in str(e) or "degraded" in str(e).lower():
                logger.warning("Primary source degraded. Triggering fallback.")
                try:
                    fallback_result = fetch_recommendations(platform_client, interaction_id, payload_fallback)
                    if fallback_result["recommendations"]:
                        logger.info("Fallback source returned valid recommendations.")
                        return fallback_result["recommendations"]
                except Exception as fallback_err:
                    logger.error(f"Fallback failed: {fallback_err}")
                    raise RuntimeError("Both primary and fallback content sources unavailable.")
            raise
        except ApiException as e:
            logger.error(f"API error during polling: {e.status} {e.reason}")
            time.sleep(2)
            
    raise TimeoutError("Recommendation retrieval timed out.")

Step 5: Synchronize via Webhook and Generate Audit Logs

You must track retrieval latency, relevance scores, and synchronize delivery events with external training platforms. Structured audit logs ensure compliance verification. The webhook callback uses httpx for asynchronous delivery.

import httpx
import time
import json
from datetime import datetime, timezone
from typing import List, Dict, Any

def calculate_latency(start_time: float) -> float:
    return round(time.time() - start_time, 3)

def generate_audit_log(
    interaction_id: str,
    recommendations: List[Dict[str, Any]],
    latency: float,
    source: str
) -> Dict[str, Any]:
    """Generate structured audit log for compliance verification."""
    scores = [r["combined_score"] for r in recommendations]
    return {
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "interaction_id": interaction_id,
        "content_source": source,
        "retrieval_latency_seconds": latency,
        "recommendation_count": len(recommendations),
        "avg_relevance_score": sum(scores) / len(scores) if scores else 0.0,
        "max_relevance_score": max(scores) if scores else 0.0,
        "compliance_status": "verified",
        "audit_id": f"audit-{interaction_id}-{int(time.time())}"
    }

def sync_webhook_callback(
    webhook_url: str,
    audit_log: Dict[str, Any],
    recommendations: List[Dict[str, Any]]
) -> bool:
    """Synchronize recommendation delivery events with external training platforms."""
    payload = {
        "event": "agentassist.recommendations.delivered",
        "audit": audit_log,
        "content": [
            {"id": r["id"], "title": r["title"], "score": r["combined_score"]}
            for r in recommendations[:5]
        ]
    }
    
    try:
        with httpx.Client(timeout=10.0) as client:
            response = client.post(
                webhook_url,
                json=payload,
                headers={"Content-Type": "application/json", "X-Event-Source": "genesys-agentassist"}
            )
            response.raise_for_status()
            return True
    except httpx.HTTPError as e:
        logger.error(f"Webhook synchronization failed: {e}")
        return False

Complete Working Example

The following module combines all components into a production-ready recommendation retriever. Replace the placeholder credentials and configuration values before execution.

import os
import time
import logging
import numpy as np
from typing import Dict, Any, List, Optional
from genesyscloud.platform_client import PlatformClient
from pydantic import BaseModel, Field, validator

# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger("agentassist_retriever")

class RetrievalPayload(BaseModel):
    content_source_id: str
    max_results: int = Field(default=10, le=50)
    min_relevance: float = Field(default=0.6, ge=0.0, le=1.0)

def initialize_platform_client() -> PlatformClient:
    platform_client = PlatformClient()
    platform_client.set_auth(
        client_id=os.getenv("GENESYS_CLIENT_ID"),
        client_secret=os.getenv("GENESYS_CLIENT_SECRET"),
        base_url=os.getenv("GENESYS_BASE_URL", "https://api.mypurecloud.com")
    )
    return platform_client

def build_interaction_context(channel: str, user_id: str, intent: str, keywords: list[str]) -> Dict[str, Any]:
    return {
        "channel": channel,
        "user": {"id": user_id},
        "intent": {"name": intent},
        "keywords": keywords,
        "metadata": {"source": "api_retriever", "session_type": "live_agent_support"}
    }

def create_interaction(platform_client: PlatformClient, context: Dict[str, Any]) -> str:
    from genesyscloud.agentassist.rest import ApiException
    agent_assist = platform_client.agent_assist
    try:
        response = agent_assist.post_agentassist_interactions(body=context)
        return response.id
    except ApiException as e:
        if e.status == 401:
            raise RuntimeError("OAuth token expired or invalid.")
        if e.status == 403:
            raise RuntimeError("Missing agentassist:interaction:write scope.")
        raise RuntimeError(f"Failed to create interaction: {e.reason}")

def fetch_recommendations(platform_client: PlatformClient, interaction_id: str, payload: RetrievalPayload) -> Dict[str, Any]:
    from genesyscloud.agentassist.rest import ApiException
    agent_assist = platform_client.agent_assist
    request_body = {
        "contentSourceId": payload.content_source_id,
        "maxResults": payload.max_results,
        "filter": {"minRelevance": payload.min_relevance}
    }
    try:
        response = agent_assist.post_agentassist_interactions_content_retrieve(
            interaction_id=interaction_id, body=request_body
        )
        return {
            "id": response.id,
            "status": response.status,
            "recommendations": [
                {"id": r.id, "title": r.title, "relevance": r.relevance, "content_type": r.contentType, "url": r.url}
                for r in response.recommendations
            ],
            "etag": response.etag
        }
    except ApiException as e:
        if e.status == 429:
            raise RuntimeError("Concurrent request quota exceeded.")
        if e.status == 403:
            raise RuntimeError("Missing agentassist:content:read scope.")
        raise RuntimeError(f"Retrieval failed: {e.reason}")

def calculate_semantic_similarity(context_vector: np.ndarray, content_vectors: List[np.ndarray], recommendations: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    context_norm = np.linalg.norm(context_vector)
    if context_norm == 0:
        return recommendations
    ranked = []
    for rec, vec in zip(recommendations, content_vectors):
        vec_norm = np.linalg.norm(vec)
        if vec_norm == 0:
            continue
        cosine_sim = np.dot(context_vector, vec) / (context_norm * vec_norm)
        combined_score = (rec["relevance"] * 0.7) + (cosine_sim * 0.3)
        ranked.append({**rec, "semantic_score": float(cosine_sim), "combined_score": float(combined_score)})
    return sorted(ranked, key=lambda x: x["combined_score"], reverse=True)

def apply_rule_based_filter(recommendations: List[Dict[str, Any]], allowed_types: List[str], min_combined_score: float = 0.65) -> List[Dict[str, Any]]:
    return [rec for rec in recommendations if rec["content_type"] in allowed_types and rec["combined_score"] >= min_combined_score]

def poll_recommendations_streaming(
    platform_client: PlatformClient,
    interaction_id: str,
    primary_source: str,
    fallback_source: str,
    etag_cache: Dict[str, str],
    timeout_seconds: int = 30
) -> List[Dict[str, Any]]:
    start_time = time.time()
    payload_primary = RetrievalPayload(content_source_id=primary_source)
    payload_fallback = RetrievalPayload(content_source_id=fallback_source)
    
    while time.time() - start_time < timeout_seconds:
        try:
            result = fetch_recommendations(platform_client, interaction_id, payload_primary)
            current_etag = result["etag"]
            if current_etag != etag_cache.get(primary_source):
                etag_cache[primary_source] = current_etag
                logger.info("Cache invalidated. New content version detected.")
                if result["status"] == "completed" and result["recommendations"]:
                    return result["recommendations"]
            time.sleep(1.5)
        except RuntimeError as e:
            if "quota exceeded" in str(e):
                logger.warning("Primary source degraded. Triggering fallback.")
                try:
                    fallback_result = fetch_recommendations(platform_client, interaction_id, payload_fallback)
                    if fallback_result["recommendations"]:
                        return fallback_result["recommendations"]
                except Exception as fallback_err:
                    logger.error(f"Fallback failed: {fallback_err}")
                    raise RuntimeError("Both primary and fallback content sources unavailable.")
            raise
        except Exception as e:
            logger.error(f"Polling error: {e}")
            time.sleep(2)
    raise TimeoutError("Recommendation retrieval timed out.")

def generate_audit_log(interaction_id: str, recommendations: List[Dict[str, Any]], latency: float, source: str) -> Dict[str, Any]:
    import json
    from datetime import datetime, timezone
    scores = [r["combined_score"] for r in recommendations]
    return {
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "interaction_id": interaction_id,
        "content_source": source,
        "retrieval_latency_seconds": latency,
        "recommendation_count": len(recommendations),
        "avg_relevance_score": sum(scores) / len(scores) if scores else 0.0,
        "max_relevance_score": max(scores) if scores else 0.0,
        "compliance_status": "verified",
        "audit_id": f"audit-{interaction_id}-{int(time.time())}"
    }

def sync_webhook_callback(webhook_url: str, audit_log: Dict[str, Any], recommendations: List[Dict[str, Any]]) -> bool:
    import httpx
    payload = {
        "event": "agentassist.recommendations.delivered",
        "audit": audit_log,
        "content": [{"id": r["id"], "title": r["title"], "score": r["combined_score"]} for r in recommendations[:5]]
    }
    try:
        with httpx.Client(timeout=10.0) as client:
            response = client.post(webhook_url, json=payload, headers={"Content-Type": "application/json", "X-Event-Source": "genesys-agentassist"})
            response.raise_for_status()
            return True
    except httpx.HTTPError as e:
        logger.error(f"Webhook synchronization failed: {e}")
        return False

class AgentAssistRecommendationRetriever:
    def __init__(self, platform_client: PlatformClient, webhook_url: str):
        self.platform_client = platform_client
        self.webhook_url = webhook_url
        self.etag_cache: Dict[str, str] = {}
        
    def retrieve_and_process(
        self,
        channel: str,
        user_id: str,
        intent: str,
        keywords: list[str],
        primary_source: str,
        fallback_source: str,
        context_embedding: np.ndarray,
        content_embeddings: List[np.ndarray]
    ) -> Dict[str, Any]:
        start_time = time.time()
        
        context = build_interaction_context(channel, user_id, intent, keywords)
        interaction_id = create_interaction(self.platform_client, context)
        logger.info(f"Created interaction: {interaction_id}")
        
        raw_recommendations = poll_recommendations_streaming(
            self.platform_client, interaction_id, primary_source, fallback_source, self.etag_cache
        )
        
        scored = calculate_semantic_similarity(context_embedding, content_embeddings, raw_recommendations)
        filtered = apply_rule_based_filter(scored, allowed_types=["knowledge_article", "procedure_guide"])
        
        latency = time.time() - start_time
        audit = generate_audit_log(interaction_id, filtered, latency, primary_source)
        logger.info(f"Retrieval complete. Latency: {latency}s. Recommendations: {len(filtered)}")
        
        synced = sync_webhook_callback(self.webhook_url, audit, filtered)
        if not synced:
            logger.warning("Webhook sync failed. Proceeding with local delivery.")
            
        return {
            "interaction_id": interaction_id,
            "recommendations": filtered,
            "latency_seconds": latency,
            "audit_log": audit,
            "webhook_synced": synced
        }

if __name__ == "__main__":
    client = initialize_platform_client()
    retriever = AgentAssistRecommendationRetriever(client, webhook_url="https://training.internal/api/v1/events")
    
    # Mock embeddings for demonstration
    ctx_vec = np.array([0.8, 0.2, 0.5, 0.1])
    content_vecs = [np.array([0.7, 0.3, 0.6, 0.2]), np.array([0.1, 0.9, 0.2, 0.8])]
    
    result = retriever.retrieve_and_process(
        channel="voice",
        user_id="agent-1024",
        intent="billing_inquiry",
        keywords=["invoice", "payment", "late_fee"],
        primary_source="src-primary-kb",
        fallback_source="src-fallback-kb",
        context_embedding=ctx_vec,
        content_embeddings=content_vecs
    )
    print(json.dumps(result, indent=2))

Common Errors & Debugging

Error: 401 Unauthorized

• What causes it: The OAuth access token has expired or the client credentials are incorrect.
• How to fix it: Verify GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET environment variables. Ensure the client is registered as a confidential or public client with the correct scopes. Restart the process to trigger a fresh token acquisition.
• Code showing the fix: The initialize_platform_client function raises a clear RuntimeError on 401. Wrap calls in a retry loop with token refresh if running in a long-lived process.

Error: 403 Forbidden

• What causes it: The OAuth client lacks agentassist:interaction:write or agentassist:content:read scopes, or the content source reference is invalid.
• How to fix it: Navigate to the OAuth client configuration in the Genesys Cloud admin console and append the missing scopes. Verify that the content_source_id matches an active content source in your organization.
• Code showing the fix: The fetch_recommendations function explicitly checks for 403 and raises a descriptive error. Log the content_source_id to validate against the Genesys Cloud content repository.

Error: 429 Too Many Requests

• What causes it: Concurrent request quotas are exceeded on the content source or the API rate limit is reached.
• How to fix it: Implement exponential backoff with jitter. Reduce max_results or split retrieval across multiple interactions. Monitor the Retry-After header in the response.
• Code showing the fix: The polling loop catches RuntimeError containing “quota exceeded” and triggers the fallback source. Add a time.sleep(min(2**attempt, 30)) pattern before retrying.

Error: Cache Invalidation Failures

• What causes it: The etag comparison logic fails when the content source returns null etags or when multiple workers update the cache simultaneously.
• How to fix it: Use a thread-safe dictionary or external cache store (Redis) for etag_cache. Treat missing etags as cache misses.
• Code showing the fix: Replace etag_cache with a threading.Lock protected dictionary in production deployments.

Official References

• Agent Assist API Documentation
• Genesys Cloud Python SDK Reference
• OAuth 2.0 Client Credentials Flow
• Content Retrieval Payload Schema