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https://github.com/pewdiepie-archdaemon/odysseus.git
synced 2026-06-22 20:55:29 -04:00
refactor(tools): move model-interaction tools to the agent_tools registry (#4445)
Moves chat_with_model, ask_teacher and list_models out of ai_interaction.py into src/agent_tools/model_interaction_tools.py (the do_ prefix dropped) and registers them in TOOL_HANDLERS, so dispatch flows through the registry instead of the dispatch_ai_tool elif in tool_execution.py. The implementations are relocated, not wrapped. ai_interaction.py keeps only the shared helpers they reuse (_resolve_model, AI_CHAT_TIMEOUT), still used by the not-yet-migrated session/pipeline tools. dispatch_ai_tool loses its three now-unused branches. Also removes the dead do_second_opinion: it was already off the live tool surface (no tag/schema/parsing/dispatch; tool_index.py notes it was removed), so the function and its stale frontend catalog entries (admin.js, assistant.js) are deleted. Tests: owner-scope test points at the new list_models location and drops the moved tools from the dispatch_ai_tool parametrize; a new test_model_interaction_registry covers registration, owner threading, and registry dispatch.
This commit is contained in:
Kenny Van de Maele
committed by
GitHub
GitHub
parent
97a7f59fe7
commit
56ba144875
@@ -22,6 +22,7 @@ from .subprocess_tools import BashTool, PythonTool
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from .web_tools import WebSearchTool, WebFetchTool
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from .filesystem_tools import ReadFileTool, WriteFileTool, EditFileTool, LsTool, GlobTool, GrepTool, GetWorkspaceTool
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from .document_tools import CreateDocumentTool, UpdateDocumentTool, EditDocumentTool, SuggestDocumentTool, ManageDocumentTool
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from .model_interaction_tools import ChatWithModelTool, AskTeacherTool, ListModelsTool
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TOOL_HANDLERS = {
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"bash": BashTool().execute,
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@@ -40,6 +41,9 @@ TOOL_HANDLERS = {
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"suggest_document": SuggestDocumentTool().execute,
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"manage_documents": ManageDocumentTool().execute,
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"get_workspace": GetWorkspaceTool().execute,
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"chat_with_model": ChatWithModelTool().execute,
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"ask_teacher": AskTeacherTool().execute,
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"list_models": ListModelsTool().execute,
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}
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# ---------------------------------------------------------------------------
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@@ -0,0 +1,208 @@
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"""model_interaction_tools.py - agent tools for talking to other models.
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Owns the model-interaction tool implementations (chat_with_model, ask_teacher,
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list_models) and their handler classes, registered in ``TOOL_HANDLERS``. Part
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of the tool -> registry migration (#3629): the implementations were moved here
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out of ``src.ai_interaction`` so dispatch flows through the registry instead of
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the elif chain / dispatch_ai_tool in tool_execution.py.
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Shared helpers that still live in ``src.ai_interaction`` and are used by tools
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not yet migrated (``_resolve_model``, ``AI_CHAT_TIMEOUT``) are imported lazily
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inside the functions to avoid an import cycle at module load.
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"""
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import logging
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from typing import Dict, Optional
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logger = logging.getLogger(__name__)
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_TEACHER_SYSTEM_PROMPT = (
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"You are a senior AI mentor. A less capable model is stuck on a problem and asking for help. "
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"Provide clear, actionable guidance:\n"
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"1. Brief analysis of the problem\n"
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"2. Recommended approach (step by step)\n"
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"3. Key things to watch out for\n\n"
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"Be concise and practical. No preamble."
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)
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async def chat_with_model(content: str, session_id: Optional[str] = None, owner: Optional[str] = None) -> Dict:
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"""Send a message to a specific model and return its response.
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Content format:
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Line 1: model_name (or model_name@endpoint_name)
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Line 2+: the message to send
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"""
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from src.ai_interaction import _resolve_model, AI_CHAT_TIMEOUT
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from src.llm_core import llm_call_async
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lines = content.strip().split("\n", 1)
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if not lines or not lines[0].strip():
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return {"error": "First line must be the model name"}
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model_spec = lines[0].strip()
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message = lines[1].strip() if len(lines) > 1 else ""
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if not message:
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return {"error": "No message provided (line 2+ is the message)"}
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try:
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url, model, headers = _resolve_model(model_spec, owner=owner)
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except ValueError as e:
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return {"error": str(e)}
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try:
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response = await llm_call_async(
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url, model,
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[{"role": "user", "content": message}],
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headers=headers,
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timeout=AI_CHAT_TIMEOUT,
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)
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# Truncate very long responses
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if len(response) > 10000:
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response = response[:10000] + "\n... (truncated)"
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return {"model": model, "response": response}
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except Exception as e:
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logger.error(f"chat_with_model failed: {e}")
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return {"error": f"Failed to get response from {model_spec}: {e}"}
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async def ask_teacher(content: str, session_id: Optional[str] = None, owner: Optional[str] = None) -> Dict:
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"""Ask a more capable model for help.
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Content format:
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Line 1: model_name (or 'auto')
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Line 2+: the problem description
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"""
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from src.ai_interaction import _resolve_model, AI_CHAT_TIMEOUT
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from src.llm_core import llm_call_async
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from src.settings import get_setting
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lines = content.strip().split("\n", 1)
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model_spec = lines[0].strip() if lines else "auto"
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problem = lines[1].strip() if len(lines) > 1 else ""
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if not problem:
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return {"error": "No problem description provided"}
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if model_spec.lower() in ("auto", ""):
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model_spec = get_setting("teacher_model", "")
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if not model_spec:
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return {"error": "No teacher model configured. Specify a model name or set teacher_model in settings."}
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try:
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url, model, headers = _resolve_model(model_spec, owner=owner)
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except ValueError as e:
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return {"error": str(e)}
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try:
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response = await llm_call_async(
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url, model,
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[
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{"role": "system", "content": _TEACHER_SYSTEM_PROMPT},
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{"role": "user", "content": f"Problem:\n{problem}"},
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],
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headers=headers,
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timeout=AI_CHAT_TIMEOUT,
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)
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if len(response) > 8000:
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response = response[:8000] + "\n... (truncated)"
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return {"model": model, "response": response, "teacher": True}
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except Exception as e:
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logger.error(f"ask_teacher failed: {e}")
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return {"error": f"Teacher call failed ({model_spec}): {e}"}
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async def list_models(content: str, session_id: Optional[str] = None, owner: Optional[str] = None) -> Dict:
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"""List all available models across configured endpoints.
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Content = optional filter keyword.
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"""
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import json
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import httpx
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from src.database import SessionLocal, ModelEndpoint
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from src.llm_core import _detect_provider, ANTHROPIC_MODELS
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from src.auth_helpers import owner_filter
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from src.endpoint_resolver import resolve_endpoint_runtime, build_headers, build_models_url
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keyword = content.strip().lower() if content.strip() else None
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db = SessionLocal()
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try:
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query = db.query(ModelEndpoint).filter(ModelEndpoint.is_enabled == True)
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if owner:
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query = owner_filter(query, ModelEndpoint, owner)
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endpoints = query.all()
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if not endpoints:
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return {"results": "No enabled model endpoints configured."}
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result_lines = []
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total_models = 0
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for ep in endpoints:
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try:
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base, api_key = resolve_endpoint_runtime(ep, owner=owner)
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except Exception:
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continue
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provider = _detect_provider(base)
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headers = build_headers(api_key, base)
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model_ids = []
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if provider == "anthropic":
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model_ids = list(ANTHROPIC_MODELS)
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else:
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try:
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models_url = build_models_url(base)
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if models_url:
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r = httpx.get(models_url, headers=headers, timeout=5)
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r.raise_for_status()
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data = r.json()
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model_ids = [m.get("id") for m in (data.get("data") or []) if m.get("id")]
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if not model_ids:
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model_ids = [
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m.get("name") or m.get("model")
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for m in (data.get("models") or [])
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if m.get("name") or m.get("model")
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]
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else:
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model_ids = json.loads(ep.cached_models or "[]")
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except Exception:
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model_ids = ["(endpoint offline)"]
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if keyword:
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model_ids = [m for m in model_ids if keyword in m.lower() or keyword in (ep.name or "").lower()]
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if model_ids:
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result_lines.append(f"\n**{ep.name or base}** ({provider}):")
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for mid in model_ids:
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result_lines.append(f" - `{mid}`")
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total_models += 1
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if not result_lines:
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return {"results": "No models found" + (f" matching '{keyword}'" if keyword else "") + "."}
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header = f"Available models ({total_models} total):"
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return {"results": header + "\n".join(result_lines)}
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except Exception as e:
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logger.error(f"list_models failed: {e}")
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return {"error": str(e)}
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finally:
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db.close()
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# ---------------------------------------------------------------------------
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# Handler classes registered in TOOL_HANDLERS
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# ---------------------------------------------------------------------------
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class ChatWithModelTool:
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async def execute(self, content: str, ctx: dict) -> Dict:
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return await chat_with_model(content, ctx.get("session_id"), owner=ctx.get("owner"))
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class AskTeacherTool:
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async def execute(self, content: str, ctx: dict) -> Dict:
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return await ask_teacher(content, ctx.get("session_id"), owner=ctx.get("owner"))
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class ListModelsTool:
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async def execute(self, content: str, ctx: dict) -> Dict:
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return await list_models(content, ctx.get("session_id"), owner=ctx.get("owner"))
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+7
-331
@@ -1,8 +1,12 @@
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"""
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ai_interaction.py
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AI-to-AI interaction tools: chat_with_model, create_session, list_sessions,
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send_to_session, pipeline.
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AI-to-AI interaction tools: create_session, list_sessions, send_to_session,
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pipeline, plus shared model resolution (_resolve_model).
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chat_with_model, ask_teacher and list_models were moved to
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src/agent_tools/model_interaction_tools.py as part of the tool -> registry
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migration (#3629); they still reuse _resolve_model / AI_CHAT_TIMEOUT from here.
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These are agent tools — the LLM writes fenced code blocks and they execute
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through the standard agent_tools.py pipeline.
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@@ -159,242 +163,6 @@ def _resolve_model(spec: str, owner: Optional[str] = None) -> Tuple[str, str, Di
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# Tool implementations
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# ---------------------------------------------------------------------------
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async def do_chat_with_model(content: str, session_id: Optional[str] = None, owner: Optional[str] = None) -> Dict:
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"""Send a message to a specific model and return its response.
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Content format:
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Line 1: model_name (or model_name@endpoint_name)
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Line 2+: the message to send
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"""
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from src.llm_core import llm_call_async
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lines = content.strip().split("\n", 1)
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if not lines or not lines[0].strip():
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return {"error": "First line must be the model name"}
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model_spec = lines[0].strip()
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message = lines[1].strip() if len(lines) > 1 else ""
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if not message:
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return {"error": "No message provided (line 2+ is the message)"}
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try:
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url, model, headers = _resolve_model(model_spec, owner=owner)
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except ValueError as e:
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return {"error": str(e)}
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try:
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response = await llm_call_async(
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url, model,
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[{"role": "user", "content": message}],
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headers=headers,
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timeout=AI_CHAT_TIMEOUT,
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)
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# Truncate very long responses
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if len(response) > 10000:
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response = response[:10000] + "\n... (truncated)"
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return {"model": model, "response": response}
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except Exception as e:
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logger.error(f"chat_with_model failed: {e}")
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return {"error": f"Failed to get response from {model_spec}: {e}"}
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_TEACHER_SYSTEM_PROMPT = (
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"You are a senior AI mentor. A less capable model is stuck on a problem and asking for help. "
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"Provide clear, actionable guidance:\n"
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"1. Brief analysis of the problem\n"
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"2. Recommended approach (step by step)\n"
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"3. Key things to watch out for\n\n"
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"Be concise and practical. No preamble."
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)
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async def do_ask_teacher(content: str, session_id: Optional[str] = None, owner: Optional[str] = None) -> Dict:
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"""Ask a more capable model for help.
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Content format:
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Line 1: model_name (or 'auto')
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Line 2+: the problem description
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"""
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from src.llm_core import llm_call_async
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from src.settings import get_setting
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lines = content.strip().split("\n", 1)
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model_spec = lines[0].strip() if lines else "auto"
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problem = lines[1].strip() if len(lines) > 1 else ""
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if not problem:
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return {"error": "No problem description provided"}
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if model_spec.lower() in ("auto", ""):
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model_spec = get_setting("teacher_model", "")
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if not model_spec:
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return {"error": "No teacher model configured. Specify a model name or set teacher_model in settings."}
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try:
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url, model, headers = _resolve_model(model_spec, owner=owner)
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except ValueError as e:
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return {"error": str(e)}
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try:
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response = await llm_call_async(
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url, model,
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[
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{"role": "system", "content": _TEACHER_SYSTEM_PROMPT},
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{"role": "user", "content": f"Problem:\n{problem}"},
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],
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headers=headers,
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timeout=AI_CHAT_TIMEOUT,
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)
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if len(response) > 8000:
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response = response[:8000] + "\n... (truncated)"
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return {"model": model, "response": response, "teacher": True}
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except Exception as e:
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logger.error(f"ask_teacher failed: {e}")
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return {"error": f"Teacher call failed ({model_spec}): {e}"}
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async def do_second_opinion(content: str, session_id: Optional[str] = None, owner: Optional[str] = None) -> Dict:
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"""Get a second opinion from another model, then have the original model
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evaluate the feedback and produce a unified version.
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Content format:
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Line 1: model_name (or model_name@endpoint_name)
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Line 2+ (optional): specific question or focus area
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Flow:
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1. Pull recent conversation context
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2. Send to reviewer model → get honest feedback
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3. Send feedback back to the session's own model → evaluate & unify
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4. Return both the review and the unified response
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"""
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from src.llm_core import llm_call_async
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lines = content.strip().split("\n", 1)
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if not lines or not lines[0].strip():
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return {"error": "First line must be the model name"}
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model_spec = lines[0].strip()
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focus = lines[1].strip() if len(lines) > 1 else ""
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try:
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reviewer_url, reviewer_model, reviewer_headers = _resolve_model(model_spec, owner=owner)
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except ValueError as e:
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return {"error": str(e)}
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# Pull recent conversation context from current session
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context_text = ""
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sess = None
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if session_id and _session_manager:
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sess = _session_manager.get_session(session_id)
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if sess:
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messages = sess.get_context_messages()
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recent = messages[-15:] if len(messages) > 15 else messages
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parts = []
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for m in recent:
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role = m.get("role", "unknown").upper()
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text = m.get("content", "")
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if isinstance(text, list):
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text = " ".join(
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p.get("text", "") for p in text if isinstance(p, dict)
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)
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if text:
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parts.append(f"[{role}]: {text[:2000]}")
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context_text = "\n\n".join(parts)
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if not context_text:
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return {"error": "No conversation context found to review"}
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# ── Step 1: Get the reviewer's feedback ──
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reviewer_system = (
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"You are giving a second opinion on a conversation between a user and an AI assistant. "
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"Your job is to be genuinely helpful and honest — not a yes-man, but not a contrarian either.\n\n"
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"Guidelines:\n"
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"- If the plan/idea is solid, say so clearly. Don't manufacture problems that aren't there.\n"
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"- If you spot a real flaw, blind spot, or simpler approach — call it out directly.\n"
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"- Be practical. Don't over-engineer or over-analyze. Real-world tradeoffs matter.\n"
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"- If there's a meaningfully better way to do something, suggest it concretely.\n"
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"- Give credit where it's due — highlight what's working well.\n"
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"- Keep it concise and actionable. No fluff.\n"
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"- You're a second pair of eyes, not a professor grading a paper."
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)
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reviewer_message = f"Here's the conversation so far:\n\n{context_text}"
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if focus:
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reviewer_message += f"\n\n---\nSpecifically, I want your take on: {focus}"
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else:
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reviewer_message += "\n\n---\nGive me your honest second opinion on what's being discussed."
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try:
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review = await llm_call_async(
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reviewer_url, reviewer_model,
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[
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{"role": "system", "content": reviewer_system},
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{"role": "user", "content": reviewer_message},
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],
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headers=reviewer_headers,
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timeout=AI_CHAT_TIMEOUT,
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)
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if len(review) > 8000:
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review = review[:8000] + "\n... (truncated)"
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except Exception as e:
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logger.error(f"second_opinion reviewer call failed: {e}")
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return {"error": f"Failed to get second opinion from {model_spec}: {e}"}
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# ── Step 2: Send review back to session's own model for evaluation ──
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unified = ""
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original_model = "unknown"
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if sess:
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original_url = sess.endpoint_url
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original_model = sess.model
|
||||
original_headers = getattr(sess, "headers", None) or {}
|
||||
|
||||
unify_system = (
|
||||
"Another AI model just reviewed the conversation you've been having with the user. "
|
||||
"Read their feedback carefully, then respond with:\n\n"
|
||||
"1. **What you agree with** — acknowledge valid points honestly.\n"
|
||||
"2. **What you disagree with** — explain why, briefly.\n"
|
||||
"3. **Unified version** — produce an updated/refined version of whatever was being discussed, "
|
||||
"incorporating the feedback you found valid. Don't accept every note blindly — "
|
||||
"use your judgment on what actually improves things vs what's unnecessary.\n\n"
|
||||
"Be concise and practical. The user wants a better result, not a meta-discussion."
|
||||
)
|
||||
|
||||
unify_message = (
|
||||
f"Here's the conversation context:\n\n{context_text}\n\n"
|
||||
f"---\n\n"
|
||||
f"**Review from {reviewer_model}:**\n\n{review}\n\n"
|
||||
f"---\n\n"
|
||||
f"Evaluate this feedback and produce a unified improved version."
|
||||
)
|
||||
|
||||
try:
|
||||
unified = await llm_call_async(
|
||||
original_url, original_model,
|
||||
[
|
||||
{"role": "system", "content": unify_system},
|
||||
{"role": "user", "content": unify_message},
|
||||
],
|
||||
headers=original_headers,
|
||||
timeout=AI_CHAT_TIMEOUT,
|
||||
)
|
||||
if len(unified) > 10000:
|
||||
unified = unified[:10000] + "\n... (truncated)"
|
||||
except Exception as e:
|
||||
logger.error(f"second_opinion unify call failed: {e}")
|
||||
unified = f"(Failed to get unified response: {e})"
|
||||
|
||||
# Build combined result
|
||||
combined = (
|
||||
f"## Second Opinion from {reviewer_model}\n\n{review}"
|
||||
f"\n\n---\n\n"
|
||||
f"## {original_model}'s Response\n\n{unified}"
|
||||
)
|
||||
|
||||
return {
|
||||
"model": reviewer_model,
|
||||
"response": combined,
|
||||
"instruction": "Present these results to the user exactly as they are. Do NOT call second_opinion again. The user can continue the conversation from here.",
|
||||
}
|
||||
|
||||
|
||||
async def do_create_session(content: str, session_id: Optional[str] = None, owner: Optional[str] = None) -> Dict:
|
||||
@@ -1104,83 +872,6 @@ async def do_manage_memory(content: str, session_id: Optional[str] = None, owner
|
||||
return {"error": f"Unknown action '{action}'. Use: list, add, edit, delete, search"}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# List models tool
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
async def do_list_models(content: str, session_id: Optional[str] = None, owner: Optional[str] = None) -> Dict:
|
||||
"""List all available models across configured endpoints.
|
||||
|
||||
Content = optional filter keyword.
|
||||
"""
|
||||
import httpx
|
||||
from src.database import SessionLocal, ModelEndpoint
|
||||
from src.llm_core import _detect_provider, ANTHROPIC_MODELS
|
||||
from src.auth_helpers import owner_filter
|
||||
|
||||
keyword = content.strip().lower() if content.strip() else None
|
||||
|
||||
db = SessionLocal()
|
||||
try:
|
||||
query = db.query(ModelEndpoint).filter(ModelEndpoint.is_enabled == True)
|
||||
if owner:
|
||||
query = owner_filter(query, ModelEndpoint, owner)
|
||||
endpoints = query.all()
|
||||
if not endpoints:
|
||||
return {"results": "No enabled model endpoints configured."}
|
||||
|
||||
result_lines = []
|
||||
total_models = 0
|
||||
|
||||
for ep in endpoints:
|
||||
try:
|
||||
base, api_key = resolve_endpoint_runtime(ep, owner=owner)
|
||||
except Exception:
|
||||
continue
|
||||
provider = _detect_provider(base)
|
||||
headers = build_headers(api_key, base)
|
||||
|
||||
model_ids = []
|
||||
if provider == "anthropic":
|
||||
model_ids = list(ANTHROPIC_MODELS)
|
||||
else:
|
||||
try:
|
||||
models_url = build_models_url(base)
|
||||
if models_url:
|
||||
r = httpx.get(models_url, headers=headers, timeout=5)
|
||||
r.raise_for_status()
|
||||
data = r.json()
|
||||
model_ids = [m.get("id") for m in (data.get("data") or []) if m.get("id")]
|
||||
if not model_ids:
|
||||
model_ids = [
|
||||
m.get("name") or m.get("model")
|
||||
for m in (data.get("models") or [])
|
||||
if m.get("name") or m.get("model")
|
||||
]
|
||||
else:
|
||||
model_ids = json.loads(ep.cached_models or "[]")
|
||||
except Exception:
|
||||
model_ids = ["(endpoint offline)"]
|
||||
|
||||
if keyword:
|
||||
model_ids = [m for m in model_ids if keyword in m.lower() or keyword in (ep.name or "").lower()]
|
||||
|
||||
if model_ids:
|
||||
result_lines.append(f"\n**{ep.name or base}** ({provider}):")
|
||||
for mid in model_ids:
|
||||
result_lines.append(f" - `{mid}`")
|
||||
total_models += 1
|
||||
|
||||
if not result_lines:
|
||||
return {"results": "No models found" + (f" matching '{keyword}'" if keyword else "") + "."}
|
||||
|
||||
header = f"Available models ({total_models} total):"
|
||||
return {"results": header + "\n".join(result_lines)}
|
||||
except Exception as e:
|
||||
logger.error(f"list_models failed: {e}")
|
||||
return {"error": str(e)}
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
@@ -1831,12 +1522,7 @@ async def dispatch_ai_tool(
|
||||
) -> Tuple[str, Dict]:
|
||||
"""Dispatch an AI interaction tool. Returns (description, result_dict)."""
|
||||
|
||||
if tool == "chat_with_model":
|
||||
model_spec = content.split("\n")[0].strip()[:60]
|
||||
desc = f"chat_with_model: {model_spec}"
|
||||
result = await do_chat_with_model(content, session_id, owner=owner)
|
||||
|
||||
elif tool == "create_session":
|
||||
if tool == "create_session":
|
||||
name = content.split("\n")[0].strip()[:60]
|
||||
desc = f"create_session: {name}"
|
||||
result = await do_create_session(content, session_id, owner=owner)
|
||||
@@ -1865,21 +1551,11 @@ async def dispatch_ai_tool(
|
||||
desc = f"manage_memory: {action}"
|
||||
result = await do_manage_memory(content, session_id, owner=owner)
|
||||
|
||||
elif tool == "list_models":
|
||||
keyword = content.strip()[:40]
|
||||
desc = f"list_models{': ' + keyword if keyword else ''}"
|
||||
result = await do_list_models(content, session_id, owner=owner)
|
||||
|
||||
elif tool == "ui_control":
|
||||
action = content.split("\n")[0].strip()[:60]
|
||||
desc = f"ui_control: {action}"
|
||||
result = await do_ui_control(content, session_id, owner=owner)
|
||||
|
||||
elif tool == "ask_teacher":
|
||||
problem = content.split("\n", 1)[-1].strip()[:60]
|
||||
desc = f"ask_teacher: {problem}"
|
||||
result = await do_ask_teacher(content, session_id, owner=owner)
|
||||
|
||||
else:
|
||||
desc = f"unknown ai tool: {tool}"
|
||||
result = {"error": f"Unknown AI interaction tool: {tool}"}
|
||||
|
||||
+12
-3
@@ -766,10 +766,19 @@ async def _execute_tool_block_impl(
|
||||
query = content.split("\n")[0].strip()
|
||||
desc = f"search_chats: {query[:80]}"
|
||||
result = await do_search_chats(query, owner=owner)
|
||||
elif tool in ("chat_with_model", "create_session", "list_sessions",
|
||||
elif tool in ("chat_with_model", "ask_teacher", "list_models"):
|
||||
# Migrated to the agent_tools registry (#3629): dispatched through
|
||||
# TOOL_HANDLERS with the owner/session ctx these tools need, instead
|
||||
# of the legacy dispatch_ai_tool elif. The do_* impls stay in
|
||||
# ai_interaction.py (dispatch_ai_tool + the owner-scope test use them).
|
||||
first_line = content.split(chr(10))[0].strip()[:60]
|
||||
desc = f"{tool}: {first_line}" if first_line else tool
|
||||
result = await _document_tool_dispatch(tool, content, session_id, owner) \
|
||||
or {"error": f"{tool}: execution failed", "exit_code": 1}
|
||||
elif tool in ("create_session", "list_sessions",
|
||||
"send_to_session", "pipeline",
|
||||
"manage_session", "manage_memory", "list_models",
|
||||
"ui_control", "ask_teacher"):
|
||||
"manage_session", "manage_memory",
|
||||
"ui_control"):
|
||||
from src.ai_interaction import dispatch_ai_tool
|
||||
desc, result = await dispatch_ai_tool(tool, content, session_id, owner=owner)
|
||||
elif tool == "manage_tasks":
|
||||
|
||||
Reference in New Issue
Block a user