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feat: add config v2 to simplify billing (#428)

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* feat: add model config v2

* chore: centralize user org selection

* chore: move preferences to platform settings

* fix: decouple org preference and ai model preferences
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Abhishek 2026-06-09 16:10:26 +05:30 • committed by GitHub
parent 49e68b49d5
commit cdbd06c8d9
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42 changed files with 5135 additions and 264 deletions
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42
api/services/pipecat/run_pipeline.py
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@ -195,14 +195,17 @@ async def run_pipeline_telephony(
# Resolve effective user config here so the transport can tune its
# bot-stopped-speaking fallback based on is_realtime; pass the resolved
# values into _run_pipeline so it doesn't fetch them again.
from api.services.configuration.resolve import resolve_effective_config
from api.services.configuration.ai_model_configuration import (
get_effective_ai_model_configuration_for_workflow,
)
user_config = await db_client.get_user_configurations(user_id)
run_configs = (
(workflow_run.definition.workflow_configurations or {}) if workflow_run else {}
)
user_config = resolve_effective_config(
user_config, run_configs.get("model_overrides")
user_config = await get_effective_ai_model_configuration_for_workflow(
user_id=user_id,
organization_id=workflow.organization_id if workflow else None,
workflow_configurations=run_configs,
)
is_realtime = bool(user_config.is_realtime and user_config.realtime is not None)
@ -272,15 +275,18 @@ async def run_pipeline_smallwebrtc(
# Resolve workflow_run + effective user_config here so the transport can
# tune its bot-stopped-speaking fallback based on is_realtime. _run_pipeline
# reuses these via kwargs so we don't fetch twice.
from api.services.configuration.resolve import resolve_effective_config
from api.services.configuration.ai_model_configuration import (
get_effective_ai_model_configuration_for_workflow,
)
workflow_run = await db_client.get_workflow_run(workflow_run_id, user_id)
user_config = await db_client.get_user_configurations(user_id)
run_configs = (
(workflow_run.definition.workflow_configurations or {}) if workflow_run else {}
)
user_config = resolve_effective_config(
user_config, run_configs.get("model_overrides")
user_config = await get_effective_ai_model_configuration_for_workflow(
user_id=user_id,
organization_id=workflow.organization_id if workflow else None,
workflow_configurations=run_configs,
)
is_realtime = bool(user_config.is_realtime and user_config.realtime is not None)
@ -380,11 +386,14 @@ async def _run_pipeline(
# Resolve model overrides from the version onto global user config (skip
# when the caller already resolved it).
if resolved_user_config is None:
from api.services.configuration.resolve import resolve_effective_config
from api.services.configuration.ai_model_configuration import (
get_effective_ai_model_configuration_for_workflow,
)
user_config = await db_client.get_user_configurations(user_id)
user_config = resolve_effective_config(
user_config, run_configs.get("model_overrides")
user_config = await get_effective_ai_model_configuration_for_workflow(
user_id=user_id,
organization_id=workflow.organization_id,
workflow_configurations=run_configs,
)
else:
user_config = resolved_user_config
@ -508,10 +517,17 @@ async def _run_pipeline(
embeddings_endpoint = None
embeddings_api_version = None
if user_config and user_config.embeddings:
from api.services.configuration.ai_model_configuration import (
apply_managed_embeddings_base_url,
)
embeddings_api_key = user_config.embeddings.api_key
embeddings_model = user_config.embeddings.model
embeddings_provider = getattr(user_config.embeddings, "provider", None)
embeddings_base_url = getattr(user_config.embeddings, "base_url", None)
embeddings_base_url = apply_managed_embeddings_base_url(
provider=embeddings_provider,
base_url=getattr(user_config.embeddings, "base_url", None),
)
embeddings_endpoint = getattr(user_config.embeddings, "endpoint", None)
embeddings_api_version = getattr(user_config.embeddings, "api_version", None)