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* Cloudflare Workers AI REST API\n *\n * Edge-native implementation for Cloudflare Workers AI via REST API.\n * Supports both standard and streaming responses.\n */\n\nimport type { Credentials } from \"./types.js\";\n\n// =============================================================================\n// Types\n// =============================================================================\n\nexport interface CloudflareRestResponse {\n result: {\n response: string;\n output?: Array<{ type: string; role: string; content?: Array<{ text?: string }> }>;\n usage?: {\n prompt_tokens: number;\n completion_tokens: number;\n total_tokens: number;\n };\n };\n success: boolean;\n errors: unknown[];\n messages: unknown[];\n}\n\n// =============================================================================\n// Cloudflare REST API\n// =============================================================================\n\nfunction validateCloudflareCredentials(credentials: Credentials) {\n const { cloudflareApiKey, cloudflareEmail, cloudflareAccountId } = credentials;\n if (!cloudflareApiKey || !cloudflareEmail || !cloudflareAccountId) {\n throw new Error(\"Cloudflare REST API requires cloudflareApiKey, cloudflareEmail, and cloudflareAccountId\");\n }\n return { cloudflareApiKey, cloudflareEmail, cloudflareAccountId };\n}\n\nfunction buildRequestBody(model: string, messages: Array<{ role: string; content: string }>, stream = false): Record {\n if (model.includes(\"gpt-oss\")) {\n const userMessage = messages.find(m => m.role === \"user\")?.content || \"\";\n const systemMessage = messages.find(m => m.role === \"system\")?.content || \"\";\n return {\n input: userMessage,\n ...(stream && { stream: true }),\n ...(systemMessage && { instructions: systemMessage })\n };\n }\n return {\n messages,\n ...(stream && { stream: true }),\n ...(stream && model.includes('qwen') && { max_tokens: 1024 })\n };\n}\n\n/**\n * Call Cloudflare Workers AI via REST API\n */\nexport async function callCloudflareRest(\n model: string,\n messages: Array<{ role: string; content: string }>,\n credentials: Credentials\n): Promise {\n const { cloudflareApiKey, cloudflareEmail, cloudflareAccountId } = validateCloudflareCredentials(credentials);\n const requestBody = buildRequestBody(model, messages);\n\n const response = await fetch(\n `https://api.cloudflare.com/client/v4/accounts/${cloudflareAccountId}/ai/run/${model}`,\n {\n method: \"POST\",\n headers: {\n \"X-Auth-Email\": cloudflareEmail,\n \"X-Auth-Key\": cloudflareApiKey,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(requestBody),\n }\n );\n\n if (!response.ok) {\n const text = await response.text();\n throw new Error(`Cloudflare API error: ${response.status} ${text}`);\n }\n\n return response.json();\n}\n\n/**\n * Parse SSE lines and extract response chunks\n */\nfunction* parseSSELines(lines: string[]): Generator {\n for (const line of lines) {\n const trimmed = line.trim();\n if (!trimmed || !trimmed.startsWith(\"data: \")) continue;\n const data = trimmed.slice(6);\n if (data === \"[DONE]\") return;\n try {\n const parsed = JSON.parse(data);\n if (parsed.response !== undefined && parsed.response !== \"\") {\n yield parsed.response;\n }\n } catch { /* skip malformed JSON */ }\n }\n}\n\n/**\n * Call Cloudflare Workers AI via REST API with streaming\n */\nexport async function* callCloudflareRestStream(\n model: string,\n messages: Array<{ role: string; content: string }>,\n credentials: Credentials\n): AsyncGenerator {\n const { cloudflareApiKey, cloudflareEmail, cloudflareAccountId } = validateCloudflareCredentials(credentials);\n\n const response = await fetch(\n `https://api.cloudflare.com/client/v4/accounts/${cloudflareAccountId}/ai/run/${model}`,\n {\n method: \"POST\",\n headers: {\n \"X-Auth-Email\": cloudflareEmail,\n \"X-Auth-Key\": cloudflareApiKey,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(buildRequestBody(model, messages, true)),\n }\n );\n\n if (!response.ok) {\n const text = await response.text();\n throw new Error(`Cloudflare API error: ${response.status} ${text}`);\n }\n\n if (!response.body) throw new Error(\"No response body for streaming\");\n\n const reader = response.body.getReader();\n const decoder = new TextDecoder();\n let buffer = \"\";\n\n while (true) {\n const { done, value } = await reader.read();\n if (done) break;\n buffer += decoder.decode(value, { stream: true });\n const lines = buffer.split(\"\\n\");\n buffer = lines.pop() ?? \"\";\n yield* parseSSELines(lines);\n }\n}\n\n/**\n * Extract response text from gpt-oss format\n */\nexport function extractGptOssResponse(result: CloudflareRestResponse): string {\n if (result.result?.output) {\n const assistantMessage = result.result.output.find(\n (o) => o.type === \"message\" && o.role === \"assistant\"\n );\n return assistantMessage?.content?.[0]?.text || \"\";\n }\n return result.result?.response || \"\";\n}\n","/**\n * Provider-Specific Factory Functions\n *\n * Edge-native implementations for each LLM provider's API.\n * Each function uses pure fetch API with no external dependencies.\n */\n\n// =============================================================================\n// Response Types\n// =============================================================================\n\ninterface ProviderResponse {\n text: string;\n usage?: { promptTokens: number; completionTokens: number };\n}\n\ninterface AnthropicApiResponse {\n content?: Array<{ text?: string }>;\n usage?: { input_tokens: number; output_tokens: number };\n}\n\ninterface OpenAIApiResponse {\n choices?: Array<{ message?: { content?: string } }>;\n usage?: { prompt_tokens: number; completion_tokens: number };\n}\n\ninterface GeminiApiResponse {\n candidates?: Array<{ content?: { parts?: Array<{ text?: string }> } }>;\n usageMetadata?: { promptTokenCount: number; candidatesTokenCount: number };\n}\n\n// =============================================================================\n// Anthropic\n// =============================================================================\n\nexport async function generateWithAnthropic(\n model: string,\n messages: Array<{ role: string; content: string }>,\n apiKey: string,\n options: { temperature?: number; maxTokens?: number } = {}\n): Promise {\n const anthropicMessages = messages.map(msg => ({\n role: msg.role === \"user\" ? \"user\" : \"assistant\",\n content: msg.content,\n }));\n\n const systemIndex = messages.findIndex(m => m.role === \"system\");\n const system = systemIndex >= 0 ? messages[systemIndex].content : undefined;\n const finalMessages = systemIndex >= 0\n ? anthropicMessages.filter((_, i) => i !== systemIndex)\n : anthropicMessages;\n\n const response = await fetch(\"https://api.anthropic.com/v1/messages\", {\n method: \"POST\",\n headers: {\n \"x-api-key\": apiKey,\n \"anthropic-version\": \"2023-06-01\",\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n model, messages: finalMessages, system,\n max_tokens: options.maxTokens || 4096,\n temperature: options.temperature || 0.7,\n }),\n });\n\n if (!response.ok) {\n const text = await response.text();\n throw new Error(`Anthropic API error: ${response.status} ${text}`);\n }\n\n const result = await response.json() as AnthropicApiResponse;\n return {\n text: result.content?.[0]?.text || \"\",\n usage: result.usage ? {\n promptTokens: result.usage.input_tokens,\n completionTokens: result.usage.output_tokens,\n } : undefined,\n };\n}\n\n// =============================================================================\n// OpenAI\n// =============================================================================\n\nexport async function generateWithOpenAI(\n model: string,\n messages: Array<{ role: string; content: string }>,\n apiKey: string,\n options: { temperature?: number; maxTokens?: number } = {}\n): Promise {\n const response = await fetch(\"https://api.openai.com/v1/chat/completions\", {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n model, messages,\n temperature: options.temperature || 0.7,\n max_tokens: options.maxTokens,\n }),\n });\n\n if (!response.ok) {\n const text = await response.text();\n throw new Error(`OpenAI API error: ${response.status} ${text}`);\n }\n\n const result = await response.json() as OpenAIApiResponse;\n return {\n text: result.choices?.[0]?.message?.content || \"\",\n usage: result.usage ? {\n promptTokens: result.usage.prompt_tokens,\n completionTokens: result.usage.completion_tokens,\n } : undefined,\n };\n}\n\n// =============================================================================\n// Groq\n// =============================================================================\n\nexport async function generateWithGroq(\n model: string,\n messages: Array<{ role: string; content: string }>,\n apiKey: string,\n options: { temperature?: number; maxTokens?: number } = {}\n): Promise {\n const response = await fetch(\"https://api.groq.com/openai/v1/chat/completions\", {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n model, messages,\n temperature: options.temperature || 0.7,\n max_tokens: options.maxTokens,\n }),\n });\n\n if (!response.ok) {\n const text = await response.text();\n throw new Error(`Groq API error: ${response.status} ${text}`);\n }\n\n const result = await response.json() as OpenAIApiResponse;\n return {\n text: result.choices?.[0]?.message?.content || \"\",\n usage: result.usage ? {\n promptTokens: result.usage.prompt_tokens,\n completionTokens: result.usage.completion_tokens,\n } : undefined,\n };\n}\n\n// =============================================================================\n// Gemini\n// =============================================================================\n\nexport async function generateWithGemini(\n model: string,\n messages: Array<{ role: string; content: string }>,\n apiKey: string,\n options: { temperature?: number; maxTokens?: number } = {}\n): Promise {\n const contents = messages\n .filter(m => m.role !== \"system\")\n .map(m => ({\n role: m.role === \"assistant\" ? \"model\" : \"user\",\n parts: [{ text: m.content }],\n }));\n\n const systemInstruction = messages.find(m => m.role === \"system\")?.content;\n\n const response = await fetch(\n `https://generativelanguage.googleapis.com/v1beta/models/${model}:generateContent?key=${apiKey}`,\n {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n contents,\n ...(systemInstruction && { systemInstruction: { parts: [{ text: systemInstruction }] } }),\n generationConfig: {\n temperature: options.temperature || 0.7,\n maxOutputTokens: options.maxTokens,\n },\n }),\n }\n );\n\n if (!response.ok) {\n const text = await response.text();\n throw new Error(`Gemini API error: ${response.status} ${text}`);\n }\n\n const result = await response.json() as GeminiApiResponse;\n return {\n text: result.candidates?.[0]?.content?.parts?.[0]?.text || \"\",\n usage: result.usageMetadata ? {\n promptTokens: result.usageMetadata.promptTokenCount,\n completionTokens: result.usageMetadata.candidatesTokenCount,\n } : undefined,\n };\n}\n\n// Re-export Cloudflare functions\nexport {\n callCloudflareRest,\n callCloudflareRestStream,\n extractGptOssResponse,\n} from \"./factory-cloudflare.js\";\n\nexport type { CloudflareRestResponse } from \"./factory-cloudflare.js\";\n","/**\n * Edge-Native Provider Factory\n *\n * Core factory function and utilities for routing to provider-specific\n * implementations. Pure fetch API - no external dependencies.\n */\n\nimport type {\n ModelSpec,\n ParsedModelSpec,\n ProviderType,\n Credentials,\n} from \"./types.js\";\nimport {\n generateWithAnthropic,\n generateWithOpenAI,\n generateWithGroq,\n generateWithGemini,\n callCloudflareRest,\n extractGptOssResponse,\n} from \"./factory-providers.js\";\n\n// Re-export provider functions for backward compatibility\nexport {\n generateWithAnthropic,\n generateWithOpenAI,\n generateWithGroq,\n generateWithGemini,\n callCloudflareRest,\n callCloudflareRestStream,\n} from \"./factory-providers.js\";\n\nexport type { CloudflareRestResponse } from \"./factory-providers.js\";\n\n// =============================================================================\n// Core API\n// =============================================================================\n\n/**\n * Parse a ModelSpec string into its components\n */\nexport function parseModelSpec(spec: string): ParsedModelSpec {\n const colonIndex = spec.indexOf(\":\");\n if (colonIndex === -1) {\n throw new Error(`Invalid ModelSpec: \"${spec}\". Expected format: \"provider:model\"`);\n }\n\n const provider = spec.slice(0, colonIndex) as ProviderType;\n const model = spec.slice(colonIndex + 1);\n\n if (![\"anthropic\", \"openai\", \"groq\", \"gemini\", \"cloudflare\"].includes(provider)) {\n throw new Error(`Unknown provider: \"${provider}\". Expected: anthropic, openai, groq, gemini, or cloudflare`);\n }\n\n if (!model) {\n throw new Error(`Model ID is required in spec: \"${spec}\"`);\n }\n\n return {\n provider,\n model,\n spec: spec as ModelSpec,\n };\n}\n\n/**\n * Create a model spec string\n */\nexport function createModelSpec(provider: ProviderType, model: string): ModelSpec {\n return `${provider}:${model}` as ModelSpec;\n}\n\n// =============================================================================\n// Edge-Native Generation\n// =============================================================================\n\ntype GenerateFn = (model: string, messages: Array<{ role: string; content: string }>, apiKey: string, options: { temperature?: number; maxTokens?: number }) => Promise<{ text: string; usage?: { promptTokens: number; completionTokens: number } }>;\n\nconst PROVIDER_GENERATORS: Record = {\n anthropic: { keyField: \"anthropicApiKey\", fn: generateWithAnthropic },\n openai: { keyField: \"openaiApiKey\", fn: generateWithOpenAI },\n groq: { keyField: \"groqApiKey\", fn: generateWithGroq },\n gemini: { keyField: \"geminiApiKey\", fn: generateWithGemini },\n};\n\nasync function generateWithCloudflare(\n model: string,\n messages: Array<{ role: string; content: string }>,\n credentials: Credentials\n): Promise<{ text: string; usage?: { promptTokens: number; completionTokens: number } }> {\n if (!credentials.cloudflareApiKey || !credentials.cloudflareEmail || !credentials.cloudflareAccountId) {\n throw new Error(\"Cloudflare models require cloudflareApiKey, cloudflareEmail, and cloudflareAccountId\");\n }\n const result = await callCloudflareRest(model, messages, credentials);\n return {\n text: result.result.response || extractGptOssResponse(result),\n usage: result.result.usage ? {\n promptTokens: result.result.usage.prompt_tokens,\n completionTokens: result.result.usage.completion_tokens,\n } : undefined,\n };\n}\n\n/**\n * Generate text using any supported provider (edge-native)\n */\nexport async function generate(\n spec: ModelSpec | string,\n messages: Array<{ role: string; content: string }>,\n credentials: Credentials,\n options: { temperature?: number; maxTokens?: number } = {}\n): Promise<{ text: string; usage?: { promptTokens: number; completionTokens: number } }> {\n const { provider, model } = parseModelSpec(spec);\n\n if (provider === \"cloudflare\") {\n return generateWithCloudflare(model, messages, credentials);\n }\n\n const config = PROVIDER_GENERATORS[provider];\n if (!config) throw new Error(`Unknown provider: ${provider}`);\n\n const apiKey = credentials[config.keyField];\n if (!apiKey) throw new Error(`${config.keyField} is required for ${provider} models`);\n\n return config.fn(model, messages, apiKey, options);\n}\n\n// =============================================================================\n// Convenience Functions\n// =============================================================================\n\n/**\n * Check if credentials are available for a provider\n */\nexport function hasCredentials(provider: ProviderType, credentials: Credentials): boolean {\n switch (provider) {\n case \"anthropic\":\n return !!credentials.anthropicApiKey;\n case \"openai\":\n return !!credentials.openaiApiKey;\n case \"groq\":\n return !!credentials.groqApiKey;\n case \"gemini\":\n return !!credentials.geminiApiKey;\n case \"cloudflare\":\n return !!(credentials.cloudflareApiKey && credentials.cloudflareEmail && credentials.cloudflareAccountId);\n }\n}\n\n/**\n * Get credentials from environment variables\n */\nexport function getCredentialsFromEnv(): Credentials {\n return {\n anthropicApiKey: process.env.ANTHROPIC_API_KEY,\n openaiApiKey: process.env.OPENAI_API_KEY,\n groqApiKey: process.env.GROQ_API_KEY,\n geminiApiKey: process.env.GEMINI_API_KEY,\n cloudflareApiKey: process.env.CLOUDFLARE_API_KEY,\n cloudflareEmail: process.env.CLOUDFLARE_EMAIL,\n cloudflareAccountId: process.env.CLOUDFLARE_ACCOUNT_ID,\n };\n}\n","/**\n * Model Data Definitions\n *\n * All available models with metadata, indexed by ModelSpec.\n * Verified working as of 2025-12-17, updated 2025-01-15.\n */\n\nimport type { ModelSpec, ModelInfo } from \"./types.js\";\n\n/**\n * All available models with metadata\n */\nexport const MODELS: ModelInfo[] = [\n // Anthropic models (verified 2025-12-26)\n {\n spec: \"anthropic:claude-opus-4-5-20251101\",\n provider: \"anthropic\",\n model: \"claude-opus-4-5-20251101\",\n name: \"Claude Opus 4.5 (Most Intelligent)\",\n contextWindow: 200000,\n speed: \"slow\",\n cost: \"high\",\n },\n {\n spec: \"anthropic:claude-haiku-4-5-20251001\",\n provider: \"anthropic\",\n model: \"claude-haiku-4-5-20251001\",\n name: \"Claude Haiku 4.5 (Ultra Fast)\",\n contextWindow: 200000,\n speed: \"fast\",\n cost: \"low\",\n },\n {\n spec: \"anthropic:claude-sonnet-4-5-20250929\",\n provider: \"anthropic\",\n model: \"claude-sonnet-4-5-20250929\",\n name: \"Claude Sonnet 4.5 (Best for Coding)\",\n contextWindow: 200000,\n speed: \"fast\",\n cost: \"medium\",\n },\n {\n spec: \"anthropic:claude-opus-4-1-20250805\",\n provider: \"anthropic\",\n model: \"claude-opus-4-1-20250805\",\n name: \"Claude Opus 4.1\",\n contextWindow: 200000,\n speed: \"slow\",\n cost: \"high\",\n },\n {\n spec: \"anthropic:claude-opus-4-20250514\",\n provider: \"anthropic\",\n model: \"claude-opus-4-20250514\",\n name: \"Claude Opus 4\",\n contextWindow: 200000,\n speed: \"slow\",\n cost: \"high\",\n },\n {\n spec: \"anthropic:claude-sonnet-4-20250514\",\n provider: \"anthropic\",\n model: \"claude-sonnet-4-20250514\",\n name: \"Claude Sonnet 4\",\n contextWindow: 200000,\n speed: \"fast\",\n cost: \"medium\",\n },\n {\n spec: \"anthropic:claude-3-5-haiku-20241022\",\n provider: \"anthropic\",\n model: \"claude-3-5-haiku-20241022\",\n name: \"Claude 3.5 Haiku\",\n contextWindow: 200000,\n speed: \"fast\",\n cost: \"low\",\n },\n {\n spec: \"anthropic:claude-3-haiku-20240307\",\n provider: \"anthropic\",\n model: \"claude-3-haiku-20240307\",\n name: \"Claude 3 Haiku\",\n contextWindow: 200000,\n speed: \"fast\",\n cost: \"low\",\n },\n\n // OpenAI models (verified 2025-12-26)\n {\n spec: \"openai:gpt-4o\",\n provider: \"openai\",\n model: \"gpt-4o\",\n name: \"GPT-4o (Latest)\",\n contextWindow: 128000,\n speed: \"fast\",\n cost: \"high\",\n },\n {\n spec: \"openai:gpt-4o-mini\",\n provider: \"openai\",\n model: \"gpt-4o-mini\",\n name: \"GPT-4o Mini\",\n contextWindow: 128000,\n speed: \"fast\",\n cost: \"low\",\n },\n {\n spec: \"openai:gpt-4o-2024-11-20\",\n provider: \"openai\",\n model: \"gpt-4o-2024-11-20\",\n name: \"GPT-4o (2024-11-20)\",\n contextWindow: 128000,\n speed: \"fast\",\n cost: \"high\",\n },\n {\n spec: \"openai:gpt-4o-2024-08-06\",\n provider: \"openai\",\n model: \"gpt-4o-2024-08-06\",\n name: \"GPT-4o (2024-08-06)\",\n contextWindow: 128000,\n speed: \"fast\",\n cost: \"high\",\n },\n {\n spec: \"openai:gpt-4-turbo\",\n provider: \"openai\",\n model: \"gpt-4-turbo\",\n name: \"GPT-4 Turbo\",\n contextWindow: 128000,\n speed: \"medium\",\n cost: \"high\",\n },\n {\n spec: \"openai:gpt-4-turbo-preview\",\n provider: \"openai\",\n model: \"gpt-4-turbo-preview\",\n name: \"GPT-4 Turbo Preview\",\n contextWindow: 128000,\n speed: \"medium\",\n cost: \"high\",\n },\n {\n spec: \"openai:gpt-4\",\n provider: \"openai\",\n model: \"gpt-4\",\n name: \"GPT-4\",\n contextWindow: 8192,\n speed: \"slow\",\n cost: \"high\",\n },\n {\n spec: \"openai:gpt-3.5-turbo\",\n provider: \"openai\",\n model: \"gpt-3.5-turbo\",\n name: \"GPT-3.5 Turbo\",\n contextWindow: 16385,\n speed: \"fast\",\n cost: \"low\",\n },\n {\n spec: \"openai:gpt-3.5-turbo-0125\",\n provider: \"openai\",\n model: \"gpt-3.5-turbo-0125\",\n name: \"GPT-3.5 Turbo (0125)\",\n contextWindow: 16385,\n speed: \"fast\",\n cost: \"low\",\n },\n\n // Groq models (verified 2025-01-15)\n {\n spec: \"groq:llama-3.1-8b-instant\",\n provider: \"groq\",\n model: \"llama-3.1-8b-instant\",\n name: \"Llama 3.1 8B Instant\",\n contextWindow: 131072,\n speed: \"fast\",\n cost: \"low\",\n },\n {\n spec: \"groq:llama-3.3-70b-versatile\",\n provider: \"groq\",\n model: \"llama-3.3-70b-versatile\",\n name: \"Llama 3.3 70B Versatile\",\n contextWindow: 131072,\n speed: \"medium\",\n cost: \"medium\",\n },\n {\n spec: \"groq:meta-llama/llama-guard-4-12b\",\n provider: \"groq\",\n model: \"meta-llama/llama-guard-4-12b\",\n name: \"Llama Guard 4 12B\",\n contextWindow: 131072,\n speed: \"fast\",\n cost: \"low\",\n },\n {\n spec: \"groq:openai/gpt-oss-120b\",\n provider: \"groq\",\n model: \"openai/gpt-oss-120b\",\n name: \"GPT-OSS 120B\",\n contextWindow: 131072,\n speed: \"medium\",\n cost: \"low\",\n },\n {\n spec: \"groq:openai/gpt-oss-20b\",\n provider: \"groq\",\n model: \"openai/gpt-oss-20b\",\n name: \"GPT-OSS 20B\",\n contextWindow: 131072,\n speed: \"fast\",\n cost: \"low\",\n },\n {\n spec: \"groq:groq/compound\",\n provider: \"groq\",\n model: \"groq/compound\",\n name: \"Groq Compound (Web Search + Code)\",\n contextWindow: 131072,\n speed: \"medium\",\n cost: \"medium\",\n },\n {\n spec: \"groq:groq/compound-mini\",\n provider: \"groq\",\n model: \"groq/compound-mini\",\n name: \"Groq Compound Mini (Web Search + Code)\",\n contextWindow: 131072,\n speed: \"medium\",\n cost: \"low\",\n },\n];\n","/**\n * Gemini Model Definitions\n *\n * Google Gemini model metadata for all supported versions.\n */\n\nimport type { ModelInfo } from \"./types.js\";\n\n/**\n * Gemini models (verified 2025-01-15)\n */\nexport const GEMINI_MODELS: ModelInfo[] = [\n // Gemini 3 Series (Latest)\n {\n spec: \"gemini:gemini-3-pro-preview\",\n provider: \"gemini\",\n model: \"gemini-3-pro-preview\",\n name: \"Gemini 3 Pro (Preview)\",\n contextWindow: 1048576,\n speed: \"slow\",\n cost: \"high\",\n },\n {\n spec: \"gemini:gemini-3-flash-preview\",\n provider: \"gemini\",\n model: \"gemini-3-flash-preview\",\n name: \"Gemini 3 Flash (Preview)\",\n contextWindow: 1048576,\n speed: \"fast\",\n cost: \"medium\",\n },\n\n // Gemini 2.5 Series\n {\n spec: \"gemini:gemini-2.5-pro\",\n provider: \"gemini\",\n model: \"gemini-2.5-pro\",\n name: \"Gemini 2.5 Pro\",\n contextWindow: 1048576,\n speed: \"slow\",\n cost: \"high\",\n },\n {\n spec: \"gemini:gemini-2.5-flash\",\n provider: \"gemini\",\n model: \"gemini-2.5-flash\",\n name: \"Gemini 2.5 Flash\",\n contextWindow: 1048576,\n speed: \"fast\",\n cost: \"medium\",\n },\n\n // Gemini 2.0 Series\n {\n spec: \"gemini:gemini-2.0-flash\",\n provider: \"gemini\",\n model: \"gemini-2.0-flash\",\n name: \"Gemini 2.0 Flash\",\n contextWindow: 1048576,\n speed: \"fast\",\n cost: \"low\",\n },\n {\n spec: \"gemini:gemini-2.0-flash-lite\",\n provider: \"gemini\",\n model: \"gemini-2.0-flash-lite\",\n name: \"Gemini 2.0 Flash Lite\",\n contextWindow: 1048576,\n speed: \"fast\",\n cost: \"low\",\n },\n\n // Gemini 1.5 Series (Legacy but Active)\n {\n spec: \"gemini:gemini-1.5-pro-002\",\n provider: \"gemini\",\n model: \"gemini-1.5-pro-002\",\n name: \"Gemini 1.5 Pro\",\n contextWindow: 1048576,\n speed: \"medium\",\n cost: \"medium\",\n },\n {\n spec: \"gemini:gemini-1.5-flash-002\",\n provider: \"gemini\",\n model: \"gemini-1.5-flash-002\",\n name: \"Gemini 1.5 Flash\",\n contextWindow: 1048576,\n speed: \"fast\",\n cost: \"low\",\n },\n];\n","/**\n * Cloudflare Workers AI Model Definitions\n *\n * Cloudflare Workers AI model metadata for all supported models.\n */\n\nimport type { ModelInfo } from \"./types.js\";\n\n/**\n * Cloudflare Workers AI models (verified 2025-01-15)\n */\nexport const CLOUDFLARE_MODELS: ModelInfo[] = [\n // OpenAI Models\n {\n spec: \"cloudflare:@cf/openai/gpt-oss-120b\",\n provider: \"cloudflare\",\n model: \"@cf/openai/gpt-oss-120b\",\n name: \"GPT-OSS 120B\",\n contextWindow: 128000,\n speed: \"medium\",\n cost: \"medium\",\n },\n {\n spec: \"cloudflare:@cf/openai/gpt-oss-20b\",\n provider: \"cloudflare\",\n model: \"@cf/openai/gpt-oss-20b\",\n name: \"GPT-OSS 20B\",\n contextWindow: 128000,\n speed: \"fast\",\n cost: \"low\",\n },\n\n // Meta Llama Models\n {\n spec: \"cloudflare:@cf/meta/llama-4-scout-17b-16e-instruct\",\n provider: \"cloudflare\",\n model: \"@cf/meta/llama-4-scout-17b-16e-instruct\",\n name: \"Llama 4 Scout 17B (Multimodal)\",\n contextWindow: 131072,\n speed: \"medium\",\n cost: \"free\",\n },\n {\n spec: \"cloudflare:@cf/meta/llama-3.3-70b-instruct-fp8-fast\",\n provider: \"cloudflare\",\n model: \"@cf/meta/llama-3.3-70b-instruct-fp8-fast\",\n name: \"Llama 3.3 70B (FP8 Fast)\",\n contextWindow: 131072,\n speed: \"fast\",\n cost: \"free\",\n },\n {\n spec: \"cloudflare:@cf/meta/llama-3.1-70b-instruct\",\n provider: \"cloudflare\",\n model: \"@cf/meta/llama-3.1-70b-instruct\",\n name: \"Llama 3.1 70B Instruct\",\n contextWindow: 131072,\n speed: \"medium\",\n cost: \"free\",\n },\n {\n spec: \"cloudflare:@cf/meta/llama-3.1-8b-instruct-fast\",\n provider: \"cloudflare\",\n model: \"@cf/meta/llama-3.1-8b-instruct-fast\",\n name: \"Llama 3.1 8B Instruct (Fast)\",\n contextWindow: 131072,\n speed: \"fast\",\n cost: \"free\",\n },\n {\n spec: \"cloudflare:@cf/meta/llama-3.1-8b-instruct\",\n provider: \"cloudflare\",\n model: \"@cf/meta/llama-3.1-8b-instruct\",\n name: \"Llama 3.1 8B Instruct\",\n contextWindow: 131072,\n speed: \"fast\",\n cost: \"free\",\n },\n\n // IBM Models\n {\n spec: \"cloudflare:@cf/ibm/granite-4.0-h-micro\",\n provider: \"cloudflare\",\n model: \"@cf/ibm/granite-4.0-h-micro\",\n name: \"IBM Granite 4.0 H Micro\",\n contextWindow: 131072,\n speed: \"fast\",\n cost: \"free\",\n },\n\n // MistralAI Models\n {\n spec: \"cloudflare:@cf/mistralai/mistral-small-3.1-24b-instruct\",\n provider: \"cloudflare\",\n model: \"@cf/mistralai/mistral-small-3.1-24b-instruct\",\n name: \"Mistral Small 3.1 24B\",\n contextWindow: 131072,\n speed: \"medium\",\n cost: \"free\",\n },\n {\n spec: \"cloudflare:@cf/mistralai/mistral-7b-instruct-v0.2\",\n provider: \"cloudflare\",\n model: \"@cf/mistralai/mistral-7b-instruct-v0.2\",\n name: \"Mistral 7B Instruct v0.2\",\n contextWindow: 32768,\n speed: \"fast\",\n cost: \"free\",\n },\n\n // Google Models\n {\n spec: \"cloudflare:@cf/google/gemma-3-12b-it\",\n provider: \"cloudflare\",\n model: \"@cf/google/gemma-3-12b-it\",\n name: \"Gemma 3 12B IT\",\n contextWindow: 128000,\n speed: \"fast\",\n cost: \"free\",\n },\n\n // Qwen Models\n {\n spec: \"cloudflare:@cf/qwen/qwq-32b\",\n provider: \"cloudflare\",\n model: \"@cf/qwen/qwq-32b\",\n name: \"QwQ 32B (Reasoning)\",\n contextWindow: 131072,\n speed: \"medium\",\n cost: \"free\",\n },\n {\n spec: \"cloudflare:@cf/qwen/qwen2.5-coder-32b-instruct\",\n provider: \"cloudflare\",\n model: \"@cf/qwen/qwen2.5-coder-32b-instruct\",\n name: \"Qwen 2.5 Coder 32B\",\n contextWindow: 131072,\n speed: \"medium\",\n cost: \"free\",\n },\n];\n","/**\n * Model Registry and Utilities\n *\n * Aggregates model data from provider-specific files and provides\n * lookup, filtering, and query functions.\n */\n\nimport type { ModelSpec, ModelInfo, ProviderType, Credentials } from \"./types.js\";\nimport { MODELS as BASE_MODELS } from \"./models-data.js\";\nimport { GEMINI_MODELS } from \"./models-gemini.js\";\nimport { CLOUDFLARE_MODELS } from \"./models-cloudflare.js\";\n\n// =============================================================================\n// Aggregated Model List\n// =============================================================================\n\n/**\n * All available models with metadata\n */\nexport const MODELS: ModelInfo[] = [\n ...BASE_MODELS,\n ...GEMINI_MODELS,\n ...CLOUDFLARE_MODELS,\n];\n\n// =============================================================================\n// Model Lookup\n// =============================================================================\n\n/**\n * Model lookup by spec\n */\nexport const MODEL_BY_SPEC: Record = Object.fromEntries(\n MODELS.map((m) => [m.spec, m])\n);\n\n/**\n * Get model info by spec\n */\nexport function getModelInfo(spec: ModelSpec | string): ModelInfo | undefined {\n return MODEL_BY_SPEC[spec];\n}\n\n/**\n * Get models by provider\n */\nexport function getModelsByProvider(provider: ProviderType): ModelInfo[] {\n return MODELS.filter((m) => m.provider === provider);\n}\n\n/**\n * Get all model specs\n */\nexport function getAllSpecs(): ModelSpec[] {\n return MODELS.map((m) => m.spec);\n}\n\n/**\n * Get recommended models (fast + low/free cost)\n */\nexport function getRecommendedModels(): ModelInfo[] {\n return MODELS.filter(\n (m) => m.speed === \"fast\" && (m.cost === \"free\" || m.cost === \"low\")\n );\n}\n\n/**\n * Default model specs for each provider\n */\nexport const DEFAULT_SPECS: Record = {\n anthropic: \"anthropic:claude-sonnet-4-5-20250929\",\n openai: \"openai:gpt-4o-mini\",\n groq: \"groq:llama-3.1-8b-instant\",\n gemini: \"gemini:gemini-2.0-flash\",\n cloudflare: \"cloudflare:@cf/meta/llama-3.3-70b-instruct-fp8-fast\",\n};\n\n/**\n * Check if a spec is valid\n */\nexport function isValidSpec(spec: string): spec is ModelSpec {\n return spec in MODEL_BY_SPEC;\n}\n\n/**\n * Get available providers based on credentials\n */\nexport function getAvailableProviders(credentials: Credentials): ProviderType[] {\n const providers: ProviderType[] = [];\n if (credentials.anthropicApiKey) providers.push(\"anthropic\");\n if (credentials.openaiApiKey) providers.push(\"openai\");\n if (credentials.groqApiKey) providers.push(\"groq\");\n if (credentials.geminiApiKey) providers.push(\"gemini\");\n if (credentials.cloudflareAccountId) providers.push(\"cloudflare\");\n return providers;\n}\n\n/**\n * Get available models based on credentials\n */\nexport function getAvailableModels(credentials: Credentials): ModelInfo[] {\n const providers = getAvailableProviders(credentials);\n return MODELS.filter((m) => providers.includes(m.provider));\n}\n","/**\n * Error Types for LLM Provider\n *\n * Unified error handling across all providers (Groq, Gemini, Cloudflare)\n */\n\nimport type { ProviderType } from \"./types.js\";\n\n/**\n * Error codes for categorization\n */\nexport type LLMErrorCode =\n | \"RATE_LIMIT\" // 429 - Too many requests\n | \"AUTH\" // 401/403 - Authentication/Authorization failed\n | \"TIMEOUT\" // Request timeout\n | \"INVALID_RESPONSE\" // Invalid or unparseable response\n | \"NETWORK\" // Network connectivity issues\n | \"SERVER_ERROR\" // 500+ server errors\n | \"VALIDATION\" // Schema validation failed\n | \"UNKNOWN\"; // Unknown error\n\n/**\n * Main error class for LLM provider errors\n */\nexport class LLMProviderError extends Error {\n readonly code: LLMErrorCode;\n readonly provider: ProviderType | \"unknown\";\n readonly retryable: boolean;\n readonly statusCode?: number;\n readonly cause?: Error;\n\n constructor(options: {\n message: string;\n code: LLMErrorCode;\n provider?: ProviderType;\n statusCode?: number;\n retryable?: boolean;\n cause?: Error;\n }) {\n super(options.message);\n this.name = \"LLMProviderError\";\n this.code = options.code;\n this.provider = options.provider ?? \"unknown\";\n this.statusCode = options.statusCode;\n this.retryable = options.retryable ?? isRetryableCode(options.code);\n this.cause = options.cause;\n\n // Maintains proper stack trace for where our error was thrown (only in V8)\n if (Error.captureStackTrace) {\n Error.captureStackTrace(this, LLMProviderError);\n }\n }\n\n /**\n * Create a user-friendly error message\n */\n toUserMessage(): string {\n switch (this.code) {\n case \"RATE_LIMIT\":\n return \"Too many requests. Please wait a moment and try again.\";\n case \"AUTH\":\n return \"Authentication failed. Please check your API credentials.\";\n case \"TIMEOUT\":\n return \"Request timed out. Please try again.\";\n case \"INVALID_RESPONSE\":\n return \"Received an invalid response from the AI service.\";\n case \"NETWORK\":\n return \"Network error. Please check your connection.\";\n case \"SERVER_ERROR\":\n return \"The AI service is temporarily unavailable. Please try again later.\";\n case \"VALIDATION\":\n return \"The response did not match the expected format.\";\n default:\n return \"An unexpected error occurred.\";\n }\n }\n}\n\n/**\n * Determine if an error code is retryable by default\n */\nexport function isRetryableCode(code: LLMErrorCode): boolean {\n switch (code) {\n case \"RATE_LIMIT\":\n case \"TIMEOUT\":\n case \"NETWORK\":\n case \"SERVER_ERROR\":\n return true;\n case \"AUTH\":\n case \"INVALID_RESPONSE\":\n case \"VALIDATION\":\n case \"UNKNOWN\":\n return false;\n }\n}\n\n/**\n * Parse an HTTP status code to an error code\n */\nexport function statusToErrorCode(status: number): LLMErrorCode {\n if (status === 429) return \"RATE_LIMIT\";\n if (status === 401 || status === 403) return \"AUTH\";\n if (status === 408) return \"TIMEOUT\";\n if (status >= 500) return \"SERVER_ERROR\";\n return \"UNKNOWN\";\n}\n\n/**\n * Detect error code from message content\n */\nfunction detectErrorCode(lowerMessage: string): { code: LLMErrorCode; statusCode?: number } {\n const patterns: Array<{ keywords: string[]; code: LLMErrorCode; statusCode?: number }> = [\n { keywords: [\"rate limit\", \"429\"], code: \"RATE_LIMIT\", statusCode: 429 },\n { keywords: [\"timeout\", \"timed out\"], code: \"TIMEOUT\" },\n { keywords: [\"unauthorized\", \"authentication\", \"api key\", \"401\", \"403\"], code: \"AUTH\" },\n { keywords: [\"network\", \"econnrefused\", \"enotfound\", \"fetch failed\"], code: \"NETWORK\" },\n { keywords: [\"500\", \"internal server\"], code: \"SERVER_ERROR\", statusCode: 500 },\n ];\n for (const pattern of patterns) {\n if (pattern.keywords.some(k => lowerMessage.includes(k))) {\n return { code: pattern.code, statusCode: pattern.statusCode };\n }\n }\n return { code: \"UNKNOWN\" };\n}\n\n/**\n * Wrap any error into an LLMProviderError\n */\nexport function wrapError(\n error: unknown,\n provider?: ProviderType,\n context?: string\n): LLMProviderError {\n if (error instanceof LLMProviderError) {\n return error;\n }\n\n if (error instanceof Error) {\n const message = context ? `${context}: ${error.message}` : error.message;\n const detected = detectErrorCode(error.message.toLowerCase());\n return new LLMProviderError({\n message,\n code: detected.code,\n provider,\n statusCode: detected.statusCode,\n cause: error,\n });\n }\n\n return new LLMProviderError({\n message: context ? `${context}: ${String(error)}` : String(error),\n code: \"UNKNOWN\",\n provider,\n });\n}\n\n/**\n * Check if an error is an LLMProviderError with a specific code\n */\nexport function isLLMError(\n error: unknown,\n code?: LLMErrorCode\n): error is LLMProviderError {\n if (!(error instanceof LLMProviderError)) return false;\n if (code === undefined) return true;\n return error.code === code;\n}\n\n/**\n * Check if an error is retryable\n */\nexport function isRetryable(error: unknown): boolean {\n if (error instanceof LLMProviderError) {\n return error.retryable;\n }\n return false;\n}\n","/**\n * Retry Logic for LLM Provider\n *\n * Exponential backoff with jitter for handling transient errors\n */\n\nimport {\n LLMProviderError,\n LLMErrorCode,\n wrapError,\n} from \"./errors.js\";\n\n/**\n * Configuration for retry behavior\n */\nexport interface RetryConfig {\n /** Maximum number of retry attempts (default: 3) */\n maxRetries?: number;\n /** Base delay in milliseconds (default: 100) */\n baseDelay?: number;\n /** Maximum delay in milliseconds (default: 10000) */\n maxDelay?: number;\n /** Error codes to retry on (default: all retryable codes) */\n retryOn?: LLMErrorCode[];\n /** Callback when a retry occurs */\n onRetry?: (attempt: number, error: LLMProviderError, delay: number) => void;\n /** Custom delay calculator (overrides exponential backoff) */\n delayFn?: (attempt: number, baseDelay: number) => number;\n}\n\n/**\n * Default retry configuration\n */\nexport const DEFAULT_RETRY_CONFIG: Required> = {\n maxRetries: 3,\n baseDelay: 100,\n maxDelay: 10000,\n};\n\n/**\n * Default retryable error codes\n */\nexport const DEFAULT_RETRYABLE_CODES: LLMErrorCode[] = [\n \"RATE_LIMIT\",\n \"TIMEOUT\",\n \"NETWORK\",\n \"SERVER_ERROR\",\n];\n\n/**\n * Calculate delay with exponential backoff and jitter\n *\n * @param attempt - Current attempt number (0-indexed)\n * @param baseDelay - Base delay in milliseconds\n * @param maxDelay - Maximum delay cap\n * @returns Delay in milliseconds\n */\nexport function calculateDelay(\n attempt: number,\n baseDelay: number,\n maxDelay: number\n): number {\n // Exponential backoff: baseDelay * 2^attempt\n const exponentialDelay = baseDelay * Math.pow(2, attempt);\n\n // Add jitter (random factor between 0.5 and 1.5)\n const jitter = 0.5 + Math.random();\n const delayWithJitter = exponentialDelay * jitter;\n\n // Cap at maxDelay\n return Math.min(delayWithJitter, maxDelay);\n}\n\n/**\n * Sleep for a specified duration\n */\nexport function sleep(ms: number): Promise {\n return new Promise((resolve) => setTimeout(resolve, ms));\n}\n\n/**\n * Execute a function with retry logic\n *\n * @example\n * ```typescript\n * const result = await withRetry(\n * () => generateText({ model, prompt }),\n * {\n * maxRetries: 3,\n * baseDelay: 100,\n * retryOn: ['RATE_LIMIT', 'TIMEOUT'],\n * onRetry: (attempt, error) => console.log(`Retry ${attempt}...`),\n * }\n * );\n * ```\n */\nexport async function withRetry(\n fn: () => Promise,\n config: RetryConfig = {}\n): Promise {\n const {\n maxRetries = DEFAULT_RETRY_CONFIG.maxRetries,\n baseDelay = DEFAULT_RETRY_CONFIG.baseDelay,\n maxDelay = DEFAULT_RETRY_CONFIG.maxDelay,\n retryOn,\n onRetry,\n delayFn,\n } = config;\n\n // Determine which codes to retry on\n const retryableCodes = retryOn ?? DEFAULT_RETRYABLE_CODES;\n\n let lastError: LLMProviderError | null = null;\n\n for (let attempt = 0; attempt <= maxRetries; attempt++) {\n try {\n return await fn();\n } catch (error) {\n // Wrap error for consistent handling\n const wrappedError = wrapError(error);\n lastError = wrappedError;\n\n // Check if we should retry\n const isLastAttempt = attempt === maxRetries;\n const shouldRetry =\n !isLastAttempt && retryableCodes.includes(wrappedError.code);\n\n if (!shouldRetry) {\n throw wrappedError;\n }\n\n // Calculate delay\n const delay = delayFn\n ? delayFn(attempt, baseDelay)\n : calculateDelay(attempt, baseDelay, maxDelay);\n\n // Notify callback\n if (onRetry) {\n onRetry(attempt + 1, wrappedError, delay);\n }\n\n // Wait before next attempt\n await sleep(delay);\n }\n }\n\n // Should never reach here, but TypeScript needs this\n throw lastError ?? new Error(\"Retry failed\");\n}\n\n/**\n * Result of withRetryResult - includes metadata about retries\n */\nexport interface RetryResult {\n /** The successful result */\n result: T;\n /** Number of attempts made */\n attempts: number;\n /** Total time spent including retries (ms) */\n totalTime: number;\n /** Whether any retries occurred */\n retried: boolean;\n}\n\n/**\n * Execute a function with retry logic, returning metadata about the retries\n *\n * @example\n * ```typescript\n * const { result, attempts, totalTime } = await withRetryResult(\n * () => generateText({ model, prompt }),\n * { maxRetries: 3 }\n * );\n * console.log(`Success after ${attempts} attempts in ${totalTime}ms`);\n * ```\n */\nexport async function withRetryResult(\n fn: () => Promise,\n config: RetryConfig = {}\n): Promise> {\n const startTime = Date.now();\n let attempts = 0;\n\n const result = await withRetry(async () => {\n attempts++;\n return fn();\n }, config);\n\n return {\n result,\n attempts,\n totalTime: Date.now() - startTime,\n retried: attempts > 1,\n };\n}\n\n/**\n * Create a retry wrapper with pre-configured options\n *\n * @example\n * ```typescript\n * const retryWithBackoff = createRetryWrapper({\n * maxRetries: 5,\n * baseDelay: 200,\n * onRetry: (attempt) => console.log(`Retry ${attempt}...`),\n * });\n *\n * // Use later\n * const result = await retryWithBackoff(() => generateText({ model, prompt }));\n * ```\n */\nexport function createRetryWrapper(defaultConfig: RetryConfig) {\n return async function (\n fn: () => Promise,\n overrideConfig?: RetryConfig\n ): Promise {\n return withRetry(fn, { ...defaultConfig, ...overrideConfig });\n };\n}\n","/**\n * Structured Output Functions (Edge-Native)\n *\n * Pure implementation without AI SDK dependencies\n * Uses JSON extraction from text responses with Zod validation\n */\n\nimport type { z } from \"zod\";\nimport { LLMProviderError } from \"./errors.js\";\nimport { generate } from \"./factory.js\";\nimport type { ModelSpec, Credentials } from \"./types.js\";\n\n// =============================================================================\n// Types\n// =============================================================================\n\nexport interface GenerateObjectOptions {\n /** Model specification (e.g., \"groq:llama-3.1-8b-instant\") */\n model: ModelSpec | string;\n /** API credentials */\n credentials: Credentials;\n /** Zod schema for the expected output */\n schema: T;\n /** User prompt */\n prompt: string;\n /** Optional system prompt */\n system?: string;\n /** Temperature (0-1) */\n temperature?: number;\n /** Max tokens */\n maxTokens?: number;\n}\n\nexport interface GenerateObjectResult {\n /** Parsed and validated object */\n object: T;\n /** Raw text response */\n rawText: string;\n /** Token usage */\n usage?: {\n promptTokens: number;\n completionTokens: number;\n };\n}\n\n// =============================================================================\n// Core Functions\n// =============================================================================\n\n/**\n * Generate a structured object using edge-native implementation\n *\n * @example\n * ```typescript\n * const result = await generateObject({\n * model: \"groq:llama-3.1-8b-instant\",\n * credentials: { groqApiKey: process.env.GROQ_API_KEY },\n * schema: z.object({ name: z.string(), age: z.number() }),\n * prompt: \"Generate a person\",\n * });\n * ```\n */\nexport async function generateObject(\n options: GenerateObjectOptions\n): Promise>> {\n const { model, credentials, schema, prompt, system, temperature, maxTokens } = options;\n\n // Create JSON prompt\n const jsonPrompt = `${prompt}\n\nRespond with valid JSON only, no other text. The JSON must match this schema:\n${JSON.stringify(schema._def, null, 2)}\n\nExample format: {\"field1\": \"value1\", \"field2\": 123}`;\n\n const messages = [\n ...(system ? [{ role: \"system\", content: system }] : []),\n { role: \"user\", content: jsonPrompt },\n ];\n\n const result = await generate(model, messages, credentials, {\n temperature,\n maxTokens,\n });\n\n const object = extractJSON(result.text, schema);\n\n return {\n object,\n rawText: result.text,\n usage: result.usage,\n };\n}\n\n// =============================================================================\n// Utilities\n// =============================================================================\n\n/**\n * Extract JSON from text response (edge-native)\n *\n * @example\n * ```typescript\n * const response = \"Here's the data: {\\\"name\\\": \\\"test\\\", \\\"value\\\": 42}\";\n * const schema = z.object({ name: z.string(), value: z.number() });\n * const result = extractJSON(response, schema);\n * // => { name: \"test\", value: 42 }\n * ```\n */\nexport function extractJSON(\n text: string,\n schema?: T\n): T extends z.ZodType ? z.infer : unknown {\n // Try to find JSON in the text\n const jsonMatch = text.match(/\\{[\\s\\S]*\\}/);\n if (!jsonMatch) {\n throw new LLMProviderError({\n message: \"No JSON object found in response\",\n code: \"INVALID_RESPONSE\",\n });\n }\n\n try {\n const parsed = JSON.parse(jsonMatch[0]);\n\n if (schema) {\n const result = schema.safeParse(parsed);\n\n if (!result.success) {\n throw new LLMProviderError({\n message: `Schema validation failed: ${result.error.message}`,\n code: \"VALIDATION\",\n });\n }\n\n return result.data;\n }\n\n return parsed;\n } catch (error) {\n if (error instanceof LLMProviderError) throw error;\n\n throw new LLMProviderError({\n message: `Failed to parse JSON: ${error instanceof Error ? error.message : String(error)}`,\n code: \"INVALID_RESPONSE\",\n cause: error instanceof Error ? error : undefined,\n });\n }\n}","/**\n * Edge-native streaming utilities using Web Streams API\n * \n * Provides WebStreams-compatible streaming for edge computing environments\n * like Cloudflare Workers, Vercel Edge Functions, etc.\n */\n\nimport type { Credentials } from \"./types.js\";\n\n// =============================================================================\n// WebStreams-compatible Cloudflare API\n// =============================================================================\n\nexport interface CloudflareStreamChunk {\n response: string;\n finished?: boolean;\n}\n\n/**\n * Stream Cloudflare Workers AI responses using ReadableStream (WebStreams API)\n * This is more suitable for edge environments than AsyncGenerator.\n * \n * @example\n * ```typescript\n * const stream = createCloudflareStream(model, messages, credentials);\n * \n * const reader = stream.getReader();\n * while (true) {\n * const { done, value } = await reader.read();\n * if (done) break;\n * console.log(value); // CloudflareStreamChunk\n * }\n * ```\n */\n/**\n * Process SSE lines and emit CloudflareStreamChunk events\n * Returns true if [DONE] was encountered\n */\nfunction processSSELines(lines: string[], controller: ReadableStreamDefaultController): boolean {\n for (const line of lines) {\n const trimmed = line.trim();\n if (!trimmed || !trimmed.startsWith(\"data: \")) continue;\n\n const data = trimmed.slice(6);\n if (data === \"[DONE]\") {\n controller.enqueue({ response: \"\", finished: true });\n return true;\n }\n\n try {\n const parsed = JSON.parse(data);\n if (parsed.response) {\n controller.enqueue({ response: parsed.response });\n }\n } catch { /* skip malformed JSON */ }\n }\n return false;\n}\n\nasync function readSSEStream(body: ReadableStream, controller: ReadableStreamDefaultController): Promise {\n const reader = body.getReader();\n const decoder = new TextDecoder();\n let buffer = \"\";\n\n while (true) {\n const { done, value } = await reader.read();\n if (done) break;\n\n buffer += decoder.decode(value, { stream: true });\n const lines = buffer.split(\"\\n\");\n buffer = lines.pop() ?? \"\";\n\n if (processSSELines(lines, controller)) {\n controller.close();\n return;\n }\n }\n controller.close();\n}\n\nexport function createCloudflareStream(\n model: string,\n messages: Array<{ role: string; content: string }>,\n credentials: Credentials\n): ReadableStream {\n const { cloudflareApiKey, cloudflareEmail, cloudflareAccountId } = credentials;\n\n if (!cloudflareApiKey || !cloudflareEmail || !cloudflareAccountId) {\n throw new Error(\"Cloudflare REST API requires cloudflareApiKey, cloudflareEmail, and cloudflareAccountId\");\n }\n\n return new ReadableStream({\n async start(controller) {\n try {\n const response = await fetch(\n `https://api.cloudflare.com/client/v4/accounts/${cloudflareAccountId}/ai/run/${model}`,\n {\n method: \"POST\",\n headers: { \"X-Auth-Email\": cloudflareEmail, \"X-Auth-Key\": cloudflareApiKey, \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ messages, stream: true }),\n }\n );\n\n if (!response.ok) throw new Error(`Cloudflare API error: ${response.status} ${await response.text()}`);\n if (!response.body) throw new Error(\"No response body for streaming\");\n\n await readSSEStream(response.body, controller);\n } catch (error) {\n controller.error(error);\n }\n },\n });\n}\n\n/**\n * Convert a CloudflareStreamChunk stream to text chunks\n * Useful for integrating with other streaming APIs\n * \n * @example\n * ```typescript\n * const cloudflareStream = createCloudflareStream(model, messages, credentials);\n * const textStream = cloudflareStreamToText(cloudflareStream);\n * \n * const reader = textStream.getReader();\n * while (true) {\n * const { done, value } = await reader.read();\n * if (done) break;\n * process.stdout.write(value); // string\n * }\n * ```\n */\nexport function cloudflareStreamToText(\n stream: ReadableStream\n): ReadableStream {\n return new ReadableStream({\n start(controller) {\n const reader = stream.getReader();\n\n async function pump(): Promise {\n try {\n while (true) {\n const { done, value } = await reader.read();\n if (done) {\n controller.close();\n break;\n }\n\n if (value.finished) {\n controller.close();\n break;\n }\n\n if (value.response) {\n controller.enqueue(value.response);\n }\n }\n } catch (error) {\n controller.error(error);\n }\n }\n\n pump();\n },\n });\n}\n\n// =============================================================================\n// Response utilities\n// =============================================================================\n\n/**\n * Convert a ReadableStream to Response object\n * Useful for returning streaming responses in edge functions\n * \n * @example\n * ```typescript\n * export async function POST(request: Request) {\n * const stream = createCloudflareStream(model, messages, credentials);\n * const textStream = cloudflareStreamToText(stream);\n * \n * return streamToResponse(textStream, {\n * headers: { \"Content-Type\": \"text/plain; charset=utf-8\" }\n * });\n * }\n * ```\n */\nexport function streamToResponse(\n stream: ReadableStream,\n init?: ResponseInit\n): Response {\n // Convert string stream to Uint8Array stream for Response\n const encoder = new TextEncoder();\n const uint8Stream = new ReadableStream({\n start(controller) {\n const reader = stream.getReader();\n\n async function pump(): Promise {\n try {\n while (true) {\n const { done, value } = await reader.read();\n if (done) {\n controller.close();\n break;\n }\n controller.enqueue(encoder.encode(value));\n }\n } catch (error) {\n controller.error(error);\n }\n }\n\n pump();\n },\n });\n\n return new Response(uint8Stream, {\n ...init,\n headers: {\n \"Content-Type\": \"text/plain; charset=utf-8\",\n \"Cache-Control\": \"no-cache\",\n ...init?.headers,\n },\n });\n}\n\n/**\n * Convert ReadableStream to AsyncIterator for easier consumption\n * \n * @example\n * ```typescript\n * const stream = createCloudflareStream(model, messages, credentials);\n * \n * for await (const chunk of streamToAsyncIterator(stream)) {\n * console.log(chunk.response);\n * }\n * ```\n */\nexport async function* streamToAsyncIterator(\n stream: ReadableStream\n): AsyncIterableIterator {\n const reader = stream.getReader();\n try {\n while (true) {\n const { done, value } = await reader.read();\n if (done) break;\n yield value;\n }\n } finally {\n reader.releaseLock();\n }\n}","/**\n * Memory optimization utilities for edge computing environments\n * \n * Edge environments like Cloudflare Workers have strict memory limits.\n * These utilities help optimize memory usage when working with LLMs.\n */\n\nimport type { Credentials } from \"./types.js\";\n\n// =============================================================================\n// Message Optimization\n// =============================================================================\n\nexport interface OptimizedMessage {\n role: \"system\" | \"user\" | \"assistant\";\n content: string;\n tokens?: number;\n}\n\n/**\n * Truncate message history to stay within token limits\n * Useful for edge environments with memory constraints\n * \n * @example\n * ```typescript\n * const messages = [\n * { role: \"system\", content: \"You are a helpful assistant.\" },\n * { role: \"user\", content: \"Hello\" },\n * { role: \"assistant\", content: \"Hi there!\" },\n * // ... many more messages\n * ];\n * \n * // Keep only recent messages within token limit\n * const optimized = truncateMessages(messages, 2000);\n * ```\n */\nexport function truncateMessages(\n messages: Array<{ role: string; content: string }>,\n maxTokens: number,\n tokensPerChar = 0.25 // Rough estimation\n): OptimizedMessage[] {\n let totalTokens = 0;\n\n // Always keep system message if it exists\n const systemMessage = messages.find(msg => msg.role === \"system\");\n const systemTokens = systemMessage \n ? Math.ceil(systemMessage.content.length * tokensPerChar)\n : 0;\n totalTokens += systemTokens;\n\n // Add messages from newest to oldest until we hit token limit\n const otherMessages = messages\n .filter(msg => msg.role !== \"system\")\n .reverse(); // Start from newest\n\n const selectedOtherMessages: OptimizedMessage[] = [];\n for (const message of otherMessages) {\n const tokens = Math.ceil(message.content.length * tokensPerChar);\n \n if (totalTokens + tokens > maxTokens) {\n break;\n }\n\n selectedOtherMessages.unshift({ ...message, tokens } as OptimizedMessage);\n totalTokens += tokens;\n }\n\n // Combine system message (if exists) with selected other messages\n const result: OptimizedMessage[] = [];\n if (systemMessage) {\n result.push({ ...systemMessage, tokens: systemTokens } as OptimizedMessage);\n }\n result.push(...selectedOtherMessages);\n\n return result;\n}\n\n/**\n * Compress long messages by removing unnecessary whitespace and newlines\n */\nexport function compressMessage(content: string): string {\n return content\n .replace(/[ \\t]+/g, \" \") // Replace multiple spaces/tabs with single space\n .replace(/\\n\\s*\\n/g, \"\\n\") // Replace multiple newlines with single newline\n .replace(/\\s+\\n/g, \"\\n\") // Remove trailing spaces before newlines\n .replace(/\\n\\s+/g, \"\\n\") // Remove leading spaces after newlines\n .trim();\n}\n\n// =============================================================================\n// Streaming Buffer Management\n// =============================================================================\n\n/**\n * Memory-efficient buffer for streaming responses\n * Automatically flushes when buffer gets too large\n */\nexport class StreamingBuffer {\n private buffer: string[] = [];\n private maxSize: number;\n private onFlush?: (content: string) => void;\n\n constructor(maxSize = 1024, onFlush?: (content: string) => void) {\n this.maxSize = maxSize;\n this.onFlush = onFlush;\n }\n\n append(chunk: string): void {\n this.buffer.push(chunk);\n \n if (this.getTotalLength() > this.maxSize) {\n this.flush();\n }\n }\n\n flush(): string {\n const content = this.buffer.join(\"\");\n this.buffer = [];\n \n if (this.onFlush) {\n this.onFlush(content);\n }\n \n return content;\n }\n\n getContent(): string {\n return this.buffer.join(\"\");\n }\n\n getTotalLength(): number {\n return this.buffer.reduce((sum, chunk) => sum + chunk.length, 0);\n }\n\n clear(): void {\n this.buffer = [];\n }\n}\n\n// =============================================================================\n// Cache Utilities\n// =============================================================================\n\n/**\n * Simple LRU cache for edge environments\n * Useful for caching model responses or credentials\n */\nexport class EdgeCache {\n private cache = new Map();\n private maxSize: number;\n private ttl: number; // Time to live in milliseconds\n\n constructor(maxSize = 100, ttl = 5 * 60 * 1000) { // Default 5 minutes\n this.maxSize = maxSize;\n this.ttl = ttl;\n }\n\n get(key: string): T | undefined {\n const entry = this.cache.get(key);\n \n if (!entry) {\n return undefined;\n }\n\n // Check if expired\n if (Date.now() - entry.timestamp > this.ttl) {\n this.cache.delete(key);\n return undefined;\n }\n\n // Move to end (most recently used)\n this.cache.delete(key);\n this.cache.set(key, entry);\n \n return entry.value;\n }\n\n set(key: string, value: T): void {\n // Remove oldest if at capacity\n if (this.cache.size >= this.maxSize && !this.cache.has(key)) {\n const firstKey = this.cache.keys().next().value;\n if (firstKey !== undefined) {\n this.cache.delete(firstKey);\n }\n }\n\n this.cache.set(key, {\n value,\n timestamp: Date.now(),\n });\n }\n\n has(key: string): boolean {\n return this.get(key) !== undefined;\n }\n\n delete(key: string): boolean {\n return this.cache.delete(key);\n }\n\n clear(): void {\n this.cache.clear();\n }\n\n size(): number {\n return this.cache.size;\n }\n\n cleanup(): number {\n const now = Date.now();\n let removed = 0;\n\n for (const [key, entry] of this.cache.entries()) {\n if (now - entry.timestamp > this.ttl) {\n this.cache.delete(key);\n removed++;\n }\n }\n\n return removed;\n }\n}\n\n// =============================================================================\n// Memory Monitoring\n// =============================================================================\n\n/**\n * Get rough memory usage estimate\n * Note: This is a rough approximation as JS doesn't expose precise memory info\n */\nexport function getMemoryEstimate(): {\n heapUsed?: number;\n heapTotal?: number;\n external?: number;\n} {\n // In Node.js environment\n if (typeof process !== \"undefined\" && process.memoryUsage) {\n const usage = process.memoryUsage();\n return {\n heapUsed: usage.heapUsed,\n heapTotal: usage.heapTotal,\n external: usage.external,\n };\n }\n\n // In browser/edge environments (limited info)\n if (typeof performance !== \"undefined\" && \"memory\" in performance) {\n const memory = (performance as unknown as { memory?: { usedJSHeapSize: number; totalJSHeapSize: number } }).memory;\n return {\n heapUsed: memory?.usedJSHeapSize,\n heapTotal: memory?.totalJSHeapSize,\n };\n }\n\n return {};\n}\n\n/**\n * Create a memory-aware credentials cache\n * Automatically cleans up when memory gets low\n */\nexport function createCredentialsCache(maxSize = 10, ttl = 10 * 60 * 1000): EdgeCache {\n const cache = new EdgeCache(maxSize, ttl);\n\n // Periodically cleanup expired entries\n if (typeof setInterval !== \"undefined\") {\n setInterval(() => {\n cache.cleanup();\n }, ttl / 2);\n }\n\n return cache;\n}","/**\n * Groq-specific streaming handlers\n *\n * Dedicated handlers for Groq-hosted models including GPT-OSS and Llama variants.\n */\n\nimport type { StreamHandler } from \"./streaming-handlers.js\";\n\n// =============================================================================\n// GPT-OSS Models (OpenAI Open Source on Groq)\n// =============================================================================\n\n/**\n * GPT-OSS-120B specific handler\n * This model needs special care for long outputs and may stop early\n */\nexport const gptOss120bHandler: StreamHandler = {\n model: 'openai/gpt-oss-120b',\n async createStream(messages, apiKey, options = {}) {\n const requestBody = {\n model: 'openai/gpt-oss-120b',\n messages,\n stream: true,\n temperature: options.temperature ?? 0.7,\n max_tokens: options.maxTokens ?? 8192,\n top_p: options.topP ?? 1.0,\n frequency_penalty: 0,\n presence_penalty: 0,\n stop: options.stopSequences === undefined ? [] : options.stopSequences,\n n: 1,\n };\n\n const response = await fetch(\"https://api.groq.com/openai/v1/chat/completions\", {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(requestBody),\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n throw new Error(`GPT-OSS-120B streaming failed: ${response.status} - ${errorText}`);\n }\n\n let buffer = '';\n\n return response.body!\n .pipeThrough(new TextDecoderStream())\n .pipeThrough(new TransformStream({\n transform(chunk, controller) {\n buffer += chunk;\n const lines = buffer.split('\\n');\n buffer = lines.pop() || '';\n\n for (const line of lines) {\n const trimmed = line.trim();\n if (!trimmed || !trimmed.startsWith('data: ')) continue;\n\n const data = trimmed.slice(6);\n if (data === '[DONE]') continue;\n\n try {\n const parsed = JSON.parse(data);\n const content = parsed.choices?.[0]?.delta?.content;\n if (content !== undefined && content !== null) {\n controller.enqueue(content);\n }\n } catch { /* ignore parse errors */ }\n }\n },\n\n flush(controller) {\n if (buffer.trim()) {\n if (buffer.trim().startsWith('data: ')) {\n const data = buffer.trim().slice(6);\n if (data !== '[DONE]') {\n try {\n const parsed = JSON.parse(data);\n const content = parsed.choices?.[0]?.delta?.content;\n if (content) {\n controller.enqueue(content);\n }\n } catch { /* ignore incomplete chunk */ }\n }\n }\n }\n }\n }));\n }\n};\n\n/**\n * GPT-OSS-20B specific handler\n */\nexport const gptOss20bHandler: StreamHandler = {\n model: 'openai/gpt-oss-20b',\n async createStream(messages, apiKey, options = {}) {\n const requestBody = {\n model: 'openai/gpt-oss-20b',\n messages,\n stream: true,\n temperature: options.temperature ?? 0.7,\n max_tokens: options.maxTokens ?? 4096,\n top_p: options.topP ?? 1.0,\n stop: options.stopSequences === undefined ? [] : options.stopSequences,\n n: 1,\n };\n\n const response = await fetch(\"https://api.groq.com/openai/v1/chat/completions\", {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(requestBody),\n });\n\n if (!response.ok) {\n throw new Error(`GPT-OSS-20B streaming failed: ${response.status}`);\n }\n\n let buffer = '';\n return response.body!\n .pipeThrough(new TextDecoderStream())\n .pipeThrough(new TransformStream({\n transform(chunk, controller) {\n buffer += chunk;\n const lines = buffer.split('\\n');\n buffer = lines.pop() || '';\n\n for (const line of lines) {\n const trimmed = line.trim();\n if (!trimmed || !trimmed.startsWith('data: ')) continue;\n\n const data = trimmed.slice(6);\n if (data === '[DONE]') continue;\n\n try {\n const parsed = JSON.parse(data);\n const content = parsed.choices?.[0]?.delta?.content;\n if (content !== undefined && content !== null) {\n controller.enqueue(content);\n }\n } catch { /* ignore parse errors */ }\n }\n }\n }));\n }\n};\n\n// =============================================================================\n// Llama Models\n// =============================================================================\n\n/**\n * Llama 3.1 8B Instant handler\n */\nexport const llama31InstantHandler: StreamHandler = {\n model: 'llama-3.1-8b-instant',\n async createStream(messages, apiKey, options = {}) {\n const requestBody = {\n model: 'llama-3.1-8b-instant',\n messages,\n stream: true,\n temperature: options.temperature ?? 0.7,\n max_tokens: options.maxTokens ?? 2048,\n stop: options.stopSequences,\n };\n\n const response = await fetch(\"https://api.groq.com/openai/v1/chat/completions\", {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(requestBody),\n });\n\n if (!response.ok) {\n throw new Error(`Llama 3.1 8B streaming failed: ${response.status}`);\n }\n\n let buffer = '';\n return response.body!\n .pipeThrough(new TextDecoderStream())\n .pipeThrough(new TransformStream({\n transform(chunk, controller) {\n buffer += chunk;\n const lines = buffer.split('\\n');\n buffer = lines.pop() || '';\n\n for (const line of lines) {\n const trimmed = line.trim();\n if (!trimmed || !trimmed.startsWith('data: ')) continue;\n\n const data = trimmed.slice(6);\n if (data === '[DONE]') continue;\n\n try {\n const parsed = JSON.parse(data);\n const content = parsed.choices?.[0]?.delta?.content;\n if (content !== undefined && content !== null) {\n controller.enqueue(content);\n }\n } catch { /* ignore parse errors */ }\n }\n }\n }));\n }\n};\n\n/**\n * Llama 3.3 70B Versatile handler\n */\nexport const llama33VersatileHandler: StreamHandler = {\n model: 'llama-3.3-70b-versatile',\n async createStream(messages, apiKey, options = {}) {\n const requestBody = {\n model: 'llama-3.3-70b-versatile',\n messages,\n stream: true,\n temperature: options.temperature ?? 0.7,\n max_tokens: options.maxTokens ?? 2048,\n stop: options.stopSequences,\n };\n\n const response = await fetch(\"https://api.groq.com/openai/v1/chat/completions\", {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(requestBody),\n });\n\n if (!response.ok) {\n throw new Error(`Llama 3.3 70B streaming failed: ${response.status}`);\n }\n\n let buffer = '';\n return response.body!\n .pipeThrough(new TextDecoderStream())\n .pipeThrough(new TransformStream({\n transform(chunk, controller) {\n buffer += chunk;\n const lines = buffer.split('\\n');\n buffer = lines.pop() || '';\n\n for (const line of lines) {\n const trimmed = line.trim();\n if (!trimmed || !trimmed.startsWith('data: ')) continue;\n\n const data = trimmed.slice(6);\n if (data === '[DONE]') continue;\n\n try {\n const parsed = JSON.parse(data);\n const content = parsed.choices?.[0]?.delta?.content;\n if (content !== undefined && content !== null) {\n controller.enqueue(content);\n }\n } catch { /* ignore parse errors */ }\n }\n }\n }));\n }\n};\n","/**\n * Gemini-specific streaming handlers\n *\n * Dedicated handlers for Google Gemini models with SSE streaming support.\n */\n\nimport type { StreamHandler } from \"./streaming-handlers.js\";\n\n// =============================================================================\n// Gemini Models\n// =============================================================================\n\n/**\n * Gemini 2.0 Flash handler\n */\nexport const gemini20FlashHandler: StreamHandler = {\n model: 'gemini-2.0-flash',\n async createStream(messages, apiKey, options = {}) {\n const contents = messages\n .filter(m => m.role !== \"system\")\n .map(m => ({\n role: m.role === \"assistant\" ? \"model\" : \"user\",\n parts: [{ text: m.content }],\n }));\n\n const systemInstruction = messages.find(m => m.role === \"system\")?.content;\n\n const response = await fetch(\n `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?alt=sse&key=${apiKey}`,\n {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n contents,\n ...(systemInstruction && { systemInstruction: { parts: [{ text: systemInstruction }] } }),\n generationConfig: {\n temperature: options.temperature ?? 0.7,\n maxOutputTokens: options.maxTokens ?? 2048,\n topP: options.topP ?? 0.95,\n stopSequences: options.stopSequences,\n },\n }),\n }\n );\n\n if (!response.ok) {\n throw new Error(`Gemini 2.0 Flash streaming failed: ${response.status}`);\n }\n\n let buffer = '';\n return response.body!\n .pipeThrough(new TextDecoderStream())\n .pipeThrough(new TransformStream({\n transform(chunk, controller) {\n buffer += chunk;\n const lines = buffer.split('\\n');\n buffer = lines.pop() || '';\n\n for (const line of lines) {\n const trimmed = line.trim();\n if (!trimmed || !trimmed.startsWith('data: ')) continue;\n\n const data = trimmed.slice(6);\n if (data === '[DONE]') continue;\n\n try {\n const parsed = JSON.parse(data);\n const text = parsed.candidates?.[0]?.content?.parts?.[0]?.text;\n if (text) {\n controller.enqueue(text);\n }\n } catch { /* ignore parse errors */ }\n }\n }\n }));\n }\n};\n\n/**\n * Gemini 1.5 Flash handler\n */\nexport const gemini15FlashHandler: StreamHandler = {\n model: 'gemini-1.5-flash',\n async createStream(messages, apiKey, options = {}) {\n const contents = messages\n .filter(m => m.role !== \"system\")\n .map(m => ({\n role: m.role === \"assistant\" ? \"model\" : \"user\",\n parts: [{ text: m.content }],\n }));\n\n const systemInstruction = messages.find(m => m.role === \"system\")?.content;\n\n const response = await fetch(\n `https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:streamGenerateContent?alt=sse&key=${apiKey}`,\n {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n contents,\n ...(systemInstruction && { systemInstruction: { parts: [{ text: systemInstruction }] } }),\n generationConfig: {\n temperature: options.temperature ?? 0.7,\n maxOutputTokens: options.maxTokens ?? 2048,\n topP: options.topP ?? 0.95,\n stopSequences: options.stopSequences,\n },\n }),\n }\n );\n\n if (!response.ok) {\n throw new Error(`Gemini 1.5 Flash streaming failed: ${response.status}`);\n }\n\n let buffer = '';\n return response.body!\n .pipeThrough(new TextDecoderStream())\n .pipeThrough(new TransformStream({\n transform(chunk, controller) {\n buffer += chunk;\n const lines = buffer.split('\\n');\n buffer = lines.pop() || '';\n\n for (const line of lines) {\n const trimmed = line.trim();\n if (!trimmed || !trimmed.startsWith('data: ')) continue;\n\n const data = trimmed.slice(6);\n if (data === '[DONE]') continue;\n\n try {\n const parsed = JSON.parse(data);\n const text = parsed.candidates?.[0]?.content?.parts?.[0]?.text;\n if (text) {\n controller.enqueue(text);\n }\n } catch { /* ignore parse errors */ }\n }\n }\n }));\n }\n};\n","/**\n * Model-specific streaming handlers - Registry\n *\n * Central registry for model-specific stream handlers.\n * Individual handler implementations are in separate files.\n */\n\nimport {\n gptOss120bHandler,\n gptOss20bHandler,\n llama31InstantHandler,\n llama33VersatileHandler,\n} from \"./streaming-handlers-groq.js\";\nimport {\n gemini20FlashHandler,\n gemini15FlashHandler,\n} from \"./streaming-handlers-gemini.js\";\n\n// =============================================================================\n// Handler Types\n// =============================================================================\n\nexport interface StreamHandler {\n model: string;\n createStream: (\n messages: Array<{ role: string; content: string }>,\n apiKey: string,\n options?: StreamOptions\n ) => Promise>;\n}\n\nexport interface StreamOptions {\n temperature?: number;\n maxTokens?: number;\n topP?: number;\n stopSequences?: string[] | null;\n}\n\n// =============================================================================\n// Handler Registry\n// =============================================================================\n\nexport const STREAM_HANDLERS: Map = new Map([\n // GPT-OSS models\n ['openai/gpt-oss-120b', gptOss120bHandler],\n ['openai/gpt-oss-20b', gptOss20bHandler],\n\n // Llama models\n ['llama-3.1-8b-instant', llama31InstantHandler],\n ['llama-3.3-70b-versatile', llama33VersatileHandler],\n\n // Gemini models\n ['gemini-2.0-flash', gemini20FlashHandler],\n ['gemini-1.5-flash', gemini15FlashHandler],\n ['gemini-1.5-flash-002', gemini15FlashHandler],\n]);\n\n/**\n * Get a stream handler for a specific model\n */\nexport function getStreamHandler(model: string): StreamHandler | undefined {\n return STREAM_HANDLERS.get(model);\n}\n\n/**\n * Check if a model has a dedicated stream handler\n */\nexport function hasStreamHandler(model: string): boolean {\n return STREAM_HANDLERS.has(model);\n}\n\n// Re-export individual handlers for direct access\nexport {\n gptOss120bHandler,\n gptOss20bHandler,\n llama31InstantHandler,\n llama33VersatileHandler,\n} from \"./streaming-handlers-groq.js\";\n\nexport {\n gemini20FlashHandler,\n gemini15FlashHandler,\n} from \"./streaming-handlers-gemini.js\";\n","/**\n * Fluent API - Streaming Implementation Functions\n *\n * Provider-specific streaming implementations for the fluent builder.\n * Creates ReadableStream for each provider's streaming API.\n */\n\nimport type { Credentials } from \"./types.js\";\nimport { callCloudflareRestStream } from \"./factory.js\";\nimport { getStreamHandler } from \"./streaming-handlers.js\";\n\n// =============================================================================\n// Provider Stream Router\n// =============================================================================\n\ninterface ProviderStreamConfig {\n provider: string;\n model: string;\n messages: Array<{ role: string; content: string }>;\n credentials: Credentials;\n options?: { temperature?: number; maxTokens?: number };\n}\n\nexport async function createProviderStream(config: ProviderStreamConfig): Promise> {\n const { provider, model, messages, credentials, options } = config;\n return routeToProvider(provider, model, messages, credentials, options);\n}\n\nfunction getApiKeyOrThrow(credentials: Credentials, provider: string): string {\n const keyMap: Record = {\n anthropic: credentials.anthropicApiKey,\n openai: credentials.openaiApiKey,\n groq: credentials.groqApiKey,\n gemini: credentials.geminiApiKey,\n };\n const key = keyMap[provider];\n if (!key) throw new Error(`${provider}ApiKey is required for ${provider} streaming`);\n return key;\n}\n\nasync function routeToProvider(provider: string, model: string, messages: Array<{ role: string; content: string }>, credentials: Credentials, options?: { temperature?: number; maxTokens?: number }): Promise> {\n if (provider === \"cloudflare\") {\n return createCloudflareReadableStream(model, messages, credentials);\n }\n const apiKey = getApiKeyOrThrow(credentials, provider);\n if (provider === \"groq\" || provider === \"gemini\") {\n const handler = getStreamHandler(model);\n if (handler) return handler.createStream(messages, apiKey, options);\n }\n const streamFn = STREAM_CREATORS[provider];\n if (!streamFn) throw new Error(`Streaming not supported for provider: ${provider}`);\n return streamFn(model, messages, apiKey, options);\n}\n\nfunction createCloudflareReadableStream(model: string, messages: Array<{ role: string; content: string }>, credentials: Credentials): ReadableStream {\n if (!credentials.cloudflareApiKey || !credentials.cloudflareEmail || !credentials.cloudflareAccountId) {\n throw new Error(\"Cloudflare credentials required for streaming\");\n }\n const iter = callCloudflareRestStream(model, messages, credentials);\n return new ReadableStream({\n async start(controller) {\n try {\n for await (const chunk of iter) { controller.enqueue(chunk); }\n controller.close();\n } catch (error) { controller.error(error); }\n }\n });\n}\n\ntype StreamCreator = (model: string, messages: Array<{ role: string; content: string }>, apiKey: string, options?: { temperature?: number; maxTokens?: number }) => Promise>;\n\nconst STREAM_CREATORS: Record = {\n anthropic: createAnthropicStream,\n openai: createOpenAIStream,\n groq: createGroqStream,\n gemini: createGeminiStream,\n};\n\n// =============================================================================\n// Shared SSE Parsing\n// =============================================================================\n\ntype ContentExtractor = (parsed: Record) => string | null;\n\nfunction createSSETransform(extract: ContentExtractor): TransformStream {\n let buffer = '';\n return new TransformStream({\n transform(chunk, controller) {\n buffer += chunk;\n const lines = buffer.split('\\n');\n buffer = lines.pop() || '';\n for (const line of lines) {\n const trimmed = line.trim();\n if (!trimmed.startsWith('data: ')) continue;\n const data = trimmed.slice(6);\n if (data === '[DONE]') continue;\n try {\n const content = extract(JSON.parse(data));\n if (content) controller.enqueue(content);\n } catch { /* ignore parse errors */ }\n }\n },\n flush(controller) {\n if (buffer.trim().startsWith('data: ')) {\n const data = buffer.trim().slice(6);\n if (data !== '[DONE]') {\n try {\n const content = extract(JSON.parse(data));\n if (content) controller.enqueue(content);\n } catch { /* ignore incomplete final chunk */ }\n }\n }\n }\n });\n}\n\nconst extractOpenAI: ContentExtractor = (p) => {\n const choices = p.choices as Array> | undefined;\n const delta = choices?.[0]?.delta as Record | undefined;\n return (delta?.content as string) || null;\n};\n\nconst extractGemini: ContentExtractor = (p) => {\n const candidates = p.candidates as Array> | undefined;\n const content = candidates?.[0]?.content as Record | undefined;\n const parts = content?.parts as Array> | undefined;\n return (parts?.[0]?.text as string) || null;\n};\n\nconst extractAnthropic: ContentExtractor = (p) => {\n if (p.type === 'content_block_delta') {\n const delta = p.delta as Record | undefined;\n return (delta?.text as string) || null;\n }\n return null;\n};\n\nconst extractGroq: ContentExtractor = (p) => {\n return extractOpenAI(p) || (p.response as string) || null;\n};\n\n// =============================================================================\n// Model Configs\n// =============================================================================\n\nconst MODEL_CONFIGS: Record = {\n 'openai/gpt-oss-120b': { maxTokens: 4096, temperature: 0.7, topP: 1.0, stopSequences: null },\n 'openai/gpt-oss-20b': { maxTokens: 4096, temperature: 0.7, topP: 1.0, stopSequences: null },\n 'llama-3.1-8b-instant': { maxTokens: 2048, temperature: 0.7 },\n 'llama-3.3-70b-versatile': { maxTokens: 2048, temperature: 0.7 },\n 'mixtral-8x7b-32768': { maxTokens: 32768, temperature: 0.7 },\n 'gemma2-9b-it': { maxTokens: 2048, temperature: 0.7 },\n};\n\nexport function getModelConfig(model: string) {\n const c = MODEL_CONFIGS[model] || {};\n return { maxTokens: c.maxTokens || 2048, temperature: c.temperature || 0.7, topP: c.topP, stopSequences: c.stopSequences };\n}\n\n// =============================================================================\n// Provider Streaming Functions\n// =============================================================================\n\nasync function fetchAndPipeSSE(url: string, headers: Record, body: Record, extract: ContentExtractor): Promise> {\n const response = await fetch(url, { method: \"POST\", headers: { ...headers, \"Content-Type\": \"application/json\" }, body: JSON.stringify(body) });\n if (!response.ok) {\n const errorText = await response.text();\n throw new Error(`Streaming failed: ${response.status} - ${errorText}`);\n }\n if (!response.body) throw new Error(\"No response body for streaming\");\n return response.body.pipeThrough(new TextDecoderStream()).pipeThrough(createSSETransform(extract));\n}\n\nexport async function createAnthropicStream(model: string, messages: Array<{ role: string; content: string }>, apiKey: string, options?: { temperature?: number; maxTokens?: number }): Promise> {\n const anthropicMessages = messages.filter(m => m.role !== \"system\").map(msg => ({ role: msg.role === \"user\" ? \"user\" : \"assistant\", content: msg.content }));\n const system = messages.find(m => m.role === \"system\")?.content;\n return fetchAndPipeSSE(\"https://api.anthropic.com/v1/messages\", { \"x-api-key\": apiKey, \"anthropic-version\": \"2023-06-01\" }, { model, messages: anthropicMessages, system, max_tokens: options?.maxTokens || 4096, temperature: options?.temperature || 0.7, stream: true }, extractAnthropic);\n}\n\nexport async function createOpenAIStream(model: string, messages: Array<{ role: string; content: string }>, apiKey: string, options?: { temperature?: number; maxTokens?: number }): Promise> {\n return fetchAndPipeSSE(\"https://api.openai.com/v1/chat/completions\", { \"Authorization\": `Bearer ${apiKey}` }, { model, messages, stream: true, temperature: options?.temperature || 0.7, max_tokens: options?.maxTokens }, extractOpenAI);\n}\n\nexport async function createGroqStream(model: string, messages: Array<{ role: string; content: string }>, apiKey: string, options?: { temperature?: number; maxTokens?: number }): Promise> {\n const config = getModelConfig(model);\n const body: Record = { model, messages, stream: true, temperature: options?.temperature ?? config.temperature, max_tokens: options?.maxTokens ?? config.maxTokens };\n if (config.topP !== undefined) body.top_p = config.topP;\n if (config.stopSequences !== undefined) body.stop = config.stopSequences;\n return fetchAndPipeSSE(\"https://api.groq.com/openai/v1/chat/completions\", { \"Authorization\": `Bearer ${apiKey}` }, body, extractGroq);\n}\n\nexport async function createGeminiStream(model: string, messages: Array<{ role: string; content: string }>, apiKey: string, options?: { temperature?: number; maxTokens?: number }): Promise> {\n const contents = messages.filter(m => m.role !== \"system\").map(m => ({ role: m.role === \"assistant\" ? \"model\" : \"user\", parts: [{ text: m.content }] }));\n const systemInstruction = messages.find(m => m.role === \"system\")?.content;\n return fetchAndPipeSSE(`https://generativelanguage.googleapis.com/v1beta/models/${model}:streamGenerateContent?alt=sse&key=${apiKey}`, {}, { contents, ...(systemInstruction && { systemInstruction: { parts: [{ text: systemInstruction }] } }), generationConfig: { temperature: options?.temperature ?? 0.7, maxOutputTokens: options?.maxTokens ?? 2048 } }, extractGemini);\n}\n","/**\n * Fluent API - Provider-Specific Shortcuts\n *\n * Pre-configured builder shortcuts for each LLM provider.\n * Provides ergonomic access to common models.\n */\n\nimport type { Stream } from \"@aid-on/nagare\";\nimport { quick } from \"./fluent.js\";\n\n// =============================================================================\n// Provider-Specific Shortcuts\n// =============================================================================\n\n/**\n * Anthropic-specific builder shortcuts with streaming support\n */\nexport const anthropic = {\n /** Claude 4.5 Sonnet - Latest 2025, most capable */\n sonnet: (apiKey: string) => Object.assign(quick(\"anthropic:claude-sonnet-4-5-20250929\", { anthropicApiKey: apiKey }), {\n stream: async (prompt: string): Promise> => {\n const builder = quick(\"anthropic:claude-sonnet-4-5-20250929\", { anthropicApiKey: apiKey });\n return builder.stream(prompt);\n }\n }),\n\n /** Claude 3.5 Haiku - Fast and cheap */\n haiku: (apiKey: string) => Object.assign(quick(\"anthropic:claude-3-5-haiku-20241022\", { anthropicApiKey: apiKey }), {\n stream: async (prompt: string): Promise> => {\n const builder = quick(\"anthropic:claude-3-5-haiku-20241022\", { anthropicApiKey: apiKey });\n return builder.stream(prompt);\n }\n }),\n};\n\n/**\n * OpenAI-specific builder shortcuts with streaming support\n */\nexport const openai = {\n /** GPT-4o - Latest and fastest */\n gpt4o: (apiKey: string) => Object.assign(quick(\"openai:gpt-4o\", { openaiApiKey: apiKey }), {\n stream: async (prompt: string): Promise> => {\n const builder = quick(\"openai:gpt-4o\", { openaiApiKey: apiKey });\n return builder.stream(prompt);\n }\n }),\n\n /** GPT-4o Mini - Cost-effective */\n mini: (apiKey: string) => Object.assign(quick(\"openai:gpt-4o-mini\", { openaiApiKey: apiKey }), {\n stream: async (prompt: string): Promise> => {\n const builder = quick(\"openai:gpt-4o-mini\", { openaiApiKey: apiKey });\n return builder.stream(prompt);\n }\n }),\n\n /** GPT-4 Turbo - High capability */\n turbo: (apiKey: string) => Object.assign(quick(\"openai:gpt-4-turbo\", { openaiApiKey: apiKey }), {\n stream: async (prompt: string): Promise> => {\n const builder = quick(\"openai:gpt-4-turbo\", { openaiApiKey: apiKey });\n return builder.stream(prompt);\n }\n }),\n\n /** GPT-3.5 Turbo - Fast and cheap */\n gpt35: (apiKey: string) => Object.assign(quick(\"openai:gpt-3.5-turbo\", { openaiApiKey: apiKey }), {\n stream: async (prompt: string): Promise> => {\n const builder = quick(\"openai:gpt-3.5-turbo\", { openaiApiKey: apiKey });\n return builder.stream(prompt);\n }\n }),\n};\n\n/**\n * Groq-specific builder shortcuts with streaming support\n */\nexport const groq = {\n /** Fastest model - 560 tokens/sec */\n instant: (apiKey: string) => Object.assign(quick(\"groq:llama-3.1-8b-instant\", { groqApiKey: apiKey }), {\n stream: async (prompt: string): Promise> => {\n const builder = quick(\"groq:llama-3.1-8b-instant\", { groqApiKey: apiKey });\n return builder.stream(prompt);\n }\n }),\n\n /** Balanced model - 280 tokens/sec */\n versatile: (apiKey: string) => Object.assign(quick(\"groq:llama-3.3-70b-versatile\", { groqApiKey: apiKey }), {\n stream: async (prompt: string): Promise> => {\n const builder = quick(\"groq:llama-3.3-70b-versatile\", { groqApiKey: apiKey });\n return builder.stream(prompt);\n }\n }),\n\n /** OpenAI OSS models */\n gpt120b: (apiKey: string) => Object.assign(quick(\"groq:openai/gpt-oss-120b\", { groqApiKey: apiKey }), {\n stream: async (prompt: string): Promise> => {\n const builder = quick(\"groq:openai/gpt-oss-120b\", { groqApiKey: apiKey });\n return builder.stream(prompt);\n }\n }),\n\n gpt20b: (apiKey: string) => Object.assign(quick(\"groq:openai/gpt-oss-20b\", { groqApiKey: apiKey }), {\n stream: async (prompt: string): Promise> => {\n const builder = quick(\"groq:openai/gpt-oss-20b\", { groqApiKey: apiKey });\n return builder.stream(prompt);\n }\n }),\n\n /** Specialized models */\n guard: (apiKey: string) => quick(\"groq:meta-llama/llama-guard-4-12b\", { groqApiKey: apiKey }),\n compound: (apiKey: string) => quick(\"groq:groq/compound\", { groqApiKey: apiKey }),\n compoundMini: (apiKey: string) => quick(\"groq:groq/compound-mini\", { groqApiKey: apiKey }),\n};\n\n/**\n * Gemini-specific builder shortcuts\n */\nexport const gemini = {\n /** Latest Gemini 3 series */\n pro3: (apiKey: string) => quick(\"gemini:gemini-3-pro-preview\", { geminiApiKey: apiKey }),\n flash3: (apiKey: string) => quick(\"gemini:gemini-3-flash-preview\", { geminiApiKey: apiKey }),\n\n /** Gemini 2.5 series */\n pro25: (apiKey: string) => quick(\"gemini:gemini-2.5-pro\", { geminiApiKey: apiKey }),\n flash25: (apiKey: string) => quick(\"gemini:gemini-2.5-flash\", { geminiApiKey: apiKey }),\n\n /** Gemini 2.0 series (recommended) */\n flash: (apiKey: string) => quick(\"gemini:gemini-2.0-flash\", { geminiApiKey: apiKey }),\n lite: (apiKey: string) => quick(\"gemini:gemini-2.0-flash-lite\", { geminiApiKey: apiKey }),\n\n /** Stable 1.5 series */\n pro: (apiKey: string) => quick(\"gemini:gemini-1.5-pro-002\", { geminiApiKey: apiKey }),\n flash15: (apiKey: string) => quick(\"gemini:gemini-1.5-flash-002\", { geminiApiKey: apiKey }),\n};\n\n/**\n * Cloudflare-specific builder shortcuts\n */\nexport const cloudflare = {\n /** GPT-OSS 120B - Production use */\n gpt120b: (creds: { apiKey: string; email: string; accountId: string }) =>\n quick(\"cloudflare:@cf/openai/gpt-oss-120b\", {\n cloudflareApiKey: creds.apiKey,\n cloudflareEmail: creds.email,\n cloudflareAccountId: creds.accountId,\n }),\n\n /** GPT-OSS 20B - Lower latency */\n gpt20b: (creds: { apiKey: string; email: string; accountId: string }) =>\n quick(\"cloudflare:@cf/openai/gpt-oss-20b\", {\n cloudflareApiKey: creds.apiKey,\n cloudflareEmail: creds.email,\n cloudflareAccountId: creds.accountId,\n }),\n\n /** Llama 4 Scout - Multimodal */\n llama4: (creds: { apiKey: string; email: string; accountId: string }) =>\n quick(\"cloudflare:@cf/meta/llama-4-scout-17b-16e-instruct\", {\n cloudflareApiKey: creds.apiKey,\n cloudflareEmail: creds.email,\n cloudflareAccountId: creds.accountId,\n }),\n\n /** Llama 3.3 70B - Fast quantized */\n llama70b: (creds: { apiKey: string; email: string; accountId: string }) =>\n quick(\"cloudflare:@cf/meta/llama-3.3-70b-instruct-fp8-fast\", {\n cloudflareApiKey: creds.apiKey,\n cloudflareEmail: creds.email,\n cloudflareAccountId: creds.accountId,\n }),\n\n /** Llama 3.1 8B - Lightweight */\n llama8b: (creds: { apiKey: string; email: string; accountId: string }) =>\n quick(\"cloudflare:@cf/meta/llama-3.1-8b-instruct\", {\n cloudflareApiKey: creds.apiKey,\n cloudflareEmail: creds.email,\n cloudflareAccountId: creds.accountId,\n }),\n\n /** QwQ 32B - Reasoning specialist */\n reasoning: (creds: { apiKey: string; email: string; accountId: string }) =>\n quick(\"cloudflare:@cf/qwen/qwq-32b\", {\n cloudflareApiKey: creds.apiKey,\n cloudflareEmail: creds.email,\n cloudflareAccountId: creds.accountId,\n }),\n\n /** Qwen 2.5 Coder - Code specialist */\n coder: (creds: { apiKey: string; email: string; accountId: string }) =>\n quick(\"cloudflare:@cf/qwen/qwen2.5-coder-32b-instruct\", {\n cloudflareApiKey: creds.apiKey,\n cloudflareEmail: creds.email,\n cloudflareAccountId: creds.accountId,\n }),\n\n /** IBM Granite - Enterprise */\n granite: (creds: { apiKey: string; email: string; accountId: string }) =>\n quick(\"cloudflare:@cf/ibm/granite-4.0-h-micro\", {\n cloudflareApiKey: creds.apiKey,\n cloudflareEmail: creds.email,\n cloudflareAccountId: creds.accountId,\n }),\n};\n","/**\n * Fluent Builder API for unillm\n *\n * Modern, type-safe, chainable interface for LLM operations\n * Inspired by Prisma, Playwright, and modern TypeScript patterns\n */\n\nimport type { z } from \"zod\";\nimport type { Stream } from \"@aid-on/nagare\";\nimport { stream as nagareStream } from \"@aid-on/nagare\";\nimport type { ModelSpec, Credentials, GenerateOptions, GenerateResult } from \"./types.js\";\nimport { generate, parseModelSpec } from \"./factory.js\";\nimport { generateObject as baseGenerateObject, extractJSON } from \"./structured.js\";\nimport { withRetry, type RetryConfig } from \"./retry.js\";\nimport { truncateMessages, compressMessage } from \"./memory.js\";\nimport { createProviderStream } from \"./fluent-streaming.js\";\n\n// =============================================================================\n// Core Builder Types\n// =============================================================================\n\ninterface FluentState {\n model?: ModelSpec | string;\n credentials?: Credentials;\n messages?: Array<{ role: string; content: string }>;\n system?: string;\n temperature?: number;\n maxTokens?: number;\n retryConfig?: RetryConfig;\n schema?: z.ZodType;\n}\n\n// =============================================================================\n// Fluent Builder Class\n// =============================================================================\n\nexport class UnillmBuilder {\n private state: FluentState = {};\n\n constructor(initialState: Partial = {}) {\n this.state = { ...initialState };\n }\n\n model(spec: ModelSpec | string): UnillmBuilder {\n return new UnillmBuilder({ ...this.state, model: spec });\n }\n\n credentials(creds: Credentials): UnillmBuilder {\n return new UnillmBuilder({ ...this.state, credentials: creds });\n }\n\n /** Alias for credentials */\n creds(creds: Credentials): UnillmBuilder { return this.credentials(creds); }\n\n temperature(temp: number): UnillmBuilder {\n return new UnillmBuilder({ ...this.state, temperature: temp });\n }\n\n /** Alias for temperature */\n temp(temp: number): UnillmBuilder { return this.temperature(temp); }\n\n maxTokens(tokens: number): UnillmBuilder {\n return new UnillmBuilder({ ...this.state, maxTokens: tokens });\n }\n\n /** Alias for maxTokens */\n tokens(tokens: number): UnillmBuilder { return this.maxTokens(tokens); }\n\n system(prompt: string): UnillmBuilder {\n return new UnillmBuilder({ ...this.state, system: prompt });\n }\n\n messages(msgs: Array<{ role: string; content: string }>): UnillmBuilder {\n return new UnillmBuilder({ ...this.state, messages: msgs });\n }\n\n user(content: string): UnillmBuilder {\n const messages = [...(this.state.messages || []), { role: \"user\", content }];\n return new UnillmBuilder({ ...this.state, messages });\n }\n\n assistant(content: string): UnillmBuilder {\n const messages = [...(this.state.messages || []), { role: \"assistant\", content }];\n return new UnillmBuilder({ ...this.state, messages });\n }\n\n schema(schema: T): UnillmStructuredBuilder {\n return new UnillmStructuredBuilder({ ...this.state, schema });\n }\n\n retry(config: RetryConfig): UnillmBuilder {\n return new UnillmBuilder({ ...this.state, retryConfig: config });\n }\n\n retries(count: number, baseDelay = 1000): UnillmBuilder {\n return this.retry({ maxRetries: count, baseDelay });\n }\n\n optimize(maxTokens = 4000): UnillmBuilder {\n const messages = this.state.messages;\n if (!messages) return this;\n const optimized = truncateMessages(messages, maxTokens);\n return new UnillmBuilder({ ...this.state, messages: optimized });\n }\n\n compress(): UnillmBuilder {\n const messages = this.state.messages?.map(m => ({\n ...m, content: compressMessage(m.content),\n }));\n return new UnillmBuilder({ ...this.state, messages });\n }\n\n // ===========================================================================\n // Generation Methods\n // ===========================================================================\n\n async generate(prompt?: string): Promise {\n this.validateRequired(prompt);\n const messages = this.prepareMessages(prompt);\n const options: GenerateOptions = {\n temperature: this.state.temperature,\n maxTokens: this.state.maxTokens,\n };\n const generateFn = () => generate(this.state.model!, messages, this.state.credentials!, options);\n return this.state.retryConfig ? withRetry(generateFn, this.state.retryConfig) : generateFn();\n }\n\n async stream(prompt?: string): Promise> {\n this.validateRequired(prompt);\n const messages = this.prepareMessages(prompt);\n const { provider, model } = parseModelSpec(this.state.model!);\n const readableStream = await createProviderStream({\n provider, model, messages, credentials: this.state.credentials!,\n options: { temperature: this.state.temperature, maxTokens: this.state.maxTokens },\n });\n return nagareStream.from(readableStream);\n }\n\n // ===========================================================================\n // Private Helpers\n // ===========================================================================\n\n private prepareMessages(prompt?: string): Array<{ role: string; content: string }> {\n let messages = this.state.messages || [];\n if (this.state.system) {\n messages = [{ role: \"system\", content: this.state.system }, ...messages];\n }\n if (prompt) {\n messages = [...messages, { role: \"user\", content: prompt }];\n }\n return messages;\n }\n\n private validateRequired(prompt?: string): void {\n if (!this.state.model) throw new Error(\"Model is required. Use .model('provider:model-name')\");\n if (!this.state.credentials) throw new Error(\"Credentials are required. Use .credentials({ ... })\");\n if (!prompt && (!this.state.messages || this.state.messages.length === 0)) {\n throw new Error(\"At least one message is required. Use .user('...') or .messages([...]), or provide a prompt to generate()\");\n }\n }\n}\n\n// =============================================================================\n// Structured Output Builder\n// =============================================================================\n\nexport class UnillmStructuredBuilder {\n private state: FluentState & { schema: T };\n\n constructor(state: FluentState & { schema: T }) {\n this.state = state;\n }\n\n async generate(prompt: string): Promise<{ object: z.infer; rawText: string; usage?: Record }> {\n if (!this.state.model || !this.state.credentials || !this.state.schema) {\n throw new Error(\"Model, credentials, and schema are required for structured generation\");\n }\n return baseGenerateObject({\n model: this.state.model,\n credentials: this.state.credentials,\n schema: this.state.schema,\n prompt,\n system: this.state.system,\n temperature: this.state.temperature,\n maxTokens: this.state.maxTokens,\n });\n }\n\n extract(text: string): z.infer {\n if (!this.state.schema) throw new Error(\"Schema is required for extraction\");\n return extractJSON(text, this.state.schema);\n }\n}\n\n// =============================================================================\n// Factory Functions\n// =============================================================================\n\nexport function unillm(initialState?: Partial): UnillmBuilder {\n return new UnillmBuilder(initialState);\n}\n\nexport function quick(model: ModelSpec | string, credentials: Credentials): UnillmBuilder {\n return new UnillmBuilder({ model, credentials });\n}\n\n// Re-export provider shortcuts\nexport { groq, gemini, cloudflare, anthropic, openai } from \"./fluent-providers.js\";\n"]}