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https://github.com/langgenius/dify.git
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829 lines
28 KiB
Python
829 lines
28 KiB
Python
import binascii
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import json
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import re
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import time
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from collections.abc import Callable, Generator, Sequence
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from typing import IO, Any
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from core.plugin.entities.plugin_daemon import (
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PluginBasicBooleanResponse,
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PluginDaemonInnerError,
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PluginLLMNumTokensResponse,
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PluginModelProviderBinding,
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PluginModelProviderEntity,
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PluginModelSchemaEntity,
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PluginStringResultResponse,
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PluginTextEmbeddingNumTokensResponse,
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PluginTTSResultResponse,
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PluginVoicesResponse,
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TTSAudioChunk,
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)
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from core.plugin.impl.base import BasePluginClient, use_plugin_daemon_request_timeout
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from core.plugin.impl.exc import PluginInvokeError, PluginLLMPollingUnsupportedError
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from graphon.model_runtime.entities.llm_entities import LLMPollingResult, LLMResultChunk
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from graphon.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
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from graphon.model_runtime.entities.model_entities import AIModelEntity, ModelType
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from graphon.model_runtime.entities.rerank_entities import MultimodalRerankInput, RerankResult
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from graphon.model_runtime.entities.text_embedding_entities import EmbeddingResult
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from graphon.model_runtime.errors.invoke import InvokeServerUnavailableError
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from graphon.model_runtime.protocols.tts_runtime import TTSModelVoice
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from graphon.model_runtime.utils.encoders import jsonable_encoder
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from services.plugin.dispatch_identity import plugin_dispatch_headers
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_POLLING_UNSUPPORTED_INVOKE_ERROR_TYPES = frozenset((NotImplementedError.__name__,))
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_POLLING_UNSUPPORTED_ERROR_MESSAGE = "does not support polling"
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_MODEL_METADATA_READINESS_BUDGET_SECONDS = 10.0
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def _metadata_gateway_unavailable(error: PluginInvokeError) -> bool:
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"""Recognize only the daemon's bounded exhausted-gateway envelope.
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This identifies a gateway failure, not startup or an unsent inference.
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Observation may retain this classification without granting replay.
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Only credential-free schema metadata may be repeated on this evidence.
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"""
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if type(error) is not PluginInvokeError or not isinstance(error.description, str) or len(error.description) > 2048:
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return False
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try:
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envelope = json.loads(error.description)
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except (ValueError, TypeError, RecursionError):
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return False
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if not isinstance(envelope, dict) or envelope.get("error_type") != "PluginDaemonInnerError":
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return False
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message = envelope.get("message")
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if not isinstance(message, str):
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return False
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return (
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re.fullmatch(
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r"Error sending request to serverless: all (?P<count>[1-9]|1[0-6]) attempts failed, "
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r"last error: attempt (?P=count)/(?P=count) failed with status code: 502",
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message,
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)
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is not None
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)
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class PluginModelClient(BasePluginClient):
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def ensure_model_metadata_ready(
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self,
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*,
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tenant_id: str,
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user_id: str | None,
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plugin_id: str,
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provider: str,
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model: str,
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plugin_unique_identifier: str,
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check: Callable[[], None],
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) -> None:
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"""Read fresh predefined LLM metadata with at most one gateway retry.
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Preconditions: the caller qualified the pinned predefined model and
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supplies current admission checks. Credentials and inference payloads
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are never sent. Success verifies matching fresh metadata; failure never
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dispatches inference or changes credentials, permissions or cache TTLs.
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The body deadline is checked per raw chunk, with up to one final read
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inactivity wait (at most five seconds); it is not a hard wall deadline.
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"""
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deadline = time.monotonic() + _MODEL_METADATA_READINESS_BUDGET_SECONDS
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def read() -> None:
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check()
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remaining = deadline - time.monotonic()
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if remaining <= 0:
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raise InvokeServerUnavailableError(description="Plugin model metadata service unavailable.")
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try:
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with use_plugin_daemon_request_timeout(min(remaining, 5.0), metadata_deadline=deadline):
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schema = self.get_model_schema(
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tenant_id=tenant_id,
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user_id=user_id,
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plugin_id=plugin_id,
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provider=provider,
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model_type=ModelType.LLM.value,
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model=model,
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credentials={},
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plugin_unique_identifier=plugin_unique_identifier,
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)
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finally:
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check()
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if time.monotonic() >= deadline:
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raise InvokeServerUnavailableError(description="Plugin model metadata service unavailable.")
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if schema is None or schema.model != model or schema.model_type != ModelType.LLM:
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raise ValueError("Plugin model metadata schema does not match the prepared model")
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try:
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read()
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except PluginInvokeError as error:
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if not _metadata_gateway_unavailable(error):
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raise
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check()
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time.sleep(min(0.5, max(0.0, deadline - time.monotonic())))
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check()
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try:
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read()
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except PluginInvokeError as retry_error:
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if not _metadata_gateway_unavailable(retry_error):
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raise
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raise InvokeServerUnavailableError(
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description="Plugin model metadata service unavailable."
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) from retry_error
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@staticmethod
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def _dispatch_payload(*, user_id: str | None, data: dict[str, Any], app_id: str | None = None) -> dict[str, Any]:
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payload: dict[str, Any] = {"data": data}
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if user_id is not None:
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payload["user_id"] = user_id
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if app_id is not None:
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payload["app_id"] = app_id
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return payload
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def fetch_model_providers(self, tenant_id: str) -> Sequence[PluginModelProviderEntity]:
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"""
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Fetch model providers for the given tenant.
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"""
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response = self._request_with_plugin_daemon_response(
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"GET",
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f"plugin/{tenant_id}/management/models",
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list[PluginModelProviderEntity],
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params={"page": 1, "page_size": 256},
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)
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return response
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def fetch_model_provider_bindings(self, tenant_id: str) -> Sequence[PluginModelProviderBinding]:
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"""Fetch only model-provider installation identities from the daemon."""
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return self._request_with_plugin_daemon_response(
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"GET",
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f"plugin/{tenant_id}/management/models/bindings",
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list[PluginModelProviderBinding],
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)
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def get_model_schema(
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self,
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tenant_id: str,
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user_id: str | None,
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plugin_id: str,
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provider: str,
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model_type: str,
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model: str,
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credentials: dict[str, Any],
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*,
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plugin_unique_identifier: str | None = None,
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) -> AIModelEntity | None:
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"""
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Get model schema
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"""
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response = self._request_with_plugin_daemon_response_stream(
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"POST",
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f"plugin/{tenant_id}/dispatch/model/schema",
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PluginModelSchemaEntity,
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data=self._dispatch_payload(
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user_id=user_id,
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data={
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"provider": provider,
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"model_type": model_type,
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"model": model,
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"credentials": credentials,
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},
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),
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headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
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)
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for resp in response:
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return resp.model_schema
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return None
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def validate_provider_credentials(
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self,
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tenant_id: str,
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user_id: str | None,
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plugin_id: str,
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provider: str,
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credentials: dict[str, Any],
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*,
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plugin_unique_identifier: str | None = None,
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) -> bool:
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"""
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validate the credentials of the provider
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"""
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response = self._request_with_plugin_daemon_response_stream(
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"POST",
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f"plugin/{tenant_id}/dispatch/model/validate_provider_credentials",
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PluginBasicBooleanResponse,
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data=self._dispatch_payload(
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user_id=user_id,
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data={
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"provider": provider,
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"credentials": credentials,
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},
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),
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headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
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)
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for resp in response:
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if resp.credentials and isinstance(resp.credentials, dict):
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credentials.update(resp.credentials)
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return resp.result
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return False
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def validate_model_credentials(
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self,
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tenant_id: str,
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user_id: str | None,
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plugin_id: str,
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provider: str,
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model_type: str,
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model: str,
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credentials: dict[str, Any],
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*,
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plugin_unique_identifier: str | None = None,
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) -> bool:
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"""
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validate the credentials of the provider
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"""
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response = self._request_with_plugin_daemon_response_stream(
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"POST",
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f"plugin/{tenant_id}/dispatch/model/validate_model_credentials",
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PluginBasicBooleanResponse,
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data=self._dispatch_payload(
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user_id=user_id,
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data={
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"provider": provider,
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"model_type": model_type,
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"model": model,
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"credentials": credentials,
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},
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),
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headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
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)
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for resp in response:
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if resp.credentials and isinstance(resp.credentials, dict):
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credentials.update(resp.credentials)
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return resp.result
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return False
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def invoke_llm(
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self,
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tenant_id: str,
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user_id: str | None,
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plugin_id: str,
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provider: str,
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model: str,
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credentials: dict[str, Any],
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prompt_messages: list[PromptMessage],
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model_parameters: dict[str, Any] | None = None,
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tools: list[PromptMessageTool] | None = None,
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stop: list[str] | None = None,
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stream: bool = True,
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app_id: str | None = None,
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*,
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plugin_unique_identifier: str | None = None,
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) -> Generator[LLMResultChunk]:
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"""
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Invoke llm
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"""
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response = self._request_with_plugin_daemon_response_stream(
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method="POST",
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path=f"plugin/{tenant_id}/dispatch/llm/invoke",
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type_=LLMResultChunk,
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data=jsonable_encoder(
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self._dispatch_payload(
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user_id=user_id,
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data={
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"provider": provider,
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"model_type": "llm",
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"model": model,
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"credentials": credentials,
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"prompt_messages": prompt_messages,
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"model_parameters": model_parameters,
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"tools": tools,
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"stop": stop,
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"stream": stream,
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},
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app_id=app_id,
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)
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),
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headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
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)
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try:
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yield from response
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except PluginDaemonInnerError as e:
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raise ValueError(e.message + str(e.code)) from e
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def start_llm_polling(
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self,
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tenant_id: str,
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user_id: str | None,
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plugin_id: str,
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|
provider: str,
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|
model: str,
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|
credentials: dict[str, Any],
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prompt_messages: list[PromptMessage],
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model_parameters: dict[str, Any] | None = None,
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tools: list[PromptMessageTool] | None = None,
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stop: list[str] | None = None,
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json_schema: dict[str, Any] | None = None,
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*,
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|
plugin_unique_identifier: str | None = None,
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|
) -> LLMPollingResult:
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"""Start an LLM polling request for plugin-backed long-running jobs."""
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try:
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return self._request_with_plugin_daemon_response(
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method="POST",
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path=f"plugin/{tenant_id}/dispatch/model/polling/start",
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type_=LLMPollingResult,
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data=jsonable_encoder(
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self._dispatch_payload(
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user_id=user_id,
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data={
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"provider": provider,
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"model_type": ModelType.LLM.value,
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"model": model,
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|
"credentials": credentials,
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"prompt_messages": prompt_messages,
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"model_parameters": model_parameters,
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"tools": tools,
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"stop": stop,
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"stream": False,
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"json_schema": json_schema,
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},
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)
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),
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headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
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)
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except PluginInvokeError as error:
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self._raise_typed_polling_unsupported_error(error)
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raise
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|
|
|
def check_llm_polling(
|
|
self,
|
|
tenant_id: str,
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|
user_id: str | None,
|
|
plugin_id: str,
|
|
provider: str,
|
|
model: str,
|
|
credentials: dict[str, Any],
|
|
plugin_state: dict[str, Any],
|
|
*,
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|
plugin_unique_identifier: str | None = None,
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|
) -> LLMPollingResult:
|
|
"""Check the latest state for a plugin-backed LLM polling job."""
|
|
try:
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|
return self._request_with_plugin_daemon_response(
|
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method="POST",
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path=f"plugin/{tenant_id}/dispatch/model/polling/check",
|
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type_=LLMPollingResult,
|
|
data=jsonable_encoder(
|
|
self._dispatch_payload(
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user_id=user_id,
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data={
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|
"provider": provider,
|
|
"model_type": ModelType.LLM.value,
|
|
"model": model,
|
|
"credentials": credentials,
|
|
"plugin_state": plugin_state,
|
|
},
|
|
)
|
|
),
|
|
headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
|
|
)
|
|
except PluginInvokeError as error:
|
|
self._raise_typed_polling_unsupported_error(error)
|
|
raise
|
|
|
|
@staticmethod
|
|
def _raise_typed_polling_unsupported_error(error: PluginInvokeError) -> None:
|
|
"""Convert plugin polling capability failures into a dedicated Dify exception."""
|
|
if error.get_error_type() == PluginLLMPollingUnsupportedError.__name__:
|
|
raise PluginLLMPollingUnsupportedError(description=error.description) from error
|
|
|
|
if (
|
|
error.get_error_type() in _POLLING_UNSUPPORTED_INVOKE_ERROR_TYPES
|
|
# This is ugly, we should not rely on error messages while checking
|
|
# error types.
|
|
and _POLLING_UNSUPPORTED_ERROR_MESSAGE in error.get_error_message().lower()
|
|
):
|
|
raise PluginLLMPollingUnsupportedError(description=error.description) from error
|
|
|
|
def get_llm_num_tokens(
|
|
self,
|
|
tenant_id: str,
|
|
user_id: str | None,
|
|
plugin_id: str,
|
|
provider: str,
|
|
model_type: str,
|
|
model: str,
|
|
credentials: dict[str, Any],
|
|
prompt_messages: list[PromptMessage],
|
|
tools: list[PromptMessageTool] | None = None,
|
|
*,
|
|
plugin_unique_identifier: str | None = None,
|
|
) -> int:
|
|
"""
|
|
Get number of tokens for llm
|
|
"""
|
|
response = self._request_with_plugin_daemon_response_stream(
|
|
method="POST",
|
|
path=f"plugin/{tenant_id}/dispatch/llm/num_tokens",
|
|
type_=PluginLLMNumTokensResponse,
|
|
data=jsonable_encoder(
|
|
self._dispatch_payload(
|
|
user_id=user_id,
|
|
data={
|
|
"provider": provider,
|
|
"model_type": model_type,
|
|
"model": model,
|
|
"credentials": credentials,
|
|
"prompt_messages": prompt_messages,
|
|
"tools": tools,
|
|
},
|
|
)
|
|
),
|
|
headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
|
|
)
|
|
|
|
for resp in response:
|
|
return resp.num_tokens
|
|
|
|
return 0
|
|
|
|
def invoke_text_embedding(
|
|
self,
|
|
tenant_id: str,
|
|
user_id: str | None,
|
|
plugin_id: str,
|
|
provider: str,
|
|
model: str,
|
|
credentials: dict[str, Any],
|
|
texts: list[str],
|
|
input_type: str,
|
|
*,
|
|
plugin_unique_identifier: str | None = None,
|
|
) -> EmbeddingResult:
|
|
"""
|
|
Invoke text embedding
|
|
"""
|
|
response = self._request_with_plugin_daemon_response_stream(
|
|
method="POST",
|
|
path=f"plugin/{tenant_id}/dispatch/text_embedding/invoke",
|
|
type_=EmbeddingResult,
|
|
data=jsonable_encoder(
|
|
self._dispatch_payload(
|
|
user_id=user_id,
|
|
data={
|
|
"provider": provider,
|
|
"model_type": "text-embedding",
|
|
"model": model,
|
|
"credentials": credentials,
|
|
"texts": texts,
|
|
"input_type": input_type,
|
|
},
|
|
)
|
|
),
|
|
headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
|
|
)
|
|
|
|
for resp in response:
|
|
return resp
|
|
|
|
raise ValueError("Failed to invoke text embedding")
|
|
|
|
def invoke_multimodal_embedding(
|
|
self,
|
|
tenant_id: str,
|
|
user_id: str | None,
|
|
plugin_id: str,
|
|
provider: str,
|
|
model: str,
|
|
credentials: dict[str, Any],
|
|
documents: list[dict],
|
|
input_type: str,
|
|
*,
|
|
plugin_unique_identifier: str | None = None,
|
|
) -> EmbeddingResult:
|
|
"""
|
|
Invoke file embedding
|
|
"""
|
|
response = self._request_with_plugin_daemon_response_stream(
|
|
method="POST",
|
|
path=f"plugin/{tenant_id}/dispatch/multimodal_embedding/invoke",
|
|
type_=EmbeddingResult,
|
|
data=jsonable_encoder(
|
|
self._dispatch_payload(
|
|
user_id=user_id,
|
|
data={
|
|
"provider": provider,
|
|
"model_type": "text-embedding",
|
|
"model": model,
|
|
"credentials": credentials,
|
|
"documents": documents,
|
|
"input_type": input_type,
|
|
},
|
|
)
|
|
),
|
|
headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
|
|
)
|
|
|
|
for resp in response:
|
|
return resp
|
|
|
|
raise ValueError("Failed to invoke file embedding")
|
|
|
|
def get_text_embedding_num_tokens(
|
|
self,
|
|
tenant_id: str,
|
|
user_id: str | None,
|
|
plugin_id: str,
|
|
provider: str,
|
|
model: str,
|
|
credentials: dict[str, Any],
|
|
texts: list[str],
|
|
*,
|
|
plugin_unique_identifier: str | None = None,
|
|
) -> list[int]:
|
|
"""
|
|
Get number of tokens for text embedding
|
|
"""
|
|
response = self._request_with_plugin_daemon_response_stream(
|
|
method="POST",
|
|
path=f"plugin/{tenant_id}/dispatch/text_embedding/num_tokens",
|
|
type_=PluginTextEmbeddingNumTokensResponse,
|
|
data=jsonable_encoder(
|
|
self._dispatch_payload(
|
|
user_id=user_id,
|
|
data={
|
|
"provider": provider,
|
|
"model_type": "text-embedding",
|
|
"model": model,
|
|
"credentials": credentials,
|
|
"texts": texts,
|
|
},
|
|
)
|
|
),
|
|
headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
|
|
)
|
|
|
|
for resp in response:
|
|
return resp.num_tokens
|
|
|
|
return []
|
|
|
|
def invoke_rerank(
|
|
self,
|
|
tenant_id: str,
|
|
user_id: str | None,
|
|
plugin_id: str,
|
|
provider: str,
|
|
model: str,
|
|
credentials: dict[str, Any],
|
|
query: str,
|
|
docs: list[str],
|
|
score_threshold: float | None = None,
|
|
top_n: int | None = None,
|
|
*,
|
|
plugin_unique_identifier: str | None = None,
|
|
) -> RerankResult:
|
|
"""
|
|
Invoke rerank
|
|
"""
|
|
response = self._request_with_plugin_daemon_response_stream(
|
|
method="POST",
|
|
path=f"plugin/{tenant_id}/dispatch/rerank/invoke",
|
|
type_=RerankResult,
|
|
data=jsonable_encoder(
|
|
self._dispatch_payload(
|
|
user_id=user_id,
|
|
data={
|
|
"provider": provider,
|
|
"model_type": "rerank",
|
|
"model": model,
|
|
"credentials": credentials,
|
|
"query": query,
|
|
"docs": docs,
|
|
"score_threshold": score_threshold,
|
|
"top_n": top_n,
|
|
},
|
|
)
|
|
),
|
|
headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
|
|
)
|
|
|
|
for resp in response:
|
|
return resp
|
|
|
|
raise ValueError("Failed to invoke rerank")
|
|
|
|
def invoke_multimodal_rerank(
|
|
self,
|
|
tenant_id: str,
|
|
user_id: str | None,
|
|
plugin_id: str,
|
|
provider: str,
|
|
model: str,
|
|
credentials: dict[str, Any],
|
|
query: MultimodalRerankInput,
|
|
docs: list[MultimodalRerankInput],
|
|
score_threshold: float | None = None,
|
|
top_n: int | None = None,
|
|
*,
|
|
plugin_unique_identifier: str | None = None,
|
|
) -> RerankResult:
|
|
"""
|
|
Invoke multimodal rerank
|
|
"""
|
|
response = self._request_with_plugin_daemon_response_stream(
|
|
method="POST",
|
|
path=f"plugin/{tenant_id}/dispatch/multimodal_rerank/invoke",
|
|
type_=RerankResult,
|
|
data=jsonable_encoder(
|
|
self._dispatch_payload(
|
|
user_id=user_id,
|
|
data={
|
|
"provider": provider,
|
|
"model_type": "rerank",
|
|
"model": model,
|
|
"credentials": credentials,
|
|
"query": query,
|
|
"docs": docs,
|
|
"score_threshold": score_threshold,
|
|
"top_n": top_n,
|
|
},
|
|
)
|
|
),
|
|
headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
|
|
)
|
|
for resp in response:
|
|
return resp
|
|
|
|
raise ValueError("Failed to invoke multimodal rerank")
|
|
|
|
def invoke_tts(
|
|
self,
|
|
tenant_id: str,
|
|
user_id: str | None,
|
|
plugin_id: str,
|
|
provider: str,
|
|
model: str,
|
|
credentials: dict[str, Any],
|
|
content_text: str,
|
|
voice: str,
|
|
*,
|
|
plugin_unique_identifier: str | None = None,
|
|
) -> Generator[TTSAudioChunk]:
|
|
"""
|
|
Invoke tts
|
|
"""
|
|
response = self._request_with_plugin_daemon_response_stream(
|
|
method="POST",
|
|
path=f"plugin/{tenant_id}/dispatch/tts/invoke",
|
|
type_=PluginTTSResultResponse,
|
|
data=jsonable_encoder(
|
|
self._dispatch_payload(
|
|
user_id=user_id,
|
|
data={
|
|
"provider": provider,
|
|
"model_type": "tts",
|
|
"model": model,
|
|
"credentials": credentials,
|
|
"tenant_id": tenant_id,
|
|
"content_text": content_text,
|
|
"voice": voice,
|
|
},
|
|
)
|
|
),
|
|
headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
|
|
)
|
|
|
|
try:
|
|
for result in response:
|
|
hex_str = result.result
|
|
yield TTSAudioChunk(binascii.unhexlify(hex_str), mime_type=result.mime_type)
|
|
except PluginDaemonInnerError as e:
|
|
raise ValueError(e.message + str(e.code)) from e
|
|
|
|
def get_tts_model_voices(
|
|
self,
|
|
tenant_id: str,
|
|
user_id: str | None,
|
|
plugin_id: str,
|
|
provider: str,
|
|
model: str,
|
|
credentials: dict[str, Any],
|
|
language: str | None = None,
|
|
*,
|
|
plugin_unique_identifier: str | None = None,
|
|
) -> list[TTSModelVoice]:
|
|
"""
|
|
Get tts model voices
|
|
"""
|
|
response = self._request_with_plugin_daemon_response_stream(
|
|
method="POST",
|
|
path=f"plugin/{tenant_id}/dispatch/tts/model/voices",
|
|
type_=PluginVoicesResponse,
|
|
data=jsonable_encoder(
|
|
self._dispatch_payload(
|
|
user_id=user_id,
|
|
data={
|
|
"provider": provider,
|
|
"model_type": "tts",
|
|
"model": model,
|
|
"credentials": credentials,
|
|
"language": language,
|
|
},
|
|
)
|
|
),
|
|
headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
|
|
)
|
|
|
|
for resp in response:
|
|
voices: list[TTSModelVoice] = []
|
|
for voice in resp.voices:
|
|
voices.append({"name": voice.name, "value": voice.value})
|
|
|
|
return voices
|
|
|
|
return []
|
|
|
|
def invoke_speech_to_text(
|
|
self,
|
|
tenant_id: str,
|
|
user_id: str | None,
|
|
plugin_id: str,
|
|
provider: str,
|
|
model: str,
|
|
credentials: dict[str, Any],
|
|
file: IO[bytes],
|
|
*,
|
|
plugin_unique_identifier: str | None = None,
|
|
) -> str:
|
|
"""
|
|
Invoke speech to text
|
|
"""
|
|
response = self._request_with_plugin_daemon_response_stream(
|
|
method="POST",
|
|
path=f"plugin/{tenant_id}/dispatch/speech2text/invoke",
|
|
type_=PluginStringResultResponse,
|
|
data=jsonable_encoder(
|
|
self._dispatch_payload(
|
|
user_id=user_id,
|
|
data={
|
|
"provider": provider,
|
|
"model_type": "speech2text",
|
|
"model": model,
|
|
"credentials": credentials,
|
|
"file": binascii.hexlify(file.read()).decode(),
|
|
},
|
|
)
|
|
),
|
|
headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
|
|
)
|
|
|
|
for resp in response:
|
|
return resp.result
|
|
|
|
raise ValueError("Failed to invoke speech to text")
|
|
|
|
def invoke_moderation(
|
|
self,
|
|
tenant_id: str,
|
|
user_id: str | None,
|
|
plugin_id: str,
|
|
provider: str,
|
|
model: str,
|
|
credentials: dict[str, Any],
|
|
text: str,
|
|
*,
|
|
plugin_unique_identifier: str | None = None,
|
|
) -> bool:
|
|
"""
|
|
Invoke moderation
|
|
"""
|
|
response = self._request_with_plugin_daemon_response_stream(
|
|
method="POST",
|
|
path=f"plugin/{tenant_id}/dispatch/moderation/invoke",
|
|
type_=PluginBasicBooleanResponse,
|
|
data=jsonable_encoder(
|
|
self._dispatch_payload(
|
|
user_id=user_id,
|
|
data={
|
|
"provider": provider,
|
|
"model_type": "moderation",
|
|
"model": model,
|
|
"credentials": credentials,
|
|
"text": text,
|
|
},
|
|
)
|
|
),
|
|
headers=plugin_dispatch_headers(plugin_id, plugin_unique_identifier),
|
|
)
|
|
|
|
for resp in response:
|
|
return resp.result
|
|
|
|
raise ValueError("Failed to invoke moderation")
|