{"repo": "aurovexlabs/dzidzaal", "files": {"backend/src/config/aiProvider.js": {"14bdf9666330bb75d652c83cc51992680291840c": {"t": 1785745543.6782424, "n": 9517, "h": "c9bd55a05c4e6a4d", "c": "\"use strict\";\n/**\n * DzidzaAI — Multi-Provider AI Abstraction Layer\n *\n * Supports: OpenAI (GPT-4o-mini), Google Gemini (1.5 Flash), DeepSeek (Chat)\n *\n * Priority:\n *   1. Env var AI_PROVIDER if explicitly set\n *   2. OPENAI_API_KEY  → OpenAI\n *   3. GEMINI_API_KEY  → Gemini\n *   4. DEEPSEEK_API_KEY → DeepSeek\n *\n * All providers expose the same interface:\n *   chat(messages, opts)   → { content, tokensUsed, provider }\n *   json(prompt, opts)     → parsed object (retries on bad JSON)\n *\n * Failover: if the primary provider throws, the next available is tried once.\n */\n\nconst NodeCache = require(\"node-cache\");\nconst responseCache = new NodeCache({ stdTTL: 300, checkperiod: 60 }); // 5-min cache\n\nconst LOCAL_VECTOR_DIMENSIONS = 64;\n\nconst localEmbedding = (text) => {\n  const crypto = require(\"crypto\");\n  const normalized = String(text || \"\")\n    .toLowerCase()\n    .replace(/[^a-z0-9\\s]/g, \" \")\n    .replace(/\\s+/g, \" \")\n    .trim();\n\n  const vector = new Array(LOCAL_VECTOR_DIMENSIONS).fill(0);\n  for (const token of normalized.split(\" \").filter(Boolean)) {\n    const hash = crypto.createHash(\"sha256\").update(token).digest();\n    const index = hash.readUInt32BE(0) % LOCAL_VECTOR_DIMENSIONS;\n    vector[index] += 1;\n  }\n\n  const magnitude =\n    Math.sqrt(vector.reduce((sum, value) => sum + value * value, 0)) || 1;\n  return vector.map((value) => value / magnitude);\n};\n\n// ─── Determine provider priority ─────────────────────────────────────────────\nconst buildProviderList = () => {\n  const explicit = process.env.AI_PROVIDER?.toLowerCase();\n  const all = [];\n\n  if (explicit) {\n    // If explicitly set, put it first — still include others for failover\n    const map = { openai: \"openai\", gemini: \"gemini\", deepseek: \"deepseek\" };\n    if (map[explicit]) all.push(map[explicit]);\n  }\n\n  if (!all.includes(\"openai\") && process.env.OPENAI_API_KEY) all.push(\"openai\");\n  if (!all.includes(\"gemini\") && process.env.GEMINI_API_KEY) all.push(\"gemini\");\n  if (!all.includes(\"deepseek\") && process.env.DEEPSEEK_API_KEY)\n    all.push(\"deepseek\");\n\n  return all;\n};\n\n// ─── Provider adapters ────────────────────────────────────────────────────────\n\n// OpenAI (also used for DeepSeek which is OpenAI-compatible)\nconst openAICompletions = async (\n  apiKey,\n  baseURL,\n  model,\n  messages,\n  opts = {},\n) => {\n  const { default: OpenAI } = await Promise.resolve().then(() =>\n    require(\"openai\"),\n  );\n  const client = new OpenAI({ apiKey, baseURL });\n\n  const params = {\n    model,\n    messages,\n    max_tokens: opts.maxTokens || 1200,\n    temperature: opts.temperature ?? 0.7,\n  };\n  if (opts.json) params.response_format = { type: \"json_object\" };\n\n  const res = await client.chat.completions.create(params);\n  return {\n    content: res.choices[0].message.content,\n    tokensUsed: res.usage?.total_tokens || 0,\n  };\n};\n\nconst openAIEmbeddings = async (apiKey, baseURL, model, input) => {\n  const { default: OpenAI } = await Promise.resolve().then(() =>\n    require(\"openai\"),\n  );\n  const client = new OpenAI({ apiKey, baseURL });\n  const res = await client.embeddings.create({\n    model,\n    input,\n  });\n\n  return {\n    vectors: res.data.map((item) => item.embedding),\n    tokensUsed: res.usage?.total_tokens || 0,\n  };\n};\n\n// Google Gemini\nconst geminiCompletions = async (messages, opts = {}) => {\n  const { GoogleGenerativeAI } = require(\"@google/generative-ai\");\n  const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);\n  const model = genAI.getGenerativeModel({ model: \"gemini-2.5-flash\" });\n\n  // Convert OpenAI message format to Gemini format\n  const systemMsg = messages.find((m) => m.role === \"system\");\n  const chatHistory = messages\n    .filter((m) => m.role !== \"system\")\n    .slice(0, -1)\n    .map((m) => ({\n      role: m.role === \"assistant\" ? \"model\" : \"user\",\n      parts: [{ text: m.content }],\n    }));\n  const lastMsg = messages.filter((m) => m.role !== \"system\").at(-1);\n\n  let prompt = lastMsg?.content || \"\";\n  if (systemMsg) prompt = `${systemMsg.content}\\n\\n${prompt}`;\n  if (opts.json)\n    prompt +=\n      \"\\n\\nIMPORTANT: Respond with ONLY valid JSON. No markdown, no explanation.\";\n\n  const chat = model.startChat({\n    history: chatHistory,\n    generationConfig: {\n      maxOutputTokens: opts.maxTokens || 1200,\n      temperature: opts.temperature ?? 0.7,\n    },\n  });\n\n  const result = await chat.sendMessage(prompt);\n  const text = result.response.text();\n\n  return {\n    content: text,\n    tokensUsed: result.response.usageMetadata?.totalTokenCount || 0,\n  };\n};\n\n// ─── Core completion function with failover ───────────────────────────────────\nconst complete = async (messages, opts = {}) => {\n  const providers = buildProviderList();\n\n  if (providers.length === 0) {\n    throw new Error(\n      \"No AI engine configured. Set at least one of: OPENAI_API_KEY, GEMINI_API_KEY, DEEPSEEK_API_KEY in your .env file.\",\n    );\n  }\n\n  let lastError;\n\n  for (const provider of providers) {\n    try {\n      let result;\n\n      if (provider === \"openai\") {\n        result = await openAICompletions(\n          process.env.OPENAI_API_KEY,\n          undefined,\n          \"gpt-4o-mini\",\n          messages,\n          opts,\n        );\n      } else if (provider === \"gemini\") {\n        result = await geminiCompletions(messages, opts);\n      } else if (provider === \"deepseek\") {\n        result = await openAICompletions(\n          process.env.DEEPSEEK_API_KEY,\n          \"https://api.deepseek.com\",\n          \"deepseek-chat\",\n          messages,\n          opts,\n        );\n      }\n\n      return { ...result, provider };\n    } catch (err) {\n      console.error(`[AI] Provider \"${provider}\" failed: ${err.message}`);\n      lastError = err;\n      // Continue to next provider\n    }\n  }\n\n  throw new Error(`All AI engines failed. Last error: ${lastError?.message}`);\n};\n\n// ─── JSON helper with retry + repair ─────────────────────────────────────────\nconst completeJSON = async (prompt, opts = {}) => {\n  const MAX_RETRIES = 2;\n  let lastError;\n\n  for (let attempt = 0; attempt <= MAX_RETRIES; attempt++) {\n    const result = await complete([{ role: \"user\", content: prompt }], {\n      ...opts,\n      json: true,\n    });\n\n    let text = result.content.trim();\n\n    // Strip markdown code fences if present (Gemini sometimes adds them)\n    text = text.replace(/^```(?:json)?\\s*/i, \"\").replace(/\\s*```$/i, \"\");\n\n    // Find first { and last } to extract JSON\n    const start = text.indexOf(\"{\");\n    const end = text.lastIndexOf(\"}\");\n    if (start !== -1 && end !== -1) {\n      text = text.slice(start, end + 1);\n    }\n\n    try {\n      const parsed = JSON.parse(text);\n      return {\n        data: parsed,\n        provider: result.provider,\n        tokensUsed: result.tokensUsed,\n      };\n    } catch (parseErr) {\n      console.error(\n        `[AI] JSON parse failed (attempt ${attempt + 1}):`,\n        parseErr.message,\n      );\n      lastError = parseErr;\n\n      if (attempt < MAX_RETRIES) {\n        // Ask the model to fix its output\n        prompt = `${prompt}\\n\\nYour previous response could not be parsed as JSON. Return ONLY raw JSON with no markdown or explanations.`;\n      }\n    }\n  }\n\n  throw new Error(\n    `AI returned invalid JSON after ${MAX_RETRIES + 1} attempts: ${lastError?.message}`,\n  );\n};\n\n// ─── Cache wrapper ────────────────────────────────────────────────────────────\nconst completeWithCache = async (cacheKey, messages, opts = {}) => {\n  if (opts.noCache) return complete(messages, opts);\n  const cached = responseCache.get(cacheKey);\n  if (cached) return { ...cached, fromCache: true };\n  const result = await complete(messages, opts);\n  responseCache.set(cacheKey, result);\n  return result;\n};\n\nconst embedMany = async (texts, opts = {}) => {\n  const inputs = Array.isArray(texts) ? texts : [texts];\n  const cleaned = inputs.map((text) => String(text || \"\").trim());\n\n  if (!cleaned.length) {\n    return { vectors: [], provider: null, model: null, tokensUsed: 0 };\n  }\n\n  const provider = (\n    opts.provider ||\n    process.env.EMBEDDING_PROVIDER ||\n    \"\"\n  ).toLowerCase();\n  const model =\n    opts.model || process.env.EMBEDDING_MODEL || \"text-embedding-3-small\";\n\n  if (\n    (provider === \"openai\" || provider === \"\") &&\n    process.env.OPENAI_API_KEY\n  ) {\n    const result = await openAIEmbeddings(\n      process.env.OPENAI_API_KEY,\n      undefined,\n      model,\n      cleaned,\n    );\n    return {\n      vectors: result.vectors,\n      provider: \"openai\",\n      model,\n      tokensUsed: result.tokensUsed,\n    };\n  }\n\n  if (\n    (provider === \"deepseek\" || provider === \"\") &&\n    process.env.DEEPSEEK_API_KEY\n  ) {\n    const result = await openAIEmbeddings(\n      process.env.DEEPSEEK_API_KEY,\n      \"https://api.deepseek.com\",\n      model,\n      cleaned,\n    );\n    return {\n      vectors: result.vectors,\n      provider: \"deepseek\",\n      model,\n      tokensUsed: result.tokensUsed,\n    };\n  }\n\n  return {\n    vectors: cleaned.map(localEmbedding),\n    provider: \"local-hash\",\n    model: \"hash-64\",\n    tokensUsed: 0,\n  };\n};\n\nconst embedText = async (text, opts = {}) => {\n  const result = await embedMany([text], opts);\n  if (result.vectors.length && result.vectors[0]) {\n    return {\n      vector: result.vectors[0],\n      provider: result.provider,\n      model: result.model,\n      tokensUsed: result.tokensUsed,\n    };\n  }\n\n  return {\n    vector: localEmbedding(text),\n    provider: \"local-hash\",\n    model: \"hash-64\",\n    tokensUsed: 0,\n  };\n};\n\nmodule.exports = {\n  complete,\n  completeJSON,\n  completeWithCache,\n  embedMany,\n  embedText,\n};\n"}, "ba81ae500f221be17b2f2863a49c67082a35183c": {"t": 1785754063.1805475, "n": 9517, "h": "c9bd55a05c4e6a4d", "c": "\"use strict\";\n/**\n * DzidzaAI — Multi-Provider AI Abstraction Layer\n *\n * Supports: OpenAI (GPT-4o-mini), Google Gemini (1.5 Flash), DeepSeek (Chat)\n *\n * Priority:\n *   1. Env var AI_PROVIDER if explicitly set\n *   2. OPENAI_API_KEY  → OpenAI\n *   3. GEMINI_API_KEY  → Gemini\n *   4. DEEPSEEK_API_KEY → DeepSeek\n *\n * All providers expose the same interface:\n *   chat(messages, opts)   → { content, tokensUsed, provider }\n *   json(prompt, opts)     → parsed object (retries on bad JSON)\n *\n * Failover: if the primary provider throws, the next available is tried once.\n */\n\nconst NodeCache = require(\"node-cache\");\nconst responseCache = new NodeCache({ stdTTL: 300, checkperiod: 60 }); // 5-min cache\n\nconst LOCAL_VECTOR_DIMENSIONS = 64;\n\nconst localEmbedding = (text) => {\n  const crypto = require(\"crypto\");\n  const normalized = String(text || \"\")\n    .toLowerCase()\n    .replace(/[^a-z0-9\\s]/g, \" \")\n    .replace(/\\s+/g, \" \")\n    .trim();\n\n  const vector = new Array(LOCAL_VECTOR_DIMENSIONS).fill(0);\n  for (const token of normalized.split(\" \").filter(Boolean)) {\n    const hash = crypto.createHash(\"sha256\").update(token).digest();\n    const index = hash.readUInt32BE(0) % LOCAL_VECTOR_DIMENSIONS;\n    vector[index] += 1;\n  }\n\n  const magnitude =\n    Math.sqrt(vector.reduce((sum, value) => sum + value * value, 0)) || 1;\n  return vector.map((value) => value / magnitude);\n};\n\n// ─── Determine provider priority ─────────────────────────────────────────────\nconst buildProviderList = () => {\n  const explicit = process.env.AI_PROVIDER?.toLowerCase();\n  const all = [];\n\n  if (explicit) {\n    // If explicitly set, put it first — still include others for failover\n    const map = { openai: \"openai\", gemini: \"gemini\", deepseek: \"deepseek\" };\n    if (map[explicit]) all.push(map[explicit]);\n  }\n\n  if (!all.includes(\"openai\") && process.env.OPENAI_API_KEY) all.push(\"openai\");\n  if (!all.includes(\"gemini\") && process.env.GEMINI_API_KEY) all.push(\"gemini\");\n  if (!all.includes(\"deepseek\") && process.env.DEEPSEEK_API_KEY)\n    all.push(\"deepseek\");\n\n  return all;\n};\n\n// ─── Provider adapters ────────────────────────────────────────────────────────\n\n// OpenAI (also used for DeepSeek which is OpenAI-compatible)\nconst openAICompletions = async (\n  apiKey,\n  baseURL,\n  model,\n  messages,\n  opts = {},\n) => {\n  const { default: OpenAI } = await Promise.resolve().then(() =>\n    require(\"openai\"),\n  );\n  const client = new OpenAI({ apiKey, baseURL });\n\n  const params = {\n    model,\n    messages,\n    max_tokens: opts.maxTokens || 1200,\n    temperature: opts.temperature ?? 0.7,\n  };\n  if (opts.json) params.response_format = { type: \"json_object\" };\n\n  const res = await client.chat.completions.create(params);\n  return {\n    content: res.choices[0].message.content,\n    tokensUsed: res.usage?.total_tokens || 0,\n  };\n};\n\nconst openAIEmbeddings = async (apiKey, baseURL, model, input) => {\n  const { default: OpenAI } = await Promise.resolve().then(() =>\n    require(\"openai\"),\n  );\n  const client = new OpenAI({ apiKey, baseURL });\n  const res = await client.embeddings.create({\n    model,\n    input,\n  });\n\n  return {\n    vectors: res.data.map((item) => item.embedding),\n    tokensUsed: res.usage?.total_tokens || 0,\n  };\n};\n\n// Google Gemini\nconst geminiCompletions = async (messages, opts = {}) => {\n  const { GoogleGenerativeAI } = require(\"@google/generative-ai\");\n  const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);\n  const model = genAI.getGenerativeModel({ model: \"gemini-2.5-flash\" });\n\n  // Convert OpenAI message format to Gemini format\n  const systemMsg = messages.find((m) => m.role === \"system\");\n  const chatHistory = messages\n    .filter((m) => m.role !== \"system\")\n    .slice(0, -1)\n    .map((m) => ({\n      role: m.role === \"assistant\" ? \"model\" : \"user\",\n      parts: [{ text: m.content }],\n    }));\n  const lastMsg = messages.filter((m) => m.role !== \"system\").at(-1);\n\n  let prompt = lastMsg?.content || \"\";\n  if (systemMsg) prompt = `${systemMsg.content}\\n\\n${prompt}`;\n  if (opts.json)\n    prompt +=\n      \"\\n\\nIMPORTANT: Respond with ONLY valid JSON. No markdown, no explanation.\";\n\n  const chat = model.startChat({\n    history: chatHistory,\n    generationConfig: {\n      maxOutputTokens: opts.maxTokens || 1200,\n      temperature: opts.temperature ?? 0.7,\n    },\n  });\n\n  const result = await chat.sendMessage(prompt);\n  const text = result.response.text();\n\n  return {\n    content: text,\n    tokensUsed: result.response.usageMetadata?.totalTokenCount || 0,\n  };\n};\n\n// ─── Core completion function with failover ───────────────────────────────────\nconst complete = async (messages, opts = {}) => {\n  const providers = buildProviderList();\n\n  if (providers.length === 0) {\n    throw new Error(\n      \"No AI engine configured. Set at least one of: OPENAI_API_KEY, GEMINI_API_KEY, DEEPSEEK_API_KEY in your .env file.\",\n    );\n  }\n\n  let lastError;\n\n  for (const provider of providers) {\n    try {\n      let result;\n\n      if (provider === \"openai\") {\n        result = await openAICompletions(\n          process.env.OPENAI_API_KEY,\n          undefined,\n          \"gpt-4o-mini\",\n          messages,\n          opts,\n        );\n      } else if (provider === \"gemini\") {\n        result = await geminiCompletions(messages, opts);\n      } else if (provider === \"deepseek\") {\n        result = await openAICompletions(\n          process.env.DEEPSEEK_API_KEY,\n          \"https://api.deepseek.com\",\n          \"deepseek-chat\",\n          messages,\n          opts,\n        );\n      }\n\n      return { ...result, provider };\n    } catch (err) {\n      console.error(`[AI] Provider \"${provider}\" failed: ${err.message}`);\n      lastError = err;\n      // Continue to next provider\n    }\n  }\n\n  throw new Error(`All AI engines failed. Last error: ${lastError?.message}`);\n};\n\n// ─── JSON helper with retry + repair ─────────────────────────────────────────\nconst completeJSON = async (prompt, opts = {}) => {\n  const MAX_RETRIES = 2;\n  let lastError;\n\n  for (let attempt = 0; attempt <= MAX_RETRIES; attempt++) {\n    const result = await complete([{ role: \"user\", content: prompt }], {\n      ...opts,\n      json: true,\n    });\n\n    let text = result.content.trim();\n\n    // Strip markdown code fences if present (Gemini sometimes adds them)\n    text = text.replace(/^```(?:json)?\\s*/i, \"\").replace(/\\s*```$/i, \"\");\n\n    // Find first { and last } to extract JSON\n    const start = text.indexOf(\"{\");\n    const end = text.lastIndexOf(\"}\");\n    if (start !== -1 && end !== -1) {\n      text = text.slice(start, end + 1);\n    }\n\n    try {\n      const parsed = JSON.parse(text);\n      return {\n        data: parsed,\n        provider: result.provider,\n        tokensUsed: result.tokensUsed,\n      };\n    } catch (parseErr) {\n      console.error(\n        `[AI] JSON parse failed (attempt ${attempt + 1}):`,\n        parseErr.message,\n      );\n      lastError = parseErr;\n\n      if (attempt < MAX_RETRIES) {\n        // Ask the model to fix its output\n        prompt = `${prompt}\\n\\nYour previous response could not be parsed as JSON. Return ONLY raw JSON with no markdown or explanations.`;\n      }\n    }\n  }\n\n  throw new Error(\n    `AI returned invalid JSON after ${MAX_RETRIES + 1} attempts: ${lastError?.message}`,\n  );\n};\n\n// ─── Cache wrapper ────────────────────────────────────────────────────────────\nconst completeWithCache = async (cacheKey, messages, opts = {}) => {\n  if (opts.noCache) return complete(messages, opts);\n  const cached = responseCache.get(cacheKey);\n  if (cached) return { ...cached, fromCache: true };\n  const result = await complete(messages, opts);\n  responseCache.set(cacheKey, result);\n  return result;\n};\n\nconst embedMany = async (texts, opts = {}) => {\n  const inputs = Array.isArray(texts) ? texts : [texts];\n  const cleaned = inputs.map((text) => String(text || \"\").trim());\n\n  if (!cleaned.length) {\n    return { vectors: [], provider: null, model: null, tokensUsed: 0 };\n  }\n\n  const provider = (\n    opts.provider ||\n    process.env.EMBEDDING_PROVIDER ||\n    \"\"\n  ).toLowerCase();\n  const model =\n    opts.model || process.env.EMBEDDING_MODEL || \"text-embedding-3-small\";\n\n  if (\n    (provider === \"openai\" || provider === \"\") &&\n    process.env.OPENAI_API_KEY\n  ) {\n    const result = await openAIEmbeddings(\n      process.env.OPENAI_API_KEY,\n      undefined,\n      model,\n      cleaned,\n    );\n    return {\n      vectors: result.vectors,\n      provider: \"openai\",\n      model,\n      tokensUsed: result.tokensUsed,\n    };\n  }\n\n  if (\n    (provider === \"deepseek\" || provider === \"\") &&\n    process.env.DEEPSEEK_API_KEY\n  ) {\n    const result = await openAIEmbeddings(\n      process.env.DEEPSEEK_API_KEY,\n      \"https://api.deepseek.com\",\n      model,\n      cleaned,\n    );\n    return {\n      vectors: result.vectors,\n      provider: \"deepseek\",\n      model,\n      tokensUsed: result.tokensUsed,\n    };\n  }\n\n  return {\n    vectors: cleaned.map(localEmbedding),\n    provider: \"local-hash\",\n    model: \"hash-64\",\n    tokensUsed: 0,\n  };\n};\n\nconst embedText = async (text, opts = {}) => {\n  const result = await embedMany([text], opts);\n  if (result.vectors.length && result.vectors[0]) {\n    return {\n      vector: result.vectors[0],\n      provider: result.provider,\n      model: result.model,\n      tokensUsed: result.tokensUsed,\n    };\n  }\n\n  return {\n    vector: localEmbedding(text),\n    provider: \"local-hash\",\n    model: \"hash-64\",\n    tokensUsed: 0,\n  };\n};\n\nmodule.exports = {\n  complete,\n  completeJSON,\n  completeWithCache,\n  embedMany,\n  embedText,\n};\n"}}}}