#!/usr/bin/env python3 """X Search tool backed by xAI's built-in ``x_search`` Responses API tool. Registers when either xAI credential path is available (``XAI_API_KEY`` or ``hermes auth add xai-oauth``). At call time an explicit ``XAI_API_KEY`` wins (``prefer_api_key=True``): x_search is API-metered and the subscription OAuth bearer answers ``/v1/responses`` in a degraded no-citation mode (#88040). Defensive output: ``from_date``/``to_date`` are validated client-side (strict ``YYYY-MM-DD``, ``from <= to``, ``from`` not in the future) so malformed windows fail fast instead of burning a billable call. Successful responses carry ``degraded``/``degraded_reason``: True when a narrowing filter was active AND xAI returned no citations in either channel, meaning the answer came from the model's own knowledge rather than the X index. Salvaged from PR #10786 (originally by @Jaaneek). """ from __future__ import annotations import json import logging import time from datetime import date, datetime, timezone from typing import Any, Dict, List, Optional, Tuple import requests from tools.registry import registry, tool_error from tools.xai_http import DEFAULT_XAI_BASE_URL, hermes_xai_user_agent, resolve_xai_http_credentials logger = logging.getLogger(__name__) DEFAULT_X_SEARCH_MODEL = "grok-4.5" DEFAULT_X_SEARCH_TIMEOUT_SECONDS = 180 DEFAULT_X_SEARCH_RETRIES = 2 X_SEARCH_REASONING_EFFORTS = ("low", "medium", "high", "xhigh") MAX_HANDLES = 10 # --------------------------------------------------------------------------- # Config # --------------------------------------------------------------------------- def _load_x_search_config() -> Dict[str, Any]: try: from hermes_cli.config import load_config return load_config().get("x_search", {}) or {} except Exception: return {} def _get_x_search_model() -> str: return str(_load_x_search_config().get("model") or "").strip() or DEFAULT_X_SEARCH_MODEL def _get_x_search_reasoning_effort() -> Optional[str]: raw_value = _load_x_search_config().get("reasoning_effort") if raw_value is None or not str(raw_value).strip(): return None effort = str(raw_value).strip().lower() if effort not in X_SEARCH_REASONING_EFFORTS: allowed = ", ".join(X_SEARCH_REASONING_EFFORTS) raise ValueError( f"x_search.reasoning_effort must be one of: {allowed} " f"(got {raw_value!r})" ) return effort def _get_x_search_int(key: str, default: int, floor: int) -> int: raw_value = _load_x_search_config().get(key, default) try: return max(floor, int(raw_value)) except Exception: return default def _get_x_search_timeout_seconds() -> int: return _get_x_search_int("timeout_seconds", DEFAULT_X_SEARCH_TIMEOUT_SECONDS, 30) def _get_x_search_retries() -> int: return _get_x_search_int("retries", DEFAULT_X_SEARCH_RETRIES, 0) # --------------------------------------------------------------------------- # Credential resolution # --------------------------------------------------------------------------- def _resolve_xai_bearer() -> Tuple[str, str, str]: """Return ``(api_key, base_url, source)``; ``source`` is ``"xai-oauth"`` or ``"xai"``. Raises ``RuntimeError`` when no credential is usable so a credential that expires between registration and invocation yields a clean tool error, not a 401. ``prefer_api_key=True``: see module docstring (#88040). """ creds = resolve_xai_http_credentials(prefer_api_key=True) api_key = str(creds.get("api_key") or "").strip() if not api_key: raise RuntimeError( "No xAI credentials available. Run `hermes auth add xai-oauth` " "to sign in with your SuperGrok subscription, or set XAI_API_KEY." ) base_url = str(creds.get("base_url") or DEFAULT_XAI_BASE_URL).strip().rstrip("/") source = str(creds.get("provider") or "xai") return api_key, base_url, source def check_x_search_requirements() -> bool: """True when xAI credentials resolve to a non-empty bearer (OAuth auto-refreshed).""" try: creds = resolve_xai_http_credentials() return bool(str(creds.get("api_key") or "").strip()) except Exception: return False # --------------------------------------------------------------------------- # Helpers # --------------------------------------------------------------------------- def _normalize_handles(handles: Optional[List[str]], field_name: str) -> List[str]: cleaned = [h for h in (str(handle or "").strip().lstrip("@") for handle in handles or []) if h] if len(cleaned) > MAX_HANDLES: raise ValueError(f"{field_name} supports at most {MAX_HANDLES} handles") return cleaned def _parse_iso_date(value: str, field_name: str) -> date: """Parse a strict YYYY-MM-DD string (xAI silently accepts malformed dates and returns no citations).""" raw = value.strip() try: return datetime.strptime(raw, "%Y-%m-%d").date() except ValueError as exc: raise ValueError( f"{field_name} must be YYYY-MM-DD (got {raw!r})" ) from exc def _validate_date_range(from_date: str, to_date: str) -> None: """Both parse as YYYY-MM-DD; from <= to; from not after today UTC (to may be in the future).""" parsed_from = _parse_iso_date(from_date, "from_date") if from_date.strip() else None parsed_to = _parse_iso_date(to_date, "to_date") if to_date.strip() else None if parsed_from and parsed_to and parsed_from > parsed_to: raise ValueError( f"from_date ({parsed_from.isoformat()}) must be on or before " f"to_date ({parsed_to.isoformat()})" ) if parsed_from is not None: today_utc = datetime.now(timezone.utc).date() if parsed_from > today_utc: raise ValueError( f"from_date ({parsed_from.isoformat()}) is in the future; " f"X Search only indexes past posts (today UTC is " f"{today_utc.isoformat()})" ) def _message_contents(payload: Dict[str, Any]): for item in payload.get("output", []) or []: if item.get("type") == "message": yield from item.get("content", []) or [] def _extract_response_text(payload: Dict[str, Any]) -> str: output_text = str(payload.get("output_text") or "").strip() if output_text: return output_text parts: List[str] = [] for content in _message_contents(payload): if content.get("type") in {"output_text", "text"}: text = str(content.get("text") or "").strip() if text: parts.append(text) return "\n\n".join(parts).strip() def _extract_inline_citations(payload: Dict[str, Any]) -> List[Dict[str, Any]]: return [ { "url": annotation.get("url", ""), "title": annotation.get("title", ""), "start_index": annotation.get("start_index"), "end_index": annotation.get("end_index"), } for content in _message_contents(payload) for annotation in content.get("annotations", []) or [] if annotation.get("type") == "url_citation" ] def _http_error_message(exc: requests.HTTPError) -> str: response = getattr(exc, "response", None) if response is None: return str(exc) try: payload = response.json() except Exception: payload = None if isinstance(payload, dict): code = str(payload.get("code") or "").strip() error = str(payload.get("error") or "").strip() message = error or str(payload) if code and code not in message: message = f"{code}: {message}" return message or str(exc) text = str(getattr(response, "text", "") or "").strip() if text: return text[:500] return str(exc) def _error_json(error: str, exc: BaseException) -> str: return json.dumps( { "success": False, "provider": "xai", "tool": "x_search", "error": error, "error_type": type(exc).__name__, }, ensure_ascii=False, ) def _post_with_retries(url: str, headers: Dict[str, str], payload: Dict[str, Any]) -> requests.Response: """POST with retries on 5xx / timeout / connection errors; re-raises the last failure.""" timeout_seconds = _get_x_search_timeout_seconds() max_retries = _get_x_search_retries() response: Optional[requests.Response] = None for attempt in range(max_retries + 1): try: response = requests.post(url, headers=headers, json=payload, timeout=timeout_seconds) response.raise_for_status() break except requests.HTTPError as e: status_code = getattr(getattr(e, "response", None), "status_code", None) if status_code is None or status_code < 500 or attempt >= max_retries: raise logger.warning( "x_search upstream failure on attempt %s/%s: %s", attempt + 1, max_retries + 1, _http_error_message(e), ) time.sleep(min(5.0, 1.5 * (attempt + 1))) except (requests.ReadTimeout, requests.ConnectionError) as e: if attempt >= max_retries: raise logger.warning( "x_search transient failure on attempt %s/%s: %s", attempt + 1, max_retries + 1, e, ) time.sleep(min(5.0, 1.5 * (attempt + 1))) if response is None: raise RuntimeError("x_search request did not return a response") return response # --------------------------------------------------------------------------- # Tool implementation # --------------------------------------------------------------------------- def x_search_tool( query: str, allowed_x_handles: Optional[List[str]] = None, excluded_x_handles: Optional[List[str]] = None, from_date: str = "", to_date: str = "", enable_image_understanding: bool = False, enable_video_understanding: bool = False, ) -> str: if not query or not query.strip(): return tool_error("query is required for x_search") try: api_key, base_url, source = _resolve_xai_bearer() except RuntimeError as exc: return tool_error(str(exc)) try: allowed = _normalize_handles(allowed_x_handles, "allowed_x_handles") excluded = _normalize_handles(excluded_x_handles, "excluded_x_handles") if allowed and excluded: return tool_error("allowed_x_handles and excluded_x_handles cannot be used together") try: _validate_date_range(from_date, to_date) reasoning_effort = _get_x_search_reasoning_effort() except ValueError as exc: return tool_error(str(exc)) from_date, to_date = from_date.strip(), to_date.strip() tool_def: Dict[str, Any] = {"type": "x_search"} active_filters: List[str] = [] for key, value in ( ("allowed_x_handles", allowed), ("excluded_x_handles", excluded), ("from_date", from_date), ("to_date", to_date), ): if value: tool_def[key] = value active_filters.append(key) if enable_image_understanding: tool_def["enable_image_understanding"] = True if enable_video_understanding: tool_def["enable_video_understanding"] = True payload = { "model": _get_x_search_model(), "input": [{"role": "user", "content": query.strip()}], "tools": [tool_def], "store": False, } if reasoning_effort: payload["reasoning"] = {"effort": reasoning_effort} response = _post_with_retries( f"{base_url}/responses", { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json", "User-Agent": hermes_xai_user_agent(), }, payload, ) data = response.json() citations = list(data.get("citations") or []) inline_citations = _extract_inline_citations(data) # xAI returns 200 with a synthesized answer even when no posts match the # narrowing filters; with both citation channels empty the answer came # from training data, so flag it as degraded. degraded = bool(active_filters) and not citations and not inline_citations return json.dumps( { "success": True, "provider": "xai", "credential_source": source, "tool": "x_search", "model": payload["model"], "query": query.strip(), "answer": _extract_response_text(data), "citations": citations, "inline_citations": inline_citations, "degraded": degraded, "degraded_reason": ( f"no citations returned despite filters: {', '.join(active_filters)}" if degraded else None ), }, ensure_ascii=False, ) except requests.HTTPError as e: logger.error("x_search failed: %s", e, exc_info=True) return _error_json(_http_error_message(e), e) except requests.ReadTimeout as e: logger.error("x_search timed out: %s", e, exc_info=True) return _error_json(f"xAI x_search timed out after {_get_x_search_timeout_seconds()} seconds", e) except Exception as e: logger.error("x_search failed: %s", e, exc_info=True) return _error_json(str(e), e) X_SEARCH_SCHEMA = { "name": "x_search", "description": ( "Search X (Twitter) posts, profiles, and threads using xAI's built-in " "X Search tool. Read-only discovery only: use this for current " "discussion, reactions, or claims on public X rather than general web " "pages. Do not use it to post, reply, like, DM, upload media, delete, " "or inspect the user's authenticated X account — those require a " "separate authenticated X API surface outside this tool. Available " "when xAI credentials are configured (SuperGrok OAuth or XAI_API_KEY)." ), "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "What to look up on X.", }, "allowed_x_handles": { "type": "array", "items": {"type": "string"}, "description": "Optional list of X handles to include exclusively (max 10).", }, "excluded_x_handles": { "type": "array", "items": {"type": "string"}, "description": "Optional list of X handles to exclude (max 10).", }, "from_date": { "type": "string", "description": "Optional start date in YYYY-MM-DD format.", }, "to_date": { "type": "string", "description": "Optional end date in YYYY-MM-DD format.", }, "enable_image_understanding": { "type": "boolean", "description": "Whether xAI should analyze images attached to matching X posts.", "default": False, }, "enable_video_understanding": { "type": "boolean", "description": "Whether xAI should analyze videos attached to matching X posts.", "default": False, }, }, "required": ["query"], }, } def _handle_x_search(args, **kw): return x_search_tool( query=args.get("query", ""), allowed_x_handles=args.get("allowed_x_handles"), excluded_x_handles=args.get("excluded_x_handles"), from_date=args.get("from_date", ""), to_date=args.get("to_date", ""), enable_image_understanding=bool(args.get("enable_image_understanding", False)), enable_video_understanding=bool(args.get("enable_video_understanding", False)), ) registry.register( name="x_search", toolset="x_search", schema=X_SEARCH_SCHEMA, handler=_handle_x_search, check_fn=check_x_search_requirements, requires_env=["XAI_API_KEY"], emoji="🐦", max_result_size_chars=100_000, )