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Merge remote-tracking branch 'origin/main' into feat/user-onboarding

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This commit is contained in:
Abhishek Kumar 2026-06-12 18:54:48 +05:30
commit 093e888ce4
148 changed files with 10908 additions and 2815 deletions
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4
api/services/workflow/pipecat_engine.py
View file

@ -35,6 +35,7 @@ import asyncio
from loguru import logger
from api.services.managed_model_services import MPS_CORRELATION_ID_CONTEXT_KEY
from api.services.workflow import pipecat_engine_callbacks as engine_callbacks
from api.services.workflow.mcp_tool_session import McpToolSession
from api.services.workflow.pipecat_engine_context_composer import (
@ -382,6 +383,9 @@ class PipecatEngine:
embeddings_provider=self._embeddings_provider,
embeddings_endpoint=self._embeddings_endpoint,
embeddings_api_version=self._embeddings_api_version,
correlation_id=self._call_context_vars.get(
MPS_CORRELATION_ID_CONTEXT_KEY
),
tracing_context=self._get_otel_context(),
)

25
api/services/workflow/qa/llm_config.py
View file

@ -2,7 +2,6 @@
import random
from api.db import db_client
from api.db.models import WorkflowRunModel
from api.services.workflow.dto import QANodeData
@ -43,7 +42,7 @@ async def resolve_llm_config(
async def resolve_user_llm_config(
workflow_run: WorkflowRunModel,
) -> tuple[str, str, str, dict]:
"""Resolve the user's configured LLM (from UserConfiguration).
"""Resolve the user's configured LLM (from EffectiveAIModelConfiguration).
Returns:
(provider, model, api_key, service_kwargs) tuple
@ -54,7 +53,27 @@ async def resolve_user_llm_config(
llm_config: dict = {}
if user_id:
user_configuration = await db_client.get_user_configurations(user_id)
from api.services.configuration.ai_model_configuration import (
get_effective_ai_model_configuration_for_workflow,
)
workflow_configurations = {}
if workflow_run.definition:
workflow_configurations = (
workflow_run.definition.workflow_configurations or {}
)
elif workflow_run.workflow:
workflow_configurations = (
workflow_run.workflow.workflow_configurations or {}
)
user_configuration = await get_effective_ai_model_configuration_for_workflow(
user_id=user_id,
organization_id=workflow_run.workflow.organization_id
if workflow_run.workflow
else None,
workflow_configurations=workflow_configurations,
)
llm_config = user_configuration.model_dump(exclude_none=True).get("llm", {})
provider = llm_config.get("provider", "openai")

41
api/services/workflow/run_usage_response.py Normal file
View file

@ -0,0 +1,41 @@
"""Format workflow run usage for public API responses."""
def format_public_usage_info(usage_info: dict | None) -> dict | None:
if not usage_info:
return None
return {
"llm": usage_info.get("llm") or {},
"tts": usage_info.get("tts") or {},
"stt": usage_info.get("stt") or {},
"call_duration_seconds": usage_info.get("call_duration_seconds"),
}
def format_public_cost_info(
cost_info: dict | None, usage_info: dict | None
) -> dict | None:
"""Return the legacy response shape without doing local cost accounting."""
duration = None
if usage_info and usage_info.get("call_duration_seconds") is not None:
duration = int(round(usage_info.get("call_duration_seconds") or 0))
elif cost_info and cost_info.get("call_duration_seconds") is not None:
duration = int(round(cost_info.get("call_duration_seconds") or 0))
dograh_token_usage = 0
if cost_info:
if "dograh_token_usage" in cost_info:
dograh_token_usage = cost_info.get("dograh_token_usage") or 0
elif "total_cost_usd" in cost_info:
dograh_token_usage = round(
float(cost_info.get("total_cost_usd", 0)) * 100, 2
)
if duration is None and dograh_token_usage == 0:
return None
return {
"dograh_token_usage": dograh_token_usage,
"call_duration_seconds": duration,
}

46
api/services/workflow/text_chat_runner.py
View file

@ -32,7 +32,6 @@ from pipecat.utils.run_context import set_current_org_id
from api.db import db_client
from api.enums import WorkflowRunMode, WorkflowRunState
from api.services.configuration.resolve import resolve_effective_config
from api.services.pipecat.audio_config import create_audio_config
from api.services.pipecat.pipeline_builder import create_pipeline_task
from api.services.pipecat.pipeline_metrics_aggregator import (
@ -410,14 +409,31 @@ async def execute_text_chat_pending_turn(
run_definition = workflow_run.definition
run_configs = run_definition.workflow_configurations or {}
user_config = await db_client.get_user_configurations(workflow_run.workflow.user.id)
user_config = resolve_effective_config(
user_config, run_configs.get("model_overrides")
from api.services.configuration.ai_model_configuration import (
get_effective_ai_model_configuration_for_workflow,
)
user_config = await get_effective_ai_model_configuration_for_workflow(
user_id=workflow_run.workflow.user.id,
organization_id=workflow.organization_id,
workflow_configurations=run_configs,
)
if user_config.llm is None:
raise ValueError("Text chat requires an LLM configuration")
llm = create_llm_service(user_config)
from api.services.managed_model_services import (
MPS_CORRELATION_ID_CONTEXT_KEY,
ensure_mps_correlation_id,
)
base_initial_context = dict(workflow_run.initial_context or {})
mps_correlation_id = await ensure_mps_correlation_id(
ai_model_config=user_config,
workflow_run_id=workflow_run_id,
initial_context=base_initial_context,
)
llm = create_llm_service(user_config, correlation_id=mps_correlation_id)
inference_llm = llm
runtime_configuration = {
@ -425,9 +441,15 @@ async def execute_text_chat_pending_turn(
"llm_model": user_config.llm.model,
}
initial_context = {
**(workflow_run.initial_context or {}),
**base_initial_context,
"runtime_configuration": runtime_configuration,
}
if mps_correlation_id:
initial_context[MPS_CORRELATION_ID_CONTEXT_KEY] = mps_correlation_id
await db_client.update_workflow_run(
workflow_run_id,
initial_context=initial_context,
)
workflow_graph = WorkflowGraph(
ReactFlowDTO.model_validate(run_definition.workflow_json)
@ -466,9 +488,17 @@ async def execute_text_chat_pending_turn(
embeddings_model = None
embeddings_base_url = None
if 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_base_url = getattr(user_config.embeddings, "base_url", None)
embeddings_provider = getattr(user_config.embeddings, "provider", None)
embeddings_base_url = apply_managed_embeddings_base_url(
provider=embeddings_provider,
base_url=getattr(user_config.embeddings, "base_url", None),
)
has_recordings = await db_client.has_active_recordings(workflow.organization_id)
context_compaction_enabled = (workflow.workflow_configurations or {}).get(
@ -606,8 +636,10 @@ async def execute_text_chat_pending_turn(
"Transportless text chat pipeline failed while closing run {}",
workflow_run_id,
)
await engine.close_mcp_sessions()
await engine.cleanup()
raise
await engine.close_mcp_sessions()
await engine.cleanup()
gathered_context = await engine.get_gathered_context()

20
api/services/workflow/text_chat_session_service.py
View file

@ -4,17 +4,11 @@ from datetime import UTC, datetime
from typing import Any
from uuid import uuid4
from loguru import logger
from api.db import db_client
from api.db.models import WorkflowRunTextSessionModel
from api.db.workflow_run_text_session_client import (
WorkflowRunTextSessionRevisionConflictError,
)
from api.services.pricing.workflow_run_cost import (
apply_usage_delta_to_organization,
build_workflow_run_cost_info,
)
from api.services.workflow.text_chat_logs import (
build_text_chat_realtime_feedback_events,
)
@ -261,20 +255,6 @@ async def execute_pending_text_chat_turn(
state=execution.state,
is_completed=execution.is_completed,
)
workflow_run = await db_client.get_workflow_run_by_id(run_id)
if workflow_run:
try:
# Apply the per-turn delta so org usage tracks cumulative run cost
# without replaying the full session totals on every turn.
await apply_usage_delta_to_organization(workflow_run, execution.usage)
except Exception as e:
logger.error(
f"Failed to update organization usage for text chat run {run_id}: {e}"
)
cost_info = await build_workflow_run_cost_info(workflow_run)
if cost_info is not None:
await db_client.update_workflow_run(run_id, cost_info=cost_info)
return await _reload_text_chat_session(run_id)

15
api/services/workflow/tools/knowledge_base.py
View file

@ -29,6 +29,7 @@ async def retrieve_from_knowledge_base(
embeddings_provider: Optional[str] = None,
embeddings_endpoint: Optional[str] = None,
embeddings_api_version: Optional[str] = None,
correlation_id: Optional[str] = None,
tracing_context=None,
) -> Dict[str, Any]:
"""Retrieve relevant information from the knowledge base using vector similarity search.
@ -75,6 +76,7 @@ async def retrieve_from_knowledge_base(
embeddings_provider,
embeddings_endpoint,
embeddings_api_version,
correlation_id,
)
# Create span with parent context
@ -115,6 +117,7 @@ async def retrieve_from_knowledge_base(
embeddings_provider,
embeddings_endpoint,
embeddings_api_version,
correlation_id,
)
# Add result metadata to span
@ -192,6 +195,7 @@ async def retrieve_from_knowledge_base(
embeddings_provider,
embeddings_endpoint,
embeddings_api_version,
correlation_id,
)
else:
# Tracing is disabled - perform retrieval without tracing
@ -206,6 +210,7 @@ async def retrieve_from_knowledge_base(
embeddings_provider,
embeddings_endpoint,
embeddings_api_version,
correlation_id,
)
@ -220,6 +225,7 @@ async def _perform_retrieval(
embeddings_provider: Optional[str] = None,
embeddings_endpoint: Optional[str] = None,
embeddings_api_version: Optional[str] = None,
correlation_id: Optional[str] = None,
) -> Dict[str, Any]:
"""Internal function to perform the actual retrieval operation.
@ -272,11 +278,20 @@ async def _perform_retrieval(
api_version=embeddings_api_version or "2024-02-15-preview",
)
else:
default_headers = None
if (
embeddings_provider == ServiceProviders.DOGRAH.value
and correlation_id
):
default_headers = {
"X-Dograh-Correlation-Id": correlation_id,
}
embedding_service = OpenAIEmbeddingService(
db_client=db_client,
api_key=embeddings_api_key,
model_id=embeddings_model or "text-embedding-3-small",
base_url=embeddings_base_url,
default_headers=default_headers,
)
results = await embedding_service.search_similar_chunks(