"""Cognee runtime configuration for the local Cerebras/Gemma setup."""

from __future__ import annotations

import json
import os
import importlib
from typing import Any, Callable

from app.agents.cerebras_client import CerebrasClient

COGNEE_LLM_MODEL = "openai/gemma-4-31b"
COGNEE_CEREBRAS_BASE_URL = "https://api.cerebras.ai/v1"
COGNEE_INSTRUCTOR_MODE = "json_schema_mode"
COGNEE_SKIP_CONNECTION_TEST = "true"
COGNEE_OPENAI_ADAPTER_MODULE = (
    "cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.openai.adapter"
)


def _cognee_llm_args() -> dict[str, Any]:
    """LiteLLM args shared by all Cognee structured-output calls."""
    return {
        "temperature": 0,
        "top_p": 1,
        "seed": 0,
    }


def configure_cognee_llm(
    cognee_config: Any,
    *,
    clear_llm_client_cache: Callable[[], None] | None = None,
) -> dict[str, Any]:
    """Force Cognee's LiteLLM/instructor path onto Cerebras strict JSON mode.

    StudyBuddy's own ``CerebrasClient`` already uses provider-native
    ``response_format={"type": "json_schema", ... "strict": true}``. Cognee
    reaches Cerebras through LiteLLM + instructor instead, so we configure both
    the Cognee config object and the environment it reads from before any Cognee
    client can be cached.
    """
    cerebras_key = os.environ.get("CEREBRAS_API_KEY", "")
    llm_args = _cognee_llm_args()
    config = {
        "structured_output_framework": "instructor",
        "llm_provider": "openai",
        "llm_model": COGNEE_LLM_MODEL,
        "llm_endpoint": COGNEE_CEREBRAS_BASE_URL,
        "llm_api_key": cerebras_key,
        "llm_instructor_mode": COGNEE_INSTRUCTOR_MODE,
        "llm_temperature": 0.0,
        "llm_max_completion_tokens": 16384,
        "llm_args": llm_args,
    }

    os.environ["OPENAI_API_KEY"] = cerebras_key
    os.environ["OPENAI_API_BASE"] = COGNEE_CEREBRAS_BASE_URL
    os.environ["LLM_API_KEY"] = cerebras_key
    os.environ["LLM_API_BASE"] = COGNEE_CEREBRAS_BASE_URL
    os.environ["LLM_ENDPOINT"] = COGNEE_CEREBRAS_BASE_URL
    os.environ["LLM_MODEL"] = COGNEE_LLM_MODEL
    os.environ["LLM_PROVIDER"] = "openai"
    os.environ["LLM_INSTRUCTOR_MODE"] = COGNEE_INSTRUCTOR_MODE
    os.environ["LLM_TEMPERATURE"] = "0"
    os.environ["LLM_ARGS"] = json.dumps(llm_args)
    os.environ["COGNEE_SKIP_CONNECTION_TEST"] = COGNEE_SKIP_CONNECTION_TEST

    cognee_config.set_llm_config(config)
    patch_cognee_cerebras_structured_output()
    if clear_llm_client_cache is not None:
        clear_llm_client_cache()
    return config


def patch_cognee_cerebras_structured_output() -> bool:
    """Route Cognee+Cerebras structured outputs through native strict JSON schema.

    Cognee's default OpenAI adapter uses LiteLLM + instructor. With Cerebras/Gemma
    this can produce fenced or schema-shaped JSON that fails Cognee's Pydantic
    models during memify/cognify. ResearchMate's own Cerebras client already uses
    provider-native strict JSON schema; this patch makes Cognee use the same path
    for BaseModel structured outputs while preserving the original adapter for
    plain string calls and non-Cerebras endpoints.
    """
    module = importlib.import_module(COGNEE_OPENAI_ADAPTER_MODULE)
    adapter_cls = getattr(module, "OpenAIAdapter")
    if getattr(adapter_cls, "_researchmate_cerebras_patch", False):
        return True

    original = getattr(adapter_cls, "acreate_structured_output", None)

    async def _native_cerebras_structured_output(
        self: Any,
        text_input: str,
        system_prompt: str,
        response_model: type[Any],
        **kwargs: Any,
    ) -> Any:
        if response_model is str:
            if hasattr(self, "acreate_str_output"):
                return await self.acreate_str_output(text_input, system_prompt, **kwargs)
            if original is not None:
                return await original(self, text_input, system_prompt, response_model, **kwargs)

        model = str(getattr(self, "model", "") or COGNEE_LLM_MODEL)
        endpoint = str(getattr(self, "endpoint", "") or "")
        is_cerebras = COGNEE_CEREBRAS_BASE_URL in endpoint or "gemma" in model.lower()
        if not is_cerebras and original is not None:
            return await original(self, text_input, system_prompt, response_model, **kwargs)

        merged_kwargs = {**(getattr(self, "llm_args", {}) or {}), **kwargs}
        max_completion_tokens = getattr(self, "max_completion_tokens", None)
        if max_completion_tokens and "max_completion_tokens" not in merged_kwargs:
            merged_kwargs["max_completion_tokens"] = max_completion_tokens
        native_model = model.removeprefix("openai/")
        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": text_input},
        ]

        api_key = getattr(self, "api_key", None) or None
        try:
            client = CerebrasClient(api_key=api_key)
        except TypeError:
            client = CerebrasClient()
        return await _run_sync_structured_complete(
            client=client,
            messages=messages,
            response_model=response_model,
            model=native_model,
            kwargs=merged_kwargs,
        )

    adapter_cls.acreate_structured_output = _native_cerebras_structured_output
    adapter_cls._researchmate_cerebras_patch = True
    return True


async def _run_sync_structured_complete(
    *,
    client: CerebrasClient,
    messages: list[dict[str, Any]],
    response_model: type[Any],
    model: str,
    kwargs: dict[str, Any],
) -> Any:
    import asyncio

    return await asyncio.to_thread(
        lambda: client.structured_complete(messages, response_model, model=model, **kwargs)
    )