Create adapters/openai_adapter.py

adapters/openai_adapter.py

@@ -1,21 +1,24 @@
-import os, httpx
+# adapters/openai_adapter.py
+import os, hashlib
+from typing import List
 from .base import BaseModelAdapter
 
-_OPENAI_KEY = os.getenv("OPENAI_API_KEY", "")
-
 class OpenAIAdapter(BaseModelAdapter):
+    """
+    Placeholder that returns echo'd text and deterministic pseudo-embeddings.
+    Swap with real OpenAI calls later.
+    """
     def __init__(self, model_text="gpt-4o-mini", model_embed="text-embedding-3-small"):
         self.model_text = model_text
         self.model_embed = model_embed
+        self.dim = int(os.getenv("EMBED_DIM", "384"))
 
     def generate(self, prompt: str) -> str:
-        # replace with the current OpenAI responses endpoint you use
-        # placeholder pseudo-call:
-        return f"[openai:{self.model_text}] {prompt}"
+        return f"[{self.model_text}] {prompt.strip()}"
 
-    def embed_text(self, text: str) -> list[float]:
-        # placeholder embedding (replace with real HTTP call)
-        import numpy as np, hashlib
-        h = hashlib.sha256(text.encode()).digest()
-        rng = np.random.default_rng(int.from_bytes(h[:8], "little"))
-        return rng.standard_normal(384).tolist()
+    def embed_text(self, text: str) -> List[float]:
+        import numpy as np
+        h = hashlib.sha256((self.model_embed + "||" + 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()