Clean up stale references and dead code

CHANGELOG.md

@@ -1,6 +1,26 @@
 # Changelog
 ## [Unreleased]
 
+### Added
+
+- **RAG skill** (`haiku.rag.skills.rag`): haiku.skills integration with search, list_documents, get_document, ask, analyze, and research tools plus managed `RAGState`
+- **`HaikuRAG.research()`**: Client method for multi-agent research
+- **haiku.skills entry point**: `rag = "haiku.rag.skills.rag:create_skill"`
+
+### Changed
+
+- **Chat TUI**: Rebuilt on RAG skill + haiku.skills `SkillToolset`
+- **Web app backend**: Rebuilt on RAG skill + `AGUIAdapter`
+- **Toolsets simplified**: Removed `ToolContext`, `SessionState`, `AgentDeps`, `Toolkit`; kept core `FunctionToolset` factories
+- **Research graph**: Removed `session_context` and conversational output mode
+
+### Removed
+
+- **`agents/chat/`**: Entire chat agent module (replaced by RAG skill)
+- **`--deep` flag**: Removed from `ask` CLI (use `research` command instead)
+- **`--context`/`--context-file`**: Removed from `ask` CLI
+- **`tools/` state machinery**: `ToolContext`, `ToolContextCache`, `SessionState`, `AgentDeps`, `Toolkit`, etc.
+
 ## [0.30.2] - 2026-02-19
 
 ### Fixed

README.md

@@ -59,9 +59,6 @@ haiku-rag search "attention mechanism"
 # Ask questions with citations
 haiku-rag ask "What datasets were used for evaluation?" --cite
 
-# Deep QA — decomposes complex questions into sub-queries
-haiku-rag ask "How does the proposed method compare to the baseline on MMLU?" --deep
-
 # Research mode — iterative planning and search
 haiku-rag research "What are the limitations of the approach?"
 

docs/agents/index.md

@@ -64,8 +64,7 @@ stateDiagram-v2
   synthesize --> [*]
 
   note right of plan_next
-    Receives session_context as background
+    Uses prior_answers from previous iterations.
-    and prior_answers from conversation history.
     Uses a different prompt when prior answers exist.
   end note
 ```
@@ -73,8 +72,7 @@ stateDiagram-v2
 The graph receives a `ResearchContext` containing:
 
 - `original_question` — the user's question
-- `session_context` — summary of conversation history (injected as `<background>` XML)
+- `qa_responses` — prior answers from previous iterations (injected as `<prior_answers>` XML)
-- `qa_responses` — prior answers from semantic matching or previous iterations (injected as `<prior_answers>` XML)
 
 When prior answers are provided, the planner uses a context-aware prompt that evaluates whether existing evidence is sufficient. If it is, the planner marks `is_complete=True` and the graph skips directly to synthesis without any searches.
 
@@ -88,7 +86,6 @@ When prior answers are provided, the planner uses a context-aware prompt that ev
 
 - Each iteration: planner evaluates context → proposes one question → search answers it → loop back
 - Planner can decompose complex questions (e.g., "benefits and drawbacks" → start with "benefits")
-- Session context resolves ambiguous references and informs planning
 - Prior answers let the planner skip redundant searches
 - Loop terminates when planner marks `is_complete=True` or `max_iterations` is reached
 

docs/architecture.md

@@ -24,7 +24,7 @@ flowchart TB
 
     subgraph Agents["Agent Layer"]
         QA[QA Agent]
-        Chat[Chat Agent]
+        Skill[RAG Skill]
         Research[Research Graph]
         RLM[RLM Agent]
     end
@@ -98,7 +98,7 @@ flowchart LR
 
 ### Agent Layer
 
-Four agent types for different use cases:
+Three agent types and a RAG skill for different use cases:
 
 ```mermaid
 flowchart TB
@@ -107,12 +107,12 @@ flowchart TB
         S1 --> A1[Answer]
     end
 
-    subgraph Chat["Chat Agent"]
+    subgraph Skill["RAG Skill"]
         Q2[Question] --> Tools[Tool Selection]
-        Tools --> S2[Search / Ask / Get]
+        Tools --> S2[Search / Ask / Analyze]
         S2 --> A2[Answer]
-        A2 --> History[Session History]
+        A2 --> State[RAG State]
-        History -.-> Q2
+        State -.-> Q2
     end
 
     subgraph Research["Research Graph"]
@@ -138,17 +138,16 @@ flowchart TB
 - Expands context around results
 - Generates answer with optional citations
 
-**Chat Agent** - Multi-turn conversational RAG:
+**RAG Skill** - Multi-turn conversational RAG via [haiku.skills](https://github.com/ggozad/haiku.skills):
 
-- Composed from reusable [toolsets](tools.md) (search, documents, QA, analysis)
+- Bundles search, list_documents, get_document, ask, analyze, and research tools
-- Maintains session history with prior answer recall
+- Managed `RAGState` for session state (citations, QA history, document filters)
-- Background summarization for context continuity
+- Integrates with any pydantic-ai agent via `SkillToolset`
-- Session-level document filtering
+- Powers both the Chat TUI and web application
 
 **Research Graph** - Iterative research workflow:
 
 - 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
 

docs/benchmarks.md

@@ -60,27 +60,9 @@ evaluations run repliqa --config /path/to/haiku.rag.yaml --db /path/to/custom.la
 - `--skip-qa` - Skip QA benchmark
 - `--limit N` - Limit number of test cases
 - `--name NAME` - Override the evaluation name
-- `--deep` - Use deep QA mode (multi-step reasoning with research graph)
 
 If no config file is specified, the script searches standard locations: `./haiku.rag.yaml`, user config directory, then falls back to defaults.
 
-### Deep QA Mode
-
-The `--deep` flag enables multi-step reasoning using the research graph instead of the simple QA agent:
-
-```bash
-evaluations run repliqa --skip-db --deep
-```
-
-In deep mode:
-
-- Questions are decomposed into sub-questions by a planning agent
-- Each sub-question is answered by searching the knowledge base
-- A synthesis agent combines findings into a comprehensive answer
-- The graph runs for up to 2 iterations with no early exit (confidence threshold disabled)
-
-This matches the behavior of `haiku-rag ask --deep` in the CLI. Deep mode typically produces more thorough answers but requires more LLM calls per question.
-
 ## Methodology
 
 ### Retrieval Metrics

docs/cli.md

@@ -143,31 +143,17 @@ Ask questions with citations showing source documents:
 haiku-rag ask "Who is the author of haiku.rag?" --cite
 ```
 
-Use deep QA for complex questions (multi-agent decomposition):
-```bash
-haiku-rag ask "What are the main features and architecture of haiku.rag?" --deep --cite
-```
-
 Filter to specific documents:
 ```bash
 haiku-rag ask "What are the main findings?" --filter "uri LIKE '%paper%'"
 ```
 
-Provide background context for the question:
-```bash
-haiku-rag ask "What are the protocols?" --context "Focus on security best practices"
-haiku-rag ask "Summarize the findings" --context-file background.txt
-```
-
 The QA agent searches your documents for relevant information and provides a comprehensive answer. When available, citations use the document title; otherwise they fall back to the URI.
 
 Flags:
 
 - `--cite`: Include citations showing which documents were used
-- `--deep`: Decompose the question into sub-questions answered in parallel before synthesizing a final answer
 - `--filter` / `-f`: Restrict searches to documents matching the filter (see [Filtering Search Results](python.md#filtering-search-results))
-- `--context`: Background context for the question (passed to the agent as system context)
-- `--context-file`: Path to a file containing background context
 
 ## Chat
 
@@ -242,18 +228,9 @@ Filter to specific documents:
 haiku-rag research "What are the key findings?" --filter "uri LIKE '%paper%'"
 ```
 
-Provide background context for the research:
-
-```bash
-haiku-rag research "What are the safety protocols?" --context "Industrial manufacturing context"
-haiku-rag research "Analyze the methodology" --context-file research-background.txt
-```
-
 Flags:
 
 - `--filter` / `-f`: SQL WHERE clause to filter documents (see [Filtering Search Results](python.md#filtering-search-results))
-- `--context`: Background context for the research
-- `--context-file`: Path to a file containing background context
 
 Research parameters like `max_iterations` and `max_concurrency` are configured in your [configuration file](configuration/index.md) under the `research` section.
 

docs/configuration/qa-research.md

@@ -40,8 +40,6 @@ qa:
 - **max_iterations**: Maximum search iterations (default: 2)
 - **max_concurrency**: Number of concurrent search operations (default: 1)
 
-Deep QA mode (`haiku-rag ask --deep`) uses the research graph with a single iteration for quick, focused answers.
-
 ## Research Configuration
 
 Configure the multi-agent research workflow:

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

@@ -16,10 +16,6 @@ class ResearchContext(BaseModel):
     qa_responses: list[Any] = Field(
         default_factory=list, description="Structured QA pairs used during research"
     )
-    session_context: str | None = Field(
-        default=None,
-        description="Session context from previous Q&A summarization",
-    )
 
     def add_qa_response(self, qa: "SearchAnswer") -> None:
         """Add a structured QA response."""

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

@@ -27,9 +27,6 @@ def format_context_for_prompt(context: ResearchContext) -> str:
     """Format the research context as XML for prompts."""
     context_data: dict[str, object] = {}
 
-    if context.session_context:
-        context_data["background"] = context.session_context
-
     context_data["question"] = context.original_question
 
     if context.qa_responses:

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

@@ -1,7 +1,5 @@
 ITERATIVE_PLAN_PROMPT = """You are the research orchestrator planning the investigation.
 
-If a <background> section is provided, use it to understand the conversation context.
-
 Your task:
 1. Analyze the original question
 2. Propose the first question to investigate
@@ -23,7 +21,6 @@ The question must be standalone and self-contained:
 ITERATIVE_PLAN_PROMPT_WITH_CONTEXT = """You are the research orchestrator evaluating gathered evidence.
 
 You have access to context that may include:
-- <background>: Domain context for the conversation
 - <prior_answers>: Previous Q&A pairs with confidence scores
 
 Your task:

tests/agents/research/test_research_graph.py

@@ -89,22 +89,6 @@ def test_format_context_for_prompt_basic():
     assert "What is X?" in result
 
 
-def test_format_context_for_prompt_with_session_context():
-    """Test format_context_for_prompt includes session_context as background."""
-    from haiku.rag.agents.research.dependencies import ResearchContext
-    from haiku.rag.agents.research.graph import format_context_for_prompt
-
-    context = ResearchContext(
-        original_question="What is Y?",
-        session_context="Previous discussion about topic Z.",
-    )
-    result = format_context_for_prompt(context)
-
-    assert "<background>" in result
-    assert "Previous discussion" in result
-    assert "What is Y?" in result
-
-
 def test_format_context_for_prompt_with_prior_answers():
     """Test format_context_for_prompt includes prior_answers."""
     from haiku.rag.agents.research.dependencies import ResearchContext