"""
OpenAI-compatible LLM provider supporting OpenAI, Groq, Ollama, LMStudio, and MiniMax.

This provider handles all OpenAI API-compatible models including:
- OpenAI: GPT-4, GPT-4o, GPT-5, o1, o3 (reasoning models)
- Groq: Fast inference with seed control and service tiers
- Ollama: Local models with native streaming API support
- LMStudio: Local models with OpenAI-compatible API
- MiniMax: MiniMax-M2.7 models with 1M context window

Features:
- Reasoning models with extended thinking (o1, o3, GPT-5 families)
- Strict JSON schema enforcement (OpenAI)
- Provider-specific parameters (Groq seed, service tier)
- Native Ollama streaming for better structured output
- Automatic token limit handling per model family
"""

import asyncio
import io
import json
import logging
import os
import re
import time
from typing import Any
from urllib.parse import parse_qs, urlparse, urlunparse

import httpx
from openai import APIConnectionError, APIStatusError, AsyncOpenAI, LengthFinishReasonError

from hindsight_api.config import DEFAULT_LLM_TIMEOUT, ENV_LLM_TIMEOUT
from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
from hindsight_api.metrics import get_metrics_collector
from hindsight_api.worker.stage import set_stage

logger = logging.getLogger(__name__)

# Seed applied to every Groq request for deterministic behavior
DEFAULT_LLM_SEED = 4242


def _strip_code_fences(content: str) -> str:
    """Strip markdown code fences from LLM response if present.

    Many LLM providers (MiniMax, some Ollama models, Claude via proxies)
    wrap JSON responses in ```json ... ``` fences even when json_object
    response format is requested. This strips the fences while preserving
    the JSON content inside. Returns the original content unchanged if
    no fences are detected.
    """
    if "```" not in content:
        return content
    try:
        if "```json" in content:
            return content.split("```json")[1].split("```")[0].strip()
        return content.split("```")[1].split("```")[0].strip()
    except (IndexError, ValueError):
        return content


def _summarize_status_error(e: APIStatusError, body_max: int = 400) -> str:
    """Render an APIStatusError with status code + truncated response body.

    Without this, retry loops only log "API error after N attempts" with the
    bare exception message — losing the provider's actual error payload, which
    is the only thing that explains *why* a request failed (rate limit reason,
    invalid tool schema, model overloaded, etc.).
    """
    body: Any = getattr(e, "body", None)
    if body is None:
        try:
            body = e.response.text
        except Exception:
            body = None
    if isinstance(body, (dict, list)):
        try:
            body_str = json.dumps(body, default=str, ensure_ascii=False)
        except Exception:
            body_str = str(body)
    else:
        body_str = str(body or "").strip()
    if len(body_str) > body_max:
        body_str = body_str[:body_max] + "...TRUNCATED"
    return f"HTTP {e.status_code}: {body_str or '<no body>'}"


class OpenAICompatibleLLM(LLMInterface):
    """
    LLM provider for OpenAI-compatible APIs.

    Supports:
    - OpenAI: Standard models (GPT-4, GPT-4o) and reasoning models (o1, o3, GPT-5)
    - Groq: Fast inference with seed control and service tiers
    - Ollama: Local models with native streaming API for better structured output
    - LMStudio: Local models with OpenAI-compatible API
    - MiniMax: MiniMax-M2.7 models via OpenAI-compatible API (https://api.minimax.io/v1)
    """

    def __init__(
        self,
        provider: str,
        api_key: str,
        base_url: str,
        model: str,
        reasoning_effort: str = "low",
        timeout: float | None = None,
        groq_service_tier: str | None = None,
        extra_body: dict[str, Any] | None = None,
        **kwargs: Any,
    ):
        """
        Initialize OpenAI-compatible LLM provider.

        Args:
            provider: Provider name ("openai", "groq", "ollama", "lmstudio").
            api_key: API key (optional for ollama/lmstudio).
            base_url: Base URL for the API (uses defaults for groq/ollama/lmstudio if empty).
            model: Model name.
            reasoning_effort: Reasoning effort level for supported models ("low", "medium", "high").
            timeout: Request timeout in seconds (uses env var or 300s default).
            groq_service_tier: Groq service tier ("on_demand", "flex", "auto").
            extra_body: Extra body params merged into every API call.
            **kwargs: Additional provider-specific parameters.
        """
        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)

        # Validate provider
        valid_providers = ["openai", "groq", "ollama", "lmstudio", "llamacpp", "minimax", "volcano", "openrouter"]
        if self.provider not in valid_providers:
            raise ValueError(f"OpenAICompatibleLLM only supports: {', '.join(valid_providers)}. Got: {self.provider}")

        # Set default base URLs
        if not self.base_url:
            if self.provider == "groq":
                self.base_url = "https://api.groq.com/openai/v1"
            elif self.provider == "ollama":
                self.base_url = "http://localhost:11434/v1"
            elif self.provider == "lmstudio":
                self.base_url = "http://localhost:1234/v1"
            elif self.provider == "minimax":
                self.base_url = "https://api.minimax.io/v1"
            elif self.provider == "openrouter":
                self.base_url = "https://openrouter.ai/api/v1"

        # For ollama/lmstudio, use dummy key if not provided
        if self.provider in ("ollama", "lmstudio") and not self.api_key:
            self.api_key = "local"

        # Validate API key for cloud providers
        if self.provider in ("openai", "groq", "minimax", "openrouter") and not self.api_key:
            raise ValueError(f"API key is required for {self.provider}")

        # Service tier configuration (from config, not env vars)
        self.groq_service_tier = groq_service_tier
        self.openai_service_tier = kwargs.get("openai_service_tier")
        # User-configured extra body params (merged into every API call)
        self._config_extra_body = extra_body or {}

        # Get timeout config
        self.timeout = timeout or float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT)))

        # Create OpenAI client — extract query params from base_url (e.g. Azure api-version)
        client_kwargs: dict[str, Any] = {"api_key": self.api_key, "max_retries": 0}
        if self.base_url:
            parsed = urlparse(self.base_url)
            if parsed.query:
                clean_url = urlunparse(parsed._replace(query=""))
                client_kwargs["base_url"] = clean_url
                default_query = {k: v[0] for k, v in parse_qs(parsed.query).items()}
                client_kwargs["default_query"] = default_query
                self.base_url = clean_url
            else:
                client_kwargs["base_url"] = self.base_url
        if self.timeout:
            client_kwargs["timeout"] = self.timeout

        self._client = AsyncOpenAI(**client_kwargs)
        logger.info(
            f"OpenAI-compatible client initialized: provider={self.provider}, model={self.model}, "
            f"base_url={self.base_url or 'default'}"
        )

    async def verify_connection(self) -> None:
        """
        Verify that the provider is configured correctly by making a simple test call.

        Raises:
            RuntimeError: If the connection test fails.
        """
        try:
            logger.info(f"Verifying connection: {self.provider}/{self.model}")
            await self.call(
                messages=[{"role": "user", "content": "Say 'ok'"}],
                max_completion_tokens=100,
                max_retries=2,
                initial_backoff=0.5,
                max_backoff=2.0,
                scope="verification",
            )
            logger.info(f"Connection verified: {self.provider}/{self.model}")
        except Exception as e:
            raise RuntimeError(f"Connection verification failed for {self.provider}/{self.model}: {e}") from e

    def _supports_reasoning_model(self) -> bool:
        """Check if the current model is a reasoning model (o1, o3, GPT-5, DeepSeek)."""
        model_lower = self.model.lower()
        return any(x in model_lower for x in ["gpt-5", "o1", "o3", "deepseek"])

    def _get_max_reasoning_tokens(self) -> int | None:
        """Get max reasoning tokens for reasoning models."""
        model_lower = self.model.lower()

        # GPT-4 and GPT-4.1 models have different caps
        if any(x in model_lower for x in ["gpt-4.1", "gpt-4-"]):
            return 32000
        elif "gpt-4o" in model_lower:
            return 16384

        return None

    def _max_tokens_param_name(self) -> str:
        """Return the correct parameter name for limiting response tokens.

        Native OpenAI, Azure OpenAI, Groq, and llamacpp accept 'max_completion_tokens'.
        Mistral and other OpenAI-compatible endpoints that haven't adopted the newer
        parameter name require 'max_tokens', so when the openai provider is configured
        with a non-Azure custom base_url we fall back to the widely-supported
        'max_tokens'.

        Reasoning models (GPT-5, o1, o3) only accept 'max_completion_tokens' and reject
        'max_tokens' outright, so they always use the new parameter name regardless of
        base_url.
        """
        # Reasoning models (GPT-5, o1, o3, ...) only accept max_completion_tokens.
        # Azure OpenAI + GPT-5 is the canonical example: issue #978.
        if self._supports_reasoning_model():
            return "max_completion_tokens"
        # Native OpenAI (no custom base URL), Groq, and llamacpp use max_completion_tokens
        if self.provider in ("groq", "llamacpp"):
            return "max_completion_tokens"
        if self.provider == "openai" and not self.base_url:
            return "max_completion_tokens"
        # Azure OpenAI is fully OpenAI-API-compatible — detect it by hostname so users
        # can keep provider=openai + an Azure base_url (the documented setup).
        if self.provider == "openai" and self.base_url and ".openai.azure.com" in self.base_url:
            return "max_completion_tokens"
        # openai with custom base_url, ollama, lmstudio, minimax, volcano —
        # use the widely-supported max_tokens
        return "max_tokens"

    async def call(
        self,
        messages: list[dict[str, str]],
        response_format: Any | None = None,
        max_completion_tokens: int | None = None,
        temperature: float | None = None,
        scope: str = "memory",
        max_retries: int = 10,
        initial_backoff: float = 1.0,
        max_backoff: float = 60.0,
        skip_validation: bool = False,
        strict_schema: bool = False,
        return_usage: bool = False,
    ) -> Any:
        """
        Make an LLM API call with retry logic.

        Args:
            messages: List of message dicts with 'role' and 'content'.
            response_format: Optional Pydantic model for structured output.
            max_completion_tokens: Maximum tokens in response.
            temperature: Sampling temperature (0.0-2.0).
            scope: Scope identifier for tracking.
            max_retries: Maximum retry attempts.
            initial_backoff: Initial backoff time in seconds.
            max_backoff: Maximum backoff time in seconds.
            skip_validation: Return raw JSON without Pydantic validation.
            strict_schema: Use strict JSON schema enforcement (OpenAI only).
            return_usage: If True, return tuple (result, TokenUsage) instead of just result.

        Returns:
            If return_usage=False: Parsed response if response_format is provided, otherwise text content.
            If return_usage=True: Tuple of (result, TokenUsage) with token counts.

        Raises:
            OutputTooLongError: If output exceeds token limits.
            Exception: Re-raises API errors after retries exhausted.
        """
        # Handle Ollama with native API for structured output (better schema enforcement)
        if self.provider == "ollama" and response_format is not None:
            return await self._call_ollama_native(
                messages=messages,
                response_format=response_format,
                max_completion_tokens=max_completion_tokens,
                temperature=temperature,
                max_retries=max_retries,
                initial_backoff=initial_backoff,
                max_backoff=max_backoff,
                skip_validation=skip_validation,
                scope=scope,
                return_usage=return_usage,
            )

        start_time = time.time()

        # Build call parameters
        call_params: dict[str, Any] = {
            "model": self.model,
            "messages": messages,
        }

        # Check if model supports reasoning parameter
        is_reasoning_model = self._supports_reasoning_model()

        # Apply model-specific token limits
        if max_completion_tokens is not None:
            max_tokens_cap = self._get_max_reasoning_tokens()
            if max_tokens_cap and max_completion_tokens > max_tokens_cap:
                max_completion_tokens = max_tokens_cap
            # For reasoning models, enforce minimum to ensure space for reasoning + output
            if is_reasoning_model and max_completion_tokens < 16000:
                max_completion_tokens = 16000
            call_params[self._max_tokens_param_name()] = max_completion_tokens
        if temperature is not None and not is_reasoning_model:
            # MiniMax requires temperature in (0.0, 1.0] — clamp accordingly
            if self.provider == "minimax":
                temperature = max(0.01, min(temperature, 1.0))
            call_params["temperature"] = temperature

        # Set reasoning_effort for reasoning models
        if is_reasoning_model:
            call_params["reasoning_effort"] = self.reasoning_effort

        # Provider-specific parameters
        extra_body: dict[str, Any] = {**self._config_extra_body}
        if self.provider == "groq":
            call_params["seed"] = DEFAULT_LLM_SEED
            # Add service_tier if configured
            if self.groq_service_tier:
                extra_body["service_tier"] = self.groq_service_tier
            # Add reasoning parameters for reasoning models
            if is_reasoning_model:
                extra_body["include_reasoning"] = False
        if extra_body:
            call_params["extra_body"] = extra_body

        # Prepare response format ONCE before retry loop
        if response_format is not None:
            schema = None
            if hasattr(response_format, "model_json_schema"):
                schema = response_format.model_json_schema()

            if strict_schema and schema is not None:
                # Use OpenAI's strict JSON schema enforcement
                call_params["response_format"] = {
                    "type": "json_schema",
                    "json_schema": {
                        "name": "response",
                        "strict": True,
                        "schema": schema,
                    },
                }
            else:
                # Soft enforcement: add schema to prompt and use json_object mode
                if schema is not None:
                    schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2, ensure_ascii=False)}"

                    if call_params["messages"] and call_params["messages"][0].get("role") == "system":
                        first_msg = call_params["messages"][0]
                        if isinstance(first_msg, dict) and isinstance(first_msg.get("content"), str):
                            first_msg["content"] += schema_msg
                    elif call_params["messages"]:
                        first_msg = call_params["messages"][0]
                        if isinstance(first_msg, dict) and isinstance(first_msg.get("content"), str):
                            first_msg["content"] = schema_msg + "\n\n" + first_msg["content"]
                # Providers that skip json_object grammar enforcement
                skip_grammar = self.provider in ("lmstudio", "ollama", "volcano")
                if self.provider == "llamacpp":
                    from hindsight_api.config import get_config

                    skip_grammar = get_config().llamacpp_no_grammar
                if not skip_grammar:
                    call_params["response_format"] = {"type": "json_object"}

        last_exception = None

        for attempt in range(max_retries + 1):
            # Surface attempt count in worker stage so JSON-schema retry loops
            # are visible from logs (small models on strict structured output
            # often loop here). Cheap no-op outside worker context.
            if attempt > 0:
                set_stage(f"llm.{self.provider}.{scope}.attempt={attempt + 1}/{max_retries + 1}")
            try:
                if response_format is not None:
                    response = await self._client.chat.completions.create(**call_params)

                    content = response.choices[0].message.content

                    # Strip reasoning model thinking tags
                    # Supports: <think>, <thinking>, <reasoning>, |startthink|/|endthink|
                    if content:
                        original_len = len(content)
                        content = re.sub(r"<think>.*?</think>", "", content, flags=re.DOTALL)
                        content = re.sub(r"<thinking>.*?</thinking>", "", content, flags=re.DOTALL)
                        content = re.sub(r"<reasoning>.*?</reasoning>", "", content, flags=re.DOTALL)
                        content = re.sub(r"\|startthink\|.*?\|endthink\|", "", content, flags=re.DOTALL)
                        content = content.strip()
                        if len(content) < original_len:
                            logger.debug(f"Stripped {original_len - len(content)} chars of reasoning tokens")

                    # Strip markdown code fences if present — any provider may
                    # produce these (confirmed with MiniMax, some Ollama models,
                    # Claude via proxies). No-op when content is already bare JSON.
                    clean_content = _strip_code_fences(content)
                    try:
                        json_data = json.loads(clean_content)
                    except json.JSONDecodeError:
                        # Fallback to parsing raw content in case stripping was wrong
                        try:
                            json_data = json.loads(content)
                        except json.JSONDecodeError as json_err:
                            # Truncate content for logging
                            content_preview = content[:500] if content else "<empty>"
                            if content and len(content) > 700:
                                content_preview = f"{content[:500]}...TRUNCATED...{content[-200:]}"
                            logger.warning(
                                f"JSON parse error from LLM response (attempt {attempt + 1}/{max_retries + 1}): {json_err}\n"
                                f"  Model: {self.provider}/{self.model}\n"
                                f"  Content length: {len(content) if content else 0} chars\n"
                                f"  Content preview: {content_preview!r}\n"
                                f"  Finish reason: {response.choices[0].finish_reason if response.choices else 'unknown'}"
                            )
                            # Retry on JSON parse errors
                            if attempt < max_retries:
                                backoff = min(initial_backoff * (2**attempt), max_backoff)
                                await asyncio.sleep(backoff)
                                last_exception = json_err
                                continue
                            else:
                                logger.error(f"JSON parse error after {max_retries + 1} attempts, giving up")
                                raise

                    if skip_validation:
                        result = json_data
                    else:
                        result = response_format.model_validate(json_data)
                else:
                    response = await self._client.chat.completions.create(**call_params)
                    result = response.choices[0].message.content

                # Record token usage metrics
                duration = time.time() - start_time
                usage = response.usage
                input_tokens = usage.prompt_tokens or 0 if usage else 0
                output_tokens = usage.completion_tokens or 0 if usage else 0
                total_tokens = usage.total_tokens or 0 if usage else 0

                # Record LLM metrics
                metrics = get_metrics_collector()
                metrics.record_llm_call(
                    provider=self.provider,
                    model=self.model,
                    scope=scope,
                    duration=duration,
                    input_tokens=input_tokens,
                    output_tokens=output_tokens,
                    success=True,
                )

                # Record trace span
                from hindsight_api.tracing import _serialize_for_span, get_span_recorder

                finish_reason = response.choices[0].finish_reason if response.choices else None
                span_recorder = get_span_recorder()
                span_recorder.record_llm_call(
                    provider=self.provider,
                    model=self.model,
                    scope=scope,
                    messages=messages,
                    response_content=_serialize_for_span(result),
                    input_tokens=input_tokens,
                    output_tokens=output_tokens,
                    duration=duration,
                    finish_reason=finish_reason,
                    error=None,
                )

                # Log slow calls
                if duration > 10.0 and usage:
                    ratio = max(1, output_tokens) / max(1, input_tokens)
                    cached_tokens = 0
                    if hasattr(usage, "prompt_tokens_details") and usage.prompt_tokens_details:
                        cached_tokens = getattr(usage.prompt_tokens_details, "cached_tokens", 0) or 0
                    cache_info = f", cached_tokens={cached_tokens}" if cached_tokens > 0 else ""
                    logger.info(
                        f"slow llm call: scope={scope}, model={self.provider}/{self.model}, "
                        f"input_tokens={input_tokens}, output_tokens={output_tokens}, "
                        f"total_tokens={total_tokens}{cache_info}, time={duration:.3f}s, ratio out/in={ratio:.2f}"
                    )

                if return_usage:
                    token_usage = TokenUsage(
                        input_tokens=input_tokens,
                        output_tokens=output_tokens,
                        total_tokens=total_tokens,
                    )
                    return result, token_usage
                return result

            except LengthFinishReasonError as e:
                logger.warning(f"LLM output exceeded token limits: {str(e)}")
                raise OutputTooLongError(
                    "LLM output exceeded token limits. Input may need to be split into smaller chunks."
                ) from e

            except APIConnectionError as e:
                last_exception = e
                status_code = getattr(e, "status_code", None) or getattr(
                    getattr(e, "response", None), "status_code", None
                )
                logger.warning(f"APIConnectionError (HTTP {status_code}), attempt {attempt + 1}: {str(e)[:200]}")
                if attempt < max_retries:
                    backoff = min(initial_backoff * (2**attempt), max_backoff)
                    await asyncio.sleep(backoff)
                    continue
                else:
                    logger.error(f"Connection error after {max_retries + 1} attempts: {str(e)}")
                    raise

            except APIStatusError as e:
                # Fast fail only on 401 (unauthorized) and 403 (forbidden)
                if e.status_code in (401, 403):
                    logger.error(f"Auth error (HTTP {e.status_code}), not retrying: {str(e)}")
                    raise

                # Handle tool_use_failed error - model outputted in tool call format
                if e.status_code == 400 and response_format is not None:
                    try:
                        error_body = e.body if hasattr(e, "body") else {}
                        if isinstance(error_body, dict):
                            error_info: dict[str, Any] = error_body.get("error") or {}
                            if error_info.get("code") == "tool_use_failed":
                                failed_gen = error_info.get("failed_generation", "")
                                if failed_gen:
                                    # Parse tool call format and convert to expected format
                                    tool_call = json.loads(failed_gen)
                                    tool_name = tool_call.get("name", "")
                                    tool_args = tool_call.get("arguments", {})
                                    converted = {"actions": [{"tool": tool_name, **tool_args}]}
                                    if skip_validation:
                                        result = converted
                                    else:
                                        result = response_format.model_validate(converted)

                                    # Record metrics
                                    duration = time.time() - start_time
                                    metrics = get_metrics_collector()
                                    metrics.record_llm_call(
                                        provider=self.provider,
                                        model=self.model,
                                        scope=scope,
                                        duration=duration,
                                        input_tokens=0,
                                        output_tokens=0,
                                        success=True,
                                    )
                                    if return_usage:
                                        return result, TokenUsage(input_tokens=0, output_tokens=0, total_tokens=0)
                                    return result
                    except (json.JSONDecodeError, KeyError, TypeError):
                        pass  # Failed to parse tool_use_failed, continue with normal retry

                last_exception = e
                if attempt < max_retries:
                    logger.warning(
                        f"APIStatusError ({self.provider}/{self.model}, scope={scope}, "
                        f"attempt {attempt + 1}/{max_retries + 1}): {_summarize_status_error(e)}"
                    )
                    backoff = min(initial_backoff * (2**attempt), max_backoff)
                    jitter = backoff * 0.2 * (2 * (time.time() % 1) - 1)
                    sleep_time = backoff + jitter
                    await asyncio.sleep(sleep_time)
                else:
                    logger.error(
                        f"API error after {max_retries + 1} attempts ({self.provider}/{self.model}, "
                        f"scope={scope}): {_summarize_status_error(e)}"
                    )
                    raise

            except Exception:
                raise

        if last_exception:
            raise last_exception
        raise RuntimeError("LLM call failed after all retries with no exception captured")

    async def call_with_tools(
        self,
        messages: list[dict[str, Any]],
        tools: list[dict[str, Any]],
        max_completion_tokens: int | None = None,
        temperature: float | None = None,
        scope: str = "tools",
        max_retries: int = 5,
        initial_backoff: float = 1.0,
        max_backoff: float = 30.0,
        tool_choice: str | dict[str, Any] = "auto",
    ) -> LLMToolCallResult:
        """
        Make an LLM API call with tool/function calling support.

        Args:
            messages: List of message dicts. Can include tool results with role='tool'.
            tools: List of tool definitions in OpenAI format.
            max_completion_tokens: Maximum tokens in response.
            temperature: Sampling temperature (0.0-2.0).
            scope: Scope identifier for tracking.
            max_retries: Maximum retry attempts.
            initial_backoff: Initial backoff time in seconds.
            max_backoff: Maximum backoff time in seconds.
            tool_choice: How to choose tools - "auto", "none", "required", or specific function.

        Returns:
            LLMToolCallResult with content and/or tool_calls.
        """
        start_time = time.time()

        # Normalize named tool_choice dicts to "required" + filter tools.
        # Some providers (e.g. LM Studio, Ollama) reject the OpenAI named format
        # {"type": "function", "function": {"name": "..."}}.  The semantics are
        # identical to tool_choice="required" with the tools list restricted to
        # just the requested tool, so we apply that transformation universally.
        if isinstance(tool_choice, dict) and tool_choice.get("type") == "function":
            forced_name = tool_choice.get("function", {}).get("name")
            if forced_name:
                filtered = [t for t in tools if t.get("function", {}).get("name") == forced_name]
                if filtered:
                    tools = filtered
                tool_choice = "required"

        # Build call parameters
        call_params: dict[str, Any] = {
            "model": self.model,
            "messages": messages,
            "tools": tools,
            "tool_choice": tool_choice,
        }

        if max_completion_tokens is not None:
            call_params[self._max_tokens_param_name()] = max_completion_tokens
        if temperature is not None:
            # MiniMax requires temperature in (0.0, 1.0] — clamp accordingly
            if self.provider == "minimax":
                temperature = max(0.01, min(temperature, 1.0))
            call_params["temperature"] = temperature

        # Provider-specific parameters
        extra_body: dict[str, Any] = {**self._config_extra_body}
        if self.provider == "groq":
            call_params["seed"] = DEFAULT_LLM_SEED
        if extra_body:
            call_params["extra_body"] = extra_body

        last_exception = None

        for attempt in range(max_retries + 1):
            if attempt > 0:
                set_stage(f"llm.{self.provider}.tools.attempt={attempt + 1}/{max_retries + 1}")
            try:
                response = await self._client.chat.completions.create(**call_params)

                message = response.choices[0].message
                finish_reason = response.choices[0].finish_reason

                # Extract tool calls if present
                tool_calls: list[LLMToolCall] = []
                if message.tool_calls:
                    for tc in message.tool_calls:
                        try:
                            args = json.loads(tc.function.arguments) if tc.function.arguments else {}
                        except json.JSONDecodeError:
                            args = {"_raw": tc.function.arguments}
                        tool_calls.append(LLMToolCall(id=tc.id, name=tc.function.name, arguments=args))

                content = message.content

                # Record metrics
                duration = time.time() - start_time
                usage = response.usage
                input_tokens = usage.prompt_tokens or 0 if usage else 0
                output_tokens = usage.completion_tokens or 0 if usage else 0

                metrics = get_metrics_collector()
                metrics.record_llm_call(
                    provider=self.provider,
                    model=self.model,
                    scope=scope,
                    duration=duration,
                    input_tokens=input_tokens,
                    output_tokens=output_tokens,
                    success=True,
                )

                # Record OpenTelemetry span
                from hindsight_api.tracing import get_span_recorder

                span_recorder = get_span_recorder()
                # Convert LLMToolCall objects to dicts for span recording
                tool_calls_dict = (
                    [{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in tool_calls]
                    if tool_calls
                    else None
                )
                span_recorder.record_llm_call(
                    provider=self.provider,
                    model=self.model,
                    scope=scope,
                    messages=messages,
                    response_content=content,
                    input_tokens=input_tokens,
                    output_tokens=output_tokens,
                    duration=duration,
                    finish_reason=finish_reason,
                    error=None,
                    tool_calls=tool_calls_dict,
                )

                return LLMToolCallResult(
                    content=content,
                    tool_calls=tool_calls,
                    finish_reason=finish_reason,
                    input_tokens=input_tokens,
                    output_tokens=output_tokens,
                )

            except APIConnectionError as e:
                last_exception = e
                status_code = getattr(e, "status_code", None) or getattr(
                    getattr(e, "response", None), "status_code", None
                )
                if attempt < max_retries:
                    logger.warning(
                        f"APIConnectionError in tool call ({self.provider}/{self.model}, scope={scope}, "
                        f"attempt {attempt + 1}/{max_retries + 1}, HTTP {status_code}): {str(e)[:200]}"
                    )
                    await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
                    continue
                logger.error(
                    f"Connection error in tool call after {max_retries + 1} attempts "
                    f"({self.provider}/{self.model}, scope={scope}): {str(e)}"
                )
                raise

            except APIStatusError as e:
                if e.status_code in (401, 403):
                    logger.error(
                        f"Auth error in tool call (HTTP {e.status_code}, {self.provider}/{self.model}), "
                        f"not retrying: {_summarize_status_error(e)}"
                    )
                    raise
                last_exception = e
                if attempt < max_retries:
                    logger.warning(
                        f"APIStatusError in tool call ({self.provider}/{self.model}, scope={scope}, "
                        f"attempt {attempt + 1}/{max_retries + 1}): {_summarize_status_error(e)}"
                    )
                    await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
                    continue
                logger.error(
                    f"API error in tool call after {max_retries + 1} attempts "
                    f"({self.provider}/{self.model}, scope={scope}): {_summarize_status_error(e)}"
                )
                raise

            except Exception:
                raise

        if last_exception:
            raise last_exception
        raise RuntimeError("Tool call failed after all retries")

    async def _call_ollama_native(
        self,
        messages: list[dict[str, str]],
        response_format: Any,
        max_completion_tokens: int | None,
        temperature: float | None,
        max_retries: int,
        initial_backoff: float,
        max_backoff: float,
        skip_validation: bool,
        scope: str = "memory",
        return_usage: bool = False,
    ) -> Any:
        """
        Call Ollama using native API with JSON schema enforcement.

        Ollama's native API supports passing a full JSON schema in the 'format' parameter,
        which provides better structured output control than the OpenAI-compatible API.
        """
        start_time = time.time()

        # Get the JSON schema from the Pydantic model
        schema = response_format.model_json_schema() if hasattr(response_format, "model_json_schema") else None

        # Build the base URL for Ollama's native API
        # Default OpenAI-compatible URL is http://localhost:11434/v1
        # Native API is at http://localhost:11434/api/chat
        base_url = self.base_url or "http://localhost:11434/v1"
        if base_url.endswith("/v1"):
            native_url = base_url[:-3] + "/api/chat"
        else:
            native_url = base_url.rstrip("/") + "/api/chat"

        # Build request payload
        payload: dict[str, Any] = {
            "model": self.model,
            "messages": messages,
            "stream": False,
            "think": False,  # Disable thinking for reasoning models (qwen3.5, etc.)
        }

        # Add schema as format parameter for structured output
        if schema:
            payload["format"] = schema

        # Add optional parameters with optimized defaults for Ollama
        options: dict[str, Any] = {
            "num_ctx": 16384,  # 16k context window for larger prompts
            "num_batch": 512,  # Optimal batch size for prompt processing
        }
        if max_completion_tokens:
            options["num_predict"] = max_completion_tokens
        if temperature is not None:
            options["temperature"] = temperature
        payload["options"] = options

        last_exception = None

        async with httpx.AsyncClient(timeout=300.0) as client:
            for attempt in range(max_retries + 1):
                if attempt > 0:
                    set_stage(f"llm.ollama_native.{scope}.attempt={attempt + 1}/{max_retries + 1}")
                try:
                    response = await client.post(native_url, json=payload)
                    response.raise_for_status()

                    result = response.json()
                    content = result.get("message", {}).get("content", "")

                    # Strip markdown code fences if present (safety net —
                    # Ollama with schema enforcement usually returns bare JSON,
                    # but some models may still wrap in fences)
                    clean_content = _strip_code_fences(content)
                    try:
                        json_data = json.loads(clean_content)
                    except json.JSONDecodeError:
                        # Fallback to raw content
                        try:
                            json_data = json.loads(content)
                        except json.JSONDecodeError as json_err:
                            content_preview = content[:500] if content else "<empty>"
                            if content and len(content) > 700:
                                content_preview = f"{content[:500]}...TRUNCATED...{content[-200:]}"
                            logger.warning(
                                f"Ollama JSON parse error (attempt {attempt + 1}/{max_retries + 1}): {json_err}\n"
                                f"  Model: ollama/{self.model}\n"
                                f"  Content length: {len(content) if content else 0} chars\n"
                                f"  Content preview: {content_preview!r}"
                            )
                            if attempt < max_retries:
                                backoff = min(initial_backoff * (2**attempt), max_backoff)
                                await asyncio.sleep(backoff)
                                last_exception = json_err
                                continue
                            else:
                                raise

                    # Extract token usage from Ollama response
                    duration = time.time() - start_time
                    input_tokens = result.get("prompt_eval_count", 0) or 0
                    output_tokens = result.get("eval_count", 0) or 0
                    total_tokens = input_tokens + output_tokens

                    # Record LLM metrics
                    metrics = get_metrics_collector()
                    metrics.record_llm_call(
                        provider=self.provider,
                        model=self.model,
                        scope=scope,
                        duration=duration,
                        input_tokens=input_tokens,
                        output_tokens=output_tokens,
                        success=True,
                    )

                    # Validate against Pydantic model or return raw JSON
                    if skip_validation:
                        validated_result = json_data
                    else:
                        validated_result = response_format.model_validate(json_data)

                    if return_usage:
                        token_usage = TokenUsage(
                            input_tokens=input_tokens,
                            output_tokens=output_tokens,
                            total_tokens=total_tokens,
                        )
                        return validated_result, token_usage
                    return validated_result

                except httpx.HTTPStatusError as e:
                    last_exception = e
                    if attempt < max_retries:
                        logger.warning(
                            f"Ollama HTTP error (attempt {attempt + 1}/{max_retries + 1}): {e.response.status_code}"
                        )
                        backoff = min(initial_backoff * (2**attempt), max_backoff)
                        await asyncio.sleep(backoff)
                        continue
                    else:
                        logger.error(f"Ollama HTTP error after {max_retries + 1} attempts: {e}")
                        raise

                except httpx.RequestError as e:
                    last_exception = e
                    if attempt < max_retries:
                        logger.warning(f"Ollama connection error (attempt {attempt + 1}/{max_retries + 1}): {e}")
                        backoff = min(initial_backoff * (2**attempt), max_backoff)
                        await asyncio.sleep(backoff)
                        continue
                    else:
                        logger.error(f"Ollama connection error after {max_retries + 1} attempts: {e}")
                        raise

                except Exception as e:
                    logger.error(f"Unexpected error during Ollama call: {type(e).__name__}: {e}")
                    raise

        if last_exception:
            raise last_exception
        raise RuntimeError("Ollama call failed after all retries")

    async def supports_batch_api(self) -> bool:
        """Check if this provider supports batch API operations."""
        # Only OpenAI and Groq support batch API
        return self.provider in ("openai", "groq")

    async def submit_batch(
        self,
        requests: list[dict[str, Any]],
        endpoint: str = "/v1/chat/completions",
        completion_window: str = "24h",
    ) -> dict[str, Any]:
        """
        Submit a batch of requests to OpenAI/Groq Batch API.

        Args:
            requests: List of request dicts with custom_id, method, url, body
            endpoint: API endpoint (e.g., "/v1/chat/completions")
            completion_window: Completion window (e.g., "24h")

        Returns:
            Dict with batch metadata including batch_id

        Raises:
            NotImplementedError: If provider doesn't support batch API
        """
        if not await self.supports_batch_api():
            raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")

        logger.info(f"Submitting batch with {len(requests)} requests to {self.provider}")

        # Format requests as JSONL
        jsonl_content = "\n".join(json.dumps(req, ensure_ascii=False) for req in requests)

        # Upload file to provider (wrap in BytesIO with filename)
        file_bytes = io.BytesIO(jsonl_content.encode("utf-8"))
        file_bytes.name = "batch_input.jsonl"  # OpenAI SDK needs a filename

        file_response = await self._client.files.create(
            file=file_bytes,
            purpose="batch",
        )

        logger.debug(f"Uploaded batch file: {file_response.id}")

        # Create batch
        batch_response = await self._client.batches.create(
            input_file_id=file_response.id,
            endpoint=endpoint,
            completion_window=completion_window,
        )

        logger.info(f"Batch submitted: {batch_response.id}, status={batch_response.status}")

        return {
            "batch_id": batch_response.id,
            "status": batch_response.status,
            "input_file_id": file_response.id,
            "created_at": batch_response.created_at,
            "request_count": len(requests),
        }

    async def get_batch_status(self, batch_id: str) -> dict[str, Any]:
        """
        Get the status of a batch job.

        Args:
            batch_id: Batch identifier

        Returns:
            Dict with status info (batch_id, status, completed_at, etc.)
        """
        if not await self.supports_batch_api():
            raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")

        batch = await self._client.batches.retrieve(batch_id)

        result = {
            "batch_id": batch.id,
            "status": batch.status,
            "created_at": batch.created_at,
            "request_counts": {
                "total": batch.request_counts.total if batch.request_counts else 0,
                "completed": batch.request_counts.completed if batch.request_counts else 0,
                "failed": batch.request_counts.failed if batch.request_counts else 0,
            },
        }

        if batch.completed_at:
            result["completed_at"] = batch.completed_at
        if batch.output_file_id:
            result["output_file_id"] = batch.output_file_id
        if batch.error_file_id:
            result["error_file_id"] = batch.error_file_id
        if batch.errors:
            result["errors"] = batch.errors

        return result

    async def retrieve_batch_results(self, batch_id: str) -> list[dict[str, Any]]:
        """
        Retrieve completed batch results.

        Args:
            batch_id: Batch identifier

        Returns:
            List of result dicts (one per request, matched by custom_id)
        """
        if not await self.supports_batch_api():
            raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")

        # Get batch status
        batch = await self._client.batches.retrieve(batch_id)

        if batch.status != "completed":
            raise ValueError(f"Batch {batch_id} is not completed yet (status: {batch.status})")

        if not batch.output_file_id:
            raise ValueError(f"Batch {batch_id} has no output file")

        # Download results file
        logger.debug(f"Downloading results for batch {batch_id} from file {batch.output_file_id}")
        file_content = await self._client.files.content(batch.output_file_id)

        # Parse JSONL results
        results = []
        for line in file_content.text.strip().split("\n"):
            if line:
                results.append(json.loads(line))

        logger.info(f"Retrieved {len(results)} results for batch {batch_id}")

        return results

    async def cleanup(self) -> None:
        """Clean up resources (close OpenAI client connections)."""
        if hasattr(self, "_client") and self._client:
            await self._client.close()
