mirror of
https://github.com/langgenius/dify-plugin-sdks.git
synced 2026-07-22 10:25:23 -04:00
272 lines
9.9 KiB
Python
272 lines
9.9 KiB
Python
import binascii
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from collections.abc import Generator, Iterable
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import tempfile
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from werkzeug import Response
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from dify_plugin.config.config import DifyPluginEnv
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from dify_plugin.core.entities.plugin.request import (
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ModelInvokeLLMRequest,
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ModelInvokeModerationRequest,
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ModelInvokeRerankRequest,
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ModelInvokeSpeech2TextRequest,
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ModelInvokeTTSRequest,
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ModelInvokeTextEmbeddingRequest,
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ModelValidateModelCredentialsRequest,
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ModelValidateProviderCredentialsRequest,
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ToolInvokeRequest,
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ToolValidateCredentialsRequest,
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EndpointInvokeRequest,
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)
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from dify_plugin.model.large_language_model import LargeLanguageModel
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from dify_plugin.model.moderation_model import ModerationModel
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from dify_plugin.model.rerank_model import RerankModel
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from dify_plugin.model.speech2text_model import Speech2TextModel
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from dify_plugin.model.text_embedding_model import TextEmbeddingModel
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from dify_plugin.model.tts_model import TTSModel
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from dify_plugin.plugin_registration import PluginRegistration
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from dify_plugin.tool.entities import ToolRuntime
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from dify_plugin.utils.http_parser import parse_raw_request
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from dify_plugin.core.runtime import Session
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class PluginExecutor:
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def __init__(self, config: DifyPluginEnv, registration: PluginRegistration) -> None:
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self.config = config
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self.registration = registration
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def validate_tool_provider_credentials(
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self, session: Session, data: ToolValidateCredentialsRequest
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):
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provider_instance = self.registration.get_tool_provider_cls(data.provider)
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if provider_instance is None:
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raise ValueError(f"Provider `{data.provider}` not found")
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provider_instance = provider_instance()
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try:
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provider_instance.validate_credentials(data.credentials)
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except Exception as e:
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raise ValueError(
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f"Failed to validate provider credentials: {type(e).__name__}: {str(e)}"
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)
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return {"result": True}
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def invoke_tool(self, session: Session, request: ToolInvokeRequest):
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provider_cls = self.registration.get_tool_provider_cls(request.provider)
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if provider_cls is None:
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raise ValueError(f"Provider `{request.provider}` not found")
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tool_cls = self.registration.get_tool_cls(request.provider, request.tool)
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if tool_cls is None:
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raise ValueError(
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f"Tool `{request.tool}` not found for provider `{request.provider}`"
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)
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# instantiate tool
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tool = tool_cls(
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runtime=ToolRuntime(
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credentials=request.credentials,
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user_id=request.user_id,
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session_id=session.session_id,
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),
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session=session,
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)
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# invoke tool
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try:
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yield from tool.invoke(request.tool_parameters)
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except Exception as e:
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raise ValueError(f"Failed to invoke tool: {type(e).__name__}: {str(e)}")
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def validate_model_provider_credentials(
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self, session: Session, data: ModelValidateProviderCredentialsRequest
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):
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provider_instance = self.registration.get_model_provider_instance(data.provider)
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if provider_instance is None:
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raise ValueError(f"Provider `{data.provider}` not found")
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try:
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provider_instance.validate_provider_credentials(data.credentials)
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except Exception as e:
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raise ValueError(
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f"Failed to validate provider credentials: {type(e).__name__}: {str(e)}"
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)
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return {"result": True}
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def validate_model_credentials(
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self, session: Session, data: ModelValidateModelCredentialsRequest
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):
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provider_instance = self.registration.get_model_provider_instance(data.provider)
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if provider_instance is None:
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raise ValueError(f"Provider `{data.provider}` not found")
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model_instance = self.registration.get_model_instance(
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data.provider, data.model_type
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)
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if model_instance is None:
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raise ValueError(
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f"Model `{data.model_type}` not found for provider `{data.provider}`"
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)
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try:
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model_instance.validate_credentials(data.model, data.credentials)
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except Exception as e:
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raise ValueError(
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f"Failed to validate model credentials: {type(e).__name__}: {str(e)}"
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)
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return {"result": True}
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def invoke_llm(self, session: Session, data: ModelInvokeLLMRequest):
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model_instance = self.registration.get_model_instance(
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data.provider, data.model_type
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)
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if isinstance(model_instance, LargeLanguageModel):
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return model_instance.invoke(
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data.model,
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data.credentials,
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data.prompt_messages,
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data.model_parameters,
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data.tools,
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data.stop,
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data.stream,
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data.user_id,
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)
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def invoke_text_embedding(
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self, session: Session, data: ModelInvokeTextEmbeddingRequest
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):
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model_instance = self.registration.get_model_instance(
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data.provider, data.model_type
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)
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if isinstance(model_instance, TextEmbeddingModel):
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return model_instance.invoke(
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data.model,
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data.credentials,
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data.texts,
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data.user_id,
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)
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def invoke_rerank(self, session: Session, data: ModelInvokeRerankRequest):
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model_instance = self.registration.get_model_instance(
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data.provider, data.model_type
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)
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if isinstance(model_instance, RerankModel):
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return model_instance.invoke(
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data.model,
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data.credentials,
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data.query,
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data.docs,
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data.score_threshold,
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data.top_n,
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data.user_id,
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)
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def invoke_tts(self, session: Session, data: ModelInvokeTTSRequest):
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model_instance = self.registration.get_model_instance(
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data.provider, data.model_type
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)
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if isinstance(model_instance, TTSModel):
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b = model_instance.invoke(
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data.model,
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data.credentials,
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data.content_text,
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data.voice,
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data.user_id,
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)
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if isinstance(b, bytes | bytearray | memoryview):
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return {"result": binascii.hexlify(b).decode()}
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for chunk in b:
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yield {"result": binascii.hexlify(chunk).decode()}
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def invoke_speech_to_text(
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self, session: Session, data: ModelInvokeSpeech2TextRequest
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):
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model_instance = self.registration.get_model_instance(
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data.provider, data.model_type
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)
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with tempfile.NamedTemporaryFile(suffix=".mp3", mode="wb", delete=True) as temp:
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temp.write(binascii.unhexlify(data.file))
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temp.flush()
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with open(temp.name, "rb") as f:
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if isinstance(model_instance, Speech2TextModel):
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return {
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"result": model_instance.invoke(
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data.model,
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data.credentials,
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f,
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data.user_id,
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)
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}
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def invoke_moderation(self, session: Session, data: ModelInvokeModerationRequest):
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model_instance = self.registration.get_model_instance(
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data.provider, data.model_type
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)
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if isinstance(model_instance, ModerationModel):
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return {
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"result": model_instance.invoke(
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data.model,
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data.credentials,
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data.text,
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data.user_id,
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)
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}
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def invoke_endpoint(self, session: Session, data: EndpointInvokeRequest):
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bytes_data = binascii.unhexlify(data.raw_http_request)
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request = parse_raw_request(bytes_data)
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try:
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# dispatch request
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endpoint, values = self.registration.dispatch_endpoint_request(request)
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# construct response
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endpoint_instance = endpoint(session)
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response = endpoint_instance.invoke(request, values, data.settings)
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except Exception:
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response = Response("Not Found", status=404)
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# check if response is a generator
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if isinstance(response.response, Generator):
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# return headers
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yield {
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"status": response.status_code,
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"headers": {k: v for k, v in response.headers.items()},
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}
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for chunk in response.response:
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if isinstance(chunk, bytes | bytearray | memoryview):
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yield {"result": binascii.hexlify(chunk).decode()}
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else:
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yield {"result": binascii.hexlify(chunk.encode("utf-8")).decode()}
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else:
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result = {
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"status": response.status_code,
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"headers": {k: v for k, v in response.headers.items()},
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}
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if isinstance(response.response, bytes | bytearray | memoryview):
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result["result"] = binascii.hexlify(response.response).decode()
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elif isinstance(response.response, str):
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result["result"] = binascii.hexlify(
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response.response.encode("utf-8")
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).decode()
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elif isinstance(response.response, Iterable):
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result["result"] = ""
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for chunk in response.response:
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if isinstance(chunk, bytes | bytearray | memoryview):
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result["result"] += binascii.hexlify(chunk).decode()
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else:
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result["result"] += binascii.hexlify(
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chunk.encode("utf-8")
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).decode()
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yield result
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