Billy D.
408f31e56d
- Add py.typed marker for PEP 561 type hint support - Add ray_utils module for Ray handle detection and caching - Update all clients (Embeddings, LLM, TTS, STT, Reranker) to use Ray handles when running inside Ray cluster for faster internal calls - Add .pre-commit-config.yaml with ruff and standard hooks - Add pre-commit and ray[serve] to optional dependencies - Bump ruff version to 0.4.0
129 lines
4.0 KiB
Python
129 lines
4.0 KiB
Python
"""
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Embeddings service client (Infinity/BGE).
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Supports both HTTP (external) and Ray handles (internal) for optimal performance.
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"""
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import logging
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from typing import Any, Optional
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import httpx
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from handler_base.config import EmbeddingsSettings
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from handler_base.ray_utils import get_ray_handle
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from handler_base.telemetry import create_span
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logger = logging.getLogger(__name__)
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class EmbeddingsClient:
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"""
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Client for the embeddings service (Infinity with BGE models).
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When running inside Ray, automatically uses Ray handles for faster
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internal communication. Falls back to HTTP for external calls.
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Usage:
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client = EmbeddingsClient()
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embeddings = await client.embed(["Hello world"])
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"""
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# Ray Serve deployment configuration
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RAY_DEPLOYMENT_NAME = "EmbeddingsDeployment"
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RAY_APP_NAME = "embeddings"
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def __init__(self, settings: Optional[EmbeddingsSettings] = None):
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self.settings = settings or EmbeddingsSettings()
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self._client = httpx.AsyncClient(
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base_url=self.settings.embeddings_url,
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timeout=self.settings.http_timeout,
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)
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self._ray_handle: Optional[Any] = None
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self._ray_checked = False
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def _get_ray_handle(self) -> Optional[Any]:
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"""Get Ray handle, checking only once."""
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if not self._ray_checked:
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self._ray_handle = get_ray_handle(self.RAY_DEPLOYMENT_NAME, self.RAY_APP_NAME)
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self._ray_checked = True
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return self._ray_handle
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async def close(self) -> None:
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"""Close the HTTP client."""
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await self._client.aclose()
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async def embed(
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self,
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texts: list[str],
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model: Optional[str] = None,
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) -> list[list[float]]:
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"""
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Generate embeddings for a list of texts.
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Args:
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texts: List of texts to embed
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model: Model name (defaults to settings)
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Returns:
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List of embedding vectors
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"""
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model = model or self.settings.embeddings_model
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with create_span("embeddings.embed") as span:
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if span:
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span.set_attribute("embeddings.model", model)
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span.set_attribute("embeddings.batch_size", len(texts))
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# Try Ray handle first (faster internal path)
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handle = self._get_ray_handle()
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if handle:
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try:
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if span:
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span.set_attribute("embeddings.transport", "ray")
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result = await handle.embed.remote(texts, model)
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if span and result:
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span.set_attribute("embeddings.dimensions", len(result[0]) if result else 0)
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return result
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except Exception as e:
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logger.warning(f"Ray handle failed, falling back to HTTP: {e}")
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# HTTP fallback
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if span:
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span.set_attribute("embeddings.transport", "http")
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response = await self._client.post(
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"/embeddings",
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json={"input": texts, "model": model},
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)
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response.raise_for_status()
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result = response.json()
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embeddings = [d["embedding"] for d in result.get("data", [])]
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if span:
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span.set_attribute("embeddings.dimensions", len(embeddings[0]) if embeddings else 0)
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return embeddings
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async def embed_single(self, text: str, model: Optional[str] = None) -> list[float]:
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"""
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Generate embedding for a single text.
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Args:
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text: Text to embed
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model: Model name (defaults to settings)
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Returns:
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Embedding vector
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"""
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embeddings = await self.embed([text], model)
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return embeddings[0] if embeddings else []
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async def health(self) -> bool:
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"""Check if the embeddings service is healthy."""
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try:
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response = await self._client.get("/health")
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return response.status_code == 200
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except Exception:
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return False
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