Revert embeddings to use flat config

docker/README.md

@@ -30,8 +30,9 @@ embeddings:
   vector_dim: 768
 
 qa:
-  provider: ollama
+  model:
-  model: qwen3
+    provider: ollama
+    name: qwen3
 ```
 
 See [Configuration docs](https://ggozad.github.io/haiku.rag/configuration/) for all available options.

docs/config-index.md

@@ -35,9 +35,8 @@ A minimal configuration file with defaults:
 environment: production
 
 embeddings:
-  model:
+  provider: ollama
-    provider: ollama
+  model: qwen3-embedding:4b
-    name: qwen3-embedding:4b
   vector_dim: 2560
 
 qa:
@@ -70,9 +69,8 @@ lancedb:
   region: ""
 
 embeddings:
-  model:
+  provider: ollama
-    provider: ollama
+  model: qwen3-embedding:4b
-    name: qwen3-embedding:4b
   vector_dim: 2560
 
 reranking:

docs/providers.md

@@ -72,9 +72,8 @@ If you use Ollama, you can use any pulled model that supports embeddings.
 
 ```yaml
 embeddings:
-  model:
+  provider: ollama
-    provider: ollama
+  model: mxbai-embed-large
-    name: mxbai-embed-large
   vector_dim: 1024
 ```
 
@@ -104,9 +103,8 @@ uv pip install haiku.rag-slim[voyageai]
 
 ```yaml
 embeddings:
-  model:
+  provider: voyageai
-    provider: voyageai
+  model: voyage-3.5
-    name: voyage-3.5
   vector_dim: 1024
 ```
 
@@ -122,9 +120,8 @@ OpenAI embeddings are included in the default installation:
 
 ```yaml
 embeddings:
-  model:
+  provider: openai
-    provider: openai
+  model: text-embedding-3-small  # or text-embedding-3-large
-    name: text-embedding-3-small  # or text-embedding-3-large
   vector_dim: 1536
 ```
 
@@ -140,9 +137,8 @@ For high-performance local inference, you can use vLLM to serve embedding models
 
 ```yaml
 embeddings:
-  model:
+  provider: vllm
-    provider: vllm
+  model: mixedbread-ai/mxbai-embed-large-v1
-    name: mixedbread-ai/mxbai-embed-large-v1
   vector_dim: 512
 
 providers:

docs/tutorial.md

@@ -38,8 +38,9 @@ embeddings:
   vector_dim: 1536
 
 qa:
-  provider: openai
+  model:
-  model: gpt-4o-mini  # or gpt-4o, gpt-4, etc.
+    provider: openai
+    name: gpt-4o-mini  # or gpt-4o, gpt-4, etc.
 ```
 
 Set your OpenAI API key as an environment variable (API keys should not be stored in the YAML file):

evaluations/evaluations/benchmark.py

@@ -43,8 +43,8 @@ def build_experiment_metadata(
     return {
         "dataset": dataset_key,
         "test_cases": test_cases,
-        "embedder_provider": config.embeddings.model.provider,
+        "embedder_provider": config.embeddings.provider,
-        "embedder_model": config.embeddings.model.name,
+        "embedder_model": config.embeddings.model,
         "embedder_dim": config.embeddings.vector_dim,
         "chunk_size": config.processing.chunk_size,
         "context_chunk_radius": config.processing.context_chunk_radius,

haiku_rag_slim/haiku/rag/config/models.py

@@ -44,12 +44,8 @@ class LanceDBConfig(BaseModel):
 
 
 class EmbeddingsConfig(BaseModel):
-    model: ModelConfig = Field(
+    provider: str = "ollama"
-        default_factory=lambda: ModelConfig(
+    model: str = "qwen3-embedding:4b"
-            provider="ollama",
-            name="qwen3-embedding:4b",
-        )
-    )
     vector_dim: int = 2560
 
 

haiku_rag_slim/haiku/rag/embeddings/__init__.py

@@ -14,12 +14,12 @@ def get_embedder(config: AppConfig = Config) -> EmbedderBase:
         An embedder instance configured according to the config.
     """
 
-    if config.embeddings.model.provider == "ollama":
+    if config.embeddings.provider == "ollama":
         return OllamaEmbedder(
-            config.embeddings.model.name, config.embeddings.vector_dim, config
+            config.embeddings.model, config.embeddings.vector_dim, config
         )
 
-    if config.embeddings.model.provider == "voyageai":
+    if config.embeddings.provider == "voyageai":
         try:
             from haiku.rag.embeddings.voyageai import Embedder as VoyageAIEmbedder
         except ImportError:
@@ -29,23 +29,21 @@ def get_embedder(config: AppConfig = Config) -> EmbedderBase:
                 "uv pip install haiku.rag[voyageai]"
             )
         return VoyageAIEmbedder(
-            config.embeddings.model.name, config.embeddings.vector_dim, config
+            config.embeddings.model, config.embeddings.vector_dim, config
         )
 
-    if config.embeddings.model.provider == "openai":
+    if config.embeddings.provider == "openai":
         from haiku.rag.embeddings.openai import Embedder as OpenAIEmbedder
 
         return OpenAIEmbedder(
-            config.embeddings.model.name, config.embeddings.vector_dim, config
+            config.embeddings.model, config.embeddings.vector_dim, config
         )
 
-    if config.embeddings.model.provider == "vllm":
+    if config.embeddings.provider == "vllm":
         from haiku.rag.embeddings.vllm import Embedder as VllmEmbedder
 
         return VllmEmbedder(
-            config.embeddings.model.name, config.embeddings.vector_dim, config
+            config.embeddings.model, config.embeddings.vector_dim, config
         )
 
-    raise ValueError(
+    raise ValueError(f"Unsupported embedding provider: {config.embeddings.provider}")
-        f"Unsupported embedding provider: {config.embeddings.model.provider}"
-    )

haiku_rag_slim/haiku/rag/embeddings/base.py

@@ -4,7 +4,7 @@ from haiku.rag.config import AppConfig, Config
 
 
 class EmbedderBase:
-    _model: str = Config.embeddings.model.name
+    _model: str = Config.embeddings.model
     _vector_dim: int = Config.embeddings.vector_dim
     _config: AppConfig = Config
 

haiku_rag_slim/haiku/rag/utils.py

@@ -365,8 +365,8 @@ def prefetch_models():
 
     # Collect Ollama models from config
     required_models: set[str] = set()
-    if Config.embeddings.model.provider == "ollama":
+    if Config.embeddings.provider == "ollama":
-        required_models.add(Config.embeddings.model.name)
+        required_models.add(Config.embeddings.model)
     if Config.qa.model.provider == "ollama":
         required_models.add(Config.qa.model.name)
     if Config.research.model.provider == "ollama":

tests/test_embedder_config.py

@@ -7,7 +7,6 @@ from haiku.rag.config import (
     ProvidersConfig,
     VLLMConfig,
 )
-from haiku.rag.config.models import ModelConfig
 from haiku.rag.embeddings import get_embedder
 
 
@@ -15,10 +14,8 @@ def test_embedder_uses_config_from_get_embedder():
     """Test that embedders use the config passed to get_embedder."""
     custom_config = AppConfig(
         embeddings=EmbeddingsConfig(
-            model=ModelConfig(
+            provider="ollama",
-                provider="ollama",
+            model="custom-model",
-                name="custom-model",
-            ),
             vector_dim=512,
         ),
         providers=ProvidersConfig(
@@ -36,14 +33,10 @@ def test_embedder_uses_config_from_get_embedder():
 
 def test_vllm_embedder_uses_config():
     """Test that vllm embedder uses the config passed to get_embedder."""
-    from haiku.rag.config.models import ModelConfig
-
     custom_config = AppConfig(
         embeddings=EmbeddingsConfig(
-            model=ModelConfig(
+            provider="vllm",
-                provider="vllm",
+            model="custom-vllm-model",
-                name="custom-vllm-model",
-            ),
             vector_dim=768,
         ),
         providers=ProvidersConfig(
@@ -62,14 +55,11 @@ def test_vllm_embedder_uses_config():
 
 def test_openai_embedder_uses_config():
     """Test that openai embedder uses the config passed to get_embedder."""
-    from haiku.rag.config.models import ModelConfig
 
     custom_config = AppConfig(
         embeddings=EmbeddingsConfig(
-            model=ModelConfig(
+            provider="openai",
-                provider="openai",
+            model="text-embedding-3-large",
-                name="text-embedding-3-large",
-            ),
             vector_dim=3072,
         ),
     )
@@ -88,10 +78,8 @@ def test_voyageai_embedder_uses_config():
     """Test that voyageai embedder uses the config passed to get_embedder."""
     custom_config = AppConfig(
         embeddings=EmbeddingsConfig(
-            model=ModelConfig(
+            provider="voyageai",
-                provider="voyageai",
+            model="voyage-large-2",
-                name="voyage-large-2",
-            ),
             vector_dim=1536,
         ),
     )