use kb default model for agentic kb calls

agents/nucliadb/src/hyperforge_nucliadb_agentic/agent.py

@@ -9,6 +9,7 @@
 from hyperforge.configure import agent
 from hyperforge.context.agent import ContextAgent
 from hyperforge.definition import FunctionDefinition
+from hyperforge.llm_config import LLMConfig
 from hyperforge.manager import Manager, build_reasoning
 from hyperforge.memory import Chunk, Context, QuestionMemory, Source
 from hyperforge.models import JSONObject
@@ -67,16 +68,29 @@ async def choose_sources(
     question: str,
     ident: str,
     step_title: str,
+    model: LLMConfig | None = None,
 ) -> list[Source]:
     if len(sources) == 1:
         return [Source.model_construct(id=sources[0])]
+    kbid = None
+    if model is None and sources:
+        kbids = {get_ndb_driver(manager, source).config.kbid for source in sources}
+        if len(kbids) != 1:
+            raise ValueError(
+                "An explicit routing model is required for sources from different KBs"
+            )
+        kbid = kbids.pop()
+        if not kbid or not kbid.strip():
+            raise ValueError("A KB ID is required to inherit its generative model")
     return await choose_source(
         memory,
         manager,
         sources,
         question,
         ident=ident,
         step_title=step_title,
+        choose_source_model=model,
+        kbid=kbid,
     )
 
 
@@ -775,6 +789,7 @@ async def ask_agent(
             question,
             ident=self.agent_id,
             step_title=self.step_title("Choose sources"),
+            model=self.config.generative_model,
         )
 
         contexts: list[Context] = await asyncio.gather(
@@ -812,6 +827,7 @@ async def _get_question_context(
             question,
             ident=self.agent_id,
             step_title=self.step_title("Choose sources"),
+            model=self.config.generative_model,
         )
 
         missing: list[tuple[str, str] | None] = await asyncio.gather(
@@ -979,6 +995,8 @@ async def prepare_ask_request(
         rag_strategies: list[RagStrategies],
     ) -> AskRequest:
         """Apply agent defaults, search config, public ASK overrides, and runtime query."""
+        if not nucliadb_driver.config.kbid or not nucliadb_driver.config.kbid.strip():
+            raise ValueError("A KB ID is required for KB source generation")
         # Load explicit options from the public ASK request.
         ask_request: AskRequest | None = None
         if ask_request_json:
@@ -1007,14 +1025,15 @@ async def prepare_ask_request(
             )
 
         # Agent defaults fill any remaining fields.
-        fallback_values = {
+        fallback_values: dict[str, object] = {
             "show": [ResourceProperties.BASIC, ResourceProperties.ORIGIN],
             "citations": CitationsType.LLM_FOOTNOTES,
-            "generative_model": self.config.generative_model.model_id,
             "rag_strategies": rag_strategies,
             "generate_answer": self.config.generate_inner_answer,
-            "reasoning": build_reasoning(self.config.generative_model),
         }
+        if self.config.generative_model is not None:
+            fallback_values["generative_model"] = self.config.generative_model.model_id
+            fallback_values["reasoning"] = build_reasoning(self.config.generative_model)
         return ask_request.model_copy(
             update={
                 field: value
@@ -1072,6 +1091,8 @@ async def inner_rag(
             memory.arguments.get("ask_request"),
             rag_strategies,
         )
+        if ask_request.generative_model is None:
+            manager.require_kb_model_scope(nucliadb_driver.config.kbid)
 
         filter_expression = await self.build_filter_expression(
             nucliadb_driver,
@@ -1233,6 +1254,7 @@ async def facets_search(
             question,
             ident=self.agent_id,
             step_title=self.step_title("Choose sources"),
+            model=self.config.generative_model,
         )
         contexts: list[Context] = await asyncio.gather(
             *[
@@ -1287,6 +1309,7 @@ async def inner_facets_search(
                 "additionalProperties": False,
             },
             model=self.config.generative_model,
+            kbid=ndb.config.kbid,
             tracking=memory.get_tracking_info(),
         )
         labelsets_selected = selected_labels.get("label_sets", [])
@@ -1341,6 +1364,7 @@ async def inner_facets_search(
                 prompt,
                 user_id="rao-facets-answer-generation",
                 model=self.config.generative_model,
+                kbid=ndb.config.kbid,
                 tracking=memory.get_tracking_info(),
             )
             # TODO: Error if code is not success
@@ -1392,6 +1416,7 @@ async def facets_count(
             question,
             ident=self.agent_id,
             step_title=self.step_title("Choose sources"),
+            model=self.config.generative_model,
         )
         contexts: list[Context] = await asyncio.gather(
             *[
@@ -1441,6 +1466,7 @@ async def inner_facets(
                 "additionalProperties": False,
             },
             model=self.config.generative_model,
+            kbid=ndb.config.kbid,
             tracking=memory.get_tracking_info(),
         )
         labels_selected = selected_labels.get("label_sets", [])
@@ -1462,6 +1488,7 @@ async def inner_facets(
                 prompt,
                 user_id="rao-facets-answer-generation",
                 model=self.config.generative_model,
+                kbid=ndb.config.kbid,
                 tracking=memory.get_tracking_info(),
             )
             # TODO: Error if code is not success
@@ -1512,6 +1539,7 @@ async def catalog_search(
             question,
             ident=self.agent_id,
             step_title=self.step_title("Choose sources"),
+            model=self.config.generative_model,
         )
         contexts: list[Context] = await asyncio.gather(
             *[
@@ -1557,6 +1585,7 @@ async def inner_catalog_search(
                 "required": ["filters"],
             },
             model=self.config.generative_model,
+            kbid=ndb.config.kbid,
             tracking=memory.get_tracking_info(),
         )
         filters = self.parse_selected_filters(question, source, selected_filters)
@@ -1587,6 +1616,7 @@ async def inner_catalog_search(
             prompt,
             user_id="rao-catalog-search-answer",
             model=self.config.generative_model,
+            kbid=ndb.config.kbid,
             tracking=memory.get_tracking_info(),
         )
         # TODO: Error if code is not success

agents/nucliadb/src/hyperforge_nucliadb_agentic/config.py

@@ -1,7 +1,7 @@
 from typing import List, Literal, Optional, Tuple
 
 from hyperforge.context.agent import ContextAgentConfig
-from hyperforge.llm_config import LLMConfig, LLMField, llm_defaults
+from hyperforge.llm_config import LLMField
 from hyperforge.utils import WidgetType
 from pydantic import Field
 from pydantic.config import ConfigDict
@@ -20,10 +20,10 @@ class NucliaDBAgentConfig(ContextAgentConfig):
             "show_in_node": True,
         },
     )
-    generative_model: LLMField = Field(
-        default=LLMConfig(model_id=llm_defaults.default),
+    generative_model: LLMField | None = Field(
+        default=None,
         title="Generative model",
-        description="Model used to generate answers",
+        description="Model used by KB source tools. If unset, inherit the Knowledge Box's configured model.",
     )
     published_functions: Optional[Tuple[str, ...]] = Field(
         default=(