mirror of
https://github.com/langgenius/dify-plugin-sdks.git
synced 2026-07-22 18:35:29 -04:00
Yeuoly
102 lines
4.2 KiB
Python
102 lines
4.2 KiB
Python
from collections.abc import Generator
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from typing import Any
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import uuid
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from dify_plugin.core.runtime.abstract.request import AbstractRequestInterface
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from dify_plugin.core.runtime.entities.model_runtime.llm import LLMResult, LLMResultChunk
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from dify_plugin.core.runtime.entities.model_runtime.rerank import RerankResult
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from dify_plugin.core.runtime.entities.model_runtime.text_embedding import TextEmbeddingResult
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from dify_plugin.core.runtime.entities.plugin.dify import ToolProviderType
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from dify_plugin.core.runtime.entities.plugin.invocation import InvokeType
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from dify_plugin.core.runtime.entities.plugin.workflow import KnowledgeRetrievalNodeData, NodeResponse, ParameterExtractorNodeData, QuestionClassifierNodeData
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from dify_plugin.tool.entities import ToolInvokeMessage
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from dify_plugin.utils.io_writer import PluginOutputStream
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class RequestInterface(AbstractRequestInterface):
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session_id: str
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def __init__(self, session_id: str) -> None:
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self.session_id = session_id
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def _generate_backwards_request_id(self):
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return uuid.uuid4().hex
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def invoke_tool(self, provider_type: ToolProviderType, provider: str, tool_name: str,
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parameters: dict[str, Any]) -> Generator[ToolInvokeMessage, None, None]:
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"""
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Invoke tool
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"""
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backwards_request_id = self._generate_backwards_request_id()
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PluginOutputStream.session_message(
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session_id=self.session_id,
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data=PluginOutputStream.stream_invoke_object(data={
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'type': InvokeType.Tool.value,
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'backwards_request_id': backwards_request_id,
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'request': {
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'provider_type': provider_type.value,
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'provider': provider,
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'tool': tool_name,
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'parameters': parameters
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}
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}).to_dict()
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)
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yield ToolInvokeMessage(
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type=ToolInvokeMessage.MessageType.TEXT,
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message=ToolInvokeMessage.TextMessage(text='123')
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)
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def invoke_builtin_tool(self, provider: str, tool_name: str, parameters: dict[str, Any]) -> Generator[ToolInvokeMessage, None, None]:
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"""
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Invoke builtin tool
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"""
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return super().invoke_builtin_tool(provider, tool_name, parameters)
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def invoke_workflow_tool(self, provider: str, tool_name: str, parameters: dict[str, Any]) -> Generator[ToolInvokeMessage, None, None]:
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"""
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Invoke workflow tool
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"""
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return super().invoke_workflow_tool(provider, tool_name, parameters)
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def invoke_api_tool(self, provider: str, tool_name: str, parameters: dict[str, Any]) -> Generator[ToolInvokeMessage, None, None]:
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"""
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Invoke api tool
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"""
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return super().invoke_api_tool(provider, tool_name, parameters)
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def invoke_llm(self, provider: str, model_name: str, parameters: dict[str, Any]) -> Generator[LLMResultChunk, None, None] | LLMResult:
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"""
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Invoke llm
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"""
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return super().invoke_llm(provider, model_name, parameters)
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def invoke_text_embedding(self, provider: str, model_name: str, parameters: dict[str, Any]) -> TextEmbeddingResult:
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"""
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Invoke text embedding
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"""
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return super().invoke_text_embedding(provider, model_name, parameters)
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def invoke_rerank(self, provider: str, model_name: str, parameters: dict[str, Any]) -> RerankResult:
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"""
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Invoke rerank
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"""
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return super().invoke_rerank(provider, model_name, parameters)
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def invoke_question_classifier(self, node_data: QuestionClassifierNodeData, inputs: dict) -> NodeResponse:
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"""
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Invoke question classifier
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"""
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return super().invoke_question_classifier(node_data, inputs)
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def invoke_parameter_extractor(self, node_data: ParameterExtractorNodeData, inputs: dict) -> NodeResponse:
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"""
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Invoke parameter extractor
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"""
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return super().invoke_parameter_extractor(node_data, inputs)
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def invoke_knowledge_retrieval(self, node_data: KnowledgeRetrievalNodeData, inputs: dict) -> NodeResponse:
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"""
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Invoke knowledge retrieval
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"""
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return super().invoke_knowledge_retrieval(node_data, inputs) |