401 lines
14 KiB
Python
401 lines
14 KiB
Python
# pyright: reportPossiblyUnboundVariable=false
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import asyncio
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import uuid
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from collections.abc import Iterable
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from datetime import datetime
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from pathlib import Path
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from typing import TYPE_CHECKING, Any
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from haiku.rag.client import HaikuRAG
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from haiku.rag.config import get_config
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from haiku.rag.skills.rag import RAGState
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from haiku.skills.agent import SkillToolset
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from haiku.skills.models import Skill
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if TYPE_CHECKING:
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from textual.app import ComposeResult
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try:
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import logfire
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logfire.configure(send_to_logfire="if-token-present", console=False)
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logfire.instrument_pydantic_ai()
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except ImportError: # pragma: no cover
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pass
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try:
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import textual_image.widget # noqa: F401 - import early for renderer detection
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from ag_ui.core import (
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AssistantMessage,
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BaseEvent,
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EventType,
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RunAgentInput,
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StateDeltaEvent,
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TextMessageContentEvent,
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ToolCallEndEvent,
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ToolCallStartEvent,
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UserMessage,
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)
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from jsonpatch import JsonPatch
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from pydantic_ai import Agent
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from pydantic_ai.ag_ui import AGUIAdapter
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from textual.app import App, SystemCommand
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from textual.binding import Binding
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from textual.widgets import Footer, Header, Input
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from textual.worker import Worker
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from haiku.rag.chat.widgets.chat_history import ChatHistory, CitationWidget
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TEXTUAL_AVAILABLE = True
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except ImportError: # pragma: no cover
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TEXTUAL_AVAILABLE = False
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App = object # type: ignore
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SystemCommand = object # type: ignore
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RAG_STATE_NAMESPACE = "rag"
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AGENT_PREAMBLE = """You are a helpful research assistant powered by haiku.rag, a knowledge base system.
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CRITICAL RULES:
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1. For greetings or casual chat: respond directly WITHOUT using any tools
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2. NEVER make up information - always use tools to get facts from the knowledge base
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3. For questions: Use the "ask" tool - it handles search and citation automatically
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4. For searches: Use the "search" tool - copy the ENTIRE tool response to your output INCLUDING content snippets
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5. When you use the "ask" tool, summarize the key findings and always include citations in your response
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"""
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class ChatApp(App):
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"""Textual TUI for conversational RAG."""
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TITLE = "haiku.rag Chat"
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CSS = """
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Screen {
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layout: grid;
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grid-size: 1 2;
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grid-rows: 1fr auto;
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background: $surface;
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}
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#chat-history {
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height: 100%;
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}
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Header {
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background: $primary;
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}
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Footer {
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background: $surface-darken-1;
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}
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"""
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BINDINGS = [
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Binding("escape", "focus_input", "Focus Input", show=False),
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]
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def __init__(
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self,
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db_path: Path,
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skill: Skill,
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read_only: bool = False,
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before: datetime | None = None,
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model: str | None = None,
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) -> None:
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super().__init__()
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self.db_path = db_path
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self._skill = skill
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self.read_only = read_only
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self.before = before
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self._model = model or "openai:gpt-4o"
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self.client: HaikuRAG | None = None
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self.config = get_config()
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self._toolset: SkillToolset | None = None
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self._agent: Agent[None, str] | None = None
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self._messages: list[Any] = []
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self._state: dict[str, Any] = {}
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self._is_processing = False
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self._current_worker: Worker[None] | None = None
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self._document_filter: list[str] = []
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def compose(self) -> "ComposeResult":
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"""Compose the UI layout."""
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yield Header()
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yield ChatHistory(id="chat-history")
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yield Input(placeholder="Ask a question...", id="chat-input")
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yield Footer()
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def get_system_commands(self, screen: Any) -> Iterable[SystemCommand]:
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"""Add commands to the command palette."""
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yield from super().get_system_commands(screen)
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yield SystemCommand(
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"Clear chat",
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"Clear the chat history and reset session",
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self.action_clear_chat,
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)
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yield SystemCommand(
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"Filter documents",
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"Select documents to filter searches",
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self.action_show_filter,
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)
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yield SystemCommand(
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"Show visual grounding",
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"Show visual grounding for selected citation",
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self.action_show_visual,
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)
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yield SystemCommand(
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"Database info",
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"Show database information",
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self.action_show_info,
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)
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yield SystemCommand(
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"View state",
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"Show the current session state",
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self.action_view_state,
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)
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async def on_mount(self) -> None:
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"""Initialize the app when mounted."""
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self.client = HaikuRAG(
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db_path=self.db_path,
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config=self.config,
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read_only=self.read_only,
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before=self.before,
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)
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await self.client.__aenter__()
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self._toolset = SkillToolset(skills=[self._skill])
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self._agent = Agent(
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self._model,
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instructions=AGENT_PREAMBLE + self._toolset.system_prompt,
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toolsets=[self._toolset],
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)
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self._state = self._toolset.build_state_snapshot()
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self.query_one(Input).focus()
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async def on_unmount(self) -> None:
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"""Clean up when unmounting."""
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if self.client:
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await self.client.__aexit__(None, None, None)
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async def on_input_submitted(self, event: Input.Submitted) -> None:
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"""Handle user input submission."""
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user_message = event.value.strip()
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if not user_message or self._is_processing:
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return
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event.input.clear()
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chat_history = self.query_one(ChatHistory)
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await chat_history.add_message("user", user_message)
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self._messages.append(
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UserMessage(
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id=str(uuid.uuid4()),
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role="user",
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content=user_message,
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)
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)
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self._is_processing = True
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self.query_one(Input).disabled = True
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self._current_worker = self.run_worker(
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self._run_agent(user_message), exclusive=True
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)
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async def _run_agent(self, user_message: str) -> None:
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"""Run the agent in a background worker."""
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if not self._agent or not self._toolset:
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return
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chat_history = self.query_one(ChatHistory)
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await chat_history.show_thinking()
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run_input = RunAgentInput(
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thread_id="tui",
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run_id=str(uuid.uuid4()),
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messages=self._messages,
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state=self._state,
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tools=[],
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context=[],
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forwarded_props={},
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)
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adapter = AGUIAdapter(agent=self._agent, run_input=run_input)
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message = None
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accumulated_text = ""
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try:
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async for event in adapter.run_stream():
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if not isinstance(event, BaseEvent):
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continue
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if event.type == EventType.TEXT_MESSAGE_START:
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chat_history.hide_thinking()
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message = await chat_history.add_message("assistant")
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accumulated_text = ""
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elif event.type == EventType.TEXT_MESSAGE_CONTENT:
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assert isinstance(event, TextMessageContentEvent)
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accumulated_text += event.delta
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if message:
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message.update_content(accumulated_text)
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chat_history.scroll_end(animate=False)
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elif event.type == EventType.TEXT_MESSAGE_END:
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self._messages.append(
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AssistantMessage(
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id=str(uuid.uuid4()),
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role="assistant",
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content=accumulated_text,
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)
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)
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# Show citations from RAG state
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await self._show_citations(chat_history)
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elif event.type == EventType.TOOL_CALL_START:
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assert isinstance(event, ToolCallStartEvent)
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chat_history.hide_thinking()
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await chat_history.add_tool_call(
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event.tool_call_id, event.tool_call_name
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)
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await chat_history.show_thinking("Executing tasks...")
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elif event.type == EventType.TOOL_CALL_END:
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assert isinstance(event, ToolCallEndEvent)
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chat_history.mark_tool_complete(event.tool_call_id)
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elif event.type == EventType.STATE_DELTA:
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assert isinstance(event, StateDeltaEvent)
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patch = JsonPatch(event.delta)
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self._state = patch.apply(self._state)
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self._toolset.restore_state_snapshot(self._state)
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elif event.type == EventType.STATE_SNAPSHOT:
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self._state = getattr(event, "snapshot", self._state)
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self._toolset.restore_state_snapshot(self._state)
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elif event.type == EventType.RUN_FINISHED:
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chat_history.hide_thinking()
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elif event.type == EventType.RUN_ERROR:
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chat_history.hide_thinking()
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error_msg = getattr(event, "message", "Unknown error")
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await chat_history.add_message("assistant", f"Error: {error_msg}")
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except asyncio.CancelledError:
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chat_history.hide_thinking()
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await chat_history.add_message("assistant", "*Cancelled*")
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except Exception as e:
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chat_history.hide_thinking()
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await chat_history.add_message("assistant", f"Error: {e}")
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finally:
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self._is_processing = False
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self._current_worker = None
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chat_input = self.query_one(Input)
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chat_input.disabled = False
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chat_input.focus()
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async def _show_citations(self, chat_history: "ChatHistory") -> None:
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"""Show citations from the RAG state after an agent response."""
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if not self._toolset:
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return
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rag_state = self._toolset.get_namespace(RAG_STATE_NAMESPACE)
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if rag_state is None:
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return
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citations = getattr(rag_state, "citations", [])
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if citations:
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# Show only new citations (since last response)
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await chat_history.add_citations(citations)
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async def action_clear_chat(self) -> None:
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"""Clear the chat history and reset session."""
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chat_history = self.query_one(ChatHistory)
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await chat_history.clear_messages()
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self._messages.clear()
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# Reset state
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if self._toolset:
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self._state = self._toolset.build_state_snapshot()
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def action_focus_input(self) -> None:
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"""Focus the input field, or cancel if processing."""
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if self._is_processing and self._current_worker:
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self._current_worker.cancel()
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self.query_one(Input).focus()
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def _clear_citation_selection(self) -> None:
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"""Clear citation selection."""
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chat_history = self.query_one(ChatHistory)
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for widget in chat_history.query(CitationWidget):
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widget.remove_class("selected")
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def on_descendant_focus(self, _event: object) -> None:
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"""Clear citation selection when chat input is focused."""
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if isinstance(self.focused, Input) and self.focused.id == "chat-input":
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self._clear_citation_selection()
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async def action_show_visual(self) -> None:
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"""Show visual grounding for the selected citation."""
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if not self.client:
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return
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chat_history = self.query_one(ChatHistory)
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selected_widgets = list(chat_history.query(CitationWidget).filter(".selected"))
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if not selected_widgets:
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return
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citation = selected_widgets[0].citation
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chunk = await self.client.chunk_repository.get_by_id(citation.chunk_id)
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if not chunk:
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return
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from haiku.rag.inspector.widgets.visual_modal import VisualGroundingModal
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await self.push_screen(VisualGroundingModal(chunk=chunk, client=self.client))
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async def action_show_info(self) -> None:
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"""Show database info modal."""
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if not self.client:
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return
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from haiku.rag.inspector.widgets.info_modal import InfoModal
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await self.push_screen(InfoModal(self.client, self.db_path))
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def action_view_state(self) -> None:
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"""Show the current session state."""
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from haiku.skills.chat.app import StateScreen
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self.push_screen(StateScreen(self._state))
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def on_citation_widget_selected(self, event: CitationWidget.Selected) -> None:
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"""Handle citation selection."""
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chat_history = self.query_one(ChatHistory)
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for widget in chat_history.query(CitationWidget):
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widget.remove_class("selected")
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event.widget.add_class("selected")
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async def action_show_filter(self) -> None:
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"""Show document filter modal."""
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if not self.client:
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return
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from haiku.rag.chat.widgets.document_filter_modal import DocumentFilterModal
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await self.push_screen(
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DocumentFilterModal(
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client=self.client,
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selected=self._document_filter,
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)
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)
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def on_document_filter_modal_filter_changed(self, event: Any) -> None:
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"""Handle document filter changes from modal."""
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from haiku.rag.tools.filters import build_multi_document_filter
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self._document_filter = event.selected
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if self._toolset:
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rag_state = self._toolset.get_namespace(RAG_STATE_NAMESPACE)
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if isinstance(rag_state, RAGState):
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rag_state.document_filter = build_multi_document_filter(
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self._document_filter
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)
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self._state = self._toolset.build_state_snapshot()
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