import os
import re
from collections.abc import AsyncIterator
from typing import Any, cast

import pipmaster as pm

# install specific modules
if not pm.is_installed('ollama'):
    pm.install('ollama')


import numpy as np
import ollama

ollama = cast(Any, ollama)
from tenacity import (
    retry,
    retry_if_exception_type,
    stop_after_attempt,
    wait_exponential,
)

from lightrag.api import __api_version__
from lightrag.exceptions import (
    APIConnectionError,
    APITimeoutError,
    RateLimitError,
)
from lightrag.utils import (
    logger,
    wrap_embedding_func_with_attrs,
)

_OLLAMA_CLOUD_HOST = 'https://ollama.com'
_CLOUD_MODEL_SUFFIX_PATTERN = re.compile(r'(?:-cloud|:cloud)$')


def _coerce_host_for_cloud_model(host: str | None, model: object) -> str | None:
    if host:
        return host
    try:
        model_name_str = str(model) if model is not None else ''
    except (TypeError, ValueError, AttributeError) as e:
        logger.warning(f'Failed to convert model to string: {e}, using empty string')
        model_name_str = ''
    if _CLOUD_MODEL_SUFFIX_PATTERN.search(model_name_str):
        logger.debug(f"Detected cloud model '{model_name_str}', using Ollama Cloud host")
        return _OLLAMA_CLOUD_HOST
    return host


@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=10),
    retry=retry_if_exception_type((RateLimitError, APIConnectionError, APITimeoutError)),
)
async def _ollama_model_if_cache(
    model,
    prompt,
    system_prompt=None,
    history_messages=None,
    enable_cot: bool = False,
    **kwargs,
) -> str | AsyncIterator[str]:
    if history_messages is None:
        history_messages = []
    if enable_cot:
        logger.debug('enable_cot=True is not supported for ollama and will be ignored.')
    stream = bool(kwargs.get('stream'))

    kwargs.pop('max_tokens', None)
    # kwargs.pop("response_format", None) # allow json
    host = kwargs.pop('host', None)
    timeout = kwargs.pop('timeout', None)
    if timeout == 0:
        timeout = None
    kwargs.pop('hashing_kv', None)
    api_key = kwargs.pop('api_key', None)
    # fallback to environment variable when not provided explicitly
    if not api_key:
        api_key = os.getenv('OLLAMA_API_KEY')
    headers = {
        'Content-Type': 'application/json',
        'User-Agent': f'LightRAG/{__api_version__}',
    }
    if api_key:
        headers['Authorization'] = f'Bearer {api_key}'

    host = _coerce_host_for_cloud_model(host, model)

    ollama_client = cast(Any, ollama).AsyncClient(host=host, timeout=timeout, headers=headers)

    try:
        messages = []
        if system_prompt:
            messages.append({'role': 'system', 'content': system_prompt})
        messages.extend(history_messages)
        messages.append({'role': 'user', 'content': prompt})

        response = await ollama_client.chat(model=model, messages=messages, **kwargs)
        if stream:
            """cannot cache stream response and process reasoning"""

            async def inner():
                try:
                    async for chunk in response:
                        yield chunk['message']['content']
                except Exception as e:
                    logger.error(f'Error in stream response: {e!s}')
                    raise
                finally:
                    try:
                        await ollama_client._client.aclose()
                        logger.debug('Successfully closed Ollama client for streaming')
                    except Exception as close_error:
                        logger.warning(f'Failed to close Ollama client: {close_error}')

            return inner()
        else:
            model_response = response['message']['content']

            """
            If the model also wraps its thoughts in a specific tag,
            this information is not needed for the final
            response and can simply be trimmed.
            """

            return model_response
    except Exception as e:
        try:
            await ollama_client._client.aclose()
            logger.debug('Successfully closed Ollama client after exception')
        except Exception as close_error:
            logger.warning(f'Failed to close Ollama client after exception: {close_error}')
        raise e
    finally:
        if not stream:
            try:
                await ollama_client._client.aclose()
                logger.debug('Successfully closed Ollama client for non-streaming response')
            except Exception as close_error:
                logger.warning(f'Failed to close Ollama client in finally block: {close_error}')


async def ollama_model_complete(
    prompt,
    system_prompt=None,
    history_messages=None,
    enable_cot: bool = False,
    keyword_extraction=False,
    **kwargs,
) -> str | AsyncIterator[str]:
    if history_messages is None:
        history_messages = []
    keyword_extraction = kwargs.pop('keyword_extraction', None)
    if keyword_extraction:
        kwargs['format'] = 'json'
    model_name = kwargs['hashing_kv'].global_config['llm_model_name']
    return await _ollama_model_if_cache(
        model_name,
        prompt,
        system_prompt=system_prompt,
        history_messages=history_messages,
        enable_cot=enable_cot,
        **kwargs,
    )


@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192)
async def ollama_embed(texts: list[str], embed_model: str = 'bge-m3:latest', **kwargs) -> np.ndarray:
    api_key = kwargs.pop('api_key', None)
    if not api_key:
        api_key = os.getenv('OLLAMA_API_KEY')
    headers = {
        'Content-Type': 'application/json',
        'User-Agent': f'LightRAG/{__api_version__}',
    }
    if api_key:
        headers['Authorization'] = f'Bearer {api_key}'

    host = kwargs.pop('host', None)
    timeout = kwargs.pop('timeout', None)

    host = _coerce_host_for_cloud_model(host, embed_model)

    ollama_client = cast(Any, ollama).AsyncClient(host=host, timeout=timeout, headers=headers)
    try:
        options = kwargs.pop('options', {})
        data = await ollama_client.embed(model=embed_model, input=texts, options=options)
        return np.array(data['embeddings'])
    except Exception as e:
        logger.error(f'Error in ollama_embed: {e!s}')
        try:
            await ollama_client._client.aclose()
            logger.debug('Successfully closed Ollama client after exception in embed')
        except Exception as close_error:
            logger.warning(f'Failed to close Ollama client after exception in embed: {close_error}')
        raise e
    finally:
        try:
            await ollama_client._client.aclose()
            logger.debug('Successfully closed Ollama client after embed')
        except Exception as close_error:
            logger.warning(f'Failed to close Ollama client after embed: {close_error}')