7bfb9946
 tangwang
向量化模块
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"""
Embedding service (FastAPI).
API (simple list-in, list-out; aligned by index; failures -> null):
- POST /embed/text body: ["text1", "text2", ...] -> [[...], null, ...]
- POST /embed/image body: ["url_or_path1", ...] -> [[...], null, ...]
"""
import threading
from typing import Any, Dict, List, Optional
import numpy as np
from fastapi import FastAPI
from embeddings.config import CONFIG
from embeddings.bge_model import BgeTextModel
from embeddings.clip_model import ClipImageModel
app = FastAPI(title="SearchEngine Embedding Service", version="1.0.0")
_text_model = None
_image_model = None
_text_init_lock = threading.Lock()
_image_init_lock = threading.Lock()
_text_encode_lock = threading.Lock()
_image_encode_lock = threading.Lock()
def _get_text_model():
global _text_model
if _text_model is None:
with _text_init_lock:
if _text_model is None:
_text_model = BgeTextModel(model_dir=CONFIG.TEXT_MODEL_DIR)
return _text_model
def _get_image_model():
global _image_model
if _image_model is None:
with _image_init_lock:
if _image_model is None:
_image_model = ClipImageModel(
model_name=CONFIG.IMAGE_MODEL_NAME,
device=CONFIG.IMAGE_DEVICE,
)
return _image_model
def _as_list(embedding: Optional[np.ndarray]) -> Optional[List[float]]:
if embedding is None:
return None
if not isinstance(embedding, np.ndarray):
embedding = np.array(embedding, dtype=np.float32)
if embedding.ndim != 1:
embedding = embedding.reshape(-1)
return embedding.astype(np.float32).tolist()
@app.get("/health")
def health() -> Dict[str, Any]:
return {"status": "ok"}
@app.post("/embed/text")
def embed_text(texts: List[str]) -> List[Optional[List[float]]]:
model = _get_text_model()
out: List[Optional[List[float]]] = [None] * len(texts)
indexed_texts: List[tuple] = []
for i, t in enumerate(texts):
if t is None:
continue
if not isinstance(t, str):
t = str(t)
t = t.strip()
if not t:
continue
indexed_texts.append((i, t))
if not indexed_texts:
return out
batch_texts = [t for _, t in indexed_texts]
try:
with _text_encode_lock:
embs = model.encode_batch(
batch_texts, batch_size=int(CONFIG.TEXT_BATCH_SIZE), device=CONFIG.TEXT_DEVICE
)
for j, (idx, _t) in enumerate(indexed_texts):
out[idx] = _as_list(embs[j])
except Exception:
# keep Nones
pass
return out
@app.post("/embed/image")
def embed_image(images: List[str]) -> List[Optional[List[float]]]:
model = _get_image_model()
out: List[Optional[List[float]]] = [None] * len(images)
with _image_encode_lock:
for i, url_or_path in enumerate(images):
try:
if url_or_path is None:
continue
if not isinstance(url_or_path, str):
url_or_path = str(url_or_path)
url_or_path = url_or_path.strip()
if not url_or_path:
continue
emb = model.encode_image_from_url(url_or_path)
out[i] = _as_list(emb)
except Exception:
out[i] = None
return out
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