Update docs

docker/README.md

@@ -25,8 +25,9 @@ Create a configuration file `haiku.rag.yaml`:
 environment: production
 
 embeddings:
+  model:
     provider: ollama
-  model: nomic-embed-text
+    name: nomic-embed-text
     vector_dim: 768
 
 qa:

docs/configuration/index.md

@@ -35,8 +35,9 @@ A minimal configuration file with defaults:
 environment: production
 
 embeddings:
+  model:
     provider: ollama
-  model: qwen3-embedding:4b
+    name: qwen3-embedding:4b
     vector_dim: 2560
 
 qa:
@@ -69,8 +70,9 @@ lancedb:
   region: ""
 
 embeddings:
+  model:
     provider: ollama
-  model: qwen3-embedding:4b
+    name: qwen3-embedding:4b
     vector_dim: 2560
 
 reranking:
@@ -149,7 +151,7 @@ When using haiku.rag as a Python library, you can pass configuration directly to
 
 ```python
 from haiku.rag.config import AppConfig
-from haiku.rag.config.models import ModelConfig, QAConfig, EmbeddingsConfig
+from haiku.rag.config.models import EmbeddingModelConfig, ModelConfig, QAConfig, EmbeddingsConfig
 from haiku.rag.client import HaikuRAG
 
 # Create custom configuration
@@ -157,16 +159,16 @@ custom_config = AppConfig(
     qa=QAConfig(
         model=ModelConfig(
             provider="openai",
-            model="gpt-4o",
+            name="gpt-4o",
             temperature=0.7
         )
     ),
     embeddings=EmbeddingsConfig(
-        model=ModelConfig(
+        model=EmbeddingModelConfig(
             provider="ollama",
-            model="qwen3-embedding:4b"
-        ),
+            name="qwen3-embedding:4b",
             vector_dim=2560
+        )
     ),
     processing={"chunk_size": 512}
 )

docs/configuration/providers.md

@@ -73,8 +73,9 @@ If you use Ollama, you can use any pulled model that supports embeddings.
 
 ```yaml
 embeddings:
+  model:
     provider: ollama
-  model: mxbai-embed-large
+    name: mxbai-embed-large
     vector_dim: 1024
 ```
 
@@ -104,8 +105,9 @@ uv pip install haiku.rag-slim[voyageai]
 
 ```yaml
 embeddings:
+  model:
     provider: voyageai
-  model: voyage-3.5
+    name: voyage-3.5
     vector_dim: 1024
 ```
 
@@ -121,8 +123,9 @@ OpenAI embeddings are included in the default installation:
 
 ```yaml
 embeddings:
+  model:
     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
 ```
 
@@ -138,8 +141,9 @@ For high-performance local inference, you can use vLLM to serve embedding models
 
 ```yaml
 embeddings:
+  model:
     provider: vllm
-  model: mixedbread-ai/mxbai-embed-large-v1
+    name: mixedbread-ai/mxbai-embed-large-v1
     vector_dim: 512
 
 providers:
@@ -155,8 +159,9 @@ providers:
 
 ```yaml
 embeddings:
+  model:
     provider: lm_studio
-  model: text-embedding-qwen3-embedding-4b
+    name: text-embedding-qwen3-embedding-4b
     vector_dim: 2560
 
 providers:

docs/tutorial.md

@@ -33,8 +33,9 @@ Configure haiku.rag to use OpenAI. Create a `haiku.rag.yaml` file:
 
 ```yaml
 embeddings:
+  model:
     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
 
 qa: