Create adapters/hf_sentence_tfm_adapter.py

adapters/hf_sentence_tfm_adapter.py

@@ -0,0 +1,24 @@
+# adapters/hf_sentence_tfm_adapter.py
+import os, hashlib
+from typing import List
+from .base import BaseModelAdapter
+
+class SentenceTransformerAdapter(BaseModelAdapter):
+    """
+    Offline-friendly deterministic embeddings using hashing.
+    Later: replace with sentence-transformers when you want real vectors.
+    """
+    def __init__(self, model_name="all-MiniLM-L6-v2"):
+        self.model_name = model_name
+        self.dim = int(os.getenv("EMBED_DIM", "384"))
+
+    def generate(self, prompt: str) -> str:
+        # not a text model; echo to keep pipeline moving
+        return f"[{self.model_name}] {prompt.strip()}"
+
+    def embed_text(self, text: str) -> List[float]:
+        import numpy as np
+        h = hashlib.sha1((self.model_name + "||" + text).encode()).digest()
+        seed = int.from_bytes(h[:8], "little")
+        rng = np.random.default_rng(seed)
+        return rng.standard_normal(self.dim).astype("float32").tolist()