00270a5c01
- Introduce new fields `dify_api_url` and `dify_api_key` in `AssistantConfig` for Dify API integration. - Update `requirements.txt` to include `dify-client-python` for Dify SDK support. - Modify `config_resolver` to handle Dify connection information. - Add a new `globalNode` type in workflow specifications to provide unified settings across workflows. - Enhance node specifications with additional constraints and default values for better configuration management. - Update frontend components to support the new `globalNode` type and its properties, improving workflow editor functionality.
107 lines
3.8 KiB
Python
107 lines
3.8 KiB
Python
"""Local prompt assistant, including prompt-only reusable tools."""
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from __future__ import annotations
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from uuid import uuid4
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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 OutputTransportMessageUrgentFrame, TTSSpeakFrame
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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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from pipecat.services.llm_service import (
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FunctionCallParams,
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FunctionCallResultProperties,
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)
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from pipecat.utils.time import time_now_iso8601
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from services.brains.base import BaseBrain, BrainRuntime, BrainSpec
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class PromptBrain(BaseBrain):
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spec = BrainSpec(
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type="prompt",
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supported_runtime_modes=frozenset({"pipeline", "realtime"}),
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owns_context=True,
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)
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def build_llm(self, cfg: AssistantConfig, context: LLMContext) -> FrameProcessor:
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from services.pipecat.service_factory import create_llm
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return create_llm(cfg)
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async def setup(self, cfg: AssistantConfig, runtime: BrainRuntime) -> None:
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schemas: list[FunctionSchema] = []
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for tool in cfg.tools:
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if tool.type != "end_call":
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continue
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schema, handler = self._make_end_call_tool(tool, runtime)
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schemas.append(schema)
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runtime.llm.register_function(tool.function_name, handler)
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runtime.set_tools(schemas)
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@staticmethod
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def _make_end_call_tool(tool, runtime: BrainRuntime):
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config = (tool.definition or {}).get("config") or {}
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message_type = str(config.get("message_type") or "none")
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custom_message = str(config.get("custom_message") or "").strip()
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capture_reason = bool(config.get("capture_reason", True))
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async def end_call(params: FunctionCallParams) -> None:
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reason = str(params.arguments.get("reason") or "end_call_tool").strip()
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runtime.call_end.begin(reason)
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await params.result_callback(
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{"status": "success", "action": "ending_call"},
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properties=FunctionCallResultProperties(run_llm=False),
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)
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if message_type != "custom" or not custom_message:
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await runtime.call_end.finish()
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return
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turn_id = uuid4().hex
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timestamp = time_now_iso8601()
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for message in (
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{
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"type": "assistant-text-start",
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"turn_id": turn_id,
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"timestamp": timestamp,
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},
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{
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"type": "assistant-text-delta",
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"turn_id": turn_id,
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"delta": custom_message,
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},
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{
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"type": "assistant-text-end",
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"turn_id": turn_id,
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"content": custom_message,
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"interrupted": False,
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},
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):
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await runtime.queue_frame(
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OutputTransportMessageUrgentFrame(message=message)
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)
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runtime.call_end.arm_after_speech()
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await runtime.queue_frame(
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TTSSpeakFrame(custom_message, append_to_context=False)
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)
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properties = (
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{
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"reason": {
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"type": "string",
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"description": "结束本次通话的简短原因。",
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}
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}
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if capture_reason
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else {}
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)
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schema = FunctionSchema(
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name=tool.function_name,
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description=tool.description or "结束当前通话。",
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properties=properties,
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required=["reason"] if capture_reason else [],
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)
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return schema, end_call
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