"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.MessagesDAO = void 0;
const uuid_1 = require("uuid");
const vector_utils_1 = require("../core/vector-utils");
const vector_search_engine_1 = require("../core/vector-search-engine");
class MessagesDAO {
    dbManager;
    embeddingService;
    searchEngine;
    constructor(dbManager, embeddingService) {
        this.dbManager = dbManager;
        this.embeddingService = embeddingService;
        this.searchEngine = new vector_search_engine_1.VectorSearchEngine();
    }
    get db() {
        return this.dbManager.getDatabase();
    }
    async create(input) {
        const id = input.id || (0, uuid_1.v4)();
        const now = input.timestamp || new Date();
        // Generate embedding (Async)
        let embedding;
        let embeddingBlob = null;
        let embeddingModel = null;
        let embeddingGeneratedAt = null;
        try {
            embedding = await this.embeddingService.embed(input.content);
            if (embedding) {
                embeddingBlob = (0, vector_utils_1.serializeEmbedding)(embedding);
                const modelInfo = this.embeddingService.getModelInfo();
                embeddingModel = modelInfo.name;
                embeddingGeneratedAt = Date.now();
            }
        }
        catch (e) {
            console.error('Failed to generate embedding:', e);
            // Proceed without embedding
        }
        const message = {
            id,
            threadId: input.threadId,
            role: input.role,
            content: input.content,
            timestamp: now,
            metadata: input.metadata || {},
            embedding
        };
        const stmt = this.db.prepare(`
      INSERT INTO messages (
        id, thread_id, role, content, timestamp, metadata,
        embedding_blob, embedding_model, embedding_generated_at
      ) VALUES (
        ?, ?, ?, ?, ?, ?, ?, ?, ?
      )
    `);
        stmt.run(message.id, message.threadId, message.role, message.content, message.timestamp.getTime(), JSON.stringify(message.metadata || {}), embeddingBlob, embeddingModel, embeddingGeneratedAt);
        // Update thread message count
        this.updateThreadMessageCount(message.threadId);
        return message;
    }
    findByThreadId(threadId, options) {
        const { limit = 50, offset = 0 } = options || {};
        const stmt = this.db.prepare(`
      SELECT * FROM messages 
      WHERE thread_id = ? 
      ORDER BY timestamp DESC
      LIMIT ? OFFSET ?
    `);
        const rows = stmt.all(threadId, limit, offset);
        return rows.map(this.mapRowToMessage);
    }
    /**
     * Semantically search for messages
     */
    async searchSimilar(query, options = {}) {
        const { threadId, topK = 10, minScore = 0.5 } = options;
        // 1. Generate query vector
        const queryVector = await this.embeddingService.embed(query);
        // 2. Load candidate messages (all messages with embeddings)
        // Optimization: Filter by threadId in SQL if provided to reduce memory usage
        let sql = 'SELECT * FROM messages WHERE embedding_blob IS NOT NULL';
        const params = [];
        if (threadId) {
            sql += ' AND thread_id = ?';
            params.push(threadId);
        }
        const rows = this.db.prepare(sql).all(...params);
        // 3. Build corpus
        const corpus = rows.map(row => ({
            id: row.id,
            vector: (0, vector_utils_1.deserializeEmbedding)(row.embedding_blob),
            payload: this.mapRowToMessage(row)
        }));
        // 4. Perform vector search
        const results = this.searchEngine.search(queryVector, corpus, {
            topK,
            minScore
        });
        // 5. Map results
        return results.map(result => ({
            ...result.payload,
            score: result.score
        }));
    }
    deleteByThreadId(threadId) {
        const stmt = this.db.prepare('DELETE FROM messages WHERE thread_id = ?');
        stmt.run(threadId);
    }
    updateThreadMessageCount(threadId) {
        const countStmt = this.db.prepare('SELECT COUNT(*) as count FROM messages WHERE thread_id = ?');
        const result = countStmt.get(threadId);
        const updateStmt = this.db.prepare('UPDATE threads SET message_count = ? WHERE id = ?');
        updateStmt.run(result.count, threadId);
    }
    mapRowToMessage(row) {
        const message = {
            id: row.id,
            threadId: row.thread_id,
            role: row.role,
            content: row.content,
            timestamp: new Date(row.timestamp),
            metadata: JSON.parse(row.metadata || '{}')
        };
        if (row.embedding_blob) {
            message.embedding = (0, vector_utils_1.deserializeEmbedding)(row.embedding_blob);
        }
        return message;
    }
}
exports.MessagesDAO = MessagesDAO;