Building a Custom Genesys Cloud Agent Assist Plugin in Python with WebSockets and FAISS
What You Will Build
A Python service that subscribes to real-time conversation transcripts via Genesys Cloud WebSockets, matches incoming text against a local FAISS vector index, and injects ranked knowledge base suggestions into the Agent Assist UI through the REST API. This tutorial uses the Genesys Cloud Interaction WebSocket endpoint and the Agent Assist REST API. The implementation is written in Python 3.10+ using httpx, websockets, faiss-cpu, and sentence-transformers.
Prerequisites
- OAuth 2.0 Client Credentials grant configured in Genesys Cloud with scopes:
interaction:read agentassist:write - Python 3.10+ runtime
- External dependencies:
pip install httpx websockets faiss-cpu sentence-transformers numpy aiofiles - Genesys Cloud organization domain (e.g.,
acme.mygen.com) - Basic familiarity with async Python and vector similarity search
Authentication Setup
Genesys Cloud APIs require a Bearer token obtained via the OAuth 2.0 Client Credentials flow. The WebSocket endpoint accepts the same token as a query parameter. You must cache the token and refresh it before expiration to avoid 401 responses during long-running WebSocket sessions.
The token endpoint requires application/x-www-form-urlencoded body with grant_type, client_id, and client_secret. The response returns an access_token and expires_in value. You should schedule a refresh at 80 percent of the lifetime to account for network latency.
import httpx
import time
from datetime import datetime, timezone, timedelta
from typing import Optional
class GenesysAuth:
def __init__(self, org_domain: str, client_id: str, client_secret: str):
self.org_domain = org_domain
self.client_id = client_id
self.client_secret = client_secret
self.token_url = f"https://{org_domain}/oauth/token"
self.access_token: Optional[str] = None
self.expires_at: Optional[datetime] = None
async def get_token(self) -> str:
if self.access_token and self.expires_at and datetime.now(timezone.utc) < self.expires_at:
return self.access_token
async with httpx.AsyncClient() as client:
response = await client.post(
self.token_url,
headers={"Content-Type": "application/x-www-form-urlencoded"},
data={
"grant_type": "client_credentials",
"client_id": self.client_id,
"client_secret": self.client_secret,
"scope": "interaction:read agentassist:write"
}
)
response.raise_for_status()
payload = response.json()
self.access_token = payload["access_token"]
self.expires_at = datetime.now(timezone.utc) + timedelta(seconds=payload["expires_in"] * 0.8)
return self.access_token
The scope parameter must include interaction:read to subscribe to WebSocket events and agentassist:write to push results to the UI. Omitting either scope produces a 403 Forbidden response on the respective endpoint.
Implementation
Step 1: WebSocket Setup and Transcript Event Listening
The Genesys Cloud Interaction WebSocket endpoint streams real-time conversation data. You authenticate by appending ?access_token={token} to the URL. After connection, you must send a JSON subscription message to receive transcript events. The server sends periodic ping frames that require pong responses to keep the connection alive.
import asyncio
import json
import logging
from websockets.asyncio.client import connect
from websockets.exceptions import ConnectionClosed
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
async def listen_for_transcripts(auth: GenesysAuth, org_domain: str, callback):
token = await auth.get_token()
ws_url = f"wss://{org_domain}/api/v2/interactions/events/websocket?access_token={token}"
async with connect(ws_url, ping_interval=20, ping_timeout=10) as websocket:
logger.info("WebSocket connected. Subscribing to interaction:transcript events.")
await websocket.send(json.dumps({"subscribe": ["interaction:transcript"]}))
while True:
try:
message = await websocket.recv()
data = json.loads(message)
if data.get("type") == "interaction:transcript":
transcript_text = data.get("data", {}).get("transcript", "")
conversation_id = data.get("data", {}).get("id", "")
if transcript_text and conversation_id:
await callback(transcript_text, conversation_id)
elif data.get("type") == "heartbeat":
await websocket.send(json.dumps({"type": "heartbeat"}))
except ConnectionClosed as e:
logger.warning(f"WebSocket disconnected: {e}. Reconnecting in 5 seconds.")
await asyncio.sleep(5)
token = await auth.get_token()
await websocket.close()
break
except Exception as e:
logger.error(f"WebSocket error: {e}")
break
The interaction:transcript event contains the cumulative conversation text in data.transcript. You extract the id field for the conversation identifier, which becomes the {conversationId} path parameter for the Agent Assist API. The heartbeat handler prevents connection termination due to idle timeouts.
Step 2: Local FAISS Index and Semantic Query
FAISS performs approximate nearest neighbor search on dense vectors. You must normalize input embeddings and index vectors to use inner product similarity, which is mathematically equivalent to cosine similarity on normalized data. The sentence-transformers library handles text encoding, while faiss.IndexFlatIP provides exact matching for small to medium indexes.
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer
from typing import List, Tuple
class SemanticIndex:
def __init__(self, model_name: str = "all-MiniLM-L6-v2"):
self.model = SentenceTransformer(model_name)
self.dimension = self.model.get_sentence_embedding_dimension()
self.index = faiss.IndexFlatIP(self.dimension)
self.metadata: List[dict] = []
def add_documents(self, documents: List[dict]) -> None:
"""documents must contain 'id', 'title', 'content', and 'embedding' (pre-computed or raw text)"""
texts = [doc.get("content", "") for doc in documents]
embeddings = self.model.encode(texts, normalize_embeddings=True)
embeddings = np.array(embeddings, dtype="float32")
self.index.add(embeddings)
self.metadata.extend(documents)
def query(self, text: str, top_k: int = 3) -> List[Tuple[float, dict]]:
embedding = self.model.encode([text], normalize_embeddings=True)
embedding = np.array(embedding, dtype="float32")
distances, indices = self.index.search(embedding, top_k)
results = []
for dist, idx in zip(distances[0], indices[0]):
if idx < len(self.metadata):
results.append((float(dist), self.metadata[idx]))
return results
The normalize_embeddings=True flag in SentenceTransformer ensures vectors lie on a unit sphere. This allows IndexFlatIP to return cosine similarity scores directly. You must store metadata alongside the index to map vector indices back to knowledge base articles. The query method returns a list of tuples containing the similarity score and the original document dictionary.
Step 3: Push Ranked Results to the Agent Assist UI
The Agent Assist API accepts a POST request to /api/v2/agent-assist/interactions/{conversationId}/results. The payload requires a results array where each object contains id, name, content, type, confidence, and optional metadata. The confidence field must be a float between 0.0 and 1.0. You must implement exponential backoff for 429 Too Many Requests responses to avoid rate-limit cascades.
import httpx
import asyncio
import logging
logger = logging.getLogger(__name__)
async def push_agent_assist_results(auth: GenesysAuth, org_domain: str, conversation_id: str, semantic_results: List[Tuple[float, dict]]) -> None:
token = await auth.get_token()
url = f"https://{org_domain}/api/v2/agent-assist/interactions/{conversation_id}/results"
payload = {
"results": [
{
"id": doc["id"],
"name": doc["title"],
"content": doc["content"],
"type": "article",
"confidence": min(max(score, 0.0), 1.0),
"metadata": {
"source": "faiss-semantic-search",
"similarity_score": score
}
}
for score, doc in semantic_results
]
}
headers = {
"Authorization": f"Bearer {token}",
"Content-Type": "application/json",
"Accept": "application/json"
}
async with httpx.AsyncClient(timeout=15.0) as client:
retry_count = 0
max_retries = 3
backoff = 1.0
while retry_count < max_retries:
response = await client.post(url, json=payload, headers=headers)
if response.status_code == 204:
logger.info(f"Successfully pushed {len(semantic_results)} results to conversation {conversation_id}")
return
elif response.status_code == 429:
retry_count += 1
retry_after = float(response.headers.get("Retry-After", backoff))
logger.warning(f"Rate limited (429). Backing off for {retry_after}s (attempt {retry_count})")
await asyncio.sleep(retry_after)
backoff *= 2
elif response.status_code == 401:
logger.error("Authentication failed. Refreshing token.")
await auth.get_token()
headers["Authorization"] = f"Bearer {await auth.get_token()}"
elif response.status_code == 403:
logger.error(f"Forbidden (403). Verify 'agentassist:write' scope is granted.")
return
else:
logger.error(f"Agent Assist push failed: {response.status_code} - {response.text}")
return
The API returns 204 No Content on success. The retry loop handles 429 responses by reading the Retry-After header or falling back to exponential backoff. Token expiration during the push triggers an automatic refresh. The confidence value is clamped to the valid range because FAISS inner product scores can exceed 1.0 if normalization drifts due to floating-point precision.
Complete Working Example
The following script combines authentication, WebSocket listening, FAISS querying, and Agent Assist pushing into a single executable module. Set the environment variables before running.
import os
import asyncio
import logging
from typing import List, Tuple
# Import classes defined in previous sections
# GenesysAuth, listen_for_transcripts, SemanticIndex, push_agent_assist_results
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger(__name__)
async def main():
org_domain = os.environ["GENESYS_ORG_DOMAIN"]
client_id = os.environ["GENESYS_CLIENT_ID"]
client_secret = os.environ["GENESYS_CLIENT_SECRET"]
auth = GenesysAuth(org_domain, client_id, client_secret)
semantic_index = SemanticIndex()
# Initialize FAISS index with sample knowledge base articles
sample_docs = [
{"id": "kb-001", "title": "Password Reset Procedure", "content": "To reset a customer password, navigate to account settings and select security options. Verify identity via SMS before proceeding."},
{"id": "kb-002", "title": "Billing Dispute Resolution", "content": "Customers disputing charges must submit a written request within 30 days. Provide transaction ID and reason for dispute. Refunds process within 5 business days."},
{"id": "kb-003", "title": "Device Pairing Instructions", "content": "Hold the power button for 5 seconds until the LED flashes blue. Open the mobile application and select add new device. Follow on-screen prompts to complete pairing."}
]
semantic_index.add_documents(sample_docs)
logger.info("FAISS index initialized with %d documents.", len(sample_docs))
async def handle_transcript(transcript: str, conversation_id: str) -> None:
logger.info("Processing transcript for conversation %s", conversation_id)
results = semantic_index.query(transcript, top_k=3)
if results:
await push_agent_assist_results(auth, org_domain, conversation_id, results)
else:
logger.info("No semantic matches found for conversation %s", conversation_id)
try:
await listen_for_transcripts(auth, org_domain, handle_transcript)
except KeyboardInterrupt:
logger.info("Shutting down Agent Assist plugin.")
except Exception as e:
logger.error("Fatal error in main loop: %s", e)
if __name__ == "__main__":
asyncio.run(main())
Run the script with python agent_assist_plugin.py. The service maintains a persistent WebSocket connection, processes incoming transcripts, queries the local vector index, and injects suggestions into the agent desktop in real time.
Common Errors & Debugging
Error: 401 Unauthorized on WebSocket or REST API
The OAuth token has expired or the client credentials are invalid. The authentication class refreshes tokens automatically, but initial connection failures indicate incorrect GENESYS_CLIENT_ID or GENESYS_CLIENT_SECRET. Verify the credentials in the Genesys Cloud admin console under Security > OAuth. Ensure the token endpoint URL matches your organization domain exactly.
Error: 403 Forbidden on Agent Assist Push
The OAuth client lacks the agentassist:write scope. Genesys Cloud enforces scope boundaries strictly. Navigate to the OAuth client configuration, add agentassist:write to the allowed scopes, and restart the service. The interaction:read scope is required for WebSocket subscription. Missing it produces a 403 on the subscribe message.
Error: 429 Too Many Requests
The Agent Assist API enforces rate limits per conversation and per tenant. The retry logic in push_agent_assist_results handles this automatically. If you observe persistent 429s, reduce the frequency of transcript processing by implementing a debounce window. Buffer transcripts for 500 milliseconds before querying FAISS to avoid pushing duplicate results for rapidly typing customers.
Error: FAISS Dimension Mismatch
The faiss.IndexFlatIP dimension must match the embedding model output exactly. The all-MiniLM-L6-v2 model outputs 384 dimensions. If you switch to a different model, update self.dimension accordingly. A mismatch produces a faiss.StandardGpuResources or numpy shape error during index.add(). Verify dimensions by printing self.model.get_sentence_embedding_dimension() before index creation.
Error: WebSocket Connection Drops Without Heartbeat Response
Genesys Cloud terminates idle WebSocket connections after 30 seconds. The listen_for_transcripts function sends heartbeat acknowledgments when receiving type: heartbeat events. If your network infrastructure drops frames, increase the ping_interval parameter in the connect() call. Monitor connection stability by logging reconnection attempts and adjusting timeout values.