mirror of
https://github.com/MODSetter/SurfSense.git
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refactor(subagents): remove dormant research subagent
This commit is contained in:
CREDO23
parent
62cb0efb44
commit
e3ed3b2be3
9 changed files with 5 additions and 512 deletions
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"""``research`` route: ``SurfSenseSubagentSpec`` builder for deepagents."""
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from __future__ import annotations
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from typing import Any
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from langchain_core.language_models import BaseChatModel
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from langchain_core.tools import BaseTool
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from app.agents.chat.multi_agent_chat.shared.middleware.citation_state import (
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build_citation_state_mw,
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)
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from app.agents.chat.multi_agent_chat.subagents.shared.md_file_reader import (
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read_md_file,
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)
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from app.agents.chat.multi_agent_chat.subagents.shared.spec import SurfSenseSubagentSpec
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from app.agents.chat.multi_agent_chat.subagents.shared.subagent_builder import (
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pack_subagent,
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)
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from .tools.index import NAME, RULESET, load_tools
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def build_subagent(
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*,
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dependencies: dict[str, Any],
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model: BaseChatModel | None = None,
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middleware_stack: dict[str, Any] | None = None,
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mcp_tools: list[BaseTool] | None = None,
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) -> SurfSenseSubagentSpec:
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tools = [*load_tools(dependencies=dependencies), *(mcp_tools or [])]
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description = (
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read_md_file(__package__, "description").strip()
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or "Handles research tasks for this workspace."
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)
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system_prompt = read_md_file(__package__, "system_prompt").strip()
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# web_search registers WEB_RESULT citations via Command(update=...); the
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# citation-state middleware declares the channel so those [n] merge back up.
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middleware_with_citations = {
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**(middleware_stack or {}),
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"citation_state": build_citation_state_mw(),
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}
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return pack_subagent(
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name=NAME,
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description=description,
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system_prompt=system_prompt,
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tools=tools,
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ruleset=RULESET,
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dependencies=dependencies,
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model=model,
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middleware_stack=middleware_with_citations,
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)
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Specialist for external research.
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Use whenever a task requires finding sources on the web and extracting evidence to answer documentation questions.
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You are the SurfSense research operations sub-agent.
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You receive delegated instructions from a supervisor agent and return structured results for supervisor synthesis.
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<goal>
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Gather and synthesize evidence using SurfSense research tools with clear citations and uncertainty reporting.
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</goal>
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<available_tools>
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- `web_search`
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- `scrape_webpage`
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</available_tools>
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<tool_policy>
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- Use only tools in `<available_tools>`.
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- Prefer primary and recent sources when recency matters.
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- If the delegated request is underspecified, return `status=blocked` with the missing research constraints.
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- Never fabricate facts, citations, URLs, or quote text.
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</tool_policy>
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<citations>
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`web_search` returns a `<web_results>` block whose results are each prefixed with a bracketed label — `[1]`, `[2]`, `[3]`. That `[n]` is the citation label. When a finding came from a specific result, append its `[n]` to that finding, copying the label **exactly** as shown. The caller relays these labels verbatim and the server resolves each one, so a wrong number silently breaks the citation.
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- Use the exact `[n]` shown next to the result you actually used; never renumber, guess, or invent a label.
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- Before emitting an `[n]`, confirm that bracketed label appears in the `web_search` output this turn. If you can't see it, omit it.
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- Write the bare label `[n]` only — no `[citation:…]` wrapper, no markdown links.
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- Several results behind one finding → each in its own brackets with nothing between: `[1][2]`.
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- `scrape_webpage` returns raw page text with no `[n]` labels; a fact drawn only from a scrape carries no citation (report the URL in `evidence.sources` instead).
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</citations>
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<out_of_scope>
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- Do not execute connector mutations (email/calendar/docs/chat writes) or deliverable generation.
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</out_of_scope>
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<safety>
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- Report uncertainty explicitly when evidence is incomplete or conflicting.
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- Never present unverified claims as facts.
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</safety>
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<failure_policy>
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- On tool failure, return `status=error` with a concise recovery `next_step`.
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- On no useful evidence, return `status=blocked` with recommended narrower filters.
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</failure_policy>
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<output_contract>
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Return **only** one JSON object (no markdown/prose):
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{
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"status": "success" | "partial" | "blocked" | "error",
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"action_summary": string,
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"evidence": {
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"findings": string[],
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"sources": string[],
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"confidence": "high" | "medium" | "low"
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},
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"next_step": string | null,
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"missing_fields": string[] | null,
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"assumptions": string[] | null
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}
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<include snippet="output_contract_base"/>
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Route-specific rules:
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- `evidence.findings`: max 10 entries, each a single sentence stating one distinct fact. Append the supporting `[n]` to each finding drawn from a `web_search` result. Do not paste raw paragraphs, scraped pages, or quote blocks.
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- `evidence.sources`: max 10 URLs, one per finding when applicable. List each URL once. (Citations travel as `[n]`; `sources` is for transparency and for scrape-only facts that carry no `[n]`.)
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</output_contract>
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"""Research-stage tools: web search (shared) and scrape."""
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from app.agents.chat.shared.tools.web_search import create_web_search_tool
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from .scrape_webpage import create_scrape_webpage_tool
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__all__ = [
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"create_scrape_webpage_tool",
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"create_web_search_tool",
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]
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"""``research`` native tools and (empty) permission ruleset."""
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from __future__ import annotations
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from typing import Any
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from langchain_core.tools import BaseTool
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from app.agents.chat.multi_agent_chat.shared.permissions import Ruleset
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from app.agents.chat.shared.tools.web_search import create_web_search_tool
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from .scrape_webpage import create_scrape_webpage_tool
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NAME = "research"
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RULESET = Ruleset(origin=NAME, rules=[])
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def load_tools(
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*, dependencies: dict[str, Any] | None = None, **kwargs: Any
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) -> list[BaseTool]:
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d = {**(dependencies or {}), **kwargs}
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return [
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create_web_search_tool(
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workspace_id=d.get("workspace_id"),
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available_connectors=d.get("available_connectors"),
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),
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create_scrape_webpage_tool(),
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]
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"""Scrape pages via WebCrawlerConnector; YouTube URLs use the transcript API instead of HTML crawl."""
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import hashlib
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import logging
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import time
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from typing import Any
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from urllib.parse import urlparse
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from fake_useragent import UserAgent
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from langchain_core.tools import tool
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from requests import Session
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from scrapling.fetchers import AsyncFetcher
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from youtube_transcript_api import YouTubeTranscriptApi
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from app.proprietary.web_crawler import (
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CrawlOutcomeStatus,
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WebCrawlerConnector,
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)
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from app.tasks.document_processors.youtube_processor import get_youtube_video_id
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from app.utils.proxy import get_proxy_url, get_requests_proxies
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logger = logging.getLogger(__name__)
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def _bill_successful_scrape() -> None:
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"""Fold one successful crawl into the current chat turn's bill (Phase 3c).
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The cost rides the turn accumulator and settles at the premium
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``finalize_credit`` step — no separate wallet hit. Free / BYOK / anonymous
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turns (which never reserve/finalize) still record the line in the
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breakdown but are never debited. No-op when crawl billing is disabled or
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there is no active turn (e.g. non-chat callers).
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"""
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from app.services.token_tracking_service import get_current_accumulator
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from app.services.web_crawl_credit_service import WebCrawlCreditService
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if not WebCrawlCreditService.billing_enabled():
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return
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acc = get_current_accumulator()
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if acc is None:
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return
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acc.add(
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model="web_crawl",
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prompt_tokens=0,
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completion_tokens=0,
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total_tokens=0,
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cost_micros=WebCrawlCreditService.successes_to_micros(1),
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call_kind="web_crawl",
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)
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def _bill_captcha_attempts(outcome) -> None:
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"""Fold captcha solve attempts (Phase 3d) into the current chat turn's bill.
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Per *attempt*, not per success: a solve that didn't rescue the crawl still
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cost real solver money, so this runs before the success/failure branch.
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Mirrors :func:`_bill_successful_scrape` (rides the turn accumulator, settles
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at ``finalize_credit``; record-only on free/anonymous turns). No-op when
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captcha billing is off, there were no attempts, or no turn is active.
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"""
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from app.services.token_tracking_service import get_current_accumulator
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from app.services.web_crawl_credit_service import WebCrawlCreditService
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if not WebCrawlCreditService.captcha_billing_enabled():
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return
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attempts = getattr(outcome, "captcha_attempts", 0) or 0
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if attempts <= 0:
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return
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acc = get_current_accumulator()
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if acc is None:
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return
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acc.add(
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model="web_crawl_captcha",
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prompt_tokens=0,
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completion_tokens=0,
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total_tokens=0,
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cost_micros=WebCrawlCreditService.captcha_solves_to_micros(attempts),
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call_kind="web_crawl_captcha",
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)
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def extract_domain(url: str) -> str:
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"""Extract the domain from a URL."""
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try:
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parsed = urlparse(url)
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domain = parsed.netloc
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if domain.startswith("www."):
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domain = domain[4:]
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return domain
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except Exception:
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return ""
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def generate_scrape_id(url: str) -> str:
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"""Generate a unique ID for a scraped webpage."""
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hash_val = hashlib.md5(url.encode()).hexdigest()[:12]
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return f"scrape-{hash_val}"
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def truncate_content(content: str, max_length: int = 50000) -> tuple[str, bool]:
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"""
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Truncate content to a maximum length.
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Returns:
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Tuple of (truncated_content, was_truncated)
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"""
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if len(content) <= max_length:
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return content, False
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# Prefer truncating at a sentence/paragraph boundary.
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truncated = content[:max_length]
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last_period = truncated.rfind(".")
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last_newline = truncated.rfind("\n\n")
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boundary = max(last_period, last_newline)
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if boundary > max_length * 0.8: # only if the boundary isn't too far back
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truncated = content[: boundary + 1]
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return truncated + "\n\n[Content truncated...]", True
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async def _scrape_youtube_video(
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url: str, video_id: str, max_length: int
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) -> dict[str, Any]:
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"""
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Fetch YouTube video metadata and transcript via the YouTubeTranscriptApi.
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Returns a result dict in the same shape as the regular scrape_webpage output.
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"""
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scrape_id = generate_scrape_id(url)
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domain = "youtube.com"
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# --- Video metadata via oEmbed ---
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residential_proxies = get_requests_proxies()
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params = {
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"format": "json",
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"url": f"https://www.youtube.com/watch?v={video_id}",
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}
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oembed_url = "https://www.youtube.com/oembed"
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try:
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oembed_fetch_start = time.perf_counter()
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oembed_page = await AsyncFetcher.get(
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oembed_url,
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params=params,
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proxy=get_proxy_url(),
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stealthy_headers=True,
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)
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logger.info(
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"[scrape_webpage][perf] source=oembed video=%s status=%s fetch_ms=%.1f",
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video_id,
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getattr(oembed_page, "status", None),
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(time.perf_counter() - oembed_fetch_start) * 1000,
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)
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video_data = oembed_page.json()
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except Exception:
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video_data = {}
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title = video_data.get("title", "YouTube Video")
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author = video_data.get("author_name", "Unknown")
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# --- Transcript via YouTubeTranscriptApi ---
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try:
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transcript_fetch_start = time.perf_counter()
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ua = UserAgent()
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http_client = Session()
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http_client.headers.update({"User-Agent": ua.random})
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if residential_proxies:
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http_client.proxies.update(residential_proxies)
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ytt_api = YouTubeTranscriptApi(http_client=http_client)
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# Pick the first transcript (video's primary language) rather than
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# defaulting to English.
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transcript_list = ytt_api.list(video_id)
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transcript = next(iter(transcript_list))
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captions = transcript.fetch()
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logger.info(
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"[scrape_webpage][perf] source=transcript video=%s fetch_ms=%.1f",
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video_id,
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(time.perf_counter() - transcript_fetch_start) * 1000,
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)
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logger.info(
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f"[scrape_webpage] Fetched transcript for {video_id} "
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f"in {transcript.language} ({transcript.language_code})"
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)
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transcript_segments = []
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for line in captions:
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start_time = line.start
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duration = line.duration
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text = line.text
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timestamp = f"[{start_time:.2f}s-{start_time + duration:.2f}s]"
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transcript_segments.append(f"{timestamp} {text}")
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transcript_text = "\n".join(transcript_segments)
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except Exception as e:
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logger.warning(f"[scrape_webpage] No transcript for video {video_id}: {e}")
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transcript_text = f"No captions available for this video. Error: {e!s}"
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content = f"# {title}\n\n**Author:** {author}\n**Video ID:** {video_id}\n\n## Transcript\n\n{transcript_text}"
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content, was_truncated = truncate_content(content, max_length)
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word_count = len(content.split())
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description = f"YouTube video by {author}"
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return {
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"id": scrape_id,
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"assetId": url,
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"kind": "article",
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"href": url,
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"title": title,
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"description": description,
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"content": content,
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"domain": domain,
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"word_count": word_count,
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"was_truncated": was_truncated,
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"crawler_type": "youtube_transcript",
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"author": author,
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}
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def create_scrape_webpage_tool():
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"""
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Factory function to create the scrape_webpage tool.
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Returns:
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A configured tool function for scraping webpages.
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"""
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@tool
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async def scrape_webpage(
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url: str,
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max_length: int = 50000,
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) -> dict[str, Any]:
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"""
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Scrape and extract the main content from a webpage.
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Use this tool when the user wants you to read, summarize, or answer
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questions about a specific webpage's content. This tool actually
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fetches and reads the full page content. For YouTube video URLs it
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fetches the transcript directly instead of crawling the page.
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Common triggers:
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- "Read this article and summarize it"
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- "What does this page say about X?"
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- "Summarize this blog post for me"
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- "Tell me the key points from this article"
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- "What's in this webpage?"
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Args:
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url: The URL of the webpage to scrape (must be HTTP/HTTPS)
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max_length: Maximum content length to return (default: 50000 chars)
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Returns:
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A dictionary containing:
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- id: Unique identifier for this scrape
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- assetId: The URL (for deduplication)
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- kind: "article" (type of content)
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- href: The URL to open when clicked
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- title: Page title
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- description: Brief description or excerpt
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- content: The extracted main content (markdown format)
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- domain: The domain name
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- word_count: Approximate word count
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- was_truncated: Whether content was truncated
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- error: Error message (if scraping failed)
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"""
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scrape_id = generate_scrape_id(url)
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domain = extract_domain(url)
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if not url.startswith(("http://", "https://")):
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url = f"https://{url}"
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try:
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# YouTube URLs use the transcript API instead of crawling.
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video_id = get_youtube_video_id(url)
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if video_id:
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return await _scrape_youtube_video(url, video_id, max_length)
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connector = WebCrawlerConnector()
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outcome = await connector.crawl_url(url)
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# 03d: bill any captcha attempts (even if the crawl ultimately failed).
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_bill_captcha_attempts(outcome)
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if outcome.status is not CrawlOutcomeStatus.SUCCESS or not outcome.result:
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return {
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"id": scrape_id,
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"assetId": url,
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"kind": "article",
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"href": url,
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"title": domain or "Webpage",
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"domain": domain,
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"error": outcome.error or "No content returned from crawler",
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}
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result = outcome.result
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_bill_successful_scrape()
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content = result.get("content", "")
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metadata = result.get("metadata", {})
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title = metadata.get("title", "")
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if not title:
|
||||
title = domain or url.split("/")[-1] or "Webpage"
|
||||
|
||||
description = metadata.get("description", "")
|
||||
if not description and content:
|
||||
first_para = content.split("\n\n")[0] if content else ""
|
||||
description = (
|
||||
first_para[:300] + "..." if len(first_para) > 300 else first_para
|
||||
)
|
||||
|
||||
content, was_truncated = truncate_content(content, max_length)
|
||||
word_count = len(content.split())
|
||||
|
||||
return {
|
||||
"id": scrape_id,
|
||||
"assetId": url,
|
||||
"kind": "article",
|
||||
"href": url,
|
||||
"title": title,
|
||||
"description": description,
|
||||
"content": content,
|
||||
"domain": domain,
|
||||
"word_count": word_count,
|
||||
"was_truncated": was_truncated,
|
||||
"crawler_type": result.get("crawler_type", "unknown"),
|
||||
"author": metadata.get("author"),
|
||||
"date": metadata.get("date"),
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
error_message = str(e)
|
||||
logger.error(f"[scrape_webpage] Error scraping {url}: {error_message}")
|
||||
return {
|
||||
"id": scrape_id,
|
||||
"assetId": url,
|
||||
"kind": "article",
|
||||
"href": url,
|
||||
"title": domain or "Webpage",
|
||||
"domain": domain,
|
||||
"error": f"Failed to scrape: {error_message[:100]}",
|
||||
}
|
||||
|
||||
return scrape_webpage
|
||||
|
|
@ -21,9 +21,6 @@ from app.agents.chat.multi_agent_chat.subagents.builtins.knowledge_base.agent im
|
|||
from app.agents.chat.multi_agent_chat.subagents.builtins.memory.agent import (
|
||||
build_subagent as build_memory_subagent,
|
||||
)
|
||||
from app.agents.chat.multi_agent_chat.subagents.builtins.research.agent import (
|
||||
build_subagent as build_research_subagent,
|
||||
)
|
||||
from app.agents.chat.multi_agent_chat.subagents.builtins.web_crawler.agent import (
|
||||
build_subagent as build_web_crawler_subagent,
|
||||
)
|
||||
|
|
@ -112,7 +109,6 @@ SUBAGENT_BUILDERS_BY_NAME: dict[str, SubagentBuilder] = {
|
|||
"memory": build_memory_subagent,
|
||||
"notion": build_notion_subagent,
|
||||
"onedrive": build_onedrive_subagent,
|
||||
"research": build_research_subagent,
|
||||
"slack": build_slack_subagent,
|
||||
"teams": build_teams_subagent,
|
||||
"web_crawler": build_web_crawler_subagent,
|
||||
|
|
@ -127,7 +123,7 @@ def _route_resource_package(builder: SubagentBuilder) -> str:
|
|||
|
||||
def main_prompt_registry_subagent_lines(exclude: list[str]) -> list[tuple[str, str]]:
|
||||
"""(name, description) for registry specialists included for **task** (same rules as ``build_subagents``)."""
|
||||
banned = frozenset(("memory", "research")) | frozenset(exclude)
|
||||
banned = frozenset(("memory",)) | frozenset(exclude)
|
||||
rows: list[tuple[str, str]] = []
|
||||
for name in sorted(SUBAGENT_BUILDERS_BY_NAME):
|
||||
if name in banned:
|
||||
|
|
@ -197,10 +193,10 @@ def build_subagents(
|
|||
disabled_tools: list[str] | None = None,
|
||||
ask_kb_tool: BaseTool | None = None,
|
||||
) -> list[SubAgent]:
|
||||
"""Build registry subagents; skip memory/research; skip names in exclude."""
|
||||
"""Build registry subagents; skip memory; skip names in exclude."""
|
||||
mcp = mcp_tools_by_agent or {}
|
||||
specs: list[SubAgent] = []
|
||||
excluded = ["memory", "research"]
|
||||
excluded = ["memory"]
|
||||
if exclude:
|
||||
excluded.extend(exclude)
|
||||
disabled_names = frozenset(disabled_tools or ())
|
||||
|
|
|
|||
|
|
@ -19,7 +19,7 @@ from app.agents.chat.multi_agent_chat.subagents.registry import (
|
|||
|
||||
pytestmark = pytest.mark.unit
|
||||
|
||||
# The full specialist roster the main agent composes from: 6 builtins + 15
|
||||
# The full specialist roster the main agent composes from: 5 builtins + 15
|
||||
# connector routes. Adding/removing a specialist is a deliberate product change
|
||||
# and must be reflected here.
|
||||
_EXPECTED_SUBAGENTS = frozenset(
|
||||
|
|
@ -40,7 +40,6 @@ _EXPECTED_SUBAGENTS = frozenset(
|
|||
"memory",
|
||||
"notion",
|
||||
"onedrive",
|
||||
"research",
|
||||
"slack",
|
||||
"teams",
|
||||
"web_crawler",
|
||||
|
|
@ -50,7 +49,7 @@ _EXPECTED_SUBAGENTS = frozenset(
|
|||
|
||||
# Specialists that are always available regardless of connected sources, so they
|
||||
# carry no required-connector entry.
|
||||
_CONNECTORLESS = frozenset({"memory", "research"})
|
||||
_CONNECTORLESS = frozenset({"memory"})
|
||||
|
||||
|
||||
def test_registry_contains_exactly_expected_subagents():
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue