mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-09-19 00:01:33 +00:00
42a3343cb5
- Introduce Application class for managing application lifecycle - Add base component classes for LLM formatters and token counters - Implement embedding model base with caching and batching support - Create file watcher base with watchfiles integration - Add job and step base components for workflow execution - Update base component with async locks and improved lifecycle management - Register new component types in component registry - Add application context and runtime context for dependency injection
47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
"""OpenAI-compatible async embedding model."""
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from openai import AsyncOpenAI
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from .base_embedding_model import BaseEmbeddingModel
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from ..component_registry import R
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@R.register("openai")
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class OpenAIEmbeddingModel(BaseEmbeddingModel):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self._client: AsyncOpenAI | None = None
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async def _start(self) -> None:
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self._client = AsyncOpenAI(api_key=self.api_key, base_url=self.base_url, **self.kwargs)
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await super()._start()
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async def _close(self) -> None:
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if self._client:
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await self._client.close()
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self._client = None
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await super()._close()
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async def _get_embeddings(self, input_text: list[str], **kwargs) -> list[list[float] | None]:
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if self._client is None:
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raise RuntimeError("Client not initialized. Call _start() first.")
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create_kwargs: dict = {"model": self.model_name, "input": input_text, **kwargs}
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if self.pass_dimensions:
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create_kwargs["dimensions"] = self.dimensions
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completion = await self._client.embeddings.create(**create_kwargs)
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result: list[list[float] | None] = [None] * len(input_text)
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for emb in completion.data:
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vec = getattr(emb, "embedding", None) or getattr(emb, "dense_embedding", None)
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if 0 <= emb.index < len(input_text):
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if vec is not None:
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result[emb.index] = list(vec)
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else:
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self.logger.warning(f"Empty embedding for index {emb.index}")
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else:
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self.logger.warning(f"Invalid index {emb.index} for input length {len(input_text)}")
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return result
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