// LLM gateway — single interface used by Socrates' modes.
//
// Two adapters in M6:
//   - lmstudio  → local LM Studio at LMSTUDIO_BASE_URL (OpenAI-compatible)
//   - anthropic → hosted Claude via @anthropic-ai/sdk
//
// Provider is chosen via LLM_PROVIDER env var (defaults to "lmstudio" so a
// fresh checkout works against the local model the user already has).

export type Role = "system" | "user" | "assistant";

export interface Message {
  role: Role;
  content: string;
}

export interface ChatOptions {
  temperature?: number;
  maxTokens?: number;
  /** When set, the response is constrained to JSON conforming to this schema. */
  jsonSchema?: { name: string; schema: Record<string, unknown> };
  /** Force JSON-object mode (looser than schema) — for adapters that don't support strict schemas. */
  jsonObjectMode?: boolean;
}

export interface ChatResult {
  text: string;
  inputTokens: number;
  outputTokens: number;
}

export interface LLMGateway {
  /** Provider name — for logging / model attribution in DB. */
  readonly provider: "lmstudio" | "anthropic";
  /** Model id reported by the provider for the active model. */
  readonly model: string;
  chat(messages: Message[], opts?: ChatOptions): Promise<ChatResult>;
}

// ─── Provider selection ──────────────────────────────────────────────────

export function defaultGateway(): LLMGateway {
  const provider = (process.env.LLM_PROVIDER ?? "lmstudio").toLowerCase();
  if (provider === "anthropic") return makeAnthropicGateway();
  return makeLMStudioGateway();
}

// ─── LM Studio (OpenAI-compatible) ───────────────────────────────────────

function makeLMStudioGateway(): LLMGateway {
  // Lazy-import OpenAI SDK so we don't pay the cost when we're using Anthropic.
  // eslint-disable-next-line @typescript-eslint/no-require-imports
  const OpenAI = require("openai").default ?? require("openai");
  const baseURL = process.env.LMSTUDIO_BASE_URL ?? "http://localhost:1234/v1";
  const apiKey = process.env.LMSTUDIO_API_KEY ?? "lm-studio";
  const model = process.env.LMSTUDIO_MODEL ?? "local-model";

  const client = new OpenAI({ baseURL, apiKey, timeout: 120_000, maxRetries: 0 });

  return {
    provider: "lmstudio",
    model,
    async chat(messages, opts = {}) {
      const responseFormat = buildOpenAIResponseFormat(opts);
      const completion = await client.chat.completions.create({
        model,
        messages: messages as { role: Role; content: string }[],
        temperature: opts.temperature ?? 0.4,
        max_tokens: opts.maxTokens ?? 1024,
        ...(responseFormat ? { response_format: responseFormat } : {}),
      });
      const choice = completion.choices[0];
      return {
        text: choice?.message?.content ?? "",
        inputTokens: completion.usage?.prompt_tokens ?? 0,
        outputTokens: completion.usage?.completion_tokens ?? 0,
      };
    },
  };
}

function buildOpenAIResponseFormat(opts: ChatOptions): unknown {
  if (opts.jsonSchema) {
    return {
      type: "json_schema",
      json_schema: { name: opts.jsonSchema.name, schema: opts.jsonSchema.schema, strict: false },
    };
  }
  if (opts.jsonObjectMode) return { type: "json_object" };
  return undefined;
}

// ─── Anthropic ────────────────────────────────────────────────────────────

function makeAnthropicGateway(): LLMGateway {
  // eslint-disable-next-line @typescript-eslint/no-require-imports
  const Anthropic = require("@anthropic-ai/sdk").default ?? require("@anthropic-ai/sdk");
  const apiKey = process.env.ANTHROPIC_API_KEY;
  if (!apiKey) throw new Error("ANTHROPIC_API_KEY is required when LLM_PROVIDER=anthropic");

  const model = process.env.ANTHROPIC_MODEL ?? "claude-sonnet-4-6";
  const client = new Anthropic({ apiKey });

  return {
    provider: "anthropic",
    model,
    async chat(messages, opts = {}) {
      const systemBlocks = messages.filter(m => m.role === "system").map(m => m.content);
      const others = messages.filter(m => m.role !== "system");

      const result = await client.messages.create({
        model,
        max_tokens: opts.maxTokens ?? 1024,
        temperature: opts.temperature ?? 0.4,
        system: systemBlocks.length > 0 ? systemBlocks.join("\n\n---\n\n") : undefined,
        messages: others.map(m => ({ role: m.role as "user" | "assistant", content: m.content })),
      });

      // Concatenate text blocks
      const text = (result.content as Array<{ type: string; text?: string }>)
        .filter(b => b.type === "text" && typeof b.text === "string")
        .map(b => b.text!)
        .join("");

      return {
        text,
        inputTokens: result.usage?.input_tokens ?? 0,
        outputTokens: result.usage?.output_tokens ?? 0,
      };
    },
  };
}

// ─── JSON-mode helper with repair-retry (Phase 0 lessons) ────────────────

export async function chatJSON<T = unknown>(
  gateway: LLMGateway,
  messages: Message[],
  opts: ChatOptions & { maxRepairs?: number } = {}
): Promise<{ value: T; result: ChatResult }> {
  const maxRepairs = opts.maxRepairs ?? 2;
  const history: Message[] = [...messages];

  for (let attempt = 0; attempt <= maxRepairs; attempt++) {
    const result = await gateway.chat(history, opts);
    const cleaned = stripCodeFences(result.text);
    try {
      const value = JSON.parse(cleaned) as T;
      return { value, result };
    } catch (parseErr) {
      if (attempt === maxRepairs) {
        throw new Error(
          `JSON parse failed after ${maxRepairs + 1} attempts. ` +
            `Last error: ${(parseErr as Error).message}\n` +
            `Last response (first 600 chars): ${cleaned.slice(0, 600)}`
        );
      }
      history.push({ role: "assistant", content: result.text });
      history.push({
        role: "user",
        content: `That response was not valid JSON. Error: ${(parseErr as Error).message}\nReturn ONLY a valid JSON object, no prose, no code fences.`,
      });
    }
  }
  throw new Error("chatJSON exhausted repairs");
}

function stripCodeFences(text: string): string {
  const trimmed = text.trim();
  const fenceMatch = trimmed.match(/^```(?:json)?\s*([\s\S]*?)\s*```$/);
  if (fenceMatch) return fenceMatch[1].trim();
  return trimmed;
}