File size: 3,732 Bytes
c581249 fac50ab c581249 fac50ab c581249 fac50ab c581249 fac50ab c581249 fac50ab c581249 fac50ab c581249 fac50ab c581249 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
"""CLI entrypoint for the DeepSeek OCR pipeline."""
from __future__ import annotations
import logging
from .config import AssembleSettings, DescribeSettings, ExtractSettings, env
from .server import (
DeepSeekClient,
base_url_from_env,
launch_vllm,
should_launch_server,
shutdown_server,
wait_for_server,
)
from .stages import run_stage_assemble, run_stage_describe, run_stage_extract
LOGGER = logging.getLogger(__name__)
def _setup_logging() -> None:
"""Configure logging with optional rich handler."""
level = env("LOG_LEVEL", "INFO").upper()
try:
from rich.console import Console
from rich.logging import RichHandler
console = Console(
force_terminal=env("FORCE_COLOR", "").lower() in {"1", "true"}
)
handler = RichHandler(
console=console, show_time=True, show_level=True, rich_tracebacks=True
)
logging.basicConfig(
level=level, format="%(message)s", handlers=[handler], force=True
)
except ImportError:
logging.basicConfig(
level=level,
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
force=True,
)
def _create_client(
max_tokens: int, temperature: float, inference_settings
) -> DeepSeekClient:
"""Create DeepSeek client from environment."""
return DeepSeekClient(
base_url=base_url_from_env(),
model_name=env("SERVED_MODEL_NAME", "deepseek-ocr"),
max_tokens=max_tokens,
temperature=temperature,
request_timeout=inference_settings.request_timeout,
max_retries=inference_settings.max_retries,
retry_backoff_seconds=inference_settings.retry_backoff,
)
def main() -> None:
"""Main entry point for the pipeline CLI."""
_setup_logging()
stage = env("PIPELINE_STAGE", "extract").lower()
if stage not in {"extract", "describe", "assemble"}:
raise ValueError(f"Unsupported stage: {stage}")
needs_server = stage in {"extract", "describe"}
launch_server = should_launch_server() and needs_server
server_process = None
try:
if launch_server:
server_process = launch_vllm()
if needs_server:
base_url = base_url_from_env()
health_url = env("HEALTH_URL", f"{base_url}/health")
LOGGER.info("Waiting for server at %s", health_url)
if not wait_for_server(health_url):
raise RuntimeError("vLLM server did not become ready in time")
if stage == "extract":
from .config import InferenceSettings
inference = InferenceSettings.from_env("EXTRACT")
max_tokens = env("DOC_MAX_TOKENS", 2048, int)
temperature = env("DOC_TEMPERATURE", 0.0, float)
client = _create_client(max_tokens, temperature, inference)
settings = ExtractSettings.from_env(client)
settings.inference = inference
run_stage_extract(settings)
elif stage == "describe":
from .config import InferenceSettings
inference = InferenceSettings.from_env("DESCRIBE")
max_tokens = env("FIGURE_MAX_TOKENS", 512, int)
temperature = env("FIGURE_TEMPERATURE", 0.0, float)
client = _create_client(max_tokens, temperature, inference)
settings = DescribeSettings.from_env(client)
settings.inference = inference
run_stage_describe(settings)
elif stage == "assemble":
settings = AssembleSettings.from_env()
run_stage_assemble(settings)
finally:
if server_process is not None:
shutdown_server(server_process)
|