606 lines
22 KiB
Python
606 lines
22 KiB
Python
"""xAI subscription provider with capability-gated hosted X Search."""
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from __future__ import annotations
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import asyncio
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import base64
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import json
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import re
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import time
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import uuid
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from collections.abc import Awaitable, Callable
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from typing import Any
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import httpx
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from loguru import logger
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from nanobot import __version__
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from nanobot.providers.base import (
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LLMProvider,
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LLMResponse,
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ToolCallRequest,
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resolve_stream_idle_timeout_s,
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)
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from nanobot.providers.openai_responses import (
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consume_sse_with_reasoning,
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convert_messages,
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convert_tools,
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)
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from nanobot.providers.xai_oauth import (
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XAI_CLIENT_VERSION,
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XAIToken,
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get_xai_oauth_token,
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)
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DEFAULT_XAI_GROK_URL = "https://cli-chat-proxy.grok.com/v1/responses"
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DEFAULT_XAI_GROK_MODELS_URL = "https://cli-chat-proxy.grok.com/v1/models"
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DEFAULT_XAI_GROK_MODEL = "xai-grok/grok-4.5"
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_MODEL_CAPABILITIES_TTL_S = 5 * 60
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_MAX_ERROR_BODY_CHARS = 1000
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_SENSITIVE_ERROR_KEYS = {
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"accesstoken",
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"apikey",
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"authorization",
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"idtoken",
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"refreshtoken",
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}
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class XAIGrokProvider(LLMProvider):
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"""Call xAI's subscription proxy and expose supported hosted tools."""
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supports_progress_deltas = True
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def __init__(
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self,
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default_model: str = DEFAULT_XAI_GROK_MODEL,
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proxy: str | None = None,
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extra_body: dict[str, Any] | None = None,
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):
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super().__init__(api_key=None, api_base=None)
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self.default_model = default_model
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self.proxy = proxy or None
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self._extra_body = dict(extra_body or {})
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self._model_capabilities: dict[str, bool] | None = None
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self._model_capabilities_fetched_at = 0.0
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async def _supports_backend_search(self, token: XAIToken, model: str) -> bool:
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now = time.monotonic()
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capabilities = self._model_capabilities
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if (
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capabilities is None
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or now - self._model_capabilities_fetched_at >= _MODEL_CAPABILITIES_TTL_S
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):
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try:
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capabilities = await _fetch_xai_model_capabilities(
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DEFAULT_XAI_GROK_MODELS_URL,
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_build_model_headers(token),
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proxy=self.proxy,
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)
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except Exception as exc:
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logger.warning(
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"xAI model capability lookup failed; hosted X Search disabled for model {}: "
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"type={} error={}",
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model,
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type(exc).__name__,
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str(exc).strip() or "unexpected error",
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)
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capabilities = {}
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self._model_capabilities = capabilities
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self._model_capabilities_fetched_at = now
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else:
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self._model_capabilities = capabilities
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self._model_capabilities_fetched_at = now
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return capabilities.get(model, False)
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async def _call_xai(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None,
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model: str | None,
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max_tokens: int,
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temperature: float,
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reasoning_effort: str | None,
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tool_choice: str | dict[str, Any] | None,
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on_content_delta: Callable[[str], Awaitable[None]] | None = None,
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on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
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on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
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) -> LLMResponse:
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wire_model = _strip_model_prefix(model or self.default_model)
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system_prompt, input_items = convert_messages(messages)
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stage = "oauth_token"
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try:
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token = await asyncio.to_thread(get_xai_oauth_token, proxy=self.proxy)
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stage = "model_capabilities"
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supports_backend_search = await self._supports_backend_search(token, wire_model)
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converted_tools = convert_tools(tools or [])
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if supports_backend_search:
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converted_tools = [
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tool for tool in converted_tools if tool.get("name") != "x_search"
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]
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converted_tools.append({"type": "x_search"})
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body: dict[str, Any] = {
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"model": wire_model,
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"store": False,
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"stream": True,
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"instructions": system_prompt,
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"input": input_items,
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"include": ["reasoning.encrypted_content"],
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"tools": converted_tools,
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"tool_choice": tool_choice or "auto",
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"parallel_tool_calls": True,
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"stream_tool_calls": True,
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"max_output_tokens": max_tokens,
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"temperature": temperature,
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"reasoning": _build_reasoning_options(reasoning_effort),
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}
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if self._extra_body:
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body.update(self._extra_body)
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headers = _build_headers(token.access, wire_model)
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stage = "xai_request"
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try:
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result = await _request_xai(
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DEFAULT_XAI_GROK_URL,
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headers,
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body,
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proxy=self.proxy,
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on_content_delta=on_content_delta,
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on_thinking_delta=on_thinking_delta,
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on_tool_call_delta=on_tool_call_delta,
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)
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except _XAIHTTPError as exc:
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if exc.status_code != 401:
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raise
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stage = "oauth_refresh"
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token = await asyncio.to_thread(
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get_xai_oauth_token,
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proxy=self.proxy,
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force_refresh=True,
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)
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self._model_capabilities = None
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self._model_capabilities_fetched_at = 0.0
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headers = _build_headers(token.access, wire_model)
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stage = "xai_request_retry"
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result = await _request_xai(
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DEFAULT_XAI_GROK_URL,
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headers,
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body,
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proxy=self.proxy,
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on_content_delta=on_content_delta,
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on_thinking_delta=on_thinking_delta,
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on_tool_call_delta=on_tool_call_delta,
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)
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content, tool_calls, finish_reason, usage, reasoning_content = result
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return LLMResponse(
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content=content,
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tool_calls=tool_calls,
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finish_reason=finish_reason,
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usage=usage,
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reasoning_content=reasoning_content,
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)
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except Exception as exc:
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response = _xai_error_response(exc)
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logger.warning(
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"xAI subscription request failed: stage={} type={} retryable={} status={} "
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"error_type={} error_code={} response_body={}",
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stage,
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type(exc).__name__,
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response.error_should_retry,
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response.error_status_code,
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response.error_type,
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response.error_code,
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getattr(exc, "response_body", None),
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)
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return response
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async def chat(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int = 4096,
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temperature: float = 0.7,
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reasoning_effort: str | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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) -> LLMResponse:
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return await self._call_xai(
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messages, tools, model, max_tokens, temperature, reasoning_effort, tool_choice
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)
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async def chat_stream(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int = 4096,
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temperature: float = 0.7,
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reasoning_effort: str | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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on_content_delta: Callable[[str], Awaitable[None]] | None = None,
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on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
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on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
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) -> LLMResponse:
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return await self._call_xai(
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messages,
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tools,
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model,
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max_tokens,
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temperature,
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reasoning_effort,
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tool_choice,
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on_content_delta,
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on_thinking_delta,
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on_tool_call_delta,
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)
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def get_default_model(self) -> str:
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return self.default_model
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def _strip_model_prefix(model: str) -> str:
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if model.startswith("xai-grok/") or model.startswith("xai_grok/"):
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return model.split("/", 1)[1]
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return model
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def _build_reasoning_options(reasoning_effort: str | None) -> dict[str, str]:
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options = {"summary": "concise"}
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if reasoning_effort and reasoning_effort.lower() != "none":
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options["effort"] = reasoning_effort
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return options
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def _build_headers(token: str, model: str) -> dict[str, str]:
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conversation_id = str(uuid.uuid4())
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return {
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"Authorization": f"Bearer {token}",
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"X-XAI-Token-Auth": "xai-grok-cli",
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"x-authenticateresponse": "authenticate-response",
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"x-grok-client-version": XAI_CLIENT_VERSION,
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"x-grok-client-identifier": "nanobot",
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"x-grok-client-mode": "headless",
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"x-grok-conv-id": conversation_id,
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"x-grok-req-id": str(uuid.uuid4()),
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"x-grok-model-override": model,
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"x-grok-session-id": conversation_id,
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"x-grok-agent-id": str(uuid.uuid4()),
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"User-Agent": f"nanobot/{__version__} (python)",
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"accept": "text/event-stream",
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"content-type": "application/json",
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}
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def _build_model_headers(token: XAIToken) -> dict[str, str]:
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headers = {
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"Authorization": f"Bearer {token.access}",
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"X-XAI-Token-Auth": "xai-grok-cli",
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"x-grok-client-version": XAI_CLIENT_VERSION,
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"x-grok-client-identifier": "nanobot",
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"x-grok-client-mode": "headless",
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"User-Agent": f"nanobot/{__version__} (python)",
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"accept": "application/json",
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}
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claims = _decode_access_token_claims(token.access)
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user_id = claims.get("sub")
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if claims.get("principal_type") == "Team":
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user_id = claims.get("principal_id") or user_id
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if isinstance(user_id, str) and user_id:
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headers["x-userid"] = user_id
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email = claims.get("email")
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if not isinstance(email, str) or "@" not in email:
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email = token.account_id if token.account_id and "@" in token.account_id else None
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if email:
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headers["x-email"] = email
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return headers
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def _decode_access_token_claims(token: str) -> dict[str, Any]:
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"""Read identity hints from the signed token; the server still authenticates it."""
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parts = token.split(".")
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if len(parts) < 2 or not parts[1]:
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return {}
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payload = parts[1]
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try:
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decoded = base64.urlsafe_b64decode(payload + "=" * (-len(payload) % 4))
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claims = json.loads(decoded)
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except (ValueError, TypeError):
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return {}
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return claims if isinstance(claims, dict) else {}
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class _XAIHTTPError(RuntimeError):
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def __init__(
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self,
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message: str,
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*,
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status_code: int,
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retry_after: float | None = None,
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error_type: str | None = None,
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error_code: str | None = None,
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should_retry: bool | None = None,
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response_body: str | None = None,
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):
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super().__init__(message)
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self.status_code = status_code
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self.retry_after = retry_after
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self.error_type = error_type
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self.error_code = error_code
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self.should_retry = should_retry
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self.response_body = response_body
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async def _fetch_xai_model_capabilities(
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url: str,
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headers: dict[str, str],
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*,
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proxy: str | None = None,
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) -> dict[str, bool]:
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client_kwargs: dict[str, Any] = {"timeout": 10.0, "follow_redirects": False}
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if proxy:
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client_kwargs.update(proxy=proxy, trust_env=False)
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async with httpx.AsyncClient(**client_kwargs) as client:
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response = await client.get(url, headers=headers)
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if response.status_code != 200:
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raw = response.content.decode("utf-8", "ignore")
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raise _build_xai_http_error(response.status_code, response.headers, raw)
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try:
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payload = response.json()
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except ValueError as exc:
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raise RuntimeError("xAI model catalog returned invalid JSON.") from exc
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return _parse_xai_model_capabilities(payload)
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def _parse_xai_model_capabilities(payload: Any) -> dict[str, bool]:
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if isinstance(payload, dict):
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rows = payload.get("data")
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if not isinstance(rows, list):
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rows = payload.get("models")
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else:
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rows = payload
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if not isinstance(rows, list):
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return {}
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capabilities: dict[str, bool] = {}
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for row in rows:
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if not isinstance(row, dict):
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continue
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meta = row.get("_meta") if isinstance(row.get("_meta"), dict) else {}
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support_value = row.get("supportsBackendSearch")
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if not isinstance(support_value, bool):
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support_value = row.get("supports_backend_search")
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if not isinstance(support_value, bool):
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support_value = meta.get("supportsBackendSearch")
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if not isinstance(support_value, bool):
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support_value = meta.get("supports_backend_search")
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supports_backend_search = support_value if isinstance(support_value, bool) else False
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identifiers = (
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row.get("model"),
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row.get("modelId"),
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row.get("id"),
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meta.get("model"),
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meta.get("modelId"),
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)
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for identifier in identifiers:
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if isinstance(identifier, str) and identifier.strip():
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capabilities[_strip_model_prefix(identifier.strip())] = supports_backend_search
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return capabilities
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async def _request_xai(
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url: str,
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headers: dict[str, str],
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body: dict[str, Any],
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*,
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proxy: str | None = None,
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on_content_delta: Callable[[str], Awaitable[None]] | None = None,
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on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
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on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
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) -> tuple[str, list[ToolCallRequest], str, dict[str, int], str | None]:
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async def _on_response_event(event: dict[str, Any]) -> None:
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hosted_event = _xai_hosted_tool_event(event)
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if hosted_event is not None and on_tool_call_delta is not None:
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await on_tool_call_delta(hosted_event)
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client_kwargs: dict[str, Any] = {"timeout": resolve_stream_idle_timeout_s()}
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if proxy:
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client_kwargs.update(proxy=proxy, trust_env=False)
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async with httpx.AsyncClient(**client_kwargs) as client:
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async with client.stream("POST", url, headers=headers, json=body) as response:
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if response.status_code != 200:
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content = await response.aread()
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raw = content.decode("utf-8", "ignore")
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raise _build_xai_http_error(response.status_code, response.headers, raw)
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return await consume_sse_with_reasoning(
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response,
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on_content_delta=on_content_delta,
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on_tool_call_delta=on_tool_call_delta,
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on_reasoning_delta=on_thinking_delta,
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on_response_event=_on_response_event if on_tool_call_delta else None,
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)
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def _xai_hosted_tool_event(event: dict[str, Any]) -> dict[str, Any] | None:
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event_type = event.get("type")
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if event_type == "response.custom_tool_call_input.done":
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call_id = event.get("item_id") or event.get("call_id") or event.get("id")
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if not call_id:
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return None
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return {
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"kind": "hosted_tool",
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"phase": "start",
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"call_id": str(call_id),
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"name": "x_search",
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"arguments": _xai_hosted_tool_arguments(
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event.get("input", event.get("arguments"))
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),
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"result": None,
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}
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if event_type != "response.output_item.done":
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return None
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item = event.get("item")
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if not isinstance(item, dict) or item.get("type") != "custom_tool_call":
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return None
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tool_name = item.get("name")
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if not isinstance(tool_name, str) or not tool_name.startswith("x_"):
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return None
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call_id = item.get("id") or item.get("call_id") or event.get("item_id")
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if not call_id:
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return None
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return {
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"kind": "hosted_tool",
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"phase": "end",
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"call_id": str(call_id),
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"name": "x_search",
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"arguments": _xai_hosted_tool_arguments(
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item.get("input", item.get("arguments"))
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),
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# Keep the useful search subtype, but do not persist large hosted results
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# in WebUI activity messages. The model answer already carries citations.
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"result": {"name": tool_name},
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}
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def _xai_hosted_tool_arguments(value: Any) -> dict[str, Any]:
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if isinstance(value, dict):
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return dict(value)
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if not isinstance(value, str) or not value.strip():
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return {}
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try:
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parsed = json.loads(value)
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except (TypeError, ValueError):
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return {}
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return parsed if isinstance(parsed, dict) else {}
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def _build_xai_http_error(
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status_code: int,
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headers: httpx.Headers,
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raw: str,
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) -> _XAIHTTPError:
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retry_after = LLMProvider._extract_retry_after_from_headers(headers)
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error_type, error_code = LLMProvider._extract_error_type_code(raw)
|
|
response_body = _bounded_error_body(raw)
|
|
return _XAIHTTPError(
|
|
_friendly_error(status_code, response_body),
|
|
status_code=status_code,
|
|
retry_after=retry_after,
|
|
error_type=error_type,
|
|
error_code=error_code,
|
|
should_retry=_should_retry_status(status_code, error_type, error_code, raw),
|
|
response_body=response_body,
|
|
)
|
|
|
|
|
|
def _bounded_error_body(raw: str) -> str | None:
|
|
text = raw.strip()
|
|
if not text:
|
|
return None
|
|
|
|
try:
|
|
payload = json.loads(text)
|
|
except (TypeError, ValueError):
|
|
pass
|
|
else:
|
|
text = json.dumps(
|
|
_redact_error_payload(payload),
|
|
ensure_ascii=False,
|
|
separators=(",", ":"),
|
|
)
|
|
|
|
text = re.sub(r"(?i)(bearer\s+)[a-z0-9._~+/=-]+", r"\1[REDACTED]", text)
|
|
text = " ".join(text.split())
|
|
if len(text) > _MAX_ERROR_BODY_CHARS:
|
|
return f"{text[:_MAX_ERROR_BODY_CHARS]}…"
|
|
return text
|
|
|
|
|
|
def _redact_error_payload(payload: Any) -> Any:
|
|
if isinstance(payload, dict):
|
|
return {
|
|
key: "[REDACTED]" if _is_sensitive_error_key(key) else _redact_error_payload(value)
|
|
for key, value in payload.items()
|
|
}
|
|
if isinstance(payload, list):
|
|
return [_redact_error_payload(value) for value in payload]
|
|
return payload
|
|
|
|
|
|
def _is_sensitive_error_key(key: str) -> bool:
|
|
normalized = re.sub(r"[^a-z0-9]", "", key.casefold())
|
|
return normalized in _SENSITIVE_ERROR_KEYS
|
|
|
|
|
|
def _friendly_error(status_code: int, response_body: str | None = None) -> str:
|
|
if status_code == 401:
|
|
message = "xAI rejected the login. Sign in again with `nanobot provider login xai-grok`."
|
|
elif status_code == 403:
|
|
message = "This xAI account or subscription cannot access the Grok subscription endpoint."
|
|
elif status_code == 426:
|
|
message = "xAI requires a newer Grok client version. Update nanobot and try again."
|
|
elif status_code == 429:
|
|
message = "xAI usage quota or rate limit reached. Please try again later."
|
|
else:
|
|
message = f"xAI subscription endpoint returned HTTP {status_code}."
|
|
if response_body:
|
|
return f"{message} Response body: {response_body}"
|
|
return message
|
|
|
|
|
|
def _xai_error_response(exc: Exception) -> LLMResponse:
|
|
status_code = getattr(exc, "status_code", None)
|
|
should_retry = getattr(exc, "should_retry", None)
|
|
error_kind: str | None = None
|
|
if isinstance(exc, (httpx.TimeoutException, asyncio.TimeoutError)):
|
|
error_kind = "timeout"
|
|
should_retry = True if should_retry is None else should_retry
|
|
elif isinstance(exc, (httpx.NetworkError, httpx.TransportError)):
|
|
error_kind = "connection"
|
|
should_retry = True if should_retry is None else should_retry
|
|
elif isinstance(exc, _XAIHTTPError):
|
|
error_kind = "http"
|
|
if status_code is not None and should_retry is None:
|
|
should_retry = _should_retry_status(
|
|
int(status_code),
|
|
getattr(exc, "error_type", None),
|
|
getattr(exc, "error_code", None),
|
|
None,
|
|
)
|
|
message = str(exc).strip() or "unexpected error"
|
|
retry_after = getattr(exc, "retry_after", None)
|
|
return LLMResponse(
|
|
content=f"Error calling xAI ({type(exc).__name__}): {message}",
|
|
finish_reason="error",
|
|
retry_after=retry_after,
|
|
error_status_code=int(status_code) if status_code is not None else None,
|
|
error_kind=error_kind,
|
|
error_type=getattr(exc, "error_type", None),
|
|
error_code=getattr(exc, "error_code", None),
|
|
error_retry_after_s=retry_after,
|
|
error_should_retry=should_retry,
|
|
)
|
|
|
|
|
|
def _should_retry_status(
|
|
status_code: int,
|
|
error_type: str | None,
|
|
error_code: str | None,
|
|
content: str | None,
|
|
) -> bool:
|
|
if status_code == 429:
|
|
return LLMProvider._is_retryable_429_response(
|
|
LLMResponse(
|
|
content=content or "",
|
|
finish_reason="error",
|
|
error_status_code=status_code,
|
|
error_type=error_type,
|
|
error_code=error_code,
|
|
)
|
|
)
|
|
return status_code in LLMProvider._RETRYABLE_STATUS_CODES or status_code >= 500
|