32aef14ddb
- Introduce RuntimeModelResource and RuntimeKnowledgeBase classes to manage workflow resources. - Update AssistantConfig to include workflow_model_resources and workflow_knowledge_bases for better integration. - Refactor validation and processing logic in routes and services to accommodate workflow types. - Implement dynamic variable support for workflow assistants and enhance graph normalization. - Add ToolExecutor for reusable tool execution across different assistant types. - Update various services to ensure compatibility with new workflow features and improve error handling.
125 lines
3.9 KiB
Python
125 lines
3.9 KiB
Python
"""Conversation-brain contracts shared by every assistant type.
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Brain selects who owns reasoning and conversation state. The Pipecat pipeline
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still owns media transport, STT/TTS, transcript delivery, and interruption
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semantics. This keeps assistant-specific orchestration out of pipeline.py
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without coupling brains to Pipecat internals more than necessary.
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"""
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from __future__ import annotations
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from collections.abc import Awaitable, Callable
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from dataclasses import dataclass, field
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from typing import Any, Protocol, runtime_checkable
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from models import AssistantConfig
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.frames.frames import Frame
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.frame_processor import FrameProcessor
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@dataclass(frozen=True)
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class BrainSpec:
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"""Static capabilities used by validation and runtime dispatch."""
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type: str
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supported_runtime_modes: frozenset[str]
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# False means context, knowledge bases, and tools live on an external agent.
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owns_context: bool
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class CallEndPort(Protocol):
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"""Small call-lifecycle surface available to a brain."""
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@property
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def ending(self) -> bool: ...
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def begin(self, reason: str) -> None: ...
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def arm_after_speech(self) -> None: ...
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async def finish(self) -> None: ...
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@dataclass(frozen=True)
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class BrainRuntime:
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"""Pipeline-owned capabilities injected into one brain session."""
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context: LLMContext
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llm: Any
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queue_frame: Callable[[Frame], Awaitable[None]]
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set_system_prompt: Callable[[str], None]
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set_tools: Callable[[list[FunctionSchema] | None], None]
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call_end: CallEndPort
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worker: Any = None
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context_aggregator: Any = None
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transport: Any = None
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switch_services: Callable[[str | None, str | None], Awaitable[None]] | None = None
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set_knowledge_scope: Callable[[dict[str, Any]], None] | None = None
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set_input_enabled: Callable[[bool], None] | None = None
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flow_global_functions: list[Any] = field(default_factory=list)
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class BaseBrain:
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"""No-op lifecycle defaults for brains without local orchestration."""
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spec: BrainSpec
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async def greeting(self, cfg: AssistantConfig) -> str:
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return cfg.greeting
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def system_prompt(self, cfg: AssistantConfig) -> str:
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return cfg.prompt if self.spec.owns_context else ""
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def build_llm(self, cfg: AssistantConfig, context: LLMContext) -> FrameProcessor:
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raise NotImplementedError
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async def setup(self, cfg: AssistantConfig, runtime: BrainRuntime) -> None:
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"""Register tools and initialize per-call orchestration."""
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async def on_connected(self) -> None:
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"""Handle a connected client after the common greeting is queued."""
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def record_user_message(self, content: str) -> None:
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"""Observe a committed user message for brain-owned routing state."""
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async def on_assistant_text_start(self, turn_id: str) -> None:
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"""Observe the start of a generated assistant turn."""
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async def on_assistant_text_end(
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self,
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turn_id: str,
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content: str,
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interrupted: bool,
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) -> None:
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"""Observe the completion of a generated assistant turn."""
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@runtime_checkable
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class Brain(Protocol):
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"""One instance per call; implementations may keep conversation state."""
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spec: BrainSpec
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async def greeting(self, cfg: AssistantConfig) -> str: ...
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def system_prompt(self, cfg: AssistantConfig) -> str: ...
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def build_llm(self, cfg: AssistantConfig, context: LLMContext) -> FrameProcessor: ...
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async def setup(self, cfg: AssistantConfig, runtime: BrainRuntime) -> None: ...
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async def on_connected(self) -> None: ...
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def record_user_message(self, content: str) -> None: ...
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async def on_assistant_text_start(self, turn_id: str) -> None: ...
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async def on_assistant_text_end(
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self,
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turn_id: str,
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content: str,
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interrupted: bool,
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) -> None: ...
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