chore: import upstream snapshot with attribution

application/vectorstore/embeddings_local.py

@@ -0,0 +1,48 @@
+"""
+Local embeddings using SentenceTransformer.
+This module is only imported when EMBEDDINGS_BASE_URL is not set,
+to avoid loading SentenceTransformer into memory when using remote embeddings.
+"""
+
+import logging
+
+from sentence_transformers import SentenceTransformer
+
+
+class EmbeddingsWrapper:
+    def __init__(self, model_name, *args, **kwargs):
+        logging.info(f"Initializing EmbeddingsWrapper with model: {model_name}")
+        try:
+            kwargs.setdefault("trust_remote_code", True)
+            self.model = SentenceTransformer(
+                model_name,
+                config_kwargs={"allow_dangerous_deserialization": True},
+                *args,
+                **kwargs,
+            )
+            if self.model is None or self.model._first_module() is None:
+                raise ValueError(
+                    f"SentenceTransformer model failed to load properly for: {model_name}"
+                )
+            self.dimension = self.model.get_sentence_embedding_dimension()
+            logging.info(f"Successfully loaded model with dimension: {self.dimension}")
+        except Exception as e:
+            logging.error(
+                f"Failed to initialize SentenceTransformer with model {model_name}: {str(e)}",
+                exc_info=True,
+            )
+            raise
+
+    def embed_query(self, query: str):
+        return self.model.encode(query).tolist()
+
+    def embed_documents(self, documents: list):
+        return self.model.encode(documents).tolist()
+
+    def __call__(self, text):
+        if isinstance(text, str):
+            return self.embed_query(text)
+        elif isinstance(text, list):
+            return self.embed_documents(text)
+        else:
+            raise ValueError("Input must be a string or a list of strings")