chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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from __future__ import annotations
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import httpx
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from typing import Literal
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class APIStatusError(Exception):
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"""Raised when an API response has a status code of 4xx or 5xx."""
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response: httpx.Response
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status_code: int
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request_id: str | None
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def __init__(
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self, message: str, *, response: httpx.Response, body: object | None
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) -> None:
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super().__init__(message)
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self.request = response.request
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self.body = body
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self.response = response
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self.status_code = response.status_code
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self.request_id = response.headers.get("x-request-id")
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class APIConnectionError(Exception):
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def __init__(
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self, *, message: str = "Connection error.", request: httpx.Request | None
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) -> None:
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super().__init__(message)
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self.request = request
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class BadRequestError(APIStatusError):
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status_code: Literal[400] = 400 # pyright: ignore[reportIncompatibleVariableOverride]
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class AuthenticationError(APIStatusError):
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status_code: Literal[401] = 401 # pyright: ignore[reportIncompatibleVariableOverride]
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class PermissionDeniedError(APIStatusError):
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status_code: Literal[403] = 403 # pyright: ignore[reportIncompatibleVariableOverride]
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class NotFoundError(APIStatusError):
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status_code: Literal[404] = 404 # pyright: ignore[reportIncompatibleVariableOverride]
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class ConflictError(APIStatusError):
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status_code: Literal[409] = 409 # pyright: ignore[reportIncompatibleVariableOverride]
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class UnprocessableEntityError(APIStatusError):
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status_code: Literal[422] = 422 # pyright: ignore[reportIncompatibleVariableOverride]
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class RateLimitError(APIStatusError):
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status_code: Literal[429] = 429 # pyright: ignore[reportIncompatibleVariableOverride]
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class APITimeoutError(APIConnectionError):
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def __init__(self, request: httpx.Request | None) -> None:
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super().__init__(message="Request timed out.", request=request)
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class StorageNotInitializedError(RuntimeError):
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"""Raised when storage operations are attempted before initialization."""
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def __init__(self, storage_type: str = "Storage"):
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super().__init__(
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f"{storage_type} not initialized. Please ensure proper initialization:\n"
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f"\n"
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f" rag = LightRAG(...)\n"
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f" await rag.initialize_storages() # Required - auto-initializes pipeline_status\n"
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f"\n"
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f"See: https://github.com/HKUDS/LightRAG#important-initialization-requirements"
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)
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class PipelineNotInitializedError(KeyError):
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"""Raised when pipeline status is accessed before initialization."""
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def __init__(self, namespace: str = ""):
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msg = (
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f"Pipeline namespace '{namespace}' not found.\n"
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f"\n"
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f"Pipeline status should be auto-initialized by initialize_storages().\n"
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f"If you see this error, please ensure:\n"
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f"\n"
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f" 1. You called await rag.initialize_storages()\n"
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f" 2. For multi-workspace setups, each LightRAG instance was properly initialized\n"
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f"\n"
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f"Standard initialization:\n"
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f" rag = LightRAG(workspace='your_workspace')\n"
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f" await rag.initialize_storages() # Auto-initializes pipeline_status\n"
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f"\n"
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f"If you need manual control (advanced):\n"
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f" from lightrag.kg.shared_storage import initialize_pipeline_status\n"
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f" await initialize_pipeline_status(workspace='your_workspace')"
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)
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super().__init__(msg)
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class PipelineCancelledException(Exception):
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"""Raised when pipeline processing is cancelled by user request."""
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def __init__(self, message: str = "User cancelled"):
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super().__init__(message)
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self.message = message
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class IndexFlushError(Exception):
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"""Raised when a storage backend fails to flush buffered index ops.
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Carries the storage driver name and namespace so the pipeline can abort
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the batch with an actionable reason. The underlying error is preserved as
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the exception ``__cause__`` (set via ``raise ... from cause``).
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"""
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def __init__(self, storage_name: str, namespace: str, cause: BaseException):
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self.storage_name = storage_name
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self.namespace = namespace
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super().__init__(f"{storage_name}[{namespace}] index flush failed: {cause}")
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class ChunkTokenLimitExceededError(ValueError):
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"""Raised when a chunk exceeds the configured token limit."""
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def __init__(
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self,
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chunk_tokens: int,
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chunk_token_limit: int,
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chunk_preview: str | None = None,
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) -> None:
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preview = chunk_preview.strip() if chunk_preview else None
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truncated_preview = preview[:80] if preview else None
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preview_note = f" Preview: '{truncated_preview}'" if truncated_preview else ""
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message = (
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f"Chunk token length {chunk_tokens} exceeds chunk_token_size {chunk_token_limit}."
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f"{preview_note}"
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)
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super().__init__(message)
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self.chunk_tokens = chunk_tokens
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self.chunk_token_limit = chunk_token_limit
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self.chunk_preview = truncated_preview
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class ChunkBlockMatchError(ValueError):
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"""Raised when a chunk cannot be located in the document's blocks.jsonl.
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Sidecar backfill (``lightrag.sidecar.backfill``) maps F/R/V chunks back to
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their source block(s) by matching chunk content against the parse-time
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``*.blocks.jsonl`` merged text. When a sidecar-less chunk cannot be located,
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this is raised so the pipeline marks the document FAILED rather than
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persisting chunks with missing/incorrect provenance.
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"""
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def __init__(
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self,
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chunk_order_index: int,
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chunk_preview: str | None = None,
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blocks_path: str | None = None,
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) -> None:
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preview = chunk_preview.strip() if chunk_preview else None
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truncated_preview = preview[:80] if preview else None
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preview_note = f" Preview: '{truncated_preview}'" if truncated_preview else ""
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path_note = f" (blocks: {blocks_path})" if blocks_path else ""
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message = (
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f"Chunk #{chunk_order_index} could not be located in the document "
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f"blocks during sidecar backfill.{preview_note}{path_note}"
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)
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super().__init__(message)
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self.chunk_order_index = chunk_order_index
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self.chunk_preview = truncated_preview
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self.blocks_path = blocks_path
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class DataMigrationError(Exception):
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"""Raised when data migration from legacy collection/table fails."""
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def __init__(self, message: str):
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super().__init__(message)
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self.message = message
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class MultimodalAnalysisError(RuntimeError):
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"""Raised when multimodal analysis must fail the current document.
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Hard failures (missing required field, schema mismatch, model not
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available, sidecar already carries ``status="failure"``) bubble this
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exception so the pipeline marks the document failed instead of writing
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an unusable analyze result. Callers persist a ``status="failure"``
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sidecar entry alongside the raise so a re-run sees the failure.
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"""
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