Introduce embedder factory

src/haiku/rag/config.py

@@ -11,7 +11,9 @@ class AppConfig(BaseModel):
 
     OLLAMA_BASE_URL: str = "http://localhost:11434"
 
+    EMBEDDING_PROVIDER: str = "ollama"
     EMBEDDING_MODEL: str = "mxbai-embed-large"
+    EMBEDDING_VECTOR_DIM: int = 1024
     CHUNK_SIZE: int = 256
     CHUNK_OVERLAP: int = 32
 

src/haiku/rag/embeddings/__init__.py

@@ -0,0 +1,13 @@
+from haiku.rag.config import Config
+from haiku.rag.embeddings.base import EmbedderBase
+from haiku.rag.embeddings.ollama import Embedder as OllamaEmbedder
+
+
+def get_embedder() -> EmbedderBase:
+    """
+    Factory function to get the appropriate embedder based on the configuration.
+    """
+
+    if Config.EMBEDDING_PROVIDER == "ollama":
+        return OllamaEmbedder(Config.EMBEDDING_MODEL, Config.EMBEDDING_VECTOR_DIM)
+    raise ValueError(f"Unsupported embedding provider: {Config.EMBEDDING_PROVIDER}")

src/haiku/rag/embeddings/base.py

@@ -2,6 +2,10 @@ class EmbedderBase:
     _model: str = ""
     _vector_dim: int = 0
 
+    def __init__(self, model: str, vector_dim: int):
+        self._model = model
+        self._vector_dim = vector_dim
+
     async def embed(self, text: str) -> list[float]:
         raise NotImplementedError(
             "Embedder is an abstract class. Please implement the embed method in a subclass."

src/haiku/rag/store/engine.py

@@ -5,7 +5,7 @@ from typing import Literal
 
 import sqlite_vec
 
-from haiku.rag.embeddings.ollama import Embedder
+from haiku.rag.embeddings import get_embedder
 
 
 class Store:
@@ -43,7 +43,7 @@ class Store:
         """)
 
         # Create vector table for chunk embeddings
-        embedder = Embedder()
+        embedder = get_embedder()
         db.execute(f"""
             CREATE VIRTUAL TABLE IF NOT EXISTS chunk_embeddings USING vec0(
                 chunk_id INTEGER PRIMARY KEY,

src/haiku/rag/store/repositories/chunk.py

@@ -2,7 +2,7 @@ import json
 import re
 
 from haiku.rag.chunker import chunker
-from haiku.rag.embeddings.ollama import Embedder
+from haiku.rag.embeddings import get_embedder
 from haiku.rag.store.models.chunk import Chunk
 from haiku.rag.store.repositories.base import BaseRepository
 
@@ -10,9 +10,9 @@ from haiku.rag.store.repositories.base import BaseRepository
 class ChunkRepository(BaseRepository[Chunk]):
     """Repository for Chunk database operations."""
 
-    def __init__(self, store, embedder: Embedder | None = None):
+    def __init__(self, store):
         super().__init__(store)
-        self.embedder = embedder or Embedder()
+        self.embedder = get_embedder()
 
     async def create(self, entity: Chunk, commit: bool = True) -> Chunk:
         """Create a chunk in the database."""

tests/test_embedder.py

@@ -1,19 +1,19 @@
 import numpy as np
 import pytest
 
-from haiku.rag.embeddings.ollama import Embedder
+from haiku.rag.embeddings import get_embedder
 
 
 @pytest.mark.asyncio
 async def test_embedder():
-    embedder = Embedder()
+    embedder = get_embedder()
     embedding = await embedder.embed("hello world")
     assert len(embedding) == embedder._vector_dim
 
 
 @pytest.mark.asyncio
 async def test_similarity():
-    embedder = Embedder()
+    embedder = get_embedder()
     phrases = [
         "I enjoy eating great food.",
         "Python is my favorite programming language.",