Make the capability image-attachment gate a vision bool

haiku_rag_slim/haiku/rag/capabilities/_base.py

@@ -22,7 +22,7 @@ from pydantic_ai.toolsets import AgentToolset
 
 from haiku.rag.capabilities._tools import CodeExecutionEntry, search_corpus
 from haiku.rag.client import HaikuRAG
-from haiku.rag.config.models import AppConfig, ModelConfig
+from haiku.rag.config.models import AppConfig
 from haiku.rag.store.models.chunk import SearchResult
 from haiku.rag.store.models.citation import Citation, resolve_citations
 from haiku.rag.tools.search import build_binary_parts_from_results
@@ -87,7 +87,7 @@ class RAGCapabilityBase[StateT: BaseModel](AbstractCapability[Any]):
     state_type: type[StateT]
     state_namespace: str
     instruction_text: str
-    model: ModelConfig
+    vision: bool
     tool_names: frozenset[str]
     request_limit: int | None = None
     state: StateT | None = field(default=None, repr=False)
@@ -220,7 +220,7 @@ class RAGCapabilityBase[StateT: BaseModel](AbstractCapability[Any]):
             )
         state = cast(Any, self.state)
         state.searches[query] = results
-        if self.model.vision and (parts := build_binary_parts_from_results(results)):
+        if self.vision and (parts := build_binary_parts_from_results(results)):
             return ToolReturn(return_value=formatted, content=parts)
         return formatted
 

haiku_rag_slim/haiku/rag/capabilities/analysis.py

@@ -13,7 +13,7 @@ from haiku.rag.capabilities._base import (
     RAGCapabilityBase,
     resolve_db_path,
 )
-from haiku.rag.config.models import AppConfig, ModelConfig
+from haiku.rag.config.models import AppConfig
 from haiku.rag.sandbox import AnalysisContext, Sandbox
 from haiku.rag.store.models.chunk import SearchResult
 from haiku.rag.store.models.citation import Citation
@@ -129,25 +129,27 @@ def create_capability(
     *,
     defer_loading: bool = True,
     request_limit: int | None = 30,
-    model: ModelConfig | None = None,
+    vision: bool | None = None,
 ) -> AnalysisCapability:
     """Create a native Pydantic AI analysis capability.
 
-    ``model`` sets the capability's image-attachment gate and should be the
+    ``vision`` gates whether picture chunks are attached to search results as
-    model the hosting agent actually runs. Defaults to ``config.analysis.model``
+    images, and should reflect the model the hosting agent actually runs.
-    (falling back to ``config.qa.model``).
+    Defaults to ``config.analysis.model.vision`` (falling back to
+    ``config.qa.model.vision``).
     """
     if config is None:
         from haiku.rag.config import get_config
 
         config = get_config()
+    analysis_model = config.analysis.model or config.qa.model
     return AnalysisCapability(
         db_path=resolve_db_path(db_path, config),
         config=config,
         state_type=AnalysisState,
         state_namespace=STATE_NAMESPACE,
         instruction_text=instructions(),
-        model=model or config.analysis.model or config.qa.model,
+        vision=analysis_model.vision if vision is None else vision,
         tool_names=_TOOL_NAMES,
         request_limit=request_limit,
         id=_CAPABILITY_ID,

haiku_rag_slim/haiku/rag/capabilities/rag.py

@@ -12,7 +12,7 @@ from haiku.rag.capabilities._base import (
     RAGCapabilityBase,
     resolve_db_path,
 )
-from haiku.rag.config.models import AppConfig, ModelConfig
+from haiku.rag.config.models import AppConfig
 from haiku.rag.store.models.chunk import SearchResult
 from haiku.rag.store.models.citation import Citation
 
@@ -71,12 +71,13 @@ def create_capability(
     *,
     defer_loading: bool = True,
     request_limit: int | None = 20,
-    model: ModelConfig | None = None,
+    vision: bool | None = None,
 ) -> RAGCapability:
     """Create a native Pydantic AI RAG capability.
 
-    ``model`` sets the capability's image-attachment gate and should be the
+    ``vision`` gates whether picture chunks are attached to search results as
-    model the hosting agent actually runs. Defaults to ``config.qa.model``.
+    images, and should reflect the model the hosting agent actually runs.
+    Defaults to ``config.qa.model.vision``.
     """
     if config is None:
         from haiku.rag.config import get_config
@@ -88,7 +89,7 @@ def create_capability(
         state_type=RAGState,
         state_namespace=STATE_NAMESPACE,
         instruction_text=instructions(),
-        model=model or config.qa.model,
+        vision=config.qa.model.vision if vision is None else vision,
         tool_names=_TOOL_NAMES,
         request_limit=request_limit,
         id=_CAPABILITY_ID,

haiku_rag_slim/haiku/rag/chat/__init__.py

@@ -55,7 +55,7 @@ def run_chat(
                 db_path=db_path,
                 config=config,
                 defer_loading=defer_loading,
-                model=driving_model,
+                vision=driving_model.vision,
             )
         )
 
@@ -67,7 +67,7 @@ def run_chat(
                 db_path=db_path,
                 config=config,
                 defer_loading=defer_loading,
-                model=driving_model,
+                vision=driving_model.vision,
             )
         )
 

tests/chat/test_chat_app.py

@@ -47,23 +47,25 @@ def test_run_chat_defers_multiple_capabilities(temp_db_path: Path):
 
 
 @pytest.mark.parametrize(
-    ("enabled", "expected_model"),
+    ("enabled", "expected_model", "expected_vision"),
     [
-        (["analysis"], "analysis-model"),
+        (["analysis"], "analysis-model", False),
-        (["rag"], "qa-model"),
+        (["rag"], "qa-model", True),
-        (["rag", "analysis"], "qa-model"),
+        (["rag", "analysis"], "qa-model", True),
     ],
 )
 def test_run_chat_gates_capability_vision_on_driving_model(
-    temp_db_path: Path, enabled, expected_model
+    temp_db_path: Path, enabled, expected_model, expected_vision
 ):
     """Analysis-only chat runs on analysis.model; otherwise on qa.model. Every
-    attached capability's vision gate (its ``model``) tracks that one model."""
+    attached capability's vision gate tracks that one driving model."""
     from haiku.rag.config.models import AppConfig, ModelConfig
 
     config = AppConfig()
-    config.qa.model = ModelConfig(provider="openai", name="qa-model")
+    config.qa.model = ModelConfig(provider="openai", name="qa-model", vision=True)
-    config.analysis.model = ModelConfig(provider="openai", name="analysis-model")
+    config.analysis.model = ModelConfig(
+        provider="openai", name="analysis-model", vision=False
+    )
     captured: dict[str, str] = {}
 
     def fake_get_model(model_config, _config):
@@ -81,7 +83,7 @@ def test_run_chat_gates_capability_vision_on_driving_model(
 
     assert captured["name"] == expected_model
     attached = mock_app.call_args.kwargs["capabilities"]
-    assert {capability.model.name for capability in attached} == {expected_model}
+    assert {capability.vision for capability in attached} == {expected_vision}
 
 
 def _make_mock_client():