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100 lines
4.2 KiB
Python
100 lines
4.2 KiB
Python
"""Deep Research capability — agentic-engine-based deep research.
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Thin shim that delegates to :class:`ResearchPipeline`. All orchestration
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— rephrase (mini agentic loop with ``ask_user``), decompose, per-block
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research loops with ``THINK`` / ``TOOL`` / ``APPEND`` / ``FINISH``,
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queue scheduler, and iterative reporting — lives in the pipeline
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module. The capability only handles:
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* request-config validation,
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* the outline-preview two-stage flow (first call returns sub-topics
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the user edits / confirms; second call drives Phase 3+4 with the
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confirmed outline).
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Tool composition is delegated to the shared
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:mod:`deeptutor.agents._shared.tool_composition` policy — same as chat,
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so the user's composer toggles + the attached KB drive what the per-block
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research loop actually has access to. There is no separate "sources"
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knob.
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"""
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from __future__ import annotations
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from typing import Any
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from deeptutor.agents.research.pipeline import ResearchPipeline, SubTopicItem
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from deeptutor.agents.research.request_config import (
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build_research_runtime_config,
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validate_research_request_config,
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)
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from deeptutor.core.capability_protocol import BaseCapability, CapabilityManifest
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from deeptutor.core.context import UnifiedContext
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from deeptutor.core.stream_bus import StreamBus
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from deeptutor.runtime.request_contracts import get_capability_request_schema
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from deeptutor.services.config import load_config_with_main
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class DeepResearchCapability(BaseCapability):
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manifest = CapabilityManifest(
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name="deep_research",
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description="Agentic-loop deep research with iterative report generation.",
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stages=["rephrasing", "decomposing", "researching", "reporting"],
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tools_used=["rag", "web_search", "paper_search", "code_execution"],
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cli_aliases=["research"],
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request_schema=get_capability_request_schema("deep_research"),
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)
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async def run(self, context: UnifiedContext, stream: StreamBus) -> None:
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kb_name = context.knowledge_bases[0] if context.knowledge_bases else None
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request_config = validate_research_request_config(context.config_overrides)
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enabled_tools = list(context.enabled_tools or [])
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runtime_config = build_research_runtime_config(
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base_config=load_config_with_main("main.yaml"),
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request_config=request_config,
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kb_name=kb_name,
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)
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# Outline-preview two-stage flow: first call lacks a confirmed
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# outline → pipeline returns ``outline_preview`` and exits; the
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# frontend surfaces the outline editor and (after the user
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# confirms) sends a second call with ``confirmed_outline`` set.
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confirmed_outline_items: list[SubTopicItem] | None = None
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if request_config.confirmed_outline is not None:
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confirmed_outline_items = [
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SubTopicItem(title=item.title, overview=item.overview or "")
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for item in request_config.confirmed_outline
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]
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pipeline = ResearchPipeline(
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language=context.language,
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runtime_config=runtime_config,
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kb_name=kb_name,
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enabled_tools=enabled_tools,
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)
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result = await pipeline.run(
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context=context,
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topic=context.user_message,
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confirmed_outline=confirmed_outline_items,
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attachments=context.attachments,
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stream=stream,
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)
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# Outline-preview payloads carry the sub-topics + the original
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# request config so the second call has everything it needs to
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# confirm and resume. Fields live at top level so
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# ``event.metadata.outline_preview`` resolves on the FE.
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if result.get("outline_preview"):
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research_config: dict[str, Any] = {
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"mode": request_config.mode,
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"depth": request_config.depth,
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}
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if request_config.manual_subtopics is not None:
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research_config["manual_subtopics"] = request_config.manual_subtopics
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if request_config.manual_max_iterations is not None:
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research_config["manual_max_iterations"] = request_config.manual_max_iterations
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await stream.result(
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{**result, "research_config": research_config},
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source=self.name,
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)
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