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Deploy mindnet to llm-node / deploy (push) Successful in 2s
43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
from __future__ import annotations
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import os, time, json
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import urllib.request
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from typing import List, Dict, Any
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EMBED_URL = os.getenv("EMBED_URL", "http://127.0.0.1:8990/embed")
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EMBED_MODEL = os.getenv("EMBED_MODEL", "minilm-384")
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EMBED_BATCH = int(os.getenv("EMBED_BATCH", "64"))
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TIMEOUT = 60
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class EmbedError(RuntimeError): ...
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def _post_json(url: str, payload: Dict[str, Any]) -> Dict[str, Any]:
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data = json.dumps(payload).encode("utf-8")
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req = urllib.request.Request(url, data=data, headers={"Content-Type": "application/json"})
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with urllib.request.urlopen(req, timeout=TIMEOUT) as resp:
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return json.loads(resp.read().decode("utf-8"))
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def embed_texts(texts: List[str], model: str | None = None, batch_size: int | None = None) -> List[List[float]]:
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model = model or EMBED_MODEL
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batch = batch_size or EMBED_BATCH
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out: List[List[float]] = []
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i = 0
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while i < len(texts):
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chunk = texts[i:i+batch]
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# einfache Retries bei 429/500er
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for attempt in range(5):
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try:
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resp = _post_json(EMBED_URL, {"model": model, "inputs": chunk})
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vecs = resp.get("embeddings") or resp.get("vectors") or resp.get("data")
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if not isinstance(vecs, list): raise EmbedError(f"Bad embed response keys: {resp.keys()}")
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out.extend(vecs)
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break
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except Exception as e:
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if attempt == 4: raise
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time.sleep(1.5 * (attempt + 1))
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continue
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i += batch
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return out
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def embed_one(text: str, model: str | None = None) -> List[float]:
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return embed_texts([text], model=model, batch_size=1)[0]
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