mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-09-17 23:51:19 +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
67 lines
2 KiB
Python
67 lines
2 KiB
Python
"""Runtime context for managing response states and asynchronous data streaming."""
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import asyncio
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from ..enumeration import ChunkEnum
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from ..schema import Response, StreamChunk
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class RuntimeContext:
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"""Context for execution state, response metadata, and stream queues."""
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def __init__(self, **kwargs):
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self.data: dict = kwargs
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def get(self, key: str, default=None):
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return self.data.get(key, default)
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def update(self, data: dict) -> "RuntimeContext":
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self.data.update(data)
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return self
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def __getitem__(self, key: str):
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return self.data[key]
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def __setitem__(self, key: str, value):
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self.data[key] = value
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def __delitem__(self, key: str):
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del self.data[key]
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def __contains__(self, key: str) -> bool:
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return key in self.data
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@property
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def response(self) -> Response:
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return self.data.setdefault("response", Response())
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@property
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def stream_queue(self) -> asyncio.Queue:
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return self.data["stream_queue"]
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@classmethod
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def from_context(cls, context: "RuntimeContext | None" = None, **kwargs) -> "RuntimeContext":
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if context is None:
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return cls(**kwargs)
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context.update(kwargs)
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return context
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async def _enqueue(self, chunk: StreamChunk) -> None:
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if self.stream_queue:
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await self.stream_queue.put(chunk)
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async def add_stream_string(self, chunk: str, chunk_type: ChunkEnum) -> "RuntimeContext":
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await self._enqueue(StreamChunk(chunk_type=chunk_type, chunk=chunk))
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return self
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async def add_stream_done(self) -> "RuntimeContext":
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await self._enqueue(StreamChunk(chunk_type=ChunkEnum.DONE, chunk="", done=True))
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return self
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def apply_mapping(self, mapping: dict[str, str]) -> "RuntimeContext":
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if not mapping:
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return self
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for source, target in mapping.items():
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if source in self.data:
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self.data[target] = self.data[source]
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return self
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