145 lines
4.7 KiB
Python
145 lines
4.7 KiB
Python
"""织忆 MemoryWeave — bge-m3 ONNX 嵌入服务器
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OpenAI /v1/embeddings 兼容接口，Go 代码零改动切换。
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使用 ONNX Runtime CPU 推理，RTX 3050 4GB 无压力。
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启动: python3 bge_embed_server.py
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端口: 8000
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模型: /home/muc/models/bge-m3/onnx/
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"""
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import json
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import logging
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import math
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import os
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from http.server import HTTPServer, BaseHTTPRequestHandler
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import numpy as np
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import onnxruntime as ort
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from transformers import AutoTokenizer
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MODEL_PATH = os.environ.get("BGE_MODEL_PATH", "/home/muc/models/bge-m3/onnx")
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PORT = int(os.environ.get("BGE_PORT", "8000"))
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MAX_BATCH = int(os.environ.get("BGE_MAX_BATCH", "32"))
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logging.basicConfig(level=logging.INFO, format="[bge-embed] %(message)s")
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log = logging.getLogger(__name__)
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# ─── 初始化 ──────────────────────────────────────────
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log.info("加载 tokenizer: %s", MODEL_PATH)
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
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log.info("加载 ONNX 模型: %s/model.onnx", MODEL_PATH)
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sess_options = ort.SessionOptions()
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sess_options.intra_op_num_threads = 4
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sess_options.inter_op_num_threads = 2
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session = ort.InferenceSession(
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os.path.join(MODEL_PATH, "model.onnx"),
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sess_options=sess_options,
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providers=["CPUExecutionProvider"],
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)
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log.info("ONNX 模型就绪 — providers=%s", session.get_providers())
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def encode(texts: list[str]) -> list[list[float]]:
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"""批量编码 + mean pooling + L2 归一化"""
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inputs = tokenizer(
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texts,
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padding=True,
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truncation=True,
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max_length=8192,
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return_tensors="np",
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)
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ort_inputs = {
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"input_ids": inputs["input_ids"],
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"attention_mask": inputs["attention_mask"],
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}
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outputs = session.run(None, ort_inputs)
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# ONNX 输出: [batch, seq_len, 1024] — token-level embeddings
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embeddings: np.ndarray = outputs[0]
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# Mean pooling — 按 attention_mask 加权平均
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attention_mask = inputs["attention_mask"].astype(np.float32)
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mask_expanded = np.expand_dims(attention_mask, -1) # [batch, seq_len, 1]
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sum_embeddings = np.sum(embeddings * mask_expanded, axis=1) # [batch, 1024]
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sum_mask = np.clip(np.sum(mask_expanded, axis=1), 1e-9, None) # [batch, 1]
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embeddings = sum_embeddings / sum_mask # [batch, 1024]
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# L2 归一化
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norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
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norms = np.maximum(norms, 1e-12)
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embeddings = embeddings / norms
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return embeddings.tolist()
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class EmbedHandler(BaseHTTPRequestHandler):
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"""OpenAI /v1/embeddings 兼容"""
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def log_message(self, fmt, *args):
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pass # 安静模式
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def _respond(self, code: int, data: dict):
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body = json.dumps(data, ensure_ascii=False).encode()
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self.send_response(code)
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self.send_header("Content-Type", "application/json")
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self.send_header("Content-Length", str(len(body)))
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self.end_headers()
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self.wfile.write(body)
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def do_GET(self):
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if self.path == "/health":
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self._respond(200, {"status": "ok", "model": "bge-m3", "backend": "onnxruntime"})
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else:
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self._respond(404, {"error": "not found"})
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def do_POST(self):
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if self.path != "/v1/embeddings":
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self._respond(404, {"error": "not found"})
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return
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content_len = int(self.headers.get("Content-Length", 0))
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body = json.loads(self.rfile.read(content_len))
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inputs = body.get("input", [])
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if isinstance(inputs, str):
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inputs = [inputs]
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if not inputs:
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self._respond(400, {"error": "empty input"})
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return
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if len(inputs) > MAX_BATCH:
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self._respond(400, {"error": f"batch size {len(inputs)} > max {MAX_BATCH}"})
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return
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try:
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embeddings = encode(inputs)
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except Exception as e:
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log.error("encode error: %s", e)
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self._respond(500, {"error": str(e)})
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return
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data = [
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{"embedding": emb, "index": i, "object": "embedding"}
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for i, emb in enumerate(embeddings)
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]
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self._respond(200, {
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"object": "list",
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"data": data,
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"model": "bge-m3",
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"usage": {"prompt_tokens": sum(len(t) for t in inputs), "total_tokens": sum(len(t) for t in inputs)},
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})
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def main():
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server = HTTPServer(("0.0.0.0", PORT), EmbedHandler)
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log.info("bge-m3 ONNX 嵌入服务器启动 — http://0.0.0.0:%d", PORT)
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log.info("端点: POST /v1/embeddings GET /health")
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try:
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server.serve_forever()
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except KeyboardInterrupt:
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log.info("关闭服务器")
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server.shutdown()
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if __name__ == "__main__":
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main()
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