Billy D.
99c97b7973
- Handler base class with graceful shutdown and signal handling - NATSClient with JetStream and msgpack serialization - Pydantic Settings for environment configuration - HealthServer for Kubernetes probes - OpenTelemetry telemetry setup - Service clients: STT, TTS, LLM, Embeddings, Reranker, Milvus
92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
"""
|
|
Embeddings service client (Infinity/BGE).
|
|
"""
|
|
import logging
|
|
from typing import Optional
|
|
|
|
import httpx
|
|
|
|
from handler_base.config import EmbeddingsSettings
|
|
from handler_base.telemetry import create_span
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class EmbeddingsClient:
|
|
"""
|
|
Client for the embeddings service (Infinity with BGE models).
|
|
|
|
Usage:
|
|
client = EmbeddingsClient()
|
|
embeddings = await client.embed(["Hello world"])
|
|
"""
|
|
|
|
def __init__(self, settings: Optional[EmbeddingsSettings] = None):
|
|
self.settings = settings or EmbeddingsSettings()
|
|
self._client = httpx.AsyncClient(
|
|
base_url=self.settings.embeddings_url,
|
|
timeout=self.settings.http_timeout,
|
|
)
|
|
|
|
async def close(self) -> None:
|
|
"""Close the HTTP client."""
|
|
await self._client.aclose()
|
|
|
|
async def embed(
|
|
self,
|
|
texts: list[str],
|
|
model: Optional[str] = None,
|
|
) -> list[list[float]]:
|
|
"""
|
|
Generate embeddings for a list of texts.
|
|
|
|
Args:
|
|
texts: List of texts to embed
|
|
model: Model name (defaults to settings)
|
|
|
|
Returns:
|
|
List of embedding vectors
|
|
"""
|
|
model = model or self.settings.embeddings_model
|
|
|
|
with create_span("embeddings.embed") as span:
|
|
if span:
|
|
span.set_attribute("embeddings.model", model)
|
|
span.set_attribute("embeddings.batch_size", len(texts))
|
|
|
|
response = await self._client.post(
|
|
"/embeddings",
|
|
json={"input": texts, "model": model},
|
|
)
|
|
response.raise_for_status()
|
|
|
|
result = response.json()
|
|
embeddings = [d["embedding"] for d in result.get("data", [])]
|
|
|
|
if span:
|
|
span.set_attribute("embeddings.dimensions", len(embeddings[0]) if embeddings else 0)
|
|
|
|
return embeddings
|
|
|
|
async def embed_single(self, text: str, model: Optional[str] = None) -> list[float]:
|
|
"""
|
|
Generate embedding for a single text.
|
|
|
|
Args:
|
|
text: Text to embed
|
|
model: Model name (defaults to settings)
|
|
|
|
Returns:
|
|
Embedding vector
|
|
"""
|
|
embeddings = await self.embed([text], model)
|
|
return embeddings[0] if embeddings else []
|
|
|
|
async def health(self) -> bool:
|
|
"""Check if the embeddings service is healthy."""
|
|
try:
|
|
response = await self._client.get("/health")
|
|
return response.status_code == 200
|
|
except Exception:
|
|
return False
|