What’s the correct approach for filtering Analytics API queries against Data Action return values? Running a Jupyter notebook with genesyscloud==3.0.12 and pandas==2.1.3. Trying to slice conversation metrics where a custom Data Action (enrich_cust_tier) sets output:tier to platinum. The queryFilter block looks like {"condition": "output.tier eq platinum"}. Backend doesn’t accept the syntax. Throws a 422 Unprocessable Entity with {"error": "invalidQuery", "message": "Dimension 'output.tier' is not supported in filter expressions"}.
Console is empty except for the SDK traceback. Tried swapping eq for contains, same result. The Data Action definitely fires, checked the debug logs in Architect and the payload hits the webhook fine. It’s just the analytics aggregation layer that seems blind to it. Timezone is locked to Asia/Singapore via pytz.timezone('Asia/Singapore'), interval uses 2023-10-01T00:00:00+08:00 to 2023-10-01T23:59:59+08:00. You’ll notice the queryFilter block expects a strict dimension key. Maybe the attribute needs a custom: prefix? Tried custom:output.tier and da_output:tier. Both get rejected. The docs mention conversation and interaction dimensions, but nothing concrete on dynamic Data Action outputs. Running queryInterval as relative helps bypass the strict ISO parsing, but the filter error persists anyway. pandas DataFrame stays empty, doing jack all on the aggregation side.
Here is the exact request dict being passed to analytics_api.query_conversations():
You might want to swap the queryFilter syntax since genesyscloud-python handles the analytics routing differently and will outright reject raw Data Action output references. The backend expects the filter block to target conversation attributes mapped via the attribute reference instead of those raw payload keys. Switching to the attributes namespace usually clears the 422. You’ll need to structure the request body exactly like this:
The suggestion above touches the namespace, but the real blocker is the condition key. The analytics query engine drops anything that doesn’t use explicit attribute, op, and value fields. Swapping to that structure clears the 422. If you’re looping through date ranges in that notebook, you’ll hit the rate limit on /api/v2/analytics/conversations/queries pretty fast. Wrap the call in a sliding window cache with a 300-second TTL to avoid hammering the endpoint. Are you hashing the full request body for the Redis key? Cache key collision happens if you only store the interval. Watch the TTL. Redis evicts on memory pressure. Stale Bearer tokens will mask the actual 422 as a 401.
the earlier post’s payload structure is spot on, but the analytics store doesn’t index Data Action outputs immediately. There’s often a sync delay or a schema mismatch causing that 422.
The filter key must be attribute, not condition. The Python SDK might be translating condition into a legacy format that the newer analytics endpoint rejects.
Check the attribute path. It usually looks like attributes.enrich_cust_tier.tier. If the Data Action output isn’t explicitly mapped to a conversation attribute in the flow, the analytics index ignores it.
Verify the output type. If tier is set as a string in the action but the analytics schema expects an enum or a different type, the query validator throws a 422 before even running the search.
Here’s how the Node.js SDK handles this for reference. The structure stays the same, but the property names are strict.