Use EmbeddingModelConfig similar to ModelConfig for embeddings

evaluations/evaluations/benchmark.py

@@ -41,9 +41,9 @@ def build_experiment_metadata(
     return {
         "dataset": dataset_key,
         "test_cases": test_cases,
-        "embedder_provider": config.embeddings.provider,
+        "embedder_provider": config.embeddings.model.provider,
-        "embedder_model": config.embeddings.model,
+        "embedder_model": config.embeddings.model.name,
-        "embedder_dim": config.embeddings.vector_dim,
+        "embedder_dim": config.embeddings.model.vector_dim,
         "chunk_size": config.processing.chunk_size,
         "context_chunk_radius": config.processing.context_chunk_radius,
         "rerank_provider": config.reranking.model.provider

evaluations/evaluations/evaluators/judge.py

@@ -38,9 +38,7 @@ class LLMJudge:
 
     def __init__(self, model: str = "gpt-oss"):
         # Create model using get_model with thinking disabled
-        model_config = ModelConfig(
+        model_config = ModelConfig(provider="ollama", name=model, enable_thinking=False)
-            provider="ollama", model=model, enable_thinking=False
-        )
         model_obj = get_model(model_config, Config)
 
         # Create Pydantic AI agent

evaluations/pyproject.toml

@@ -2,7 +2,7 @@
 
 name = "haiku.rag-evals"
 description = "Benchmarking and evaluation scripts for haiku.rag"
-version = "0.19.5"
+version = "0.19.6"
 authors = [{ name = "Yiorgis Gozadinos", email = "ggozadinos@gmail.com" }]
 license = { text = "MIT" }
 requires-python = ">=3.12"

haiku_rag_slim/haiku/rag/app.py

@@ -80,9 +80,10 @@ class HaikuRAGApp:
                 data = json.loads(raw) if isinstance(raw, str) else (raw or {})
                 stored_version = str(data.get("version", stored_version))
                 embeddings = data.get("embeddings", {})
-                embed_provider = embeddings.get("provider")
+                embed_model_obj = embeddings.get("model", {})
-                embed_model = embeddings.get("model")
+                embed_provider = embed_model_obj.get("provider")
-                vector_dim = embeddings.get("vector_dim")
+                embed_model = embed_model_obj.get("name")
+                vector_dim = embed_model_obj.get("vector_dim")
 
         # Get comprehensive table statistics
         from haiku.rag.store.engine import Store

haiku_rag_slim/haiku/rag/config/__init__.py

@@ -9,9 +9,11 @@ from haiku.rag.config.models import (
     AGUIConfig,
     AppConfig,
     ConversionOptions,
+    EmbeddingModelConfig,
     EmbeddingsConfig,
     LanceDBConfig,
     LMStudioConfig,
+    ModelConfig,
     MonitorConfig,
     OllamaConfig,
     ProcessingConfig,
@@ -28,22 +30,24 @@ __all__ = [
     "AGUIConfig",
     "AppConfig",
     "ConversionOptions",
-    "StorageConfig",
+    "EmbeddingModelConfig",
-    "MonitorConfig",
-    "LanceDBConfig",
     "EmbeddingsConfig",
-    "RerankingConfig",
+    "LanceDBConfig",
-    "QAConfig",
-    "ResearchConfig",
-    "ProcessingConfig",
-    "OllamaConfig",
     "LMStudioConfig",
-    "VLLMConfig",
+    "ModelConfig",
+    "MonitorConfig",
+    "OllamaConfig",
+    "ProcessingConfig",
     "ProvidersConfig",
+    "QAConfig",
+    "RerankingConfig",
+    "ResearchConfig",
+    "StorageConfig",
+    "VLLMConfig",
     "find_config_file",
-    "load_yaml_config",
     "generate_default_config",
     "get_config",
+    "load_yaml_config",
     "set_config",
 ]
 

haiku_rag_slim/haiku/rag/config/models.py

@@ -25,6 +25,20 @@ class ModelConfig(BaseModel):
     max_tokens: int | None = None
 
 
+class EmbeddingModelConfig(BaseModel):
+    """Configuration for an embedding model.
+
+    Attributes:
+        provider: Model provider (ollama, openai, voyageai, vllm, lm_studio)
+        name: Model name/identifier
+        vector_dim: Vector dimensions produced by the model
+    """
+
+    provider: str = "ollama"
+    name: str = "qwen3-embedding:4b"
+    vector_dim: int = 2560
+
+
 class StorageConfig(BaseModel):
     data_dir: Path = Field(default_factory=get_default_data_dir)
     vacuum_retention_seconds: int = 86400
@@ -44,9 +58,7 @@ class LanceDBConfig(BaseModel):
 
 
 class EmbeddingsConfig(BaseModel):
-    provider: str = "ollama"
+    model: EmbeddingModelConfig = Field(default_factory=EmbeddingModelConfig)
-    model: str = "qwen3-embedding:4b"
-    vector_dim: int = 2560
 
 
 class RerankingConfig(BaseModel):

haiku_rag_slim/haiku/rag/embeddings/__init__.py

@@ -13,13 +13,12 @@ def get_embedder(config: AppConfig = Config) -> EmbedderBase:
     Returns:
         An embedder instance configured according to the config.
     """
+    embedding_model = config.embeddings.model
 
-    if config.embeddings.provider == "ollama":
+    if embedding_model.provider == "ollama":
-        return OllamaEmbedder(
+        return OllamaEmbedder(embedding_model.name, embedding_model.vector_dim, config)
-            config.embeddings.model, config.embeddings.vector_dim, config
-        )
 
-    if config.embeddings.provider == "voyageai":
+    if embedding_model.provider == "voyageai":
         try:
             from haiku.rag.embeddings.voyageai import Embedder as VoyageAIEmbedder
         except ImportError:
@@ -29,28 +28,24 @@ def get_embedder(config: AppConfig = Config) -> EmbedderBase:
                 "uv pip install haiku.rag[voyageai]"
             )
         return VoyageAIEmbedder(
-            config.embeddings.model, config.embeddings.vector_dim, config
+            embedding_model.name, embedding_model.vector_dim, config
         )
 
-    if config.embeddings.provider == "openai":
+    if embedding_model.provider == "openai":
         from haiku.rag.embeddings.openai import Embedder as OpenAIEmbedder
 
-        return OpenAIEmbedder(
+        return OpenAIEmbedder(embedding_model.name, embedding_model.vector_dim, config)
-            config.embeddings.model, config.embeddings.vector_dim, config
-        )
 
-    if config.embeddings.provider == "vllm":
+    if embedding_model.provider == "vllm":
         from haiku.rag.embeddings.vllm import Embedder as VllmEmbedder
 
-        return VllmEmbedder(
+        return VllmEmbedder(embedding_model.name, embedding_model.vector_dim, config)
-            config.embeddings.model, config.embeddings.vector_dim, config
-        )
 
-    if config.embeddings.provider == "lm_studio":
+    if embedding_model.provider == "lm_studio":
         from haiku.rag.embeddings.lm_studio import Embedder as LMStudioEmbedder
 
         return LMStudioEmbedder(
-            config.embeddings.model, config.embeddings.vector_dim, config
+            embedding_model.name, embedding_model.vector_dim, config
         )
 
-    raise ValueError(f"Unsupported embedding provider: {config.embeddings.provider}")
+    raise ValueError(f"Unsupported embedding provider: {embedding_model.provider}")

haiku_rag_slim/haiku/rag/embeddings/base.py

@@ -4,8 +4,8 @@ from haiku.rag.config import AppConfig, Config
 
 
 class EmbedderBase:
-    _model: str = Config.embeddings.model
+    _model: str = Config.embeddings.model.name
-    _vector_dim: int = Config.embeddings.vector_dim
+    _vector_dim: int = Config.embeddings.model.vector_dim
     _config: AppConfig = Config
 
     def __init__(self, model: str, vector_dim: int, config: AppConfig = Config):

haiku_rag_slim/haiku/rag/store/repositories/settings.py

@@ -118,25 +118,21 @@ class SettingsRepository:
         current_config = self.store._config.model_dump(mode="json")
 
         # Check if embedding provider or model has changed
-        # Support both old flat structure and new nested structure for backward compatibility
+        # Both stored and current use nested structure: embeddings.model.{provider,name,vector_dim}
         stored_embeddings = stored_settings.get("embeddings", {})
         current_embeddings = current_config.get("embeddings", {})
 
-        # Try nested structure first, fall back to flat for old databases
+        stored_model_obj = stored_embeddings.get("model", {})
-        stored_provider = stored_embeddings.get("provider") or stored_settings.get(
+        current_model_obj = current_embeddings.get("model", {})
-            "EMBEDDINGS_PROVIDER"
-        )
-        current_provider = current_embeddings.get("provider")
 
-        stored_model = stored_embeddings.get("model") or stored_settings.get(
+        stored_provider = stored_model_obj.get("provider")
-            "EMBEDDINGS_MODEL"
+        current_provider = current_model_obj.get("provider")
-        )
-        current_model = current_embeddings.get("model")
 
-        stored_vector_dim = stored_embeddings.get("vector_dim") or stored_settings.get(
+        stored_model = stored_model_obj.get("name")
-            "EMBEDDINGS_VECTOR_DIM"
+        current_model = current_model_obj.get("name")
-        )
+
-        current_vector_dim = current_embeddings.get("vector_dim")
+        stored_vector_dim = stored_model_obj.get("vector_dim")
+        current_vector_dim = current_model_obj.get("vector_dim")
 
         # Check for incompatible changes
         incompatible_changes = []

haiku_rag_slim/haiku/rag/store/upgrades/__init__.py

@@ -56,7 +56,11 @@ def run_pending_upgrades(store: Store, from_version: str, to_version: str) -> No
 from .v0_9_3 import upgrade_fts_phrase as upgrade_0_9_3_fts  # noqa: E402
 from .v0_9_3 import upgrade_order as upgrade_0_9_3_order  # noqa: E402
 from .v0_10_1 import upgrade_add_title as upgrade_0_10_1_add_title  # noqa: E402
+from .v0_19_6 import (  # noqa: E402
+    upgrade_embeddings_model_config as upgrade_0_19_6_embeddings,
+)
 
 upgrades.append(upgrade_0_9_3_order)
 upgrades.append(upgrade_0_9_3_fts)
 upgrades.append(upgrade_0_10_1_add_title)
+upgrades.append(upgrade_0_19_6_embeddings)

haiku_rag_slim/haiku/rag/store/upgrades/v0_19_6.py

@@ -0,0 +1,65 @@
+import json
+import logging
+
+from haiku.rag.store.engine import SettingsRecord, Store
+from haiku.rag.store.upgrades import Upgrade
+
+logger = logging.getLogger(__name__)
+
+
+def _apply_embeddings_model_config(store: Store) -> None:
+    """Migrate embeddings config from flat to nested EmbeddingModelConfig structure."""
+    results = list(
+        store.settings_table.search()
+        .where("id = 'settings'")
+        .limit(1)
+        .to_pydantic(SettingsRecord)
+    )
+
+    if not results or not results[0].settings:
+        return
+
+    settings = json.loads(results[0].settings)
+    embeddings = settings.get("embeddings", {})
+
+    # Check if already migrated (model is a dict with nested structure)
+    if isinstance(embeddings.get("model"), dict):
+        return
+
+    # Migrate from flat structure to nested EmbeddingModelConfig
+    old_provider = embeddings.get("provider", "ollama")
+    old_model = embeddings.get("model", "qwen3-embedding:4b")
+    old_vector_dim = embeddings.get("vector_dim", 2560)
+
+    logger.warning(
+        "Migrating embeddings config to new nested structure: "
+        "embeddings.{provider,model,vector_dim} -> embeddings.model.{provider,name,vector_dim}"
+    )
+
+    # Create new nested structure
+    settings["embeddings"] = {
+        "model": {
+            "provider": old_provider,
+            "name": old_model,
+            "vector_dim": old_vector_dim,
+        }
+    }
+
+    store.settings_table.update(
+        where="id = 'settings'",
+        values={"settings": json.dumps(settings)},
+    )
+
+    logger.warning(
+        "Embeddings config migrated: provider=%s, name=%s, vector_dim=%d",
+        old_provider,
+        old_model,
+        old_vector_dim,
+    )
+
+
+upgrade_embeddings_model_config = Upgrade(
+    version="0.19.6",
+    apply=_apply_embeddings_model_config,
+    description="Migrate embeddings config to nested EmbeddingModelConfig structure",
+)

haiku_rag_slim/haiku/rag/utils.py

@@ -392,8 +392,8 @@ async def prefetch_models():
 
     # Collect Ollama models from config
     required_models: set[str] = set()
-    if Config.embeddings.provider == "ollama":
+    if Config.embeddings.model.provider == "ollama":
-        required_models.add(Config.embeddings.model)
+        required_models.add(Config.embeddings.model.name)
     if Config.qa.model.provider == "ollama":
         required_models.add(Config.qa.model.name)
     if Config.research.model.provider == "ollama":

haiku_rag_slim/pyproject.toml

@@ -2,7 +2,7 @@
 
 name = "haiku.rag-slim"
 description = "Opinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling - Minimal dependencies"
-version = "0.19.5"
+version = "0.19.6"
 authors = [{ name = "Yiorgis Gozadinos", email = "ggozadinos@gmail.com" }]
 license = { text = "MIT" }
 readme = { file = "README.md", content-type = "text/markdown" }

pyproject.toml

@@ -2,7 +2,7 @@
 
 name = "haiku.rag"
 description = "Opinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling"
-version = "0.19.5"
+version = "0.19.6"
 authors = [{ name = "Yiorgis Gozadinos", email = "ggozadinos@gmail.com" }]
 license = { text = "MIT" }
 readme = { file = "README.md", content-type = "text/markdown" }
@@ -22,7 +22,7 @@ classifiers = [
 ]
 
 dependencies = [
-    "haiku.rag-slim[docling,voyageai,mxbai,cohere,zeroentropy,inspector]==0.19.5",
+    "haiku.rag-slim[docling,voyageai,mxbai,cohere,zeroentropy,inspector]==0.19.6",
 ]
 
 [project.scripts]

tests/test_client.py

@@ -699,7 +699,7 @@ async def test_client_create_document_with_custom_chunks(temp_db_path):
             Chunk(
                 content="This is the second chunk",
                 metadata={"custom": "metadata2"},
-                embedding=[0.1] * Config.embeddings.vector_dim,
+                embedding=[0.1] * Config.embeddings.model.vector_dim,
                 order=1,
             ),  # With embedding
             Chunk(

tests/test_config.py

@@ -15,16 +15,17 @@ def test_load_yaml_config(tmp_path):
     config_file.write_text("""
 environment: production
 embeddings:
-  provider: ollama
+  model:
-  model: test-model
+    provider: ollama
-  vector_dim: 1024
+    name: test-model
+    vector_dim: 1024
 """)
 
     config = load_yaml_config(config_file)
     assert config["environment"] == "production"
-    assert config["embeddings"]["provider"] == "ollama"
+    assert config["embeddings"]["model"]["provider"] == "ollama"
-    assert config["embeddings"]["model"] == "test-model"
+    assert config["embeddings"]["model"]["name"] == "test-model"
-    assert config["embeddings"]["vector_dim"] == 1024
+    assert config["embeddings"]["model"]["vector_dim"] == 1024
 
 
 def test_find_config_file_cwd(tmp_path, monkeypatch):
@@ -179,7 +180,7 @@ def test_generate_default_config_completeness():
 
     # Verify config validates successfully
     assert config.environment == "production"
-    assert config.embeddings.provider == "ollama"
+    assert config.embeddings.model.provider == "ollama"
     assert config.qa.model.provider == "ollama"
     assert config.research.model.provider == "ollama"
     assert config.reranking.model is None

tests/test_embedder_config.py

@@ -2,6 +2,7 @@ import pytest
 
 from haiku.rag.config import (
     AppConfig,
+    EmbeddingModelConfig,
     EmbeddingsConfig,
     LMStudioConfig,
     OllamaConfig,
@@ -15,9 +16,9 @@ def test_embedder_uses_config_from_get_embedder():
     """Test that embedders use the config passed to get_embedder."""
     custom_config = AppConfig(
         embeddings=EmbeddingsConfig(
-            provider="ollama",
+            model=EmbeddingModelConfig(
-            model="custom-model",
+                provider="ollama", name="custom-model", vector_dim=512
-            vector_dim=512,
+            ),
         ),
         providers=ProvidersConfig(
             ollama=OllamaConfig(base_url="http://custom-ollama:8080"),
@@ -36,9 +37,9 @@ def test_vllm_embedder_uses_config():
     """Test that vllm embedder uses the config passed to get_embedder."""
     custom_config = AppConfig(
         embeddings=EmbeddingsConfig(
-            provider="vllm",
+            model=EmbeddingModelConfig(
-            model="custom-vllm-model",
+                provider="vllm", name="custom-vllm-model", vector_dim=768
-            vector_dim=768,
+            ),
         ),
         providers=ProvidersConfig(
             vllm=VLLMConfig(embeddings_base_url="http://custom-vllm:9001"),
@@ -56,12 +57,11 @@ def test_vllm_embedder_uses_config():
 
 def test_openai_embedder_uses_config():
     """Test that openai embedder uses the config passed to get_embedder."""
-
     custom_config = AppConfig(
         embeddings=EmbeddingsConfig(
-            provider="openai",
+            model=EmbeddingModelConfig(
-            model="text-embedding-3-large",
+                provider="openai", name="text-embedding-3-large", vector_dim=3072
-            vector_dim=3072,
+            ),
         ),
     )
 
@@ -76,9 +76,9 @@ def test_lm_studio_embedder_uses_config():
     """Test that lm_studio embedder uses the config passed to get_embedder."""
     custom_config = AppConfig(
         embeddings=EmbeddingsConfig(
-            provider="lm_studio",
+            model=EmbeddingModelConfig(
-            model="custom-lm-studio-model",
+                provider="lm_studio", name="custom-lm-studio-model", vector_dim=1024
-            vector_dim=1024,
+            ),
         ),
         providers=ProvidersConfig(
             lm_studio=LMStudioConfig(base_url="http://custom-lmstudio:5678"),
@@ -101,9 +101,9 @@ def test_voyageai_embedder_uses_config():
     """Test that voyageai embedder uses the config passed to get_embedder."""
     custom_config = AppConfig(
         embeddings=EmbeddingsConfig(
-            provider="voyageai",
+            model=EmbeddingModelConfig(
-            model="voyage-large-2",
+                provider="voyageai", name="voyage-large-2", vector_dim=1536
-            vector_dim=1536,
+            ),
         ),
     )
 

tests/test_info.py

@@ -125,7 +125,7 @@ async def test_app_info_with_vector_index(temp_db_path, capsys):
         [
             SettingsRecord(
                 id="settings",
-                settings='{"version": "1.0.0", "embeddings": {"provider": "ollama", "model": "test", "vector_dim": 3}}',
+                settings='{"version": "1.0.0", "embeddings": {"model": {"provider": "ollama", "name": "test", "vector_dim": 3}}}',
             )
         ]
     )

uv.lock

@@ -1264,7 +1264,7 @@ wheels = [
 
 [[package]]
 name = "haiku-rag"
-version = "0.19.5"
+version = "0.19.6"
 source = { editable = "." }
 dependencies = [
     { name = "haiku-rag-slim", extra = ["cohere", "docling", "inspector", "mxbai", "voyageai", "zeroentropy"] },
@@ -1312,7 +1312,7 @@ dev = [
 
 [[package]]
 name = "haiku-rag-evals"
-version = "0.19.5"
+version = "0.19.6"
 source = { editable = "evaluations" }
 dependencies = [
     { name = "datasets" },
@@ -1333,7 +1333,7 @@ requires-dist = [
 
 [[package]]
 name = "haiku-rag-slim"
-version = "0.19.5"
+version = "0.19.6"
 source = { editable = "haiku_rag_slim" }
 dependencies = [
     { name = "docling-core" },