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- Implement BaseComponent with async lifecycle and context management - Add ApplicationContext for managing component initialization and registry - Create Application class for orchestrating job execution and lifecycle - Add AS LLM components with OpenAI chat model wrapper - Implement AS LLM formatter components with OpenAI formatter - Add client implementations including base, HTTP and ReMe clients - Create embedding model base class with caching and batching support - Implement file store base class with vector and full-text search - Add file watcher components for monitoring file system changes - Create job components for executing workflows - Implement service components for exposing jobs via different protocols - Add configuration schema with ApplicationConfig and ComponentConfig - Include utility modules for case conversion, chunking, logging and similarity - Register component types and create component registry system
90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
"""Runtime context for managing response states and asynchronous data streaming."""
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import asyncio
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from .application_context import ApplicationContext
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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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"""Initialize the context with all keyword arguments stored in data."""
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self.data: dict = kwargs
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@property
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def response(self) -> Response:
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"""Get or create the response object."""
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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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"""Get the stream queue."""
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return self.data["stream_queue"]
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@property
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def application_context(self) -> ApplicationContext:
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"""Get the application context."""
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return self.data["application_context"]
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@classmethod
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def from_context(cls, context: "RuntimeContext | None" = None, **kwargs) -> "RuntimeContext":
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"""Create a new context from an existing instance or keywords."""
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if context is None:
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return cls(**kwargs)
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context.data.update(kwargs)
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return context
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async def _enqueue(self, chunk: StreamChunk) -> None:
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"""Internal helper to put a chunk into the queue if it exists."""
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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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"""Enqueue a stream chunk from a raw string and type."""
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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_chunk(self, stream_chunk: StreamChunk) -> "RuntimeContext":
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"""Enqueue an existing stream chunk."""
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await self._enqueue(stream_chunk)
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return self
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async def add_stream_done(self) -> "RuntimeContext":
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"""Enqueue a termination chunk to signal the end of the stream."""
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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 add_response_error(self, e: Exception) -> "RuntimeContext":
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"""Record an exception into the response object."""
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self.response.success = False
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self.response.answer = str(e)
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return self
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def apply_mapping(self, mapping: dict[str, str]) -> "RuntimeContext":
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"""Copy internal values based on a source-to-target key map."""
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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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def validate_required_keys(
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self,
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required_keys: dict[str, bool],
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context_name: str = "context",
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) -> "RuntimeContext":
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"""Ensure all required keys are present in the context.
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Args:
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required_keys: Dictionary mapping key names to boolean indicating if required
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context_name: Name of the context for error messages (e.g., operator name)
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"""
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for key, is_required in required_keys.items():
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if is_required and key not in self.data:
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raise ValueError(f"{context_name}: missing required input '{key}'")
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return self
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