diff --git a/src/haiku/rag/embeddings/base.py b/src/haiku/rag/embeddings/base.py
index 16e19d9c..369a53a6 100644
--- a/src/haiku/rag/embeddings/base.py
+++ b/src/haiku/rag/embeddings/base.py
@@ -1,6 +1,9 @@
+from haiku.rag.config import Config
+
+
 class EmbedderBase:
-    _model: str = ""
-    _vector_dim: int = 0
+    _model: str = Config.EMBEDDINGS_MODEL
+    _vector_dim: int = Config.EMBEDDINGS_VECTOR_DIM
 
     def __init__(self, model: str, vector_dim: int):
         self._model = model
diff --git a/src/haiku/rag/embeddings/ollama.py b/src/haiku/rag/embeddings/ollama.py
index d7aa97a7..600afe65 100644
--- a/src/haiku/rag/embeddings/ollama.py
+++ b/src/haiku/rag/embeddings/ollama.py
@@ -5,9 +5,6 @@ from haiku.rag.embeddings.base import EmbedderBase
 
 
 class Embedder(EmbedderBase):
-    _model: str = Config.EMBEDDINGS_MODEL
-    _vector_dim: int = 1024
-
     async def embed(self, text: str) -> list[float]:
         client = AsyncClient(host=Config.OLLAMA_BASE_URL)
         res = await client.embeddings(model=self._model, prompt=text)
diff --git a/src/haiku/rag/embeddings/openai.py b/src/haiku/rag/embeddings/openai.py
index 024705cd..818f0e5b 100644
--- a/src/haiku/rag/embeddings/openai.py
+++ b/src/haiku/rag/embeddings/openai.py
@@ -1,13 +1,9 @@
 try:
     from openai import AsyncOpenAI
 
-    from haiku.rag.config import Config
     from haiku.rag.embeddings.base import EmbedderBase
 
     class Embedder(EmbedderBase):
-        _model: str = Config.EMBEDDINGS_MODEL
-        _vector_dim: int = 1536
-
         async def embed(self, text: str) -> list[float]:
             client = AsyncOpenAI()
             response = await client.embeddings.create(
diff --git a/src/haiku/rag/embeddings/voyageai.py b/src/haiku/rag/embeddings/voyageai.py
index d37378c7..ac7aa1b6 100644
--- a/src/haiku/rag/embeddings/voyageai.py
+++ b/src/haiku/rag/embeddings/voyageai.py
@@ -1,13 +1,9 @@
 try:
     from voyageai.client import Client  # type: ignore
 
-    from haiku.rag.config import Config
     from haiku.rag.embeddings.base import EmbedderBase
 
     class Embedder(EmbedderBase):
-        _model: str = Config.EMBEDDINGS_MODEL
-        _vector_dim: int = 1024
-
         async def embed(self, text: str) -> list[float]:
             client = Client()
             res = client.embed([text], model=self._model, output_dtype="float")