feat: extract model runtime

Signed-off-by: -LAN- <laipz8200@outlook.com>

api/dify_graph/model_runtime/runtime.py

@@ -0,0 +1,159 @@
+from __future__ import annotations
+
+from collections.abc import Generator, Iterable, Sequence
+from typing import IO, Any, Protocol, Union, runtime_checkable
+
+from dify_graph.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
+from dify_graph.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
+from dify_graph.model_runtime.entities.model_entities import AIModelEntity, ModelType
+from dify_graph.model_runtime.entities.provider_entities import ProviderEntity
+from dify_graph.model_runtime.entities.rerank_entities import MultimodalRerankInput, RerankResult
+from dify_graph.model_runtime.entities.text_embedding_entities import EmbeddingInputType, EmbeddingResult
+
+
+@runtime_checkable
+class ModelRuntime(Protocol):
+    """Port for provider discovery, schema lookup, and model execution.
+
+    `provider` is the model runtime's canonical provider identifier. Adapters may
+    derive transport-specific details from it, but those details stay outside
+    this boundary.
+    """
+
+    def fetch_model_providers(self) -> Sequence[ProviderEntity]: ...
+
+    def get_provider_icon(self, *, provider: str, icon_type: str, lang: str) -> tuple[bytes, str]: ...
+
+    def validate_provider_credentials(self, *, provider: str, credentials: dict[str, Any]) -> None: ...
+
+    def validate_model_credentials(
+        self,
+        *,
+        provider: str,
+        model_type: ModelType,
+        model: str,
+        credentials: dict[str, Any],
+    ) -> None: ...
+
+    def get_model_schema(
+        self,
+        *,
+        provider: str,
+        model_type: ModelType,
+        model: str,
+        credentials: dict[str, Any],
+    ) -> AIModelEntity | None: ...
+
+    def invoke_llm(
+        self,
+        *,
+        provider: str,
+        model: str,
+        credentials: dict[str, Any],
+        model_parameters: dict[str, Any],
+        prompt_messages: Sequence[PromptMessage],
+        tools: list[PromptMessageTool] | None,
+        stop: Sequence[str] | None,
+        stream: bool,
+    ) -> Union[LLMResult, Generator[LLMResultChunk, None, None]]: ...
+
+    def get_llm_num_tokens(
+        self,
+        *,
+        provider: str,
+        model_type: ModelType,
+        model: str,
+        credentials: dict[str, Any],
+        prompt_messages: Sequence[PromptMessage],
+        tools: Sequence[PromptMessageTool] | None,
+    ) -> int: ...
+
+    def invoke_text_embedding(
+        self,
+        *,
+        provider: str,
+        model: str,
+        credentials: dict[str, Any],
+        texts: list[str],
+        input_type: EmbeddingInputType,
+    ) -> EmbeddingResult: ...
+
+    def invoke_multimodal_embedding(
+        self,
+        *,
+        provider: str,
+        model: str,
+        credentials: dict[str, Any],
+        documents: list[dict[str, Any]],
+        input_type: EmbeddingInputType,
+    ) -> EmbeddingResult: ...
+
+    def get_text_embedding_num_tokens(
+        self,
+        *,
+        provider: str,
+        model: str,
+        credentials: dict[str, Any],
+        texts: list[str],
+    ) -> list[int]: ...
+
+    def invoke_rerank(
+        self,
+        *,
+        provider: str,
+        model: str,
+        credentials: dict[str, Any],
+        query: str,
+        docs: list[str],
+        score_threshold: float | None,
+        top_n: int | None,
+    ) -> RerankResult: ...
+
+    def invoke_multimodal_rerank(
+        self,
+        *,
+        provider: str,
+        model: str,
+        credentials: dict[str, Any],
+        query: MultimodalRerankInput,
+        docs: list[MultimodalRerankInput],
+        score_threshold: float | None,
+        top_n: int | None,
+    ) -> RerankResult: ...
+
+    def invoke_tts(
+        self,
+        *,
+        provider: str,
+        model: str,
+        credentials: dict[str, Any],
+        content_text: str,
+        voice: str,
+    ) -> Iterable[bytes]: ...
+
+    def get_tts_model_voices(
+        self,
+        *,
+        provider: str,
+        model: str,
+        credentials: dict[str, Any],
+        language: str | None,
+    ) -> Any: ...
+
+    def invoke_speech_to_text(
+        self,
+        *,
+        provider: str,
+        model: str,
+        credentials: dict[str, Any],
+        file: IO[bytes],
+    ) -> str: ...
+
+    def invoke_moderation(
+        self,
+        *,
+        provider: str,
+        model: str,
+        credentials: dict[str, Any],
+        text: str,
+    ) -> bool: ...