feat: add document_query_llm config to agent-map operation (#5108)

## Summary
- Adds a new optional `document_query_llm` field to the
`AgentMapOperation` API request, allowing users to specify which LLM the
`QueryDocumentLLM` (QDLLM) tool uses when scraping web pages
- Threads the field through `AgentQueryParams` →
`setup_agent_components` → `QueryDocumentLLM` constructor
- Fully backward-compatible: when not provided, QDLLM uses its existing
default (`DEFAULT_SMALL`)

## Test plan
- [ ] Verify agent-map works without `document_query_llm` (existing
behavior unchanged)
- [ ] Verify agent-map with `document_query_llm` set to a valid
`LLMEnumPublic` value uses the specified model for QDLLM
- [ ] Verify validation rejects invalid enum values

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Sourced from commit f06e977f5c8ef0f88e9a54cad8fca39747735b98

Author: RafaelPo

src/futuresearch/generated/models/agent_map_operation.py

@@ -49,6 +49,8 @@ class AgentMapOperation:
             affected by other agents. Disables adaptive budget adjustment and straggler management, ensuring agents are not
             hurried or given iteration limits based on other agents' progress. Use this when consistent per-row behavior is
             more important than overall throughput. Default: False.
+        document_query_llm (LLMEnumPublic | None | Unset): LLM to use for the document query tool (QDLLM) that reads and
+            extracts information from web pages. If not provided, defaults to the system default.
     """
 
     input_: AgentMapOperationInputType2 | list[AgentMapOperationInputType1Item] | UUID
@@ -63,6 +65,7 @@ class AgentMapOperation:
     include_reasoning: bool | None | Unset = UNSET
     include_research: bool | None | Unset = UNSET
     enforce_row_independence: bool | Unset = False
+    document_query_llm: LLMEnumPublic | None | Unset = UNSET
     additional_properties: dict[str, Any] = _attrs_field(init=False, factory=dict)
 
     def to_dict(self) -> dict[str, Any]:
@@ -142,6 +145,14 @@ def to_dict(self) -> dict[str, Any]:
 
         enforce_row_independence = self.enforce_row_independence
 
+        document_query_llm: None | str | Unset
+        if isinstance(self.document_query_llm, Unset):
+            document_query_llm = UNSET
+        elif isinstance(self.document_query_llm, LLMEnumPublic):
+            document_query_llm = self.document_query_llm.value
+        else:
+            document_query_llm = self.document_query_llm
+
         field_dict: dict[str, Any] = {}
         field_dict.update(self.additional_properties)
         field_dict.update(
@@ -170,6 +181,8 @@ def to_dict(self) -> dict[str, Any]:
             field_dict["include_research"] = include_research
         if enforce_row_independence is not UNSET:
             field_dict["enforce_row_independence"] = enforce_row_independence
+        if document_query_llm is not UNSET:
+            field_dict["document_query_llm"] = document_query_llm
 
         return field_dict
 
@@ -321,6 +334,23 @@ def _parse_include_research(data: object) -> bool | None | Unset:
 
         enforce_row_independence = d.pop("enforce_row_independence", UNSET)
 
+        def _parse_document_query_llm(data: object) -> LLMEnumPublic | None | Unset:
+            if data is None:
+                return data
+            if isinstance(data, Unset):
+                return data
+            try:
+                if not isinstance(data, str):
+                    raise TypeError()
+                document_query_llm_type_0 = LLMEnumPublic(data)
+
+                return document_query_llm_type_0
+            except (TypeError, ValueError, AttributeError, KeyError):
+                pass
+            return cast(LLMEnumPublic | None | Unset, data)
+
+        document_query_llm = _parse_document_query_llm(d.pop("document_query_llm", UNSET))
+
         agent_map_operation = cls(
             input_=input_,
             task=task,
@@ -334,6 +364,7 @@ def _parse_include_research(data: object) -> bool | None | Unset:
             include_reasoning=include_reasoning,
             include_research=include_research,
             enforce_row_independence=enforce_row_independence,
+            document_query_llm=document_query_llm,
         )
 
         agent_map_operation.additional_properties = d

src/futuresearch/ops.py

@@ -289,6 +289,7 @@ async def agent_map(
     include_reasoning: bool | None = None,
     enforce_row_independence: bool = False,
     response_model: type[BaseModel] = DefaultAgentResponse,
+    document_query_llm: LLM | None = None,
 ) -> TableResult:
     """Execute an AI agent task on each row of the input table.
 
@@ -302,6 +303,7 @@ async def agent_map(
         iteration_budget: Number of agent iterations per row (0-20). Required when effort_level is None.
         include_reasoning: Include reasoning notes. Required when effort_level is None.
         response_model: Pydantic model for the response schema.
+        document_query_llm: LLM to use for the document query tool (QDLLM) when scraping web pages.
 
     Returns:
         TableResult containing the agent results merged with input rows.
@@ -320,6 +322,7 @@ async def agent_map(
                 include_reasoning=include_reasoning,
                 enforce_row_independence=enforce_row_independence,
                 response_model=response_model,
+                document_query_llm=document_query_llm,
             )
             result = await cohort_task.await_result()
             if isinstance(result, TableResult):
@@ -335,6 +338,7 @@ async def agent_map(
         include_reasoning=include_reasoning,
         enforce_row_independence=enforce_row_independence,
         response_model=response_model,
+        document_query_llm=document_query_llm,
     )
     result = await cohort_task.await_result()
     if isinstance(result, TableResult):
@@ -352,6 +356,7 @@ async def agent_map_async(
     include_reasoning: bool | None = None,
     enforce_row_independence: bool = False,
     response_model: type[BaseModel] = DefaultAgentResponse,
+    document_query_llm: LLM | None = None,
 ) -> EveryrowTask[BaseModel]:
     """Submit an agent_map task asynchronously."""
     input_data = _prepare_table_input(input, AgentMapOperationInputType1Item)
@@ -372,6 +377,9 @@ async def agent_map_async(
         include_reasoning=include_reasoning if include_reasoning is not None else UNSET,
         join_with_input=True,
         enforce_row_independence=enforce_row_independence,
+        document_query_llm=LLMEnumPublic(document_query_llm.value)
+        if document_query_llm is not None
+        else UNSET,
     )
 
     response = await agent_map_operations_agent_map_post.asyncio(