The web messaging transcription buffer’s choking the knowledge surfacing pipeline, and it’s tanking our AI ROI before agents even see a prompt. Architect flow msg-assist-01 throws a 429 Too Many Requests on the /v2/ai/knowledge/suggestions endpoint whenever real-time transcription pushes past 120 characters in a single payload on the Webchat SDK v5.2.
curl -X POST "$GENESYS_CLOUD_HOST/api/v2/ai/knowledge/suggestions" \
-H "Authorization: Bearer $ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"text": "'$CHUNKED_TEXT'", "language": "en-us"}'
You’re hitting the rate limit because the SDK just dumps the whole buffer at once.
- Split the transcription into 80-char chunks before calling the endpoint. 2. Add a 300ms delay between requests to keep the oauth token valid and avoid the 429.
The API spec explicitly calls out that 120-character limit, so batch aggregation is your only move to clear the 429s. Step one: buffer the stream in a ConcurrentLinkedQueue until it hits 400 characters, then fire a single api.knowledgeSuggestionsPost() call with the combined text. Step two: strip the If-Match header since the endpoint doesn’t enforce versioning, which you’ll see clear up the throttling without tanking your latency.
Queue<String> buffer = new ConcurrentLinkedQueue<>();
// accumulate chunks until > 400 chars
String combined = String.join(" ", buffer);
PureCloudPlatformClientV2 client = PureCloudPlatformClientV2.create();
KnowledgeApi api = client.createApi(KnowledgeApi.class);
// POST /api/v2/ai/knowledge/suggestions
api.knowledgeSuggestionsPost(new KnowledgeSuggestionsPostRequest().text(combined).language("en-us"));
Cause: The 429 errors hit because the Webchat SDK v5.2 flushes the transcription buffer as a single POST when concurrent sessions spike. It’s messy. You’ll notice the endpoint has a hard throughput cap around 15 requests per second per org. When you push past that, the gateway drops the payload before it even hits the AI service. Batching helps, but you still need to control the request rate at the load generator level to avoid the initial spike.
Solution: The chunking approach above works, but you should throttle the actual HTTP calls instead of just splitting the string. Here is a JMeter config that mimics the production load pattern without tripping the rate limiter. Set the loop count to your target concurrent chats, and use a Constant Throughput Timer to cap the requests at 12/sec.
<kg.apc.jmeter.timers.VariableThroughputTimer guiclass="kg.apc.jmeter.timers.VariableThroughputTimerGui" testclass="kg.apc.jmeter.timers.VariableThroughputTimer" testname="Constant Throughput Timer">
<stringProp name="TargetThroughput">1200</stringProp>
<stringProp name="ConstantThroughput">true</stringProp>
</kg.apc.jmeter.timers.VariableThroughputTimer>
Pair that with a simple JS pre-processor to join the 80-char chunks before the sampler fires. This keeps the pipeline stable under 800 concurrent webchats. The dashboard stops rendering blank cards once the queue drains properly. You can drop the timer threshold to 8 if your org hits the WebSocket connection limits early. The baseline latency drops immediately.
The Python SDK v2.6.1 serializer in genesyscloud/models/conversations/webchat/message_send_request.py line 104 still merges transcription buffers into a single payload before hitting /v2/ai/knowledge/suggestions, which triggers the 429. Watch out for the built-in streaming callback since it bypasses the truncation logic entirely. Try intercepting the request builder to split the text array at 100 characters before JSON encoding, or are you routing this through a custom Architect flow action?