{"version":3,"file":"index.cjs","sources":["../src/logger.ts","../src/ModelUsage.ts","../src/config.ts","../src/parsers.ts","../src/utils.ts","../src/LLM.ts","../src/anthropic.ts","../src/ollama.ts","../src/openai.ts","../src/google.ts","../src/APIv1.ts","../src/xai.ts","../src/groq.ts","../src/deepseek.ts","../src/index.ts"],"sourcesContent":["interface LoggerConfig {\n enabled: boolean;\n color: string;\n lastTime?: number;\n}\n\ninterface Logger {\n (message: any, ...args: any[]): void;\n debug: (message: any, ...args: any[]) => void;\n warn: (message: any, ...args: any[]) => void;\n error: (message: any, ...args: any[]) => void;\n namespace: string;\n enabled: boolean;\n}\n\nclass LoggerManager {\n private loggers = new Map();\n private patterns: string[] = [];\n private isNode = typeof process !== 'undefined' && process.env;\n private colors = [\n '#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4',\n '#46f0f0', '#f032e6', '#bcf60c', '#fabebe', '#008080', '#e6beff',\n '#9a6324', '#fffac8', '#800000', '#aaffc3', '#808000', '#ffd8b1',\n '#000075', '#808080'\n ];\n \n constructor() {\n this.updatePatterns();\n }\n\n private updatePatterns(): void {\n let debugEnv = '';\n \n if (this.isNode) {\n debugEnv = typeof process !== 'undefined' && process.env.DEBUG || '';\n } else {\n // Browser: check localStorage and global config\n if (typeof localStorage !== 'undefined') {\n debugEnv = localStorage.getItem('DEBUG') || '';\n }\n if (typeof globalThis !== 'undefined' && (globalThis as any).DEBUG) {\n debugEnv = (globalThis as any).DEBUG;\n }\n }\n\n this.patterns = debugEnv.split(',').map(p => p.trim()).filter(Boolean);\n \n // Update existing loggers\n for (const [namespace, config] of this.loggers) {\n config.enabled = this.isEnabled(namespace);\n }\n }\n\n private isEnabled(namespace: string): boolean {\n if (this.patterns.length === 0) return false;\n\n for (const pattern of this.patterns) {\n if (pattern.startsWith('-')) {\n // Exclusion pattern\n const excludePattern = pattern.slice(1);\n if (this.matchPattern(namespace, excludePattern)) {\n return false;\n }\n } else {\n // Inclusion pattern\n if (this.matchPattern(namespace, pattern)) {\n return true;\n }\n }\n }\n\n return false;\n }\n\n private matchPattern(namespace: string, pattern: string): boolean {\n if (pattern === '*') return true;\n if (pattern === namespace) return true;\n \n // Convert glob pattern to regex\n const regexPattern = pattern\n .replace(/\\*/g, '.*')\n .replace(/\\?/g, '.')\n .replace(/\\+/g, '\\\\+')\n .replace(/\\[/g, '\\\\[')\n .replace(/\\]/g, '\\\\]')\n .replace(/\\(/g, '\\\\(')\n .replace(/\\)/g, '\\\\)')\n .replace(/\\{/g, '\\\\{')\n .replace(/\\}/g, '\\\\}')\n .replace(/\\^/g, '\\\\^')\n .replace(/\\$/g, '\\\\$')\n .replace(/\\|/g, '\\\\|');\n \n const regex = new RegExp(`^${regexPattern}$`);\n return regex.test(namespace);\n }\n\n private getColor(namespace: string): string {\n // Simple hash function to consistently assign colors\n let hash = 0;\n for (let i = 0; i < namespace.length; i++) {\n const char = namespace.charCodeAt(i);\n hash = ((hash << 5) - hash) + char;\n hash = hash & hash; // Convert to 32bit integer\n }\n return this.colors[Math.abs(hash) % this.colors.length];\n }\n\n private formatMessage(namespace: string, level: string, message: any, args: any[]): string {\n const now = Date.now();\n const config = this.loggers.get(namespace)!;\n \n let prefix = namespace;\n let timeDiff = '';\n \n if (config.lastTime) {\n timeDiff = `+${now - config.lastTime}ms`;\n }\n config.lastTime = now;\n\n if (this.isNode) {\n // Node.js: Use ANSI color codes\n const colorCode = this.getAnsiColor(config.color);\n prefix = `\\x1b[${colorCode}m${namespace}\\x1b[0m`;\n if (timeDiff) {\n prefix += ` \\x1b[90m${timeDiff}\\x1b[0m`;\n }\n } else {\n // Browser: Use CSS colors\n if (timeDiff) {\n prefix += ` ${timeDiff}`;\n }\n }\n\n const levelPrefix = level !== 'debug' ? `[${level.toUpperCase()}]` : '';\n return `${prefix}${levelPrefix ? ' ' + levelPrefix : ''} ${message}`;\n }\n\n private getAnsiColor(hexColor: string): string {\n // Convert hex color to closest ANSI color code\n const colorMap: { [key: string]: string } = {\n '#e6194b': '31', // red\n '#3cb44b': '32', // green \n '#ffe119': '33', // yellow\n '#4363d8': '34', // blue\n '#f58231': '35', // magenta\n '#911eb4': '36', // cyan\n };\n return colorMap[hexColor] || '37'; // default to white\n }\n\n private log(namespace: string, level: 'debug' | 'warn' | 'error', message: any, ...args: any[]): void {\n const config = this.loggers.get(namespace);\n if (!config || !config.enabled) return;\n\n if (typeof message === \"object\" || Array.isArray(message)) {\n message = JSON.stringify(message);\n }\n\n for (let i = 0; i < args.length; i++) {\n if (typeof args[i] === \"object\" || Array.isArray(args[i])) {\n args[i] = JSON.stringify(args[i]);\n }\n }\n\n const formattedMessage = this.formatMessage(namespace, level, message, args);\n \n if (this.isNode) {\n // Node.js: Write to stderr for debug, stdout for warn/error\n const output = level === 'debug' ? process.stderr : process.stdout;\n output.write(formattedMessage + ' ');\n if (args.length > 0) {\n output.write(args.map(arg => \n typeof arg === 'object' ? JSON.stringify(arg, null, 2) : String(arg)\n ).join(' '));\n }\n\n output.write(\"\\n\");\n } else {\n // Browser: Use console with CSS styling\n const styles = `color: ${config.color}; font-weight: bold;`;\n if (level === 'warn') {\n console.warn(`%c${formattedMessage}`, styles, ...args);\n } else if (level === 'error') {\n console.error(`%c${formattedMessage}`, styles, ...args);\n } else {\n console.log(`%c${formattedMessage}`, styles, ...args);\n }\n }\n }\n\n createLogger(namespace: string): Logger {\n if (!this.loggers.has(namespace)) {\n this.loggers.set(namespace, {\n enabled: this.isEnabled(namespace),\n color: this.getColor(namespace)\n });\n }\n\n const config = this.loggers.get(namespace)!;\n\n const logger = ((message: any, ...args: any[]) => {\n this.log(namespace, 'debug', message, ...args);\n }) as Logger;\n\n logger.debug = (message: any, ...args: any[]) => {\n this.log(namespace, 'debug', message, ...args);\n };\n\n logger.warn = (message: any, ...args: any[]) => {\n this.log(namespace, 'warn', message, ...args);\n };\n\n logger.error = (message: any, ...args: any[]) => {\n this.log(namespace, 'error', message, ...args);\n };\n\n logger.namespace = namespace;\n \n Object.defineProperty(logger, 'enabled', {\n get: () => config.enabled\n });\n\n return logger;\n }\n\n // Update patterns when environment changes\n refresh(): void {\n this.updatePatterns();\n }\n}\n\n// Singleton instance\nconst manager = new LoggerManager();\n\n// Factory function to create loggers\nfunction createLogger(namespace: string): Logger {\n return manager.createLogger(namespace);\n}\n\n// Utility to refresh patterns (useful when DEBUG env changes)\ncreateLogger.refresh = () => manager.refresh();\n\nexport default createLogger;\nexport { Logger, createLogger };","import data from \"../data/model_prices_and_context_window.json\";\n\nlet modelData = data;\n\nimport type { ServiceName, QualityFilter } from \"./LLM.types\";\n\nlet customModels: Record = {\n};\n\n/**\n * @category Usage\n */\nexport type ModelUsageType = {\n mode: string;\n service: string;\n model: string;\n max_tokens: number;\n max_input_tokens: number;\n max_output_tokens: number;\n input_cost_per_token: number;\n output_cost_per_token: number;\n output_cost_per_reasoning_token: number;\n supports_reasoning: boolean;\n supported_modalities: string[];\n}\n\n/**\n * @category Usage\n */\nexport default class ModelUsage {\n static readonly DEFAULT_BASE_URL: string = \"https://raw.githubusercontent.com/BerriAI/litellm/refs/heads/main/model_prices_and_context_window.json\";\n\n service: ServiceName;\n\n constructor(service: ServiceName) {\n this.service = service;\n }\n\n getModel(model: string, quality_filter: QualityFilter = {}): ModelUsageType | null {\n return ModelUsage.get(this.service, model, quality_filter);\n }\n\n async refresh() {\n await ModelUsage.refresh();\n }\n\n models() {\n return ModelUsage.getByService(this.service);\n }\n\n static get(service: ServiceName, model: string, quality_filter: QualityFilter = {}): ModelUsageType | null {\n let info = this.getByServiceModel(service, model);\n if (info) return info;\n\n if (!quality_filter.allowSimilar) return null;\n\n info = this.getByServiceModel(service, `${model}-latest`);\n if (info) return info;\n\n info = this.getByServiceModel(service, `${model}-beta`);\n if (info) return info;\n\n info = this.getByServiceModel(service, `${service}/${model}`);\n if (info) return info;\n\n info = this.getByServiceModel(service, `${service}/${model}-beta`);\n if (info) return info;\n\n info = this.getByServiceModel(service, model.replace(\"-beta\", \"\"));\n if (info) return info;\n\n info = this.getByServiceModel(service, model.replace(\"-thinking\", \"\"));\n if (info) return info;\n\n info = this.getByServiceModel(service, model.replace(\"-exp\", \"\"));\n if (info) return info;\n\n info = this.getByServiceModel(service, model.replace(\"-experimental\", \"\"));\n if (info) return info;\n\n info = this.getByServiceModel(service, model.replace(\"-thinking-exp\", \"\"));\n if (info) return info;\n\n info = this.getByServiceModel(service, model.replace(\"-preview\", \"\"));\n if (info) return info;\n\n return null;\n }\n\n static getAll(): ModelUsageType[] {\n return this.factories(data).filter(this.filter());\n }\n\n static getByService(service?: ServiceName): ModelUsageType[] {\n return this.factories(data).filter(this.filter(service));\n }\n\n static getByServiceModel(service: ServiceName, model: string): ModelUsageType | null {\n return this.getByService(service).find(m => m.model === model) || null;\n }\n\n static filter(service?: ServiceName): (model: ModelUsageType) => boolean {\n return (model: ModelUsageType) => {\n if (model.mode !== \"chat\" && model.mode !== \"responses\") return false;\n if (service === \"google\" && model.service === \"gemini\") return true;\n return service ? model.service === service : true;\n }\n }\n\n static factories(data: any): ModelUsageType[] {\n const custom = Object.assign({}, data, customModels);\n\n return Object.keys(custom).map(key => {\n const m = custom[key];\n const max_input_tokens = m.max_input_tokens || 0;\n const max_output_tokens = m.max_output_tokens || 0;\n const max_tokens = (m.max_tokens ? m.max_tokens : max_input_tokens + max_output_tokens);\n\n const input_cost_per_token = m.input_cost_per_token || 0;\n const output_cost_per_token = m.output_cost_per_token || 0;\n const output_cost_per_reasoning_token = m.output_cost_per_reasoning_token || 0;\n\n const supported_modalities = m.supported_modalities || [];\n\n let model = key;\n if (key.includes(\"/\")) model = key.split(\"/\").slice(1).join(\"/\");\n\n return {\n service: m.litellm_provider || m.service,\n mode: m.mode,\n model,\n max_tokens,\n max_input_tokens,\n max_output_tokens,\n input_cost_per_token,\n output_cost_per_token,\n output_cost_per_reasoning_token,\n supports_reasoning: m.supports_reasoning || false,\n supported_modalities,\n }\n });\n }\n\n static async refresh(service?: ServiceName): Promise {\n const response = await fetch(this.DEFAULT_BASE_URL);\n const data = await response.json();\n modelData = data;\n return this.factories(data).filter(this.filter(service));\n }\n\n static addCustom(info: ModelUsageType) {\n customModels[`${info.service}/${info.model}`] = Object.assign({}, { mode: \"chat\" }, info);\n }\n static getCustom(service: ServiceName, model: string): ModelUsageType | null { return customModels[`${service}/${model}`] || null }\n static getCustoms() { return customModels }\n static removeCustom(service: ServiceName, model: string) { delete customModels[`${service}/${model}`] }\n static clearCustom() { customModels = {} }\n}","import type { ServiceName } from \"./LLM.types\";\n\ntype Config = {\n service: ServiceName;\n max_tokens: number;\n}\n\nexport default {\n service: \"ollama\",\n max_tokens: 1024,\n} as Config;","import logger from \"./logger\";\n\nconst log = logger(\"llm.js:parsers\");\n\n/**\n * @category Parsers\n */\nexport function codeBlock(blockType: string) {\n return function (content: string) : string {\n try {\n return content.split(\"```\" + blockType)[1].split(\"```\")[0].trim();\n } catch (e) {\n log.error(`error parsing code block of type ${blockType} from content`, content);\n throw e;\n }\n }\n}\n\n/**\n * @category Parsers\n */\nexport function markdown(content: string) : string {\n try {\n return codeBlock(\"markdown\")(content);\n } catch (e) {\n return codeBlock(\"md\")(content);\n }\n}\n\n/**\n * @category Parsers\n */\nexport function json(content: string) : any {\n try {\n return JSON.parse(content);\n } catch (e) {\n const parser = codeBlock(\"json\");\n try {\n return JSON.parse(parser(content));\n } catch (e) {\n log.error(`error parsing json from content`, content);\n throw e;\n }\n }\n}\n\n/**\n * @category Parsers\n */\nexport function xml(tag: string) {\n return function (content: string) : string {\n try {\n const inner = content.split(`<${tag}>`)[1].split(``)[0].trim();\n if (!inner || inner.length == 0) {\n throw new Error(`No content found inside of XML tag ${tag}`);\n }\n return inner;\n } catch (e) {\n log.error(`error parsing xml tag ${tag} from content`, content);\n throw e;\n }\n }\n}","import logger from \"./logger\";\nimport type { Message, MessageRole, Tool, ToolCall, WrappedTool, WrappedToolCall } from \"./LLM.types\";\n\nconst log = logger(\"llm.js:utils\");\n\nexport async function handleErrorResponse(response: Response, error=\"Error while handling response\") {\n if (response.ok) return true;\n let data;\n try {\n data = await response.json();\n } catch (e) {\n let err = \"Unable to parse response\";\n if (response.status && response.statusText) {\n err = `${response.status} ${response.statusText}`;\n }\n\n throw new Error(err);\n }\n\n let err = error;\n if (data.error && typeof data.error === \"string\") {\n err = data.error;\n } else if (data.error && typeof data.error === \"object\" && data.error.type && data.error.message) {\n err = `${data.error.type}: ${data.error.message}`;\n } else if (data.error && typeof data.error === \"object\") {\n err = JSON.stringify(data.error);\n }\n\n throw new Error(err);\n}\n\nexport async function *parseStream(stream: ReadableStream) : AsyncGenerator {\n const reader = stream.getReader();\n const decoder = new TextDecoder();\n let buffer = \"\";\n\n let done = false;\n while (!done) {\n let { done, value } = await reader.read();\n if (done) break;\n\n let jsonchunk = decoder.decode(value, { stream: true });\n if (!jsonchunk) continue;\n\n const jsonlines = jsonchunk.split(\"\\n\");\n let unparsedLines: string[] = [];\n\n for (let jsonline of jsonlines) {\n if (jsonline.startsWith(\"event: \")) continue;\n if (jsonline.startsWith(\"data: \")) jsonline = jsonline.slice(6);\n if (jsonline.length === 0) continue;\n unparsedLines.push(jsonline);\n }\n\n buffer = unparsedLines.join(\"\\n\");\n unparsedLines = [];\n\n const lines = buffer.split(\"\\n\");\n for (const line of lines) {\n try {\n const data = JSON.parse(line);\n yield data;\n if (data.type === \"message_stop\" || data.type === \"response.completed\" || data.done) {\n done = true;\n break;\n }\n } catch (e) {\n unparsedLines.push(line);\n }\n }\n\n buffer = unparsedLines.join(\"\\n\");\n }\n\n if (buffer.length > 0) {\n try {\n const data = JSON.parse(buffer);\n yield data;\n } catch (e) {\n log.error(\"Error parsing JSON LINE:\", buffer);\n }\n }\n}\n\nexport function filterMessageRole(messages: Message[], role: MessageRole): Message[] {\n return messages.filter(message => message.role === role);\n}\n\nexport function filterNotMessageRole(messages: Message[], role: MessageRole): Message[] {\n return messages.filter(message => message.role !== role);\n}\n\nexport function uuid() {\n return crypto.randomUUID();\n}\n\nexport function wrapTool(tool: Tool) : WrappedTool {\n if (!tool.name) throw new Error(\"Tool name is required\");\n if (!tool.description) throw new Error(\"Tool description is required\");\n if (!tool.input_schema) throw new Error(\"Tool input schema is required\");\n\n return {\n type: \"function\",\n function: { name: tool.name, description: tool.description, parameters: tool.input_schema },\n };\n}\n\nexport function unwrapToolCall(tool_call: WrappedToolCall) : ToolCall {\n if (!tool_call.function) throw new Error(\"Tool call function is required\");\n if (!tool_call.function.id) tool_call.function.id = crypto.randomUUID();\n if (!tool_call.function.name) throw new Error(\"Tool call function name is required\");\n if (!tool_call.function.arguments) throw new Error(\"Tool call function arguments is required\");\n\n let args = tool_call.function.arguments;\n if (typeof args === \"string\") args = JSON.parse(args);\n\n return { id: tool_call.function.id, name: tool_call.function.name, input: args };\n}\n\nexport function keywordFilter(str: string, keywords: string[]): boolean {\n for (const keyword of keywords) {\n if (str.includes(`${keyword}-`)) return false;\n if (str.includes(`-${keyword}`)) return false;\n }\n return true;\n}\n\nexport function isBrowser(): boolean {\n return typeof window !== 'undefined' && !isNode();\n}\n\nexport function isNode(): boolean {\n return typeof process !== 'undefined' && !isBrowser();\n}\n\nexport function apiKeys() {\n if (isBrowser()) {\n return Object.fromEntries(Object.entries(localStorage).filter(([key]) => key.indexOf(\"API_KEY\") !== -1));\n } else {\n return Object.fromEntries(Object.entries(process.env).filter(([key]) => key.indexOf(\"API_KEY\") !== -1));\n }\n}\n\n/* @ts-ignore */\nexport const environment = isBrowser() ? import.meta.env : process.env;\n\n\nexport function getenv(key: string) {\n return environment[key] ?? environment[`VITE_${key}`];\n}\n\nexport function join(...pathSegments: string[]): string {\n if (pathSegments.length === 0) return \".\";\n \n const firstSegment = pathSegments[0];\n const isUrl = firstSegment.startsWith(\"http://\") || firstSegment.startsWith(\"https://\");\n \n if (isUrl) {\n const [protocol, ...urlParts] = firstSegment.split(\"://\");\n const remainingSegments = pathSegments.slice(1);\n \n const allSegments = [urlParts.join(\"://\"), ...remainingSegments];\n const joinedPath = allSegments.join(\"/\").replace(/\\/+/g, \"/\"); // Remove duplicate slashes\n \n return `${protocol}://${joinedPath}`;\n }\n \n let parts: string[] = [];\n for (let i = 0, l = pathSegments.length; i < l; i++) {\n parts = parts.concat(pathSegments[i].split(\"/\"));\n }\n\n const newParts: string[] = [];\n for (let i = 0, l = parts.length; i < l; i++) {\n const part = parts[i];\n if (!part || part === \".\") continue;\n if (part === \"..\") newParts.pop();\n else newParts.push(part);\n }\n\n if (parts[0] === \"\") newParts.unshift(\"\");\n return newParts.join(\"/\") || (newParts.length ? \"/\" : \".\");\n}","import logger from './logger';\nconst log = logger(\"llm.js:index\");\n\nimport ModelUsage from \"./ModelUsage\";\nimport type { ModelUsageType } from \"./ModelUsage\";\nimport config from \"./config\";\nimport * as parsers from \"./parsers\";\nimport { parseStream, handleErrorResponse, isBrowser, isNode, join } from \"./utils\";\nimport type {\n ServiceName, Options, InputOutputTokens, Usage, Response, PartialStreamResponse, StreamResponse, QualityFilter,\n Message, Parsers, Input, Model, MessageRole, ParserResponse, Tool, MessageContent, ToolCall, StreamingToolCall } from \"./LLM.types\";\n\n/**\n * LLM Base Class\n * \n * @category LLMs\n */\nexport default class LLM {\n static parsers = parsers;\n static readonly service: ServiceName;\n static DEFAULT_BASE_URL: string;\n static DEFAULT_MODEL: string;\n static isLocal: boolean = false;\n static isBearerAuth: boolean = false;\n\n service: ServiceName;\n messages: Message[];\n model: string;\n baseUrl: string;\n options: Options;\n modelUsage: ModelUsage;\n stream: boolean;\n max_tokens: number;\n max_thinking_tokens?: number;\n extended: boolean;\n think: boolean;\n temperature?: number;\n parser?: ParserResponse;\n json?: boolean;\n tools?: Tool[];\n qualityFilter: QualityFilter;\n protected abortController: AbortController | null = null;\n protected cache: Record = {};\n\n constructor(input?: Input | Options, options: Options = {}) {\n const LLM = this.constructor as LLMConstructor;\n\n this.service = options.service ?? (this.constructor as typeof LLM).service;\n this.messages = [];\n if (input && typeof input === \"string\") this.user(input);\n else if (input && Array.isArray(input)) this.messages = input;\n this.options = options;\n this.model = options.model ?? LLM.DEFAULT_MODEL;\n this.baseUrl = options.baseUrl ?? LLM.DEFAULT_BASE_URL;\n this.modelUsage = new ModelUsage(this.service);\n this.stream = options.stream ?? false;\n this.max_tokens = options.max_tokens ?? config.max_tokens;\n this.extended = options.extended ?? false;\n this.think = options.think ?? false;\n this.qualityFilter = options.qualityFilter ?? {};\n if (typeof options.temperature === \"number\") this.temperature = options.temperature;\n if (typeof options.max_thinking_tokens === \"number\") this.max_thinking_tokens = options.max_thinking_tokens;\n if (typeof options.parser === \"string\") this.parser = this.parsers[options.parser];\n if (typeof options.json === \"boolean\") this.json = options.json;\n if (this.json && !this.parser) this.parser = parsers.json;\n if (Array.isArray(options.tools)) this.tools = options.tools as Tool[];\n if (this.think) this.extended = true;\n if (this.tools && this.tools.length > 0) this.extended = true;\n\n log.debug(`LLM ${this.service} constructor`);\n }\n\n get isLocal() { return (this.constructor as typeof LLM).isLocal }\n get apiKey() {\n if (this.options.apiKey) return this.options.apiKey;\n if (isBrowser()) {\n if (localStorage.getItem(`${this.service.toUpperCase()}_API_KEY`)) return localStorage.getItem(`${this.service.toUpperCase()}_API_KEY`);\n } else if (isNode()) {\n if (typeof process !== 'undefined' && process.env?.[`${this.service.toUpperCase()}_API_KEY`]) return process.env[`${this.service.toUpperCase()}_API_KEY`];\n }\n return undefined;\n }\n get llmOptions(): Options {\n const options = {\n model: this.model,\n messages: this.parseMessages(this.messages),\n stream: this.stream,\n max_tokens: this.max_tokens,\n think: this.think,\n } as Options;\n if (typeof this.max_thinking_tokens === \"number\") options.max_thinking_tokens = this.max_thinking_tokens;\n if (typeof this.temperature === \"number\") options.temperature = this.temperature;\n if (this.tools) options.tools = this.tools;\n return options;\n }\n\n get llmHeaders() {\n const headers: Record = {\n \"Content-Type\": \"application/json\",\n };\n if ((this.constructor as typeof LLM).isBearerAuth) headers[\"Authorization\"] = `Bearer ${this.apiKey}`;\n else if (this.apiKey) headers[\"x-api-key\"] = this.apiKey;\n return headers;\n }\n\n get chatUrl() { return join(this.baseUrl, \"api/chat\") }\n get modelsUrl() { return join(this.baseUrl, \"api/tags\") }\n getChatUrl(opts: Options) { return this.chatUrl }\n getModelsUrl() { return this.modelsUrl }\n get parsers(): Parsers {\n return {\n thinking: this.parseThinkingChunk.bind(this),\n content: this.parseContentChunk.bind(this),\n usage: this.parseTokenUsage.bind(this),\n tool_calls: this.parseToolsChunk.bind(this),\n }\n }\n\n addMessage(role: MessageRole, content: MessageContent) { this.messages.push({ role, content }) }\n user(content: string) { this.addMessage(\"user\", content) }\n assistant(content: string) { this.addMessage(\"assistant\", content) }\n system(content: string) { this.addMessage(\"system\", content) }\n thinking(content: string) { this.addMessage(\"thinking\", content) }\n toolCall(tool: ToolCall) { this.addMessage(\"tool_call\", tool) }\n\n async chat(input: string, options?: Options): Promise | Response | PartialStreamResponse> {\n this.user(input);\n return await this.send(options);\n }\n\n abort() { \n if (this.abortController) {\n this.abortController.abort();\n }\n }\n\n async send(options?: Options): Promise | Response | PartialStreamResponse> {\n const vanillaOptions = { ...this.llmOptions, ...options || {} };\n const opts = this.parseOptions(JSON.parse(JSON.stringify(vanillaOptions)));\n\n this.resetCache();\n\n if (opts.tools && opts.tools.length > 0) this.extended = true;\n\n log.debug(`LLM ${this.service} send`);\n\n this.abortController = new AbortController();\n\n const response = await fetch(this.getChatUrl(opts), {\n method: \"POST\",\n body: JSON.stringify(opts),\n headers: this.llmHeaders,\n signal: this.abortController.signal,\n mode: \"cors\",\n credentials: \"omit\",\n });\n\n await handleErrorResponse(response, \"Failed to send request\");\n\n if (this.stream) {\n const body = response.body;\n if (!body) throw new Error(\"No body found\");\n if (this.extended) return this.extendedStreamResponse(body, vanillaOptions);\n return this.streamResponse(body);\n }\n\n try {\n const data = await response.json();\n if (this.extended) return this.extendedResponse(data, vanillaOptions);\n return this.response(data);\n } finally {\n this.abortController = null;\n }\n }\n\n response(data: any) : string {\n let content = this.parseContent(data);\n if (this.parser) content = this.parser(content) as string;\n\n if (content) this.assistant(content);\n return content;\n }\n\n protected extendedResponse(data: any, options: Options): Response {\n const response = {\n service: this.service,\n options,\n } as Response;\n\n const tokenUsage = this.parseTokenUsage(data);\n if (tokenUsage) {\n response.usage = this.parseUsage(tokenUsage);\n }\n\n if (options.think) {\n const thinking = this.parseThinking(data);\n if (thinking) {\n response.thinking = thinking;\n this.thinking(thinking);\n }\n }\n\n let content = this.parseContent(data);\n if (this.parser) content = this.parser(content) as string;\n if (content) this.assistant(content);\n\n if (this.tools && this.tools.length > 0) {\n response.tool_calls = this.parseTools(data) as ToolCall[];\n for (const tool of response.tool_calls) {\n if (tool && Object.keys(tool).length > 0) { this.toolCall(tool) }\n }\n }\n\n response.content = content;\n response.messages = JSON.parse(JSON.stringify(this.messages));\n\n return response;\n }\n\n protected async *streamResponse(stream: ReadableStream): AsyncGenerator {\n const restream = this.streamResponses(stream, { content: this.parseContentChunk.bind(this) });\n for await (const chunk of restream) {\n if (chunk.type === \"content\") {\n yield chunk.content as string;\n }\n }\n\n this.abortController = null;\n }\n\n protected async *streamResponses(stream: ReadableStream, parsers: Parsers): AsyncGenerator> {\n const reader = await parseStream(stream);\n let buffers : Record = { \"type\": \"buffers\" };;\n\n for await (const chunk of reader) {\n for (const [name, parser] of Object.entries(parsers)) {\n const content = parser(chunk);\n if (!content) continue;\n\n if (name === \"usage\") {\n buffers[name] = content as InputOutputTokens;\n yield { type: name, content: content as InputOutputTokens };\n } else if (name === \"tool_calls\") {\n if (!Array.isArray(content) || content.length === 0) continue;\n if (!buffers[name]) buffers[name] = [];\n (buffers[name] as ToolCall[]).push(...content as unknown as ToolCall[]);\n yield { type: name, content: content as unknown as ToolCall[] };\n } else {\n if (!buffers[name]) buffers[name] = \"\";\n buffers[name] += content as string;\n yield { type: name, content: content as string };\n }\n }\n }\n\n this.saveBuffers(buffers);\n\n return buffers;\n }\n\n protected saveBuffers(buffers: Record) {\n for (let [name, content] of Object.entries(buffers)) {\n if (name === \"thinking\") {\n this.thinking(content as string);\n } else if (name === \"tool_calls\") {\n for (const tool of content as unknown as ToolCall[]) {\n if (tool && Object.keys(tool).length > 0) { this.toolCall(tool) }\n }\n } else if (name === \"content\") {\n if (this.parser) {\n content = this.parser(content) as string;\n buffers[name] = content;\n }\n if (content) this.assistant(content as string);\n }\n }\n }\n\n protected async *restream(stream: AsyncGenerator>, callback?: (chunk: Record) => void): AsyncGenerator> {\n while (true) {\n const { value, done } = await stream.next();\n if (callback && value) callback(value);\n if (done) break;\n yield value;\n }\n }\n\n protected extendedStreamResponse(body: ReadableStream, options: Options): PartialStreamResponse {\n let usage: Usage;\n\n let thinking = \"\";\n let content = \"\";\n let tool_calls: ToolCall[] = [];\n\n const complete = async (): Promise => {\n const messages = JSON.parse(JSON.stringify(this.messages));\n const response = { service: this.service, options, usage, messages, content } as StreamResponse;\n if (thinking) response.thinking = thinking;\n if (tool_calls.length > 0) response.tool_calls = tool_calls;\n\n this.abortController = null;\n\n return response;\n }\n\n const stream = this.streamResponses(body, this.parsers);\n const restream = this.restream(stream as AsyncGenerator>, (chunk) => {\n if (chunk.type === \"usage\" && chunk.content && typeof chunk.content === \"object\") {\n const tokenUsage = chunk.content as InputOutputTokens;\n usage = this.parseUsage(tokenUsage);\n }\n\n if (chunk.type === \"tool_calls\" && chunk.content && Array.isArray(chunk.content)) {\n tool_calls.push(...chunk.content as unknown as ToolCall[]);\n }\n\n if (chunk.type !== \"buffers\") return;\n if (chunk.thinking) thinking = chunk.thinking as string;\n if (chunk.content) content = chunk.content as string;\n });\n\n return { service: this.service, options, stream: restream, complete, think: this.think ?? false }\n }\n\n async fetchModels(): Promise {\n const options = { headers: this.llmHeaders } as RequestInit;\n log.debug(`LLM ${this.service} fetchModels`);\n const response = await fetch(this.getModelsUrl(), options);\n await handleErrorResponse(response, \"Failed to fetch models\");\n\n const data = await response.json();\n let models = [];\n if (Array.isArray(data)) models = data;\n else if (Array.isArray(data.models)) models = data.models;\n else if (Array.isArray(data.data)) models = data.data;\n\n if (!models) throw new Error(\"No models found\");\n return models.map(this.parseModel);\n }\n\n async verifyConnection(): Promise { return (await this.fetchModels()).length > 0 }\n\n async getModels(quality_filter: QualityFilter = {}): Promise {\n const models = await this.fetchModels();\n return models.map(model => {\n let usage = ModelUsage.get(this.service, model.model, quality_filter);\n if (!usage) {\n if (quality_filter.allowUnknown) {\n usage = { input_cost_per_token: 0, output_cost_per_token: 0, output_cost_per_reasoning_token: 0 } as ModelUsageType;\n } else {\n throw new Error(`model info not found for ${model.model}`);\n }\n }\n\n if (this.isLocal) {\n usage.input_cost_per_token = 0;\n usage.output_cost_per_token = 0;\n usage.output_cost_per_reasoning_token = 0;\n }\n return { ...usage, name: model.name, model: model.model, created: model.created, service: this.service, raw: model } as Model;\n }).filter(this.filterQualityModel);\n }\n\n filterQualityModel(model: Model): boolean {\n return true;\n }\n\n async getQualityModels(): Promise {\n return this.getModels({ allowUnknown: true, allowSimilar: true, topModels: true });\n }\n\n async refreshModelUsage(): Promise {\n await this.modelUsage.refresh();\n }\n\n parseContent(data: any): string { throw new Error(\"parseContent not implemented\") }\n parseTools(data: any): ToolCall[] { return [] }\n parseToolsChunk(chunk: any): ToolCall[] { return this.parseTools(chunk) }\n parseContentChunk(chunk: any): string { return this.parseContent(chunk) }\n parseThinking(data: any): string { return \"\" }\n parseThinkingChunk(chunk: any): string { return this.parseThinking(chunk) }\n parseModel(model: any): Model { throw new Error(\"parseModel not implemented\") }\n parseMessages(messages: Message[]): Message[] {\n return messages.map(message => {\n if (message.role === \"thinking\" || message.role === \"tool_call\") message.role = \"assistant\";\n return message;\n });\n }\n\n parseOptions(options: Options): Options {\n if (!options) return {};\n return options;\n }\n parseTokenUsage(usage: any): InputOutputTokens | null { return usage }\n parseUsage(tokenUsage: InputOutputTokens): Usage {\n const modelUsage = this.modelUsage.getModel(this.model, this.qualityFilter);\n let inputCostPerToken = modelUsage?.input_cost_per_token || 0;\n let outputCostPerToken = modelUsage?.output_cost_per_token || 0;\n\n if (this.isLocal) {\n inputCostPerToken = 0;\n outputCostPerToken = 0;\n }\n\n const input_cost = tokenUsage.input_tokens * inputCostPerToken;\n const output_cost = tokenUsage.output_tokens * outputCostPerToken;\n const total_cost = input_cost + output_cost;\n\n return {\n ...tokenUsage,\n local: this.isLocal,\n total_tokens: tokenUsage.input_tokens + tokenUsage.output_tokens,\n input_cost,\n output_cost,\n total_cost,\n }\n }\n\n protected resetCache() {\n this.cache = {};\n }\n}\n\nexport type LLMConstructor = typeof LLM;","import LLM from \"./LLM\";\nimport type { Model, ServiceName, Options, ToolCall } from \"./LLM.types\";\nimport { isBrowser, join } from \"./utils\";\n\n/**\n * @category Options\n */\nexport interface AnthropicOptions extends Options {\n thinking: {\n type: \"enabled\" | \"disabled\";\n budget_tokens: number;\n }\n}\n\n/**\n * @category LLMs\n */\nexport default class Anthropic extends LLM {\n static readonly service: ServiceName = \"anthropic\";\n static DEFAULT_BASE_URL: string = \"https://api.anthropic.com/v1\";\n static DEFAULT_MODEL: string = \"claude-opus-4-20250514\";\n static API_VERSION: string = \"2023-06-01\";\n\n get chatUrl() { return join(this.baseUrl, \"messages\") }\n get modelsUrl() { return join(this.baseUrl, \"models\") }\n get llmHeaders() {\n const headers = Object.assign({\n \"anthropic-version\": Anthropic.API_VERSION,\n }, super.llmHeaders);\n\n if (isBrowser()) {\n headers[\"anthropic-dangerous-direct-browser-access\"] = \"true\";\n }\n\n return headers;\n }\n\n parseOptions(options: AnthropicOptions): AnthropicOptions {\n if (options.think) {\n const budget_tokens = Math.floor((options.max_tokens || 0) / 2);\n options.thinking = { type: \"enabled\", budget_tokens };\n }\n\n if (typeof options.max_thinking_tokens === \"number\") {\n options.thinking.budget_tokens = options.max_thinking_tokens;\n delete options.max_thinking_tokens;\n }\n\n delete options.think;\n return options as AnthropicOptions;\n }\n\n parseThinking(data: any): string {\n const messages = data.content ?? [];\n for (const message of messages) {\n if (message.type !== \"thinking\") continue;\n if (!message.thinking) continue;\n return message.thinking;\n }\n return \"\";\n }\n\n parseThinkingChunk(chunk: any): string {\n if (!chunk || chunk.type !== \"content_block_delta\" || !chunk.delta) return \"\";\n const delta = chunk.delta;\n if (delta.type !== \"thinking_delta\" || !delta.thinking) return \"\";\n return delta.thinking;\n }\n\n parseTokenUsage(data: any) {\n if (!data) return null;\n const input_tokens = data.message?.usage?.input_tokens || data.usage?.input_tokens;\n const output_tokens = data.message?.usage?.output_tokens || data.usage?.output_tokens;\n if (typeof input_tokens !== \"number\") return null;\n if (typeof output_tokens !== \"number\") return null;\n return { input_tokens, output_tokens };\n }\n\n parseContent(data: any): string {\n const messages = data.content ?? [];\n for (const message of messages) {\n if (message.type !== \"text\" || !message.text) continue;\n return message.text;\n }\n return \"\";\n }\n\n parseContentChunk(chunk: any): string {\n if (chunk.type !== \"content_block_delta\" || !chunk.delta || chunk.delta.type !== \"text_delta\" || !chunk.delta.text) return \"\";\n return chunk.delta.text;\n }\n\n parseToolsChunk(data: any): ToolCall[] {\n if (data.type === \"content_block_start\" && data.content_block && data.content_block.type === \"tool_use\") {\n this.cache[\"tool_call\"] = data.content_block;\n }\n\n if (this.cache[\"tool_call\"] && data.type === \"content_block_delta\" && data.delta && data.delta.type === \"input_json_delta\") {\n if (!this.cache[\"tool_call_input\"]) this.cache[\"tool_call_input\"] = \"\";\n this.cache[\"tool_call_input\"] += data.delta.partial_json;\n }\n\n if (!this.cache[\"tool_call\"]) return [];\n if (!this.cache[\"tool_call_input\"]) return [];\n\n try {\n const input = JSON.parse(this.cache[\"tool_call_input\"]);\n const tool_call = { id: this.cache[\"tool_call\"].id, name: this.cache[\"tool_call\"].name, input } as ToolCall;\n\n delete this.cache[\"tool_call\"];\n delete this.cache[\"tool_call_input\"];\n\n return [tool_call];\n } catch (error) {\n return [];\n }\n }\n\n parseTools(data: any): ToolCall[] {\n if (!data || !data.content || !Array.isArray(data.content)) return [];\n const tools: ToolCall[] = [];\n for (const content of data.content) {\n if (content.type !== \"tool_use\" || !content.id || !content.name || !content.input) continue;\n tools.push({ id: content.id, name: content.name, input: content.input });\n }\n return tools;\n }\n\n parseModel(model: any): Model {\n return { name: model.display_name, model: model.id, created: new Date(model.created_at) } as Model;\n }\n\n filterQualityModel(model: Model): boolean {\n if (model.mode !== \"chat\") return false;\n if (model.model.startsWith(\"claude-2\")) return false;\n return true;\n }\n}","import LLM from \"./LLM\";\nimport type { Options, Model, ServiceName, ToolCall, Tool, WrappedToolCall } from \"./LLM.types\";\nimport { unwrapToolCall, wrapTool, join } from \"./utils\";\n\n/**\n * @category Options\n */\nexport interface OllamaOptions extends Options {\n think?: boolean;\n options?: {\n num_predict?: number;\n }\n}\n\n/**\n * @category LLMs\n */\nexport default class Ollama extends LLM {\n static readonly service: ServiceName = \"ollama\";\n static DEFAULT_BASE_URL: string = \"http://localhost:11434\";\n static DEFAULT_MODEL: string = \"gemma3:4b\";\n static isLocal: boolean = true;\n\n get chatUrl() { return join(this.baseUrl, \"api/chat\") }\n get modelsUrl() { return join(this.baseUrl, \"api/tags\") }\n\n get llmHeaders() {\n const headers = super.llmHeaders;\n delete headers[\"x-api-key\"];\n return headers;\n }\n\n parseOptions(options: OllamaOptions): OllamaOptions {\n if (options.max_tokens) {\n const max_tokens = options.max_tokens;\n delete options.max_tokens;\n if (!options.options) options.options = {};\n options.options.num_predict = max_tokens;\n }\n\n if (options.tools) {\n const tools = options.tools.map(tool => wrapTool(tool as Tool));\n options.tools = tools;\n }\n\n delete options.apiKey;\n\n return options;\n }\n\n parseThinking(data: any): string {\n if (!data || !data.message || !data.message.thinking) return \"\";\n return data.message.thinking;\n }\n\n parseTokenUsage(usage: any) {\n if (!usage) return null;\n if (typeof usage.prompt_eval_count !== \"number\") return null;\n if (typeof usage.eval_count !== \"number\") return null;\n\n return { input_tokens: usage.prompt_eval_count, output_tokens: usage.eval_count };\n }\n\n parseContent(data: any): string {\n if (!data || !data.message || !data.message.content) return \"\";\n return data.message.content;\n }\n\n parseContentChunk(chunk: any): string {\n if (!chunk || !chunk.message || !chunk.message.content || chunk.message.role !== \"assistant\") return \"\";\n return chunk.message.content;\n }\n\n parseTools(data: any): ToolCall[] {\n if (!data || !data.message || !data.message.tool_calls) return [];\n return data.message.tool_calls.map((tool_call: WrappedToolCall) => unwrapToolCall(tool_call));\n }\n\n parseModel(model: any): Model {\n return { name: model.model, model: model.model, created: new Date(model.modified_at) } as Model;\n }\n\n async verifyConnection(): Promise {\n const response = await fetch(`${this.baseUrl}`);\n return await response.text() === \"Ollama is running\";\n }\n}\n","import LLM from \"./LLM\";\nimport type { Message, Model, Options, ServiceName, ToolCall, Tool } from \"./LLM.types\";\nimport { keywordFilter, join } from \"./utils\";\n\n/**\n * @category Options\n */\nexport interface OpenAIOptions extends Options {\n input?: string | Message[];\n max_output_tokens?: number;\n reasoning?: { effort: \"low\" | \"medium\" | \"high\", summary: \"auto\" | \"concise\" | \"detailed\" };\n}\n\n/**\n * @category Tools\n */\nexport interface OpenAITool {\n name: string;\n parameters: any;\n strict: boolean;\n type: \"function\";\n description: string;\n}\n\n/**\n * @category LLMs\n */\nexport default class OpenAI extends LLM {\n static readonly service: ServiceName = \"openai\";\n static DEFAULT_BASE_URL: string = \"https://api.openai.com/v1\";\n static DEFAULT_MODEL: string = \"gpt-4o-mini\";\n static isBearerAuth: boolean = true;\n\n get chatUrl() { return join(this.baseUrl, \"responses\") }\n get modelsUrl() { return join(this.baseUrl, \"models\") }\n\n parseOptions(options: OpenAIOptions): OpenAIOptions {\n options.input = options.messages;\n delete options.messages;\n\n if (options.max_tokens) {\n const max_tokens = options.max_tokens;\n delete options.max_tokens;\n options.max_output_tokens = max_tokens;\n }\n\n if (options.tools) {\n const tools = options.tools.map(tool => wrapTool(tool as Tool));\n options.tools = tools;\n }\n\n if (options.think && !options.reasoning) {\n options.reasoning = { effort: \"medium\", summary: \"detailed\" };\n }\n delete options.think;\n\n return options;\n }\n\n parseContent(data: any): string {\n if (!data || !data.output || !Array.isArray(data.output)) return \"\";\n if (data.object !== \"response\" || data.status !== \"completed\") return \"\";\n for (const output of data.output) {\n if (output.type !== \"message\" || output.role !== \"assistant\" || output.status !== \"completed\" || !output.content || !Array.isArray(output.content)) continue;\n for (const content of output.content) { \n if (content.type !== \"output_text\" || !content.text) continue;\n return content.text;\n }\n }\n\n return \"\";\n }\n\n parseTokenUsage(data: any) {\n if (data.response && data.type === \"response.completed\") data = data.response;\n\n if (!data || !data.usage || !data.usage.input_tokens || !data.usage.output_tokens) return null;\n\n return {\n input_tokens: data.usage.input_tokens,\n output_tokens: data.usage.output_tokens,\n };\n }\n\n parseTools(data: any): ToolCall[] {\n if (!data || !data.output || !Array.isArray(data.output)) return [];\n if (data.object !== \"response\" || data.status !== \"completed\") return [];\n\n const tool_calls: ToolCall[] = [];\n for (const output of data.output) {\n if (output.type !== \"function_call\" || output.status !== \"completed\" || !output.call_id || !output.name || !output.arguments) continue;\n tool_calls.push({\n id: output.call_id,\n name: output.name,\n input: JSON.parse(output.arguments),\n });\n }\n\n return tool_calls;\n }\n\n parseToolsChunk(data: any): ToolCall[] {\n if (data.type === \"response.output_item.added\" && data.item && data.item.type === \"function_call\") {\n this.cache[\"tool_call\"] = data.item;\n }\n\n if (this.cache[\"tool_call\"] && data.type === \"response.function_call_arguments.done\") {\n this.cache[\"tool_call_input\"] = data.arguments;\n }\n\n if (!this.cache[\"tool_call\"]) return [];\n if (!this.cache[\"tool_call_input\"]) return [];\n\n try {\n const input = JSON.parse(this.cache[\"tool_call_input\"]);\n const tool_call = { id: this.cache[\"tool_call\"].id, name: this.cache[\"tool_call\"].name, input } as ToolCall;\n\n delete this.cache[\"tool_call\"];\n delete this.cache[\"tool_call_input\"];\n\n return [tool_call];\n } catch (error) {\n return [];\n }\n }\n\n parseThinking(data: any): string {\n if (!data || !data.output || !Array.isArray(data.output)) return \"\";\n if (data.object !== \"response\" || data.status !== \"completed\") return \"\";\n for (const output of data.output) {\n if (output.type !== \"reasoning\" || !output.summary || !Array.isArray(output.summary)) continue;\n for (const part of output.summary) {\n if (part.type !== \"summary_text\" || !part.text) continue;\n return part.text;\n }\n }\n return \"\";\n }\n\n parseThinkingChunk(chunk: any): string {\n if (!chunk || chunk.type !== \"response.reasoning_summary_text.delta\" || !chunk.delta) return \"\";\n return chunk.delta;\n }\n\n parseContentChunk(chunk: any): string {\n if (!chunk || !chunk.delta || chunk.type !== \"response.output_text.delta\") return \"\";\n return chunk.delta;\n }\n\n parseModel(model: any): Model {\n return {\n name: model.model,\n model: model.id,\n created: new Date(model.created * 1000),\n } as Model;\n }\n\n filterQualityModel(model: Model): boolean {\n const keywords = [\"audio\", \"image\", \"davinci\", \"babbage\", \"dall-e\", \"tts\", \"whisper\", \"embedding\", \"vision\", \"moderation\", \"realtime\",\n \"computer-use\", \"transcribe\", \"instruct\", \"codex\"];\n return keywordFilter(model.model, keywords);\n }\n\n}\n\nfunction wrapTool(tool: Tool): OpenAITool {\n return {\n name: tool.name,\n parameters: Object.assign({}, tool.input_schema, { additionalProperties: false }),\n strict: true,\n type: \"function\",\n description: tool.description,\n } as OpenAITool;\n}\n","import LLM from \"./LLM\";\nimport type { ServiceName, Options, Model, ToolCall, Tool } from \"./LLM.types\";\nimport { filterMessageRole, filterNotMessageRole, keywordFilter, uuid, join } from \"./utils\";\n\n/**\n * @category Message\n */\nexport interface GoogleMessage {\n role: \"user\" | \"model\" | \"assistant\";\n content: string;\n}\n\n/**\n * @category Tools\n */\nexport interface GoogleTool {\n name: string;\n description: string;\n parameters: Record;\n}\n\n/**\n * @category Options\n */\nexport interface GoogleOptions extends Options {\n system_instruction?: {\n parts: { text: string }[];\n }\n contents?: {\n parts: { text: string }[];\n }[];\n generationConfig?: {\n temperature?: number;\n maxOutputTokens?: number;\n thinkingConfig?: {\n includeThoughts: boolean;\n }\n }\n}\n\n/**\n * @category LLMs\n */\nexport default class Google extends LLM {\n static readonly service: ServiceName = \"google\";\n static DEFAULT_BASE_URL: string = \"https://generativelanguage.googleapis.com/v1beta/\";\n static DEFAULT_MODEL: string = \"gemini-2.5-flash-preview-05-20\";\n\n get chatUrl() { return join(this.baseUrl, \"chat/completions\") }\n get modelsUrl() { return join(this.baseUrl, \"models\") }\n\n getChatUrl(opts: Options) {\n return join(this.baseUrl, \"models\", `${opts.model}:generateContent?key=${this.apiKey}`);\n }\n\n getModelsUrl() { return `${this.modelsUrl}?key=${this.apiKey}` }\n\n parseOptions(options: GoogleOptions): GoogleOptions {\n const messages = JSON.parse(JSON.stringify(options.messages || [])).map((m: GoogleMessage) => {\n if (m.role === \"assistant\") m.role = \"model\";\n return m;\n });\n\n const system = filterMessageRole(messages, \"system\");\n const nonSystem = filterNotMessageRole(messages, \"system\");\n delete options.messages;\n\n if (system.length > 0) { options.system_instruction = { parts: system.map(message => ({ text: message.content })) } }\n if (nonSystem.length > 0) { options.contents = nonSystem.map(message => ({ role: message.role, parts: [{ text: message.content }] })) }\n\n if (!options.generationConfig) options.generationConfig = {};\n if (typeof options.temperature === \"number\") options.generationConfig.temperature = options.temperature;\n if (typeof options.max_tokens === \"number\") options.generationConfig.maxOutputTokens = options.max_tokens;\n if (!options.generationConfig.maxOutputTokens) options.generationConfig.maxOutputTokens = this.max_tokens;\n\n if (options.tools) {\n options.tools = [ { functionDeclarations: options.tools.map(tool => ({\n name: (tool as Tool).name,\n description: (tool as Tool).description,\n parameters: (tool as Tool).input_schema,\n })) } ] as any;\n }\n\n if (options.think) {\n if (!options.generationConfig) options.generationConfig = {};\n options.generationConfig.thinkingConfig = { includeThoughts: true };\n delete options.think;\n }\n\n delete options.think;\n delete options.max_tokens;\n delete options.temperature;\n delete options.stream;\n\n return options;\n }\n\n get llmHeaders() {\n // Google needs endpoint to not send preflight...so we remove \"x-\" header and we're passing through query params\n const headers = super.llmHeaders;\n delete headers[\"x-api-key\"];\n return headers;\n }\n \n parseContent(data: any): string {\n if (!data?.candidates?.[0]?.content?.parts) return \"\";\n const parts = data.candidates[0].content.parts as any[];\n for (const part of parts) {\n if (part.thought) continue;\n return part.text;\n }\n return \"\";\n }\n\n parseTokenUsage(data: any) {\n if (!data?.usageMetadata?.promptTokenCount || !data?.usageMetadata?.candidatesTokenCount) return null;\n const usage = data.usageMetadata;\n return { input_tokens: usage.promptTokenCount, output_tokens: usage.candidatesTokenCount };\n }\n\n parseModel(model: any): Model {\n return {\n name: model.displayName,\n model: model.name.replace(/^models\\//, \"\"),\n created: new Date(), // :(\n } as Model;\n }\n\n parseTools(data: any): ToolCall[] {\n if (!data?.candidates?.[0]?.content?.parts?.[0]?.functionCall) return [];\n const functionCall = data.candidates[0].content.parts[0].functionCall;\n return [ { id: uuid(), name: functionCall.name, input: functionCall.args, } as ToolCall ];\n }\n\n parseThinking(data: any): string {\n if (!data?.candidates?.[0]?.content?.parts) return \"\";\n const parts = data.candidates[0].content.parts as any[];\n for (const part of parts) {\n if (part.thought !== true) continue;\n return part.text;\n }\n return \"\";\n }\n\n filterQualityModel(model: Model): boolean {\n const keywords = [\"embedding\", \"vision\", \"learnlm\", \"image-generation\", \"gemma-3\", \"gemma-3n\", \"gemini-1.5\", \"embedding\"];\n return keywordFilter(model.model, keywords);\n }\n}\n","import LLM from \"./LLM\";\nimport type { ServiceName, Options, Model, ToolCall, WrappedToolCall, Tool } from \"./LLM.types\";\nimport { unwrapToolCall, wrapTool, join } from \"./utils\";\nimport { keywordFilter } from \"./utils\";\n\n/**\n * @category Options\n */\nexport type APIv1Options = Options & {\n stream_options?: {\n include_usage?: boolean;\n },\n reasoning_effort?: \"low\" | \"medium\" | \"high\"\n}\n\n/**\n * OpenAI API v1 Compatible Base Class\n * \n * @category LLMs\n */\nexport default class APIv1 extends LLM {\n static readonly service: ServiceName = \"openai\";\n static DEFAULT_BASE_URL: string = \"\";\n static DEFAULT_MODEL: string = \"\";\n static isBearerAuth: boolean = true;\n static KEY_REASONING_CONTENT: string = \"reasoning_content\";\n\n get chatUrl() { return join(this.baseUrl, \"chat/completions\") }\n get modelsUrl() { return join(this.baseUrl, \"models\") }\n\n parseOptions(options: APIv1Options): APIv1Options {\n if (options.think && !options.reasoning_effort) {\n options.reasoning_effort = \"high\";\n }\n delete options.think;\n\n if (options.tools) {\n const tools = options.tools.map(tool => wrapTool(tool as Tool));\n options.tools = tools;\n }\n\n if (options.stream) {\n options.stream_options = { include_usage: true };\n }\n return options;\n }\n\n parseContent(data: any): string {\n if (!data) return \"\";\n if (!data.choices) return \"\";\n if (!data.choices[0]) return \"\";\n if (!data.choices[0].message) return \"\";\n return data.choices[0].message.content;\n }\n\n parseContentChunk(data: any): string {\n if (!data) return \"\";\n if (!data.choices) return \"\";\n if (!data.choices[0]) return \"\";\n if (!data.choices[0].delta) return \"\";\n if (!data.choices[0].delta.content) return \"\";\n return data.choices[0].delta.content;\n }\n\n parseThinking(data: any): string {\n const key = (this.constructor as typeof APIv1).KEY_REASONING_CONTENT;\n if (!data || !data.choices || !data.choices[0] || !data.choices[0].message || !data.choices[0].message[key]) return \"\";\n return data.choices[0].message[key];\n }\n\n parseThinkingChunk(data: any): string {\n const key = (this.constructor as typeof APIv1).KEY_REASONING_CONTENT;\n if (!data || !data.choices || !data.choices[0] || !data.choices[0].delta || !data.choices[0].delta[key]) return \"\";\n return data.choices[0].delta[key];\n }\n\n parseTokenUsage(data: any) {\n if (!data) return null;\n if (!data.usage) return null;\n if (!data.usage.prompt_tokens) return null;\n if (!data.usage.completion_tokens) return null;\n\n return {\n input_tokens: data.usage.prompt_tokens,\n output_tokens: data.usage.completion_tokens,\n };\n }\n\n parseModel(model: any): Model {\n let created = (model.created ? new Date(model.created * 1000) : new Date());\n return { name: model.model, model: model.id, created } as Model;\n }\n\n parseTools(data: any): ToolCall[] {\n if (!data || !data.choices || !data.choices[0] || !data.choices[0].message || !data.choices[0].message.tool_calls) return [];\n return data.choices[0].message.tool_calls.map((tool_call: WrappedToolCall) => unwrapToolCall(tool_call));\n }\n\n parseToolsChunk(data: any): ToolCall[] {\n if (!data || !data.choices || !data.choices[0] || !data.choices[0].delta || !data.choices[0].delta.tool_calls) return [];\n return data.choices[0].delta.tool_calls.map((tool_call: WrappedToolCall) => unwrapToolCall(tool_call));\n }\n\n filterQualityModel(model: Model): boolean {\n const keywords = [\"audio\", \"vision\", \"image\"];\n return keywordFilter(model.model, keywords);\n }\n}","import type { ServiceName, Options } from \"./LLM.types\";\nimport APIv1 from \"./APIv1\";\n\n/**\n * @category LLMs\n */\nexport default class xAI extends APIv1 {\n static readonly service: ServiceName = \"xai\";\n static DEFAULT_BASE_URL: string = \"https://api.x.ai/v1/\";\n static DEFAULT_MODEL: string = \"grok-3\";\n}\n","import type { ServiceName, Model } from \"./LLM.types\";\nimport APIv1, { APIv1Options } from \"./APIv1\";\nimport { keywordFilter } from \"./utils\";\n\n/**\n * @category Options\n */\nexport type GroqOptions = APIv1Options & {\n reasoning_effort?: \"none\" | \"default\";\n reasoning_format?: \"parsed\" | \"raw\" | \"hidden\";\n}\n\n/**\n * @category LLMs\n */\nexport default class Groq extends APIv1 {\n static readonly service: ServiceName = \"groq\";\n static DEFAULT_BASE_URL: string = \"https://api.groq.com/openai/v1/\";\n static DEFAULT_MODEL: string = \"deepseek-r1-distill-llama-70b\";\n static KEY_REASONING_CONTENT: string = \"reasoning\";\n\n parseOptions(options: GroqOptions): GroqOptions {\n options = super.parseOptions(options) as GroqOptions;\n // groq reasoning support is strange...implied by model usually\n if (options.reasoning_effort === \"high\") {\n delete options.reasoning_effort;\n if (!options.reasoning_format) options.reasoning_format = \"parsed\";\n }\n return options;\n }\n\n // groq wraps usage in x_groq for streaming\n parseTokenUsage(data: any): { input_tokens: any; output_tokens: any; } | null {\n if (!data) return null;\n if (!data.usage && data.x_groq && data.x_groq.usage) data = data.x_groq;\n\n if (!data || !data.usage) return null;\n if (!data.usage.prompt_tokens) return null;\n if (!data.usage.completion_tokens) return null;\n\n return {\n input_tokens: data.usage.prompt_tokens,\n output_tokens: data.usage.completion_tokens,\n };\n }\n\n filterQualityModel(model: Model): boolean {\n return keywordFilter(model.model, [\"whisper\", \"tts\"]);\n }\n}\n","import type { ServiceName } from \"./LLM.types\";\nimport APIv1 from \"./APIv1\";\n\n/**\n * @category LLMs\n */\nexport default class DeepSeek extends APIv1 {\n static readonly service: ServiceName = \"deepseek\";\n static DEFAULT_BASE_URL: string = \"https://api.deepseek.com/v1/\";\n static DEFAULT_MODEL: string = \"deepseek-chat\";\n}\n","import type { Options, Input, Message, ServiceName, Response, PartialStreamResponse } from \"./LLM.types\";\n\nimport Anthropic from \"./anthropic\";\nimport Ollama from \"./ollama\";\nimport OpenAI from \"./openai\";\nimport Google from \"./google\";\nimport xAI from \"./xai\";\nimport Groq from \"./groq\";\nimport DeepSeek from \"./deepseek\";\nimport LLM from \"./LLM\";\nimport APIv1 from \"./APIv1\";\n\nimport ModelUsage from \"./ModelUsage\";\n\nimport * as parsers from \"./parsers\";\nimport config from \"./config\";\n\nexport type * from \"./LLM.types\"\n\n/**\n * @category Usage\n */\nexport type { ModelUsageType } from \"./ModelUsage\";\n\n/**\n * @category LLMs\n */\nexport type LLMServices = Anthropic | Ollama | OpenAI | Google | xAI | Groq | DeepSeek | APIv1;\n\n/**\n * @category LLMs\n */\nexport type { LLM, Anthropic, Ollama, OpenAI, Google, xAI, Groq, DeepSeek, APIv1 };\n\n/**\n * @category Parsers\n */\nexport type { parsers };\n\n/**\n * @category Usage\n */\nexport type { ModelUsage };\n\nexport type { Input, Message };\nexport type { AnthropicOptions } from \"./anthropic\";\nexport type { OllamaOptions } from \"./ollama\";\nexport type { OpenAIOptions, OpenAITool } from \"./openai\";\nexport type { GoogleOptions, GoogleTool } from \"./google\";\nexport type { GroqOptions } from \"./groq\";\nexport type { APIv1Options } from \"./APIv1\";\n\nlet SERVICES = [Anthropic, Ollama, OpenAI, Google, xAI, Groq, DeepSeek];\n\n/**\n * @category LLMs\n */\nexport interface LLMInterface {\n (input: Input, options?: Options): Promise;\n (options: Options): Promise;\n \n new (input: Input, options?: Options): LLMServices;\n new (options: Options): LLMServices;\n new (): LLMServices;\n\n parsers: typeof parsers;\n services: any[];\n\n ModelUsage: typeof ModelUsage;\n\n LLM: typeof LLM;\n Anthropic: typeof Anthropic;\n Ollama: typeof Ollama;\n OpenAI: typeof OpenAI;\n Google: typeof Google;\n xAI: typeof xAI;\n Groq: typeof Groq;\n DeepSeek: typeof DeepSeek;\n APIv1: typeof APIv1;\n\n register(LLMClass: typeof LLM): void;\n unregister(LLMClass: typeof LLM): void;\n}\n\nfunction LLMShortHandImpl(\n initOrOpts?: Input | Options,\n opts?: Options\n): Promise | LLMServices {\n\n let input: Input | undefined;\n let options : Options;\n\n if (typeof initOrOpts === \"string\" || Array.isArray(initOrOpts)) {\n input = initOrOpts as Input;\n options = opts || {};\n } else if (typeof initOrOpts === \"object\" && initOrOpts !== null) {\n input = undefined;\n options = initOrOpts as Options;\n } else {\n input = undefined;\n options = {};\n }\n\n let llm;\n\n const service = options?.service ?? config.service as ServiceName;\n let LLMClass = SERVICES.find(Service => Service.service === service);\n if (!LLMClass) LLMClass = APIv1;\n llm = new LLMClass(input, options);\n\n if (new.target) return llm;\n\n const response = llm.send() as Promise;\n return response;\n};\n\nconst LLMWrapper = LLMShortHandImpl as LLMInterface;\n\nLLMWrapper.parsers = parsers;\nLLMWrapper.services = SERVICES;\nLLMWrapper.ModelUsage = ModelUsage;\n\nLLMWrapper.Anthropic = Anthropic;\nLLMWrapper.Ollama = Ollama;\nLLMWrapper.OpenAI = OpenAI;\nLLMWrapper.Google = Google;\nLLMWrapper.xAI = xAI;\nLLMWrapper.Groq = Groq;\nLLMWrapper.DeepSeek = DeepSeek;\nLLMWrapper.APIv1 = APIv1;\nLLMWrapper.LLM = LLM;\n\nLLMWrapper.register = (LLMClass: typeof LLM) => {\n SERVICES.push(LLMClass as 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