/**
 * MCP gno_embed tool - embed unembedded chunks.
 *
 * @module src/mcp/tools/embed
 */

import type { ToolContext } from "../server";

import { MCP_ERRORS } from "../../core/errors";
import { acquireWriteLock, type WriteLockHandle } from "../../core/file-lock";
import { JobError } from "../../core/job-manager";
import { recordIndexMutation } from "../../core/mutation-generations";
import { embedBacklog } from "../../embed";
import { LlmAdapter } from "../../llm/nodeLlamaCpp/adapter";
import { resolveModelUri } from "../../llm/registry";
import {
  createVectorIndexPort,
  createVectorStatsPort,
} from "../../store/vector";
import { runTool, type ToolResult } from "./index";

interface EmbedInput {
  collection?: string;
}

interface EmbedResultOutput {
  jobId: string;
  status: "started";
  model: string;
}

function formatEmbedResult(result: EmbedResultOutput): string {
  const lines: string[] = [];
  lines.push(`Job: ${result.jobId}`);
  lines.push(`Status: ${result.status}`);
  lines.push(`Model: ${result.model}`);
  return lines.join("\n");
}

export function handleEmbed(
  args: EmbedInput,
  ctx: ToolContext
): Promise<ToolResult> {
  return runTool(
    ctx,
    "gno_embed",
    async () => {
      if (!ctx.enableWrite) {
        throw new Error("Write tools disabled. Start MCP with --enable-write.");
      }

      let lock: WriteLockHandle | null = null;
      let handedOff = false;

      try {
        lock = await acquireWriteLock(ctx.writeLockPath);
        if (!lock) {
          throw new Error(
            `${MCP_ERRORS.LOCKED.code}: ${MCP_ERRORS.LOCKED.message}`
          );
        }

        const requestedCollection = args.collection?.trim();
        const collection = requestedCollection
          ? ctx.collections.find((item) => item.name === requestedCollection)
          : null;

        if (requestedCollection && !collection) {
          throw new Error(
            `${MCP_ERRORS.NOT_FOUND.code}: Collection not found: ${requestedCollection}`
          );
        }

        const modelUri = resolveModelUri(
          ctx.config,
          "embed",
          undefined,
          collection?.name
        );

        const jobId = await ctx.jobManager.startTypedJobWithLock(
          "embed",
          lock,
          async () => {
            const lease = ctx.acquireModelLease?.();
            try {
              // Create LLM adapter with offline policy (fail-fast, no download)
              const llm = ctx.getModelAdapter?.() ?? new LlmAdapter(ctx.config);
              const embedResult = await llm.createEmbeddingPort(modelUri, {
                egressCollections: collection ? [collection.name] : "all",
                policy: { offline: true, allowDownload: false },
              });

              if (!embedResult.ok) {
                throw new Error(
                  `MODEL_NOT_FOUND: Embedding model not cached. ` +
                    `Model: ${modelUri}. ` +
                    `Run 'gno models pull embed' first.`
                );
              }

              const embedPort = embedResult.value;

              try {
                // Initialize and get dimensions from port interface
                const initResult = await embedPort.init();
                if (!initResult.ok) {
                  throw new Error(initResult.error.message);
                }
                const dimensions = embedPort.dimensions();

                // Create vector index port
                const db = ctx.store.getRawDb();
                const vectorResult = await createVectorIndexPort(db, {
                  model: modelUri,
                  dimensions,
                });
                if (!vectorResult.ok) {
                  throw new Error(vectorResult.error.message);
                }
                const vectorIndex = vectorResult.value;

                // Create stats port for backlog
                const statsPort = createVectorStatsPort(db);

                // Run embedding
                const result = await embedBacklog({
                  statsPort,
                  embedPort,
                  vectorIndex,
                  collection: collection?.name,
                  modelUri,
                  batchSize: 32,
                });

                if (!result.ok) {
                  throw new Error(result.error.message);
                }
                recordIndexMutation(
                  result.value.embedded,
                  ctx.markIndexMutation
                );

                return {
                  kind: "embed" as const,
                  value: result.value,
                };
              } finally {
                await embedPort.dispose();
              }
            } finally {
              lease?.release();
            }
          }
        );

        handedOff = true;

        const result: EmbedResultOutput = {
          jobId,
          status: "started",
          model: modelUri,
        };

        return result;
      } catch (error) {
        if (error instanceof JobError) {
          throw new Error(`${error.code}: ${error.message}`);
        }
        throw error;
      } finally {
        if (lock && !handedOff) {
          await lock.release();
        }
      }
    },
    formatEmbedResult
  );
}