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feat: refactor node spec and add mcp tools (#244)
* refactor: carve out extraction panel * refactor: create spec versions for node types * refactor: create a GenericNode and remove custom nodes * feat: add python and typescript sdk * add dograh sdk * fix: fetch draft workflow definition over published one * fix: fix routes of SDKs to use code gen * chore: remove doclink dependency to reduce image size * chore: format files * chore: bump pipecat * feat: let mcp fetch archived workflows on demand * chore: fix tests * feat: add sdk documentation * chore: change banner and add badge
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Abhishek
GitHub
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162 changed files with 14355 additions and 3554 deletions
196
api/services/workflow/node_specs/qa.py
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api/services/workflow/node_specs/qa.py
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"""Spec for the QA Analysis node — runs an LLM quality review on the call
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transcript after completion."""
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from api.services.workflow.node_specs._base import (
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DisplayOptions,
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NodeCategory,
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NodeExample,
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NodeSpec,
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PropertyOption,
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PropertySpec,
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PropertyType,
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)
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DEFAULT_QA_SYSTEM_PROMPT = """You are a QA analyst evaluating a specific segment of a voice AI conversation.
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## Node Purpose
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{{node_summary}}
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## Previous Conversation Context (For start of conversation, previous conversation summary can be empty.)
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{{previous_conversation_summary}}
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## Tags to evaluate
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Examine the conversation carefully and identify which of the following tags apply:
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- UNCLEAR_CONVERSATION - The conversation is not coherent or clear, messages don't connect logically
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- ASSISTANT_IN_LOOP - The assistant asks the same question multiple times or gets stuck repeating itself
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- ASSISTANT_REPLY_IMPROPER - The assistant did not reply properly to the user's question/query or seems confused by what the user said
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- USER_FRUSTRATED - The user seems angry, frustrated, or is complaining about something in the call
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- USER_NOT_UNDERSTANDING - The user explicitly says they don't understand or repeatedly asks for clarification
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- HEARING_ISSUES - Either party can't hear the other ("hello?", "are you there?", "can you hear me?")
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- DEAD_AIR - Unusually long silences in the conversation (use the timestamps to judge)
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- USER_REQUESTING_FEATURE - The user asks for something the assistant can't fulfill
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- ASSISTANT_LACKS_EMPATHY - The assistant ignores the user's personal situation or emotional state and continues pitching or pushing the agenda.
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- USER_DETECTS_AI - The user suspects or identifies that they are talking to an AI/robot/bot rather than a real human.
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## Call metrics (pre-computed)
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Use these alongside the transcript for your analysis:
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{{metrics}}
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## Output format
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Return ONLY a valid JSON object (no markdown):
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{
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"tags": [
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{
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"tag": "TAG_NAME",
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"reason": "Short reason with evidence from the transcript"
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}
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],
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"overall_sentiment": "positive|neutral|negative",
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"call_quality_score": <1-10>,
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"summary": "1-2 sentence summary of this segment"
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}
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If no tags apply, return an empty tags list. Always provide sentiment, score, and summary."""
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SPEC = NodeSpec(
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name="qa",
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display_name="QA Analysis",
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description="Run LLM quality analysis on the call transcript.",
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llm_hint=(
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"Runs an LLM quality review on the call transcript after completion. "
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"Per-node analysis splits the conversation by node and evaluates each "
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"segment against the configured system prompt. Sampling, minimum "
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"duration, and voicemail filters are supported."
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),
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category=NodeCategory.integration,
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icon="ClipboardCheck",
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properties=[
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PropertySpec(
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name="name",
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type=PropertyType.string,
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display_name="Name",
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description="Short identifier for this QA configuration.",
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required=True,
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min_length=1,
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default="QA Analysis",
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),
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PropertySpec(
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name="qa_enabled",
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type=PropertyType.boolean,
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display_name="Enabled",
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description="When false, the QA run is skipped.",
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default=True,
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),
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PropertySpec(
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name="qa_system_prompt",
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type=PropertyType.string,
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display_name="System Prompt",
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description=(
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"Instructions to the QA reviewer LLM. Supports placeholders: "
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"`{node_summary}`, `{previous_conversation_summary}`, "
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"`{transcript}`, `{metrics}`."
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),
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editor="textarea",
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default=DEFAULT_QA_SYSTEM_PROMPT,
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),
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PropertySpec(
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name="qa_min_call_duration",
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type=PropertyType.number,
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display_name="Minimum Call Duration (seconds)",
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description="Calls shorter than this are skipped.",
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default=15,
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min_value=0,
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),
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PropertySpec(
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name="qa_voicemail_calls",
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type=PropertyType.boolean,
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display_name="Include Voicemail Calls",
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description="When false, calls flagged as voicemail are skipped.",
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default=False,
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),
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PropertySpec(
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name="qa_sample_rate",
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type=PropertyType.number,
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display_name="Sample Rate (%)",
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description=(
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"Percent of eligible calls QA'd. 100 means every call; lower "
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"values use random sampling."
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),
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default=100,
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min_value=1,
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max_value=100,
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),
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# ---- LLM configuration ----
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PropertySpec(
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name="qa_use_workflow_llm",
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type=PropertyType.boolean,
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display_name="Use Workflow's LLM",
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description=(
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"When true, the QA pass uses the same LLM the workflow runs "
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"with. Set false to specify a separate provider/model."
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),
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default=True,
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),
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PropertySpec(
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name="qa_provider",
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type=PropertyType.options,
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display_name="QA LLM Provider",
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description="LLM provider used for the QA pass.",
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display_options=DisplayOptions(show={"qa_use_workflow_llm": [False]}),
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options=[
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PropertyOption(value="openai", label="OpenAI"),
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PropertyOption(value="azure", label="Azure OpenAI"),
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PropertyOption(value="openrouter", label="OpenRouter"),
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PropertyOption(value="anthropic", label="Anthropic"),
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],
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),
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PropertySpec(
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name="qa_model",
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type=PropertyType.string,
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display_name="QA Model",
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description=(
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"Model identifier (e.g., 'gpt-4o', 'claude-sonnet-4-6'). "
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"Provider-specific."
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),
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display_options=DisplayOptions(show={"qa_use_workflow_llm": [False]}),
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default="default",
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),
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PropertySpec(
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name="qa_api_key",
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type=PropertyType.string,
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display_name="API Key",
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description="API key for the chosen provider.",
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display_options=DisplayOptions(show={"qa_use_workflow_llm": [False]}),
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),
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PropertySpec(
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name="qa_endpoint",
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type=PropertyType.url,
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display_name="Azure Endpoint",
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description="Required for the Azure provider.",
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display_options=DisplayOptions(
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show={"qa_use_workflow_llm": [False], "qa_provider": ["azure"]}
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),
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),
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],
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examples=[
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NodeExample(
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name="basic_qa",
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data={
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"name": "Compliance Check",
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"qa_enabled": True,
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"qa_system_prompt": (
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"You are a compliance reviewer. Review the transcript and "
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"produce a JSON object with `tags`, `summary`, "
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"`call_quality_score`, and `overall_sentiment`."
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),
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"qa_min_call_duration": 30,
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"qa_sample_rate": 100,
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},
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),
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],
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)
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