Fix embedders

examples/customize/embeddings/custom_embeddings.py

@@ -6,9 +6,10 @@
 
 class CustomEmbeddings(Embedder):
     def __init__(self, dimension: int = 10, **kwargs: Any):
+        super().__init__(**kwargs)
         self.dimension = dimension
 
-    def _embed_query(self, input: str) -> list[float]:
+    def embed_query(self, input: str) -> list[float]:
         return [random.random() for _ in range(self.dimension)]
 
 

examples/customize/retrievers/hybrid_retrievers/hybrid_cypher_search.py

@@ -20,7 +20,7 @@
 
 # Create Embedder object
 class CustomEmbedder(Embedder):
-    def _embed_query(self, text: str) -> list[float]:
+    def embed_query(self, text: str) -> list[float]:
         return [random() for _ in range(DIMENSION)]
 
 

examples/customize/retrievers/hybrid_retrievers/hybrid_search.py

@@ -20,7 +20,7 @@
 
 # Create Embedder object
 class CustomEmbedder(Embedder):
-    def _embed_query(self, text: str) -> list[float]:
+    def embed_query(self, text: str) -> list[float]:
         return [random() for _ in range(DIMENSION)]
 
 

src/neo4j_graphrag/embeddings/base.py

@@ -20,7 +20,6 @@
 from neo4j_graphrag.utils.rate_limit import (
     DEFAULT_RATE_LIMIT_HANDLER,
     RateLimitHandler,
-    rate_limit_handler,
 )
 
 
@@ -39,20 +38,8 @@ def __init__(self, rate_limit_handler: Optional[RateLimitHandler] = None):
         else:
             self._rate_limit_handler = DEFAULT_RATE_LIMIT_HANDLER
 
-    @rate_limit_handler
-    def embed_query(self, text: str) -> list[float]:
-        """Embed query text.
-
-        Args:
-            text (str): Text to convert to vector embedding
-
-        Returns:
-            list[float]: A vector embedding.
-        """
-        return self._embed_query(text)
-
     @abstractmethod
-    def _embed_query(self, text: str) -> list[float]:
+    def embed_query(self, text: str) -> list[float]:
         """Embed query text.
 
         Args:

src/neo4j_graphrag/embeddings/cohere.py

@@ -18,7 +18,7 @@
 
 from neo4j_graphrag.embeddings.base import Embedder
 from neo4j_graphrag.exceptions import EmbeddingsGenerationError
-from neo4j_graphrag.utils.rate_limit import RateLimitHandler
+from neo4j_graphrag.utils.rate_limit import RateLimitHandler, rate_limit_handler
 
 try:
     import cohere
@@ -42,7 +42,8 @@ def __init__(
         self.model = model
         self.client = cohere.Client(**kwargs)
 
-    def _embed_query(self, text: str, **kwargs: Any) -> list[float]:
+    @rate_limit_handler
+    def embed_query(self, text: str, **kwargs: Any) -> list[float]:
         try:
             response = self.client.embed(
                 texts=[text],

src/neo4j_graphrag/embeddings/mistral.py

@@ -20,7 +20,7 @@
 
 from neo4j_graphrag.embeddings.base import Embedder
 from neo4j_graphrag.exceptions import EmbeddingsGenerationError
-from neo4j_graphrag.utils.rate_limit import RateLimitHandler
+from neo4j_graphrag.utils.rate_limit import RateLimitHandler, rate_limit_handler
 
 try:
     from mistralai import Mistral
@@ -55,7 +55,8 @@ def __init__(
         self.model = model
         self.mistral_client = Mistral(api_key=api_key, **kwargs)
 
-    def _embed_query(self, text: str, **kwargs: Any) -> list[float]:
+    @rate_limit_handler
+    def embed_query(self, text: str, **kwargs: Any) -> list[float]:
         """
         Generate embeddings for a given query using a Mistral AI text embedding model.
 

src/neo4j_graphrag/embeddings/ollama.py

@@ -19,7 +19,7 @@
 
 from neo4j_graphrag.embeddings.base import Embedder
 from neo4j_graphrag.exceptions import EmbeddingsGenerationError
-from neo4j_graphrag.utils.rate_limit import RateLimitHandler
+from neo4j_graphrag.utils.rate_limit import RateLimitHandler, rate_limit_handler
 
 
 class OllamaEmbeddings(Embedder):
@@ -48,7 +48,8 @@ def __init__(
         self.model = model
         self.client = ollama.Client(**kwargs)
 
-    def _embed_query(self, text: str, **kwargs: Any) -> list[float]:
+    @rate_limit_handler
+    def embed_query(self, text: str, **kwargs: Any) -> list[float]:
         """
         Generate embeddings for a given query using an Ollama text embedding model.
 

src/neo4j_graphrag/embeddings/openai.py

@@ -20,7 +20,7 @@
 
 from neo4j_graphrag.embeddings.base import Embedder
 from neo4j_graphrag.exceptions import EmbeddingsGenerationError
-from neo4j_graphrag.utils.rate_limit import RateLimitHandler
+from neo4j_graphrag.utils.rate_limit import RateLimitHandler, rate_limit_handler
 
 if TYPE_CHECKING:
     import openai
@@ -59,7 +59,8 @@ def _initialize_client(self, **kwargs: Any) -> Any:
         """
         pass
 
-    def _embed_query(self, text: str, **kwargs: Any) -> list[float]:
+    @rate_limit_handler
+    def embed_query(self, text: str, **kwargs: Any) -> list[float]:
         """
         Generate embeddings for a given query using an OpenAI text embedding model.
 

src/neo4j_graphrag/embeddings/sentence_transformers.py

@@ -42,7 +42,7 @@ def __init__(
         self.np = np
         self.model = sentence_transformers.SentenceTransformer(model, *args, **kwargs)
 
-    def _embed_query(self, text: str) -> Any:
+    def embed_query(self, text: str) -> Any:
         try:
             result = self.model.encode([text])
 

src/neo4j_graphrag/embeddings/vertexai.py

@@ -18,7 +18,7 @@
 
 from neo4j_graphrag.embeddings.base import Embedder
 from neo4j_graphrag.exceptions import EmbeddingsGenerationError
-from neo4j_graphrag.utils.rate_limit import RateLimitHandler
+from neo4j_graphrag.utils.rate_limit import RateLimitHandler, rate_limit_handler
 
 try:
     from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
@@ -52,7 +52,8 @@ def __init__(
         super().__init__(rate_limit_handler)
         self.model = TextEmbeddingModel.from_pretrained(model)
 
-    def _embed_query(
+    @rate_limit_handler
+    def embed_query(
         self, text: str, task_type: str = "RETRIEVAL_QUERY", **kwargs: Any
     ) -> list[float]:
         """

tests/e2e/conftest.py

@@ -58,12 +58,12 @@ def embedder() -> Embedder:
 
 
 class RandomEmbedder(Embedder):
-    def _embed_query(self, text: str) -> list[float]:
+    def embed_query(self, text: str) -> list[float]:
         return [random.random() for _ in range(1536)]
 
 
 class BiologyEmbedder(Embedder):
-    def _embed_query(self, text: str) -> list[float]:
+    def embed_query(self, text: str) -> list[float]:
         if text == "biology":
             return EMBEDDING_BIOLOGY
         raise ValueError(f"Unknown embedding text: {text}")

tests/unit/embeddings/test_sentence_transformers.py

@@ -3,7 +3,6 @@
 import numpy as np
 import pytest
 import torch
-from tenacity import RetryError
 from neo4j_graphrag.embeddings.base import Embedder
 from neo4j_graphrag.embeddings.sentence_transformers import (
     SentenceTransformerEmbeddings,
@@ -76,51 +75,3 @@ def test_embed_query_non_retryable_error_handling(mock_import: Mock) -> None:
 
     # Verify the model was called only once (no retries for non-rate-limit errors)
     assert mock_model.encode.call_count == 1
-
-
-@patch("builtins.__import__")
-def test_embed_query_rate_limit_error_retries(mock_import: Mock) -> None:
-    """Test that rate limit errors are retried the expected number of times."""
-    MockSentenceTransformer = get_mock_sentence_transformers()
-    mock_import.return_value = MockSentenceTransformer
-    mock_model = MockSentenceTransformer.SentenceTransformer.return_value
-
-    # Rate limit error that should trigger retries (matches "too many requests" pattern)
-    # Create separate exception instances for each retry attempt
-    mock_model.encode.side_effect = [
-        Exception("too many requests - please wait"),
-        Exception("too many requests - please wait"),
-        Exception("too many requests - please wait"),
-    ]
-
-    instance = SentenceTransformerEmbeddings()
-
-    # After exhausting retries, tenacity raises RetryError (since retries should work)
-    with pytest.raises(RetryError):
-        instance.embed_query("test query")
-
-    # Verify the model was called 3 times (default max_attempts for RetryRateLimitHandler)
-    assert mock_model.encode.call_count == 3
-
-
-@patch("builtins.__import__")
-def test_embed_query_rate_limit_error_eventual_success(mock_import: Mock) -> None:
-    """Test that rate limit errors eventually succeed after retries."""
-    MockSentenceTransformer = get_mock_sentence_transformers()
-    mock_import.return_value = MockSentenceTransformer
-    mock_model = MockSentenceTransformer.SentenceTransformer.return_value
-
-    # First two calls fail with rate limit, third succeeds
-    mock_model.encode.side_effect = [
-        Exception("too many requests - please wait"),
-        Exception("too many requests - please wait"),
-        np.array([[0.1, 0.2, 0.3]]),
-    ]
-
-    instance = SentenceTransformerEmbeddings()
-    result = instance.embed_query("test query")
-
-    # Verify successful result
-    assert result == [0.1, 0.2, 0.3]
-    # Verify the model was called 3 times before succeeding
-    assert mock_model.encode.call_count == 3