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https://github.com/dograh-hq/dograh.git
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feat: agent versioning and model configurations override (#227)
* feat: add tests and migrations * feat: workflow versioning among published and draft * feat: add a new settings page to simplify workflow detail page * fix: fix tsclient generation
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Abhishek
GitHub
parent
f5fa9ce717
commit
38d1d928b7
62 changed files with 10158 additions and 3131 deletions
83
api/services/configuration/resolve.py
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83
api/services/configuration/resolve.py
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@ -0,0 +1,83 @@
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"""Resolve effective config by merging per-workflow model overrides onto global config."""
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from __future__ import annotations
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from api.schemas.user_configuration import UserConfiguration
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from api.services.configuration.registry import (
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REGISTRY,
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ServiceType,
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)
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# Maps override key → (UserConfiguration field, ServiceType for registry lookup)
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_SECTION_MAP: dict[str, ServiceType] = {
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"llm": ServiceType.LLM,
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"tts": ServiceType.TTS,
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"stt": ServiceType.STT,
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"realtime": ServiceType.REALTIME,
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}
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def _build_section_from_override(service_type: ServiceType, override: dict):
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"""Construct a typed config object from a raw override dict using the registry."""
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provider = override.get("provider")
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if not provider:
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return None
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registry = REGISTRY.get(service_type, {})
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config_cls = registry.get(provider)
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if config_cls is None:
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return None
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return config_cls(**override)
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def resolve_effective_config(
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user_config: UserConfiguration,
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model_overrides: dict | None,
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) -> UserConfiguration:
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"""Deep-merge workflow model_overrides onto global user config.
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- If model_overrides is None or empty, returns a copy of user_config unchanged.
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- For each section (llm, tts, stt, realtime), if the override contains that key:
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- If the global section is None, construct a new config from the override.
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- If the provider changes, construct a new config from the override.
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- Otherwise, merge override fields onto the existing config (model_copy).
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- is_realtime is a simple boolean override.
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- Sections not in the override are inherited from global unchanged.
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- The original user_config is never mutated.
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"""
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if not model_overrides:
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return user_config.model_copy(deep=True)
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effective = user_config.model_copy(deep=True)
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# Handle is_realtime boolean
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if "is_realtime" in model_overrides:
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effective.is_realtime = model_overrides["is_realtime"]
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# Handle service sections
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for section_key, service_type in _SECTION_MAP.items():
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if section_key not in model_overrides:
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continue
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override = model_overrides[section_key]
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base = getattr(effective, section_key)
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if base is None:
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# No global config for this section — build from override
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setattr(
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effective,
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section_key,
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_build_section_from_override(service_type, override),
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)
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elif "provider" in override and override["provider"] != base.provider:
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# Provider changed — must construct new typed object
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setattr(
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effective,
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section_key,
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_build_section_from_override(service_type, override),
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)
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else:
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# Same provider — merge fields onto existing config
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merged = base.model_copy(update=override)
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setattr(effective, section_key, merged)
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return effective
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@ -76,13 +76,15 @@ class LoopTalkPipelineBuilder:
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pipeline_sample_rate=16000,
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)
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# Use published definition for graph + configs
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released_def = workflow.released_definition
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wf_json = released_def.workflow_json
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wf_configs = released_def.workflow_configurations or {}
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# Extract keyterms from workflow configurations
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keyterms = None
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if (
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workflow.workflow_configurations
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and "dictionary" in workflow.workflow_configurations
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):
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dictionary = workflow.workflow_configurations["dictionary"]
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if wf_configs and "dictionary" in wf_configs:
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dictionary = wf_configs["dictionary"]
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if dictionary and isinstance(dictionary, str):
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keyterms = [
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term.strip() for term in dictionary.split(",") if term.strip()
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@ -90,6 +92,12 @@ class LoopTalkPipelineBuilder:
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if keyterms:
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logger.info(f"Using {len(keyterms)} keyterms for STT: {keyterms}")
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# Resolve model overrides from the version onto global user config
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from api.services.configuration.resolve import resolve_effective_config
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model_overrides = wf_configs.get("model_overrides")
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user_config = resolve_effective_config(user_config, model_overrides)
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# Create services
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stt = create_stt_service(user_config, audio_config, keyterms=keyterms)
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llm = create_llm_service(user_config)
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@ -98,9 +106,7 @@ class LoopTalkPipelineBuilder:
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logger.debug(f"Created services for {role}: STT={stt}, LLM={llm}, TTS={tts}")
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# Get workflow graph
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workflow_graph = WorkflowGraph(
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ReactFlowDTO.model_validate(workflow.workflow_definition_with_fallback)
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)
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workflow_graph = WorkflowGraph(ReactFlowDTO.model_validate(wf_json))
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# Create engine first (needed for create_pipeline_components)
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engine = PipecatEngine(
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@ -562,50 +562,49 @@ async def _run_pipeline(
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# Get user configuration
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user_config = await db_client.get_user_configurations(user_id)
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# Get workflow first so we can extract configurations before creating services
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# Get workflow for metadata (name, organization_id, call_disposition_codes)
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workflow = await db_client.get_workflow(workflow_id, user_id)
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if not workflow:
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raise HTTPException(status_code=404, detail="Workflow not found")
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# Extract configurations from workflow configurations
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# Use the run's pinned definition for graph + configs (not the workflow's current)
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run_definition = workflow_run.definition
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run_workflow_json = run_definition.workflow_json
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run_configs = run_definition.workflow_configurations or {}
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# Extract configurations from the version's workflow_configurations
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max_call_duration_seconds = 300 # Default 5 minutes
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max_user_idle_timeout = 10.0 # Default 10 seconds
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smart_turn_stop_secs = 2.0 # Default 2 seconds for incomplete turn timeout
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turn_stop_strategy = "transcription" # Default to transcription-based detection
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keyterms = None # Dictionary words for STT boosting
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if workflow.workflow_configurations:
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# Use workflow-specific max call duration if provided
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if "max_call_duration" in workflow.workflow_configurations:
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max_call_duration_seconds = workflow.workflow_configurations[
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"max_call_duration"
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]
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if run_configs:
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if "max_call_duration" in run_configs:
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max_call_duration_seconds = run_configs["max_call_duration"]
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# Use workflow-specific max user idle timeout if provided
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if "max_user_idle_timeout" in workflow.workflow_configurations:
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max_user_idle_timeout = workflow.workflow_configurations[
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"max_user_idle_timeout"
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]
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if "max_user_idle_timeout" in run_configs:
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max_user_idle_timeout = run_configs["max_user_idle_timeout"]
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# Use workflow-specific smart turn stop timeout if provided
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if "smart_turn_stop_secs" in workflow.workflow_configurations:
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smart_turn_stop_secs = workflow.workflow_configurations[
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"smart_turn_stop_secs"
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]
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if "smart_turn_stop_secs" in run_configs:
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smart_turn_stop_secs = run_configs["smart_turn_stop_secs"]
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# Use workflow-specific turn stop strategy if provided
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if "turn_stop_strategy" in workflow.workflow_configurations:
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turn_stop_strategy = workflow.workflow_configurations["turn_stop_strategy"]
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if "turn_stop_strategy" in run_configs:
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turn_stop_strategy = run_configs["turn_stop_strategy"]
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# Extract dictionary words and convert to keyterms list
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if "dictionary" in workflow.workflow_configurations:
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dictionary = workflow.workflow_configurations["dictionary"]
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if "dictionary" in run_configs:
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dictionary = run_configs["dictionary"]
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if dictionary and isinstance(dictionary, str):
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# Split by comma and strip whitespace from each term
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keyterms = [
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term.strip() for term in dictionary.split(",") if term.strip()
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]
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# Resolve model overrides from the version onto global user config
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from api.services.configuration.resolve import resolve_effective_config
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model_overrides = run_configs.get("model_overrides")
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user_config = resolve_effective_config(user_config, model_overrides)
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# Detect realtime mode (speech-to-speech services like OpenAI Realtime, Gemini Live)
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is_realtime = user_config.is_realtime and user_config.realtime is not None
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@ -619,9 +618,7 @@ async def _run_pipeline(
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tts = create_tts_service(user_config, audio_config)
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llm = create_llm_service(user_config)
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workflow_graph = WorkflowGraph(
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ReactFlowDTO.model_validate(workflow.workflow_definition_with_fallback)
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)
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workflow_graph = WorkflowGraph(ReactFlowDTO.model_validate(run_workflow_json))
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# Pre-call fetch: fire early so it runs concurrently with remaining setup
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pre_call_fetch_task = None
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@ -325,8 +325,6 @@ def create_tts_service(user_config, audio_config: "AudioConfig"):
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silence_time_s=1.0,
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)
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elif user_config.tts.provider == ServiceProviders.RIME.value:
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from pipecat.transcriptions.language import Language
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speed = getattr(user_config.tts, "speed", None)
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language_code = getattr(user_config.tts, "language", None) or "en"
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rime_language_mapping = {
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@ -74,13 +74,17 @@ async def duplicate_workflow(
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if source is None:
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raise ValueError(f"Workflow with id {workflow_id} not found")
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workflow_definition = copy.deepcopy(source.workflow_definition_with_fallback)
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# 2. Prefer draft over released definition (duplicate latest state)
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draft = await db_client.get_draft_version(workflow_id)
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source_def = draft if draft else source.released_definition
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# 2. Regenerate trigger UUIDs to avoid conflicts
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workflow_definition = copy.deepcopy(source_def.workflow_json)
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# 3. Regenerate trigger UUIDs to avoid conflicts
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if workflow_definition:
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workflow_definition = _regenerate_trigger_uuids(workflow_definition)
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# 3. Create the new workflow
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# 4. Create the new workflow
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new_name = f"{source.name} - Duplicate"
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new_workflow = await db_client.create_workflow(
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name=new_name,
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@ -89,21 +93,20 @@ async def duplicate_workflow(
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organization_id=organization_id,
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)
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# 4. Copy template_context_variables and workflow_configurations
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has_extra_fields = (
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source.template_context_variables or source.workflow_configurations
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)
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if has_extra_fields:
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# 5. Copy template_context_variables and workflow_configurations from source definition
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source_tcv = source_def.template_context_variables
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source_wc = source_def.workflow_configurations
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if source_tcv or source_wc:
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new_workflow = await db_client.update_workflow(
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workflow_id=new_workflow.id,
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name=None,
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workflow_definition=None,
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template_context_variables=copy.deepcopy(source.template_context_variables),
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workflow_configurations=copy.deepcopy(source.workflow_configurations),
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template_context_variables=copy.deepcopy(source_tcv),
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workflow_configurations=copy.deepcopy(source_wc),
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organization_id=organization_id,
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)
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# 5. Copy recordings with new IDs and storage paths scoped to new workflow
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# 6. Copy recordings with new IDs and storage paths scoped to new workflow
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recording_id_map = await _duplicate_recordings(
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source_workflow_id=workflow_id,
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new_workflow_id=new_workflow.id,
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@ -111,7 +114,7 @@ async def duplicate_workflow(
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user_id=user_id,
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)
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# 6. Replace old recording IDs with new ones in the workflow definition
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# 7. Replace old recording IDs with new ones in the workflow definition
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if recording_id_map:
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workflow_definition = _replace_recording_ids(
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workflow_definition, recording_id_map
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@ -125,7 +128,7 @@ async def duplicate_workflow(
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organization_id=organization_id,
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)
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# 7. Sync triggers for the new workflow
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# 8. Sync triggers for the new workflow
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if workflow_definition:
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trigger_paths = _extract_trigger_paths(workflow_definition)
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if trigger_paths:
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