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fix: fix speech to speech model transitions (#545)

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* fix: fix transition logic for realtime providers

* chore: run formatter

* chore: generate SDK and fix other realtime providers

* fix: fix ultravox node transitions
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Abhishek 2026-07-15 18:36:36 +05:30 • committed by GitHub
parent 348cd8427b
commit 01acf6ac30
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34 changed files with 1282 additions and 617 deletions
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25
api/services/integrations/paygent/completion.py
View file

@ -12,6 +12,7 @@ Mirrors ``tuner/completion.py`` exactly:
- call ``deliver(config, snapshot)``
- collect results keyed by ``paygent_{node_id}``
"""
from __future__ import annotations
from datetime import UTC, datetime
@ -38,8 +39,8 @@ def _build_snapshot(
# snapshot value is never used to override it, preventing billing drift
# if the log is stale or corrupted.
session_id=str(workflow_run_id),
agent_id=raw.get("agent_id", ""), # filled from node config below
customer_id=raw.get("customer_id", ""), # filled from node config below
agent_id=raw.get("agent_id", ""), # filled from node config below
customer_id=raw.get("customer_id", ""), # filled from node config below
is_realtime=raw.get("is_realtime", False),
stt_provider=raw.get("stt_provider", ""),
stt_model=raw.get("stt_model", ""),
@ -90,7 +91,9 @@ async def run_completion(
continue
# ---- Build typed objects -------------------------------------------
snapshot = _build_snapshot(raw_snapshot, workflow_run_id=context.workflow_run_id)
snapshot = _build_snapshot(
raw_snapshot, workflow_run_id=context.workflow_run_id
)
# Inject node-level credentials into the snapshot
snapshot.agent_id = (node_data.paygent_agent_id or "").strip()
snapshot.customer_id = (node_data.paygent_customer_id or "").strip()
@ -102,7 +105,11 @@ async def run_completion(
# Only fallback to pipeline-level llm usage if this is NOT a realtime pipeline.
# In realtime pipelines, the collector properly segregates STS and LLM tokens;
# falling back here would duplicate the STS tokens into the LLM bucket.
if snapshot.llm_prompt_tokens == 0 and snapshot.llm_completion_tokens == 0 and not snapshot.is_realtime:
if (
snapshot.llm_prompt_tokens == 0
and snapshot.llm_completion_tokens == 0
and not snapshot.is_realtime
):
llm_providers: list[str] = []
llm_models: list[str] = []
for key, val in usage_info.get("llm", {}).items():
@ -112,7 +119,9 @@ async def run_completion(
continue
snapshot.llm_prompt_tokens += val.get("prompt_tokens", 0)
snapshot.llm_completion_tokens += val.get("completion_tokens", 0)
snapshot.llm_cached_tokens += val.get("cache_read_input_tokens", 0) + val.get("cache_creation_input_tokens", 0)
snapshot.llm_cached_tokens += val.get(
"cache_read_input_tokens", 0
) + val.get("cache_creation_input_tokens", 0)
parts = key.split("|||")
if len(parts) == 2:
llm_providers.append(parts[0])
@ -153,7 +162,11 @@ async def run_completion(
# substitute total_duration_seconds — that would overbill wall-clock time
# (silence, hold, agent speech) as STT input.
except Exception as exc:
logger.warning("[paygent] Failed to apply usage_info fallback for run {}: {}", context.workflow_run_id, exc)
logger.warning(
"[paygent] Failed to apply usage_info fallback for run {}: {}",
context.workflow_run_id,
exc,
)
try:
config = PaygentDeliveryConfig(