Fu DaiandClaude Opus 4.8
1a585693be
FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM: legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint, a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual example outputs, and quantization/concurrency benchmarks. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
from dataclasses import dataclass, field
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from typing import Any, List, Optional
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from PIL import Image
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PROMPT_TYPE_LAYOUT = "layout"
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PROMPT_TYPE_BLOCK = "block"
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PROMPT_TYPE_TABLE_REC = "table_rec"
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PROMPT_TYPE_HIGH_ACCURACY_BBOX = "high_accuracy_bbox"
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@dataclass
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class BatchInputItem:
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image: Image.Image
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prompt_type: str
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prompt: Optional[str] = None # If set, overrides the default prompt for prompt_type
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max_tokens: Optional[int] = None
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request_logprobs: bool = False
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# vllm-native guided decoding — JSON schema, regex, or grammar string.
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# When set, the server constrains the decode tokens to match the schema.
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guided_json: Optional[dict] = None
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guided_regex: Optional[str] = None
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metadata: dict = field(default_factory=dict) # Free-form, passes through to output
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@dataclass
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class GenerationResult:
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raw: str
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token_count: int
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error: bool = False
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# Mean of exp(logprob) across response tokens, if logprobs requested
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mean_token_prob: Optional[float] = None
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# Per-token logprobs (raw OpenAI-style content list), if requested - phase 2 use
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logprobs: Optional[List[Any]] = None
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@dataclass
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class BatchOutputItem:
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raw: str
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token_count: int
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error: bool
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mean_token_prob: Optional[float] = None
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logprobs: Optional[List[Any]] = None
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metadata: dict = field(default_factory=dict)
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