mirror of
https://github.com/dograh-hq/dograh.git
synced 2026-06-28 08:49:42 +02:00
fix: changes to update pipecat version to 0.0.100 (#122)
* feat: add stt evals * add smart turn as provider * chore: remove deprecations * chore: format files * fix: remove deprecated UserIdleProcessor * fix: remove deprecated TranscriptProcessor * chore: update pipecat submodule * feat: add evals visualisation * fix: trigger llm generation on client connected and pipeline started * chore: update pipecat * chore: update pipecat submodule * Add tests * fix: slow loading of workflow page * chore: update pipecat submodule * Show version after release * Fixes #99 * fix: provider check for websocket connection * Fixes #107 * Fix #96 * chore: fix documentation * fix: cloudonix campaign call error --------- Co-authored-by: Sabiha Khan <sabihak89@gmail.com>
This commit is contained in:
Abhishek
GitHub
parent
a4367bd83b
commit
911c5ed416
104 changed files with 16919 additions and 597 deletions
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@ -49,8 +49,7 @@ from api.tasks.campaign_tasks import (
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from api.tasks.knowledge_base_processing import process_knowledge_base_document
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from api.tasks.run_integrations import run_integrations_post_workflow_run
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from api.tasks.s3_upload import (
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upload_audio_to_s3,
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upload_transcript_to_s3,
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process_workflow_completion,
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upload_voicemail_audio_to_s3,
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)
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@ -59,9 +58,8 @@ class WorkerSettings:
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functions = [
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calculate_workflow_run_cost,
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run_integrations_post_workflow_run,
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upload_audio_to_s3,
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upload_transcript_to_s3,
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upload_voicemail_audio_to_s3,
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process_workflow_completion,
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sync_campaign_source,
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process_campaign_batch,
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monitor_campaign_progress,
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@ -1,8 +1,7 @@
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class FunctionNames:
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CALCULATE_WORKFLOW_RUN_COST = "calculate_workflow_run_cost"
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RUN_INTEGRATIONS_POST_WORKFLOW_RUN = "run_integrations_post_workflow_run"
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UPLOAD_AUDIO_TO_S3 = "upload_audio_to_s3"
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UPLOAD_TRANSCRIPT_TO_S3 = "upload_transcript_to_s3"
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PROCESS_WORKFLOW_COMPLETION = "process_workflow_completion"
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UPLOAD_VOICEMAIL_AUDIO_TO_S3 = "upload_voicemail_audio_to_s3"
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SYNC_CAMPAIGN_SOURCE = "sync_campaign_source"
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PROCESS_CAMPAIGN_BATCH = "process_campaign_batch"
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@ -1,10 +1,11 @@
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"""Execute webhook integrations after workflow run completion."""
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from typing import Any, Dict
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from typing import Any, Dict, Optional
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import httpx
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from loguru import logger
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from api.constants import BACKEND_API_ENDPOINT
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from api.db import db_client
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from api.db.models import WorkflowRunModel
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from api.utils.credential_auth import build_auth_header
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@ -54,10 +55,13 @@ async def run_integrations_post_workflow_run(_ctx, workflow_run_id: int):
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logger.info(f"Found {len(webhook_nodes)} webhook nodes to execute")
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# Step 4: Build render context
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render_context = _build_render_context(workflow_run)
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# Step 4: Generate public access token (on-demand, only when webhooks exist)
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public_token = await db_client.ensure_public_access_token(workflow_run_id)
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# Step 5: Execute each webhook node
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# Step 5: Build render context
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render_context = _build_render_context(workflow_run, public_token)
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# Step 6: Execute each webhook node
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for node in webhook_nodes:
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webhook_data = node.get("data", {})
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try:
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@ -77,9 +81,19 @@ async def run_integrations_post_workflow_run(_ctx, workflow_run_id: int):
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raise
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def _build_render_context(workflow_run: WorkflowRunModel) -> Dict[str, Any]:
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"""Build the context dict for template rendering."""
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return {
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def _build_render_context(
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workflow_run: WorkflowRunModel, public_token: Optional[str] = None
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) -> Dict[str, Any]:
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"""Build the context dict for template rendering.
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Args:
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workflow_run: The workflow run model
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public_token: Optional public access token for download URLs
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Returns:
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Dict containing all fields available for template rendering
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"""
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context = {
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# Top-level fields
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"workflow_run_id": workflow_run.id,
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"workflow_run_name": workflow_run.name,
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@ -89,10 +103,25 @@ def _build_render_context(workflow_run: WorkflowRunModel) -> Dict[str, Any]:
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"initial_context": workflow_run.initial_context or {},
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"gathered_context": workflow_run.gathered_context or {},
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"cost_info": workflow_run.usage_info or {},
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"recording_url": getattr(workflow_run, "recording_url", None),
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"transcript_url": getattr(workflow_run, "transcript_url", None),
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}
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# Add public download URLs if token is available
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if public_token:
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base_url = (
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f"{BACKEND_API_ENDPOINT}/api/v1/public/download/workflow/{public_token}"
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)
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context["recording_url"] = (
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f"{base_url}/recording" if workflow_run.recording_url else None
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)
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context["transcript_url"] = (
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f"{base_url}/transcript" if workflow_run.transcript_url else None
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)
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else:
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context["recording_url"] = workflow_run.recording_url
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context["transcript_url"] = workflow_run.transcript_url
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return context
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async def _execute_webhook_node(
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webhook_data: Dict[str, Any],
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@ -1,129 +1,27 @@
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import os
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from typing import Optional
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from loguru import logger
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from pipecat.utils.context import set_current_run_id
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from api.db import db_client
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from api.services.storage import get_current_storage_backend, storage_fs
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async def upload_audio_to_s3(ctx, workflow_run_id: int, temp_file_path: str):
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"""Upload audio file from temp path to S3."""
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run_id = str(workflow_run_id)
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set_current_run_id(run_id)
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logger.info(f"Starting audio upload to S3 from {temp_file_path}")
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try:
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# Verify temp file exists
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if not os.path.exists(temp_file_path):
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logger.error(f"Temp audio file not found: {temp_file_path}")
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raise FileNotFoundError(f"Temp audio file not found: {temp_file_path}")
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file_size = os.path.getsize(temp_file_path)
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logger.debug(f"Audio file size: {file_size} bytes")
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recording_url = f"recordings/{workflow_run_id}.wav"
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storage_backend = get_current_storage_backend()
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logger.info(
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f"UPLOAD: Using {storage_backend.name} (value: {storage_backend.value}) for audio upload - workflow_run_id: {workflow_run_id}"
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)
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await storage_fs.aupload_file(temp_file_path, recording_url)
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# Update DB with recording URL and storage backend
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await db_client.update_workflow_run(
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run_id=workflow_run_id,
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recording_url=recording_url,
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storage_backend=storage_backend.value,
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)
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logger.info(
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f"Successfully uploaded audio to {storage_backend.name}: {recording_url} (stored backend: {storage_backend.name})"
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)
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except Exception as e:
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logger.error(f"Error uploading audio to S3 for workflow {workflow_run_id}: {e}")
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raise
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finally:
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# Clean up temp file
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if os.path.exists(temp_file_path):
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try:
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os.remove(temp_file_path)
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logger.debug(f"Cleaned up temp audio file: {temp_file_path}")
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except Exception as e:
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logger.warning(
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f"Failed to clean up temp audio file {temp_file_path}: {e}"
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)
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async def upload_transcript_to_s3(ctx, workflow_run_id: int, temp_file_path: str):
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"""Upload transcript file from temp path to S3."""
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run_id = str(workflow_run_id)
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set_current_run_id(run_id)
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logger.info(f"Starting transcript upload to S3 from {temp_file_path}")
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try:
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# Verify temp file exists
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if not os.path.exists(temp_file_path):
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logger.error(f"Temp transcript file not found: {temp_file_path}")
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raise FileNotFoundError(f"Temp transcript file not found: {temp_file_path}")
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file_size = os.path.getsize(temp_file_path)
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logger.debug(f"Transcript file size: {file_size} bytes")
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transcript_url = f"transcripts/{workflow_run_id}.txt"
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storage_backend = get_current_storage_backend()
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logger.info(
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f"UPLOAD: Using {storage_backend.name} (value: {storage_backend.value}) for transcript upload - workflow_run_id: {workflow_run_id}"
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)
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await storage_fs.aupload_file(temp_file_path, transcript_url)
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# Update DB with transcript URL and storage backend
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await db_client.update_workflow_run(
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run_id=workflow_run_id,
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transcript_url=transcript_url,
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storage_backend=storage_backend.value,
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)
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logger.info(
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f"Successfully uploaded transcript to {storage_backend.name}: {transcript_url} (stored backend: {storage_backend.name})"
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)
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except Exception as e:
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logger.error(
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f"Error uploading transcript to S3 for workflow {workflow_run_id}: {e}"
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)
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raise
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finally:
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# Clean up temp file
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if os.path.exists(temp_file_path):
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try:
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os.remove(temp_file_path)
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logger.debug(f"Cleaned up temp transcript file: {temp_file_path}")
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except Exception as e:
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logger.warning(
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f"Failed to clean up temp transcript file {temp_file_path}: {e}"
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)
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from api.tasks.run_integrations import run_integrations_post_workflow_run
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from pipecat.utils.context import set_current_run_id
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async def upload_voicemail_audio_to_s3(
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ctx,
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_ctx,
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workflow_run_id: int,
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temp_file_path: str,
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s3_key: str,
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):
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"""Upload voicemail detection audio from temp file to S3.
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This function is similar to upload_audio_to_s3 but handles voicemail-specific
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paths and doesn't update the workflow run's recording_url field.
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Handles voicemail-specific paths and doesn't update the workflow run's
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recording_url field.
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Args:
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ctx: ARQ context
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_ctx: ARQ context (unused)
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workflow_run_id: The workflow run ID
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temp_file_path: Path to the temporary WAV file
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s3_key: The S3 key where the file should be uploaded
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@ -161,7 +59,7 @@ async def upload_voicemail_audio_to_s3(
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)
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raise
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finally:
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# Clean up temp file (same pattern as upload_audio_to_s3)
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# Clean up temp file
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if os.path.exists(temp_file_path):
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try:
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os.remove(temp_file_path)
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@ -170,3 +68,104 @@ async def upload_voicemail_audio_to_s3(
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logger.warning(
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f"Failed to clean up temp voicemail audio file {temp_file_path}: {e}"
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)
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async def process_workflow_completion(
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_ctx,
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workflow_run_id: int,
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audio_temp_path: Optional[str] = None,
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transcript_temp_path: Optional[str] = None,
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):
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"""Process workflow completion: upload artifacts and run integrations.
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This task combines audio upload, transcript upload, and webhook integrations
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into a single sequential task to ensure integrations run after uploads complete.
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Args:
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_ctx: ARQ context (unused)
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workflow_run_id: The workflow run ID
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audio_temp_path: Optional path to temp audio file
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transcript_temp_path: Optional path to temp transcript file
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"""
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run_id = str(workflow_run_id)
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set_current_run_id(run_id)
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logger.info(f"Processing workflow completion for run {workflow_run_id}")
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storage_backend = get_current_storage_backend()
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# Step 1: Upload audio if provided
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if audio_temp_path:
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try:
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if os.path.exists(audio_temp_path):
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file_size = os.path.getsize(audio_temp_path)
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logger.debug(f"Audio file size: {file_size} bytes")
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recording_url = f"recordings/{workflow_run_id}.wav"
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logger.info(
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f"Uploading audio to {storage_backend.name} - workflow_run_id: {workflow_run_id}"
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)
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await storage_fs.aupload_file(audio_temp_path, recording_url)
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await db_client.update_workflow_run(
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run_id=workflow_run_id,
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recording_url=recording_url,
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storage_backend=storage_backend.value,
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)
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logger.info(f"Successfully uploaded audio: {recording_url}")
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else:
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logger.warning(f"Audio temp file not found: {audio_temp_path}")
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except Exception as e:
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logger.error(f"Error uploading audio for workflow {workflow_run_id}: {e}")
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finally:
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if audio_temp_path and os.path.exists(audio_temp_path):
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try:
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os.remove(audio_temp_path)
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logger.debug(f"Cleaned up temp audio file: {audio_temp_path}")
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except Exception as e:
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logger.warning(f"Failed to clean up temp audio file: {e}")
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# Step 2: Upload transcript if provided
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if transcript_temp_path:
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try:
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if os.path.exists(transcript_temp_path):
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file_size = os.path.getsize(transcript_temp_path)
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logger.debug(f"Transcript file size: {file_size} bytes")
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transcript_url = f"transcripts/{workflow_run_id}.txt"
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logger.info(
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f"Uploading transcript to {storage_backend.name} - workflow_run_id: {workflow_run_id}"
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)
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await storage_fs.aupload_file(transcript_temp_path, transcript_url)
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await db_client.update_workflow_run(
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run_id=workflow_run_id,
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transcript_url=transcript_url,
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storage_backend=storage_backend.value,
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)
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logger.info(f"Successfully uploaded transcript: {transcript_url}")
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else:
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logger.warning(
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f"Transcript temp file not found: {transcript_temp_path}"
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)
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except Exception as e:
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logger.error(
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f"Error uploading transcript for workflow {workflow_run_id}: {e}"
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)
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finally:
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if transcript_temp_path and os.path.exists(transcript_temp_path):
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try:
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os.remove(transcript_temp_path)
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logger.debug(
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f"Cleaned up temp transcript file: {transcript_temp_path}"
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)
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except Exception as e:
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logger.warning(f"Failed to clean up temp transcript file: {e}")
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# Step 3: Run webhook integrations (after uploads are complete)
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try:
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await run_integrations_post_workflow_run(_ctx, workflow_run_id)
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except Exception as e:
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logger.error(f"Error running integrations for workflow {workflow_run_id}: {e}")
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logger.info(f"Completed workflow completion processing for run {workflow_run_id}")
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