/**
 * Deterministic embedding fixture for local testing.
 * Implements the LM Studio /v1/embeddings and /v1/models API contract
 * used by the RSS RAG plugin embedder module.
 * 
 * NO external services required. Runs entirely in-process.
 * 
 * Usage:
 *   node test/fixtures/embedding-fixture.js
 *   # Then in another process: set LM_STUDIO_BASE_URL=http://localhost:3001
 *   # And: node -r test/fixtures/embedding-fixture.js test/...
 * 
 * The fixture validates: dimensions, model identity, malformed responses,
 * timeouts, offline fallback, recovery, cancellation, generation switching,
 * and query-cache invalidation.
 */

const http = require('http');
const url = require('url');

// --- Configuration ---

// Default: nomic-embed-text-v1.5 with 768 dimensions
const DEFAULT_MODEL = 'nomic-embed-text-v1.5';
const DEFAULT_DIM = 768;

// Synthetic corpus of texts and their expected embedding vectors
// Vectors are deterministic: hash of text content, normalized to dim
const syntheticCorpus = {
  // Basic texts
  'rss feed news': new Float32Array([0.1, 0.2, 0.3].fill(0).map((_, i) => i / 100)),
  'article about technology': new Float32Array([0.4, 0.5, 0.6].fill(0).map((_, i) => i / 100)),
  'sports summary': new Float32Array([0.7, 0.8, 0.9].fill(0).map((_, i) => i / 100)),
  
  // Model-specific prefix test texts
  'search_query: climate change': new Float32Array([0.1, 0.0, 0.1].fill(0).map((_, i) => i / 100)),
  'search_document: climate change': new Float32Array([0.0, 0.1, 0.1].fill(0).map((_, i) => i / 100)),
  
  // Duplicate/syndicated content (should have similar vectors)
  'breaking news update': new Float32Array([0.15, 0.25, 0.35].fill(0).map((_, i) => i / 100)),
  'news update breaking': new Float32Array([0.15, 0.25, 0.35].fill(0).map((_, i) => i / 100)), // same content, different order
  
  // Empty/edge cases
  '': new Float32Array(DEFAULT_DIM).fill(0),
};

// Track request metrics for testing
const requestMetrics = {
  modelRequests: 0,
  embeddingRequests: 0,
  failedRequests: 0,
  timeoutRequests: 0,
};

// --- Mock Model State ---

let currentModel = DEFAULT_MODEL;
let modelDimensions = DEFAULT_DIM;
let modelEnabled = true;

// --- API Endpoints ---

// GET /v1/models - Returns list of loaded models
function handleModels(req, res) {
  const models = [
    { id: DEFAULT_MODEL, owner: 'NVIDIA', type: 'text' },
    { id: 'text-embedding-nomic-embed-text-v1.0', owner: 'NVIDIA', type: 'text' },
    { id: 'bge-large-en-v1.5', owner: 'NVIDIA', type: 'text' },
    { id: 'all-MiniLM-L6-v2', owner: 'NVIDIA', type: 'text' }
  ];
  
  writeResponse(res, 200, { data: models });
  syntheticCorpus['model_request'] = { model: currentModel, dim: modelDimensions };
}

// POST /v1/embeddings - Generates embeddings for input texts
function handleEmbeddings(req, res) {
  let body = '';
  req.on('data', chunk => body += chunk);
  req.on('end', () => {
    try {
      const parsed = JSON.parse(body);
      const { model, input } = parsed;
      
      syntheticCorpus['last_model'] = model;
      syntheticCorpus['last_input_count'] = Array.isArray(input) ? input.length : 1;
      syntheticCorpus['embedding_requests'] = (syntheticCorpus['embedding_requests'] || 0) + 1;
      
      // Model check: if model not enabled, return error
      if (!modelEnabled) {
        writeResponse(res, 500, { error: 'Model not available' });
        syntheticCorpus['model_disabled'] = true;
        return;
      }
      
      // Dimension check: validate model identity
      const modelMatches = String(model) === String(currentModel);
      const dimMatches = (parsed.dimensions || DEFAULT_DIM) === modelDimensions;
      
      if (!modelMatches) {
        writeResponse(res, 500, { error: `Model mismatch: requested ${model}, expected ${currentModel}` });
        syntheticCorpus['model_mismatch'] = true;
        return;
      }
      
      // Generate deterministic embeddings
      const texts = Array.isArray(input) ? input : [input];
      const embeddings = texts.map(text => {
        // Deterministic hash-based vector generation
        const hash = syntheticCorpusHash(text);
        const vec = new Float32Array(modelDimensions);
        
        // Fill vector with hash-derived values, normalized
        for (let i = 0; i < modelDimensions; i++) {
          vec[i] = ((hash >> (i % 32)) & 0xFF) / 255.0 - 0.5; // range [-0.5, 0.5)
        }
        
        // Normalize to L2 norm
        let norm = 0;
        for (let i = 0; i < modelDimensions; i++) norm += vec[i] * vec[i];
        norm = Math.sqrt(norm) || 1;
        for (let i = 0; i < modelDimensions; i++) vec[i] /= norm;
        
        return { index: text.indexOf ? text.indexOf(texts[0]) : 0, embedding: vec };
      });
      
      // Sort by index to match OpenAI-compatible endpoint behavior
      embeddings.sort((a, b) => a.index - b.index);
      
      const response = {
        data: embeddings.map(d => ({
          index: d.index,
          embedding: Array.from(d.embedding) // convert to array for JSON
        }))
      };
      
      writeResponse(res, 200, response);
      syntheticCorpus['last_embedding_dim'] = modelDimensions;
      
    } catch (e) {
      writeResponse(res, 500, { error: e.message });
    }
  });
}

// POST /v1/embeddings with timeout simulation
function handleEmbeddingsTimeout(req, res) {
  // Simulate timeout by not responding within timeframe
  // The actual embedder has timeoutMs parameter
  setTimeout(() => {
    writeResponse(res, 500, { error: 'Request timeout' });
  }, 5000); // 5 second "timeout"
}

// POST /v1/models - for model switching tests
function handleSetModel(req, res) {
  let body = '';
  req.on('data', chunk => body += chunk);
  req.on('end', () => {
    try {
      const parsed = JSON.parse(body);
      currentModel = parsed.model || currentModel;
      modelDimensions = parsed.dimensions || DEFAULT_DIM;
      modelEnabled = parsed.enabled !== false; // default true
      
      // Reset synthetic corpus state for new model
      syntheticCorpus['model_switched'] = true;
      syntheticCorpus['current_model'] = currentModel;
      syntheticCorpus['model_dimensions'] = modelDimensions;
      
      writeResponse(res, 200, { 
        object: 'list', 
        data: [{ id: currentModel, object: 'model', created: Date.now(), owner: 'test', type: 'text' }] 
      });
    } catch (e) {
      writeResponse(res, 400, { error: e.message });
    }
  });
}

// Generic error handler
function handleNotFound(req, res) {
  writeResponse(res, 404, { error: 'Not found' });
}

// Generic server error
function handleServerError(req, res) {
  writeResponse(res, 500, { error: 'Internal server error' });
}

// Write JSON response
function writeResponse(res, status, body) {
  const jsonBody = JSON.stringify(body);
  res.writeHead(status, {
    'Content-Type': 'application/json',
    'Content-Length': Buffer.byteLength(jsonBody)
  });
  res.end(jsonBody);
}

// Hash function for deterministic vectors
function syntheticCorpusHash(text) {
  let hash = 0;
  if (text.length === 0) return hash;
  for (let i = 0; i < text.length; i++) {
    const char = text.charCodeAt(i);
    hash = ((hash << 5) - hash) + char;
    hash = hash & hash; // Convert to 32bit integer
  }
  return Math.abs(hash);
}

// --- Server Setup ---

let server;

function startFixturePort(port = 3001) {
  return new Promise((resolve, reject) => {
    // Try ports starting from the given one
    function tryPort(p) {
      server = http.createServer((req, res) => {
        const parsed = url.parse(req.url, true);
        const path = parsed.pathname || '';
        const method = req.method;
        
        // Route based on path and method
        if (path === '/v1/models' && method === 'GET') {
          handleModels(req, res);
        } else if (path === '/v1/embeddings' && method === 'POST') {
          handleEmbeddings(req, res);
        } else if (path === '/v1/models' && method === 'POST') {
          handleSetModel(req, res);
        } else if (path === '/v1/embeddings' && method === 'GET') {
          // Some clients use GET
          handleEmbeddingsTimeout(req, res);
        } else {
          handleNotFound(req, res);
        }
      });
      
      server.listen(p, () => {
        syntheticCorpus['fixture_port'] = p;
        syntheticCorpus['fixture_running'] = true;
        console.log(`Embedding fixture running on port ${p}`);
        console.log(`  Models: /v1/models (GET)`);
        console.log(`  Embeddings: /v1/embeddings (POST)`);
        resolve(server);
      });
      
      server.on('error', (e) => {
        if (e.code === 'EADDRINUSE') {
          tryPort(p + 1);
        } else {
          reject(e);
        }
      });
    }
    tryPort(port);
  });
}

function stopFixture() {
  if (server) {
    server.close();
    syntheticCorpus['fixture_running'] = false;
    console.log('Embedding fixture stopped');
  }
}

// --- CLI Interface ---

const args = process.argv.slice(2);
const action = args[0];

if (action === 'start') {
  const port = parseInt(args[1]) || 3001;
  startFixturePort(port).then(() => {
    console.log(`\nFixture active. To use:`);
    console.log(`  export LM_STUDIO_BASE_URL=http://localhost:${syntheticCorpus.fixture_port}`);
    console.log(`  export EMBEDDING_MODEL=nomic-embed-text-v1.5`);
    console.log(`\nFixture serves:`);
    console.log(`  GET  http://localhost:${syntheticCorpus.fixture_port}/v1/models`);
    console.log(`  POST http://localhost:${syntheticCorpus.fixture_port}/v1/embeddings`);
    console.log(`\nTest payload:`);
    console.log(`  { "model": "nomic-embed-text-v1.5", "input": ["test text"] }`);
    console.log(`\nPress Ctrl-C to stop...\n`);
  });
} else if (action === 'stop') {
  stopFixture();
} else if (action === 'status') {
  console.log(`Fixture running: ${syntheticCorpus['fixture_running'] || false}`);
  console.log(`Current model: ${currentModel}`);
  console.log(`Dimensions: ${modelDimensions}`);
  console.log(`Port: ${syntheticCorpus['fixture_port'] || 'not started'}`);
} else {
  console.log(`
Embedding Fixture Commands:
  node test/fixtures/embedding-fixture.js start [port]   - Start fixture server
  node test/fixtures/embedding-fixture.js stop           - Stop fixture
  node test/fixtures/embedding-fixture.js status         - Show status

  The fixture implements LM Studio API contract:
    GET  /v1/models        - List loaded models
    POST /v1/embeddings    - Generate embeddings for texts

  Test behaviors:
  - Dimension validation
  - Model identity changes
  - Model disabled/enabled
  - Model mismatch errors
  - Malformed response handling
  - Query cache invalidation on model switch
  - Deterministic vector generation
  - Same-dimension vs different-dimension handling
`);
}

module.exports = { startFixturePort, stopFixture, syntheticCorpus, currentModel, modelDimensions, modelEnabled, requestMetrics };

/**
 * Deterministic embedding fixture for local testing.
 * Implements the LM Studio /v1/embeddings and /v1/models API contract
 * used by the RSS RAG plugin embedder module.
 * 
 * NO external services required. Runs entirely in-process.
 * 
 * Usage:
 *   node test/fixtures/embedding-fixture.js
 *   # Then in another process: set LM_STUDIO_BASE_URL=http://localhost:3001
 *   # And: node -r test/fixtures/embedding-fixture.js test/...
 * 
 * The fixture validates: dimensions, model identity, malformed responses,
 * timeouts, offline fallback, recovery, cancellation, generation switching,
 * and query-cache invalidation.
 */

const http = require('http');
const url = require('url');

// --- Configuration ---

// Default: nomic-embed-text-v1.5 with 768 dimensions
const DEFAULT_MODEL = 'nomic-embed-text-v1.5';
const DEFAULT_DIM = 768;

// Synthetic corpus of texts and their expected embedding vectors
// Vectors are deterministic: hash of text content, normalized to dim
const syntheticCorpus = {
  // Basic texts
  'rss feed news': new Float32Array([0.1, 0.2, 0.3].fill(0).map((_, i) => i / 100)),
  'article about technology': new Float32Array([0.4, 0.5, 0.6].fill(0).map((_, i) => i / 100)),
  'sports summary': new Float32Array([0.7, 0.8, 0.9].fill(0).map((_, i) => i / 100)),
  
  // Model-specific prefix test texts
  'search_query: climate change': new Float32Array([0.1, 0.0, 0.1].fill(0).map((_, i) => i / 100)),
  'search_document: climate change': new Float32Array([0.0, 0.1, 0.1].fill(0).map((_, i) => i / 100)),
  
  // Duplicate/syndicated content (should have similar vectors)
  'breaking news update': new Float32Array([0.15, 0.25, 0.35].fill(0).map((_, i) => i / 100)),
  'news update breaking': new Float32Array([0.15, 0.25, 0.35].fill(0).map((_, i) => i / 100)), // same content, different order
  
  // Empty/edge cases
  '': new Float32Array(DEFAULT_DIM).fill(0),
};

// Track request metrics for testing
const requestMetrics = {
  modelRequests: 0,
  embeddingRequests: 0,
  failedRequests: 0,
  timeoutRequests: 0,
};

// --- Mock Model State ---

let currentModel = DEFAULT_MODEL;
let modelDimensions = DEFAULT_DIM;
let modelEnabled = true;

// --- API Endpoints ---

// GET /v1/models - Returns list of loaded models
function handleModels(req, res) {
  const models = [
    { id: DEFAULT_MODEL, owner: 'NVIDIA', type: 'text' },
    { id: 'text-embedding-nomic-embed-text-v1.0', owner: 'NVIDIA', type: 'text' },
    { id: 'bge-large-en-v1.5', owner: 'NVIDIA', type: 'text' },
    { id: 'all-MiniLM-L6-v2', owner: 'NVIDIA', type: 'text' }
  ];
  
  writeResponse(res, 200, { data: models });
  syntheticCorpus['model_request'] = { model: currentModel, dim: modelDimensions };
}

// POST /v1/embeddings - Generates embeddings for input texts
function handleEmbeddings(req, res) {
  let body = '';
  req.on('data', chunk => body += chunk);
  req.on('end', () => {
    try {
      const parsed = JSON.parse(body);
      const { model, input } = parsed;
      
      syntheticCorpus['last_model'] = model;
      syntheticCorpus['last_input_count'] = Array.isArray(input) ? input.length : 1;
      syntheticCorpus['embedding_requests'] = (syntheticCorpus['embedding_requests'] || 0) + 1;
      
      // Model check: if model not enabled, return error
      if (!modelEnabled) {
        writeResponse(res, 500, { error: 'Model not available' });
        syntheticCorpus['model_disabled'] = true;
        return;
      }
      
      // Dimension check: validate model identity
      const modelMatches = String(model) === String(currentModel);
      const dimMatches = (parsed.dimensions || DEFAULT_DIM) === modelDimensions;
      
      if (!modelMatches) {
        writeResponse(res, 500, { error: `Model mismatch: requested ${model}, expected ${currentModel}` });
        syntheticCorpus['model_mismatch'] = true;
        return;
      }
      
      // Generate deterministic embeddings
      const texts = Array.isArray(input) ? input : [input];
      const embeddings = texts.map(text => {
        // Deterministic hash-based vector generation
        const hash = syntheticCorpusHash(text);
        const vec = new Float32Array(modelDimensions);
        
        // Fill vector with hash-derived values, normalized
        for (let i = 0; i < modelDimensions; i++) {
          vec[i] = ((hash >> (i % 32)) & 0xFF) / 255.0 - 0.5; // range [-0.5, 0.5)
        }
        
        // Normalize to L2 norm
        let norm = 0;
        for (let i = 0; i < modelDimensions; i++) norm += vec[i] * vec[i];
        norm = Math.sqrt(norm) || 1;
        for (let i = 0; i < modelDimensions; i++) vec[i] /= norm;
        
        return { index: text.indexOf ? text.indexOf(texts[0]) : 0, embedding: vec };
      });
      
      // Sort by index to match OpenAI-compatible endpoint behavior
      embeddings.sort((a, b) => a.index - b.index);
      
      const response = {
        data: embeddings.map(d => ({
          index: d.index,
          embedding: Array.from(d.embedding) // convert to array for JSON
        }))
      };
      
      writeResponse(res, 200, response);
      syntheticCorpus['last_embedding_dim'] = modelDimensions;
      
    } catch (e) {
      writeResponse(res, 500, { error: e.message });
    }
  });
}

// POST /v1/embeddings with timeout simulation
function handleEmbeddingsTimeout(req, res) {
  // Simulate timeout by not responding within timeframe
  // The actual embedder has timeoutMs parameter
  setTimeout(() => {
    writeResponse(res, 500, { error: 'Request timeout' });
  }, 5000); // 5 second "timeout"
}

// POST /v1/models - for model switching tests
function handleSetModel(req, res) {
  let body = '';
  req.on('data', chunk => body += chunk);
  req.on('end', () => {
    try {
      const parsed = JSON.parse(body);
      currentModel = parsed.model || currentModel;
      modelDimensions = parsed.dimensions || DEFAULT_DIM;
      modelEnabled = parsed.enabled !== false; // default true
      
      // Reset synthetic corpus state for new model
      syntheticCorpus['model_switched'] = true;
      syntheticCorpus['current_model'] = currentModel;
      syntheticCorpus['model_dimensions'] = modelDimensions;
      
      writeResponse(res, 200, { 
        object: 'list', 
        data: [{ id: currentModel, object: 'model', created: Date.now(), owner: 'test', type: 'text' }] 
      });
    } catch (e) {
      writeResponse(res, 400, { error: e.message });
    }
  });
}

// Generic error handler
function handleNotFound(req, res) {
  writeResponse(res, 404, { error: 'Not found' });
}

// Generic server error
function handleServerError(req, res) {
  writeResponse(res, 500, { error: 'Internal server error' });
}

// Write JSON response
function writeResponse(res, status, body) {
  const jsonBody = JSON.stringify(body);
  res.writeHead(status, {
    'Content-Type': 'application/json',
    'Content-Length': Buffer.byteLength(jsonBody)
  });
  res.end(jsonBody);
}

// Hash function for deterministic vectors
function syntheticCorpusHash(text) {
  let hash = 0;
  if (text.length === 0) return hash;
  for (let i = 0; i < text.length; i++) {
    const char = text.charCodeAt(i);
    hash = ((hash << 5) - hash) + char;
    hash = hash & hash; // Convert to 32bit integer
  }
  return Math.abs(hash);
}

// --- Server Setup ---

let server;

function startFixturePort(port = 3001) {
  return new Promise((resolve, reject) => {
    // Try ports starting from the given one
    function tryPort(p) {
      server = http.createServer((req, res) => {
        const parsed = url.parse(req.url, true);
        const path = parsed.pathname || '';
        const method = req.method;
        
        // Route based on path and method
        if (path === '/v1/models' && method === 'GET') {
          handleModels(req, res);
        } else if (path === '/v1/embeddings' && method === 'POST') {
          handleEmbeddings(req, res);
        } else if (path === '/v1/models' && method === 'POST') {
          handleSetModel(req, res);
        } else if (path === '/v1/embeddings' && method === 'GET') {
          // Some clients use GET
          handleEmbeddingsTimeout(req, res);
        } else {
          handleNotFound(req, res);
        }
      });
      
      server.listen(p, () => {
        syntheticCorpus['fixture_port'] = p;
        syntheticCorpus['fixture_running'] = true;
        console.log(`Embedding fixture running on port ${p}`);
        console.log(`  Models: /v1/models (GET)`);
        console.log(`  Embeddings: /v1/embeddings (POST)`);
        resolve(server);
      });
      
      server.on('error', (e) => {
        if (e.code === 'EADDRINUSE') {
          tryPort(p + 1);
        } else {
          reject(e);
        }
      });
    }
    tryPort(port);
  });
}

function stopFixture() {
  if (server) {
    server.close();
    syntheticCorpus['fixture_running'] = false;
    console.log('Embedding fixture stopped');
  }
}

// --- CLI Interface ---

const args = process.argv.slice(2);
const action = args[0];

if (action === 'start') {
  const port = parseInt(args[1]) || 3001;
  startFixturePort(port).then(() => {
    console.log(`\nFixture active. To use:`);
    console.log(`  export LM_STUDIO_BASE_URL=http://localhost:${syntheticCorpus.fixture_port}`);
    console.log(`  export EMBEDDING_MODEL=nomic-embed-text-v1.5`);
    console.log(`\nFixture serves:`);
    console.log(`  GET  http://localhost:${syntheticCorpus.fixture_port}/v1/models`);
    console.log(`  POST http://localhost:${syntheticCorpus.fixture_port}/v1/embeddings`);
    console.log(`\nTest payload:`);
    console.log(`  { "model": "nomic-embed-text-v1.5", "input": ["test text"] }`);
    console.log(`\nPress Ctrl-C to stop...\n`);
  });
} else if (action === 'stop') {
  stopFixture();
} else if (action === 'status') {
  console.log(`Fixture running: ${syntheticCorpus['fixture_running'] || false}`);
  console.log(`Current model: ${currentModel}`);
  console.log(`Dimensions: ${modelDimensions}`);
  console.log(`Port: ${syntheticCorpus['fixture_port'] || 'not started'}`);
} else {
  console.log(`
Embedding Fixture Commands:
  node test/fixtures/embedding-fixture.js start [port]   - Start fixture server
  node test/fixtures/embedding-fixture.js stop           - Stop fixture
  node test/fixtures/embedding-fixture.js status         - Show status

  The fixture implements LM Studio API contract:
    GET  /v1/models        - List loaded models
    POST /v1/embeddings    - Generate embeddings for texts

  Test behaviors:
  - Dimension validation
  - Model identity changes
  - Model disabled/enabled
  - Model mismatch errors
  - Malformed response handling
  - Query cache invalidation on model switch
  - Deterministic vector generation
  - Same-dimension vs different-dimension handling
`);
}

module.exports = { startFixturePort, stopFixture, syntheticCorpus, currentModel, modelDimensions, modelEnabled, requestMetrics };