Remove unused conversational output mode from research graph

app/backend/main.py

@@ -16,7 +16,7 @@ from starlette.routing import Route
 from haiku.rag.client import HaikuRAG
 from haiku.rag.config import load_yaml_config
 from haiku.rag.config.models import AppConfig
-from haiku.rag.skills.rag import create_skill
+from haiku.rag.skills.rag import AGENT_PREAMBLE, create_skill
 from haiku.skills import SkillDeps, SkillToolset
 
 load_dotenv(find_dotenv(usecwd=True))
@@ -66,16 +66,6 @@ def get_client() -> HaikuRAG:
 skill = create_skill(db_path=db_path, config=Config)
 toolset = SkillToolset(skills=[skill])
 
-AGENT_PREAMBLE = """You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-CRITICAL RULES:
-1. For greetings or casual chat: respond directly WITHOUT using any tools
-2. NEVER make up information - always use tools to get facts from the knowledge base
-3. For questions: Use the "ask" tool - it handles search and citation automatically
-4. For searches: Use the "search" tool - copy the ENTIRE tool response to your output INCLUDING content snippets
-5. When you use the "ask" tool, summarize the key findings and always include citations in your response
-"""
-
 agent = Agent(
     os.getenv("HAIKU_CHAT_MODEL", "openai:gpt-4o"),
     instructions=AGENT_PREAMBLE + toolset.system_prompt,

docs/agents/index.md

@@ -84,15 +84,6 @@ When prior answers are provided, the planner uses a context-aware prompt that ev
 - **search_one**: Answers a single question using the knowledge base (up to 3 search calls per question). Each answer is added to `ResearchContext.qa_responses` for the next planning iteration.
 - **synthesize**: Generates the final output from all gathered evidence.
 
-**Output modes:**
-
-The graph supports two output modes via `build_research_graph(output_mode=...)`:
-
-| Mode | Output type | Used by |
-|------|-------------|---------|
-| `"report"` | `ResearchReport` (title, executive summary, findings, conclusions, recommendations) | CLI `haiku-rag research`, Python API |
-| `"conversational"` | `ConversationalAnswer` (answer, citations, confidence) | Chat agent's `ask` tool |
-
 **Iterative flow:**
 
 - Each iteration: planner evaluates context → proposes one question → search answers it → loop back
@@ -160,40 +151,6 @@ async with HaikuRAG(path_to_db) as client:
     report = await graph.run(state=state, deps=deps)
 ```
 
-**Conversational mode with prior answers:**
-
-```python
-from haiku.rag.config import Config
-from haiku.rag.agents.research.dependencies import ResearchContext
-from haiku.rag.agents.research.graph import build_research_graph
-from haiku.rag.agents.research.models import SearchAnswer
-from haiku.rag.agents.research.state import ResearchDeps, ResearchState
-
-# Conversational mode returns ConversationalAnswer instead of ResearchReport
-graph = build_research_graph(config=Config, output_mode="conversational")
-
-# Pass session context and prior answers from conversation history
-context = ResearchContext(
-    original_question="How does it handle authentication?",
-    session_context="User is building a Python web app with FastAPI.",
-    qa_responses=[
-        SearchAnswer(
-            query="What authentication methods are supported?",
-            answer="JWT and OAuth2 are supported.",
-            confidence=0.95,
-            cited_chunks=["chunk-1"],
-        )
-    ],
-)
-state = ResearchState.from_config(context=context, config=Config)
-deps = ResearchDeps(client=client)
-
-result = await graph.run(state=state, deps=deps)
-print(result.answer)       # Direct conversational answer
-print(result.confidence)   # 0.0-1.0
-print(result.citations)    # Deduplicated citations from all searches
-```
-
 ### Filtering Documents
 
 Restrict searches to specific documents via the `search_filter` parameter:

docs/architecture.md

@@ -150,7 +150,7 @@ flowchart TB
 - Proposes one question at a time, evaluates the answer, then decides whether to continue
 - Session context resolves ambiguous references
 - Prior answers let the planner skip redundant searches
-- Synthesizes structured report or conversational answer
+- Synthesizes structured report
 
 **RLM Agent** - Complex analytical tasks via code execution:
 

haiku_rag_slim/haiku/rag/agents/research/graph.py

@@ -1,5 +1,4 @@
 import asyncio
-from typing import Literal, overload
 
 from pydantic_ai import Agent, RunContext, format_as_xml
 from pydantic_ai.output import ToolOutput
@@ -7,15 +6,12 @@ from pydantic_graph.beta import Graph, GraphBuilder, StepContext
 
 from haiku.rag.agents.research.dependencies import ResearchContext, ResearchDependencies
 from haiku.rag.agents.research.models import (
-    Citation,
-    ConversationalAnswer,
     IterativePlanResult,
     RawSearchAnswer,
     ResearchReport,
     SearchAnswer,
 )
 from haiku.rag.agents.research.prompts import (
-    CONVERSATIONAL_SYNTHESIS_PROMPT,
     ITERATIVE_PLAN_PROMPT,
     ITERATIVE_PLAN_PROMPT_WITH_CONTEXT,
     SEARCH_PROMPT,
@@ -169,29 +165,13 @@ async def _search_one_step_logic(
         return SearchAnswer(query=sub_q, answer="", confidence=0.0)
 
 
-@overload
-def build_research_graph(
-    config: AppConfig = ...,
-    output_mode: Literal["report"] = ...,
-) -> Graph[ResearchState, ResearchDeps, None, ResearchReport]: ...
-
-
-@overload
-def build_research_graph(
-    config: AppConfig = ...,
-    output_mode: Literal["conversational"] = ...,
-) -> Graph[ResearchState, ResearchDeps, None, ConversationalAnswer]: ...
-
-
 def build_research_graph(
     config: AppConfig = Config,
-    output_mode: Literal["report", "conversational"] = "report",
+) -> Graph[ResearchState, ResearchDeps, None, ResearchReport]:
-) -> Graph[ResearchState, ResearchDeps, None, ResearchReport | ConversationalAnswer]:
     """Build the iterative research graph.
 
     Args:
         config: AppConfig object (uses config.research for provider, model, and graph parameters)
-        output_mode: Output format - "report" for ResearchReport, "conversational" for ConversationalAnswer
 
     Returns:
         Configured research graph with iterative planning
@@ -199,18 +179,14 @@ def build_research_graph(
     model_config = config.research.model
 
     search_prompt = build_prompt(SEARCH_PROMPT, config)
-
+    synthesis_prompt = build_prompt(
-    if output_mode == "report":
+        config.prompts.synthesis or SYNTHESIS_PROMPT, config
-        synthesis_prompt = build_prompt(
+    )
-            config.prompts.synthesis or SYNTHESIS_PROMPT, config
-        )
-    else:
-        synthesis_prompt = build_prompt(CONVERSATIONAL_SYNTHESIS_PROMPT, config)
 
     g = GraphBuilder(
         state_type=ResearchState,
         deps_type=ResearchDeps,
-        output_type=ResearchReport if output_mode == "report" else ConversationalAnswer,
+        output_type=ResearchReport,
     )
 
     @g.step
@@ -236,81 +212,35 @@ def build_research_graph(
                 confidence=0.0,
             )
 
-    if output_mode == "report":
+    @g.step
+    async def synthesize(
+        ctx: StepContext[ResearchState, ResearchDeps, IterativePlanResult],
+    ) -> ResearchReport:
+        """Generate final research report."""
+        state = ctx.state
+        deps = ctx.deps
 
-        @g.step
+        agent: Agent[ResearchDependencies, ResearchReport] = Agent(  # type: ignore[assignment]
-        async def synthesize(
+            model=get_model(model_config, config),
-            ctx: StepContext[ResearchState, ResearchDeps, IterativePlanResult],
+            output_type=ResearchReport,
-        ) -> ResearchReport:
+            instructions=synthesis_prompt,
-            """Generate final research report."""
+            retries=3,
-            state = ctx.state
+            output_retries=3,
-            deps = ctx.deps
+            deps_type=ResearchDependencies,
+        )
 
-            agent: Agent[ResearchDependencies, ResearchReport] = Agent(  # type: ignore[assignment]
+        context_xml = format_context_for_prompt(state.context)
-                model=get_model(model_config, config),
+        prompt = (
-                output_type=ResearchReport,
+            "Generate a comprehensive research report based on all gathered information.\n\n"
-                instructions=synthesis_prompt,
+            f"{context_xml}\n\n"
-                retries=3,
+            "Create a detailed report that synthesizes all findings into a coherent response."
-                output_retries=3,
+        )
-                deps_type=ResearchDependencies,
+        agent_deps = ResearchDependencies(
-            )
+            client=deps.client,
-
+            context=state.context,
-            context_xml = format_context_for_prompt(state.context)
+        )
-            prompt = (
+        result = await agent.run(prompt, deps=agent_deps)
-                "Generate a comprehensive research report based on all gathered information.\n\n"
+        return result.output
-                f"{context_xml}\n\n"
-                "Create a detailed report that synthesizes all findings into a coherent response."
-            )
-            agent_deps = ResearchDependencies(
-                client=deps.client,
-                context=state.context,
-            )
-            result = await agent.run(prompt, deps=agent_deps)
-            return result.output
-
-    else:
-
-        @g.step
-        async def synthesize(
-            ctx: StepContext[ResearchState, ResearchDeps, IterativePlanResult],
-        ) -> ConversationalAnswer:
-            """Generate conversational answer from gathered evidence."""
-            state = ctx.state
-            deps = ctx.deps
-
-            agent: Agent[ResearchDependencies, ConversationalAnswer] = Agent(  # type: ignore[assignment]
-                model=get_model(model_config, config),
-                output_type=ConversationalAnswer,
-                instructions=synthesis_prompt,
-                retries=3,
-                output_retries=3,
-                deps_type=ResearchDependencies,
-            )
-
-            context_xml = format_context_for_prompt(state.context)
-            prompt = (
-                f"Answer the question based on the gathered evidence.\n\n{context_xml}"
-            )
-            agent_deps = ResearchDependencies(
-                client=deps.client,
-                context=state.context,
-            )
-            result = await agent.run(prompt, deps=agent_deps)
-
-            # Collect unique citations from qa_responses (dedupe by chunk_id)
-            seen_chunks: set[str] = set()
-            unique_citations: list[Citation] = []
-            for qa in state.context.qa_responses:
-                for c in qa.citations:
-                    if c.chunk_id not in seen_chunks:
-                        seen_chunks.add(c.chunk_id)
-                        unique_citations.append(c)
-
-            return ConversationalAnswer(
-                answer=result.output.answer,
-                citations=unique_citations,
-                confidence=result.output.confidence,
-            )
 
     # Build graph edges: iterative loop
     #

haiku_rag_slim/haiku/rag/agents/research/models.py

@@ -111,18 +111,6 @@ def resolve_citations(
     return citations
 
 
-class ConversationalAnswer(BaseModel):
-    """Conversational answer for chat context."""
-
-    answer: str = Field(description="Direct answer to the question")
-    citations: list[Citation] = Field(
-        default_factory=list, description="Citations supporting the answer"
-    )
-    confidence: float = Field(
-        default=1.0, description="Confidence score (0-1)", ge=0.0, le=1.0
-    )
-
-
 class ResearchReport(BaseModel):
     """Final research report structure."""
 

haiku_rag_slim/haiku/rag/agents/research/prompts.py

@@ -115,21 +115,3 @@ Style:
 - Be professional, objective, and specific.
 - NEVER use meta-commentary like "This report covers..." or "The findings show...".
   Instead, state the actual information directly."""
-
-CONVERSATIONAL_SYNTHESIS_PROMPT = """Generate a direct, conversational answer
-to the question based on the gathered evidence.
-
-Output:
-- answer: Direct, comprehensive answer with a natural, helpful tone.
-  Write the actual answer, not a description of what you found.
-  Use as many sentences as needed to fully address the question.
-- confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-Guidelines:
-- Base your answer solely on the evidence provided in the context.
-- If a <background> section is provided, use it to frame your answer appropriately.
-- Be thorough - include all relevant information from the evidence.
-- Use formatting (bullet points, numbered lists) when it improves clarity.
-- Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-  Instead, directly state the information.
-- If the evidence is incomplete, acknowledge limitations briefly."""

haiku_rag_slim/haiku/rag/chat/app.py

@@ -8,7 +8,7 @@ from typing import TYPE_CHECKING, Any
 
 from haiku.rag.client import HaikuRAG
 from haiku.rag.config import get_config
-from haiku.rag.skills.rag import RAGState
+from haiku.rag.skills.rag import AGENT_PREAMBLE, RAGState
 from haiku.skills.agent import SkillToolset
 from haiku.skills.models import Skill
 
@@ -55,16 +55,6 @@ except ImportError:  # pragma: no cover
 
 RAG_STATE_NAMESPACE = "rag"
 
-AGENT_PREAMBLE = """You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-CRITICAL RULES:
-1. For greetings or casual chat: respond directly WITHOUT using any tools
-2. NEVER make up information - always use tools to get facts from the knowledge base
-3. For questions: Use the "ask" tool - it handles search and citation automatically
-4. For searches: Use the "search" tool - copy the ENTIRE tool response to your output INCLUDING content snippets
-5. When you use the "ask" tool, summarize the key findings and always include citations in your response
-"""
-
 
 class ChatApp(App):
     """Textual TUI for conversational RAG."""

haiku_rag_slim/haiku/rag/client.py

@@ -9,7 +9,7 @@ from dataclasses import dataclass
 from datetime import datetime
 from enum import Enum
 from pathlib import Path
-from typing import TYPE_CHECKING, Literal, overload
+from typing import TYPE_CHECKING, overload
 from urllib.parse import urlparse
 
 import httpx
@@ -33,7 +33,6 @@ if TYPE_CHECKING:
 
     from haiku.rag.agents.research.models import (
         Citation,
-        ConversationalAnswer,
         ResearchReport,
     )
     from haiku.rag.agents.rlm.models import RLMResult
@@ -1328,50 +1327,28 @@ class HaikuRAG:
         qa_agent = get_qa_agent(self, config=self._config, system_prompt=system_prompt)
         return await qa_agent.answer(question, filter=filter)
 
-    @overload
     async def research(
         self,
         question: str,
         *,
-        output_mode: Literal["report"] = ...,
-        filter: str | None = ...,
-        max_iterations: int | None = ...,
-    ) -> "ResearchReport": ...
-
-    @overload
-    async def research(
-        self,
-        question: str,
-        *,
-        output_mode: Literal["conversational"],
-        filter: str | None = ...,
-        max_iterations: int | None = ...,
-    ) -> "ConversationalAnswer": ...
-
-    async def research(
-        self,
-        question: str,
-        *,
-        output_mode: Literal["report", "conversational"] = "report",
         filter: str | None = None,
         max_iterations: int | None = None,
-    ) -> "ResearchReport | ConversationalAnswer":
+    ) -> "ResearchReport":
         """Run multi-agent research to investigate a question.
 
         Args:
             question: The research question to investigate.
-            output_mode: "report" for ResearchReport, "conversational" for ConversationalAnswer.
             filter: SQL WHERE clause to filter documents.
             max_iterations: Override max iterations (None uses config default).
 
         Returns:
-            ResearchReport or ConversationalAnswer based on output_mode.
+            ResearchReport with structured findings.
         """
         from haiku.rag.agents.research.dependencies import ResearchContext
         from haiku.rag.agents.research.graph import build_research_graph
         from haiku.rag.agents.research.state import ResearchDeps, ResearchState
 
-        graph = build_research_graph(config=self._config, output_mode=output_mode)
+        graph = build_research_graph(config=self._config)
         context = ResearchContext(original_question=question)
         state = ResearchState.from_config(
             context=context, config=self._config, max_iterations=max_iterations

haiku_rag_slim/haiku/rag/skills/rag.py

@@ -13,6 +13,16 @@ from haiku.skills.models import Skill, SkillSource
 from haiku.skills.parser import parse_skill_md
 from haiku.skills.state import SkillRunDeps
 
+AGENT_PREAMBLE = """You are a helpful research assistant powered by haiku.rag, a knowledge base system.
+
+CRITICAL RULES:
+1. For greetings or casual chat: respond directly WITHOUT using any tools
+2. NEVER make up information - always use tools to get facts from the knowledge base
+3. For questions: Use the "ask" tool - it handles search and citation automatically
+4. For searches: Use the "search" tool - copy the ENTIRE tool response to your output INCLUDING content snippets
+5. When you use the "ask" tool, summarize the key findings and always include citations in your response
+"""
+
 
 class ResearchEntry(BaseModel):
     question: str

tests/agents/research/test_research_graph.py

@@ -68,64 +68,15 @@ def test_iterative_plan_result_model():
     assert continue_result.next_question == "What are the specific requirements?"
 
 
-# =============================================================================
+def test_build_research_graph_returns_graph():
-# Conversational Graph Tests
+    """Test build_research_graph returns a valid Graph instance."""
-# =============================================================================
-
-
-def test_build_research_graph_conversational_mode_returns_graph():
-    """Test build_research_graph with output_mode='conversational' returns a valid Graph instance."""
     from pydantic_graph.beta import Graph
 
-    graph = build_research_graph(output_mode="conversational")
+    graph = build_research_graph()
     assert graph is not None
     assert isinstance(graph, Graph)
 
 
-def test_build_research_graph_report_mode_returns_graph():
-    """Test build_research_graph with output_mode='report' returns a valid Graph instance."""
-    from pydantic_graph.beta import Graph
-
-    graph = build_research_graph(output_mode="report")
-    assert graph is not None
-    assert isinstance(graph, Graph)
-
-
-def test_conversational_answer_model():
-    """Test ConversationalAnswer model can be created with all fields."""
-    from haiku.rag.agents.research.models import Citation, ConversationalAnswer
-
-    citation = Citation(
-        index=1,
-        document_id="doc-1",
-        chunk_id="chunk-1",
-        document_uri="test.md",
-        document_title="Test Doc",
-        content="Test content",
-    )
-
-    answer = ConversationalAnswer(
-        answer="The answer is 42.",
-        citations=[citation],
-        confidence=0.95,
-    )
-
-    assert answer.answer == "The answer is 42."
-    assert len(answer.citations) == 1
-    assert answer.confidence == 0.95
-
-
-def test_conversational_answer_default_values():
-    """Test ConversationalAnswer uses correct default values."""
-    from haiku.rag.agents.research.models import ConversationalAnswer
-
-    answer = ConversationalAnswer(answer="Just the answer.")
-
-    assert answer.answer == "Just the answer."
-    assert answer.citations == []
-    assert answer.confidence == 1.0
-
-
 def test_format_context_for_prompt_basic():
     """Test format_context_for_prompt with basic context."""
     from haiku.rag.agents.research.dependencies import ResearchContext

tests/test_client_research.py

@@ -3,7 +3,7 @@ from unittest.mock import AsyncMock, patch
 
 import pytest
 
-from haiku.rag.agents.research.models import ConversationalAnswer, ResearchReport
+from haiku.rag.agents.research.models import ResearchReport
 from haiku.rag.client import HaikuRAG
 
 
@@ -32,9 +32,6 @@ async def test_client_research_report(temp_db_path):
 
         assert result is mock_report
         mock_build.assert_called_once()
-        # Verify output_mode passed correctly
-        _, kwargs = mock_build.call_args
-        assert kwargs["output_mode"] == "report"
 
         # Verify graph.run was called with correct state/deps
         mock_graph.run.assert_called_once()
@@ -43,29 +40,6 @@ async def test_client_research_report(temp_db_path):
         assert isinstance(call_kwargs["deps"].client, HaikuRAG)
 
 
-async def test_client_research_conversational(temp_db_path):
-    """Test client.research() with conversational output mode."""
-    mock_answer = ConversationalAnswer(
-        answer="The answer is 42.",
-        confidence=0.95,
-    )
-
-    with patch("haiku.rag.agents.research.graph.build_research_graph") as mock_build:
-        mock_graph = AsyncMock()
-        mock_graph.run = AsyncMock(return_value=mock_answer)
-        mock_build.return_value = mock_graph
-
-        async with HaikuRAG(temp_db_path, create=True) as client:
-            result = await client.research(
-                question="What is X?",
-                output_mode="conversational",
-            )
-
-        assert result is mock_answer
-        _, kwargs = mock_build.call_args
-        assert kwargs["output_mode"] == "conversational"
-
-
 async def test_client_research_passes_filter(temp_db_path):
     """Test client.research() passes filter to state."""
     mock_report = ResearchReport(