feat: add Tuner Integration to Dograh (#311)

* Add tuner integration * bump pipecat version * chore: update pipecat submodule to match upstream and use tuner-pipecat-sdk 0.2.0 Update pipecat submodule from 0.0.109.dev23 to 13e98d0d9 (the exact commit upstream dograh-hq/dograh uses after v1.30.1). This installs pipecat-ai as 1.1.0.post277 via setuptools_scm, satisfying tuner-pipecat-sdk 0.2.0's pipecat-ai>=1.0.0 requirement. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * wire tuner * feat: refactor integrations into self contained packages * chore: simplify ensure_public_access_token * fix: remove NodeSpec and make DTOs the source of truth * feat: send relevant signal to mcp using to_mcp_dict * fix: fix tests * cleanup: remove nango integrations * feat: add agents.md for integrations --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: Abhishek Kumar <abhishek@a6k.me>
2026-06-22 08:38:13 +02:00 · 2026-05-20 10:07:33 +01:00 · 2026-05-20 10:07:33 +01:00 · 5f28c1b2a9
commit 5f28c1b2a9
parent afa78fe859
93 changed files with 3388 additions and 3414 deletions
--- a/api/services/integrations/tuner/runtime.py
+++ b/api/services/integrations/tuner/runtime.py
@ -0,0 +1,101 @@
+from __future__ import annotations
+
+from typing import Any
+
+from api.services.configuration.registry import ServiceProviders
+from api.services.integrations.base import (
+    IntegrationRuntimeContext,
+    IntegrationRuntimeSession,
+)
+
+from .collector import TunerCollector, mode_to_tuner_call_type
+
+
+def _format_model_label(provider: str | None, model: str | None) -> str:
+    if provider and model:
+        return f"{provider}/{model}"
+    if model:
+        return model
+    return provider or ""
+
+
+def _resolve_model_labels(context: IntegrationRuntimeContext) -> tuple[str, str, str]:
+    user_config = context.user_config
+
+    if context.is_realtime and user_config.realtime:
+        realtime_provider = user_config.realtime.provider
+        realtime_model = user_config.realtime.model
+        llm_model = _format_model_label(realtime_provider, realtime_model)
+        if realtime_provider in {
+            ServiceProviders.GOOGLE_REALTIME.value,
+            ServiceProviders.GOOGLE_VERTEX_REALTIME.value,
+            ServiceProviders.OPENAI_REALTIME.value,
+        }:
+            return "", llm_model, ""
+        return "", llm_model, ""
+
+    return (
+        _format_model_label(
+            getattr(user_config.stt, "provider", None),
+            getattr(user_config.stt, "model", None),
+        ),
+        _format_model_label(
+            getattr(user_config.llm, "provider", None),
+            getattr(user_config.llm, "model", None),
+        ),
+        _format_model_label(
+            getattr(user_config.tts, "provider", None),
+            getattr(user_config.tts, "model", None),
+        ),
+    )
+
+
+class TunerRuntimeSession(IntegrationRuntimeSession):
+    name = "tuner"
+
+    def __init__(self, collector: TunerCollector) -> None:
+        self._collector = collector
+
+    def attach(self, task: Any) -> None:
+        self._collector.attach_turn_tracking_observer(task.turn_tracking_observer)
+        self._collector.attach_latency_observer(task.user_bot_latency_observer)
+        task.add_observer(self._collector)
+
+    async def on_call_finished(
+        self,
+        *,
+        gathered_context: dict[str, Any],
+    ) -> dict[str, Any] | None:
+        self._collector.set_disconnection_reason(
+            gathered_context.get("call_disposition")
+        )
+        payload = self._collector.build_payload_snapshot()
+        if payload is None:
+            return None
+        return {"tuner_payload": payload}
+
+
+def create_runtime_sessions(
+    context: IntegrationRuntimeContext,
+) -> list[IntegrationRuntimeSession]:
+    tuner_nodes = [
+        node
+        for node in context.workflow_graph.nodes.values()
+        if node.node_type == "tuner" and getattr(node.data, "tuner_enabled", True)
+    ]
+    if not tuner_nodes:
+        return []
+
+    asr_model, llm_model, tts_model = _resolve_model_labels(context)
+
+    collector = TunerCollector(
+        workflow_run_id=context.workflow_run_id,
+        call_type=mode_to_tuner_call_type(context.workflow_run.mode),
+        asr_model=asr_model,
+        llm_model=llm_model,
+        tts_model=tts_model,
+        agent_version=getattr(context.run_definition, "version_number", None),
+    )
+    collector.attach_context(context.context_messages_provider)
+
+    return [TunerRuntimeSession(collector)]