feat!: add variable store to embeddings registry (#2112)

BREAKING CHANGE: embedding function implementations in Node need to now
call `resolveVariables()` in their constructors and should **not**
implement `toJSON()`.

This tries to address the handling of secrets. In Node, they are
currently lost. In Python, they are currently leaked into the table
schema metadata.

This PR introduces an in-memory variable store on the function registry.
It also allows embedding function definitions to label certain config
values as "sensitive", and the preprocessing logic will raise an error
if users try to pass in hard-coded values.

Closes #2110
Closes #521

---------

Co-authored-by: Weston Pace <weston.pace@gmail.com>

python/python/tests/test_table.py

@@ -887,7 +887,7 @@ def test_create_with_embedding_function(mem_db: DBConnection):
         text: str
         vector: Vector(10)
 
-    func = MockTextEmbeddingFunction()
+    func = MockTextEmbeddingFunction.create()
     texts = ["hello world", "goodbye world", "foo bar baz fizz buzz"]
     df = pd.DataFrame({"text": texts, "vector": func.compute_source_embeddings(texts)})
 
@@ -934,7 +934,7 @@ def test_create_f16_table(mem_db: DBConnection):
 
 
 def test_add_with_embedding_function(mem_db: DBConnection):
-    emb = EmbeddingFunctionRegistry.get_instance().get("test")()
+    emb = EmbeddingFunctionRegistry.get_instance().get("test").create()
 
     class MyTable(LanceModel):
         text: str = emb.SourceField()
@@ -1128,7 +1128,7 @@ def test_count_rows(mem_db: DBConnection):
 
 def setup_hybrid_search_table(db: DBConnection, embedding_func):
     # Create a LanceDB table schema with a vector and a text column
-    emb = EmbeddingFunctionRegistry.get_instance().get(embedding_func)()
+    emb = EmbeddingFunctionRegistry.get_instance().get(embedding_func).create()
 
     class MyTable(LanceModel):
         text: str = emb.SourceField()