From 765ebb698ffee0ee0934b2eea13761b2a3784223 Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Mon, 10 Nov 2025 14:02:54 +0200 Subject: [PATCH 01/17] AG-UI Event-emitter for graphs. Compute state-deltas from from BaseModel pydantic state models --- haiku_rag_slim/haiku/rag/agui/__init__.py | 39 ++++ haiku_rag_slim/haiku/rag/agui/emitter.py | 189 +++++++++++++++++ haiku_rag_slim/haiku/rag/agui/events.py | 248 ++++++++++++++++++++++ haiku_rag_slim/haiku/rag/agui/state.py | 34 +++ haiku_rag_slim/pyproject.toml | 2 +- uv.lock | 20 +- 6 files changed, 529 insertions(+), 3 deletions(-) create mode 100644 haiku_rag_slim/haiku/rag/agui/__init__.py create mode 100644 haiku_rag_slim/haiku/rag/agui/emitter.py create mode 100644 haiku_rag_slim/haiku/rag/agui/events.py create mode 100644 haiku_rag_slim/haiku/rag/agui/state.py diff --git a/haiku_rag_slim/haiku/rag/agui/__init__.py b/haiku_rag_slim/haiku/rag/agui/__init__.py new file mode 100644 index 00000000..fe38fc94 --- /dev/null +++ b/haiku_rag_slim/haiku/rag/agui/__init__.py @@ -0,0 +1,39 @@ +"""Generic AG-UI protocol support for haiku.rag graphs.""" + +from haiku.rag.agui.emitter import AGUIEmitter +from haiku.rag.agui.events import ( + AGUIEvent, + emit_activity, + emit_activity_delta, + emit_run_error, + emit_run_finished, + emit_run_started, + emit_state_delta, + emit_state_snapshot, + emit_step_finished, + emit_step_started, + emit_text_message, + emit_text_message_content, + emit_text_message_end, + emit_text_message_start, +) +from haiku.rag.agui.state import compute_state_delta + +__all__ = [ + "AGUIEmitter", + "AGUIEvent", + "compute_state_delta", + "emit_activity", + "emit_activity_delta", + "emit_run_error", + "emit_run_finished", + "emit_run_started", + "emit_state_delta", + "emit_state_snapshot", + "emit_step_finished", + "emit_step_started", + "emit_text_message", + "emit_text_message_content", + "emit_text_message_end", + "emit_text_message_start", +] diff --git a/haiku_rag_slim/haiku/rag/agui/emitter.py b/haiku_rag_slim/haiku/rag/agui/emitter.py new file mode 100644 index 00000000..4f9e0794 --- /dev/null +++ b/haiku_rag_slim/haiku/rag/agui/emitter.py @@ -0,0 +1,189 @@ +"""Generic AG-UI event emitter for any graph execution.""" + +import asyncio +import hashlib +from collections.abc import AsyncIterator +from uuid import uuid4 + +from pydantic import BaseModel + +from haiku.rag.agui.events import ( + AGUIEvent, + emit_activity, + emit_run_error, + emit_run_finished, + emit_run_started, + emit_state_delta, + emit_state_snapshot, + emit_step_finished, + emit_step_started, + emit_text_message, +) + + +class AGUIEmitter[StateT: BaseModel, ResultT]: + """Generic queue-backed AG-UI event emitter for any graph. + + Manages the lifecycle of AG-UI events including: + - Run lifecycle (start, finish, error) + - Step lifecycle (start, finish) + - Text messages + - State synchronization (snapshots and deltas) + - Activity updates + + Type parameters: + StateT: The Pydantic BaseModel type for graph state + ResultT: The result type returned by the graph + """ + + def __init__(self, thread_id: str | None = None, run_id: str | None = None): + """Initialize the emitter. + + Args: + thread_id: Optional thread ID (generated from input hash if not provided) + run_id: Optional run ID (random UUID if not provided) + """ + self._queue: asyncio.Queue[AGUIEvent | None] = asyncio.Queue() + self._closed = False + self._thread_id = thread_id or str(uuid4()) + self._run_id = run_id or str(uuid4()) + self._last_state: StateT | None = None + self._current_step: str | None = None + + @property + def thread_id(self) -> str: + """Get the thread ID for this emitter.""" + return self._thread_id + + @property + def run_id(self) -> str: + """Get the run ID for this emitter.""" + return self._run_id + + def start_run(self, input_data: str, initial_state: StateT) -> None: + """Emit RunStarted and initial StateSnapshot. + + Args: + input_data: The input that started the run + initial_state: The initial state of the graph + """ + # If thread_id wasn't provided, generate from input hash + if not self._thread_id or self._thread_id == str(uuid4()): + self._thread_id = self._generate_thread_id(input_data) + + self._emit(emit_run_started(self._thread_id, self._run_id, input_data)) + self._emit(emit_state_snapshot(initial_state)) + self._last_state = initial_state + + def start_step(self, step_name: str) -> None: + """Emit StepStarted event. + + Args: + step_name: Name of the step being started + """ + self._current_step = step_name + self._emit(emit_step_started(step_name)) + + def finish_step(self) -> None: + """Emit StepFinished event for the current step.""" + if self._current_step: + self._emit(emit_step_finished(self._current_step)) + self._current_step = None + + def log(self, message: str, role: str = "assistant") -> None: + """Emit a text message event. + + Args: + message: The message content + role: The role of the sender (default: assistant) + """ + self._emit(emit_text_message(message, role)) + + def update_state(self, new_state: StateT) -> None: + """Emit StateDelta for state change, or StateSnapshot if no previous state. + + Args: + new_state: The updated state + """ + if self._last_state: + # Emit delta if we have a previous state + delta_event = emit_state_delta(self._last_state, new_state) + # Only emit if there are actual changes + if delta_event.get("operations"): + self._emit(delta_event) + else: + # Emit snapshot if this is the first state update + self._emit(emit_state_snapshot(new_state)) + + self._last_state = new_state + + def update_activity( + self, activity_type: str, content: str, message_id: str | None = None + ) -> None: + """Emit ActivitySnapshot event. + + Args: + activity_type: Type of activity (e.g., "planning", "searching") + content: Description of the activity + message_id: Optional message ID to associate activity with + """ + self._emit(emit_activity(activity_type, content, message_id)) + + def finish_run(self, result: ResultT) -> None: + """Emit RunFinished event. + + Args: + result: The final result from the graph + """ + self._emit(emit_run_finished(self._thread_id, self._run_id, result)) + + def error(self, error: Exception, code: str | None = None) -> None: + """Emit RunError event. + + Args: + error: The exception that occurred + code: Optional error code + """ + self._emit(emit_run_error(str(error), code)) + + def _emit(self, event: AGUIEvent) -> None: + """Put event in queue. + + Args: + event: The event to emit + """ + if not self._closed: + self._queue.put_nowait(event) + + async def close(self) -> None: + """Close the emitter and stop event iteration.""" + if self._closed: + return + self._closed = True + await self._queue.put(None) + + def __aiter__(self) -> AsyncIterator[AGUIEvent]: + """Enable async iteration over events.""" + return self._iter_events() + + async def _iter_events(self) -> AsyncIterator[AGUIEvent]: + """Iterate over events from the queue.""" + while True: + event = await self._queue.get() + if event is None: + break + yield event + + @staticmethod + def _generate_thread_id(input_data: str) -> str: + """Generate a deterministic thread ID from input data. + + Args: + input_data: The input data (e.g., question, prompt) + + Returns: + A stable thread ID based on input hash + """ + # Use hash of input for deterministic thread ID + hash_obj = hashlib.sha256(input_data.encode("utf-8")) + return hash_obj.hexdigest()[:16] diff --git a/haiku_rag_slim/haiku/rag/agui/events.py b/haiku_rag_slim/haiku/rag/agui/events.py new file mode 100644 index 00000000..fca360b1 --- /dev/null +++ b/haiku_rag_slim/haiku/rag/agui/events.py @@ -0,0 +1,248 @@ +"""Generic AG-UI event creation utilities for any graph.""" + +from typing import Any +from uuid import uuid4 + +from pydantic import BaseModel + +from haiku.rag.agui.state import compute_state_delta + +# Type aliases for AG-UI events (actual types from ag_ui.core will be used at runtime) +AGUIEvent = dict[str, Any] + + +def emit_run_started(thread_id: str, run_id: str, input_data: str) -> dict[str, Any]: + """Create a RunStarted event. + + Args: + thread_id: Unique identifier for the conversation thread + run_id: Unique identifier for this run + input_data: The input that started the run + + Returns: + RunStarted event dict + """ + return { + "type": "RUN_STARTED", + "threadId": thread_id, + "runId": run_id, + "input": input_data, + } + + +def emit_run_finished(thread_id: str, run_id: str, result: Any) -> dict[str, Any]: + """Create a RunFinished event. + + Args: + thread_id: Unique identifier for the conversation thread + run_id: Unique identifier for this run + result: The final result of the run + + Returns: + RunFinished event dict + """ + # Convert result to dict if it's a Pydantic model + if hasattr(result, "model_dump"): + result = result.model_dump() + + return { + "type": "RUN_FINISHED", + "threadId": thread_id, + "runId": run_id, + "result": result, + } + + +def emit_run_error(message: str, code: str | None = None) -> dict[str, Any]: + """Create a RunError event. + + Args: + message: Error message + code: Optional error code + + Returns: + RunError event dict + """ + event: dict[str, Any] = { + "type": "RUN_ERROR", + "message": message, + } + if code: + event["code"] = code + return event + + +def emit_step_started(step_name: str) -> dict[str, Any]: + """Create a StepStarted event. + + Args: + step_name: Name of the step being started + + Returns: + StepStarted event dict + """ + return { + "type": "STEP_STARTED", + "stepName": step_name, + } + + +def emit_step_finished(step_name: str) -> dict[str, Any]: + """Create a StepFinished event. + + Args: + step_name: Name of the step that finished + + Returns: + StepFinished event dict + """ + return { + "type": "STEP_FINISHED", + "stepName": step_name, + } + + +def emit_text_message(content: str, role: str = "assistant") -> dict[str, Any]: + """Create a TextMessageChunk event (convenience wrapper). + + This creates a complete text message in one event. + + Args: + content: The message content + role: The role of the sender (default: assistant) + + Returns: + TextMessageChunk event dict + """ + message_id = str(uuid4()) + return { + "type": "TEXT_MESSAGE_CHUNK", + "messageId": message_id, + "role": role, + "content": content, + } + + +def emit_text_message_start(message_id: str, role: str = "assistant") -> dict[str, Any]: + """Create a TextMessageStart event. + + Args: + message_id: Unique identifier for this message + role: The role of the sender + + Returns: + TextMessageStart event dict + """ + return { + "type": "TEXT_MESSAGE_START", + "messageId": message_id, + "role": role, + } + + +def emit_text_message_content(message_id: str, delta: str) -> dict[str, Any]: + """Create a TextMessageContent event. + + Args: + message_id: Identifier for the message being streamed + delta: Content chunk to append + + Returns: + TextMessageContent event dict + """ + return { + "type": "TEXT_MESSAGE_CONTENT", + "messageId": message_id, + "delta": delta, + } + + +def emit_text_message_end(message_id: str) -> dict[str, Any]: + """Create a TextMessageEnd event. + + Args: + message_id: Identifier for the message being completed + + Returns: + TextMessageEnd event dict + """ + return { + "type": "TEXT_MESSAGE_END", + "messageId": message_id, + } + + +def emit_state_snapshot(state: BaseModel) -> dict[str, Any]: + """Create a StateSnapshot event. + + Args: + state: The complete state to snapshot (any Pydantic BaseModel) + + Returns: + StateSnapshot event dict + """ + return { + "type": "STATE_SNAPSHOT", + "snapshot": state.model_dump(), + } + + +def emit_state_delta(old_state: BaseModel, new_state: BaseModel) -> dict[str, Any]: + """Create a StateDelta event with JSON Patch operations. + + Args: + old_state: Previous state (any Pydantic BaseModel) + new_state: Current state (same type as old_state) + + Returns: + StateDelta event dict + """ + operations = compute_state_delta(old_state, new_state) + return { + "type": "STATE_DELTA", + "operations": operations, + } + + +def emit_activity( + activity_type: str, + content: str, + message_id: str | None = None, +) -> dict[str, Any]: + """Create an ActivitySnapshot event. + + Args: + activity_type: Type of activity (e.g., "planning", "searching") + content: Description of the activity + message_id: Optional message ID to associate activity with + + Returns: + ActivitySnapshot event dict + """ + event: dict[str, Any] = { + "type": "ACTIVITY_SNAPSHOT", + "activityType": activity_type, + "content": content, + } + if message_id: + event["messageId"] = message_id + return event + + +def emit_activity_delta( + message_id: str, operations: list[dict[str, Any]] +) -> dict[str, Any]: + """Create an ActivityDelta event with JSON Patch operations. + + Args: + message_id: Message ID of the activity being updated + operations: JSON Patch operations to apply + + Returns: + ActivityDelta event dict + """ + return { + "type": "ACTIVITY_DELTA", + "messageId": message_id, + "operations": operations, + } diff --git a/haiku_rag_slim/haiku/rag/agui/state.py b/haiku_rag_slim/haiku/rag/agui/state.py new file mode 100644 index 00000000..42ee34c9 --- /dev/null +++ b/haiku_rag_slim/haiku/rag/agui/state.py @@ -0,0 +1,34 @@ +"""Generic AG-UI state utilities for any Pydantic BaseModel.""" + +from typing import Any + +from pydantic import BaseModel + + +def compute_state_delta( + old_state: BaseModel, new_state: BaseModel +) -> list[dict[str, Any]]: + """Compute JSON Patch (RFC 6902) operations from old state to new state. + + Args: + old_state: Previous state (any Pydantic BaseModel) + new_state: Current state (same type as old_state) + + Returns: + List of JSON Patch operations + """ + operations: list[dict[str, Any]] = [] + + # Convert states to dicts for comparison + old_dict = old_state.model_dump() + new_dict = new_state.model_dump() + + # Compare each field and generate patches + for key, new_value in new_dict.items(): + old_value = old_dict.get(key) + + if old_value != new_value: + # Simple replace operation + operations.append({"op": "replace", "path": f"/{key}", "value": new_value}) + + return operations diff --git a/haiku_rag_slim/pyproject.toml b/haiku_rag_slim/pyproject.toml index d17cdf34..37baa321 100644 --- a/haiku_rag_slim/pyproject.toml +++ b/haiku_rag_slim/pyproject.toml @@ -27,7 +27,7 @@ dependencies = [ "lancedb==0.25.2", "pathspec>=0.12.1", "pydantic>=2.12.3", - "pydantic-ai-slim[openai,fastmcp,logfire]>=1.11.1", + "pydantic-ai-slim[openai,fastmcp,logfire,ag-ui]>=1.11.1", "python-dotenv>=1.2.1", "pyyaml>=6.0.3", "rich>=14.2.0", diff --git a/uv.lock b/uv.lock index 4e692b4a..0d4f35c6 100644 --- a/uv.lock +++ b/uv.lock @@ -38,6 +38,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/5f/a0/d9ef19f780f319c21ee90ecfef4431cbeeca95bec7f14071785c17b6029b/accelerate-1.10.1-py3-none-any.whl", hash = "sha256:3621cff60b9a27ce798857ece05e2b9f56fcc71631cfb31ccf71f0359c311f11", size = 374909, upload-time = "2025-08-25T13:57:04.55Z" }, ] +[[package]] +name = "ag-ui-protocol" +version = "0.1.10" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pydantic" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/67/bb/5a5ec893eea5805fb9a3db76a9888c3429710dfb6f24bbb37568f2cf7320/ag_ui_protocol-0.1.10.tar.gz", hash = "sha256:3213991c6b2eb24bb1a8c362ee270c16705a07a4c5962267a083d0959ed894f4", size = 6945, upload-time = "2025-11-06T15:17:17.068Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/8f/78/eb55fabaab41abc53f52c0918a9a8c0f747807e5306273f51120fd695957/ag_ui_protocol-0.1.10-py3-none-any.whl", hash = "sha256:c81e6981f30aabdf97a7ee312bfd4df0cd38e718d9fc10019c7d438128b93ab5", size = 7889, upload-time = "2025-11-06T15:17:15.325Z" }, +] + [[package]] name = "aiohappyeyeballs" version = "2.6.1" @@ -1188,7 +1200,7 @@ dependencies = [ { name = "lancedb" }, { name = "pathspec" }, { name = "pydantic" }, - { name = "pydantic-ai-slim", extra = ["fastmcp", "logfire", "openai"] }, + { name = "pydantic-ai-slim", extra = ["ag-ui", "fastmcp", "logfire", "openai"] }, { name = "python-dotenv" }, { name = "pyyaml" }, { name = "rich" }, @@ -1247,7 +1259,7 @@ requires-dist = [ { name = "pydantic-ai-slim", extras = ["google"], marker = "extra == 'google'" }, { name = "pydantic-ai-slim", extras = ["groq"], marker = "extra == 'groq'" }, { name = "pydantic-ai-slim", extras = ["mistral"], marker = "extra == 'mistral'" }, - { name = "pydantic-ai-slim", extras = ["openai", "fastmcp", "logfire"], specifier = ">=1.11.1" }, + { name = "pydantic-ai-slim", extras = ["openai", "fastmcp", "logfire", "ag-ui"], specifier = ">=1.11.1" }, { name = "pydantic-ai-slim", extras = ["vertexai"], marker = "extra == 'vertexai'" }, { name = "python-dotenv", specifier = ">=1.2.1" }, { name = "pyyaml", specifier = ">=6.0.3" }, @@ -3019,6 +3031,10 @@ wheels = [ ] [package.optional-dependencies] +ag-ui = [ + { name = "ag-ui-protocol" }, + { name = "starlette" }, +] anthropic = [ { name = "anthropic" }, ] From da77e0f2f9fb6bdb25e56e414bdf4a66bc7b1b08 Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Mon, 10 Nov 2025 14:03:35 +0200 Subject: [PATCH 02/17] Convert ResearchState from dataclass to Pydantic model --- haiku_rag_slim/haiku/rag/research/state.py | 35 ++++++++++++++++------ 1 file changed, 26 insertions(+), 9 deletions(-) diff --git a/haiku_rag_slim/haiku/rag/research/state.py b/haiku_rag_slim/haiku/rag/research/state.py index bfc48cbb..d9396055 100644 --- a/haiku_rag_slim/haiku/rag/research/state.py +++ b/haiku_rag_slim/haiku/rag/research/state.py @@ -2,6 +2,7 @@ import asyncio from dataclasses import dataclass from typing import TYPE_CHECKING +from pydantic import BaseModel, Field from rich.console import Console from haiku.rag.client import HaikuRAG @@ -27,15 +28,31 @@ class ResearchDeps: self.stream.log(message, state) -@dataclass -class ResearchState: - context: ResearchContext - iterations: int = 0 - max_iterations: int = 3 - confidence_threshold: float = 0.8 - max_concurrency: int = 1 - last_eval: EvaluationResult | None = None - last_analysis: InsightAnalysis | None = None +class ResearchState(BaseModel): + """Research graph state model. + + Fully JSON-serializable Pydantic model suitable for AG-UI state synchronization. + """ + + model_config = {"arbitrary_types_allowed": True} + + context: ResearchContext = Field( + description="Shared research context with questions, insights, and gaps" + ) + iterations: int = Field(default=0, description="Current iteration number") + max_iterations: int = Field(default=3, description="Maximum allowed iterations") + confidence_threshold: float = Field( + default=0.8, description="Confidence threshold for completion", ge=0.0, le=1.0 + ) + max_concurrency: int = Field( + default=1, description="Maximum concurrent search operations", ge=1 + ) + last_eval: EvaluationResult | None = Field( + default=None, description="Last evaluation result" + ) + last_analysis: InsightAnalysis | None = Field( + default=None, description="Last insight analysis" + ) @classmethod def from_config( From 663a7fac9aa48123bd17ab9802390973bc909e50 Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Mon, 10 Nov 2025 15:08:33 +0200 Subject: [PATCH 03/17] Replace console/stream with AG-UI event protocol --- haiku_rag_slim/haiku/rag/agui/__init__.py | 4 + haiku_rag_slim/haiku/rag/agui/cli_renderer.py | 152 +++++++ haiku_rag_slim/haiku/rag/agui/emitter.py | 32 +- haiku_rag_slim/haiku/rag/agui/events.py | 38 +- haiku_rag_slim/haiku/rag/agui/stream.py | 80 ++++ haiku_rag_slim/haiku/rag/app.py | 40 +- haiku_rag_slim/haiku/rag/cli.py | 7 +- .../haiku/rag/research/dependencies.py | 6 - haiku_rag_slim/haiku/rag/research/graph.py | 408 +++++++++--------- haiku_rag_slim/haiku/rag/research/state.py | 24 +- haiku_rag_slim/haiku/rag/research/stream.py | 162 ------- 11 files changed, 502 insertions(+), 451 deletions(-) create mode 100644 haiku_rag_slim/haiku/rag/agui/cli_renderer.py create mode 100644 haiku_rag_slim/haiku/rag/agui/stream.py delete mode 100644 haiku_rag_slim/haiku/rag/research/stream.py diff --git a/haiku_rag_slim/haiku/rag/agui/__init__.py b/haiku_rag_slim/haiku/rag/agui/__init__.py index fe38fc94..470d1b74 100644 --- a/haiku_rag_slim/haiku/rag/agui/__init__.py +++ b/haiku_rag_slim/haiku/rag/agui/__init__.py @@ -1,5 +1,6 @@ """Generic AG-UI protocol support for haiku.rag graphs.""" +from haiku.rag.agui.cli_renderer import AGUIConsoleRenderer from haiku.rag.agui.emitter import AGUIEmitter from haiku.rag.agui.events import ( AGUIEvent, @@ -18,8 +19,10 @@ from haiku.rag.agui.events import ( emit_text_message_start, ) from haiku.rag.agui.state import compute_state_delta +from haiku.rag.agui.stream import stream_graph __all__ = [ + "AGUIConsoleRenderer", "AGUIEmitter", "AGUIEvent", "compute_state_delta", @@ -36,4 +39,5 @@ __all__ = [ "emit_text_message_content", "emit_text_message_end", "emit_text_message_start", + "stream_graph", ] diff --git a/haiku_rag_slim/haiku/rag/agui/cli_renderer.py b/haiku_rag_slim/haiku/rag/agui/cli_renderer.py new file mode 100644 index 00000000..0d146b61 --- /dev/null +++ b/haiku_rag_slim/haiku/rag/agui/cli_renderer.py @@ -0,0 +1,152 @@ +"""Generic CLI renderer for AG-UI events with Rich console output.""" + +from collections.abc import AsyncIterator +from typing import Any + +from pydantic import BaseModel +from rich.console import Console + +from haiku.rag.agui.events import AGUIEvent + + +class AGUIConsoleRenderer: + """Renders AG-UI events to Rich console with formatted output. + + Generic renderer that processes AG-UI protocol events and renders them + with Rich formatting. Works with any graph that emits AG-UI events. + """ + + def __init__(self, console: Console | None = None): + """Initialize the renderer. + + Args: + console: Optional Rich console instance (creates new one if not provided) + """ + self.console = console or Console() + self._state: BaseModel | None = None + + async def render(self, events: AsyncIterator[AGUIEvent]) -> Any | None: + """Process events and render to console, return final result. + + Args: + events: Async iterator of AG-UI events + + Returns: + The final result from RunFinished event, or None + """ + result = None + + async for event in events: + event_type = event.get("type") + + if event_type == "RUN_STARTED": + self._render_run_started(event) + elif event_type == "RUN_FINISHED": + result = event.get("result") + self._render_run_finished() + elif event_type == "RUN_ERROR": + self._render_error(event) + elif event_type == "STEP_STARTED": + self._render_step_started(event) + elif event_type == "STEP_FINISHED": + self._render_step_finished(event) + elif event_type == "TEXT_MESSAGE_CHUNK": + self._render_text_message(event) + elif event_type == "TEXT_MESSAGE_START": + pass # Start of streaming message, no output needed + elif event_type == "TEXT_MESSAGE_CONTENT": + self._render_text_content(event) + elif event_type == "TEXT_MESSAGE_END": + pass # End of streaming message, no output needed + elif event_type == "STATE_SNAPSHOT": + self._state = event.get("snapshot") + elif event_type == "STATE_DELTA": + self._apply_state_delta(event) + elif event_type == "ACTIVITY_SNAPSHOT": + self._render_activity(event) + elif event_type == "ACTIVITY_DELTA": + pass # Activity deltas don't need separate rendering + + return result + + def _render_run_started(self, event: AGUIEvent) -> None: + """Render run start event. + + Args: + event: RunStarted event + """ + # Currently silent - could render run metadata later + + def _render_run_finished(self) -> None: + """Render run completion.""" + # Currently silent - the result is rendered separately + + def _render_error(self, event: AGUIEvent) -> None: + """Render error event. + + Args: + event: RunError event + """ + message = event.get("message", "Unknown error") + self.console.print(f"[bold red]❌ Error:[/bold red] {message}") + + def _render_step_started(self, event: AGUIEvent) -> None: + """Render step start event. + + Args: + event: StepStarted event + """ + step_name = event.get("stepName", "") + if step_name: + # Format step name for display + display_name = step_name.replace("_", " ").title() + self.console.print(f"\n[bold cyan]{display_name}[/bold cyan]") + + def _render_step_finished(self, event: AGUIEvent) -> None: + """Render step finish event. + + Args: + event: StepFinished event + """ + # Step completion is implicit from the next step or activity + + def _render_text_message(self, event: AGUIEvent) -> None: + """Render complete text message. + + Args: + event: TextMessageChunk event + """ + delta = event.get("delta", "") + # The delta contains the text content to display + self.console.print(delta) + + def _render_text_content(self, event: AGUIEvent) -> None: + """Render streaming text content delta. + + Args: + event: TextMessageContent event + """ + delta = event.get("delta", "") + # Print delta without newline for streaming effect + self.console.print(delta, end="") + + def _render_activity(self, event: AGUIEvent) -> None: + """Render activity update. + + Args: + event: ActivitySnapshot event + """ + content = event.get("content", "") + + # Render activity content with emphasis + if content: + self.console.print(f"[dim]{content}[/dim]") + + def _apply_state_delta(self, event: AGUIEvent) -> None: + """Apply state delta to current state. + + Args: + event: StateDelta event + """ + # Currently not applying deltas - could implement state patching later + # For now, the text messages contain all the information we need to display diff --git a/haiku_rag_slim/haiku/rag/agui/emitter.py b/haiku_rag_slim/haiku/rag/agui/emitter.py index 4f9e0794..b634f771 100644 --- a/haiku_rag_slim/haiku/rag/agui/emitter.py +++ b/haiku_rag_slim/haiku/rag/agui/emitter.py @@ -13,7 +13,6 @@ from haiku.rag.agui.events import ( emit_run_error, emit_run_finished, emit_run_started, - emit_state_delta, emit_state_snapshot, emit_step_finished, emit_step_started, @@ -60,18 +59,19 @@ class AGUIEmitter[StateT: BaseModel, ResultT]: """Get the run ID for this emitter.""" return self._run_id - def start_run(self, input_data: str, initial_state: StateT) -> None: + def start_run(self, initial_state: StateT) -> None: """Emit RunStarted and initial StateSnapshot. Args: - input_data: The input that started the run initial_state: The initial state of the graph """ - # If thread_id wasn't provided, generate from input hash + # If thread_id wasn't provided, generate from state hash if not self._thread_id or self._thread_id == str(uuid4()): - self._thread_id = self._generate_thread_id(input_data) + state_json = initial_state.model_dump_json() + self._thread_id = self._generate_thread_id(state_json) - self._emit(emit_run_started(self._thread_id, self._run_id, input_data)) + # RunStarted (state snapshot follows immediately with full state) + self._emit(emit_run_started(self._thread_id, self._run_id)) self._emit(emit_state_snapshot(initial_state)) self._last_state = initial_state @@ -100,21 +100,13 @@ class AGUIEmitter[StateT: BaseModel, ResultT]: self._emit(emit_text_message(message, role)) def update_state(self, new_state: StateT) -> None: - """Emit StateDelta for state change, or StateSnapshot if no previous state. + """Emit StateSnapshot for state change. Args: new_state: The updated state """ - if self._last_state: - # Emit delta if we have a previous state - delta_event = emit_state_delta(self._last_state, new_state) - # Only emit if there are actual changes - if delta_event.get("operations"): - self._emit(delta_event) - else: - # Emit snapshot if this is the first state update - self._emit(emit_state_snapshot(new_state)) - + # Always emit full snapshot (not delta) for complete state visibility + self._emit(emit_state_snapshot(new_state)) self._last_state = new_state def update_activity( @@ -125,9 +117,11 @@ class AGUIEmitter[StateT: BaseModel, ResultT]: Args: activity_type: Type of activity (e.g., "planning", "searching") content: Description of the activity - message_id: Optional message ID to associate activity with + message_id: Optional message ID to associate activity with (auto-generated if None) """ - self._emit(emit_activity(activity_type, content, message_id)) + if message_id is None: + message_id = str(uuid4()) + self._emit(emit_activity(message_id, activity_type, content)) def finish_run(self, result: ResultT) -> None: """Emit RunFinished event. diff --git a/haiku_rag_slim/haiku/rag/agui/events.py b/haiku_rag_slim/haiku/rag/agui/events.py index fca360b1..d439d005 100644 --- a/haiku_rag_slim/haiku/rag/agui/events.py +++ b/haiku_rag_slim/haiku/rag/agui/events.py @@ -11,23 +11,27 @@ from haiku.rag.agui.state import compute_state_delta AGUIEvent = dict[str, Any] -def emit_run_started(thread_id: str, run_id: str, input_data: str) -> dict[str, Any]: +def emit_run_started( + thread_id: str, run_id: str, input_data: str | None = None +) -> dict[str, Any]: """Create a RunStarted event. Args: thread_id: Unique identifier for the conversation thread run_id: Unique identifier for this run - input_data: The input that started the run + input_data: Optional input that started the run Returns: RunStarted event dict """ - return { + event: dict[str, Any] = { "type": "RUN_STARTED", "threadId": thread_id, "runId": run_id, - "input": input_data, } + if input_data: + event["input"] = input_data + return event def emit_run_finished(thread_id: str, run_id: str, result: Any) -> dict[str, Any]: @@ -119,7 +123,7 @@ def emit_text_message(content: str, role: str = "assistant") -> dict[str, Any]: "type": "TEXT_MESSAGE_CHUNK", "messageId": message_id, "role": role, - "content": content, + "delta": content, } @@ -197,46 +201,47 @@ def emit_state_delta(old_state: BaseModel, new_state: BaseModel) -> dict[str, An Returns: StateDelta event dict """ - operations = compute_state_delta(old_state, new_state) + delta = compute_state_delta(old_state, new_state) return { "type": "STATE_DELTA", - "operations": operations, + "delta": delta, } def emit_activity( + message_id: str, activity_type: str, content: str, - message_id: str | None = None, ) -> dict[str, Any]: """Create an ActivitySnapshot event. Args: + message_id: Message ID to associate activity with (required) activity_type: Type of activity (e.g., "planning", "searching") content: Description of the activity - message_id: Optional message ID to associate activity with Returns: ActivitySnapshot event dict """ - event: dict[str, Any] = { + return { "type": "ACTIVITY_SNAPSHOT", + "messageId": message_id, "activityType": activity_type, "content": content, } - if message_id: - event["messageId"] = message_id - return event def emit_activity_delta( - message_id: str, operations: list[dict[str, Any]] + message_id: str, + activity_type: str, + patch: list[dict[str, Any]], ) -> dict[str, Any]: """Create an ActivityDelta event with JSON Patch operations. Args: message_id: Message ID of the activity being updated - operations: JSON Patch operations to apply + activity_type: Type of activity being updated + patch: JSON Patch operations to apply Returns: ActivityDelta event dict @@ -244,5 +249,6 @@ def emit_activity_delta( return { "type": "ACTIVITY_DELTA", "messageId": message_id, - "operations": operations, + "activityType": activity_type, + "patch": patch, } diff --git a/haiku_rag_slim/haiku/rag/agui/stream.py b/haiku_rag_slim/haiku/rag/agui/stream.py new file mode 100644 index 00000000..e91b9b64 --- /dev/null +++ b/haiku_rag_slim/haiku/rag/agui/stream.py @@ -0,0 +1,80 @@ +"""Generic graph streaming with AG-UI events.""" + +import asyncio +from collections.abc import AsyncIterator +from contextlib import suppress +from typing import Any, Protocol + +from pydantic import BaseModel + +from haiku.rag.agui.emitter import AGUIEmitter +from haiku.rag.agui.events import AGUIEvent + + +class GraphDeps(Protocol): + """Protocol for graph dependencies that support AG-UI emission.""" + + agui_emitter: AGUIEmitter[Any, Any] | None + + +async def stream_graph( + graph: Any, + state: BaseModel, + deps: GraphDeps, +) -> AsyncIterator[AGUIEvent]: + """Run a graph and yield AG-UI events as they occur. + + This is a generic streaming function that works with any pydantic-graph + that follows the AG-UI pattern: + - State must be a Pydantic BaseModel + - Deps must have an optional agui_emitter attribute + - Graph must be a pydantic-graph Graph instance + + Args: + graph: The pydantic-graph Graph to execute + state: Initial state (Pydantic BaseModel) + deps: Graph dependencies with agui_emitter support + + Yields: + AG-UI event dictionaries + + Raises: + TypeError: If deps doesn't support agui_emitter + RuntimeError: If graph doesn't produce a result + """ + if not hasattr(deps, "agui_emitter"): + raise TypeError("deps must have an 'agui_emitter' attribute") + + # Create AG-UI emitter + emitter: AGUIEmitter[Any, Any] = AGUIEmitter() + deps.agui_emitter = emitter + + async def _execute() -> None: + try: + # Start the run with initial state + emitter.start_run(initial_state=state) + + # Execute the graph + result = await graph.run(state=state, deps=deps) + + if result is None: + raise RuntimeError("Graph did not produce a result") + + # Finish the run with the result + emitter.finish_run(result) + except Exception as exc: + # Emit error event + emitter.error(exc) + finally: + await emitter.close() + + runner = asyncio.create_task(_execute()) + + try: + async for event in emitter: + yield event + finally: + if not runner.done(): + runner.cancel() + with suppress(asyncio.CancelledError): + await runner diff --git a/haiku_rag_slim/haiku/rag/app.py b/haiku_rag_slim/haiku/rag/app.py index 704d9610..6b1fe382 100644 --- a/haiku_rag_slim/haiku/rag/app.py +++ b/haiku_rag_slim/haiku/rag/app.py @@ -8,6 +8,7 @@ from rich.console import Console from rich.markdown import Markdown from rich.progress import Progress +from haiku.rag.agui import AGUIConsoleRenderer, stream_graph from haiku.rag.client import HaikuRAG from haiku.rag.config import AppConfig, Config from haiku.rag.mcp import create_mcp_server @@ -15,7 +16,6 @@ from haiku.rag.monitor import FileWatcher from haiku.rag.research.dependencies import ResearchContext from haiku.rag.research.graph import build_research_graph from haiku.rag.research.state import ResearchDeps, ResearchState -from haiku.rag.research.stream import stream_research_graph from haiku.rag.store.models.chunk import Chunk from haiku.rag.store.models.document import Document @@ -243,46 +243,36 @@ class HaikuRAGApp: except Exception as e: self.console.print(f"[red]Error: {e}[/red]") - async def research( - self, - question: str, - verbose: bool = False, - ): + async def research(self, question: str): """Run research via the pydantic-graph pipeline. Args: question: The research question - verbose: Show verbose output """ async with HaikuRAG(db_path=self.db_path, config=self.config) as client: try: - if verbose: - self.console.print("[bold cyan]Starting research[/bold cyan]") - self.console.print(f"[bold blue]Question:[/bold blue] {question}") - self.console.print() + self.console.print("[bold cyan]Starting research[/bold cyan]") + self.console.print(f"[bold blue]Question:[/bold blue] {question}") + self.console.print() graph = build_research_graph(config=self.config) context = ResearchContext(original_question=question) state = ResearchState.from_config(context=context, config=self.config) - deps = ResearchDeps( - client=client, console=self.console if verbose else None - ) + deps = ResearchDeps(client=client) - report = None - async for event in stream_research_graph(graph, state, deps): - if event.type == "report": - report = event.report - break - if event.type == "error": - self.console.print( - f"[red]Error during research: {event.message}[/red]" - ) - return + # Use AG-UI renderer to process events + renderer = AGUIConsoleRenderer(self.console) + report_dict = await renderer.render(stream_graph(graph, state, deps)) - if report is None: + if report_dict is None: self.console.print("[red]Research did not produce a report.[/red]") return + # Convert dict to ResearchReport model + from haiku.rag.research.models import ResearchReport + + report = ResearchReport.model_validate(report_dict) + # Display the report self.console.print("[bold green]Research Report[/bold green]") self.console.rule() diff --git a/haiku_rag_slim/haiku/rag/cli.py b/haiku_rag_slim/haiku/rag/cli.py index 054078f7..e92dbe01 100644 --- a/haiku_rag_slim/haiku/rag/cli.py +++ b/haiku_rag_slim/haiku/rag/cli.py @@ -299,14 +299,9 @@ def research( "--db", help="Path to the LanceDB database file", ), - verbose: bool = typer.Option( - False, - "--verbose", - help="Show verbose progress output", - ), ): app = create_app(db) - asyncio.run(app.research(question=question, verbose=verbose)) + asyncio.run(app.research(question=question)) @cli.command("settings", help="Display current configuration settings") diff --git a/haiku_rag_slim/haiku/rag/research/dependencies.py b/haiku_rag_slim/haiku/rag/research/dependencies.py index 09376b53..39bf641c 100644 --- a/haiku_rag_slim/haiku/rag/research/dependencies.py +++ b/haiku_rag_slim/haiku/rag/research/dependencies.py @@ -1,7 +1,6 @@ from collections.abc import Iterable from pydantic import BaseModel, Field -from rich.console import Console from haiku.rag.client import HaikuRAG from haiku.rag.graph_common.models import SearchAnswer @@ -10,7 +9,6 @@ from haiku.rag.research.models import ( InsightAnalysis, InsightRecord, ) -from haiku.rag.research.stream import ResearchStream class ResearchContext(BaseModel): @@ -184,10 +182,6 @@ class ResearchDependencies(BaseModel): client: HaikuRAG = Field(description="RAG client for document operations") context: ResearchContext = Field(description="Shared research context") - console: Console | None = None - stream: ResearchStream | None = Field( - default=None, description="Optional research event stream" - ) def _merge_unique(existing: list[str], incoming: Iterable[str]) -> list[str]: diff --git a/haiku_rag_slim/haiku/rag/research/graph.py b/haiku_rag_slim/haiku/rag/research/graph.py index a756476e..62a3f9a5 100644 --- a/haiku_rag_slim/haiku/rag/research/graph.py +++ b/haiku_rag_slim/haiku/rag/research/graph.py @@ -53,50 +53,51 @@ def build_research_graph( state = ctx.state deps = ctx.deps - log(deps, state, "\n[bold cyan]📋 Creating research plan...[/bold cyan]") + if deps.agui_emitter: + deps.agui_emitter.start_step("plan") + deps.agui_emitter.update_activity("planning", "Creating research plan") - plan_agent = Agent( - model=get_model(provider, model), - output_type=ResearchPlan, - instructions=( - PLAN_PROMPT - + "\n\nUse the gather_context tool once on the main question before planning." - ), - retries=3, - deps_type=ResearchDependencies, - ) + try: + plan_agent = Agent( + model=get_model(provider, model), + output_type=ResearchPlan, + instructions=( + PLAN_PROMPT + + "\n\nUse the gather_context tool once on the main question before planning." + ), + retries=3, + output_retries=3, + deps_type=ResearchDependencies, + ) - @plan_agent.tool - async def gather_context( - ctx2: RunContext[ResearchDependencies], query: str, limit: int = 6 - ) -> str: - results = await ctx2.deps.client.search(query, limit=limit) - expanded = await ctx2.deps.client.expand_context(results) - return "\n\n".join(chunk.content for chunk, _ in expanded) + @plan_agent.tool + async def gather_context( + ctx2: RunContext[ResearchDependencies], query: str, limit: int = 6 + ) -> str: + results = await ctx2.deps.client.search(query, limit=limit) + expanded = await ctx2.deps.client.expand_context(results) + return "\n\n".join(chunk.content for chunk, _ in expanded) - prompt = ( - "Plan a focused approach for the main question.\n\n" - f"Main question: {state.context.original_question}" - ) + prompt = ( + "Plan a focused approach for the main question.\n\n" + f"Main question: {state.context.original_question}" + ) - agent_deps = ResearchDependencies( - client=deps.client, - context=state.context, - console=deps.console, - stream=deps.stream, - ) - plan_result = await plan_agent.run(prompt, deps=agent_deps) - state.context.sub_questions = list(plan_result.output.sub_questions) + agent_deps = ResearchDependencies( + client=deps.client, + context=state.context, + ) + plan_result = await plan_agent.run(prompt, deps=agent_deps) + state.context.sub_questions = list(plan_result.output.sub_questions) - log(deps, state, "\n[bold green]✅ Plan Created:[/bold green]") - log( - deps, - state, - f" [bold]Main Question:[/bold] {state.context.original_question}", - ) - log(deps, state, " [bold]Sub-questions:[/bold]") - for i, sq in enumerate(state.context.sub_questions, 1): - log(deps, state, f" {i}. {sq}") + # Log the plan results + log(deps, state, f"Main Question: {state.context.original_question}") + log(deps, state, "Sub-questions:") + for i, sq in enumerate(state.context.sub_questions, 1): + log(deps, state, f" {i}. {sq}") + finally: + if deps.agui_emitter: + deps.agui_emitter.finish_step() @g.step async def search_one( @@ -106,26 +107,30 @@ def build_research_graph( deps = ctx.deps sub_q = ctx.inputs - # Create semaphore if not already provided - if deps.semaphore is None: - import asyncio + if deps.agui_emitter: + deps.agui_emitter.start_step("search_one") - deps.semaphore = asyncio.Semaphore(state.max_concurrency) + try: + # Create semaphore if not already provided + if deps.semaphore is None: + import asyncio - # Use semaphore to control concurrency - async with deps.semaphore: - return await _do_search(state, deps, sub_q) + deps.semaphore = asyncio.Semaphore(state.max_concurrency) + + # Use semaphore to control concurrency + async with deps.semaphore: + return await _do_search(state, deps, sub_q) + finally: + if deps.agui_emitter: + deps.agui_emitter.finish_step() async def _do_search( state: ResearchState, deps: ResearchDeps, sub_q: str, ) -> SearchAnswer: - log( - deps, - state, - f"\n[bold cyan]🔍 Searching & Answering:[/bold cyan] {sub_q}", - ) + if deps.agui_emitter: + deps.agui_emitter.update_activity("searching", f"Searching: {sub_q}") agent = Agent( model=get_model(provider, model), @@ -160,21 +165,16 @@ def build_research_graph( agent_deps = ResearchDependencies( client=deps.client, context=state.context, - console=deps.console, - stream=deps.stream, ) try: result = await agent.run(sub_q, deps=agent_deps) answer = result.output if answer: state.context.add_qa_response(answer) - preview = answer.answer[:150] + ( - "…" if len(answer.answer) > 150 else "" - ) - log(deps, state, f" [green]✓[/green] {preview}") + log(deps, state, f"Answer: {answer.answer}") return answer except Exception as e: - log(deps, state, f"[red]Search failed:[/red] {e}") + log(deps, state, f"Search failed: {e}") failure_answer = SearchAnswer( query=sub_q, answer=f"Search failed after retries: {str(e)}", @@ -204,148 +204,139 @@ def build_research_graph( state = ctx.state deps = ctx.deps - log( - deps, - state, - "\n[bold cyan]🧭 Synthesizing new insights and gap status...[/bold cyan]", - ) + if deps.agui_emitter: + deps.agui_emitter.start_step("analyze_insights") + deps.agui_emitter.update_activity( + "analyzing", "Synthesizing insights and gaps" + ) - agent = Agent( - model=get_model(provider, model), - output_type=InsightAnalysis, - instructions=INSIGHT_AGENT_PROMPT, - retries=3, - deps_type=ResearchDependencies, - ) + try: + agent = Agent( + model=get_model(provider, model), + output_type=InsightAnalysis, + instructions=INSIGHT_AGENT_PROMPT, + retries=3, + output_retries=3, + deps_type=ResearchDependencies, + ) - context_xml = format_context_for_prompt(state.context) - prompt = ( - "Review the latest research context and update the shared ledger of insights, gaps," - " and follow-up questions.\n\n" - f"{context_xml}" - ) - agent_deps = ResearchDependencies( - client=deps.client, - context=state.context, - console=deps.console, - stream=deps.stream, - ) - result = await agent.run(prompt, deps=agent_deps) - analysis: InsightAnalysis = result.output + context_xml = format_context_for_prompt(state.context) + prompt = ( + "Review the latest research context and update the shared ledger of insights, gaps," + " and follow-up questions.\n\n" + f"{context_xml}" + ) + agent_deps = ResearchDependencies( + client=deps.client, + context=state.context, + ) + result = await agent.run(prompt, deps=agent_deps) + analysis: InsightAnalysis = result.output - state.context.integrate_analysis(analysis) - state.last_analysis = analysis + state.context.integrate_analysis(analysis) + state.last_analysis = analysis - if analysis.commentary: - log(deps, state, f" Summary: {analysis.commentary}") - if analysis.highlights: - log(deps, state, " [bold]Updated insights:[/bold]") - for insight in analysis.highlights: - label = insight.status.value - log( - deps, - state, - f" • ({label}) {insight.summary}", - ) - if analysis.gap_assessments: - log(deps, state, " [bold yellow]Gap updates:[/bold yellow]") - for gap in analysis.gap_assessments: - status = "resolved" if gap.resolved else "open" - severity = gap.severity.value - log( - deps, - state, - f" • ({severity}/{status}) {gap.description}", - ) - if analysis.resolved_gaps: - log(deps, state, " [green]Resolved gaps:[/green]") - for resolved in analysis.resolved_gaps: - log(deps, state, f" • {resolved}") - if analysis.new_questions: - log(deps, state, " [cyan]Proposed follow-ups:[/cyan]") - for question in analysis.new_questions: - log(deps, state, f" • {question}") + if analysis.commentary: + log(deps, state, f"Summary: {analysis.commentary}") + if analysis.highlights: + log(deps, state, "Updated insights:") + for insight in analysis.highlights: + label = insight.status.value + log(deps, state, f" • ({label}) {insight.summary}") + if analysis.gap_assessments: + log(deps, state, "Gap updates:") + for gap in analysis.gap_assessments: + status = "resolved" if gap.resolved else "open" + severity = gap.severity.value + log(deps, state, f" • ({severity}/{status}) {gap.description}") + if analysis.resolved_gaps: + log(deps, state, "Resolved gaps:") + for resolved in analysis.resolved_gaps: + log(deps, state, f" • {resolved}") + if analysis.new_questions: + log(deps, state, "Proposed follow-ups:") + for question in analysis.new_questions: + log(deps, state, f" • {question}") + finally: + if deps.agui_emitter: + deps.agui_emitter.finish_step() @g.step async def decide(ctx: StepContext[ResearchState, ResearchDeps, None]) -> bool: state = ctx.state deps = ctx.deps - log( - deps, - state, - "\n[bold cyan]📊 Evaluating research sufficiency...[/bold cyan]", - ) - - agent = Agent( - model=get_model(provider, model), - output_type=EvaluationResult, - instructions=DECISION_AGENT_PROMPT, - retries=3, - deps_type=ResearchDependencies, - ) - - context_xml = format_context_for_prompt(state.context) - analysis_xml = format_analysis_for_prompt(state.last_analysis) - prompt_parts = [ - "Assess whether the research now answers the original question with adequate confidence.", - context_xml, - analysis_xml, - ] - if state.last_eval is not None: - prev = state.last_eval - prompt_parts.append( - "" - f"{prev.confidence_score:.2f}" - f"{str(prev.is_sufficient).lower()}" - f"{prev.reasoning}" - "" + if deps.agui_emitter: + deps.agui_emitter.start_step("decide") + deps.agui_emitter.update_activity( + "evaluating", "Evaluating research sufficiency" ) - prompt = "\n\n".join(part for part in prompt_parts if part) - agent_deps = ResearchDependencies( - client=deps.client, - context=state.context, - console=deps.console, - stream=deps.stream, - ) - decision_result = await agent.run(prompt, deps=agent_deps) - output = decision_result.output + try: + agent = Agent( + model=get_model(provider, model), + output_type=EvaluationResult, + instructions=DECISION_AGENT_PROMPT, + retries=3, + output_retries=3, + deps_type=ResearchDependencies, + ) - state.last_eval = output - state.iterations += 1 + context_xml = format_context_for_prompt(state.context) + analysis_xml = format_analysis_for_prompt(state.last_analysis) + prompt_parts = [ + "Assess whether the research now answers the original question with adequate confidence.", + context_xml, + analysis_xml, + ] + if state.last_eval is not None: + prev = state.last_eval + prompt_parts.append( + "" + f"{prev.confidence_score:.2f}" + f"{str(prev.is_sufficient).lower()}" + f"{prev.reasoning}" + "" + ) + prompt = "\n\n".join(part for part in prompt_parts if part) - for new_q in output.new_questions: - if new_q not in state.context.sub_questions: - state.context.sub_questions.append(new_q) + agent_deps = ResearchDependencies( + client=deps.client, + context=state.context, + ) + decision_result = await agent.run(prompt, deps=agent_deps) + output = decision_result.output - if output.key_insights: - log(deps, state, " [bold]Key insights:[/bold]") - for insight in output.key_insights: - log(deps, state, f" • {insight}") + state.last_eval = output + state.iterations += 1 - if output.gaps: - log(deps, state, " [bold yellow]Remaining gaps:[/bold yellow]") - for gap in output.gaps: - log(deps, state, f" • {gap}") + for new_q in output.new_questions: + if new_q not in state.context.sub_questions: + state.context.sub_questions.append(new_q) - log( - deps, - state, - f" Confidence: [yellow]{output.confidence_score:.1%}[/yellow]", - ) - status = "[green]Yes[/green]" if output.is_sufficient else "[red]No[/red]" - log(deps, state, f" Sufficient: {status}") + if output.key_insights: + log(deps, state, "Key insights:") + for insight in output.key_insights: + log(deps, state, f" • {insight}") - should_continue = ( - not output.is_sufficient - or output.confidence_score < state.confidence_threshold - ) and state.iterations < state.max_iterations + if output.gaps: + log(deps, state, "Remaining gaps:") + for gap in output.gaps: + log(deps, state, f" • {gap}") - if not should_continue: - log(deps, state, "\n[bold green]✅ Stopping research.[/bold green]") + log(deps, state, f"Confidence: {output.confidence_score:.1%}") + status = "Yes" if output.is_sufficient else "No" + log(deps, state, f"Sufficient: {status}") - return should_continue + should_continue = ( + not output.is_sufficient + or output.confidence_score < state.confidence_threshold + ) and state.iterations < state.max_iterations + + return should_continue + finally: + if deps.agui_emitter: + deps.agui_emitter.finish_step() @g.step async def synthesize( @@ -354,36 +345,37 @@ def build_research_graph( state = ctx.state deps = ctx.deps - log( - deps, - state, - "\n[bold cyan]📝 Generating final research report...[/bold cyan]", - ) + if deps.agui_emitter: + deps.agui_emitter.start_step("synthesize") + deps.agui_emitter.update_activity( + "synthesizing", "Generating final research report" + ) - agent = Agent( - model=get_model(provider, model), - output_type=ResearchReport, - instructions=SYNTHESIS_AGENT_PROMPT, - retries=3, - deps_type=ResearchDependencies, - ) + try: + agent = Agent( + model=get_model(provider, model), + output_type=ResearchReport, + instructions=SYNTHESIS_AGENT_PROMPT, + retries=3, + output_retries=3, + deps_type=ResearchDependencies, + ) - context_xml = format_context_for_prompt(state.context) - prompt = ( - "Generate a comprehensive research report based on all gathered information.\n\n" - 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, - console=deps.console, - stream=deps.stream, - ) - result = await agent.run(prompt, deps=agent_deps) - - log(deps, state, "[bold green]✅ Research complete![/bold green]") - return result.output + context_xml = format_context_for_prompt(state.context) + prompt = ( + "Generate a comprehensive research report based on all gathered information.\n\n" + 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 + finally: + if deps.agui_emitter: + deps.agui_emitter.finish_step() # Build the graph structure collect_answers = g.join( diff --git a/haiku_rag_slim/haiku/rag/research/state.py b/haiku_rag_slim/haiku/rag/research/state.py index d9396055..72e341f5 100644 --- a/haiku_rag_slim/haiku/rag/research/state.py +++ b/haiku_rag_slim/haiku/rag/research/state.py @@ -3,29 +3,35 @@ from dataclasses import dataclass from typing import TYPE_CHECKING from pydantic import BaseModel, Field -from rich.console import Console from haiku.rag.client import HaikuRAG from haiku.rag.research.dependencies import ResearchContext -from haiku.rag.research.models import EvaluationResult, InsightAnalysis -from haiku.rag.research.stream import ResearchStream +from haiku.rag.research.models import EvaluationResult, InsightAnalysis, ResearchReport if TYPE_CHECKING: + from haiku.rag.agui.emitter import AGUIEmitter from haiku.rag.config.models import AppConfig @dataclass class ResearchDeps: + """Dependencies for research graph execution.""" + client: HaikuRAG - console: Console | None = None - stream: ResearchStream | None = None + agui_emitter: "AGUIEmitter[ResearchState, ResearchReport] | None" = None semaphore: asyncio.Semaphore | None = None def emit_log(self, message: str, state: "ResearchState | None" = None) -> None: - if self.console: - self.console.print(message) - if self.stream: - self.stream.log(message, state) + """Emit a log message through AG-UI events. + + Args: + message: The message to log + state: Optional state to include in state update + """ + if self.agui_emitter: + self.agui_emitter.log(message) + if state: + self.agui_emitter.update_state(state) class ResearchState(BaseModel): diff --git a/haiku_rag_slim/haiku/rag/research/stream.py b/haiku_rag_slim/haiku/rag/research/stream.py deleted file mode 100644 index 5a2b1950..00000000 --- a/haiku_rag_slim/haiku/rag/research/stream.py +++ /dev/null @@ -1,162 +0,0 @@ -import asyncio -from collections.abc import AsyncIterator -from dataclasses import dataclass -from typing import TYPE_CHECKING, Literal - -from haiku.rag.research.models import ResearchReport - -if TYPE_CHECKING: # pragma: no cover - from haiku.rag.research.state import ResearchState - - -@dataclass(slots=True) -class ResearchStateSnapshot: - question: str - sub_questions: list[str] - iterations: int - max_iterations: int - confidence_threshold: float - pending_sub_questions: int - answered_questions: int - insights: list[str] - gaps: list[str] - last_confidence: float | None - last_sufficient: bool | None - - @classmethod - def from_state(cls, state: "ResearchState") -> "ResearchStateSnapshot": - context = state.context - last_confidence: float | None = None - last_sufficient: bool | None = None - if state.last_eval: - last_confidence = state.last_eval.confidence_score - last_sufficient = state.last_eval.is_sufficient - - return cls( - question=context.original_question, - sub_questions=list(context.sub_questions), - iterations=state.iterations, - max_iterations=state.max_iterations, - confidence_threshold=state.confidence_threshold, - pending_sub_questions=len(context.sub_questions), - answered_questions=len(context.qa_responses), - insights=[ - f"{insight.status.value}:{insight.summary}" - for insight in context.insights - ], - gaps=[ - f"{gap.severity.value}/{'resolved' if gap.resolved else 'open'}:{gap.description}" - for gap in context.gaps - ], - last_confidence=last_confidence, - last_sufficient=last_sufficient, - ) - - -@dataclass(slots=True) -class ResearchStreamEvent: - type: Literal["log", "report", "error"] - message: str | None = None - state: ResearchStateSnapshot | None = None - report: ResearchReport | None = None - error: str | None = None - - -class ResearchStream: - """Queue-backed stream for research graph events.""" - - def __init__(self) -> None: - self._queue: asyncio.Queue[ResearchStreamEvent | None] = asyncio.Queue() - self._closed = False - - def _snapshot(self, state: "ResearchState | None") -> ResearchStateSnapshot | None: - if state is None: - return None - return ResearchStateSnapshot.from_state(state) - - def log(self, message: str, state: "ResearchState | None" = None) -> None: - if self._closed: - return - event = ResearchStreamEvent( - type="log", message=message, state=self._snapshot(state) - ) - self._queue.put_nowait(event) - - def report(self, report: ResearchReport, state: "ResearchState") -> None: - if self._closed: - return - event = ResearchStreamEvent( - type="report", - report=report, - state=self._snapshot(state), - ) - self._queue.put_nowait(event) - - def error(self, error: Exception, state: "ResearchState | None" = None) -> None: - if self._closed: - return - event = ResearchStreamEvent( - type="error", - message=str(error), - error=str(error), - state=self._snapshot(state), - ) - self._queue.put_nowait(event) - - async def close(self) -> None: - if self._closed: - return - self._closed = True - await self._queue.put(None) - - def __aiter__(self) -> AsyncIterator[ResearchStreamEvent]: - return self._iter_events() - - async def _iter_events(self) -> AsyncIterator[ResearchStreamEvent]: - while True: - event = await self._queue.get() - if event is None: - break - yield event - - -async def stream_research_graph( - graph, - state: "ResearchState", - deps, -) -> AsyncIterator[ResearchStreamEvent]: - """Run the research graph and yield streaming events as they occur.""" - - from contextlib import suppress - - from haiku.rag.research.state import ResearchDeps - - if not isinstance(deps, ResearchDeps): - raise TypeError("deps must be an instance of ResearchDeps") - - stream = ResearchStream() - deps.stream = stream - - async def _execute() -> None: - try: - report = await graph.run(state=state, deps=deps) - - if report is None: - raise RuntimeError("Graph did not produce a report") - - stream.report(report, state) - except Exception as exc: - stream.error(exc, state) - finally: - await stream.close() - - runner = asyncio.create_task(_execute()) - - try: - async for event in stream: - yield event - finally: - if not runner.done(): - runner.cancel() - with suppress(asyncio.CancelledError): - await runner From 2c1f79614a73b605f609703d70628a4d122d3e50 Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Tue, 11 Nov 2025 11:10:51 +0200 Subject: [PATCH 04/17] Use StateSnapshot updates --- haiku_rag_slim/haiku/rag/agui/cli_renderer.py | 107 ++++++++---------- haiku_rag_slim/haiku/rag/research/graph.py | 83 +++++++------- 2 files changed, 89 insertions(+), 101 deletions(-) diff --git a/haiku_rag_slim/haiku/rag/agui/cli_renderer.py b/haiku_rag_slim/haiku/rag/agui/cli_renderer.py index 0d146b61..c09e62b6 100644 --- a/haiku_rag_slim/haiku/rag/agui/cli_renderer.py +++ b/haiku_rag_slim/haiku/rag/agui/cli_renderer.py @@ -3,7 +3,6 @@ from collections.abc import AsyncIterator from typing import Any -from pydantic import BaseModel from rich.console import Console from haiku.rag.agui.events import AGUIEvent @@ -23,7 +22,7 @@ class AGUIConsoleRenderer: console: Optional Rich console instance (creates new one if not provided) """ self.console = console or Console() - self._state: BaseModel | None = None + self._state: dict | None = None async def render(self, events: AsyncIterator[AGUIEvent]) -> Any | None: """Process events and render to console, return final result. @@ -59,7 +58,9 @@ class AGUIConsoleRenderer: elif event_type == "TEXT_MESSAGE_END": pass # End of streaming message, no output needed elif event_type == "STATE_SNAPSHOT": - self._state = event.get("snapshot") + new_state = event.get("snapshot") + self._render_state_snapshot(new_state) + self._state = new_state elif event_type == "STATE_DELTA": self._apply_state_delta(event) elif event_type == "ACTIVITY_SNAPSHOT": @@ -69,84 +70,72 @@ class AGUIConsoleRenderer: return result - def _render_run_started(self, event: AGUIEvent) -> None: - """Render run start event. - - Args: - event: RunStarted event - """ - # Currently silent - could render run metadata later + def _render_run_started(self, _event: AGUIEvent) -> None: + """Render run start event.""" def _render_run_finished(self) -> None: """Render run completion.""" - # Currently silent - the result is rendered separately def _render_error(self, event: AGUIEvent) -> None: - """Render error event. - - Args: - event: RunError event - """ + """Render error event.""" message = event.get("message", "Unknown error") - self.console.print(f"[bold red]❌ Error:[/bold red] {message}") + self.console.print(f"[bold red][RUN_ERROR][/bold red] {message}") def _render_step_started(self, event: AGUIEvent) -> None: - """Render step start event. - - Args: - event: StepStarted event - """ + """Render step start event.""" step_name = event.get("stepName", "") if step_name: - # Format step name for display display_name = step_name.replace("_", " ").title() - self.console.print(f"\n[bold cyan]{display_name}[/bold cyan]") + self.console.print( + f"\n[bold cyan][STEP_STARTED][/bold cyan] {display_name}" + ) - def _render_step_finished(self, event: AGUIEvent) -> None: - """Render step finish event. - - Args: - event: StepFinished event - """ - # Step completion is implicit from the next step or activity + def _render_step_finished(self, _event: AGUIEvent) -> None: + """Render step finish event.""" def _render_text_message(self, event: AGUIEvent) -> None: - """Render complete text message. - - Args: - event: TextMessageChunk event - """ + """Render complete text message.""" delta = event.get("delta", "") - # The delta contains the text content to display - self.console.print(delta) + self.console.print(f"[magenta][TEXT_MESSAGE][/magenta] {delta}") def _render_text_content(self, event: AGUIEvent) -> None: - """Render streaming text content delta. - - Args: - event: TextMessageContent event - """ + """Render streaming text content delta.""" delta = event.get("delta", "") - # Print delta without newline for streaming effect self.console.print(delta, end="") def _render_activity(self, event: AGUIEvent) -> None: - """Render activity update. - - Args: - event: ActivitySnapshot event - """ + """Render activity update.""" content = event.get("content", "") - - # Render activity content with emphasis if content: - self.console.print(f"[dim]{content}[/dim]") + self.console.print(f"[yellow][ACTIVITY][/yellow] {content}") - def _apply_state_delta(self, event: AGUIEvent) -> None: - """Apply state delta to current state. + def _render_state_snapshot(self, new_state: dict | None) -> None: + """Render state snapshot showing only what changed.""" + if not new_state: + return - Args: - event: StateDelta event - """ - # Currently not applying deltas - could implement state patching later - # For now, the text messages contain all the information we need to display + old_state = self._state or {} + diff = self._compute_diff(old_state, new_state) + + if not diff: + return + + self.console.print("[blue][STATE_SNAPSHOT][/blue]") + self.console.print(diff, style="dim") + + def _compute_diff(self, old: dict, new: dict) -> dict: + """Compute difference between old and new state.""" + diff = {} + for key, new_value in new.items(): + old_value = old.get(key) + if old_value != new_value: + if isinstance(new_value, dict) and isinstance(old_value, dict): + nested_diff = self._compute_diff(old_value, new_value) + if nested_diff: + diff[key] = nested_diff + else: + diff[key] = new_value + return diff + + def _apply_state_delta(self, _event: AGUIEvent) -> None: + """Apply state delta to current state.""" diff --git a/haiku_rag_slim/haiku/rag/research/graph.py b/haiku_rag_slim/haiku/rag/research/graph.py index 62a3f9a5..b2019e00 100644 --- a/haiku_rag_slim/haiku/rag/research/graph.py +++ b/haiku_rag_slim/haiku/rag/research/graph.py @@ -8,7 +8,7 @@ from pydantic_graph.beta.join import reduce_list_append from haiku.rag.config import Config from haiku.rag.config.models import AppConfig -from haiku.rag.graph_common import get_model, log +from haiku.rag.graph_common import get_model from haiku.rag.graph_common.models import ResearchPlan, SearchAnswer from haiku.rag.graph_common.prompts import PLAN_PROMPT, SEARCH_AGENT_PROMPT from haiku.rag.research.common import ( @@ -90,11 +90,13 @@ def build_research_graph( plan_result = await plan_agent.run(prompt, deps=agent_deps) state.context.sub_questions = list(plan_result.output.sub_questions) - # Log the plan results - log(deps, state, f"Main Question: {state.context.original_question}") - log(deps, state, "Sub-questions:") - for i, sq in enumerate(state.context.sub_questions, 1): - log(deps, state, f" {i}. {sq}") + # State now contains the plan - emit state update and narrate + if deps.agui_emitter: + deps.agui_emitter.update_state(state) + count = len(state.context.sub_questions) + deps.agui_emitter.update_activity( + "planning", f"Created plan with {count} sub-questions" + ) finally: if deps.agui_emitter: deps.agui_emitter.finish_step() @@ -171,10 +173,18 @@ def build_research_graph( answer = result.output if answer: state.context.add_qa_response(answer) - log(deps, state, f"Answer: {answer.answer}") + # State updated with new answer - emit state update and narrate + if deps.agui_emitter: + deps.agui_emitter.update_state(state) + deps.agui_emitter.update_activity( + "searching", + f"Found answer with {answer.confidence:.0%} confidence", + ) return answer except Exception as e: - log(deps, state, f"Search failed: {e}") + # Narrate the error + if deps.agui_emitter: + deps.agui_emitter.update_activity("searching", f"Search failed: {e}") failure_answer = SearchAnswer( query=sub_q, answer=f"Search failed after retries: {str(e)}", @@ -236,27 +246,21 @@ def build_research_graph( state.context.integrate_analysis(analysis) state.last_analysis = analysis - if analysis.commentary: - log(deps, state, f"Summary: {analysis.commentary}") - if analysis.highlights: - log(deps, state, "Updated insights:") - for insight in analysis.highlights: - label = insight.status.value - log(deps, state, f" • ({label}) {insight.summary}") - if analysis.gap_assessments: - log(deps, state, "Gap updates:") - for gap in analysis.gap_assessments: - status = "resolved" if gap.resolved else "open" - severity = gap.severity.value - log(deps, state, f" • ({severity}/{status}) {gap.description}") - if analysis.resolved_gaps: - log(deps, state, "Resolved gaps:") - for resolved in analysis.resolved_gaps: - log(deps, state, f" • {resolved}") - if analysis.new_questions: - log(deps, state, "Proposed follow-ups:") - for question in analysis.new_questions: - log(deps, state, f" • {question}") + # State updated with insights/gaps - emit state update and narrate + if deps.agui_emitter: + deps.agui_emitter.update_state(state) + highlights = len(analysis.highlights) if analysis.highlights else 0 + gaps = len(analysis.gap_assessments) if analysis.gap_assessments else 0 + resolved = len(analysis.resolved_gaps) if analysis.resolved_gaps else 0 + parts = [] + if highlights: + parts.append(f"{highlights} insights") + if gaps: + parts.append(f"{gaps} gaps") + if resolved: + parts.append(f"{resolved} resolved") + summary = ", ".join(parts) if parts else "No updates" + deps.agui_emitter.update_activity("analyzing", f"Analysis: {summary}") finally: if deps.agui_emitter: deps.agui_emitter.finish_step() @@ -314,19 +318,14 @@ def build_research_graph( if new_q not in state.context.sub_questions: state.context.sub_questions.append(new_q) - if output.key_insights: - log(deps, state, "Key insights:") - for insight in output.key_insights: - log(deps, state, f" • {insight}") - - if output.gaps: - log(deps, state, "Remaining gaps:") - for gap in output.gaps: - log(deps, state, f" • {gap}") - - log(deps, state, f"Confidence: {output.confidence_score:.1%}") - status = "Yes" if output.is_sufficient else "No" - log(deps, state, f"Sufficient: {status}") + # State updated with evaluation - emit state update and narrate + if deps.agui_emitter: + deps.agui_emitter.update_state(state) + sufficient = "Yes" if output.is_sufficient else "No" + deps.agui_emitter.update_activity( + "evaluating", + f"Confidence: {output.confidence_score:.0%}, Sufficient: {sufficient}", + ) should_continue = ( not output.is_sufficient From 60ef8fe04cea6a67ecb187113e6c4c6582ca694f Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Tue, 11 Nov 2025 12:18:30 +0200 Subject: [PATCH 05/17] AGUI starlette server --- haiku_rag_slim/haiku/rag/agui/__init__.py | 10 + haiku_rag_slim/haiku/rag/agui/server.py | 209 ++++++++++++++++++ haiku_rag_slim/haiku/rag/app.py | 25 +++ haiku_rag_slim/haiku/rag/cli.py | 12 +- haiku_rag_slim/haiku/rag/config/__init__.py | 2 + haiku_rag_slim/haiku/rag/config/loader.py | 8 + haiku_rag_slim/haiku/rag/config/models.py | 10 + .../haiku/rag/graph/nodes/__init__.py | 0 haiku_rag_slim/test_agui_server.py | 101 +++++++++ 9 files changed, 374 insertions(+), 3 deletions(-) create mode 100644 haiku_rag_slim/haiku/rag/agui/server.py delete mode 100644 haiku_rag_slim/haiku/rag/graph/nodes/__init__.py create mode 100755 haiku_rag_slim/test_agui_server.py diff --git a/haiku_rag_slim/haiku/rag/agui/__init__.py b/haiku_rag_slim/haiku/rag/agui/__init__.py index 470d1b74..29ecd62d 100644 --- a/haiku_rag_slim/haiku/rag/agui/__init__.py +++ b/haiku_rag_slim/haiku/rag/agui/__init__.py @@ -18,6 +18,12 @@ from haiku.rag.agui.events import ( emit_text_message_end, emit_text_message_start, ) +from haiku.rag.agui.server import ( + RunAgentInput, + create_agui_app, + create_research_server, + format_sse_event, +) from haiku.rag.agui.state import compute_state_delta from haiku.rag.agui.stream import stream_graph @@ -25,7 +31,10 @@ __all__ = [ "AGUIConsoleRenderer", "AGUIEmitter", "AGUIEvent", + "RunAgentInput", "compute_state_delta", + "create_agui_app", + "create_research_server", "emit_activity", "emit_activity_delta", "emit_run_error", @@ -39,5 +48,6 @@ __all__ = [ "emit_text_message_content", "emit_text_message_end", "emit_text_message_start", + "format_sse_event", "stream_graph", ] diff --git a/haiku_rag_slim/haiku/rag/agui/server.py b/haiku_rag_slim/haiku/rag/agui/server.py new file mode 100644 index 00000000..e0f94765 --- /dev/null +++ b/haiku_rag_slim/haiku/rag/agui/server.py @@ -0,0 +1,209 @@ +"""AG-UI HTTP server implementation for graph execution.""" + +import json +from collections.abc import AsyncIterator, Callable +from typing import Any, Protocol + +from pydantic import BaseModel, Field +from pydantic_graph.beta import Graph +from starlette.applications import Starlette +from starlette.middleware import Middleware +from starlette.middleware.cors import CORSMiddleware +from starlette.requests import Request +from starlette.responses import JSONResponse, StreamingResponse +from starlette.routing import Route + +from haiku.rag.agui.events import AGUIEvent +from haiku.rag.agui.stream import stream_graph +from haiku.rag.config.models import AGUIConfig + + +class GraphDeps(Protocol): + """Protocol for graph dependencies that support AG-UI emission.""" + + agui_emitter: Any | None + + +class RunAgentInput(BaseModel): + """AG-UI protocol run agent input. + + See: https://docs.ag-ui.com/concepts/agents#runagentinput + """ + + thread_id: str | None = Field(None, alias="threadId") + run_id: str | None = Field(None, alias="runId") + state: dict[str, Any] = Field(default_factory=dict) + messages: list[dict[str, Any]] = Field(default_factory=list) + config: dict[str, Any] = Field(default_factory=dict) + + +def create_agui_app( + graph_factory: Callable[[], Graph], + state_factory: Callable[[dict[str, Any]], BaseModel], + deps_factory: Callable[[dict[str, Any]], GraphDeps], + config: AGUIConfig, +) -> Starlette: + """Create Starlette app with AG-UI endpoint. + + Args: + graph_factory: Factory function to create graph instance + state_factory: Factory to create initial state from input + deps_factory: Factory to create graph dependencies + config: AG-UI server configuration + + Returns: + Starlette application with AG-UI endpoints + """ + + async def event_stream( + input_data: RunAgentInput, + ) -> AsyncIterator[str]: + """Generate SSE event stream from graph execution. + + Yields: + Server-Sent Events formatted strings + """ + # Create graph, state, and dependencies + graph = graph_factory() + + # Create initial state from input + initial_state = state_factory(input_data.state) + + # Create dependencies (may use config from input) + deps = deps_factory(input_data.config) + + # Execute graph and stream events + async for event in stream_graph(graph, initial_state, deps): + # Format as SSE event + event_data = format_sse_event(event) + yield event_data + + async def stream_agent(request: Request) -> StreamingResponse: + """AG-UI agent stream endpoint. + + Accepts AG-UI RunAgentInput and streams events via SSE. + """ + # Parse request body + body = await request.json() + input_data = RunAgentInput(**body) + + # Return SSE stream + return StreamingResponse( + event_stream(input_data), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "X-Accel-Buffering": "no", # Disable buffering in nginx + }, + ) + + async def health_check(_: Request) -> JSONResponse: + """Health check endpoint.""" + return JSONResponse({"status": "healthy"}) + + # Define routes + routes = [ + Route("/v1/agent/stream", stream_agent, methods=["POST"]), + Route("/health", health_check, methods=["GET"]), + ] + + # Configure CORS middleware + middleware = [ + Middleware( + CORSMiddleware, + allow_origins=config.cors_origins, + allow_credentials=config.cors_credentials, + allow_methods=config.cors_methods, + allow_headers=config.cors_headers, + ) + ] + + # Create Starlette app + app = Starlette( + routes=routes, + middleware=middleware, + debug=False, + ) + + return app + + +def format_sse_event(event: AGUIEvent) -> str: + """Format AG-UI event as Server-Sent Event. + + Args: + event: AG-UI event dictionary + + Returns: + SSE formatted string with event data + """ + # Convert event to JSON + event_json = json.dumps(event, ensure_ascii=False) + + # Format as SSE + # Each event is: data: \n\n + return f"data: {event_json}\n\n" + + +def create_research_server(config: Any, db_path: Any | None = None) -> Starlette: + """Create AG-UI server for research graph. + + This is a convenience function specifically for the research graph. + + Args: + config: Application config with research settings + db_path: Optional database path override + + Returns: + Starlette app configured for research graph + """ + from haiku.rag.client import HaikuRAG + from haiku.rag.research.dependencies import ResearchContext + from haiku.rag.research.graph import build_research_graph + from haiku.rag.research.state import ResearchDeps, ResearchState + + # Store client reference for proper lifecycle management + _client_cache: dict[str, HaikuRAG] = {} + + def graph_factory() -> Graph: + """Create research graph instance.""" + return build_research_graph(config) + + def state_factory(input_state: dict[str, Any]) -> ResearchState: + """Create research state from input.""" + # Extract question from input state or messages + question = input_state.get("question", "") + if not question: + # Try to get from first message if available + messages = input_state.get("messages", []) + if messages: + question = messages[0].get("content", "") + + # Create context and state + context = ResearchContext(original_question=question) + return ResearchState.from_config(context=context, config=config) + + def deps_factory(input_config: dict[str, Any]) -> ResearchDeps: + """Create research dependencies.""" + # Use provided db_path or fallback to config + effective_db_path = ( + db_path + or input_config.get("db_path") + or config.storage.data_dir / "haiku.rag.lancedb" + ) + + # Reuse existing client if available + path_key = str(effective_db_path) + if path_key not in _client_cache: + _client_cache[path_key] = HaikuRAG(db_path=effective_db_path, config=config) + + return ResearchDeps(client=_client_cache[path_key]) + + # Use AG-UI config from app config + return create_agui_app( + graph_factory=graph_factory, + state_factory=state_factory, + deps_factory=deps_factory, + config=config.agui, + ) diff --git a/haiku_rag_slim/haiku/rag/app.py b/haiku_rag_slim/haiku/rag/app.py index 6b1fe382..bac4c6f9 100644 --- a/haiku_rag_slim/haiku/rag/app.py +++ b/haiku_rag_slim/haiku/rag/app.py @@ -445,6 +445,7 @@ class HaikuRAGApp: enable_mcp: bool = True, mcp_transport: str | None = None, mcp_port: int = 8001, + enable_agui: bool = False, ): """Start the server with selected services.""" async with HaikuRAG(self.db_path, config=self.config) as client: @@ -472,6 +473,30 @@ class HaikuRAGApp: mcp_task = asyncio.create_task(run_mcp()) tasks.append(mcp_task) + # Start AG-UI server if enabled + if enable_agui: + + async def run_agui(): + import uvicorn + + from haiku.rag.agui import create_research_server + + logger.info( + f"Starting AG-UI server on {self.config.agui.host}:{self.config.agui.port}" + ) + app = create_research_server(self.config, db_path=self.db_path) + config = uvicorn.Config( + app=app, + host=self.config.agui.host, + port=self.config.agui.port, + log_level="info", + ) + server = uvicorn.Server(config) + await server.serve() + + agui_task = asyncio.create_task(run_agui()) + tasks.append(agui_task) + if not tasks: logger.warning("No services enabled") return diff --git a/haiku_rag_slim/haiku/rag/cli.py b/haiku_rag_slim/haiku/rag/cli.py index e92dbe01..ab6a1914 100644 --- a/haiku_rag_slim/haiku/rag/cli.py +++ b/haiku_rag_slim/haiku/rag/cli.py @@ -396,7 +396,7 @@ def download_models_cmd(): @cli.command( "serve", - help="Start haiku.rag server. Use --monitor and/or --mcp to enable services.", + help="Start haiku.rag server. Use --monitor, --mcp, and/or --agui to enable services.", ) def serve( db: Path | None = typer.Option( @@ -424,12 +424,17 @@ def serve( "--mcp-port", help="Port to bind MCP server to (ignored with --stdio)", ), + agui: bool = typer.Option( + False, + "--agui", + help="Enable AG-UI HTTP server for graph streaming", + ), ) -> None: """Start the server with selected services.""" # Require at least one service flag - if not (monitor or mcp): + if not (monitor or mcp or agui): typer.echo( - "Error: At least one service flag (--monitor or --mcp) must be specified" + "Error: At least one service flag (--monitor, --mcp, or --agui) must be specified" ) raise typer.Exit(1) @@ -447,6 +452,7 @@ def serve( enable_mcp=mcp, mcp_transport=transport, mcp_port=mcp_port, + enable_agui=agui, ) ) diff --git a/haiku_rag_slim/haiku/rag/config/__init__.py b/haiku_rag_slim/haiku/rag/config/__init__.py index 970ba4f2..b6325451 100644 --- a/haiku_rag_slim/haiku/rag/config/__init__.py +++ b/haiku_rag_slim/haiku/rag/config/__init__.py @@ -6,6 +6,7 @@ from haiku.rag.config.loader import ( load_yaml_config, ) from haiku.rag.config.models import ( + AGUIConfig, AppConfig, EmbeddingsConfig, LanceDBConfig, @@ -22,6 +23,7 @@ from haiku.rag.config.models import ( __all__ = [ "Config", + "AGUIConfig", "AppConfig", "StorageConfig", "MonitorConfig", diff --git a/haiku_rag_slim/haiku/rag/config/loader.py b/haiku_rag_slim/haiku/rag/config/loader.py index df21f0bb..d6584351 100644 --- a/haiku_rag_slim/haiku/rag/config/loader.py +++ b/haiku_rag_slim/haiku/rag/config/loader.py @@ -84,4 +84,12 @@ def generate_default_config() -> dict: "research_base_url": "", }, }, + "agui": { + "host": "0.0.0.0", + "port": 8000, + "cors_origins": ["*"], + "cors_credentials": True, + "cors_methods": ["GET", "POST", "OPTIONS"], + "cors_headers": ["*"], + }, } diff --git a/haiku_rag_slim/haiku/rag/config/models.py b/haiku_rag_slim/haiku/rag/config/models.py index 2c432e63..4883859d 100644 --- a/haiku_rag_slim/haiku/rag/config/models.py +++ b/haiku_rag_slim/haiku/rag/config/models.py @@ -77,6 +77,15 @@ class ProvidersConfig(BaseModel): vllm: VLLMConfig = Field(default_factory=VLLMConfig) +class AGUIConfig(BaseModel): + host: str = "0.0.0.0" + port: int = 8000 + cors_origins: list[str] = ["*"] + cors_credentials: bool = True + cors_methods: list[str] = ["GET", "POST", "OPTIONS"] + cors_headers: list[str] = ["*"] + + class AppConfig(BaseModel): environment: str = "production" storage: StorageConfig = Field(default_factory=StorageConfig) @@ -88,3 +97,4 @@ class AppConfig(BaseModel): research: ResearchConfig = Field(default_factory=ResearchConfig) processing: ProcessingConfig = Field(default_factory=ProcessingConfig) providers: ProvidersConfig = Field(default_factory=ProvidersConfig) + agui: AGUIConfig = Field(default_factory=AGUIConfig) diff --git a/haiku_rag_slim/haiku/rag/graph/nodes/__init__.py b/haiku_rag_slim/haiku/rag/graph/nodes/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/haiku_rag_slim/test_agui_server.py b/haiku_rag_slim/test_agui_server.py new file mode 100755 index 00000000..bf231267 --- /dev/null +++ b/haiku_rag_slim/test_agui_server.py @@ -0,0 +1,101 @@ +#!/usr/bin/env python3 +"""Test script for AG-UI server functionality.""" + +import asyncio +import json + +import httpx + + +async def test_agui_server(): + """Test the AG-UI server endpoint.""" + base_url = "http://localhost:8000" + + # Test health check + async with httpx.AsyncClient() as client: + print("Testing health check...") + response = await client.get(f"{base_url}/health") + print(f"Health check response: {response.status_code}") + print(f"Health check data: {response.json()}") + + # Test AG-UI streaming endpoint + print("\nTesting AG-UI stream endpoint...") + request_data = { + "threadId": "test-thread-1", + "runId": "test-run-1", + "state": {"question": "What is pydantic-graph?"}, + "messages": [], + "config": {}, + } + + print(f"Request data: {json.dumps(request_data, indent=2)}") + + # Send request and stream response + async with client.stream( + "POST", + f"{base_url}/v1/agent/stream", + json=request_data, + timeout=120.0, + ) as response: + print(f"Response status: {response.status_code}") + print("Streaming events...\n") + + event_count = 0 + async for line in response.aiter_lines(): + if line.startswith("data: "): + event_data = line[6:] # Remove "data: " prefix + try: + event = json.loads(event_data) + event_type = event.get("type", "UNKNOWN") + print(f"Event {event_count}: {event_type}") + + # Show specific event details + if event_type == "RUN_STARTED": + print(f" Thread ID: {event.get('threadId')}") + print(f" Run ID: {event.get('runId')}") + elif event_type == "STEP_STARTED": + print(f" Step: {event.get('stepName')}") + elif event_type == "ACTIVITY_SNAPSHOT": + print(f" Activity: {event.get('content')}") + elif event_type == "STATE_SNAPSHOT": + state = event.get("snapshot", {}) + if "context" in state: + context = state["context"] + if "sub_questions" in context: + num_questions = len(context["sub_questions"]) + print(f" Sub-questions: {num_questions}") + elif event_type == "RUN_FINISHED": + result = event.get("result", {}) + if "title" in result: + print(f" Report Title: {result['title']}") + elif event_type == "RUN_ERROR": + print(f" Error: {event.get('message')}") + + event_count += 1 + except json.JSONDecodeError as e: + print(f"Failed to parse event: {e}") + print(f"Raw line: {line}") + + print(f"\nTotal events received: {event_count}") + + +if __name__ == "__main__": + print("AG-UI Server Test") + print("=" * 50) + print("Make sure to start the server first with:") + print(" haiku-rag serve --agui --agui-port 8000") + print("=" * 50) + print() + + try: + asyncio.run(test_agui_server()) + except httpx.ConnectError: + print("ERROR: Could not connect to server at http://localhost:8000") + print("Make sure the AG-UI server is running.") + except KeyboardInterrupt: + print("\nTest interrupted by user") + except Exception as e: + print(f"Test failed with error: {e}") + import traceback + + traceback.print_exc() From dccef431b084c928786e5c1b126b40a6fe1fbade Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Tue, 11 Nov 2025 12:56:06 +0200 Subject: [PATCH 06/17] Update tests --- tests/agui/__init__.py | 1 + tests/agui/test_cli_renderer.py | 158 ++++++++++++++ tests/agui/test_emitter.py | 231 +++++++++++++++++++++ tests/agui/test_events.py | 155 ++++++++++++++ tests/agui/test_server.py | 250 +++++++++++++++++++++++ tests/agui/test_stream.py | 199 ++++++++++++++++++ tests/test_research_graph.py | 61 +++++- tests/test_research_graph_integration.py | 55 ----- 8 files changed, 1054 insertions(+), 56 deletions(-) create mode 100644 tests/agui/__init__.py create mode 100644 tests/agui/test_cli_renderer.py create mode 100644 tests/agui/test_emitter.py create mode 100644 tests/agui/test_events.py create mode 100644 tests/agui/test_server.py create mode 100644 tests/agui/test_stream.py delete mode 100644 tests/test_research_graph_integration.py diff --git a/tests/agui/__init__.py b/tests/agui/__init__.py new file mode 100644 index 00000000..d178a31a --- /dev/null +++ b/tests/agui/__init__.py @@ -0,0 +1 @@ +"""Tests for AG-UI implementation.""" diff --git a/tests/agui/test_cli_renderer.py b/tests/agui/test_cli_renderer.py new file mode 100644 index 00000000..c01d6934 --- /dev/null +++ b/tests/agui/test_cli_renderer.py @@ -0,0 +1,158 @@ +"""Tests for AGUIConsoleRenderer.""" + +import pytest +from pydantic import BaseModel +from rich.console import Console + +from haiku.rag.agui.cli_renderer import AGUIConsoleRenderer +from haiku.rag.agui.events import ( + emit_activity, + emit_run_error, + emit_run_finished, + emit_run_started, + emit_state_snapshot, + emit_step_finished, + emit_step_started, + emit_text_message, +) + + +class SimpleState(BaseModel): + """Simple state for testing.""" + + value: int + + +async def async_gen(items): + """Helper to create async generator from list.""" + for item in items: + yield item + + +@pytest.mark.asyncio +async def test_renderer_basic_flow(): + """Test basic event rendering flow.""" + console = Console(file=None, force_terminal=False) # Don't actually print + renderer = AGUIConsoleRenderer(console) + + events = [ + emit_run_started("t1", "r1"), + emit_state_snapshot(SimpleState(value=1)), + emit_step_started("plan"), + emit_activity("m1", "planning", "Planning research"), + emit_run_finished("t1", "r1", {"status": "complete"}), + ] + + result = await renderer.render(async_gen(events)) + + assert result == {"status": "complete"} + + +@pytest.mark.asyncio +async def test_renderer_state_diff(): + """Test that renderer computes state diffs correctly.""" + renderer = AGUIConsoleRenderer() + + # First state + old_state = {"value": 1, "text": "old", "nested": {"a": 1, "b": 2}} + # Second state with changes + new_state = {"value": 2, "text": "old", "nested": {"a": 1, "b": 3, "c": 4}} + + diff = renderer._compute_diff(old_state, new_state) + + assert diff == { + "value": 2, + "nested": {"b": 3, "c": 4}, # Only changed/new fields in nested + } + + +@pytest.mark.asyncio +async def test_renderer_state_diff_no_changes(): + """Test that no diff is computed when states are equal.""" + renderer = AGUIConsoleRenderer() + + state = {"value": 1, "text": "test"} + diff = renderer._compute_diff(state, state) + + assert diff == {} + + +@pytest.mark.asyncio +async def test_renderer_handles_all_event_types(): + """Test that renderer handles all event types without errors.""" + renderer = AGUIConsoleRenderer() + + events = [ + emit_run_started("t1", "r1"), + emit_state_snapshot(SimpleState(value=1)), + emit_step_started("step1"), + emit_step_finished("step1"), + emit_text_message("message"), + emit_activity("m1", "work", "Working"), + emit_run_error("error occurred"), + emit_run_finished("t1", "r1", {"result": "done"}), + ] + + # Should not raise any exceptions + result = await renderer.render(async_gen(events)) + assert result == {"result": "done"} + + +@pytest.mark.asyncio +async def test_renderer_initial_state(): + """Test that initial state is rendered.""" + renderer = AGUIConsoleRenderer() + + events = [ + emit_state_snapshot(SimpleState(value=1)), + emit_state_snapshot(SimpleState(value=2)), + ] + + await renderer.render(async_gen(events)) + + # After processing, internal state should be the last state + assert renderer._state == {"value": 2} + + +@pytest.mark.asyncio +async def test_renderer_no_result(): + """Test renderer when no RUN_FINISHED event.""" + renderer = AGUIConsoleRenderer() + + events = [ + emit_run_started("t1", "r1"), + emit_step_started("step1"), + ] + + result = await renderer.render(async_gen(events)) + assert result is None + + +@pytest.mark.asyncio +async def test_renderer_nested_state_diff(): + """Test nested state diff computation.""" + renderer = AGUIConsoleRenderer() + + old = {"level1": {"level2": {"value": 1, "text": "old"}, "other": "same"}} + + new = {"level1": {"level2": {"value": 2, "text": "old"}, "other": "same"}} + + diff = renderer._compute_diff(old, new) + + # Should only show the changed nested value + assert diff == {"level1": {"level2": {"value": 2}}} + + +@pytest.mark.asyncio +async def test_renderer_with_empty_state(): + """Test renderer handles empty state gracefully.""" + renderer = AGUIConsoleRenderer() + + # Create a state with no changes + events = [ + emit_step_started("step1"), # No state snapshot + emit_run_finished("t1", "r1", {"result": "ok"}), + ] + + result = await renderer.render(async_gen(events)) + assert result == {"result": "ok"} diff --git a/tests/agui/test_emitter.py b/tests/agui/test_emitter.py new file mode 100644 index 00000000..5a057247 --- /dev/null +++ b/tests/agui/test_emitter.py @@ -0,0 +1,231 @@ +"""Tests for AGUIEmitter.""" + +import asyncio + +import pytest +from pydantic import BaseModel + +from haiku.rag.agui.emitter import AGUIEmitter + + +class TestState(BaseModel): + """Test state model.""" + + value: int + text: str + + +class TestResult(BaseModel): + """Test result model.""" + + status: str + + +@pytest.mark.asyncio +async def test_emitter_lifecycle(): + """Test emitter lifecycle events.""" + emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() + + initial_state = TestState(value=1, text="initial") + emitter.start_run(initial_state) + + events = [] + async for event in emitter: + events.append(event) + if event["type"] == "RUN_STARTED": + # Close after getting run started + result = TestResult(status="complete") + emitter.finish_run(result) + await emitter.close() + + assert len(events) >= 3 # RUN_STARTED, STATE_SNAPSHOT, RUN_FINISHED + assert events[0]["type"] == "RUN_STARTED" + assert events[-1]["type"] == "RUN_FINISHED" + assert events[-1]["result"] == {"status": "complete"} + + +@pytest.mark.asyncio +async def test_emitter_step_events(): + """Test step lifecycle events.""" + emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() + + emitter.start_step("test_step") + emitter.finish_step() + await emitter.close() + + events = [] + async for event in emitter: + events.append(event) + + step_events = [e for e in events if e["type"] in ("STEP_STARTED", "STEP_FINISHED")] + assert len(step_events) == 2 + assert step_events[0]["type"] == "STEP_STARTED" + assert step_events[0]["stepName"] == "test_step" + assert step_events[1]["type"] == "STEP_FINISHED" + assert step_events[1]["stepName"] == "test_step" + + +@pytest.mark.asyncio +async def test_emitter_state_updates(): + """Test state update events.""" + emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() + + state1 = TestState(value=1, text="first") + state2 = TestState(value=2, text="second") + + emitter.update_state(state1) + emitter.update_state(state2) + await emitter.close() + + events = [] + async for event in emitter: + events.append(event) + + state_events = [e for e in events if e["type"] == "STATE_SNAPSHOT"] + assert len(state_events) == 2 + assert state_events[0]["snapshot"] == {"value": 1, "text": "first"} + assert state_events[1]["snapshot"] == {"value": 2, "text": "second"} + + +@pytest.mark.asyncio +async def test_emitter_activity_events(): + """Test activity events.""" + emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() + + emitter.update_activity("processing", "Processing data") + emitter.update_activity("done", "Completed", message_id="msg-1") + await emitter.close() + + events = [] + async for event in emitter: + events.append(event) + + activity_events = [e for e in events if e["type"] == "ACTIVITY_SNAPSHOT"] + assert len(activity_events) == 2 + assert activity_events[0]["activityType"] == "processing" + assert activity_events[0]["content"] == "Processing data" + assert activity_events[1]["messageId"] == "msg-1" + assert activity_events[1]["activityType"] == "done" + + +@pytest.mark.asyncio +async def test_emitter_text_messages(): + """Test text message events.""" + emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() + + emitter.log("Test message", role="assistant") + emitter.log("Another message", role="user") + await emitter.close() + + events = [] + async for event in emitter: + events.append(event) + + text_events = [e for e in events if e["type"] == "TEXT_MESSAGE_CHUNK"] + assert len(text_events) == 2 + assert text_events[0]["delta"] == "Test message" + assert text_events[0]["role"] == "assistant" + assert text_events[1]["delta"] == "Another message" + assert text_events[1]["role"] == "user" + + +@pytest.mark.asyncio +async def test_emitter_error(): + """Test error event emission.""" + emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() + + error = ValueError("Test error") + emitter.error(error, code="TEST_ERROR") + await emitter.close() + + events = [] + async for event in emitter: + events.append(event) + + error_events = [e for e in events if e["type"] == "RUN_ERROR"] + assert len(error_events) == 1 + assert error_events[0]["message"] == "Test error" + assert error_events[0]["code"] == "TEST_ERROR" + + +@pytest.mark.asyncio +async def test_emitter_thread_and_run_ids(): + """Test thread and run ID management.""" + emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter( + thread_id="thread-1", run_id="run-1" + ) + + assert emitter.thread_id == "thread-1" + assert emitter.run_id == "run-1" + + initial_state = TestState(value=1, text="test") + emitter.start_run(initial_state) + await emitter.close() + + events = [] + async for event in emitter: + events.append(event) + + run_started = [e for e in events if e["type"] == "RUN_STARTED"][0] + assert run_started["threadId"] == "thread-1" + assert run_started["runId"] == "run-1" + + +@pytest.mark.asyncio +async def test_emitter_generates_thread_id(): + """Test that thread ID is generated from state hash when not provided.""" + emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() + + initial_state = TestState(value=42, text="test") + emitter.start_run(initial_state) + + # Thread ID should be generated deterministically from state + assert emitter.thread_id is not None + assert len(emitter.thread_id) > 0 + + await emitter.close() + async for _ in emitter: + pass + + +@pytest.mark.asyncio +async def test_emitter_closes_properly(): + """Test that emitter closes and stops iteration.""" + emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() + + emitter.log("Message 1") + await emitter.close() + + # Attempting to iterate after close should work and stop + events = [] + async for event in emitter: + events.append(event) + + # Should have received the message and then stopped + assert len(events) == 1 + + +@pytest.mark.asyncio +async def test_emitter_concurrent_emission(): + """Test that multiple events can be emitted concurrently.""" + emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() + + async def emit_many(): + for i in range(10): + emitter.log(f"Message {i}") + await asyncio.sleep(0.001) # Simulate some work + await emitter.close() + + # Start emission in background + emit_task = asyncio.create_task(emit_many()) + + # Collect events + events = [] + async for event in emitter: + events.append(event) + + await emit_task + + # Should have all 10 messages + text_events = [e for e in events if e["type"] == "TEXT_MESSAGE_CHUNK"] + assert len(text_events) == 10 diff --git a/tests/agui/test_events.py b/tests/agui/test_events.py new file mode 100644 index 00000000..67db2e22 --- /dev/null +++ b/tests/agui/test_events.py @@ -0,0 +1,155 @@ +"""Tests for AG-UI event creation utilities.""" + +from pydantic import BaseModel + +from haiku.rag.agui.events import ( + emit_activity, + emit_run_error, + emit_run_finished, + emit_run_started, + emit_state_snapshot, + emit_step_finished, + emit_step_started, + emit_text_message, +) + + +class TestState(BaseModel): + """Test state model.""" + + value: int + + +class TestResult(BaseModel): + """Test result model.""" + + status: str + + +def test_emit_run_started(): + """Test RUN_STARTED event creation.""" + event = emit_run_started("thread-1", "run-1") + + assert event["type"] == "RUN_STARTED" + assert event["threadId"] == "thread-1" + assert event["runId"] == "run-1" + assert "input" not in event + + +def test_emit_run_started_with_input(): + """Test RUN_STARTED event with input data.""" + event = emit_run_started("thread-1", "run-1", input_data="test input") + + assert event["type"] == "RUN_STARTED" + assert event["input"] == "test input" + + +def test_emit_run_finished(): + """Test RUN_FINISHED event creation.""" + result = TestResult(status="complete") + event = emit_run_finished("thread-1", "run-1", result) + + assert event["type"] == "RUN_FINISHED" + assert event["threadId"] == "thread-1" + assert event["runId"] == "run-1" + assert event["result"] == {"status": "complete"} + + +def test_emit_run_finished_with_dict(): + """Test RUN_FINISHED event with dict result.""" + result = {"status": "complete", "count": 42} + event = emit_run_finished("thread-1", "run-1", result) + + assert event["type"] == "RUN_FINISHED" + assert event["result"] == result + + +def test_emit_run_error(): + """Test RUN_ERROR event creation.""" + event = emit_run_error("Something went wrong") + + assert event["type"] == "RUN_ERROR" + assert event["message"] == "Something went wrong" + assert "code" not in event + + +def test_emit_run_error_with_code(): + """Test RUN_ERROR event with error code.""" + event = emit_run_error("Something went wrong", code="ERR_001") + + assert event["type"] == "RUN_ERROR" + assert event["message"] == "Something went wrong" + assert event["code"] == "ERR_001" + + +def test_emit_step_started(): + """Test STEP_STARTED event creation.""" + event = emit_step_started("plan") + + assert event["type"] == "STEP_STARTED" + assert event["stepName"] == "plan" + + +def test_emit_step_finished(): + """Test STEP_FINISHED event creation.""" + event = emit_step_finished("plan") + + assert event["type"] == "STEP_FINISHED" + assert event["stepName"] == "plan" + + +def test_emit_text_message(): + """Test TEXT_MESSAGE_CHUNK event creation.""" + event = emit_text_message("Hello world") + + assert event["type"] == "TEXT_MESSAGE_CHUNK" + assert event["delta"] == "Hello world" + assert event["role"] == "assistant" + assert "messageId" in event + + +def test_emit_text_message_with_role(): + """Test TEXT_MESSAGE_CHUNK event with custom role.""" + event = emit_text_message("Hello", role="user") + + assert event["type"] == "TEXT_MESSAGE_CHUNK" + assert event["role"] == "user" + + +def test_emit_state_snapshot(): + """Test STATE_SNAPSHOT event creation.""" + state = TestState(value=42) + event = emit_state_snapshot(state) + + assert event["type"] == "STATE_SNAPSHOT" + assert event["snapshot"] == {"value": 42} + + +def test_emit_activity(): + """Test ACTIVITY_SNAPSHOT event creation.""" + event = emit_activity("msg-1", "processing", "Working on task") + + assert event["type"] == "ACTIVITY_SNAPSHOT" + assert event["messageId"] == "msg-1" + assert event["activityType"] == "processing" + assert event["content"] == "Working on task" + + +def test_event_structure_consistency(): + """Test that all events have consistent structure.""" + events = [ + emit_run_started("t1", "r1"), + emit_run_finished("t1", "r1", {"result": "done"}), + emit_run_error("error"), + emit_step_started("step1"), + emit_step_finished("step1"), + emit_text_message("text"), + emit_state_snapshot(TestState(value=1)), + emit_activity("m1", "type", "content"), + ] + + for event in events: + assert isinstance(event, dict) + assert "type" in event + assert isinstance(event["type"], str) + assert event["type"].isupper() # Event types are uppercase diff --git a/tests/agui/test_server.py b/tests/agui/test_server.py new file mode 100644 index 00000000..2752b153 --- /dev/null +++ b/tests/agui/test_server.py @@ -0,0 +1,250 @@ +"""Tests for AG-UI server.""" + +import pytest +from pydantic import BaseModel +from starlette.testclient import TestClient + +from haiku.rag.agui.server import RunAgentInput, create_agui_app, format_sse_event +from haiku.rag.config.models import AGUIConfig + + +class SimpleState(BaseModel): + """Simple state for testing.""" + + question: str + + +class SimpleResult(BaseModel): + """Simple result for testing.""" + + answer: str + + +class MockGraph: + """Mock graph that returns immediately.""" + + async def run(self, state, deps): # type: ignore[no-untyped-def] + """Return a simple result.""" + return SimpleResult(answer=f"Answer to: {state.question}") + + +def test_run_agent_input_parsing(): + """Test RunAgentInput model parsing.""" + data = { + "threadId": "thread-1", + "runId": "run-1", + "state": {"question": "What is AI?"}, + "messages": [], + "config": {}, + } + + input_data = RunAgentInput(**data) + + assert input_data.thread_id == "thread-1" + assert input_data.run_id == "run-1" + assert input_data.state == {"question": "What is AI?"} + + +def test_run_agent_input_defaults(): + """Test RunAgentInput with defaults.""" + input_data = RunAgentInput() # type: ignore[call-arg] + + assert input_data.thread_id is None + assert input_data.run_id is None + assert input_data.state == {} + assert input_data.messages == [] + assert input_data.config == {} + + +def test_format_sse_event(): + """Test SSE event formatting.""" + event = {"type": "TEST_EVENT", "data": "test"} + + sse = format_sse_event(event) + + assert sse.startswith("data: ") + assert sse.endswith("\n\n") + assert '{"type": "TEST_EVENT"' in sse + + +def test_create_agui_app_basic(): + """Test basic app creation.""" + config = AGUIConfig( + host="localhost", + port=8000, + cors_origins=["http://localhost"], + ) + + def graph_factory(): + return MockGraph() + + def state_factory(input_state): + return SimpleState(question=input_state.get("question", "")) + + def deps_factory(input_config): + from dataclasses import dataclass + + @dataclass + class SimpleDeps: + agui_emitter: None = None + + return SimpleDeps() + + app = create_agui_app( + graph_factory=graph_factory, # type: ignore[arg-type] + state_factory=state_factory, + deps_factory=deps_factory, + config=config, + ) + + # Should return a Starlette app + assert app is not None + assert hasattr(app, "routes") + + +def test_server_health_endpoint(): + """Test health check endpoint.""" + config = AGUIConfig() + + def graph_factory(): + return MockGraph() + + def state_factory(input_state): + return SimpleState(question="") + + def deps_factory(input_config): + from dataclasses import dataclass + + @dataclass + class SimpleDeps: + agui_emitter: None = None + + return SimpleDeps() + + app = create_agui_app( + graph_factory=graph_factory, # type: ignore[arg-type] + state_factory=state_factory, + deps_factory=deps_factory, + config=config, + ) + + client = TestClient(app) + response = client.get("/health") + + assert response.status_code == 200 + assert response.json() == {"status": "healthy"} + + +@pytest.mark.asyncio +async def test_server_stream_endpoint(): + """Test AG-UI streaming endpoint.""" + config = AGUIConfig() + + def graph_factory(): + return MockGraph() + + def state_factory(input_state): + question = input_state.get("question", "") + return SimpleState(question=question) + + def deps_factory(input_config): + from dataclasses import dataclass + + @dataclass + class SimpleDeps: + agui_emitter: None = None + + return SimpleDeps() + + app = create_agui_app( + graph_factory=graph_factory, # type: ignore[arg-type] + state_factory=state_factory, + deps_factory=deps_factory, + config=config, + ) + + client = TestClient(app) + + request_data = { + "threadId": "test-1", + "runId": "run-1", + "state": {"question": "What is pydantic-graph?"}, + "messages": [], + "config": {}, + } + + response = client.post("/v1/agent/stream", json=request_data) + + assert response.status_code == 200 + assert response.headers["content-type"] == "text/event-stream; charset=utf-8" + + # Read the streamed events + events = [] + for line in response.iter_lines(): + if line.startswith("data: "): + import json + + event_data = line[6:] # Remove "data: " prefix + event = json.loads(event_data) + events.append(event) + + # Should have received multiple events + assert len(events) > 0 + + # Should have RUN_STARTED and RUN_FINISHED + event_types = [e["type"] for e in events] + assert "RUN_STARTED" in event_types + assert "RUN_FINISHED" in event_types + + +def test_server_cors_headers(): + """Test CORS middleware is configured.""" + config = AGUIConfig( + cors_origins=["http://example.com"], + cors_credentials=True, + ) + + def graph_factory(): + return MockGraph() + + def state_factory(input_state): + return SimpleState(question="") + + def deps_factory(input_config): + from dataclasses import dataclass + + @dataclass + class SimpleDeps: + agui_emitter: None = None + + return SimpleDeps() + + app = create_agui_app( + graph_factory=graph_factory, # type: ignore[arg-type] + state_factory=state_factory, + deps_factory=deps_factory, + config=config, + ) + + client = TestClient(app) + + # GET request with Origin header should get CORS headers + response = client.get("/health", headers={"Origin": "http://example.com"}) + + assert response.status_code == 200 + # CORS middleware should add access-control headers + assert ( + "access-control-allow-origin" in response.headers or response.status_code == 200 + ) + + +def test_agui_config_defaults(): + """Test AGUIConfig default values.""" + config = AGUIConfig() + + assert config.host == "0.0.0.0" + assert config.port == 8000 + assert config.cors_origins == ["*"] + assert config.cors_credentials is True + assert "GET" in config.cors_methods + assert "POST" in config.cors_methods diff --git a/tests/agui/test_stream.py b/tests/agui/test_stream.py new file mode 100644 index 00000000..1aca01b4 --- /dev/null +++ b/tests/agui/test_stream.py @@ -0,0 +1,199 @@ +"""Tests for stream_graph function.""" + +from dataclasses import dataclass + +import pytest +from pydantic import BaseModel + +from haiku.rag.agui.emitter import AGUIEmitter +from haiku.rag.agui.stream import stream_graph + + +class TestState(BaseModel): + """Test state model.""" + + value: int + + +@dataclass +class TestDeps: + """Test dependencies.""" + + agui_emitter: AGUIEmitter | None = None + + +class MockGraph: + """Mock graph for testing.""" + + def __init__(self, result: str | dict[str, str | int] = "done"): + self.result = result + self.run_called = False + + async def run(self, state, deps): # type: ignore[no-untyped-def] + """Mock run method.""" + self.run_called = True + + # Emit some events through the emitter + if deps.agui_emitter: + deps.agui_emitter.start_step("mock_step") + deps.agui_emitter.update_activity("working", "Doing work") + deps.agui_emitter.finish_step() + + return self.result + + +@pytest.mark.asyncio +async def test_stream_graph_basic(): + """Test basic graph streaming.""" + graph = MockGraph(result="success") + state = TestState(value=1) + deps = TestDeps() + + events = [] + async for event in stream_graph(graph, state, deps): + events.append(event) + + # Should have collected events + assert len(events) > 0 + + # Should have RUN_STARTED and RUN_FINISHED + event_types = [e["type"] for e in events] + assert "RUN_STARTED" in event_types + assert "RUN_FINISHED" in event_types + + # Graph should have been executed + assert graph.run_called + + +@pytest.mark.asyncio +async def test_stream_graph_emits_initial_state(): + """Test that initial state is emitted.""" + graph = MockGraph() + state = TestState(value=42) + deps = TestDeps() + + events = [] + async for event in stream_graph(graph, state, deps): + events.append(event) + + # Should have initial state snapshot + state_snapshots = [e for e in events if e["type"] == "STATE_SNAPSHOT"] + assert len(state_snapshots) > 0 + # First snapshot should be initial state + assert state_snapshots[0]["snapshot"] == {"value": 42} + + +@pytest.mark.asyncio +async def test_stream_graph_emits_step_events(): + """Test that step events from graph are emitted.""" + graph = MockGraph() + state = TestState(value=1) + deps = TestDeps() + + events = [] + async for event in stream_graph(graph, state, deps): + events.append(event) + + # Should have step events from MockGraph + step_started = [e for e in events if e["type"] == "STEP_STARTED"] + assert len(step_started) > 0 + assert step_started[0]["stepName"] == "mock_step" + + +@pytest.mark.asyncio +async def test_stream_graph_emits_activity(): + """Test that activity events from graph are emitted.""" + graph = MockGraph() + state = TestState(value=1) + deps = TestDeps() + + events = [] + async for event in stream_graph(graph, state, deps): + events.append(event) + + # Should have activity events from MockGraph + activities = [e for e in events if e["type"] == "ACTIVITY_SNAPSHOT"] + assert len(activities) > 0 + assert activities[0]["content"] == "Doing work" + + +@pytest.mark.asyncio +async def test_stream_graph_handles_error(): + """Test that graph errors are captured and emitted.""" + + class ErrorGraph: + async def run(self, state, deps): + raise ValueError("Test error") + + graph = ErrorGraph() + state = TestState(value=1) + deps = TestDeps() + + events = [] + async for event in stream_graph(graph, state, deps): + events.append(event) + + # Should have error event + errors = [e for e in events if e["type"] == "RUN_ERROR"] + assert len(errors) > 0 + assert "Test error" in errors[0]["message"] + + +@pytest.mark.asyncio +async def test_stream_graph_closes_emitter(): + """Test that emitter is properly closed.""" + + class NeverReturnsGraph: + async def run(self, state, deps): + # Don't return anything + pass + + graph = NeverReturnsGraph() + state = TestState(value=1) + deps = TestDeps() + + events = [] + try: + async for event in stream_graph(graph, state, deps): + events.append(event) + except RuntimeError: + # Expected - graph didn't return a result + pass + + # Should have error event about no result + errors = [e for e in events if e["type"] == "RUN_ERROR"] + assert len(errors) > 0 + + +@pytest.mark.asyncio +async def test_stream_graph_without_emitter_support(): + """Test error when deps doesn't support agui_emitter.""" + + @dataclass + class BadDeps: + pass + + graph = MockGraph() + state = TestState(value=1) + deps = BadDeps() + + with pytest.raises(TypeError, match="agui_emitter"): + async for _ in stream_graph(graph, state, deps): # type: ignore[arg-type] + pass + + +@pytest.mark.asyncio +async def test_stream_graph_result_in_finish_event(): + """Test that graph result is included in RUN_FINISHED event.""" + graph = MockGraph(result={"status": "complete", "count": 42}) + state = TestState(value=1) + deps = TestDeps() + + events = [] + async for event in stream_graph(graph, state, deps): + events.append(event) + + # Find RUN_FINISHED event + finished = [e for e in events if e["type"] == "RUN_FINISHED"] + assert len(finished) == 1 + assert finished[0]["result"] == {"status": "complete", "count": 42} diff --git a/tests/test_research_graph.py b/tests/test_research_graph.py index 0a7c0b05..94f1f6a3 100644 --- a/tests/test_research_graph.py +++ b/tests/test_research_graph.py @@ -1,8 +1,13 @@ import asyncio +import pytest +from pydantic_ai.models.test import TestModel + +from haiku.rag.agui.stream import stream_graph +from haiku.rag.client import HaikuRAG from haiku.rag.research.dependencies import ResearchContext from haiku.rag.research.graph import build_research_graph -from haiku.rag.research.state import ResearchState +from haiku.rag.research.state import ResearchDeps, ResearchState def test_build_graph_and_state(): @@ -24,3 +29,57 @@ def test_async_loop_available(): # Ensure an event loop can be created in test env loop = asyncio.new_event_loop() loop.close() + + +@pytest.mark.asyncio +async def test_graph_end_to_end_with_test_model(monkeypatch, temp_db_path): + """Test research graph with mocked LLM using AG-UI events.""" + + # Mock get_model to return TestModel which generates valid schema-compliant data + def test_model_factory(_provider, _model): + return TestModel() + + monkeypatch.setattr("haiku.rag.graph_common.utils.get_model", test_model_factory) + monkeypatch.setattr("haiku.rag.research.graph.get_model", test_model_factory) + + graph = build_research_graph() + + state = ResearchState( + context=ResearchContext(original_question="What is haiku.rag?"), + max_iterations=1, + confidence_threshold=0.5, + max_concurrency=2, + ) + + # Use real client but with TestModel for LLM calls + client = HaikuRAG(temp_db_path) + deps = ResearchDeps(client=client) + + events = [] + result = None + async for event in stream_graph(graph, state, deps): + events.append(event) + if event["type"] == "RUN_FINISHED": + result = event["result"] + elif event["type"] == "RUN_ERROR": + pytest.fail(f"Graph execution failed: {event['message']}") + + # TestModel will generate valid structured output for each node + assert result is not None, ( + f"No result. Events collected: {[e['type'] for e in events]}" + ) + # Result is serialized as dict in AG-UI events + assert isinstance(result, dict) + assert "title" in result + assert isinstance(result["title"], str) + assert "executive_summary" in result + assert "main_findings" in result + + # Verify AG-UI events were emitted + event_types = [e["type"] for e in events] + assert "RUN_STARTED" in event_types + assert "RUN_FINISHED" in event_types + assert "STATE_SNAPSHOT" in event_types + assert "STEP_STARTED" in event_types + + client.close() diff --git a/tests/test_research_graph_integration.py b/tests/test_research_graph_integration.py deleted file mode 100644 index 5bdf40a6..00000000 --- a/tests/test_research_graph_integration.py +++ /dev/null @@ -1,55 +0,0 @@ -import pytest -from pydantic_ai.models.test import TestModel - -from haiku.rag.client import HaikuRAG -from haiku.rag.research.dependencies import ResearchContext -from haiku.rag.research.graph import build_research_graph -from haiku.rag.research.models import ResearchReport -from haiku.rag.research.state import ResearchDeps, ResearchState -from haiku.rag.research.stream import stream_research_graph - - -@pytest.mark.asyncio -async def test_graph_end_to_end_with_test_model(monkeypatch, temp_db_path): - """Test research graph with mocked LLM using TestModel.""" - - # Mock get_model to return TestModel which generates valid schema-compliant data - def test_model_factory(provider, model): - return TestModel() - - monkeypatch.setattr("haiku.rag.graph_common.utils.get_model", test_model_factory) - monkeypatch.setattr("haiku.rag.research.graph.get_model", test_model_factory) - - graph = build_research_graph() - - state = ResearchState( - context=ResearchContext(original_question="What is haiku.rag?"), - max_iterations=1, - confidence_threshold=0.5, - max_concurrency=2, - ) - - # Use real client but with TestModel for LLM calls - client = HaikuRAG(temp_db_path) - deps = ResearchDeps(client=client, console=None) - - collected = [] - report = None - async for event in stream_research_graph(graph, state, deps): - collected.append(event) - if event.type == "report": - report = event.report - break - elif event.type == "error": - pytest.fail(f"Graph execution failed: {event.error}") - - # TestModel will generate valid structured output for each node - assert report is not None, ( - f"No report generated. Events collected: {[e.type for e in collected]}" - ) - assert isinstance(report, ResearchReport) - assert report.title is not None - assert isinstance(report.title, str) - assert any(evt.type == "log" for evt in collected) - - client.close() From d5b6ad544de7100bb32b89089244013706b48bdc Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Tue, 11 Nov 2025 13:09:27 +0200 Subject: [PATCH 07/17] Bring back verbose --- haiku_rag_slim/haiku/rag/app.py | 18 ++++++++++++++---- haiku_rag_slim/haiku/rag/cli.py | 7 ++++++- 2 files changed, 20 insertions(+), 5 deletions(-) diff --git a/haiku_rag_slim/haiku/rag/app.py b/haiku_rag_slim/haiku/rag/app.py index bac4c6f9..40a20fe6 100644 --- a/haiku_rag_slim/haiku/rag/app.py +++ b/haiku_rag_slim/haiku/rag/app.py @@ -243,11 +243,12 @@ class HaikuRAGApp: except Exception as e: self.console.print(f"[red]Error: {e}[/red]") - async def research(self, question: str): + async def research(self, question: str, verbose: bool = False): """Run research via the pydantic-graph pipeline. Args: question: The research question + verbose: Show AG-UI event stream during execution """ async with HaikuRAG(db_path=self.db_path, config=self.config) as client: try: @@ -260,9 +261,18 @@ class HaikuRAGApp: state = ResearchState.from_config(context=context, config=self.config) deps = ResearchDeps(client=client) - # Use AG-UI renderer to process events - renderer = AGUIConsoleRenderer(self.console) - report_dict = await renderer.render(stream_graph(graph, state, deps)) + if verbose: + # Use AG-UI renderer to process and display events + renderer = AGUIConsoleRenderer(self.console) + report_dict = await renderer.render( + stream_graph(graph, state, deps) + ) + else: + # Run without rendering events, just get the result + report = await graph.run(state=state, deps=deps) + report_dict = ( + report.model_dump() if hasattr(report, "model_dump") else report + ) if report_dict is None: self.console.print("[red]Research did not produce a report.[/red]") diff --git a/haiku_rag_slim/haiku/rag/cli.py b/haiku_rag_slim/haiku/rag/cli.py index ab6a1914..dc99ec2a 100644 --- a/haiku_rag_slim/haiku/rag/cli.py +++ b/haiku_rag_slim/haiku/rag/cli.py @@ -299,9 +299,14 @@ def research( "--db", help="Path to the LanceDB database file", ), + verbose: bool = typer.Option( + False, + "--verbose", + help="Show planning, searching previews, evaluation summary, and stop reason", + ), ): app = create_app(db) - asyncio.run(app.research(question=question)) + asyncio.run(app.research(question=question, verbose=verbose)) @cli.command("settings", help="Display current configuration settings") From ed27acc1a207f1c9f62faf8aebaf5e5d13664a02 Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Tue, 11 Nov 2025 13:11:31 +0200 Subject: [PATCH 08/17] Update docs --- README.md | 20 ++++---- docs/agents.md | 24 +++++----- docs/cli.md | 37 +++++++++----- docs/configuration.md | 57 ++++++++++++++++++++++ docs/server.md | 109 +++++++++++++++++++++++++++++++++++++++++- 5 files changed, 210 insertions(+), 37 deletions(-) diff --git a/README.md b/README.md index 78173afc..e374b79c 100644 --- a/README.md +++ b/README.md @@ -84,6 +84,7 @@ To customize settings, create a `haiku.rag.yaml` config file (see [Configuration ## Python Usage ```python +from haiku.rag.agui.stream import stream_graph from haiku.rag.client import HaikuRAG from haiku.rag.config import Config from haiku.rag.research import ( @@ -91,7 +92,6 @@ from haiku.rag.research import ( ResearchDeps, ResearchState, build_research_graph, - stream_research_graph, ) async with HaikuRAG("database.lancedb") as client: @@ -126,15 +126,17 @@ async with HaikuRAG("database.lancedb") as client: report = await graph.run(state=state, deps=deps) print(report.title) - # Streaming progress (log/report/error events) - async for event in stream_research_graph(graph, state, deps): - if event.type == "log": - iteration = event.state.iterations if event.state else state.iterations - print(f"[{iteration}] {event.message}") - elif event.type == "report": + # Streaming progress (AG-UI events) + async for event in stream_graph(graph, state, deps): + if event["type"] == "STEP_STARTED": + print(f"Starting step: {event['stepName']}") + elif event["type"] == "ACTIVITY_SNAPSHOT": + print(f" {event['content']}") + elif event["type"] == "RUN_FINISHED": print("\nResearch complete!\n") - print(event.report.title) - print(event.report.executive_summary) + result = event["result"] + print(result["title"]) + print(result["executive_summary"]) ``` ## MCP Server diff --git a/docs/agents.md b/docs/agents.md index e9fb688a..73355100 100644 --- a/docs/agents.md +++ b/docs/agents.md @@ -250,15 +250,15 @@ deps = ResearchDeps(client=client) result = await graph.run(state=state, deps=deps) ``` -Python usage (streamed events): +Python usage (streamed AG-UI events): ```python +from haiku.rag.agui.stream import stream_graph from haiku.rag.client import HaikuRAG from haiku.rag.config import Config from haiku.rag.research.dependencies import ResearchContext from haiku.rag.research.graph import build_research_graph from haiku.rag.research.state import ResearchDeps, ResearchState -from haiku.rag.research.stream import stream_research_graph async with HaikuRAG(path_to_db) as client: graph = build_research_graph(config=Config) @@ -267,16 +267,14 @@ async with HaikuRAG(path_to_db) as client: state = ResearchState.from_config(context=context, config=Config) deps = ResearchDeps(client=client) - async for event in stream_research_graph( - graph, - state, - deps, - ): - if event.type == "log": - iteration = event.state.iterations if event.state else state.iterations - print(f"[{iteration}] {event.message}") - elif event.type == "report": + async for event in stream_graph(graph, state, deps): + if event["type"] == "STEP_STARTED": + print(f"Starting step: {event['stepName']}") + elif event["type"] == "ACTIVITY_SNAPSHOT": + print(f" {event['content']}") + elif event["type"] == "RUN_FINISHED": print("\nResearch complete!\n") - print(event.report.title) - print(event.report.executive_summary) + result = event["result"] + print(result["title"]) + print(result["executive_summary"]) ``` diff --git a/docs/cli.md b/docs/cli.md index 11e2a5b0..9f433cd7 100644 --- a/docs/cli.md +++ b/docs/cli.md @@ -146,20 +146,23 @@ When available, citations use the document title; otherwise they fall back to th Run the multi-step research graph: ```bash -haiku-rag research "How does haiku.rag organize and query documents?" \ - --max-iterations 2 \ - --confidence-threshold 0.8 \ - --max-concurrency 3 \ - --verbose +haiku-rag research "How does haiku.rag organize and query documents?" +``` + +With verbose output to see progress: + +```bash +haiku-rag research "How does haiku.rag organize and query documents?" --verbose ``` Flags: -- `--max-iterations, -n`: maximum search/evaluate cycles (default: 3) -- `--confidence-threshold`: stop once evaluation confidence meets/exceeds this (default: 0.8) -- `--max-concurrency`: number of sub-questions searched in parallel each iteration (default: 3) -- `--verbose`: show planning, searching previews, evaluation summary, and stop reason +- `--verbose`: Show planning, searching previews, evaluation summary, and stop reason -When `--verbose` is set the CLI also consumes the internal research stream, printing every `log` event as agents progress through planning, search, evaluation, and synthesis. If you build your own integration, call `stream_research_graph` to access the same `log`, `report`, and `error` events and render them however you like while the graph is running. +Research parameters like `max_iterations`, `confidence_threshold`, and `max_concurrency` are configured in your [configuration file](configuration.md) under the `research` section. + +When `--verbose` is set, the CLI consumes the research graph's AG-UI event stream, displaying step events and activity snapshots as agents progress through planning, search, evaluation, and synthesis. Without `--verbose`, only the final research report is displayed. + +If you build your own integration, import `stream_graph` from `haiku.rag.agui.stream` to access AG-UI events (`STEP_STARTED`, `ACTIVITY_SNAPSHOT`, `STATE_SNAPSHOT`, `RUN_FINISHED`, etc.) and render them however you like while the graph is running. ## Server @@ -174,10 +177,18 @@ haiku-rag serve --mcp --stdio # File monitoring only haiku-rag serve --monitor -# Both services -haiku-rag serve --monitor --mcp +# AG-UI server only +haiku-rag serve --agui -# Custom port +# Multiple services +haiku-rag serve --monitor --mcp +haiku-rag serve --monitor --agui +haiku-rag serve --mcp --agui + +# All services +haiku-rag serve --monitor --mcp --agui + +# Custom MCP port haiku-rag serve --mcp --mcp-port 9000 ``` diff --git a/docs/configuration.md b/docs/configuration.md index db0571d4..3f8da060 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -83,6 +83,17 @@ qa: research: provider: "" # Empty to use qa settings model: "" + max_iterations: 3 + confidence_threshold: 0.8 + max_concurrency: 1 + +agui: + host: "0.0.0.0" + port: 8000 + cors_origins: ["*"] + cors_credentials: true + cors_methods: ["GET", "POST", "OPTIONS"] + cors_headers: ["*"] processing: chunk_size: 256 @@ -460,6 +471,52 @@ providers: **Note:** vLLM reranking uses the `/rerank` API endpoint. You need to run a vLLM server separately with a reranking model loaded. Consult the specific model's documentation for proper vLLM serving configuration. +## Research Configuration + +Configure the multi-agent research workflow: + +```yaml +research: + provider: "" # Empty to use qa settings + model: "" # Empty to use qa model + max_iterations: 3 # Maximum search/evaluate cycles + confidence_threshold: 0.8 # Stop when confidence meets/exceeds this + max_concurrency: 1 # Sub-questions searched in parallel per iteration +``` + +- **provider/model**: LLM provider and model for research. Leave empty to use the same settings as `qa`. +- **max_iterations**: Maximum number of search/evaluate cycles before stopping (default: 3) +- **confidence_threshold**: Stop research when evaluation confidence score meets or exceeds this threshold (default: 0.8) +- **max_concurrency**: Number of sub-questions to search in parallel during each iteration (default: 1) + +The research workflow plans sub-questions, searches in parallel batches, evaluates findings, and iterates until reaching the confidence threshold or max iterations. + +## AG-UI Server Configuration + +Configure the AG-UI HTTP server for streaming graph execution events: + +```yaml +agui: + host: "0.0.0.0" + port: 8000 + cors_origins: ["*"] + cors_credentials: true + cors_methods: ["GET", "POST", "OPTIONS"] + cors_headers: ["*"] +``` + +Start the AG-UI server with: + +```bash +haiku-rag serve --agui +``` + +The server exposes: +- `GET /health` - Health check endpoint +- `POST /v1/agent/stream` - Research graph streaming endpoint (Server-Sent Events) + +See [Server Mode](server.md) for more details. + ## Other Settings ### Database and Storage diff --git a/docs/server.md b/docs/server.md index 23a74f98..e53232df 100644 --- a/docs/server.md +++ b/docs/server.md @@ -1,10 +1,10 @@ # Server Mode -The server provides automatic file monitoring and MCP functionality. +The server provides automatic file monitoring, MCP functionality, and AG-UI graph streaming. ## Starting the Server -The `serve` command requires at least one service flag. You can enable file monitoring, MCP server, or both: +The `serve` command requires at least one service flag. You can enable file monitoring, MCP server, AG-UI server, or any combination: ### MCP Server Only @@ -93,3 +93,108 @@ The server can parse 40+ file formats including: - And more... URLs are also supported for web content. + +## AG-UI Server + +The AG-UI server provides HTTP streaming of research graph execution using Server-Sent Events (SSE). + +### Starting the AG-UI Server + +```bash +haiku-rag serve --agui +``` + +This starts an HTTP server (default: http://0.0.0.0:8000) that exposes: + +- `GET /health` - Health check endpoint +- `POST /v1/agent/stream` - Research graph streaming endpoint + +### Configuration + +Configure the AG-UI server in your `haiku.rag.yaml`: + +```yaml +agui: + host: "0.0.0.0" + port: 8000 + cors_origins: ["*"] + cors_credentials: true +``` + +See [Configuration](configuration.md#ag-ui-server-configuration) for all available options. + +### Using the Streaming Endpoint + +The `/v1/agent/stream` endpoint accepts POST requests with research parameters and streams AG-UI events: + +**Request format:** +```json +{ + "threadId": "optional-thread-id", + "runId": "optional-run-id", + "state": { + "context": { + "original_question": "What are the key features of haiku.rag?" + } + }, + "messages": [], + "config": {} +} +``` + +**Example with curl:** +```bash +curl -X POST http://localhost:8000/v1/agent/stream \ + -H "Content-Type: application/json" \ + -d '{ + "state": { + "context": { + "original_question": "What are the key features of haiku.rag?" + } + } + }' \ + --no-buffer +``` + +The `--no-buffer` flag ensures curl displays events as they arrive instead of buffering them. + +**Response:** Server-Sent Events stream with AG-UI protocol events: +- `RUN_STARTED` - Graph execution started +- `STATE_SNAPSHOT` - Current state snapshot +- `STEP_STARTED` - Node execution started +- `STEP_FINISHED` - Node execution completed +- `ACTIVITY_SNAPSHOT` - Progress update +- `RUN_FINISHED` - Graph execution completed with result +- `RUN_ERROR` - Error during execution + +Example event output: +``` +data: {"type":"RUN_STARTED","threadId":"abc123","runId":"xyz789"} + +data: {"type":"STATE_SNAPSHOT","snapshot":{"context":{"original_question":"What are the key features of haiku.rag?"},"iterations":0}} + +data: {"type":"STEP_STARTED","stepName":"plan"} + +data: {"type":"ACTIVITY_SNAPSHOT","messageId":"msg-1","activityType":"planning","content":"Creating research plan"} + +data: {"type":"STEP_FINISHED","stepName":"plan"} + +data: {"type":"RUN_FINISHED","threadId":"abc123","runId":"xyz789","result":{"title":"Research Report","executive_summary":"..."}} +``` + +The endpoint follows the [AG-UI protocol](https://docs.ag-ui.com/concepts/events) for event streaming. + +### Running Multiple Services + +You can run any combination of services: + +```bash +# File monitoring + AG-UI +haiku-rag serve --monitor --agui + +# MCP + AG-UI +haiku-rag serve --mcp --agui + +# All services +haiku-rag serve --monitor --mcp --agui +``` From 8f0597e89e84adcb8f7359f0a50ffb1ce058b728 Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Tue, 11 Nov 2025 14:18:52 +0200 Subject: [PATCH 09/17] Update deep ask graph --- docs/server.md | 38 ++- haiku_rag_slim/haiku/rag/agui/__init__.py | 4 +- haiku_rag_slim/haiku/rag/agui/server.py | 152 +++++++++-- haiku_rag_slim/haiku/rag/app.py | 26 +- haiku_rag_slim/haiku/rag/qa/deep/graph.py | 314 +++++++++++----------- haiku_rag_slim/haiku/rag/qa/deep/state.py | 31 ++- tests/test_app.py | 18 +- tests/test_deep_qa.py | 4 +- 8 files changed, 353 insertions(+), 234 deletions(-) diff --git a/docs/server.md b/docs/server.md index e53232df..74c039c4 100644 --- a/docs/server.md +++ b/docs/server.md @@ -96,7 +96,7 @@ URLs are also supported for web content. ## AG-UI Server -The AG-UI server provides HTTP streaming of research graph execution using Server-Sent Events (SSE). +The AG-UI server provides HTTP streaming of both research and deep ask graph execution using Server-Sent Events (SSE). ### Starting the AG-UI Server @@ -107,7 +107,8 @@ haiku-rag serve --agui This starts an HTTP server (default: http://0.0.0.0:8000) that exposes: - `GET /health` - Health check endpoint -- `POST /v1/agent/stream` - Research graph streaming endpoint +- `POST /v1/research/stream` - Research graph streaming endpoint +- `POST /v1/deep-ask/stream` - Deep ask graph streaming endpoint ### Configuration @@ -123,9 +124,9 @@ agui: See [Configuration](configuration.md#ag-ui-server-configuration) for all available options. -### Using the Streaming Endpoint +### Using the Streaming Endpoints -The `/v1/agent/stream` endpoint accepts POST requests with research parameters and streams AG-UI events: +Both endpoints accept POST requests with the same AG-UI RunAgentInput format and stream AG-UI events. **Request format:** ```json @@ -133,24 +134,33 @@ The `/v1/agent/stream` endpoint accepts POST requests with research parameters a "threadId": "optional-thread-id", "runId": "optional-run-id", "state": { - "context": { - "original_question": "What are the key features of haiku.rag?" - } + "question": "What are the key features of haiku.rag?" }, "messages": [], "config": {} } ``` -**Example with curl:** +**Research endpoint example:** ```bash -curl -X POST http://localhost:8000/v1/agent/stream \ +curl -X POST http://localhost:8000/v1/research/stream \ -H "Content-Type: application/json" \ -d '{ "state": { - "context": { - "original_question": "What are the key features of haiku.rag?" - } + "question": "What are the key features of haiku.rag?" + } + }' \ + --no-buffer +``` + +**Deep ask endpoint example:** +```bash +curl -X POST http://localhost:8000/v1/deep-ask/stream \ + -H "Content-Type: application/json" \ + -d '{ + "state": { + "question": "How does haiku.rag handle document chunking?", + "use_citations": true } }' \ --no-buffer @@ -158,6 +168,10 @@ curl -X POST http://localhost:8000/v1/agent/stream \ The `--no-buffer` flag ensures curl displays events as they arrive instead of buffering them. +**Note:** The `state` object can include: +- `question`: The question to answer (required) +- `use_citations`: Enable citations in deep ask responses (optional, deep ask only) + **Response:** Server-Sent Events stream with AG-UI protocol events: - `RUN_STARTED` - Graph execution started - `STATE_SNAPSHOT` - Current state snapshot diff --git a/haiku_rag_slim/haiku/rag/agui/__init__.py b/haiku_rag_slim/haiku/rag/agui/__init__.py index 29ecd62d..fab7a016 100644 --- a/haiku_rag_slim/haiku/rag/agui/__init__.py +++ b/haiku_rag_slim/haiku/rag/agui/__init__.py @@ -21,7 +21,7 @@ from haiku.rag.agui.events import ( from haiku.rag.agui.server import ( RunAgentInput, create_agui_app, - create_research_server, + create_agui_server, format_sse_event, ) from haiku.rag.agui.state import compute_state_delta @@ -34,7 +34,7 @@ __all__ = [ "RunAgentInput", "compute_state_delta", "create_agui_app", - "create_research_server", + "create_agui_server", "emit_activity", "emit_activity_delta", "emit_run_error", diff --git a/haiku_rag_slim/haiku/rag/agui/server.py b/haiku_rag_slim/haiku/rag/agui/server.py index e0f94765..ff1be032 100644 --- a/haiku_rag_slim/haiku/rag/agui/server.py +++ b/haiku_rag_slim/haiku/rag/agui/server.py @@ -146,19 +146,20 @@ def format_sse_event(event: AGUIEvent) -> str: return f"data: {event_json}\n\n" -def create_research_server(config: Any, db_path: Any | None = None) -> Starlette: - """Create AG-UI server for research graph. - - This is a convenience function specifically for the research graph. +def create_agui_server(config: Any, db_path: Any | None = None) -> Starlette: + """Create AG-UI server with both research and deep ask endpoints. Args: - config: Application config with research settings + config: Application config with research and qa settings db_path: Optional database path override Returns: - Starlette app configured for research graph + Starlette app with research and deep ask endpoints """ from haiku.rag.client import HaikuRAG + from haiku.rag.qa.deep.dependencies import DeepQAContext + from haiku.rag.qa.deep.graph import build_deep_qa_graph + from haiku.rag.qa.deep.state import DeepQADeps, DeepQAState from haiku.rag.research.dependencies import ResearchContext from haiku.rag.research.graph import build_research_graph from haiku.rag.research.state import ResearchDeps, ResearchState @@ -166,44 +167,139 @@ def create_research_server(config: Any, db_path: Any | None = None) -> Starlette # Store client reference for proper lifecycle management _client_cache: dict[str, HaikuRAG] = {} - def graph_factory() -> Graph: - """Create research graph instance.""" + def get_client(effective_db_path: Any) -> HaikuRAG: + """Get or create cached client.""" + path_key = str(effective_db_path) + if path_key not in _client_cache: + _client_cache[path_key] = HaikuRAG(db_path=effective_db_path, config=config) + return _client_cache[path_key] + + # Research graph factories + def research_graph_factory() -> Graph: return build_research_graph(config) - def state_factory(input_state: dict[str, Any]) -> ResearchState: - """Create research state from input.""" - # Extract question from input state or messages + def research_state_factory(input_state: dict[str, Any]) -> ResearchState: question = input_state.get("question", "") if not question: - # Try to get from first message if available messages = input_state.get("messages", []) if messages: question = messages[0].get("content", "") - - # Create context and state context = ResearchContext(original_question=question) return ResearchState.from_config(context=context, config=config) - def deps_factory(input_config: dict[str, Any]) -> ResearchDeps: - """Create research dependencies.""" - # Use provided db_path or fallback to config + def research_deps_factory(input_config: dict[str, Any]) -> ResearchDeps: effective_db_path = ( db_path or input_config.get("db_path") or config.storage.data_dir / "haiku.rag.lancedb" ) + return ResearchDeps(client=get_client(effective_db_path)) - # Reuse existing client if available - path_key = str(effective_db_path) - if path_key not in _client_cache: - _client_cache[path_key] = HaikuRAG(db_path=effective_db_path, config=config) + # Deep ask graph factories + def deep_ask_graph_factory() -> Graph: + return build_deep_qa_graph(config) - return ResearchDeps(client=_client_cache[path_key]) + def deep_ask_state_factory(input_state: dict[str, Any]) -> DeepQAState: + question = input_state.get("question", "") + if not question: + messages = input_state.get("messages", []) + if messages: + question = messages[0].get("content", "") + use_citations = input_state.get("use_citations", False) + context = DeepQAContext(original_question=question, use_citations=use_citations) + return DeepQAState.from_config(context=context, config=config) - # Use AG-UI config from app config - return create_agui_app( - graph_factory=graph_factory, - state_factory=state_factory, - deps_factory=deps_factory, - config=config.agui, + def deep_ask_deps_factory(input_config: dict[str, Any]) -> DeepQADeps: + effective_db_path = ( + db_path + or input_config.get("db_path") + or config.storage.data_dir / "haiku.rag.lancedb" + ) + return DeepQADeps(client=get_client(effective_db_path)) + + # Create event stream functions for each graph type + async def research_event_stream( + input_data: RunAgentInput, + ) -> AsyncIterator[str]: + """Generate SSE event stream from research graph execution.""" + graph = research_graph_factory() + initial_state = research_state_factory(input_data.state) + deps = research_deps_factory(input_data.config) + + async for event in stream_graph(graph, initial_state, deps): + event_data = format_sse_event(event) + yield event_data + + async def deep_ask_event_stream( + input_data: RunAgentInput, + ) -> AsyncIterator[str]: + """Generate SSE event stream from deep ask graph execution.""" + graph = deep_ask_graph_factory() + initial_state = deep_ask_state_factory(input_data.state) + deps = deep_ask_deps_factory(input_data.config) + + async for event in stream_graph(graph, initial_state, deps): + event_data = format_sse_event(event) + yield event_data + + # Endpoint handlers + async def stream_research(request: Request) -> StreamingResponse: + """Research graph streaming endpoint.""" + body = await request.json() + input_data = RunAgentInput(**body) + + return StreamingResponse( + research_event_stream(input_data), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "X-Accel-Buffering": "no", + }, + ) + + async def stream_deep_ask(request: Request) -> StreamingResponse: + """Deep ask graph streaming endpoint.""" + body = await request.json() + input_data = RunAgentInput(**body) + + return StreamingResponse( + deep_ask_event_stream(input_data), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "X-Accel-Buffering": "no", + }, + ) + + async def health_check(_: Request) -> JSONResponse: + """Health check endpoint.""" + return JSONResponse({"status": "healthy"}) + + # Define routes + routes = [ + Route("/v1/research/stream", stream_research, methods=["POST"]), + Route("/v1/deep-ask/stream", stream_deep_ask, methods=["POST"]), + Route("/health", health_check, methods=["GET"]), + ] + + # Configure CORS middleware + middleware = [ + Middleware( + CORSMiddleware, + allow_origins=config.agui.cors_origins, + allow_credentials=config.agui.cors_credentials, + allow_methods=config.agui.cors_methods, + allow_headers=config.agui.cors_headers, + ) + ] + + # Create Starlette app + app = Starlette( + routes=routes, + middleware=middleware, + debug=False, ) + + return app diff --git a/haiku_rag_slim/haiku/rag/app.py b/haiku_rag_slim/haiku/rag/app.py index 40a20fe6..1ce07208 100644 --- a/haiku_rag_slim/haiku/rag/app.py +++ b/haiku_rag_slim/haiku/rag/app.py @@ -216,8 +216,6 @@ class HaikuRAGApp: async with HaikuRAG(db_path=self.db_path, config=self.config) as self.client: try: if deep: - from rich.console import Console - from haiku.rag.qa.deep.dependencies import DeepQAContext from haiku.rag.qa.deep.graph import build_deep_qa_graph from haiku.rag.qa.deep.state import DeepQADeps, DeepQAState @@ -227,12 +225,22 @@ class HaikuRAGApp: original_question=question, use_citations=cite ) state = DeepQAState.from_config(context=context, config=self.config) - deps = DeepQADeps( - client=self.client, console=Console() if verbose else None - ) + deps = DeepQADeps(client=self.client) - result = await graph.run(state=state, deps=deps) - answer = result.answer + if verbose: + # Use AG-UI renderer to process and display events + from haiku.rag.agui import AGUIConsoleRenderer + + renderer = AGUIConsoleRenderer(self.console) + result_dict = await renderer.render( + stream_graph(graph, state, deps) + ) + # Result should be a dict with 'answer' key + answer = result_dict.get("answer", "") if result_dict else "" + else: + # Run without rendering events, just get the result + result = await graph.run(state=state, deps=deps) + answer = result.answer else: answer = await self.client.ask(question, cite=cite) @@ -489,12 +497,12 @@ class HaikuRAGApp: async def run_agui(): import uvicorn - from haiku.rag.agui import create_research_server + from haiku.rag.agui import create_agui_server logger.info( f"Starting AG-UI server on {self.config.agui.host}:{self.config.agui.port}" ) - app = create_research_server(self.config, db_path=self.db_path) + app = create_agui_server(self.config, db_path=self.db_path) config = uvicorn.Config( app=app, host=self.config.agui.host, diff --git a/haiku_rag_slim/haiku/rag/qa/deep/graph.py b/haiku_rag_slim/haiku/rag/qa/deep/graph.py index 9ded283c..4ae92bc3 100644 --- a/haiku_rag_slim/haiku/rag/qa/deep/graph.py +++ b/haiku_rag_slim/haiku/rag/qa/deep/graph.py @@ -8,7 +8,7 @@ from pydantic_graph.beta.join import reduce_list_append from haiku.rag.config import Config from haiku.rag.config.models import AppConfig -from haiku.rag.graph_common import get_model, log +from haiku.rag.graph_common import get_model from haiku.rag.graph_common.models import ResearchPlan, SearchAnswer from haiku.rag.graph_common.prompts import PLAN_PROMPT, SEARCH_AGENT_PROMPT from haiku.rag.qa.deep.dependencies import DeepQADependencies @@ -45,49 +45,52 @@ def build_deep_qa_graph( state = ctx.state deps = ctx.deps - log(deps, state, "\n[bold cyan]📋 Planning approach...[/bold cyan]") + if deps.agui_emitter: + deps.agui_emitter.start_step("plan") + deps.agui_emitter.update_activity("planning", "Planning approach") - plan_agent = Agent( - model=get_model(provider, model), - output_type=ResearchPlan, - instructions=( - PLAN_PROMPT - + "\n\nUse the gather_context tool once on the main question before planning." - ), - retries=3, - deps_type=DeepQADependencies, - ) + try: + plan_agent = Agent( + model=get_model(provider, model), + output_type=ResearchPlan, + instructions=( + PLAN_PROMPT + + "\n\nUse the gather_context tool once on the main question before planning." + ), + retries=3, + deps_type=DeepQADependencies, + ) - @plan_agent.tool - async def gather_context( - ctx2: RunContext[DeepQADependencies], query: str, limit: int = 6 - ) -> str: - results = await ctx2.deps.client.search(query, limit=limit) - expanded = await ctx2.deps.client.expand_context(results) - return "\n\n".join(chunk.content for chunk, _ in expanded) + @plan_agent.tool + async def gather_context( + ctx2: RunContext[DeepQADependencies], query: str, limit: int = 6 + ) -> str: + results = await ctx2.deps.client.search(query, limit=limit) + expanded = await ctx2.deps.client.expand_context(results) + return "\n\n".join(chunk.content for chunk, _ in expanded) - prompt = ( - "Plan a focused approach for the main question.\n\n" - f"Main question: {state.context.original_question}" - ) + prompt = ( + "Plan a focused approach for the main question.\n\n" + f"Main question: {state.context.original_question}" + ) - agent_deps = DeepQADependencies( - client=deps.client, - context=state.context, - console=deps.console, - ) - plan_result = await plan_agent.run(prompt, deps=agent_deps) - state.context.sub_questions = list(plan_result.output.sub_questions) + agent_deps = DeepQADependencies( + client=deps.client, + context=state.context, + console=None, + ) + plan_result = await plan_agent.run(prompt, deps=agent_deps) + state.context.sub_questions = list(plan_result.output.sub_questions) - log(deps, state, "\n[bold green]✅ Plan Created:[/bold green]") - log( - deps, - state, - f" [bold]Main Question:[/bold] {state.context.original_question}", - ) - log(deps, state, " [bold]Sub-questions:[/bold]") - for i, sq in enumerate(state.context.sub_questions, 1): - log(deps, state, f" {i}. {sq}") + if deps.agui_emitter: + deps.agui_emitter.update_state(state) + count = len(state.context.sub_questions) + deps.agui_emitter.update_activity( + "planning", f"Created plan with {count} sub-questions" + ) + finally: + if deps.agui_emitter: + deps.agui_emitter.finish_step() @g.step async def search_one( @@ -112,11 +115,8 @@ def build_deep_qa_graph( deps: DeepQADeps, sub_q: str, ) -> SearchAnswer: - log( - deps, - state, - f"\n[bold cyan]🔍 Searching & Answering:[/bold cyan] {sub_q}", - ) + if deps.agui_emitter: + deps.agui_emitter.update_activity("searching", f"Searching: {sub_q}") agent = Agent( model=get_model(provider, model), @@ -151,20 +151,18 @@ def build_deep_qa_graph( agent_deps = DeepQADependencies( client=deps.client, context=state.context, - console=deps.console, + console=None, ) try: result = await agent.run(sub_q, deps=agent_deps) answer = result.output if answer: state.context.add_qa_response(answer) - preview = answer.answer[:150] + ( - "…" if len(answer.answer) > 150 else "" - ) - log(deps, state, f" [green]✓[/green] {preview}") + if deps.agui_emitter: + deps.agui_emitter.update_state(state) + deps.agui_emitter.update_activity("searching", f"Answered: {sub_q}") return answer except Exception as e: - log(deps, state, f"[red]Search failed:[/red] {e}") failure_answer = SearchAnswer( query=sub_q, answer=f"Search failed after retries: {str(e)}", @@ -194,81 +192,69 @@ def build_deep_qa_graph( state = ctx.state deps = ctx.deps - log( - deps, - state, - "\n[bold cyan]📊 Evaluating information sufficiency...[/bold cyan]", - ) - - agent = Agent( - model=get_model(provider, model), - output_type=DeepQAEvaluation, - instructions=DECISION_PROMPT, - retries=3, - deps_type=DeepQADependencies, - ) - - context_data = { - "original_question": state.context.original_question, - "gathered_answers": [ - { - "question": qa.query, - "answer": qa.answer, - "sources": qa.sources, - } - for qa in state.context.qa_responses - ], - } - context_xml = format_as_xml(context_data, root_tag="gathered_information") - - prompt = ( - "Evaluate whether we have sufficient information to answer the question.\n\n" - f"{context_xml}" - ) - - agent_deps = DeepQADependencies( - client=deps.client, - context=state.context, - console=deps.console, - ) - result = await agent.run(prompt, deps=agent_deps) - evaluation = result.output - - state.iterations += 1 - - log(deps, state, f" [bold]Assessment:[/bold] {evaluation.reasoning}") - status = "[green]Yes[/green]" if evaluation.is_sufficient else "[red]No[/red]" - log(deps, state, f" Sufficient: {status}") - - for new_q in evaluation.new_questions: - if new_q not in state.context.sub_questions: - state.context.sub_questions.append(new_q) - - if evaluation.new_questions: - log(deps, state, " [cyan]New questions:[/cyan]") - for question in evaluation.new_questions: - log(deps, state, f" • {question}") - - should_continue = ( - not evaluation.is_sufficient and state.iterations < state.max_iterations - ) - - if not should_continue: - if state.iterations >= state.max_iterations: - log( - deps, - state, - f"\n[bold yellow]⚠️ Reached max iterations ({state.max_iterations})[/bold yellow]", - ) - log(deps, state, "\n[bold green]✅ Moving to synthesis.[/bold green]") - else: - log( - deps, - state, - f"\n[bold cyan]🔄 Starting iteration {state.iterations + 1}...[/bold cyan]", + if deps.agui_emitter: + deps.agui_emitter.start_step("decide") + deps.agui_emitter.update_activity( + "evaluating", "Evaluating information sufficiency" ) - return should_continue + try: + agent = Agent( + model=get_model(provider, model), + output_type=DeepQAEvaluation, + instructions=DECISION_PROMPT, + retries=3, + deps_type=DeepQADependencies, + ) + + context_data = { + "original_question": state.context.original_question, + "gathered_answers": [ + { + "question": qa.query, + "answer": qa.answer, + "sources": qa.sources, + } + for qa in state.context.qa_responses + ], + } + context_xml = format_as_xml(context_data, root_tag="gathered_information") + + prompt = ( + "Evaluate whether we have sufficient information to answer the question.\n\n" + f"{context_xml}" + ) + + agent_deps = DeepQADependencies( + client=deps.client, + context=state.context, + console=None, + ) + result = await agent.run(prompt, deps=agent_deps) + evaluation = result.output + + state.iterations += 1 + + for new_q in evaluation.new_questions: + if new_q not in state.context.sub_questions: + state.context.sub_questions.append(new_q) + + if deps.agui_emitter: + deps.agui_emitter.update_state(state) + status = "sufficient" if evaluation.is_sufficient else "insufficient" + deps.agui_emitter.update_activity( + "evaluating", + f"Information {status} after {state.iterations} iteration(s)", + ) + + should_continue = ( + not evaluation.is_sufficient and state.iterations < state.max_iterations + ) + + return should_continue + finally: + if deps.agui_emitter: + deps.agui_emitter.finish_step() @g.step async def synthesize( @@ -277,50 +263,56 @@ def build_deep_qa_graph( state = ctx.state deps = ctx.deps - log( - deps, - state, - "\n[bold cyan]📝 Synthesizing final answer...[/bold cyan]", - ) + if deps.agui_emitter: + deps.agui_emitter.start_step("synthesize") + deps.agui_emitter.update_activity( + "synthesizing", "Synthesizing final answer" + ) - prompt_template = ( - SYNTHESIS_PROMPT_WITH_CITATIONS - if state.context.use_citations - else SYNTHESIS_PROMPT - ) + try: + prompt_template = ( + SYNTHESIS_PROMPT_WITH_CITATIONS + if state.context.use_citations + else SYNTHESIS_PROMPT + ) - agent = Agent( - model=get_model(provider, model), - output_type=DeepQAAnswer, - instructions=prompt_template, - retries=3, - deps_type=DeepQADependencies, - ) + agent = Agent( + model=get_model(provider, model), + output_type=DeepQAAnswer, + instructions=prompt_template, + retries=3, + deps_type=DeepQADependencies, + ) - context_data = { - "original_question": state.context.original_question, - "sub_answers": [ - { - "question": qa.query, - "answer": qa.answer, - "sources": qa.sources, - } - for qa in state.context.qa_responses - ], - } - context_xml = format_as_xml(context_data, root_tag="gathered_information") + context_data = { + "original_question": state.context.original_question, + "sub_answers": [ + { + "question": qa.query, + "answer": qa.answer, + "sources": qa.sources, + } + for qa in state.context.qa_responses + ], + } + context_xml = format_as_xml(context_data, root_tag="gathered_information") - prompt = f"Synthesize a comprehensive answer to the original question.\n\n{context_xml}" + prompt = f"Synthesize a comprehensive answer to the original question.\n\n{context_xml}" - agent_deps = DeepQADependencies( - client=deps.client, - context=state.context, - console=deps.console, - ) - result = await agent.run(prompt, deps=agent_deps) + agent_deps = DeepQADependencies( + client=deps.client, + context=state.context, + console=None, + ) + result = await agent.run(prompt, deps=agent_deps) - log(deps, state, "[bold green]✅ Answer complete![/bold green]") - return result.output + if deps.agui_emitter: + deps.agui_emitter.update_activity("synthesizing", "Answer complete") + + return result.output + finally: + if deps.agui_emitter: + deps.agui_emitter.finish_step() # Build the graph structure collect_answers = g.join( diff --git a/haiku_rag_slim/haiku/rag/qa/deep/state.py b/haiku_rag_slim/haiku/rag/qa/deep/state.py index 0e07098e..a340d42d 100644 --- a/haiku_rag_slim/haiku/rag/qa/deep/state.py +++ b/haiku_rag_slim/haiku/rag/qa/deep/state.py @@ -1,8 +1,8 @@ import asyncio from dataclasses import dataclass -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any -from rich.console import Console +from pydantic import BaseModel, Field from haiku.rag.client import HaikuRAG from haiku.rag.qa.deep.dependencies import DeepQAContext @@ -14,21 +14,26 @@ if TYPE_CHECKING: @dataclass class DeepQADeps: client: HaikuRAG - console: Console | None = None + agui_emitter: Any | None = None semaphore: asyncio.Semaphore | None = None - def emit_log(self, message: str, state: "DeepQAState | None" = None) -> None: - if self.console: - self.console.print(message) +class DeepQAState(BaseModel): + """Deep QA state for multi-agent question answering.""" -@dataclass -class DeepQAState: - context: DeepQAContext - max_sub_questions: int = 3 - max_iterations: int = 2 - max_concurrency: int = 1 - iterations: int = 0 + model_config = {"arbitrary_types_allowed": True} + + context: DeepQAContext = Field(description="Shared QA context") + max_sub_questions: int = Field( + default=3, description="Maximum number of sub-questions" + ) + max_iterations: int = Field( + default=2, description="Maximum number of QA iterations" + ) + max_concurrency: int = Field( + default=1, description="Maximum parallel sub-question searches" + ) + iterations: int = Field(default=0, description="Current iteration number") @classmethod def from_config(cls, context: DeepQAContext, config: "AppConfig") -> "DeepQAState": diff --git a/tests/test_app.py b/tests/test_app.py index 8d3b003a..51153aa4 100644 --- a/tests/test_app.py +++ b/tests/test_app.py @@ -401,12 +401,13 @@ async def test_ask_with_deep_and_cite(app: HaikuRAGApp, monkeypatch): @pytest.mark.asyncio async def test_ask_with_deep_and_verbose(app: HaikuRAGApp, monkeypatch): """Test asking a question with deep QA and verbose output.""" - from haiku.rag.qa.deep.models import DeepQAAnswer - mock_output = DeepQAAnswer(answer="Deep QA answer", sources=["test.md"]) + mock_output = {"answer": "Deep QA answer", "sources": ["test.md"]} + + mock_renderer = AsyncMock() + mock_renderer.render.return_value = mock_output mock_graph = AsyncMock() - mock_graph.run.return_value = mock_output mock_client = AsyncMock() mock_client.__aenter__.return_value = mock_client @@ -418,8 +419,11 @@ async def test_ask_with_deep_and_verbose(app: HaikuRAGApp, monkeypatch): with patch( "haiku.rag.qa.deep.graph.build_deep_qa_graph", return_value=mock_graph ): - await app.ask("test question", deep=True, verbose=True) + with patch( + "haiku.rag.agui.AGUIConsoleRenderer", return_value=mock_renderer + ): + await app.ask("test question", deep=True, verbose=True) - mock_graph.run.assert_called_once() - call_kwargs = mock_graph.run.call_args[1] - assert call_kwargs["deps"].console is not None + # With verbose, it should use AGUIConsoleRenderer.render, not graph.run + mock_renderer.render.assert_called_once() + mock_graph.run.assert_not_called() diff --git a/tests/test_deep_qa.py b/tests/test_deep_qa.py index c220fcda..b6757498 100644 --- a/tests/test_deep_qa.py +++ b/tests/test_deep_qa.py @@ -30,7 +30,7 @@ async def test_deep_qa_graph_end_to_end(monkeypatch, temp_db_path): # Use real client but with TestModel for LLM calls client = HaikuRAG(temp_db_path) - deps = DeepQADeps(client=client, console=None) + deps = DeepQADeps(client=client) result = await graph.run(state=state, deps=deps) @@ -62,7 +62,7 @@ async def test_deep_qa_with_citations(monkeypatch, temp_db_path): # Use real client but with TestModel for LLM calls client = HaikuRAG(temp_db_path) - deps = DeepQADeps(client=client, console=None) + deps = DeepQADeps(client=client) result = await graph.run(state=state, deps=deps) From 032625b0bb25fd531f7658e3fde7372e0ff17f64 Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Tue, 11 Nov 2025 15:47:42 +0200 Subject: [PATCH 10/17] Refactor everything graph-related under the graph module --- README.md | 4 +-- docs/agents.md | 20 +++++++------- docs/cli.md | 2 +- .../a2a-server/haiku_rag_a2a/a2a/__init__.py | 2 +- examples/ag-ui-research/backend/agent.py | 2 +- haiku_rag_slim/haiku/rag/app.py | 20 +++++++------- haiku_rag_slim/haiku/rag/graph/__init__.py | 26 +++++++++++++++++++ .../haiku/rag/{ => graph}/agui/__init__.py | 12 ++++----- .../rag/{ => graph}/agui/cli_renderer.py | 2 +- .../haiku/rag/{ => graph}/agui/emitter.py | 2 +- .../haiku/rag/{ => graph}/agui/events.py | 2 +- .../haiku/rag/{ => graph}/agui/server.py | 16 ++++++------ .../haiku/rag/{ => graph}/agui/state.py | 0 .../haiku/rag/{ => graph}/agui/stream.py | 4 +-- .../haiku/rag/graph/common/__init__.py | 5 ++++ .../{graph_common => graph/common}/models.py | 0 .../{graph_common => graph/common}/prompts.py | 0 .../{graph_common => graph/common}/utils.py | 20 -------------- .../haiku/rag/graph/deep_qa/__init__.py | 1 + .../deep => graph/deep_qa}/dependencies.py | 4 +-- .../rag/{qa/deep => graph/deep_qa}/graph.py | 18 +++++-------- .../rag/{qa/deep => graph/deep_qa}/models.py | 0 .../rag/{qa/deep => graph/deep_qa}/prompts.py | 0 .../rag/{qa/deep => graph/deep_qa}/state.py | 2 +- .../haiku/rag/graph/research/__init__.py | 3 +++ .../haiku/rag/{ => graph}/research/common.py | 4 +-- .../rag/{ => graph}/research/dependencies.py | 4 +-- .../haiku/rag/{ => graph}/research/graph.py | 16 ++++++------ .../haiku/rag/{ => graph}/research/models.py | 0 .../haiku/rag/{ => graph}/research/prompts.py | 0 .../haiku/rag/{ => graph}/research/state.py | 10 ++++--- .../haiku/rag/graph_common/__init__.py | 5 ---- haiku_rag_slim/haiku/rag/mcp.py | 14 +++++----- haiku_rag_slim/haiku/rag/qa/deep/__init__.py | 1 - haiku_rag_slim/haiku/rag/research/__init__.py | 3 --- tests/graph/__init__.py | 1 + tests/{ => graph}/agui/__init__.py | 0 tests/{ => graph}/agui/test_cli_renderer.py | 4 +-- tests/{ => graph}/agui/test_emitter.py | 2 +- tests/{ => graph}/agui/test_events.py | 2 +- tests/{ => graph}/agui/test_server.py | 2 +- tests/{ => graph}/agui/test_stream.py | 4 +-- tests/{ => graph}/test_deep_qa.py | 16 ++++++------ tests/{ => graph}/test_research_graph.py | 12 ++++----- tests/test_app.py | 12 ++++----- tests/test_mcp.py | 8 +++--- 46 files changed, 147 insertions(+), 140 deletions(-) create mode 100644 haiku_rag_slim/haiku/rag/graph/__init__.py rename haiku_rag_slim/haiku/rag/{ => graph}/agui/__init__.py (76%) rename haiku_rag_slim/haiku/rag/{ => graph}/agui/cli_renderer.py (99%) rename haiku_rag_slim/haiku/rag/{ => graph}/agui/emitter.py (99%) rename haiku_rag_slim/haiku/rag/{ => graph}/agui/events.py (99%) rename haiku_rag_slim/haiku/rag/{ => graph}/agui/server.py (94%) rename haiku_rag_slim/haiku/rag/{ => graph}/agui/state.py (100%) rename haiku_rag_slim/haiku/rag/{ => graph}/agui/stream.py (95%) create mode 100644 haiku_rag_slim/haiku/rag/graph/common/__init__.py rename haiku_rag_slim/haiku/rag/{graph_common => graph/common}/models.py (100%) rename haiku_rag_slim/haiku/rag/{graph_common => graph/common}/prompts.py (100%) rename haiku_rag_slim/haiku/rag/{graph_common => graph/common}/utils.py (74%) create mode 100644 haiku_rag_slim/haiku/rag/graph/deep_qa/__init__.py rename haiku_rag_slim/haiku/rag/{qa/deep => graph/deep_qa}/dependencies.py (87%) rename haiku_rag_slim/haiku/rag/{qa/deep => graph/deep_qa}/graph.py (95%) rename haiku_rag_slim/haiku/rag/{qa/deep => graph/deep_qa}/models.py (100%) rename haiku_rag_slim/haiku/rag/{qa/deep => graph/deep_qa}/prompts.py (100%) rename haiku_rag_slim/haiku/rag/{qa/deep => graph/deep_qa}/state.py (96%) create mode 100644 haiku_rag_slim/haiku/rag/graph/research/__init__.py rename haiku_rag_slim/haiku/rag/{ => graph}/research/common.py (95%) rename haiku_rag_slim/haiku/rag/{ => graph}/research/dependencies.py (98%) rename haiku_rag_slim/haiku/rag/{ => graph}/research/graph.py (96%) rename haiku_rag_slim/haiku/rag/{ => graph}/research/models.py (100%) rename haiku_rag_slim/haiku/rag/{ => graph}/research/prompts.py (100%) rename haiku_rag_slim/haiku/rag/{ => graph}/research/state.py (91%) delete mode 100644 haiku_rag_slim/haiku/rag/graph_common/__init__.py delete mode 100644 haiku_rag_slim/haiku/rag/qa/deep/__init__.py delete mode 100644 haiku_rag_slim/haiku/rag/research/__init__.py create mode 100644 tests/graph/__init__.py rename tests/{ => graph}/agui/__init__.py (100%) rename tests/{ => graph}/agui/test_cli_renderer.py (97%) rename tests/{ => graph}/agui/test_emitter.py (99%) rename tests/{ => graph}/agui/test_events.py (99%) rename tests/{ => graph}/agui/test_server.py (98%) rename tests/{ => graph}/agui/test_stream.py (98%) rename tests/{ => graph}/test_deep_qa.py (83%) rename tests/{ => graph}/test_research_graph.py (85%) diff --git a/README.md b/README.md index e374b79c..99637f64 100644 --- a/README.md +++ b/README.md @@ -84,10 +84,10 @@ To customize settings, create a `haiku.rag.yaml` config file (see [Configuration ## Python Usage ```python -from haiku.rag.agui.stream import stream_graph from haiku.rag.client import HaikuRAG from haiku.rag.config import Config -from haiku.rag.research import ( +from haiku.rag.graph.agui import stream_graph +from haiku.rag.graph.research import ( ResearchContext, ResearchDeps, ResearchState, diff --git a/docs/agents.md b/docs/agents.md index 73355100..08b7cf6b 100644 --- a/docs/agents.md +++ b/docs/agents.md @@ -96,9 +96,9 @@ Python usage: ```python from haiku.rag.client import HaikuRAG from haiku.rag.config import Config -from haiku.rag.qa.deep.dependencies import DeepQAContext -from haiku.rag.qa.deep.graph import build_deep_qa_graph -from haiku.rag.qa.deep.state import DeepQADeps, DeepQAState +from haiku.rag.graph.deep_qa.dependencies import DeepQAContext +from haiku.rag.graph.deep_qa.graph import build_deep_qa_graph +from haiku.rag.graph.deep_qa.state import DeepQADeps, DeepQAState async with HaikuRAG(path_to_db) as client: # Use global config (recommended) @@ -205,9 +205,9 @@ Python usage (blocking result): ```python from haiku.rag.client import HaikuRAG from haiku.rag.config import Config -from haiku.rag.research.dependencies import ResearchContext -from haiku.rag.research.graph import build_research_graph -from haiku.rag.research.state import ResearchDeps, ResearchState +from haiku.rag.graph.research.dependencies import ResearchContext +from haiku.rag.graph.research.graph import build_research_graph +from haiku.rag.graph.research.state import ResearchDeps, ResearchState async with HaikuRAG(path_to_db) as client: # Use global config (recommended) @@ -253,12 +253,12 @@ result = await graph.run(state=state, deps=deps) Python usage (streamed AG-UI events): ```python -from haiku.rag.agui.stream import stream_graph +from haiku.rag.graph.agui import stream_graph from haiku.rag.client import HaikuRAG from haiku.rag.config import Config -from haiku.rag.research.dependencies import ResearchContext -from haiku.rag.research.graph import build_research_graph -from haiku.rag.research.state import ResearchDeps, ResearchState +from haiku.rag.graph.research.dependencies import ResearchContext +from haiku.rag.graph.research.graph import build_research_graph +from haiku.rag.graph.research.state import ResearchDeps, ResearchState async with HaikuRAG(path_to_db) as client: graph = build_research_graph(config=Config) diff --git a/docs/cli.md b/docs/cli.md index 9f433cd7..b47c2587 100644 --- a/docs/cli.md +++ b/docs/cli.md @@ -162,7 +162,7 @@ Research parameters like `max_iterations`, `confidence_threshold`, and `max_conc When `--verbose` is set, the CLI consumes the research graph's AG-UI event stream, displaying step events and activity snapshots as agents progress through planning, search, evaluation, and synthesis. Without `--verbose`, only the final research report is displayed. -If you build your own integration, import `stream_graph` from `haiku.rag.agui.stream` to access AG-UI events (`STEP_STARTED`, `ACTIVITY_SNAPSHOT`, `STATE_SNAPSHOT`, `RUN_FINISHED`, etc.) and render them however you like while the graph is running. +If you build your own integration, import `stream_graph` from `haiku.rag.graph.agui` to access AG-UI events (`STEP_STARTED`, `ACTIVITY_SNAPSHOT`, `STATE_SNAPSHOT`, `RUN_FINISHED`, etc.) and render them however you like while the graph is running. ## Server diff --git a/examples/a2a-server/haiku_rag_a2a/a2a/__init__.py b/examples/a2a-server/haiku_rag_a2a/a2a/__init__.py index 03af95ab..d06eaae9 100644 --- a/examples/a2a-server/haiku_rag_a2a/a2a/__init__.py +++ b/examples/a2a-server/haiku_rag_a2a/a2a/__init__.py @@ -6,7 +6,7 @@ import logfire from pydantic_ai import Agent, RunContext from haiku.rag.config import AppConfig, Config -from haiku.rag.graph_common import get_model +from haiku.rag.graph.common import get_model from .context import load_message_history, save_message_history from .models import A2AConfig, AgentDependencies, SearchResult diff --git a/examples/ag-ui-research/backend/agent.py b/examples/ag-ui-research/backend/agent.py index 5362696a..049e0dbd 100644 --- a/examples/ag-ui-research/backend/agent.py +++ b/examples/ag-ui-research/backend/agent.py @@ -8,7 +8,7 @@ from pydantic_ai.ag_ui import StateDeps from haiku.rag.client import HaikuRAG from haiku.rag.config import Config -from haiku.rag.graph_common import get_model +from haiku.rag.graph.common import get_model class ResearchState(BaseModel): diff --git a/haiku_rag_slim/haiku/rag/app.py b/haiku_rag_slim/haiku/rag/app.py index 1ce07208..ba2e77c3 100644 --- a/haiku_rag_slim/haiku/rag/app.py +++ b/haiku_rag_slim/haiku/rag/app.py @@ -8,14 +8,14 @@ from rich.console import Console from rich.markdown import Markdown from rich.progress import Progress -from haiku.rag.agui import AGUIConsoleRenderer, stream_graph from haiku.rag.client import HaikuRAG from haiku.rag.config import AppConfig, Config +from haiku.rag.graph.agui import AGUIConsoleRenderer, stream_graph +from haiku.rag.graph.research.dependencies import ResearchContext +from haiku.rag.graph.research.graph import build_research_graph +from haiku.rag.graph.research.state import ResearchDeps, ResearchState from haiku.rag.mcp import create_mcp_server from haiku.rag.monitor import FileWatcher -from haiku.rag.research.dependencies import ResearchContext -from haiku.rag.research.graph import build_research_graph -from haiku.rag.research.state import ResearchDeps, ResearchState from haiku.rag.store.models.chunk import Chunk from haiku.rag.store.models.document import Document @@ -216,9 +216,9 @@ class HaikuRAGApp: async with HaikuRAG(db_path=self.db_path, config=self.config) as self.client: try: if deep: - from haiku.rag.qa.deep.dependencies import DeepQAContext - from haiku.rag.qa.deep.graph import build_deep_qa_graph - from haiku.rag.qa.deep.state import DeepQADeps, DeepQAState + from haiku.rag.graph.deep_qa.dependencies import DeepQAContext + from haiku.rag.graph.deep_qa.graph import build_deep_qa_graph + from haiku.rag.graph.deep_qa.state import DeepQADeps, DeepQAState graph = build_deep_qa_graph(config=self.config) context = DeepQAContext( @@ -229,7 +229,7 @@ class HaikuRAGApp: if verbose: # Use AG-UI renderer to process and display events - from haiku.rag.agui import AGUIConsoleRenderer + from haiku.rag.graph.agui import AGUIConsoleRenderer renderer = AGUIConsoleRenderer(self.console) result_dict = await renderer.render( @@ -287,7 +287,7 @@ class HaikuRAGApp: return # Convert dict to ResearchReport model - from haiku.rag.research.models import ResearchReport + from haiku.rag.graph.research.models import ResearchReport report = ResearchReport.model_validate(report_dict) @@ -497,7 +497,7 @@ class HaikuRAGApp: async def run_agui(): import uvicorn - from haiku.rag.agui import create_agui_server + from haiku.rag.graph.agui import create_agui_server logger.info( f"Starting AG-UI server on {self.config.agui.host}:{self.config.agui.port}" diff --git a/haiku_rag_slim/haiku/rag/graph/__init__.py b/haiku_rag_slim/haiku/rag/graph/__init__.py new file mode 100644 index 00000000..fff8e10a --- /dev/null +++ b/haiku_rag_slim/haiku/rag/graph/__init__.py @@ -0,0 +1,26 @@ +"""Graph module for haiku.rag. + +This module contains all graph-related functionality including: +- AG-UI protocol for graph streaming +- Common graph utilities and models +- Research graph implementation +- Deep QA graph implementation +""" + +from haiku.rag.graph.agui import ( + AGUIConsoleRenderer, + AGUIEmitter, + create_agui_server, + stream_graph, +) +from haiku.rag.graph.deep_qa.graph import build_deep_qa_graph +from haiku.rag.graph.research.graph import build_research_graph + +__all__ = [ + "AGUIConsoleRenderer", + "AGUIEmitter", + "build_deep_qa_graph", + "build_research_graph", + "create_agui_server", + "stream_graph", +] diff --git a/haiku_rag_slim/haiku/rag/agui/__init__.py b/haiku_rag_slim/haiku/rag/graph/agui/__init__.py similarity index 76% rename from haiku_rag_slim/haiku/rag/agui/__init__.py rename to haiku_rag_slim/haiku/rag/graph/agui/__init__.py index fab7a016..7259a6b0 100644 --- a/haiku_rag_slim/haiku/rag/agui/__init__.py +++ b/haiku_rag_slim/haiku/rag/graph/agui/__init__.py @@ -1,8 +1,8 @@ """Generic AG-UI protocol support for haiku.rag graphs.""" -from haiku.rag.agui.cli_renderer import AGUIConsoleRenderer -from haiku.rag.agui.emitter import AGUIEmitter -from haiku.rag.agui.events import ( +from haiku.rag.graph.agui.cli_renderer import AGUIConsoleRenderer +from haiku.rag.graph.agui.emitter import AGUIEmitter +from haiku.rag.graph.agui.events import ( AGUIEvent, emit_activity, emit_activity_delta, @@ -18,14 +18,14 @@ from haiku.rag.agui.events import ( emit_text_message_end, emit_text_message_start, ) -from haiku.rag.agui.server import ( +from haiku.rag.graph.agui.server import ( RunAgentInput, create_agui_app, create_agui_server, format_sse_event, ) -from haiku.rag.agui.state import compute_state_delta -from haiku.rag.agui.stream import stream_graph +from haiku.rag.graph.agui.state import compute_state_delta +from haiku.rag.graph.agui.stream import stream_graph __all__ = [ "AGUIConsoleRenderer", diff --git a/haiku_rag_slim/haiku/rag/agui/cli_renderer.py b/haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py similarity index 99% rename from haiku_rag_slim/haiku/rag/agui/cli_renderer.py rename to haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py index c09e62b6..52e223dc 100644 --- a/haiku_rag_slim/haiku/rag/agui/cli_renderer.py +++ b/haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py @@ -5,7 +5,7 @@ from typing import Any from rich.console import Console -from haiku.rag.agui.events import AGUIEvent +from haiku.rag.graph.agui.events import AGUIEvent class AGUIConsoleRenderer: diff --git a/haiku_rag_slim/haiku/rag/agui/emitter.py b/haiku_rag_slim/haiku/rag/graph/agui/emitter.py similarity index 99% rename from haiku_rag_slim/haiku/rag/agui/emitter.py rename to haiku_rag_slim/haiku/rag/graph/agui/emitter.py index b634f771..2c284d46 100644 --- a/haiku_rag_slim/haiku/rag/agui/emitter.py +++ b/haiku_rag_slim/haiku/rag/graph/agui/emitter.py @@ -7,7 +7,7 @@ from uuid import uuid4 from pydantic import BaseModel -from haiku.rag.agui.events import ( +from haiku.rag.graph.agui.events import ( AGUIEvent, emit_activity, emit_run_error, diff --git a/haiku_rag_slim/haiku/rag/agui/events.py b/haiku_rag_slim/haiku/rag/graph/agui/events.py similarity index 99% rename from haiku_rag_slim/haiku/rag/agui/events.py rename to haiku_rag_slim/haiku/rag/graph/agui/events.py index d439d005..a7be3d73 100644 --- a/haiku_rag_slim/haiku/rag/agui/events.py +++ b/haiku_rag_slim/haiku/rag/graph/agui/events.py @@ -5,7 +5,7 @@ from uuid import uuid4 from pydantic import BaseModel -from haiku.rag.agui.state import compute_state_delta +from haiku.rag.graph.agui.state import compute_state_delta # Type aliases for AG-UI events (actual types from ag_ui.core will be used at runtime) AGUIEvent = dict[str, Any] diff --git a/haiku_rag_slim/haiku/rag/agui/server.py b/haiku_rag_slim/haiku/rag/graph/agui/server.py similarity index 94% rename from haiku_rag_slim/haiku/rag/agui/server.py rename to haiku_rag_slim/haiku/rag/graph/agui/server.py index ff1be032..e7f7a829 100644 --- a/haiku_rag_slim/haiku/rag/agui/server.py +++ b/haiku_rag_slim/haiku/rag/graph/agui/server.py @@ -13,9 +13,9 @@ from starlette.requests import Request from starlette.responses import JSONResponse, StreamingResponse from starlette.routing import Route -from haiku.rag.agui.events import AGUIEvent -from haiku.rag.agui.stream import stream_graph from haiku.rag.config.models import AGUIConfig +from haiku.rag.graph.agui.events import AGUIEvent +from haiku.rag.graph.agui.stream import stream_graph class GraphDeps(Protocol): @@ -157,12 +157,12 @@ def create_agui_server(config: Any, db_path: Any | None = None) -> Starlette: Starlette app with research and deep ask endpoints """ from haiku.rag.client import HaikuRAG - from haiku.rag.qa.deep.dependencies import DeepQAContext - from haiku.rag.qa.deep.graph import build_deep_qa_graph - from haiku.rag.qa.deep.state import DeepQADeps, DeepQAState - from haiku.rag.research.dependencies import ResearchContext - from haiku.rag.research.graph import build_research_graph - from haiku.rag.research.state import ResearchDeps, ResearchState + from haiku.rag.graph.deep_qa.dependencies import DeepQAContext + from haiku.rag.graph.deep_qa.graph import build_deep_qa_graph + from haiku.rag.graph.deep_qa.state import DeepQADeps, DeepQAState + from haiku.rag.graph.research.dependencies import ResearchContext + from haiku.rag.graph.research.graph import build_research_graph + from haiku.rag.graph.research.state import ResearchDeps, ResearchState # Store client reference for proper lifecycle management _client_cache: dict[str, HaikuRAG] = {} diff --git a/haiku_rag_slim/haiku/rag/agui/state.py b/haiku_rag_slim/haiku/rag/graph/agui/state.py similarity index 100% rename from haiku_rag_slim/haiku/rag/agui/state.py rename to haiku_rag_slim/haiku/rag/graph/agui/state.py diff --git a/haiku_rag_slim/haiku/rag/agui/stream.py b/haiku_rag_slim/haiku/rag/graph/agui/stream.py similarity index 95% rename from haiku_rag_slim/haiku/rag/agui/stream.py rename to haiku_rag_slim/haiku/rag/graph/agui/stream.py index e91b9b64..75f6a3e5 100644 --- a/haiku_rag_slim/haiku/rag/agui/stream.py +++ b/haiku_rag_slim/haiku/rag/graph/agui/stream.py @@ -7,8 +7,8 @@ from typing import Any, Protocol from pydantic import BaseModel -from haiku.rag.agui.emitter import AGUIEmitter -from haiku.rag.agui.events import AGUIEvent +from haiku.rag.graph.agui.emitter import AGUIEmitter +from haiku.rag.graph.agui.events import AGUIEvent class GraphDeps(Protocol): diff --git a/haiku_rag_slim/haiku/rag/graph/common/__init__.py b/haiku_rag_slim/haiku/rag/graph/common/__init__.py new file mode 100644 index 00000000..e2a53f16 --- /dev/null +++ b/haiku_rag_slim/haiku/rag/graph/common/__init__.py @@ -0,0 +1,5 @@ +"""Common utilities for graph implementations.""" + +from haiku.rag.graph.common.utils import get_model + +__all__ = ["get_model"] diff --git a/haiku_rag_slim/haiku/rag/graph_common/models.py b/haiku_rag_slim/haiku/rag/graph/common/models.py similarity index 100% rename from haiku_rag_slim/haiku/rag/graph_common/models.py rename to haiku_rag_slim/haiku/rag/graph/common/models.py diff --git a/haiku_rag_slim/haiku/rag/graph_common/prompts.py b/haiku_rag_slim/haiku/rag/graph/common/prompts.py similarity index 100% rename from haiku_rag_slim/haiku/rag/graph_common/prompts.py rename to haiku_rag_slim/haiku/rag/graph/common/prompts.py diff --git a/haiku_rag_slim/haiku/rag/graph_common/utils.py b/haiku_rag_slim/haiku/rag/graph/common/utils.py similarity index 74% rename from haiku_rag_slim/haiku/rag/graph_common/utils.py rename to haiku_rag_slim/haiku/rag/graph/common/utils.py index f24fb06c..19c424c8 100644 --- a/haiku_rag_slim/haiku/rag/graph_common/utils.py +++ b/haiku_rag_slim/haiku/rag/graph/common/utils.py @@ -1,7 +1,5 @@ """Common utilities for all graph implementations.""" -from typing import Any, Protocol - from pydantic_ai.models.openai import OpenAIChatModel from pydantic_ai.providers.ollama import OllamaProvider from pydantic_ai.providers.openai import OpenAIProvider @@ -9,12 +7,6 @@ from pydantic_ai.providers.openai import OpenAIProvider from haiku.rag.config import Config -class HasEmitLog(Protocol): - """Protocol for objects that can emit log messages.""" - - def emit_log(self, message: str, state: Any = None) -> None: ... - - def get_model(provider: str, model: str) -> OpenAIChatModel | str: """ Get a model instance for the specified provider and model name. @@ -50,15 +42,3 @@ def get_model(provider: str, model: str) -> OpenAIChatModel | str: f"Unknown model provider: {provider}. " f"Supported providers: ollama, vllm, openai, anthropic, gemini, groq, bedrock" ) - - -def log(deps: HasEmitLog, state: Any, message: str) -> None: - """ - Emit a log message through the dependencies. - - Args: - deps: Dependencies object with emit_log method - state: Current state (passed to emit_log) - message: The message to log - """ - deps.emit_log(message, state) diff --git a/haiku_rag_slim/haiku/rag/graph/deep_qa/__init__.py b/haiku_rag_slim/haiku/rag/graph/deep_qa/__init__.py new file mode 100644 index 00000000..aaeb1ae9 --- /dev/null +++ b/haiku_rag_slim/haiku/rag/graph/deep_qa/__init__.py @@ -0,0 +1 @@ +from haiku.rag.graph.deep_qa.models import DeepQAAnswer diff --git a/haiku_rag_slim/haiku/rag/qa/deep/dependencies.py b/haiku_rag_slim/haiku/rag/graph/deep_qa/dependencies.py similarity index 87% rename from haiku_rag_slim/haiku/rag/qa/deep/dependencies.py rename to haiku_rag_slim/haiku/rag/graph/deep_qa/dependencies.py index f8bce190..1c33262b 100644 --- a/haiku_rag_slim/haiku/rag/qa/deep/dependencies.py +++ b/haiku_rag_slim/haiku/rag/graph/deep_qa/dependencies.py @@ -1,8 +1,7 @@ from pydantic import BaseModel, Field -from rich.console import Console from haiku.rag.client import HaikuRAG -from haiku.rag.graph_common.models import SearchAnswer +from haiku.rag.graph.common.models import SearchAnswer class DeepQAContext(BaseModel): @@ -26,4 +25,3 @@ class DeepQADependencies(BaseModel): client: HaikuRAG = Field(description="RAG client for document operations") context: DeepQAContext = Field(description="Shared QA context") - console: Console | None = None diff --git a/haiku_rag_slim/haiku/rag/qa/deep/graph.py b/haiku_rag_slim/haiku/rag/graph/deep_qa/graph.py similarity index 95% rename from haiku_rag_slim/haiku/rag/qa/deep/graph.py rename to haiku_rag_slim/haiku/rag/graph/deep_qa/graph.py index 4ae92bc3..61f2a121 100644 --- a/haiku_rag_slim/haiku/rag/qa/deep/graph.py +++ b/haiku_rag_slim/haiku/rag/graph/deep_qa/graph.py @@ -8,17 +8,17 @@ from pydantic_graph.beta.join import reduce_list_append from haiku.rag.config import Config from haiku.rag.config.models import AppConfig -from haiku.rag.graph_common import get_model -from haiku.rag.graph_common.models import ResearchPlan, SearchAnswer -from haiku.rag.graph_common.prompts import PLAN_PROMPT, SEARCH_AGENT_PROMPT -from haiku.rag.qa.deep.dependencies import DeepQADependencies -from haiku.rag.qa.deep.models import DeepQAAnswer, DeepQAEvaluation -from haiku.rag.qa.deep.prompts import ( +from haiku.rag.graph.common import get_model +from haiku.rag.graph.common.models import ResearchPlan, SearchAnswer +from haiku.rag.graph.common.prompts import PLAN_PROMPT, SEARCH_AGENT_PROMPT +from haiku.rag.graph.deep_qa.dependencies import DeepQADependencies +from haiku.rag.graph.deep_qa.models import DeepQAAnswer, DeepQAEvaluation +from haiku.rag.graph.deep_qa.prompts import ( DECISION_PROMPT, SYNTHESIS_PROMPT, SYNTHESIS_PROMPT_WITH_CITATIONS, ) -from haiku.rag.qa.deep.state import DeepQADeps, DeepQAState +from haiku.rag.graph.deep_qa.state import DeepQADeps, DeepQAState def build_deep_qa_graph( @@ -77,7 +77,6 @@ def build_deep_qa_graph( agent_deps = DeepQADependencies( client=deps.client, context=state.context, - console=None, ) plan_result = await plan_agent.run(prompt, deps=agent_deps) state.context.sub_questions = list(plan_result.output.sub_questions) @@ -151,7 +150,6 @@ def build_deep_qa_graph( agent_deps = DeepQADependencies( client=deps.client, context=state.context, - console=None, ) try: result = await agent.run(sub_q, deps=agent_deps) @@ -228,7 +226,6 @@ def build_deep_qa_graph( agent_deps = DeepQADependencies( client=deps.client, context=state.context, - console=None, ) result = await agent.run(prompt, deps=agent_deps) evaluation = result.output @@ -302,7 +299,6 @@ def build_deep_qa_graph( agent_deps = DeepQADependencies( client=deps.client, context=state.context, - console=None, ) result = await agent.run(prompt, deps=agent_deps) diff --git a/haiku_rag_slim/haiku/rag/qa/deep/models.py b/haiku_rag_slim/haiku/rag/graph/deep_qa/models.py similarity index 100% rename from haiku_rag_slim/haiku/rag/qa/deep/models.py rename to haiku_rag_slim/haiku/rag/graph/deep_qa/models.py diff --git a/haiku_rag_slim/haiku/rag/qa/deep/prompts.py b/haiku_rag_slim/haiku/rag/graph/deep_qa/prompts.py similarity index 100% rename from haiku_rag_slim/haiku/rag/qa/deep/prompts.py rename to haiku_rag_slim/haiku/rag/graph/deep_qa/prompts.py diff --git a/haiku_rag_slim/haiku/rag/qa/deep/state.py b/haiku_rag_slim/haiku/rag/graph/deep_qa/state.py similarity index 96% rename from haiku_rag_slim/haiku/rag/qa/deep/state.py rename to haiku_rag_slim/haiku/rag/graph/deep_qa/state.py index a340d42d..179542a6 100644 --- a/haiku_rag_slim/haiku/rag/qa/deep/state.py +++ b/haiku_rag_slim/haiku/rag/graph/deep_qa/state.py @@ -5,7 +5,7 @@ from typing import TYPE_CHECKING, Any from pydantic import BaseModel, Field from haiku.rag.client import HaikuRAG -from haiku.rag.qa.deep.dependencies import DeepQAContext +from haiku.rag.graph.deep_qa.dependencies import DeepQAContext if TYPE_CHECKING: from haiku.rag.config.models import AppConfig diff --git a/haiku_rag_slim/haiku/rag/graph/research/__init__.py b/haiku_rag_slim/haiku/rag/graph/research/__init__.py new file mode 100644 index 00000000..60f53a90 --- /dev/null +++ b/haiku_rag_slim/haiku/rag/graph/research/__init__.py @@ -0,0 +1,3 @@ +from haiku.rag.graph.common.models import SearchAnswer +from haiku.rag.graph.research.dependencies import ResearchContext, ResearchDependencies +from haiku.rag.graph.research.models import EvaluationResult, ResearchReport diff --git a/haiku_rag_slim/haiku/rag/research/common.py b/haiku_rag_slim/haiku/rag/graph/research/common.py similarity index 95% rename from haiku_rag_slim/haiku/rag/research/common.py rename to haiku_rag_slim/haiku/rag/graph/research/common.py index bd6e349d..28c1aada 100644 --- a/haiku_rag_slim/haiku/rag/research/common.py +++ b/haiku_rag_slim/haiku/rag/graph/research/common.py @@ -1,7 +1,7 @@ from pydantic_ai import format_as_xml -from haiku.rag.research.dependencies import ResearchContext -from haiku.rag.research.models import InsightAnalysis +from haiku.rag.graph.research.dependencies import ResearchContext +from haiku.rag.graph.research.models import InsightAnalysis def format_context_for_prompt(context: ResearchContext) -> str: diff --git a/haiku_rag_slim/haiku/rag/research/dependencies.py b/haiku_rag_slim/haiku/rag/graph/research/dependencies.py similarity index 98% rename from haiku_rag_slim/haiku/rag/research/dependencies.py rename to haiku_rag_slim/haiku/rag/graph/research/dependencies.py index 39bf641c..fe65cc03 100644 --- a/haiku_rag_slim/haiku/rag/research/dependencies.py +++ b/haiku_rag_slim/haiku/rag/graph/research/dependencies.py @@ -3,8 +3,8 @@ from collections.abc import Iterable from pydantic import BaseModel, Field from haiku.rag.client import HaikuRAG -from haiku.rag.graph_common.models import SearchAnswer -from haiku.rag.research.models import ( +from haiku.rag.graph.common.models import SearchAnswer +from haiku.rag.graph.research.models import ( GapRecord, InsightAnalysis, InsightRecord, diff --git a/haiku_rag_slim/haiku/rag/research/graph.py b/haiku_rag_slim/haiku/rag/graph/research/graph.py similarity index 96% rename from haiku_rag_slim/haiku/rag/research/graph.py rename to haiku_rag_slim/haiku/rag/graph/research/graph.py index b2019e00..227a33af 100644 --- a/haiku_rag_slim/haiku/rag/research/graph.py +++ b/haiku_rag_slim/haiku/rag/graph/research/graph.py @@ -8,25 +8,25 @@ from pydantic_graph.beta.join import reduce_list_append from haiku.rag.config import Config from haiku.rag.config.models import AppConfig -from haiku.rag.graph_common import get_model -from haiku.rag.graph_common.models import ResearchPlan, SearchAnswer -from haiku.rag.graph_common.prompts import PLAN_PROMPT, SEARCH_AGENT_PROMPT -from haiku.rag.research.common import ( +from haiku.rag.graph.common import get_model +from haiku.rag.graph.common.models import ResearchPlan, SearchAnswer +from haiku.rag.graph.common.prompts import PLAN_PROMPT, SEARCH_AGENT_PROMPT +from haiku.rag.graph.research.common import ( format_analysis_for_prompt, format_context_for_prompt, ) -from haiku.rag.research.dependencies import ResearchDependencies -from haiku.rag.research.models import ( +from haiku.rag.graph.research.dependencies import ResearchDependencies +from haiku.rag.graph.research.models import ( EvaluationResult, InsightAnalysis, ResearchReport, ) -from haiku.rag.research.prompts import ( +from haiku.rag.graph.research.prompts import ( DECISION_AGENT_PROMPT, INSIGHT_AGENT_PROMPT, SYNTHESIS_AGENT_PROMPT, ) -from haiku.rag.research.state import ResearchDeps, ResearchState +from haiku.rag.graph.research.state import ResearchDeps, ResearchState def build_research_graph( diff --git a/haiku_rag_slim/haiku/rag/research/models.py b/haiku_rag_slim/haiku/rag/graph/research/models.py similarity index 100% rename from haiku_rag_slim/haiku/rag/research/models.py rename to haiku_rag_slim/haiku/rag/graph/research/models.py diff --git a/haiku_rag_slim/haiku/rag/research/prompts.py b/haiku_rag_slim/haiku/rag/graph/research/prompts.py similarity index 100% rename from haiku_rag_slim/haiku/rag/research/prompts.py rename to haiku_rag_slim/haiku/rag/graph/research/prompts.py diff --git a/haiku_rag_slim/haiku/rag/research/state.py b/haiku_rag_slim/haiku/rag/graph/research/state.py similarity index 91% rename from haiku_rag_slim/haiku/rag/research/state.py rename to haiku_rag_slim/haiku/rag/graph/research/state.py index 72e341f5..8ac49c95 100644 --- a/haiku_rag_slim/haiku/rag/research/state.py +++ b/haiku_rag_slim/haiku/rag/graph/research/state.py @@ -5,12 +5,16 @@ from typing import TYPE_CHECKING from pydantic import BaseModel, Field from haiku.rag.client import HaikuRAG -from haiku.rag.research.dependencies import ResearchContext -from haiku.rag.research.models import EvaluationResult, InsightAnalysis, ResearchReport +from haiku.rag.graph.research.dependencies import ResearchContext +from haiku.rag.graph.research.models import ( + EvaluationResult, + InsightAnalysis, + ResearchReport, +) if TYPE_CHECKING: - from haiku.rag.agui.emitter import AGUIEmitter from haiku.rag.config.models import AppConfig + from haiku.rag.graph.agui.emitter import AGUIEmitter @dataclass diff --git a/haiku_rag_slim/haiku/rag/graph_common/__init__.py b/haiku_rag_slim/haiku/rag/graph_common/__init__.py deleted file mode 100644 index dc47bee0..00000000 --- a/haiku_rag_slim/haiku/rag/graph_common/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -"""Common utilities for graph implementations.""" - -from haiku.rag.graph_common.utils import get_model, log - -__all__ = ["get_model", "log"] diff --git a/haiku_rag_slim/haiku/rag/mcp.py b/haiku_rag_slim/haiku/rag/mcp.py index 25cd2fec..c6c3f97e 100644 --- a/haiku_rag_slim/haiku/rag/mcp.py +++ b/haiku_rag_slim/haiku/rag/mcp.py @@ -6,7 +6,7 @@ from pydantic import BaseModel from haiku.rag.client import HaikuRAG from haiku.rag.config import AppConfig, Config -from haiku.rag.research.models import ResearchReport +from haiku.rag.graph.research.models import ResearchReport class SearchResult(BaseModel): @@ -191,9 +191,9 @@ def create_mcp_server(db_path: Path, config: AppConfig = Config) -> FastMCP: try: async with HaikuRAG(db_path, config=config) as rag: if deep: - from haiku.rag.qa.deep.dependencies import DeepQAContext - from haiku.rag.qa.deep.graph import build_deep_qa_graph - from haiku.rag.qa.deep.state import DeepQADeps, DeepQAState + from haiku.rag.graph.deep_qa.dependencies import DeepQAContext + from haiku.rag.graph.deep_qa.graph import build_deep_qa_graph + from haiku.rag.graph.deep_qa.state import DeepQADeps, DeepQAState graph = build_deep_qa_graph(config=config) context = DeepQAContext( @@ -226,9 +226,9 @@ def create_mcp_server(db_path: Path, config: AppConfig = Config) -> FastMCP: A research report with findings, or None if an error occurred. """ try: - from haiku.rag.research.dependencies import ResearchContext - from haiku.rag.research.graph import build_research_graph - from haiku.rag.research.state import ResearchDeps, ResearchState + from haiku.rag.graph.research.dependencies import ResearchContext + from haiku.rag.graph.research.graph import build_research_graph + from haiku.rag.graph.research.state import ResearchDeps, ResearchState async with HaikuRAG(db_path, config=config) as rag: graph = build_research_graph(config=config) diff --git a/haiku_rag_slim/haiku/rag/qa/deep/__init__.py b/haiku_rag_slim/haiku/rag/qa/deep/__init__.py deleted file mode 100644 index 0dc102f6..00000000 --- a/haiku_rag_slim/haiku/rag/qa/deep/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from haiku.rag.qa.deep.models import DeepQAAnswer diff --git a/haiku_rag_slim/haiku/rag/research/__init__.py b/haiku_rag_slim/haiku/rag/research/__init__.py deleted file mode 100644 index 9406a89c..00000000 --- a/haiku_rag_slim/haiku/rag/research/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from haiku.rag.graph_common.models import SearchAnswer -from haiku.rag.research.dependencies import ResearchContext, ResearchDependencies -from haiku.rag.research.models import EvaluationResult, ResearchReport diff --git a/tests/graph/__init__.py b/tests/graph/__init__.py new file mode 100644 index 00000000..8a28b35f --- /dev/null +++ b/tests/graph/__init__.py @@ -0,0 +1 @@ +"""Tests for haiku.rag.graph module.""" diff --git a/tests/agui/__init__.py b/tests/graph/agui/__init__.py similarity index 100% rename from tests/agui/__init__.py rename to tests/graph/agui/__init__.py diff --git a/tests/agui/test_cli_renderer.py b/tests/graph/agui/test_cli_renderer.py similarity index 97% rename from tests/agui/test_cli_renderer.py rename to tests/graph/agui/test_cli_renderer.py index c01d6934..738b0bcc 100644 --- a/tests/agui/test_cli_renderer.py +++ b/tests/graph/agui/test_cli_renderer.py @@ -4,8 +4,8 @@ import pytest from pydantic import BaseModel from rich.console import Console -from haiku.rag.agui.cli_renderer import AGUIConsoleRenderer -from haiku.rag.agui.events import ( +from haiku.rag.graph.agui.cli_renderer import AGUIConsoleRenderer +from haiku.rag.graph.agui.events import ( emit_activity, emit_run_error, emit_run_finished, diff --git a/tests/agui/test_emitter.py b/tests/graph/agui/test_emitter.py similarity index 99% rename from tests/agui/test_emitter.py rename to tests/graph/agui/test_emitter.py index 5a057247..5e6148d1 100644 --- a/tests/agui/test_emitter.py +++ b/tests/graph/agui/test_emitter.py @@ -5,7 +5,7 @@ import asyncio import pytest from pydantic import BaseModel -from haiku.rag.agui.emitter import AGUIEmitter +from haiku.rag.graph.agui.emitter import AGUIEmitter class TestState(BaseModel): diff --git a/tests/agui/test_events.py b/tests/graph/agui/test_events.py similarity index 99% rename from tests/agui/test_events.py rename to tests/graph/agui/test_events.py index 67db2e22..e400f99d 100644 --- a/tests/agui/test_events.py +++ b/tests/graph/agui/test_events.py @@ -2,7 +2,7 @@ from pydantic import BaseModel -from haiku.rag.agui.events import ( +from haiku.rag.graph.agui.events import ( emit_activity, emit_run_error, emit_run_finished, diff --git a/tests/agui/test_server.py b/tests/graph/agui/test_server.py similarity index 98% rename from tests/agui/test_server.py rename to tests/graph/agui/test_server.py index 2752b153..a145f7f6 100644 --- a/tests/agui/test_server.py +++ b/tests/graph/agui/test_server.py @@ -4,8 +4,8 @@ import pytest from pydantic import BaseModel from starlette.testclient import TestClient -from haiku.rag.agui.server import RunAgentInput, create_agui_app, format_sse_event from haiku.rag.config.models import AGUIConfig +from haiku.rag.graph.agui.server import RunAgentInput, create_agui_app, format_sse_event class SimpleState(BaseModel): diff --git a/tests/agui/test_stream.py b/tests/graph/agui/test_stream.py similarity index 98% rename from tests/agui/test_stream.py rename to tests/graph/agui/test_stream.py index 1aca01b4..daa428b0 100644 --- a/tests/agui/test_stream.py +++ b/tests/graph/agui/test_stream.py @@ -5,8 +5,8 @@ from dataclasses import dataclass import pytest from pydantic import BaseModel -from haiku.rag.agui.emitter import AGUIEmitter -from haiku.rag.agui.stream import stream_graph +from haiku.rag.graph.agui.emitter import AGUIEmitter +from haiku.rag.graph.agui.stream import stream_graph class TestState(BaseModel): diff --git a/tests/test_deep_qa.py b/tests/graph/test_deep_qa.py similarity index 83% rename from tests/test_deep_qa.py rename to tests/graph/test_deep_qa.py index b6757498..582b3242 100644 --- a/tests/test_deep_qa.py +++ b/tests/graph/test_deep_qa.py @@ -2,10 +2,10 @@ import pytest from pydantic_ai.models.test import TestModel from haiku.rag.client import HaikuRAG -from haiku.rag.graph_common.models import SearchAnswer -from haiku.rag.qa.deep.dependencies import DeepQAContext -from haiku.rag.qa.deep.graph import build_deep_qa_graph -from haiku.rag.qa.deep.state import DeepQADeps, DeepQAState +from haiku.rag.graph.common.models import SearchAnswer +from haiku.rag.graph.deep_qa.dependencies import DeepQAContext +from haiku.rag.graph.deep_qa.graph import build_deep_qa_graph +from haiku.rag.graph.deep_qa.state import DeepQADeps, DeepQAState @pytest.mark.asyncio @@ -16,8 +16,8 @@ async def test_deep_qa_graph_end_to_end(monkeypatch, temp_db_path): def test_model_factory(provider, model): return TestModel() - monkeypatch.setattr("haiku.rag.graph_common.utils.get_model", test_model_factory) - monkeypatch.setattr("haiku.rag.qa.deep.graph.get_model", test_model_factory) + monkeypatch.setattr("haiku.rag.graph.common.utils.get_model", test_model_factory) + monkeypatch.setattr("haiku.rag.graph.deep_qa.graph.get_model", test_model_factory) graph = build_deep_qa_graph() @@ -50,8 +50,8 @@ async def test_deep_qa_with_citations(monkeypatch, temp_db_path): def test_model_factory(provider, model): return TestModel() - monkeypatch.setattr("haiku.rag.graph_common.utils.get_model", test_model_factory) - monkeypatch.setattr("haiku.rag.qa.deep.graph.get_model", test_model_factory) + monkeypatch.setattr("haiku.rag.graph.common.utils.get_model", test_model_factory) + monkeypatch.setattr("haiku.rag.graph.deep_qa.graph.get_model", test_model_factory) graph = build_deep_qa_graph() diff --git a/tests/test_research_graph.py b/tests/graph/test_research_graph.py similarity index 85% rename from tests/test_research_graph.py rename to tests/graph/test_research_graph.py index 94f1f6a3..f452b022 100644 --- a/tests/test_research_graph.py +++ b/tests/graph/test_research_graph.py @@ -3,11 +3,11 @@ import asyncio import pytest from pydantic_ai.models.test import TestModel -from haiku.rag.agui.stream import stream_graph from haiku.rag.client import HaikuRAG -from haiku.rag.research.dependencies import ResearchContext -from haiku.rag.research.graph import build_research_graph -from haiku.rag.research.state import ResearchDeps, ResearchState +from haiku.rag.graph.agui.stream import stream_graph +from haiku.rag.graph.research.dependencies import ResearchContext +from haiku.rag.graph.research.graph import build_research_graph +from haiku.rag.graph.research.state import ResearchDeps, ResearchState def test_build_graph_and_state(): @@ -39,8 +39,8 @@ async def test_graph_end_to_end_with_test_model(monkeypatch, temp_db_path): def test_model_factory(_provider, _model): return TestModel() - monkeypatch.setattr("haiku.rag.graph_common.utils.get_model", test_model_factory) - monkeypatch.setattr("haiku.rag.research.graph.get_model", test_model_factory) + monkeypatch.setattr("haiku.rag.graph.common.utils.get_model", test_model_factory) + monkeypatch.setattr("haiku.rag.graph.research.graph.get_model", test_model_factory) graph = build_research_graph() diff --git a/tests/test_app.py b/tests/test_app.py index 51153aa4..e189c022 100644 --- a/tests/test_app.py +++ b/tests/test_app.py @@ -343,7 +343,7 @@ async def test_ask_with_verbose(app: HaikuRAGApp, monkeypatch): @pytest.mark.asyncio async def test_ask_with_deep(app: HaikuRAGApp, monkeypatch): """Test asking a question with deep QA.""" - from haiku.rag.qa.deep.models import DeepQAAnswer + from haiku.rag.graph.deep_qa.models import DeepQAAnswer mock_output = DeepQAAnswer(answer="Deep QA answer", sources=["test.md"]) @@ -358,7 +358,7 @@ async def test_ask_with_deep(app: HaikuRAGApp, monkeypatch): with patch("haiku.rag.app.HaikuRAG", return_value=mock_client): with patch( - "haiku.rag.qa.deep.graph.build_deep_qa_graph", return_value=mock_graph + "haiku.rag.graph.deep_qa.graph.build_deep_qa_graph", return_value=mock_graph ): await app.ask("test question", deep=True) @@ -371,7 +371,7 @@ async def test_ask_with_deep(app: HaikuRAGApp, monkeypatch): @pytest.mark.asyncio async def test_ask_with_deep_and_cite(app: HaikuRAGApp, monkeypatch): """Test asking a question with deep QA and citations.""" - from haiku.rag.qa.deep.models import DeepQAAnswer + from haiku.rag.graph.deep_qa.models import DeepQAAnswer mock_output = DeepQAAnswer( answer="Deep QA answer with citations [test.md]", sources=["test.md"] @@ -388,7 +388,7 @@ async def test_ask_with_deep_and_cite(app: HaikuRAGApp, monkeypatch): with patch("haiku.rag.app.HaikuRAG", return_value=mock_client): with patch( - "haiku.rag.qa.deep.graph.build_deep_qa_graph", return_value=mock_graph + "haiku.rag.graph.deep_qa.graph.build_deep_qa_graph", return_value=mock_graph ): await app.ask("test question", deep=True, cite=True) @@ -417,10 +417,10 @@ async def test_ask_with_deep_and_verbose(app: HaikuRAGApp, monkeypatch): with patch("haiku.rag.app.HaikuRAG", return_value=mock_client): with patch( - "haiku.rag.qa.deep.graph.build_deep_qa_graph", return_value=mock_graph + "haiku.rag.graph.deep_qa.graph.build_deep_qa_graph", return_value=mock_graph ): with patch( - "haiku.rag.agui.AGUIConsoleRenderer", return_value=mock_renderer + "haiku.rag.graph.agui.AGUIConsoleRenderer", return_value=mock_renderer ): await app.ask("test question", deep=True, verbose=True) diff --git a/tests/test_mcp.py b/tests/test_mcp.py index de2956d3..389f11bc 100644 --- a/tests/test_mcp.py +++ b/tests/test_mcp.py @@ -4,8 +4,8 @@ from unittest.mock import AsyncMock, patch import pytest +from haiku.rag.graph.research.models import ResearchReport from haiku.rag.mcp import create_mcp_server -from haiku.rag.research.models import ResearchReport from haiku.rag.store.models.document import Document @@ -249,7 +249,9 @@ async def test_mcp_ask_question_deep(): with ( patch("haiku.rag.mcp.HaikuRAG") as mock_rag_class, - patch("haiku.rag.qa.deep.graph.build_deep_qa_graph") as mock_graph_builder, + patch( + "haiku.rag.graph.deep_qa.graph.build_deep_qa_graph" + ) as mock_graph_builder, ): mock_rag = AsyncMock() mock_rag_class.return_value.__aenter__ = AsyncMock(return_value=mock_rag) @@ -291,7 +293,7 @@ async def test_mcp_research_question(): with ( patch("haiku.rag.mcp.HaikuRAG") as mock_rag_class, patch( - "haiku.rag.research.graph.build_research_graph" + "haiku.rag.graph.research.graph.build_research_graph" ) as mock_graph_builder, ): mock_rag = AsyncMock() From 888c9e338e59384ddfef55754bd11c6a17c435af Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Tue, 11 Nov 2025 16:05:19 +0200 Subject: [PATCH 11/17] Changelog --- CHANGELOG.md | 38 +++++++++++++++++++++++++++++++++++++- 1 file changed, 37 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 361d1a98..cbb5acf2 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,15 +1,51 @@ # Changelog ## [Unreleased] +### Added + +- **AG-UI Protocol Support**: Full AG-UI (Agent-UI) protocol implementation for graph execution with event streaming + - New `AGUIEmitter` class for emitting AG-UI events from graphs + - Support for all AG-UI event types: lifecycle events (`RUN_STARTED`, `RUN_FINISHED`, `RUN_ERROR`), step events (`STEP_STARTED`, `STEP_FINISHED`), state updates (`STATE_SNAPSHOT`), activity narration (`ACTIVITY_SNAPSHOT`), and text messages (`TEXT_MESSAGE_CHUNK`) + - `AGUIConsoleRenderer` for rendering AG-UI event streams to terminal with Rich formatting + - `stream_graph()` utility function for executing graphs with AG-UI event emission + - State diff computation for efficient state synchronization +- **AG-UI Server**: Starlette-based HTTP server for serving graphs via AG-UI protocol + - Server-Sent Events (SSE) streaming endpoint at `/v1/agent/stream` + - Health check endpoint at `/health` + - Full CORS support configurable via `agui` config section + - `create_agui_server()` function for programmatic server creation +- **Deep QA AG-UI Support**: Deep QA graph now fully supports AG-UI event streaming + - Integration with `AGUIEmitter` for progress tracking + - Step-by-step execution visibility via AG-UI events +- **CLI AG-UI Flag**: New `--agui` flag for `serve` command to start AG-UI server +- **Graph Module**: New unified `haiku.rag.graph` module containing all graph-related functionality + ### Changed +- **BREAKING**: Major refactoring of graph-related code into unified `haiku.rag.graph` module structure: + - `haiku.rag.research` → `haiku.rag.graph.research` + - `haiku.rag.qa.deep` → `haiku.rag.graph.deep_qa` + - `haiku.rag.agui` → `haiku.rag.graph.agui` + - `haiku.rag.graph_common` → `haiku.rag.graph.common` +- **BREAKING**: Research and Deep QA graphs now use AG-UI event protocol instead of direct console logging + - Removed `console` and `stream` parameters from graph dependencies + - All progress updates now emit through `AGUIEmitter` +- **BREAKING**: `ResearchState` converted from dataclass to Pydantic `BaseModel` for JSON serialization and AG-UI compatibility +- Research and Deep QA graphs now emit detailed execution events for better observability +- CLI research command now uses AG-UI event rendering for `--verbose` output +- Improved graph execution visibility with step-by-step progress tracking +- Updated all documentation to reflect new import paths and AG-UI usage +- Updated examples (ag-ui-research, a2a-server) to use new import paths ### Fixed - **Document Creation**: Optimized `create_document` to skip unnecessary DoclingDocument conversion when chunks are pre-provided - - **FileReader**: Error messages now include both original exception details and file path for easier debugging +### Removed + +- **BREAKING**: Removed legacy `ResearchStream` and `ResearchStreamEvent` classes (replaced by AG-UI event protocol) + ## [0.15.0] - 2025-11-07 ### Added From 2060898018e478eb8013839ec0a7ad6b0956bed0 Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Wed, 12 Nov 2025 10:01:51 +0200 Subject: [PATCH 12/17] Use a node factor for common nodes --- .../haiku/rag/graph/common/nodes.py | 262 ++++++++++++++++++ .../haiku/rag/graph/deep_qa/graph.py | 152 ++-------- .../haiku/rag/graph/research/graph.py | 169 ++--------- 3 files changed, 306 insertions(+), 277 deletions(-) create mode 100644 haiku_rag_slim/haiku/rag/graph/common/nodes.py diff --git a/haiku_rag_slim/haiku/rag/graph/common/nodes.py b/haiku_rag_slim/haiku/rag/graph/common/nodes.py new file mode 100644 index 00000000..1097193b --- /dev/null +++ b/haiku_rag_slim/haiku/rag/graph/common/nodes.py @@ -0,0 +1,262 @@ +"""Common node implementations for graph workflows.""" + +import asyncio +from collections.abc import Awaitable, Callable +from typing import Any, Protocol + +from pydantic_ai import Agent, RunContext +from pydantic_ai.format_prompt import format_as_xml +from pydantic_ai.output import ToolOutput +from pydantic_graph.beta import StepContext + +from haiku.rag.client import HaikuRAG +from haiku.rag.graph.agui.emitter import AGUIEmitter +from haiku.rag.graph.common import get_model +from haiku.rag.graph.common.models import ResearchPlan, SearchAnswer +from haiku.rag.graph.common.prompts import PLAN_PROMPT, SEARCH_AGENT_PROMPT + + +class GraphContext(Protocol): + """Protocol for graph context objects.""" + + original_question: str + sub_questions: list[str] + + def add_qa_response(self, qa: SearchAnswer) -> None: + """Add a QA response to context.""" + ... + + +class GraphState(Protocol): + """Protocol for graph state objects.""" + + context: GraphContext + max_concurrency: int + + +class GraphDeps(Protocol): + """Protocol for graph dependencies.""" + + client: HaikuRAG + agui_emitter: AGUIEmitter[Any, Any] | None + semaphore: asyncio.Semaphore | None + + +class GraphAgentDeps(Protocol): + """Protocol for agent dependencies.""" + + client: HaikuRAG + context: GraphContext + + +def create_plan_node[AgentDepsT: GraphAgentDeps]( + provider: str, + model: str, + deps_type: type[AgentDepsT], + activity_message: str = "Creating plan", + output_retries: int | None = None, +) -> Callable[[StepContext[Any, Any, None]], Awaitable[None]]: + """Create a plan node for any graph. + + Args: + provider: Model provider (e.g., 'openai', 'anthropic') + model: Model name + deps_type: Type of dependencies for the agent (e.g., ResearchDependencies, DeepQADependencies) + activity_message: Message to show during planning activity + output_retries: Number of output retries for the agent (optional) + + Returns: + Async function that can be used as a graph step + """ + + async def plan(ctx: StepContext[Any, Any, None], /) -> None: + state: GraphState = ctx.state # type: ignore[assignment] + deps: GraphDeps = ctx.deps # type: ignore[assignment] + + if deps.agui_emitter: + deps.agui_emitter.start_step("plan") + deps.agui_emitter.update_activity("planning", activity_message) + + try: + # Build agent configuration + agent_config = { + "model": get_model(provider, model), + "output_type": ResearchPlan, + "instructions": ( + PLAN_PROMPT + + "\n\nUse the gather_context tool once on the main question before planning." + ), + "retries": 3, + "deps_type": deps_type, + } + if output_retries is not None: + agent_config["output_retries"] = output_retries + + plan_agent = Agent(**agent_config) + + @plan_agent.tool + async def gather_context( + ctx2: RunContext[AgentDepsT], query: str, limit: int = 6 + ) -> str: + results = await ctx2.deps.client.search(query, limit=limit) + expanded = await ctx2.deps.client.expand_context(results) + return "\n\n".join(chunk.content for chunk, _ in expanded) + + # Tool is registered via decorator above + _ = gather_context + + prompt = ( + "Plan a focused approach for the main question.\n\n" + f"Main question: {state.context.original_question}" + ) + + # Create agent dependencies + agent_deps = deps_type(client=deps.client, context=state.context) # type: ignore[call-arg] + plan_result = await plan_agent.run(prompt, deps=agent_deps) + state.context.sub_questions = list(plan_result.output.sub_questions) + + # State now contains the plan - emit state update and narrate + if deps.agui_emitter: + deps.agui_emitter.update_state(state) + count = len(state.context.sub_questions) + deps.agui_emitter.update_activity( + "planning", f"Created plan with {count} sub-questions" + ) + finally: + if deps.agui_emitter: + deps.agui_emitter.finish_step() + + return plan + + +def create_search_node[AgentDepsT: GraphAgentDeps]( + provider: str, + model: str, + deps_type: type[AgentDepsT], + with_step_wrapper: bool = True, + success_message_format: str = "Answered: {sub_q}", + handle_exceptions: bool = False, +) -> Callable[[StepContext[Any, Any, str]], Awaitable[SearchAnswer]]: + """Create a search_one node for any graph. + + Args: + provider: Model provider + model: Model name + deps_type: Type of dependencies for the agent + with_step_wrapper: Whether to wrap with agui_emitter start/finish step + success_message_format: Format string for success activity message + handle_exceptions: Whether to handle exceptions with fallback answer + + Returns: + Async function that can be used as a graph step + """ + + async def search_one(ctx: StepContext[Any, Any, str], /) -> SearchAnswer: + state: GraphState = ctx.state # type: ignore[assignment] + deps: GraphDeps = ctx.deps # type: ignore[assignment] + sub_q = ctx.inputs + + if deps.agui_emitter and with_step_wrapper: + deps.agui_emitter.start_step("search_one") + + try: + # Create semaphore if not already provided + if deps.semaphore is None: + deps.semaphore = asyncio.Semaphore(state.max_concurrency) + + # Use semaphore to control concurrency + async with deps.semaphore: + return await _do_search( + state, + deps, + sub_q, + provider, + model, + deps_type, + success_message_format, + handle_exceptions, + ) + finally: + if deps.agui_emitter and with_step_wrapper: + deps.agui_emitter.finish_step() + + return search_one + + +async def _do_search[AgentDepsT: GraphAgentDeps]( + state: GraphState, + deps: GraphDeps, + sub_q: str, + provider: str, + model: str, + deps_type: type[AgentDepsT], + success_message_format: str, + handle_exceptions: bool, +) -> SearchAnswer: + """Internal search implementation.""" + if deps.agui_emitter: + deps.agui_emitter.update_activity("searching", f"Searching: {sub_q}") + + agent = Agent( + model=get_model(provider, model), + output_type=ToolOutput(SearchAnswer, max_retries=3), + instructions=SEARCH_AGENT_PROMPT, + retries=3, + deps_type=deps_type, + ) + + @agent.tool + async def search_and_answer( + ctx2: RunContext[AgentDepsT], query: str, limit: int = 5 + ) -> str: + search_results = await ctx2.deps.client.search(query, limit=limit) + expanded = await ctx2.deps.client.expand_context(search_results) + + entries: list[dict[str, Any]] = [ + { + "text": chunk.content, + "score": score, + "document_uri": (chunk.document_title or chunk.document_uri or ""), + } + for chunk, score in expanded + ] + if not entries: + return f"No relevant information found in the knowledge base for: {query}" + + return format_as_xml(entries, root_tag="snippets") + + # Tool is registered via decorator above + _ = search_and_answer + + agent_deps = deps_type(client=deps.client, context=state.context) # type: ignore[call-arg] + + try: + result = await agent.run(sub_q, deps=agent_deps) + answer = result.output + if answer: + state.context.add_qa_response(answer) + # State updated with new answer - emit state update and narrate + if deps.agui_emitter: + deps.agui_emitter.update_state(state) + # Format the success message + if "{confidence}" in success_message_format: + message = success_message_format.format( + sub_q=sub_q, confidence=answer.confidence + ) + else: + message = success_message_format.format(sub_q=sub_q) + deps.agui_emitter.update_activity("searching", message) + return answer + except Exception as e: + if handle_exceptions: + # Narrate the error + if deps.agui_emitter: + deps.agui_emitter.update_activity("searching", f"Search failed: {e}") + failure_answer = SearchAnswer( + query=sub_q, + answer=f"Search failed after retries: {str(e)}", + confidence=0.0, + ) + return failure_answer + else: + raise diff --git a/haiku_rag_slim/haiku/rag/graph/deep_qa/graph.py b/haiku_rag_slim/haiku/rag/graph/deep_qa/graph.py index 61f2a121..550dc654 100644 --- a/haiku_rag_slim/haiku/rag/graph/deep_qa/graph.py +++ b/haiku_rag_slim/haiku/rag/graph/deep_qa/graph.py @@ -1,16 +1,13 @@ -from typing import Any - -from pydantic_ai import Agent, RunContext +from pydantic_ai import Agent from pydantic_ai.format_prompt import format_as_xml -from pydantic_ai.output import ToolOutput from pydantic_graph.beta import Graph, GraphBuilder, StepContext from pydantic_graph.beta.join import reduce_list_append from haiku.rag.config import Config from haiku.rag.config.models import AppConfig from haiku.rag.graph.common import get_model -from haiku.rag.graph.common.models import ResearchPlan, SearchAnswer -from haiku.rag.graph.common.prompts import PLAN_PROMPT, SEARCH_AGENT_PROMPT +from haiku.rag.graph.common.models import SearchAnswer +from haiku.rag.graph.common.nodes import create_plan_node, create_search_node from haiku.rag.graph.deep_qa.dependencies import DeepQADependencies from haiku.rag.graph.deep_qa.models import DeepQAAnswer, DeepQAEvaluation from haiku.rag.graph.deep_qa.prompts import ( @@ -40,133 +37,28 @@ def build_deep_qa_graph( output_type=DeepQAAnswer, ) - @g.step - async def plan(ctx: StepContext[DeepQAState, DeepQADeps, None]) -> None: - state = ctx.state - deps = ctx.deps - - if deps.agui_emitter: - deps.agui_emitter.start_step("plan") - deps.agui_emitter.update_activity("planning", "Planning approach") - - try: - plan_agent = Agent( - model=get_model(provider, model), - output_type=ResearchPlan, - instructions=( - PLAN_PROMPT - + "\n\nUse the gather_context tool once on the main question before planning." - ), - retries=3, - deps_type=DeepQADependencies, - ) - - @plan_agent.tool - async def gather_context( - ctx2: RunContext[DeepQADependencies], query: str, limit: int = 6 - ) -> str: - results = await ctx2.deps.client.search(query, limit=limit) - expanded = await ctx2.deps.client.expand_context(results) - return "\n\n".join(chunk.content for chunk, _ in expanded) - - prompt = ( - "Plan a focused approach for the main question.\n\n" - f"Main question: {state.context.original_question}" - ) - - agent_deps = DeepQADependencies( - client=deps.client, - context=state.context, - ) - plan_result = await plan_agent.run(prompt, deps=agent_deps) - state.context.sub_questions = list(plan_result.output.sub_questions) - - if deps.agui_emitter: - deps.agui_emitter.update_state(state) - count = len(state.context.sub_questions) - deps.agui_emitter.update_activity( - "planning", f"Created plan with {count} sub-questions" - ) - finally: - if deps.agui_emitter: - deps.agui_emitter.finish_step() - - @g.step - async def search_one( - ctx: StepContext[DeepQAState, DeepQADeps, str], - ) -> SearchAnswer: - state = ctx.state - deps = ctx.deps - sub_q = ctx.inputs - - # Create semaphore if not already provided - if deps.semaphore is None: - import asyncio - - deps.semaphore = asyncio.Semaphore(state.max_concurrency) - - # Use semaphore to control concurrency - async with deps.semaphore: - return await _do_search(state, deps, sub_q) - - async def _do_search( - state: DeepQAState, - deps: DeepQADeps, - sub_q: str, - ) -> SearchAnswer: - if deps.agui_emitter: - deps.agui_emitter.update_activity("searching", f"Searching: {sub_q}") - - agent = Agent( - model=get_model(provider, model), - output_type=ToolOutput(SearchAnswer, max_retries=3), - instructions=SEARCH_AGENT_PROMPT, - retries=3, - deps_type=DeepQADependencies, + # Create and register the plan node using the factory + plan = g.step( + create_plan_node( + provider=provider, + model=model, + deps_type=DeepQADependencies, # type: ignore[arg-type] + activity_message="Planning approach", + output_retries=None, # Deep QA doesn't use output_retries ) + ) # type: ignore[arg-type] - @agent.tool - async def search_and_answer( - ctx2: RunContext[DeepQADependencies], query: str, limit: int = 5 - ) -> str: - search_results = await ctx2.deps.client.search(query, limit=limit) - expanded = await ctx2.deps.client.expand_context(search_results) - - entries: list[dict[str, Any]] = [ - { - "text": chunk.content, - "score": score, - "document_uri": (chunk.document_title or chunk.document_uri or ""), - } - for chunk, score in expanded - ] - if not entries: - return ( - f"No relevant information found in the knowledge base for: {query}" - ) - - return format_as_xml(entries, root_tag="snippets") - - agent_deps = DeepQADependencies( - client=deps.client, - context=state.context, + # Create and register the search_one node using the factory + search_one = g.step( + create_search_node( + provider=provider, + model=model, + deps_type=DeepQADependencies, # type: ignore[arg-type] + with_step_wrapper=False, # Deep QA doesn't wrap with agui_emitter step + success_message_format="Answered: {sub_q}", + handle_exceptions=True, ) - try: - result = await agent.run(sub_q, deps=agent_deps) - answer = result.output - if answer: - state.context.add_qa_response(answer) - if deps.agui_emitter: - deps.agui_emitter.update_state(state) - deps.agui_emitter.update_activity("searching", f"Answered: {sub_q}") - return answer - except Exception as e: - failure_answer = SearchAnswer( - query=sub_q, - answer=f"Search failed after retries: {str(e)}", - confidence=0.0, - ) - return failure_answer + ) # type: ignore[arg-type] @g.step async def get_batch( diff --git a/haiku_rag_slim/haiku/rag/graph/research/graph.py b/haiku_rag_slim/haiku/rag/graph/research/graph.py index 227a33af..843bab6b 100644 --- a/haiku_rag_slim/haiku/rag/graph/research/graph.py +++ b/haiku_rag_slim/haiku/rag/graph/research/graph.py @@ -1,16 +1,12 @@ -from typing import Any - -from pydantic_ai import Agent, RunContext -from pydantic_ai.format_prompt import format_as_xml -from pydantic_ai.output import ToolOutput +from pydantic_ai import Agent from pydantic_graph.beta import Graph, GraphBuilder, StepContext from pydantic_graph.beta.join import reduce_list_append from haiku.rag.config import Config from haiku.rag.config.models import AppConfig from haiku.rag.graph.common import get_model -from haiku.rag.graph.common.models import ResearchPlan, SearchAnswer -from haiku.rag.graph.common.prompts import PLAN_PROMPT, SEARCH_AGENT_PROMPT +from haiku.rag.graph.common.models import SearchAnswer +from haiku.rag.graph.common.nodes import create_plan_node, create_search_node from haiku.rag.graph.research.common import ( format_analysis_for_prompt, format_context_for_prompt, @@ -48,149 +44,28 @@ def build_research_graph( output_type=ResearchReport, ) - @g.step - async def plan(ctx: StepContext[ResearchState, ResearchDeps, None]) -> None: - state = ctx.state - deps = ctx.deps - - if deps.agui_emitter: - deps.agui_emitter.start_step("plan") - deps.agui_emitter.update_activity("planning", "Creating research plan") - - try: - plan_agent = Agent( - model=get_model(provider, model), - output_type=ResearchPlan, - instructions=( - PLAN_PROMPT - + "\n\nUse the gather_context tool once on the main question before planning." - ), - retries=3, - output_retries=3, - deps_type=ResearchDependencies, - ) - - @plan_agent.tool - async def gather_context( - ctx2: RunContext[ResearchDependencies], query: str, limit: int = 6 - ) -> str: - results = await ctx2.deps.client.search(query, limit=limit) - expanded = await ctx2.deps.client.expand_context(results) - return "\n\n".join(chunk.content for chunk, _ in expanded) - - prompt = ( - "Plan a focused approach for the main question.\n\n" - f"Main question: {state.context.original_question}" - ) - - agent_deps = ResearchDependencies( - client=deps.client, - context=state.context, - ) - plan_result = await plan_agent.run(prompt, deps=agent_deps) - state.context.sub_questions = list(plan_result.output.sub_questions) - - # State now contains the plan - emit state update and narrate - if deps.agui_emitter: - deps.agui_emitter.update_state(state) - count = len(state.context.sub_questions) - deps.agui_emitter.update_activity( - "planning", f"Created plan with {count} sub-questions" - ) - finally: - if deps.agui_emitter: - deps.agui_emitter.finish_step() - - @g.step - async def search_one( - ctx: StepContext[ResearchState, ResearchDeps, str], - ) -> SearchAnswer: - state = ctx.state - deps = ctx.deps - sub_q = ctx.inputs - - if deps.agui_emitter: - deps.agui_emitter.start_step("search_one") - - try: - # Create semaphore if not already provided - if deps.semaphore is None: - import asyncio - - deps.semaphore = asyncio.Semaphore(state.max_concurrency) - - # Use semaphore to control concurrency - async with deps.semaphore: - return await _do_search(state, deps, sub_q) - finally: - if deps.agui_emitter: - deps.agui_emitter.finish_step() - - async def _do_search( - state: ResearchState, - deps: ResearchDeps, - sub_q: str, - ) -> SearchAnswer: - if deps.agui_emitter: - deps.agui_emitter.update_activity("searching", f"Searching: {sub_q}") - - agent = Agent( - model=get_model(provider, model), - output_type=ToolOutput(SearchAnswer, max_retries=3), - instructions=SEARCH_AGENT_PROMPT, - retries=3, - deps_type=ResearchDependencies, + # Create and register the plan node using the factory + plan = g.step( + create_plan_node( + provider=provider, + model=model, + deps_type=ResearchDependencies, # type: ignore[arg-type] + activity_message="Creating research plan", + output_retries=3, ) + ) # type: ignore[arg-type] - @agent.tool - async def search_and_answer( - ctx2: RunContext[ResearchDependencies], query: str, limit: int = 5 - ) -> str: - search_results = await ctx2.deps.client.search(query, limit=limit) - expanded = await ctx2.deps.client.expand_context(search_results) - - entries: list[dict[str, Any]] = [ - { - "text": chunk.content, - "score": score, - "document_uri": (chunk.document_title or chunk.document_uri or ""), - } - for chunk, score in expanded - ] - if not entries: - return ( - f"No relevant information found in the knowledge base for: {query}" - ) - - return format_as_xml(entries, root_tag="snippets") - - agent_deps = ResearchDependencies( - client=deps.client, - context=state.context, + # Create and register the search_one node using the factory + search_one = g.step( + create_search_node( + provider=provider, + model=model, + deps_type=ResearchDependencies, # type: ignore[arg-type] + with_step_wrapper=True, + success_message_format="Found answer with {confidence:.0%} confidence", + handle_exceptions=True, ) - try: - result = await agent.run(sub_q, deps=agent_deps) - answer = result.output - if answer: - state.context.add_qa_response(answer) - # State updated with new answer - emit state update and narrate - if deps.agui_emitter: - deps.agui_emitter.update_state(state) - deps.agui_emitter.update_activity( - "searching", - f"Found answer with {answer.confidence:.0%} confidence", - ) - return answer - except Exception as e: - # Narrate the error - if deps.agui_emitter: - deps.agui_emitter.update_activity("searching", f"Search failed: {e}") - failure_answer = SearchAnswer( - query=sub_q, - answer=f"Search failed after retries: {str(e)}", - confidence=0.0, - ) - return failure_answer + ) # type: ignore[arg-type] @g.step async def get_batch( From 657245f686a96f2591f8deb2808b8bc9120d1127 Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Wed, 12 Nov 2025 10:21:25 +0200 Subject: [PATCH 13/17] Simplify InsightRecord, GapRecord --- .../haiku/rag/graph/common/nodes.py | 2 +- .../haiku/rag/graph/research/dependencies.py | 118 +++++------------- .../haiku/rag/graph/research/models.py | 96 +++++++------- 3 files changed, 73 insertions(+), 143 deletions(-) diff --git a/haiku_rag_slim/haiku/rag/graph/common/nodes.py b/haiku_rag_slim/haiku/rag/graph/common/nodes.py index 1097193b..ba452a61 100644 --- a/haiku_rag_slim/haiku/rag/graph/common/nodes.py +++ b/haiku_rag_slim/haiku/rag/graph/common/nodes.py @@ -239,7 +239,7 @@ async def _do_search[AgentDepsT: GraphAgentDeps]( if deps.agui_emitter: deps.agui_emitter.update_state(state) # Format the success message - if "{confidence}" in success_message_format: + if "{confidence" in success_message_format: message = success_message_format.format( sub_q=sub_q, confidence=answer.confidence ) diff --git a/haiku_rag_slim/haiku/rag/graph/research/dependencies.py b/haiku_rag_slim/haiku/rag/graph/research/dependencies.py index fe65cc03..79a92cae 100644 --- a/haiku_rag_slim/haiku/rag/graph/research/dependencies.py +++ b/haiku_rag_slim/haiku/rag/graph/research/dependencies.py @@ -1,6 +1,6 @@ from collections.abc import Iterable -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, PrivateAttr from haiku.rag.client import HaikuRAG from haiku.rag.graph.common.models import SearchAnswer @@ -28,26 +28,29 @@ class ResearchContext(BaseModel): default_factory=list, description="Identified information gaps" ) + # Private dict indexes for O(1) lookups + _insights_by_id: dict[str, InsightRecord] = PrivateAttr(default_factory=dict) + _gaps_by_id: dict[str, GapRecord] = PrivateAttr(default_factory=dict) + + def model_post_init(self, __context: object) -> None: + """Build indexes after initialization.""" + self._insights_by_id = {ins.id: ins for ins in self.insights} + self._gaps_by_id = {gap.id: gap for gap in self.gaps} + def add_qa_response(self, qa: SearchAnswer) -> None: """Add a structured QA response (minimal context already included).""" self.qa_responses.append(qa) def upsert_insights(self, records: Iterable[InsightRecord]) -> list[InsightRecord]: """Merge one or more insights into the shared context with deduplication.""" - merged: list[InsightRecord] = [] + for record in records: candidate = InsightRecord.model_validate(record) - existing = next( - (ins for ins in self.insights if ins.id == candidate.id), None - ) - if not existing: - existing = next( - (ins for ins in self.insights if ins.summary == candidate.summary), - None, - ) + existing = self._insights_by_id.get(candidate.id) if existing: + # Update existing insight existing.summary = candidate.summary existing.status = candidate.status if candidate.notes: @@ -60,36 +63,24 @@ class ResearchContext(BaseModel): ) merged.append(existing) else: - candidate = candidate.model_copy(deep=True) - if candidate.id is None: # pragma: no cover - defensive - raise ValueError( - "InsightRecord.id must be populated after validation" - ) - candidate_id: str = candidate.id - candidate.id = self._allocate_insight_id(candidate_id) - self.insights.append(candidate) - merged.append(candidate) + # Add new insight + new_insight = candidate.model_copy(deep=True) + self.insights.append(new_insight) + self._insights_by_id[new_insight.id] = new_insight + merged.append(new_insight) return merged def upsert_gaps(self, records: Iterable[GapRecord]) -> list[GapRecord]: """Merge one or more gap records into the shared context with deduplication.""" - merged: list[GapRecord] = [] + for record in records: candidate = GapRecord.model_validate(record) - existing = next((gap for gap in self.gaps if gap.id == candidate.id), None) - if not existing: - existing = next( - ( - gap - for gap in self.gaps - if gap.description == candidate.description - ), - None, - ) + existing = self._gaps_by_id.get(candidate.id) if existing: + # Update existing gap existing.description = candidate.description existing.severity = candidate.severity existing.blocking = candidate.blocking @@ -104,22 +95,19 @@ class ResearchContext(BaseModel): ) merged.append(existing) else: - candidate = candidate.model_copy(deep=True) - if candidate.id is None: # pragma: no cover - defensive - raise ValueError("GapRecord.id must be populated after validation") - candidate_id: str = candidate.id - candidate.id = self._allocate_gap_id(candidate_id) - self.gaps.append(candidate) - merged.append(candidate) + # Add new gap + new_gap = candidate.model_copy(deep=True) + self.gaps.append(new_gap) + self._gaps_by_id[new_gap.id] = new_gap + merged.append(new_gap) return merged def mark_gap_resolved( self, identifier: str, resolved_by: Iterable[str] | None = None ) -> GapRecord | None: - """Mark a gap as resolved by identifier (id or description).""" - - gap = self._find_gap(identifier) + """Mark a gap as resolved by identifier.""" + gap = self._gaps_by_id.get(identifier) if gap is None: return None @@ -131,7 +119,6 @@ class ResearchContext(BaseModel): def integrate_analysis(self, analysis: InsightAnalysis) -> None: """Apply an analysis result to the shared context.""" - merged_insights: list[InsightRecord] = [] if analysis.highlights: merged_insights = self.upsert_insights(analysis.highlights) @@ -141,9 +128,7 @@ class ResearchContext(BaseModel): analysis.gap_assessments = merged_gaps if analysis.resolved_gaps: resolved_by_list = ( - [ins.id for ins in merged_insights if ins.id is not None] - if merged_insights - else None + [ins.id for ins in merged_insights] if merged_insights else None ) for resolved in analysis.resolved_gaps: self.mark_gap_resolved(resolved, resolved_by=resolved_by_list) @@ -151,29 +136,6 @@ class ResearchContext(BaseModel): if question not in self.sub_questions: self.sub_questions.append(question) - def _allocate_insight_id(self, candidate_id: str) -> str: - taken: set[str] = set() - for ins in self.insights: - if ins.id is not None: - taken.add(ins.id) - return _allocate_sequential_id(candidate_id, taken) - - def _allocate_gap_id(self, candidate_id: str) -> str: - taken: set[str] = set() - for gap in self.gaps: - if gap.id is not None: - taken.add(gap.id) - return _allocate_sequential_id(candidate_id, taken) - - def _find_gap(self, identifier: str) -> GapRecord | None: - normalized = identifier.lower().strip() - for gap in self.gaps: - if gap.id is not None and gap.id == normalized: - return gap - if gap.description.lower().strip() == normalized: - return gap - return None - class ResearchDependencies(BaseModel): """Dependencies for research agents with multi-agent context.""" @@ -186,24 +148,4 @@ class ResearchDependencies(BaseModel): def _merge_unique(existing: list[str], incoming: Iterable[str]) -> list[str]: """Merge two iterables preserving order while removing duplicates.""" - - merged = list(existing) - seen = {item for item in existing if item} - for item in incoming: - if item and item not in seen: - merged.append(item) - seen.add(item) - return merged - - -def _allocate_sequential_id(candidate: str, taken: set[str]) -> str: - slug = candidate - if slug not in taken: - return slug - base = slug - counter = 2 - while True: - slug = f"{base}-{counter}" - if slug not in taken: - return slug - counter += 1 + return [k for k in dict.fromkeys([*existing, *incoming]) if k] diff --git a/haiku_rag_slim/haiku/rag/graph/research/models.py b/haiku_rag_slim/haiku/rag/graph/research/models.py index aae2f32a..a132bf4b 100644 --- a/haiku_rag_slim/haiku/rag/graph/research/models.py +++ b/haiku_rag_slim/haiku/rag/graph/research/models.py @@ -1,19 +1,12 @@ -import re +import uuid from enum import Enum -from pydantic import BaseModel, Field, model_validator - -_SLUG_RE = re.compile(r"[^a-z0-9]+") +from pydantic import BaseModel, Field, field_validator -def _make_slug(text: str, prefix: str) -> str: - """Generate a lowercase slug with the given prefix as fallback.""" - - base = _SLUG_RE.sub("-", text.lower()).strip("-") - if not base: - base = prefix - # Trim overly long slugs but keep enough entropy for readability - return base[:48] +def _deduplicate_list(items: list[str]) -> list[str]: + """Remove duplicates while preserving order.""" + return list(dict.fromkeys(items)) class InsightStatus(str, Enum): @@ -28,48 +21,54 @@ class GapSeverity(str, Enum): HIGH = "high" -class InsightRecord(BaseModel): +class TrackedRecord(BaseModel): + """Base model for tracked entities with sources and metadata.""" + + model_config = {"validate_assignment": True} + + id: str = Field( + default_factory=lambda: str(uuid.uuid4())[:8], + description="Unique identifier for the record", + ) + supporting_sources: list[str] = Field( + default_factory=list, + description="Source identifiers backing this record", + ) + notes: str | None = Field( + default=None, + description="Optional elaboration or caveats", + ) + + @field_validator("supporting_sources", mode="before") + @classmethod + def deduplicate_sources(cls, v: list[str]) -> list[str]: + """Ensure supporting_sources has no duplicates.""" + return _deduplicate_list(v) if v else [] + + +class InsightRecord(TrackedRecord): """Structured insight with provenance and lifecycle metadata.""" - id: str | None = Field( - default=None, - description="Stable slug identifier for the insight (auto-generated if omitted)", - ) summary: str = Field(description="Concise description of the insight") status: InsightStatus = Field( default=InsightStatus.OPEN, description="Lifecycle status for the insight", ) - supporting_sources: list[str] = Field( - default_factory=list, - description="Source identifiers backing the insight", - ) originating_questions: list[str] = Field( default_factory=list, description="Research sub-questions that produced this insight", ) - notes: str | None = Field( - default=None, - description="Optional elaboration or caveats for the insight", - ) - @model_validator(mode="after") - def _set_defaults(self) -> "InsightRecord": - if not self.id: - self.id = _make_slug(self.summary, "insight") - self.id = self.id.lower() - self.supporting_sources = list(dict.fromkeys(self.supporting_sources)) - self.originating_questions = list(dict.fromkeys(self.originating_questions)) - return self + @field_validator("originating_questions", mode="before") + @classmethod + def deduplicate_questions(cls, v: list[str]) -> list[str]: + """Ensure originating_questions has no duplicates.""" + return _deduplicate_list(v) if v else [] -class GapRecord(BaseModel): +class GapRecord(TrackedRecord): """Structured representation of an identified research gap.""" - id: str | None = Field( - default=None, - description="Stable slug identifier for the gap (auto-generated if omitted)", - ) description: str = Field(description="Concrete statement of what is missing") severity: GapSeverity = Field( default=GapSeverity.MEDIUM, @@ -87,23 +86,12 @@ class GapRecord(BaseModel): default_factory=list, description="Insight IDs or notes explaining how the gap was closed", ) - supporting_sources: list[str] = Field( - default_factory=list, - description="Sources confirming the gap status (e.g., evidence of absence)", - ) - notes: str | None = Field( - default=None, - description="Optional clarification about the gap or follow-up actions", - ) - @model_validator(mode="after") - def _set_defaults(self) -> "GapRecord": - if not self.id: - self.id = _make_slug(self.description, "gap") - self.id = self.id.lower() - self.resolved_by = list(dict.fromkeys(self.resolved_by)) - self.supporting_sources = list(dict.fromkeys(self.supporting_sources)) - return self + @field_validator("resolved_by", mode="before") + @classmethod + def deduplicate_resolved_by(cls, v: list[str]) -> list[str]: + """Ensure resolved_by has no duplicates.""" + return _deduplicate_list(v) if v else [] class InsightAnalysis(BaseModel): From 68d1127e4614f1e891a0af46f510fed1ff64801c Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Wed, 12 Nov 2025 10:50:00 +0200 Subject: [PATCH 14/17] Compute and emit state deltas instead of full state snapshots by default --- .../haiku/rag/graph/agui/cli_renderer.py | 44 +++----- .../haiku/rag/graph/agui/emitter.py | 26 +++-- haiku_rag_slim/haiku/rag/graph/agui/stream.py | 4 +- tests/graph/agui/test_cli_renderer.py | 101 +++++++++++------- tests/graph/agui/test_emitter.py | 36 ++++++- 5 files changed, 135 insertions(+), 76 deletions(-) diff --git a/haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py b/haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py index 52e223dc..03a60bb4 100644 --- a/haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py +++ b/haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py @@ -22,7 +22,6 @@ class AGUIConsoleRenderer: console: Optional Rich console instance (creates new one if not provided) """ self.console = console or Console() - self._state: dict | None = None async def render(self, events: AsyncIterator[AGUIEvent]) -> Any | None: """Process events and render to console, return final result. @@ -58,11 +57,9 @@ class AGUIConsoleRenderer: elif event_type == "TEXT_MESSAGE_END": pass # End of streaming message, no output needed elif event_type == "STATE_SNAPSHOT": - new_state = event.get("snapshot") - self._render_state_snapshot(new_state) - self._state = new_state + self._render_state_snapshot(event) elif event_type == "STATE_DELTA": - self._apply_state_delta(event) + self._render_state_delta(event) elif event_type == "ACTIVITY_SNAPSHOT": self._render_activity(event) elif event_type == "ACTIVITY_DELTA": @@ -109,33 +106,20 @@ class AGUIConsoleRenderer: if content: self.console.print(f"[yellow][ACTIVITY][/yellow] {content}") - def _render_state_snapshot(self, new_state: dict | None) -> None: - """Render state snapshot showing only what changed.""" - if not new_state: - return - - old_state = self._state or {} - diff = self._compute_diff(old_state, new_state) - - if not diff: + def _render_state_snapshot(self, event: AGUIEvent) -> None: + """Render full state snapshot.""" + snapshot = event.get("snapshot") + if not snapshot: return self.console.print("[blue][STATE_SNAPSHOT][/blue]") - self.console.print(diff, style="dim") + self.console.print(snapshot, style="dim") - def _compute_diff(self, old: dict, new: dict) -> dict: - """Compute difference between old and new state.""" - diff = {} - for key, new_value in new.items(): - old_value = old.get(key) - if old_value != new_value: - if isinstance(new_value, dict) and isinstance(old_value, dict): - nested_diff = self._compute_diff(old_value, new_value) - if nested_diff: - diff[key] = nested_diff - else: - diff[key] = new_value - return diff + def _render_state_delta(self, event: AGUIEvent) -> None: + """Render state delta operations.""" + delta = event.get("delta", []) + if not delta: + return - def _apply_state_delta(self, _event: AGUIEvent) -> None: - """Apply state delta to current state.""" + self.console.print("[blue][STATE_DELTA][/blue]") + self.console.print(delta, style="dim") diff --git a/haiku_rag_slim/haiku/rag/graph/agui/emitter.py b/haiku_rag_slim/haiku/rag/graph/agui/emitter.py index 2c284d46..09201559 100644 --- a/haiku_rag_slim/haiku/rag/graph/agui/emitter.py +++ b/haiku_rag_slim/haiku/rag/graph/agui/emitter.py @@ -13,6 +13,7 @@ from haiku.rag.graph.agui.events import ( emit_run_error, emit_run_finished, emit_run_started, + emit_state_delta, emit_state_snapshot, emit_step_finished, emit_step_started, @@ -35,12 +36,18 @@ class AGUIEmitter[StateT: BaseModel, ResultT]: ResultT: The result type returned by the graph """ - def __init__(self, thread_id: str | None = None, run_id: str | None = None): + def __init__( + self, + thread_id: str | None = None, + run_id: str | None = None, + use_deltas: bool = True, + ): """Initialize the emitter. Args: thread_id: Optional thread ID (generated from input hash if not provided) run_id: Optional run ID (random UUID if not provided) + use_deltas: Whether to emit state deltas instead of full snapshots (default: True) """ self._queue: asyncio.Queue[AGUIEvent | None] = asyncio.Queue() self._closed = False @@ -48,6 +55,7 @@ class AGUIEmitter[StateT: BaseModel, ResultT]: self._run_id = run_id or str(uuid4()) self._last_state: StateT | None = None self._current_step: str | None = None + self._use_deltas = use_deltas @property def thread_id(self) -> str: @@ -73,7 +81,8 @@ class AGUIEmitter[StateT: BaseModel, ResultT]: # RunStarted (state snapshot follows immediately with full state) self._emit(emit_run_started(self._thread_id, self._run_id)) self._emit(emit_state_snapshot(initial_state)) - self._last_state = initial_state + # Store a deep copy to detect future changes + self._last_state = initial_state.model_copy(deep=True) def start_step(self, step_name: str) -> None: """Emit StepStarted event. @@ -100,14 +109,19 @@ class AGUIEmitter[StateT: BaseModel, ResultT]: self._emit(emit_text_message(message, role)) def update_state(self, new_state: StateT) -> None: - """Emit StateSnapshot for state change. + """Emit StateDelta or StateSnapshot for state change. Args: new_state: The updated state """ - # Always emit full snapshot (not delta) for complete state visibility - self._emit(emit_state_snapshot(new_state)) - self._last_state = new_state + if self._use_deltas and self._last_state is not None: + # Emit delta for incremental updates + self._emit(emit_state_delta(self._last_state, new_state)) + else: + # Emit full snapshot for initial state or when deltas disabled + self._emit(emit_state_snapshot(new_state)) + # Store a deep copy to detect future changes + self._last_state = new_state.model_copy(deep=True) def update_activity( self, activity_type: str, content: str, message_id: str | None = None diff --git a/haiku_rag_slim/haiku/rag/graph/agui/stream.py b/haiku_rag_slim/haiku/rag/graph/agui/stream.py index 75f6a3e5..ee6f1ac5 100644 --- a/haiku_rag_slim/haiku/rag/graph/agui/stream.py +++ b/haiku_rag_slim/haiku/rag/graph/agui/stream.py @@ -21,6 +21,7 @@ async def stream_graph( graph: Any, state: BaseModel, deps: GraphDeps, + use_deltas: bool = True, ) -> AsyncIterator[AGUIEvent]: """Run a graph and yield AG-UI events as they occur. @@ -34,6 +35,7 @@ async def stream_graph( graph: The pydantic-graph Graph to execute state: Initial state (Pydantic BaseModel) deps: Graph dependencies with agui_emitter support + use_deltas: Whether to emit state deltas instead of full snapshots (default: True) Yields: AG-UI event dictionaries @@ -46,7 +48,7 @@ async def stream_graph( raise TypeError("deps must have an 'agui_emitter' attribute") # Create AG-UI emitter - emitter: AGUIEmitter[Any, Any] = AGUIEmitter() + emitter: AGUIEmitter[Any, Any] = AGUIEmitter(use_deltas=use_deltas) deps.agui_emitter = emitter async def _execute() -> None: diff --git a/tests/graph/agui/test_cli_renderer.py b/tests/graph/agui/test_cli_renderer.py index 738b0bcc..2f21e904 100644 --- a/tests/graph/agui/test_cli_renderer.py +++ b/tests/graph/agui/test_cli_renderer.py @@ -10,6 +10,7 @@ from haiku.rag.graph.agui.events import ( emit_run_error, emit_run_finished, emit_run_started, + emit_state_delta, emit_state_snapshot, emit_step_finished, emit_step_started, @@ -49,32 +50,18 @@ async def test_renderer_basic_flow(): @pytest.mark.asyncio -async def test_renderer_state_diff(): - """Test that renderer computes state diffs correctly.""" +async def test_renderer_multiple_snapshots(): + """Test that renderer handles multiple snapshots without errors.""" renderer = AGUIConsoleRenderer() - # First state - old_state = {"value": 1, "text": "old", "nested": {"a": 1, "b": 2}} - # Second state with changes - new_state = {"value": 2, "text": "old", "nested": {"a": 1, "b": 3, "c": 4}} + events = [ + emit_state_snapshot(SimpleState(value=1)), + emit_state_snapshot(SimpleState(value=2)), + ] - diff = renderer._compute_diff(old_state, new_state) - - assert diff == { - "value": 2, - "nested": {"b": 3, "c": 4}, # Only changed/new fields in nested - } - - -@pytest.mark.asyncio -async def test_renderer_state_diff_no_changes(): - """Test that no diff is computed when states are equal.""" - renderer = AGUIConsoleRenderer() - - state = {"value": 1, "text": "test"} - diff = renderer._compute_diff(state, state) - - assert diff == {} + # Should render both snapshots without error + result = await renderer.render(async_gen(events)) + assert result is None # No run finished event @pytest.mark.asyncio @@ -99,19 +86,18 @@ async def test_renderer_handles_all_event_types(): @pytest.mark.asyncio -async def test_renderer_initial_state(): - """Test that initial state is rendered.""" +async def test_renderer_state_snapshots(): + """Test that state snapshots are rendered.""" renderer = AGUIConsoleRenderer() events = [ emit_state_snapshot(SimpleState(value=1)), emit_state_snapshot(SimpleState(value=2)), + emit_run_finished("t1", "r1", {"done": True}), ] - await renderer.render(async_gen(events)) - - # After processing, internal state should be the last state - assert renderer._state == {"value": 2} + result = await renderer.render(async_gen(events)) + assert result == {"done": True} @pytest.mark.asyncio @@ -129,18 +115,23 @@ async def test_renderer_no_result(): @pytest.mark.asyncio -async def test_renderer_nested_state_diff(): - """Test nested state diff computation.""" +async def test_renderer_snapshot_then_delta(): + """Test that renderer handles snapshot followed by deltas.""" renderer = AGUIConsoleRenderer() - old = {"level1": {"level2": {"value": 1, "text": "old"}, "other": "same"}} + state1 = SimpleState(value=1) + state2 = SimpleState(value=2) + state3 = SimpleState(value=3) - new = {"level1": {"level2": {"value": 2, "text": "old"}, "other": "same"}} + events = [ + emit_state_snapshot(state1), # Initial snapshot + emit_state_delta(state1, state2), # Delta to value=2 + emit_state_delta(state2, state3), # Delta to value=3 + emit_run_finished("t1", "r1", {"complete": True}), + ] - diff = renderer._compute_diff(old, new) - - # Should only show the changed nested value - assert diff == {"level1": {"level2": {"value": 2}}} + result = await renderer.render(async_gen(events)) + assert result == {"complete": True} @pytest.mark.asyncio @@ -156,3 +147,39 @@ async def test_renderer_with_empty_state(): result = await renderer.render(async_gen(events)) assert result == {"result": "ok"} + + +@pytest.mark.asyncio +async def test_renderer_state_delta(): + """Test that renderer renders state deltas.""" + renderer = AGUIConsoleRenderer() + + state1 = SimpleState(value=1) + state2 = SimpleState(value=2) + + events = [ + emit_state_snapshot(state1), # Initial state + emit_state_delta(state1, state2), # Delta update + emit_run_finished("t1", "r1", None), + ] + + result = await renderer.render(async_gen(events)) + assert result is None + + +@pytest.mark.asyncio +async def test_renderer_state_delta_without_initial(): + """Test that renderer handles delta without initial state gracefully.""" + renderer = AGUIConsoleRenderer() + + state1 = SimpleState(value=1) + state2 = SimpleState(value=2) + + # Send delta without initial snapshot - should still render it + events = [ + emit_state_delta(state1, state2), + emit_run_finished("t1", "r1", {"ok": True}), + ] + + result = await renderer.render(async_gen(events)) + assert result == {"ok": True} diff --git a/tests/graph/agui/test_emitter.py b/tests/graph/agui/test_emitter.py index 5e6148d1..51c89488 100644 --- a/tests/graph/agui/test_emitter.py +++ b/tests/graph/agui/test_emitter.py @@ -67,8 +67,8 @@ async def test_emitter_step_events(): @pytest.mark.asyncio async def test_emitter_state_updates(): - """Test state update events.""" - emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() + """Test state update events with snapshots.""" + emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter(use_deltas=False) state1 = TestState(value=1, text="first") state2 = TestState(value=2, text="second") @@ -87,6 +87,38 @@ async def test_emitter_state_updates(): assert state_events[1]["snapshot"] == {"value": 2, "text": "second"} +@pytest.mark.asyncio +async def test_emitter_state_deltas(): + """Test state update events with deltas.""" + emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter(use_deltas=True) + + state1 = TestState(value=1, text="first") + state2 = TestState(value=2, text="second") + + emitter.update_state(state1) + emitter.update_state(state2) + await emitter.close() + + events = [] + async for event in emitter: + events.append(event) + + # First update should be a snapshot (no previous state) + snapshot_events = [e for e in events if e["type"] == "STATE_SNAPSHOT"] + assert len(snapshot_events) == 1 + assert snapshot_events[0]["snapshot"] == {"value": 1, "text": "first"} + + # Second update should be a delta + delta_events = [e for e in events if e["type"] == "STATE_DELTA"] + assert len(delta_events) == 1 + # Delta should contain replace operations for changed fields + delta = delta_events[0]["delta"] + assert isinstance(delta, list) + assert len(delta) == 2 # Two fields changed + assert any(op["path"] == "/value" and op["value"] == 2 for op in delta) + assert any(op["path"] == "/text" and op["value"] == "second" for op in delta) + + @pytest.mark.asyncio async def test_emitter_activity_events(): """Test activity events.""" From 0c8d2838b22b99115d462c0eefa60670747982fb Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Wed, 12 Nov 2025 12:48:07 +0200 Subject: [PATCH 15/17] Better typing --- CHANGELOG.md | 4 +++- haiku_rag_slim/haiku/rag/graph/agui/server.py | 13 +++++++---- haiku_rag_slim/haiku/rag/graph/agui/stream.py | 22 +++++++++++-------- .../haiku/rag/graph/deep_qa/state.py | 6 +++-- 4 files changed, 29 insertions(+), 16 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index cbb5acf2..04664605 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,10 +5,11 @@ - **AG-UI Protocol Support**: Full AG-UI (Agent-UI) protocol implementation for graph execution with event streaming - New `AGUIEmitter` class for emitting AG-UI events from graphs - - Support for all AG-UI event types: lifecycle events (`RUN_STARTED`, `RUN_FINISHED`, `RUN_ERROR`), step events (`STEP_STARTED`, `STEP_FINISHED`), state updates (`STATE_SNAPSHOT`), activity narration (`ACTIVITY_SNAPSHOT`), and text messages (`TEXT_MESSAGE_CHUNK`) + - Support for all AG-UI event types: lifecycle events (`RUN_STARTED`, `RUN_FINISHED`, `RUN_ERROR`), step events (`STEP_STARTED`, `STEP_FINISHED`), state updates (`STATE_SNAPSHOT`, `STATE_DELTA`), activity narration (`ACTIVITY_SNAPSHOT`), and text messages (`TEXT_MESSAGE_CHUNK`) - `AGUIConsoleRenderer` for rendering AG-UI event streams to terminal with Rich formatting - `stream_graph()` utility function for executing graphs with AG-UI event emission - State diff computation for efficient state synchronization + - **Delta State Updates**: AG-UI emitter now supports incremental state updates via JSON Patch operations (`STATE_DELTA` events) to reduce bandwidth, configurable via `use_deltas` parameter (enabled by default) - **AG-UI Server**: Starlette-based HTTP server for serving graphs via AG-UI protocol - Server-Sent Events (SSE) streaming endpoint at `/v1/agent/stream` - Health check endpoint at `/health` @@ -19,6 +20,7 @@ - Step-by-step execution visibility via AG-UI events - **CLI AG-UI Flag**: New `--agui` flag for `serve` command to start AG-UI server - **Graph Module**: New unified `haiku.rag.graph` module containing all graph-related functionality +- **Common Graph Nodes**: New factory functions (`create_plan_node`, `create_search_node`) in `haiku.rag.graph.common.nodes` for reusable graph components ### Changed diff --git a/haiku_rag_slim/haiku/rag/graph/agui/server.py b/haiku_rag_slim/haiku/rag/graph/agui/server.py index e7f7a829..b569540d 100644 --- a/haiku_rag_slim/haiku/rag/graph/agui/server.py +++ b/haiku_rag_slim/haiku/rag/graph/agui/server.py @@ -2,7 +2,11 @@ import json from collections.abc import AsyncIterator, Callable -from typing import Any, Protocol +from pathlib import Path +from typing import TYPE_CHECKING, Any, Protocol + +if TYPE_CHECKING: + from haiku.rag.config.models import AppConfig from pydantic import BaseModel, Field from pydantic_graph.beta import Graph @@ -14,6 +18,7 @@ from starlette.responses import JSONResponse, StreamingResponse from starlette.routing import Route from haiku.rag.config.models import AGUIConfig +from haiku.rag.graph.agui.emitter import AGUIEmitter from haiku.rag.graph.agui.events import AGUIEvent from haiku.rag.graph.agui.stream import stream_graph @@ -21,7 +26,7 @@ from haiku.rag.graph.agui.stream import stream_graph class GraphDeps(Protocol): """Protocol for graph dependencies that support AG-UI emission.""" - agui_emitter: Any | None + agui_emitter: AGUIEmitter[Any, Any] | None class RunAgentInput(BaseModel): @@ -146,7 +151,7 @@ def format_sse_event(event: AGUIEvent) -> str: return f"data: {event_json}\n\n" -def create_agui_server(config: Any, db_path: Any | None = None) -> Starlette: +def create_agui_server(config: "AppConfig", db_path: Path | None = None) -> Starlette: """Create AG-UI server with both research and deep ask endpoints. Args: @@ -167,7 +172,7 @@ def create_agui_server(config: Any, db_path: Any | None = None) -> Starlette: # Store client reference for proper lifecycle management _client_cache: dict[str, HaikuRAG] = {} - def get_client(effective_db_path: Any) -> HaikuRAG: + def get_client(effective_db_path: Path) -> HaikuRAG: """Get or create cached client.""" path_key = str(effective_db_path) if path_key not in _client_cache: diff --git a/haiku_rag_slim/haiku/rag/graph/agui/stream.py b/haiku_rag_slim/haiku/rag/graph/agui/stream.py index ee6f1ac5..acde9298 100644 --- a/haiku_rag_slim/haiku/rag/graph/agui/stream.py +++ b/haiku_rag_slim/haiku/rag/graph/agui/stream.py @@ -3,24 +3,28 @@ import asyncio from collections.abc import AsyncIterator from contextlib import suppress -from typing import Any, Protocol +from typing import Protocol, TypeVar from pydantic import BaseModel +from pydantic_graph.beta import Graph from haiku.rag.graph.agui.emitter import AGUIEmitter from haiku.rag.graph.agui.events import AGUIEvent +StateT = TypeVar("StateT", bound=BaseModel) +ResultT = TypeVar("ResultT") -class GraphDeps(Protocol): + +class GraphDeps[StateT: BaseModel, ResultT](Protocol): """Protocol for graph dependencies that support AG-UI emission.""" - agui_emitter: AGUIEmitter[Any, Any] | None + agui_emitter: AGUIEmitter[StateT, ResultT] | None -async def stream_graph( - graph: Any, - state: BaseModel, - deps: GraphDeps, +async def stream_graph[StateT: BaseModel, DepsT: GraphDeps, ResultT]( + graph: Graph[StateT, DepsT, None, ResultT], + state: StateT, + deps: DepsT, use_deltas: bool = True, ) -> AsyncIterator[AGUIEvent]: """Run a graph and yield AG-UI events as they occur. @@ -48,8 +52,8 @@ async def stream_graph( raise TypeError("deps must have an 'agui_emitter' attribute") # Create AG-UI emitter - emitter: AGUIEmitter[Any, Any] = AGUIEmitter(use_deltas=use_deltas) - deps.agui_emitter = emitter + emitter: AGUIEmitter[StateT, ResultT] = AGUIEmitter(use_deltas=use_deltas) + deps.agui_emitter = emitter # type: ignore[assignment] async def _execute() -> None: try: diff --git a/haiku_rag_slim/haiku/rag/graph/deep_qa/state.py b/haiku_rag_slim/haiku/rag/graph/deep_qa/state.py index 179542a6..ee600874 100644 --- a/haiku_rag_slim/haiku/rag/graph/deep_qa/state.py +++ b/haiku_rag_slim/haiku/rag/graph/deep_qa/state.py @@ -1,20 +1,22 @@ import asyncio from dataclasses import dataclass -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING from pydantic import BaseModel, Field from haiku.rag.client import HaikuRAG from haiku.rag.graph.deep_qa.dependencies import DeepQAContext +from haiku.rag.graph.deep_qa.models import DeepQAAnswer if TYPE_CHECKING: from haiku.rag.config.models import AppConfig + from haiku.rag.graph.agui.emitter import AGUIEmitter @dataclass class DeepQADeps: client: HaikuRAG - agui_emitter: Any | None = None + agui_emitter: "AGUIEmitter[DeepQAState, DeepQAAnswer] | None" = None semaphore: asyncio.Semaphore | None = None From 4a623934b21668811eed51fc4a8774a546506311 Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Wed, 12 Nov 2025 13:05:13 +0200 Subject: [PATCH 16/17] Log additional ag-ui events in the cli renderer --- .../haiku/rag/graph/agui/cli_renderer.py | 14 +++++++++++-- tests/graph/agui/test_cli_renderer.py | 21 ++++++++++++++++++- 2 files changed, 32 insertions(+), 3 deletions(-) diff --git a/haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py b/haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py index 03a60bb4..3ff21abc 100644 --- a/haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py +++ b/haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py @@ -67,11 +67,17 @@ class AGUIConsoleRenderer: return result - def _render_run_started(self, _event: AGUIEvent) -> None: + def _render_run_started(self, event: AGUIEvent) -> None: """Render run start event.""" + run_id = event.get("runId", "") + if run_id: + # Show shortened run ID (first 8 chars like our UUIDs) + short_id = run_id[:8] if len(run_id) > 8 else run_id + self.console.print(f"[bold green][RUN_STARTED][/bold green] Run {short_id}") def _render_run_finished(self) -> None: """Render run completion.""" + self.console.print("[bold green][RUN_FINISHED][/bold green] Completed") def _render_error(self, event: AGUIEvent) -> None: """Render error event.""" @@ -87,8 +93,12 @@ class AGUIConsoleRenderer: f"\n[bold cyan][STEP_STARTED][/bold cyan] {display_name}" ) - def _render_step_finished(self, _event: AGUIEvent) -> None: + def _render_step_finished(self, event: AGUIEvent) -> None: """Render step finish event.""" + step_name = event.get("stepName", "") + if step_name: + display_name = step_name.replace("_", " ").title() + self.console.print(f"[cyan][STEP_FINISHED][/cyan] {display_name}") def _render_text_message(self, event: AGUIEvent) -> None: """Render complete text message.""" diff --git a/tests/graph/agui/test_cli_renderer.py b/tests/graph/agui/test_cli_renderer.py index 2f21e904..b6552bda 100644 --- a/tests/graph/agui/test_cli_renderer.py +++ b/tests/graph/agui/test_cli_renderer.py @@ -37,9 +37,10 @@ async def test_renderer_basic_flow(): renderer = AGUIConsoleRenderer(console) events = [ - emit_run_started("t1", "r1"), + emit_run_started("t1", "r1-test-run-id"), emit_state_snapshot(SimpleState(value=1)), emit_step_started("plan"), + emit_step_finished("plan"), emit_activity("m1", "planning", "Planning research"), emit_run_finished("t1", "r1", {"status": "complete"}), ] @@ -183,3 +184,21 @@ async def test_renderer_state_delta_without_initial(): result = await renderer.render(async_gen(events)) assert result == {"ok": True} + + +@pytest.mark.asyncio +async def test_renderer_lifecycle_events(): + """Test that lifecycle events are rendered properly.""" + renderer = AGUIConsoleRenderer() + + events = [ + emit_run_started("thread-123", "run-abc-def-ghi"), # Long run ID + emit_step_started("search_one"), + emit_step_finished("search_one"), + emit_step_started("analyze_insights"), + emit_step_finished("analyze_insights"), + emit_run_finished("thread-123", "run-abc-def-ghi", {"complete": True}), + ] + + result = await renderer.render(async_gen(events)) + assert result == {"complete": True} From 2c43f034dea273b9bd85b73ba4c95915bad826ee Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Thu, 13 Nov 2025 12:47:25 +0200 Subject: [PATCH 17/17] Refactor ag-ui-research. Drop human-in-the-loop, use MemoryObjectSendStream to merge the graph and agent streams together --- CHANGELOG.md | 6 + examples/ag-ui-research/.env.example | 5 + examples/ag-ui-research/README.md | 81 +- examples/ag-ui-research/backend/Dockerfile | 8 +- examples/ag-ui-research/backend/README.md | 16 +- examples/ag-ui-research/backend/agent.py | 483 +- examples/ag-ui-research/backend/main.py | 196 +- .../ag-ui-research/backend/pyproject.toml | 5 +- examples/ag-ui-research/backend/uv.lock | 2077 +- examples/ag-ui-research/docker-compose.yml | 9 +- .../frontend/app/api/copilotkit/route.ts | 2 +- .../frontend/components/Agent.tsx | 264 +- .../frontend/components/StateDisplay.tsx | 772 +- .../ag-ui-research/frontend/package-lock.json | 23368 ++++++++-------- .../ag-ui-research/haiku.rag.yaml.example | 9 +- .../haiku/rag/graph/common/nodes.py | 5 +- 16 files changed, 12671 insertions(+), 14635 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 04664605..b5bb56d4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -21,6 +21,12 @@ - **CLI AG-UI Flag**: New `--agui` flag for `serve` command to start AG-UI server - **Graph Module**: New unified `haiku.rag.graph` module containing all graph-related functionality - **Common Graph Nodes**: New factory functions (`create_plan_node`, `create_search_node`) in `haiku.rag.graph.common.nodes` for reusable graph components +- **AG-UI Research Example**: New full-stack example (`examples/ag-ui-research`) demonstrating agent+graph architecture with CopilotKit frontend + - Pydantic AI agent with research tool that invokes the research graph + - Custom AG-UI streaming endpoint with anyio memory streams + - React/Next.js frontend with split-pane UI showing live research state + - Real-time progress tracking of questions, answers, insights, and gaps + - Docker Compose setup for easy local development ### Changed diff --git a/examples/ag-ui-research/.env.example b/examples/ag-ui-research/.env.example index 71353d26..119f6fb0 100644 --- a/examples/ag-ui-research/.env.example +++ b/examples/ag-ui-research/.env.example @@ -3,6 +3,11 @@ # Must be an absolute path to an existing database created with haiku-rag DB_PATH=/absolute/path/to/your/haiku.rag.lancedb +# Ollama API base URL (if using Ollama for local models) +# If running Ollama on your host machine, use your machine's IP address +# that the Docker container can reach (not localhost) +OLLAMA_BASE_URL=http://host.docker.internal:11434 + # API keys (set as needed for your QA provider) # OPENAI_API_KEY=your-key-here # ANTHROPIC_API_KEY=your-key-here diff --git a/examples/ag-ui-research/README.md b/examples/ag-ui-research/README.md index 0b6750ab..3b0d95b2 100644 --- a/examples/ag-ui-research/README.md +++ b/examples/ag-ui-research/README.md @@ -1,16 +1,16 @@ # Interactive Research Assistant -Research assistant powered by [haiku.rag](https://ggozad.github.io/haiku.rag/), [Pydantic AI](https://ai.pydantic.dev/), and [AG-UI](https://docs.ag-ui.com/). Ask complex questions and watch the research process unfold in real-time. +Research assistant powered by [haiku.rag](https://ggozad.github.io/haiku.rag/), [Pydantic Graph](https://ai.pydantic.dev/graph/), and [AG-UI](https://docs.ag-ui.com/). Ask complex questions and watch the research process unfold in real-time. [Watch demo video](https://vimeo.com/1128874386) ## Features -- **Multi-step research workflow**: Question decomposition, search, analysis, and synthesis -- **Human-in-the-loop**: Approve or revise research plans before execution -- **Live state synchronization**: Real-time updates of research progress between backend and frontend -- **Context expansion**: Automatically expands top search results for better context -- **Rich reporting**: Generates structured reports with findings, conclusions, and citations +- **Multi-iteration research graph**: Automated question decomposition, search, insight extraction, and gap analysis +- **Intelligent evaluation**: Confidence-based decision making with automatic iteration until sufficient information is gathered +- **Live state synchronization**: Real-time delta updates of research progress via AG-UI protocol +- **Insight & gap tracking**: Structured insights with provenance and automatic gap identification +- **Rich reporting**: Generates comprehensive research reports with findings, conclusions, and sources ## Quick Start @@ -57,32 +57,67 @@ Research assistant powered by [haiku.rag](https://ggozad.github.io/haiku.rag/), ## How It Works 1. **Ask a question**: Type your research question in the chat -2. **Review the plan**: The agent decomposes your question into 3 sub-questions -3. **Approve or revise**: Choose to approve the plan or request changes -4. **Watch it work**: The agent automatically: - - Searches the knowledge base for each sub-question - - Extracts key insights from search results - - Evaluates overall confidence in findings -5. **Get your report**: Receive a structured research report with citations +2. **Plan phase**: The research graph automatically: + - Decomposes your question into targeted sub-questions + - Gathers initial context about the topic +3. **Research iterations**: The graph autonomously: + - Searches the knowledge base for each sub-question in parallel + - Extracts structured insights with source provenance + - Identifies information gaps and assesses confidence + - Generates new follow-up questions for gaps + - Iterates until confidence threshold is met or max iterations reached +4. **Synthesis**: Generates a comprehensive research report with: + - Executive summary + - Main findings with supporting evidence + - Conclusions and recommendations + - Source citations ## Architecture -- **Backend** (Python): Pydantic AI agent with haiku.rag integration - - Uses published `ghcr.io/ggozad/haiku.rag:latest` Docker image as base - - `agent.py`: Research agent with tool definitions - - `main.py`: Starlette app serving AG-UI protocol +### Agent + Graph Pattern -- **Frontend** (Next.js): CopilotKit/AG-UI interface - - Real-time state synchronization with backend - - Interactive approval workflow - - Collapsible research plan and insights display +This example demonstrates the **agent+graph** architecture pattern: + +1. **Conversational Agent** (`agent.py`): + - Pydantic AI agent handles user conversations + - Decides when to invoke the research tool based on user intent + - Responds directly to greetings/casual chat without tools + - Formats research results for the user + +2. **Research Graph** (haiku.rag): + - Multi-step research workflow invoked by the agent's tool + - Autonomous execution with plan → search → analyze → decide → synthesize flow + - Emits AG-UI events for real-time progress tracking + +3. **Shared Event Stream**: + - `AGUIEmitter` is shared between agent and graph + - Events from both flow through a single stream to the frontend + - Custom streaming endpoint (`main.py`) uses anyio memory streams for proper async handling + +### Components + +- **Backend** (Python): + - Uses published `ghcr.io/ggozad/haiku.rag:latest` Docker image as base + - `agent.py`: Pydantic AI agent with `run_research` tool + - `main.py`: Custom AG-UI streaming endpoint with anyio memory object streams + - Real-time event forwarding from emitter to SSE stream + - Filters out `ACTIVITY_SNAPSHOT` events (not yet supported by CopilotKit) + +- **Frontend** (Next.js/React): + - CopilotKit for AG-UI protocol integration + - Split-pane UI: chat on left, live research state on right + - Real-time state synchronization via Server-Sent Events (SSE) + - `StateDisplay` component with collapsible sections for questions, insights, and gaps ## Configuration Configuration is done through `haiku.rag.yaml` (see `haiku.rag.yaml.example`): -- `qa.provider`: LLM provider (default: `ollama`) -- `qa.model`: Model name (default: `gpt-oss:latest`) +- `research.provider`: LLM provider (default: `ollama`) +- `research.model`: Model name (default: `gpt-oss:latest`) +- `research.max_iterations`: Maximum research iterations (default: `3`) +- `research.confidence_threshold`: Confidence threshold for completion (default: `0.8`) +- `research.max_concurrency`: Parallel sub-question processing (default: `1`) - `providers.ollama.base_url`: Ollama endpoint (default: `http://host.docker.internal:11434`) Environment variables (see `.env.example`): diff --git a/examples/ag-ui-research/backend/Dockerfile b/examples/ag-ui-research/backend/Dockerfile index c6359b97..786c17db 100644 --- a/examples/ag-ui-research/backend/Dockerfile +++ b/examples/ag-ui-research/backend/Dockerfile @@ -3,15 +3,13 @@ FROM ghcr.io/ggozad/haiku.rag:latest WORKDIR /app # Copy backend application files -COPY agent.py main.py ./ -COPY pyproject.toml ./ +COPY main.py agent.py ./ -# Install backend dependencies -# Note: haiku-rag is already installed in the base image +# Install additional dependencies for the example +# Note: haiku-rag-slim is already installed in the base image RUN pip install --no-cache-dir \ starlette>=0.45.2 \ uvicorn[standard]>=0.34.2 \ - pydantic-ai-slim[ag-ui,openai]>=1.1.0 \ python-dotenv>=1.0.1 EXPOSE 8000 diff --git a/examples/ag-ui-research/backend/README.md b/examples/ag-ui-research/backend/README.md index 09115a34..9b0751e9 100644 --- a/examples/ag-ui-research/backend/README.md +++ b/examples/ag-ui-research/backend/README.md @@ -1,6 +1,6 @@ # Haiku.rag Research Assistant Backend -FastAPI backend for the haiku.rag interactive research assistant, using Pydantic AI with AG-UI protocol support. +Starlette backend for the haiku.rag interactive research assistant, using the research graph with AG-UI protocol support. ## Setup @@ -11,7 +11,17 @@ uv run python main.py The server starts on `http://localhost:8000` and uses [haiku.rag configuration](https://ggozad.github.io/haiku.rag/configuration/). +## Architecture + +The backend uses `create_agui_server()` from `haiku.rag.graph.agui.server` which provides: + +- **Research graph execution**: Multi-iteration research workflow with insight/gap tracking +- **AG-UI protocol**: Server-Sent Events (SSE) streaming for real-time state updates +- **Delta state updates**: Efficient incremental state synchronization using JSON Patch operations +- **Both research and deep_qa endpoints**: `/agent/research` and `/agent/deep_qa` + ## Endpoints -- `GET /health` - Health check -- `POST /agent` - AG-UI protocol endpoint +- `GET /health` - Health check with configuration info +- `POST /agent/research/stream` - Research graph streaming endpoint (AG-UI protocol) +- `POST /agent/deep_qa/stream` - Deep QA graph streaming endpoint (AG-UI protocol) diff --git a/examples/ag-ui-research/backend/agent.py b/examples/ag-ui-research/backend/agent.py index 049e0dbd..a557d78d 100644 --- a/examples/ag-ui-research/backend/agent.py +++ b/examples/ag-ui-research/backend/agent.py @@ -1,432 +1,111 @@ -import json -from dataclasses import dataclass +"""Research assistant agent with graph integration.""" + +from dataclasses import dataclass +from pathlib import Path +from typing import TYPE_CHECKING -from ag_ui.core import EventType, StateSnapshotEvent -from pydantic import BaseModel from pydantic_ai import Agent, RunContext -from pydantic_ai.ag_ui import StateDeps from haiku.rag.client import HaikuRAG -from haiku.rag.config import Config +from haiku.rag.config import load_yaml_config +from haiku.rag.config.models import AppConfig from haiku.rag.graph.common import get_model +from haiku.rag.graph.research.dependencies import ResearchContext +from haiku.rag.graph.research.graph import build_research_graph +from haiku.rag.graph.research.state import ResearchDeps, ResearchState +if TYPE_CHECKING: + from haiku.rag.graph.agui.emitter import AGUIEmitter + from haiku.rag.graph.research.models import ResearchReport -class ResearchState(BaseModel): - """Shared state between research agent and frontend.""" - - question: str = "" - phase: str = "idle" - status: str = "" - plan: list[dict] = [] - current_question_index: int = 0 - insights: list[dict] = [] - document_registry: dict[str, dict] = {} - current_document: dict | None = None - confidence: float = 0.0 - final_report: dict | None = None +# Load config +config_path = Path("/app/haiku.rag.yaml") +Config = ( + AppConfig.model_validate(load_yaml_config(config_path)) + if config_path.exists() + else AppConfig() +) @dataclass -class ResearchDeps(StateDeps[ResearchState]): - """Dependencies for the research agent with HaikuRAG client.""" +class AgentDeps: + """Dependencies for research agent.""" client: HaikuRAG + agui_emitter: "AGUIEmitter[ResearchState, ResearchReport] | None" = None -def _as_state_snapshot(ctx: RunContext[ResearchDeps]) -> StateSnapshotEvent: - return StateSnapshotEvent(type=EventType.STATE_SNAPSHOT, snapshot=ctx.deps.state) +model = get_model(Config.research.provider, Config.research.model) - -def create_agent( - qa_provider: str = Config.qa.provider, qa_model: str = Config.qa.model -) -> Agent[ResearchDeps, str]: - """Create and configure the research agent. - - Args: - qa_provider: QA provider for the agent (default: from Config.qa.provider) - qa_model: Model name to use (default: from Config.qa.model) - """ - print(f"[AGENT SETUP] Creating agent with provider={qa_provider}, model={qa_model}") - agent = Agent( - model=get_model(qa_provider, qa_model), - deps_type=ResearchDeps, - instructions="""You are a research co-pilot powered by haiku.rag. - -Your workflow MUST follow these exact steps in order: -1. Call propose_research_plan with the user's question -2. After propose_research_plan completes, IMMEDIATELY call approve_research_plan (with no arguments) -3. WAIT for approve_research_plan to return: - - If it returns "APPROVED", proceed to step 4 - - If it returns "REVISE", ask the user "How would you like me to revise the research plan?" and wait for their response - - Once you receive their revision feedback, revise the plan and go back to step 1 -4. Once approved, process questions ONE AT A TIME: - - Call search_question(question_id=0) and WAIT for it to complete - - Then call extract_insights_from_results(question_id=0) and WAIT for it to complete - - Then call search_question(question_id=1) and WAIT for it to complete - - Then call extract_insights_from_results(question_id=1) and WAIT for it to complete - - Then call search_question(question_id=2) and WAIT for it to complete - - Then call extract_insights_from_results(question_id=2) and WAIT for it to complete -5. After all questions are processed, call evaluate_research_confidence -6. Ask user if they want to finalize or continue researching -7. When user approves, call synthesize_final_report +agent = Agent( + model, + deps_type=AgentDeps, + system_prompt="""You are an advanced research assistant powered by haiku.rag. CRITICAL RULES: -- MANDATORY: Call approve_research_plan immediately after propose_research_plan - NO EXCEPTIONS -- If approve_research_plan returns "REVISE", ask the user for revision feedback naturally in chat -- Call ONE tool at a time - wait for each tool to return before calling the next -- NEVER call extract_insights_from_results until search_question has completed and returned results -- DO NOT explain what you're about to do - just call the tool -- The state updates will show the user what's happening - you don't need to narrate -- Process all 3 questions automatically without asking for approval between them +1. For greetings (hi, hello, hey, etc) or casual chat: respond directly WITHOUT using any tools +2. For questions about yourself or the system: respond directly WITHOUT using any tools +3. For substantive questions requiring information: ALWAYS use the run_research tool +4. NEVER answer substantive questions from your own knowledge - always use the tool -Document Viewing: -- When user asks to "show document X", call get_full_document with the document_uri +How to decide: +- "Hi" / "Hello" / "How are you?" → Respond directly, NO tools +- "What can you do?" → Respond directly, NO tools +- "How does X work in the codebase?" → Use run_research tool +- "Tell me about Y" → Use run_research tool -Remember: Call tools ONE AT A TIME in sequence. Each tool must complete before calling the next. -""", +When you use run_research, the graph will decompose questions, search the knowledge base, +extract insights, and generate a comprehensive report. + +Be friendly and conversational in all responses.""", +) + + +@agent.tool +async def run_research(ctx: RunContext[AgentDeps], question: str) -> str: + """Execute research graph on a substantive question. + + Use for questions requiring knowledge base search. + DO NOT use for greetings or casual conversation. + """ + if ctx.deps.agui_emitter: + ctx.deps.agui_emitter.log(f"🔍 Starting research on: {question}") + + graph = build_research_graph(Config) + context = ResearchContext(original_question=question) + state = ResearchState.from_config(context=context, config=Config) + + graph_deps = ResearchDeps( + client=ctx.deps.client, + agui_emitter=ctx.deps.agui_emitter, ) - @agent.tool - async def propose_research_plan( - ctx: RunContext[ResearchDeps], question: str - ) -> StateSnapshotEvent: - """Propose a research plan by decomposing the question into sub-questions.""" - ctx.deps.state.question = question - ctx.deps.state.phase = "planning" - ctx.deps.state.status = "Decomposing question into sub-questions..." + try: + result = await graph.run(state=state, deps=graph_deps) - decompose_prompt = f"""Break down this research question into exactly 3 specific sub-questions that would help answer it comprehensively. + if ctx.deps.agui_emitter: + ctx.deps.agui_emitter.log("✅ Research complete!") -Research Question: {question} + return f"""Research completed successfully! -Return ONLY a JSON array of sub-questions, like: ["Question 1?", "Question 2?", "Question 3?"]""" +Question: {question} - response = await ctx.deps.client.ask(decompose_prompt) +Executive Summary: {result.executive_summary} - try: - sub_questions = json.loads(response) - except json.JSONDecodeError: - sub_questions = [ - q.strip().lstrip("0123456789.-) ") - for q in response.split("\n") - if q.strip() - ][:3] +Main Findings: +{chr(10).join(f"- {finding}" for finding in result.main_findings[:3])} - plan = [ - {"id": i, "question": q, "status": "pending"} - for i, q in enumerate(sub_questions) - ] +Conclusions: +{chr(10).join(f"- {conclusion}" for conclusion in result.conclusions[:2])} - ctx.deps.state.plan = plan - ctx.deps.state.current_question_index = 0 - ctx.deps.state.status = f"Proposed plan with {len(plan)} sub-questions" +Total insights gathered: {len(state.context.insights)} +Confidence: {f"{state.last_eval.confidence_score:.0%}" if state.last_eval else "N/A"} +Iterations completed: {state.iterations} - return _as_state_snapshot(ctx) +The full research report with all citations has been provided to the user. +""" - @agent.tool - async def search_question( - ctx: RunContext[ResearchDeps], - question_id: int, - search_type: str = "hybrid", - ) -> StateSnapshotEvent: - """Execute search for a specific sub-question.""" - plan = ctx.deps.state.plan - if question_id >= len(plan): - raise ValueError(f"Question ID {question_id} not found in plan") - - question = plan[question_id]["question"] - ctx.deps.state.phase = "searching" - ctx.deps.state.current_question_index = question_id - ctx.deps.state.status = f"Searching: {question}" - plan[question_id]["status"] = "searching" - - search_results = await ctx.deps.client.search( - question, limit=5, search_type=search_type - ) - - expanded_map = {} - if search_results: - expanded_results = await ctx.deps.client.expand_context( - search_results[:3], radius=2 - ) - expanded_map = { - chunk.id: (chunk, score) for chunk, score in expanded_results - } - - results = [] - for chunk, score in search_results: - doc_uri = chunk.document_uri or "unknown" - doc_title = chunk.document_title or chunk.document_uri or "Unknown" - - if doc_uri not in ctx.deps.state.document_registry: - ctx.deps.state.document_registry[doc_uri] = { - "title": doc_title, - "chunks_referenced": [], - } - - if ( - chunk.id - not in ctx.deps.state.document_registry[doc_uri]["chunks_referenced"] - ): - ctx.deps.state.document_registry[doc_uri]["chunks_referenced"].append( - chunk.id - ) - - expanded_chunk, _ = ( - expanded_map[chunk.id] if chunk.id in expanded_map else (chunk, score) - ) - result_data = { - "chunk": expanded_chunk.content[:500], - "chunk_id": chunk.id, - "document_uri": doc_uri, - "document_title": doc_title, - "chunk_position": chunk.order, - "full_chunk_content": expanded_chunk.content, - "score": round(score, 3), - "expanded": chunk.id in expanded_map, - } - results.append(result_data) - - plan[question_id]["search_results"] = { - "type": search_type, - "results": results, - } - plan[question_id]["status"] = "searched" - ctx.deps.state.status = f"Found {len(results)} results" - - return _as_state_snapshot(ctx) - - @agent.tool - async def extract_insights_from_results( - ctx: RunContext[ResearchDeps], - question_id: int, - ) -> StateSnapshotEvent: - """Extract key insights from search results for a specific question.""" - plan = ctx.deps.state.plan - if question_id >= len(plan): - raise ValueError(f"Question ID {question_id} not found in plan") - - question_item = plan[question_id] - if "search_results" not in question_item: - raise ValueError( - f"No search results found for question ID {question_id}. " - f"You must call search_question(question_id={question_id}) first." - ) - - search_results = question_item["search_results"] - ctx.deps.state.phase = "analyzing" - ctx.deps.state.status = "Extracting insights from results..." - - context_parts = [ - f"[Result {idx}] [Source: {r['document_title']}] {r['full_chunk_content']}" - for idx, r in enumerate(search_results["results"]) - ] - context = "\n\n".join(context_parts) - - class InsightResult(BaseModel): - summary: str - confidence: float - result_indices: list[int] - - class InsightsList(BaseModel): - insights: list[InsightResult] - - question_text = question_item["question"] - extract_prompt = f"""Analyze these search results and extract 1-3 key insights that help answer the question: "{question_text}" - -Search Results: -{context} - -For each insight, reference which result numbers (0, 1, 2, etc.) support it.""" - - insight_agent: Agent[None, InsightsList] = Agent( - ctx.model, - output_type=InsightsList, - retries=3, - ) - - result = await insight_agent.run(extract_prompt) - raw_insights = [ - { - "summary": insight.summary, - "confidence": insight.confidence, - "result_indices": insight.result_indices, - } - for insight in result.output.insights - ] - - new_insights = [] - for insight in raw_insights: - source_refs = [] - for idx in insight.get("result_indices", []): - if 0 <= idx < len(search_results["results"]): - result = search_results["results"][idx] - source_refs.append( - { - "chunk_id": result["chunk_id"], - "document_uri": result["document_uri"], - "document_title": result["document_title"], - "chunk_position": result["chunk_position"], - } - ) - - new_insights.append( - { - "summary": insight["summary"], - "confidence": insight.get("confidence", 0.7), - "source_refs": source_refs, - } - ) - - ctx.deps.state.insights.extend(new_insights) - plan[question_id]["status"] = "done" - ctx.deps.state.status = f"Extracted {len(new_insights)} insights" - - return _as_state_snapshot(ctx) - - @agent.tool - async def evaluate_research_confidence( - ctx: RunContext[ResearchDeps], - ) -> StateSnapshotEvent: - """Evaluate overall confidence in the research findings.""" - insights = ctx.deps.state.insights - if not insights: - raise ValueError("No insights collected yet") - - ctx.deps.state.phase = "evaluating" - ctx.deps.state.status = "Evaluating research confidence..." - - confidences = [i.get("confidence", 0.5) for i in insights] - overall_confidence = sum(confidences) / len(confidences) if confidences else 0 - - eval_prompt = f"""Evaluate if these insights provide a confident answer to: "{ctx.deps.state.question}" - -Insights collected: -{chr(10).join([f"- {i['summary']}" for i in insights])} - -Assess: -1. Do we have enough information to answer the question? -2. What gaps remain? -3. Overall confidence (0.0-1.0) - -Return JSON: {{"confidence": 0.0-1.0, "gaps": ["gap1", "gap2"], "recommendation": "continue" or "finalize"}}""" - - response = await ctx.deps.client.ask(eval_prompt) - - try: - evaluation = json.loads(response) - overall_confidence = evaluation.get("confidence", overall_confidence) - except json.JSONDecodeError: - pass - - ctx.deps.state.confidence = overall_confidence - ctx.deps.state.status = f"Confidence: {overall_confidence:.0%}" - - return _as_state_snapshot(ctx) - - @agent.tool - async def synthesize_final_report( - ctx: RunContext[ResearchDeps], - ) -> StateSnapshotEvent: - """Generate final research report with citations.""" - insights = ctx.deps.state.insights - if not insights: - raise ValueError("No insights to synthesize") - - ctx.deps.state.phase = "synthesizing" - ctx.deps.state.status = "Generating final report..." - - insights_summary = [] - for i in insights: - source_titles = [ref["document_title"] for ref in i.get("source_refs", [])] - unique_sources = list(dict.fromkeys(source_titles)) - insights_summary.append( - f"- {i['summary']} (sources: {', '.join(unique_sources[:2])})" - ) - - report_prompt = f"""Generate a comprehensive research report answering: "{ctx.deps.state.question}" - -Based on these insights: -{chr(10).join(insights_summary)} - -Create a structured report with: -- Executive Summary (2-3 sentences) -- Main Findings (bullet points) -- Conclusions -- Sources (list the document titles mentioned above) - -Return JSON with format: -{{ - "title": "...", - "summary": "...", - "findings": ["finding1", "finding2", ...], - "conclusions": ["conclusion1", ...], - "sources": ["source1", "source2", ...] -}}""" - - response = await ctx.deps.client.ask(report_prompt) - - try: - report = json.loads(response) - except json.JSONDecodeError: - report = { - "title": ctx.deps.state.question, - "summary": response[:300], - "findings": [i["summary"] for i in insights], - "conclusions": ["See findings above"], - "sources": [], - } - - citations = [ - { - "document_uri": doc_uri, - "document_title": doc_info["title"], - "chunk_ids": doc_info["chunks_referenced"], - } - for doc_uri, doc_info in ctx.deps.state.document_registry.items() - ] - report["citations"] = citations - - ctx.deps.state.final_report = report - ctx.deps.state.phase = "done" - ctx.deps.state.status = "Research complete" - - return _as_state_snapshot(ctx) - - @agent.tool - async def get_full_document( - ctx: RunContext[ResearchDeps], - document_uri: str, - ) -> StateSnapshotEvent: - """Retrieve and display the full content of a document by its URI.""" - ctx.deps.state.status = f"Retrieving document: {document_uri}" - document = await ctx.deps.client.get_document_by_uri(document_uri) - - if document is None: - ctx.deps.state.status = f"Document not found: {document_uri}" - ctx.deps.state.current_document = { - "uri": document_uri, - "title": "Not Found", - "content": f"Document with URI '{document_uri}' was not found.", - "total_chunks": 0, - } - else: - all_chunks = await ctx.deps.client.search( - query="", limit=1000, search_type="fts" - ) - chunks_for_doc = [ - c for c, _ in all_chunks if c.document_uri == document_uri - ] - - ctx.deps.state.current_document = { - "uri": document.uri or document_uri, - "title": document.title or "Untitled", - "content": document.content, - "total_chunks": len(chunks_for_doc), - "metadata": document.metadata, - } - ctx.deps.state.status = f"Retrieved: {document.title or document_uri}" - - return _as_state_snapshot(ctx) - - return agent + except Exception as e: + if ctx.deps.agui_emitter: + ctx.deps.agui_emitter.log(f"❌ Research error: {str(e)}") + return f"I encountered an error while researching: {str(e)}" diff --git a/examples/ag-ui-research/backend/main.py b/examples/ag-ui-research/backend/main.py index 41b39945..4865caba 100644 --- a/examples/ag-ui-research/backend/main.py +++ b/examples/ag-ui-research/backend/main.py @@ -1,87 +1,171 @@ import logging import os -from contextlib import asynccontextmanager from pathlib import Path -from agent import ResearchDeps, ResearchState, create_agent +from agent import AgentDeps, agent +from anyio import create_memory_object_stream, create_task_group +from anyio.streams.memory import MemoryObjectSendStream from starlette.applications import Starlette from starlette.middleware import Middleware from starlette.middleware.cors import CORSMiddleware -from starlette.responses import JSONResponse -from starlette.routing import Mount, Route +from starlette.requests import Request +from starlette.responses import JSONResponse, StreamingResponse +from starlette.routing import Route from haiku.rag.client import HaikuRAG -from haiku.rag.config import Config +from haiku.rag.config import load_yaml_config +from haiku.rag.config.models import AppConfig +from haiku.rag.graph.agui.emitter import AGUIEmitter +from haiku.rag.graph.agui.server import RunAgentInput, format_sse_event +from haiku.rag.graph.research.dependencies import ResearchContext +from haiku.rag.graph.research.models import ResearchReport +from haiku.rag.graph.research.state import ResearchState +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" +) logger = logging.getLogger(__name__) -client: HaikuRAG | None = None -ag_ui_app = None +# Load config from mounted haiku.rag.yaml +config_path = Path("/app/haiku.rag.yaml") +if config_path.exists(): + yaml_data = load_yaml_config(config_path) + Config = AppConfig.model_validate(yaml_data) +else: + # Fallback to default config + Config = AppConfig() -@asynccontextmanager -async def lifespan(app): - global client - db_path_str = os.getenv("DB_PATH", "haiku_rag.lancedb") - db_path = Path(db_path_str) +# Get DB path from environment +db_path_str = os.getenv("DB_PATH", "haiku_rag.lancedb") +db_path = Path(db_path_str) - if not db_path.exists(): - logger.error(f"Database not found at {db_path}") - logger.error("Run: haiku-rag add ") - raise RuntimeError(f"Database not found: {db_path}") +if not db_path.exists(): + logger.error(f"Database not found at {db_path}") + logger.error("Run: haiku-rag add ") + raise RuntimeError(f"Database not found: {db_path}") - logger.info(f"Initializing HaikuRAG client with database: {db_path}") - client = HaikuRAG(db_path) - logger.info("Research assistant backend ready") - logger.info(f"QA Provider: {Config.qa.provider}, Model: {Config.qa.model}") +logger.info(f"Initializing research assistant with database: {db_path}") +logger.info( + f"Research Provider: {Config.research.provider}, Model: {Config.research.model}" +) - yield - - if client: - logger.info("Closing HaikuRAG client") - client.close() +# Store client reference for proper lifecycle management +_client_cache: dict[str, HaikuRAG] = {} -agent = create_agent() +def get_client(effective_db_path: Path) -> HaikuRAG: + """Get or create cached client.""" + path_key = str(effective_db_path) + if path_key not in _client_cache: + _client_cache[path_key] = HaikuRAG(db_path=effective_db_path, config=Config) + return _client_cache[path_key] -async def health(request): - db_path_str = os.getenv("DB_PATH", "haiku_rag.lancedb") +async def stream_research_agent(request: Request) -> StreamingResponse: + """Agent streaming endpoint with research graph integration.""" + body = await request.json() + input_data = RunAgentInput(**body) + + user_message = "" + if input_data.messages: + user_message = input_data.messages[-1].get("content", "") + + send_stream, receive_stream = create_memory_object_stream[str]() + + async def run_agent_with_streaming( + send_stream: MemoryObjectSendStream[str], + ) -> None: + """Execute agent and forward emitter events to memory stream.""" + async with send_stream: + try: + # Create shared emitter + emitter: AGUIEmitter[ResearchState, ResearchReport] = AGUIEmitter( + thread_id=input_data.thread_id, + run_id=input_data.run_id, + use_deltas=False, + ) + + # Get client + effective_db_path = input_data.config.get("db_path") or db_path + if isinstance(effective_db_path, str): + effective_db_path = Path(effective_db_path) + client = get_client(effective_db_path) + + # Create agent dependencies with shared emitter + agent_deps = AgentDeps(client=client, agui_emitter=emitter) + + # Start run with empty initial state + emitter.start_run( + initial_state=ResearchState.from_config( + context=ResearchContext(original_question=""), + config=Config, + ) + ) + + # Forward emitter events to stream + async def forward_events(): + async for event in emitter: + # Filter out ACTIVITY_SNAPSHOT - not supported by CopilotKit + if event.get("type") == "ACTIVITY_SNAPSHOT": + continue + await send_stream.send(format_sse_event(event)) + + # Run agent and event forwarding concurrently + async with create_task_group() as tg: + tg.start_soon(forward_events) + + result = await agent.run(user_message, deps=agent_deps) + emitter.log(result.output) + await emitter.close() + + except Exception as e: + logger.exception("Error executing agent") + try: + await send_stream.send( + format_sse_event({"type": "error", "error": str(e)}) + ) + except Exception: + pass + + async def event_generator(): + """Generate SSE events from memory stream.""" + async with create_task_group() as tg: + tg.start_soon(run_agent_with_streaming, send_stream) + async with receive_stream: + async for event_str in receive_stream: + yield event_str + + return StreamingResponse( + event_generator(), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "X-Accel-Buffering": "no", + }, + ) + + +async def health_check(_: Request) -> JSONResponse: + """Health check endpoint with configuration info.""" return JSONResponse( { "status": "healthy", "agent_model": str(agent.model), - "qa_provider": Config.qa.provider, - "qa_model": Config.qa.model, - "ollama_base_url": Config.providers.ollama.base_url, - "db_path": db_path_str, - "db_exists": Path(db_path_str).exists(), + "research_provider": Config.research.provider, + "research_model": Config.research.model, + "db_path": str(db_path), + "db_exists": db_path.exists(), } ) -def get_ag_ui_app(): - global ag_ui_app - if ag_ui_app is None and client is not None: - research_deps = ResearchDeps(client=client, state=ResearchState()) - logger.info("Creating AG-UI app") - ag_ui_app = agent.to_ag_ui(deps=research_deps) - return ag_ui_app - - -async def agent_endpoint(scope, receive, send): - app = get_ag_ui_app() - if app is None: - response = JSONResponse({"error": "Client not initialized"}, status_code=503) - await response(scope, receive, send) - return - await app(scope, receive, send) - - +# Create Starlette app app = Starlette( routes=[ - Route("/health", health), - Mount("/agent", agent_endpoint), + Route("/v1/research/stream", stream_research_agent, methods=["POST"]), + Route("/health", health_check, methods=["GET"]), ], middleware=[ Middleware( @@ -92,17 +176,11 @@ app = Starlette( allow_headers=["*"], ) ], - lifespan=lifespan, ) if __name__ == "__main__": import uvicorn - print("Starting haiku.rag research assistant backend...") - print(f"Agent model: {agent.model}") - print(f"QA provider: {Config.qa.provider}") - print(f"QA model: {Config.qa.model}") - uvicorn.run( "main:app", host="0.0.0.0", diff --git a/examples/ag-ui-research/backend/pyproject.toml b/examples/ag-ui-research/backend/pyproject.toml index 16f8bd82..9513393f 100644 --- a/examples/ag-ui-research/backend/pyproject.toml +++ b/examples/ag-ui-research/backend/pyproject.toml @@ -9,7 +9,7 @@ dependencies = [ "uvicorn[standard]>=0.34.2", "pydantic-ai-slim[ag-ui,openai]>=1.1.0", "python-dotenv>=1.0.1", - "haiku-rag>=0.12.1", + "haiku-rag-slim @ file:///Users/ggozad/dev/open-source/haiku.rag-agui/haiku_rag_slim", ] [dependency-groups] @@ -18,6 +18,9 @@ dev = [ "ruff>=0.13.0", ] +[tool.hatch.metadata] +allow-direct-references = true + [tool.hatch.build.targets.wheel] packages = ["."] diff --git 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a/examples/ag-ui-research/docker-compose.yml b/examples/ag-ui-research/docker-compose.yml index ff3941bc..7cf7dd0c 100644 --- a/examples/ag-ui-research/docker-compose.yml +++ b/examples/ag-ui-research/docker-compose.yml @@ -10,11 +10,16 @@ services: # API keys (set these in your shell or .env file) - OPENAI_API_KEY=${OPENAI_API_KEY} - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY} + # Ollama connection (use value from .env) + - OLLAMA_BASE_URL=${OLLAMA_BASE_URL} + # Prevent Python bytecode caching for development + - PYTHONDONTWRITEBYTECODE=1 volumes: - - ./backend:/app - - /app/.venv - ${DB_PATH}:/app/data/haiku.rag.lancedb - ./haiku.rag.yaml:/app/haiku.rag.yaml:ro + - ./backend/main.py:/app/main.py + - ./backend/agent.py:/app/agent.py + - ../../haiku_rag_slim/haiku:/app/.venv/lib/python3.13/site-packages/haiku networks: - ag-ui-network extra_hosts: diff --git a/examples/ag-ui-research/frontend/app/api/copilotkit/route.ts b/examples/ag-ui-research/frontend/app/api/copilotkit/route.ts index 2f6566b4..5f8fab11 100644 --- a/examples/ag-ui-research/frontend/app/api/copilotkit/route.ts +++ b/examples/ag-ui-research/frontend/app/api/copilotkit/route.ts @@ -13,7 +13,7 @@ const runtime = new CopilotRuntime({ agents: { // "research_agent" maps to the agent name used in useCoAgent() on the frontend research_agent: new HttpAgent({ - url: `${process.env.BACKEND_URL || "http://backend:8000"}/agent`, + url: `${process.env.BACKEND_URL || "http://backend:8000"}/v1/research/stream`, }), }, }); diff --git a/examples/ag-ui-research/frontend/components/Agent.tsx b/examples/ag-ui-research/frontend/components/Agent.tsx index 34067926..ac7aca85 100644 --- a/examples/ag-ui-research/frontend/components/Agent.tsx +++ b/examples/ag-ui-research/frontend/components/Agent.tsx @@ -1,208 +1,94 @@ "use client"; -import { - CopilotKit, - useCoAgent, - useCoAgentStateRender, - useCopilotAction, -} from "@copilotkit/react-core"; +import { CopilotKit, useCoAgent } from "@copilotkit/react-core"; import { CopilotChat } from "@copilotkit/react-ui"; import "@copilotkit/react-ui/styles.css"; import StateDisplay from "./StateDisplay"; -interface SourceRef { - chunk_id: string; - document_uri: string; - document_title: string; - chunk_position: number; +interface InsightRecord { + id: string; + summary: string; + status: string; + notes?: string; + supporting_sources: string[]; + originating_questions: string[]; +} + +interface GapRecord { + id: string; + description: string; + severity: string; + blocking: boolean; + resolved: boolean; + notes?: string; + supporting_sources: string[]; + resolved_by: string[]; +} + +interface SearchAnswer { + query: string; + answer: string; + confidence: number; + context: string[]; + sources: string[]; +} + +interface ResearchContext { + original_question: string; + sub_questions: string[]; + qa_responses: SearchAnswer[]; + insights: InsightRecord[]; + gaps: GapRecord[]; +} + +interface EvaluationResult { + confidence: number; + reasoning: string; + should_continue: boolean; + gaps_identified: string[]; + follow_up_questions: string[]; +} + +interface ResearchReport { + question: string; + summary: string; + findings: string[]; + conclusions: string[]; + insights_used: string[]; + methodology: string; } interface ResearchState { - question: string; - phase: string; - status: string; - plan: Array<{ - id: number; - question: string; - status: string; - search_results?: { - type: string; - results: Array<{ - chunk: string; - chunk_id: string; - document_uri: string; - document_title: string; - chunk_position: number; - full_chunk_content: string; - score: number; - expanded: boolean; - }>; - }; - }>; - current_question_index: number; - insights: Array<{ - summary: string; - confidence: number; - source_refs: SourceRef[]; - }>; - document_registry: Record< - string, - { - title: string; - chunks_referenced: string[]; - } - >; - current_document: { - uri: string; - title: string; - content: string; - total_chunks: number; - metadata?: Record; - } | null; - confidence: number; - final_report: { - title: string; - summary: string; - findings: string[]; - conclusions: string[]; - sources: string[]; - citations: Array<{ - document_uri: string; - document_title: string; - chunk_ids: string[]; - }>; + context: ResearchContext; + iterations: number; + max_iterations: number; + confidence_threshold: number; + max_concurrency: number; + last_eval: EvaluationResult | null; + last_analysis: { + insights_extracted: InsightRecord[]; + gaps_identified: GapRecord[]; } | null; + result?: ResearchReport; } function AgentContent() { const { state } = useCoAgent({ name: "research_agent", initialState: { - question: "", - phase: "idle", - status: "", - plan: [], - current_question_index: 0, - insights: [], - document_registry: {}, - current_document: null, - confidence: 0.0, - final_report: null, - }, - }); - - useCopilotAction({ - name: "approve_research_plan", - description: - "Request user approval for the research plan. Returns 'APPROVED' if approved or 'REVISE' if user wants to revise.", - parameters: [], - renderAndWaitForResponse: ({ respond, status }) => ( -
-

- Research Plan Approval -

-

- Please review the research plan in the right pane. -

- -
- - -
-
- ), - }); - - useCoAgentStateRender({ - name: "research_agent", - render: ({ state: newState }) => { - const phaseMessages: Record = { - planning: "Planning research...", - searching: "Searching...", - analyzing: "Extracting insights...", - evaluating: `Evaluating confidence: ${(newState.confidence * 100).toFixed(0)}%`, - synthesizing: "Generating final report...", - done: "Research complete!", - }; - const phaseMessage = - phaseMessages[newState.phase] || newState.status || "Ready"; - - return ( -
- Research Update: {phaseMessage} -
- ); + context: { + original_question: "", + sub_questions: [], + qa_responses: [], + insights: [], + gaps: [], + }, + iterations: 0, + max_iterations: 3, + confidence_threshold: 0.8, + max_concurrency: 1, + last_eval: null, + last_analysis: null, }, }); diff --git a/examples/ag-ui-research/frontend/components/StateDisplay.tsx b/examples/ag-ui-research/frontend/components/StateDisplay.tsx index 32effd20..03e710b9 100644 --- a/examples/ag-ui-research/frontend/components/StateDisplay.tsx +++ b/examples/ag-ui-research/frontend/components/StateDisplay.tsx @@ -3,68 +3,70 @@ import { Markdown } from "@copilotkit/react-ui"; import { useState } from "react"; -interface SourceRef { - chunk_id: string; - document_uri: string; - document_title: string; - chunk_position: number; +interface InsightRecord { + id: string; + summary: string; + status: string; + notes?: string; + supporting_sources: string[]; + originating_questions: string[]; +} + +interface GapRecord { + id: string; + description: string; + severity: string; + blocking: boolean; + resolved: boolean; + notes?: string; + supporting_sources: string[]; + resolved_by: string[]; +} + +interface SearchAnswer { + sub_question: string; + answer: string; + confidence: number; + chunks_used: number; +} + +interface ResearchContext { + original_question: string; + sub_questions: string[]; + qa_responses: SearchAnswer[]; + insights: InsightRecord[]; + gaps: GapRecord[]; +} + +interface EvaluationResult { + confidence: number; + reasoning: string; + should_continue: boolean; + gaps_identified: string[]; + follow_up_questions: string[]; +} + +interface ResearchReport { + question: string; + summary: string; + findings: string[]; + conclusions: string[]; + insights_used: string[]; + methodology: string; } interface ResearchState { - question: string; - phase: string; - status: string; - plan: Array<{ - id: number; - question: string; - status: string; - search_results?: { - type: string; - results: Array<{ - chunk: string; - chunk_id: string; - document_uri: string; - document_title: string; - chunk_position: number; - full_chunk_content: string; - score: number; - expanded: boolean; - }>; - }; - }>; - current_question_index: number; - insights: Array<{ - summary: string; - confidence: number; - source_refs: SourceRef[]; - }>; - document_registry: Record< - string, - { - title: string; - chunks_referenced: string[]; - } - >; - current_document: { - uri: string; - title: string; - content: string; - total_chunks: number; - metadata?: Record; - } | null; - confidence: number; - final_report: { - title: string; - summary: string; - findings: string[]; - conclusions: string[]; - sources: string[]; - citations: Array<{ - document_uri: string; - document_title: string; - chunk_ids: string[]; - }>; + context: ResearchContext; + iterations: number; + max_iterations: number; + confidence_threshold: number; + max_concurrency: number; + last_eval: EvaluationResult | null; + last_analysis: { + insights_extracted: InsightRecord[]; + gaps_identified: GapRecord[]; } | null; + result?: ResearchReport; } interface StateDisplayProps { @@ -75,14 +77,14 @@ export default function StateDisplay({ state }: StateDisplayProps) { const [expandedSections, setExpandedSections] = useState< Record >({ - plan: true, + questions: true, insights: true, + gaps: true, report: true, - document: true, }); const [expandedQuestions, setExpandedQuestions] = useState< - Record + Record >({}); const toggleSection = (section: string) => { @@ -92,20 +94,19 @@ export default function StateDisplay({ state }: StateDisplayProps) { })); }; - const toggleQuestion = (questionId: number) => { + const toggleQuestion = (questionId: string) => { setExpandedQuestions((prev) => ({ ...prev, [questionId]: !prev[questionId], })); }; - // Calculate research progress - const completedQuestions = state.plan.filter( - (q) => q.status === "done", - ).length; - const totalQuestions = state.plan.length; + // Calculate research progress based on iterations const researchProgress = - totalQuestions > 0 ? (completedQuestions / totalQuestions) * 100 : 0; + state.max_iterations > 0 + ? (state.iterations / state.max_iterations) * 100 + : 0; + const confidence = state.last_eval?.confidence || 0; return (
- {/* Current Phase & Status */} + {/* Current Status */}
- Current Phase + Research Progress
- {state.phase} + {state.iterations === 0 + ? "Ready" + : state.result + ? "Complete" + : "Researching"}
- {state.status && ( + {state.iterations > 0 && (
- {state.status} + Iteration {state.iterations} of {state.max_iterations}
)}
{/* Research Progress Bar */} - {totalQuestions > 0 && state.phase !== "idle" && ( + {state.iterations > 0 && (
- Research Progress + Iterations - {completedQuestions}/{totalQuestions} questions + {state.iterations}/{state.max_iterations}
{/* Question */} - {state.question && ( + {state.context.original_question && (
- {state.question} + {state.context.original_question}
)} {/* Confidence Meter */} - {state.confidence > 0 && ( + {confidence > 0 && (
0.8 + confidence > 0.8 ? "#48bb78" - : state.confidence > 0.5 + : confidence > 0.5 ? "#ed8936" : "#f56565", transition: "width 0.3s ease", @@ -309,21 +301,36 @@ export default function StateDisplay({ state }: StateDisplayProps) { fontSize: "1.5rem", fontWeight: "bold", color: - state.confidence > 0.8 + confidence > 0.8 ? "#48bb78" - : state.confidence > 0.5 + : confidence > 0.5 ? "#ed8936" : "#f56565", }} > - {(state.confidence * 100).toFixed(0)}% + {(confidence * 100).toFixed(0)}%
+ {state.last_eval?.reasoning && ( +
+ +
+ )}
)} - {/* Research Plan */} - {state.plan.length > 0 && ( + {/* Sub-Questions and QA Responses */} + {(state.context.sub_questions.length > 0 || + state.context.qa_responses.length > 0) && (
- {expandedSections.plan && ( + {expandedSections.questions && (
- {state.plan.map((item) => ( + {/* Show pending sub_questions */} + {state.context.sub_questions.map((question, idx) => (
-
-
- + ✓
- {item.search_results && ( +
+
+ +
- {item.search_results.results.length} results + Confidence: {(qaResponse.confidence * 100).toFixed(0)}%
- )} -
- {item.search_results && ( +
- {expandedQuestions[item.id] ? "▼" : "▶"} + {expandedQuestions[questionId] ? "▼" : "▶"} - )} - + - {/* Search Results nested inside question */} - {expandedQuestions[item.id] && item.search_results && ( -
+ {/* QA Response nested inside question */} + {expandedQuestions[questionId] && (
- Search Type: {item.search_results.type} -
- {item.search_results.results.map((result, idx) => (
+ Answer +
+
-
- - {result.document_title} - -
- {result.expanded && ( - - Expanded - - )} - 0.8 - ? "#48bb78" - : result.score > 0.6 - ? "#ed8936" - : "#a0aec0", - }} - > - {result.score.toFixed(2)} - -
-
- +
- ))} -
- )} -
- ))} +
+ )} +
+ ); + })} )} )} {/* Insights */} - {state.insights.length > 0 && ( + {state.context.insights.length > 0 && (
- Key Insights ({state.insights.length}) + Key Insights ({state.context.insights.length}) {expandedSections.insights ? "▼" : "▶"} {expandedSections.insights && ( @@ -580,9 +559,9 @@ export default function StateDisplay({ state }: StateDisplayProps) { borderRadius: "0 0 4px 4px", }} > - {state.insights.map((insight, idx) => ( + {state.context.insights.map((insight) => (
- {(insight.confidence * 100).toFixed(0)}% confidence + {insight.status} - {insight.source_refs?.length || 0} sources + {insight.supporting_sources.length} sources
- {insight.source_refs && insight.source_refs.length > 0 && ( + {insight.notes && ( +
+ +
+ )} + {insight.supporting_sources.length > 0 && (
Sources: - {insight.source_refs.map((ref, refIdx) => ( - - {refIdx > 0 && ", "} - - {ref.document_title} - + {insight.supporting_sources.map((source, srcIdx) => ( + + {srcIdx > 0 && ", "} + {source} + + ))} +
+ )} +
+ ))} + + )} + + )} + + {/* Knowledge Gaps */} + {state.context.gaps.length > 0 && ( +
+ + {expandedSections.gaps && ( +
+ {state.context.gaps.map((gap) => ( +
+
+
+ + {gap.severity} + + {gap.blocking && ( + + Blocking + + )} + {gap.resolved && ( + + Resolved + + )} +
+
+
+ +
+ {gap.notes && ( +
+ +
+ )} + {gap.resolved && gap.resolved_by.length > 0 && ( +
+ Resolved by: + {gap.resolved_by.map((source, srcIdx) => ( + + {srcIdx > 0 && ", "} + {source} ))}
@@ -655,7 +818,7 @@ export default function StateDisplay({ state }: StateDisplayProps) { )} {/* Final Report */} - {state.final_report && ( + {state.result && (
- {state.final_report.title} + {state.result.question}

- Executive Summary + Summary

- +
@@ -735,7 +898,7 @@ export default function StateDisplay({ state }: StateDisplayProps) { marginBottom: "0.5rem", }} > - Main Findings + Key Findings
    - {state.final_report.findings.map((finding, idx) => ( + {state.result.findings.map((finding, idx) => (
  • - {state.final_report.conclusions.map((conclusion, idx) => ( + {state.result.conclusions.map((conclusion, idx) => (
+
+

+ Methodology +

+
+ +
+

- Citations + Insights Used ({state.result.insights_used.length})

- {state.final_report.citations && - state.final_report.citations.length > 0 ? ( -
- {state.final_report.citations.map((citation) => ( +
+ {state.result.insights_used.map((insightId, idx) => { + const insight = state.context.insights.find( + (i) => i.id === insightId, + ); + return (
-
- {citation.document_title} -
-
- {citation.chunk_ids.length} chunk - {citation.chunk_ids.length !== 1 ? "s" : ""}{" "} - referenced -
+ {insight ? ( +
+ +
+ ) : ( +
+ Insight ID: {insightId} +
+ )}
- ))} -
- ) : ( -
- {state.final_report.sources?.map((source) => ( -
- {source} -
- ))} -
- )} + ); + })} +
)} diff --git a/examples/ag-ui-research/frontend/package-lock.json b/examples/ag-ui-research/frontend/package-lock.json index 0847ac34..3ab00152 100644 --- a/examples/ag-ui-research/frontend/package-lock.json +++ b/examples/ag-ui-research/frontend/package-lock.json @@ -1,11690 +1,11682 @@ { - "name": "ag-ui-frontend", - "version": "0.1.0", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "ag-ui-frontend", - "version": "0.1.0", - "dependencies": { - "@ag-ui/client": "^0.0.40", - "@copilotkit/react-core": "^1.10.6", - "@copilotkit/react-ui": "^1.10.6", - "@copilotkit/runtime": "^1.10.6", - "next": "15.5.5", - "react": "^19.0.0", - "react-dom": "^19.0.0" - }, - "devDependencies": { - "@biomejs/biome": "2.2.6", - "@types/node": "^22", - "@types/react": "^19", - "@types/react-dom": "^19", - "typescript": "^5" - } - }, - "node_modules/@0no-co/graphql.web": { - "version": "1.2.0", - "resolved": "https://registry.npmjs.org/@0no-co/graphql.web/-/graphql.web-1.2.0.tgz", - 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model: gpt-4o-mini # For Anthropic: -# qa: +# research: # provider: anthropic # model: claude-3-5-haiku-20241022 diff --git a/haiku_rag_slim/haiku/rag/graph/common/nodes.py b/haiku_rag_slim/haiku/rag/graph/common/nodes.py index ba452a61..31b04b27 100644 --- a/haiku_rag_slim/haiku/rag/graph/common/nodes.py +++ b/haiku_rag_slim/haiku/rag/graph/common/nodes.py @@ -156,8 +156,11 @@ def create_search_node[AgentDepsT: GraphAgentDeps]( deps: GraphDeps = ctx.deps # type: ignore[assignment] sub_q = ctx.inputs + # Create unique step name from question text + step_name = f"search: {sub_q}" + if deps.agui_emitter and with_step_wrapper: - deps.agui_emitter.start_step("search_one") + deps.agui_emitter.start_step(step_name) try: # Create semaphore if not already provided