{"version":3,"sources":["../src/utils/logger.ts","../src/constants/agent.ts","../src/constants/memory.ts","../src/constants/database.ts","../src/constants/provider.ts","../src/constants/rag.ts","../src/constants/task.ts","../src/constants/error.ts","../src/constants/index.ts","../src/providers/openai.ts","../src/providers/ollama.ts","../src/providers/embedding.ts","../src/providers/index.ts","../src/index.ts","../src/agent.ts","../src/database.ts","../src/database/sqlite.ts","../src/database/postgresql.ts","../src/utils/index.ts","../src/utils/validation.ts","../src/utils/rag-tools.ts","../src/database/modules/user.ts","../src/plugin.ts","../src/memory.ts","../src/rag/vector.ts","../src/types/rag.ts","../src/rag/vector-db/index.ts","../src/rag/document.ts","../src/rag/pdf-parser.ts","../src/rag/index.ts","../src/chat.ts","../src/tasks/task.ts","../src/utils/intent.ts","../src/tasks/manager.ts","../src/tasks/index.ts"],"sourcesContent":["/**\n * Astreus AI - Logger Utility\n * Provides colorful console logging functionality for the framework\n */\n\n// ANSI color codes for terminal output\nconst colors = {\n reset: \"\\x1b[0m\",\n bright: \"\\x1b[1m\",\n dim: \"\\x1b[2m\",\n underscore: \"\\x1b[4m\",\n blink: \"\\x1b[5m\",\n reverse: \"\\x1b[7m\",\n hidden: \"\\x1b[8m\",\n \n // Foreground colors\n black: \"\\x1b[30m\",\n red: \"\\x1b[31m\",\n green: \"\\x1b[32m\",\n yellow: \"\\x1b[33m\",\n blue: \"\\x1b[34m\",\n magenta: \"\\x1b[35m\",\n cyan: \"\\x1b[36m\",\n white: \"\\x1b[37m\",\n gray: \"\\x1b[90m\",\n \n // Background colors\n bgBlack: \"\\x1b[40m\",\n bgRed: \"\\x1b[41m\",\n bgGreen: \"\\x1b[42m\",\n bgYellow: \"\\x1b[43m\",\n bgBlue: \"\\x1b[44m\",\n bgMagenta: \"\\x1b[45m\",\n bgCyan: \"\\x1b[46m\",\n bgWhite: \"\\x1b[47m\",\n};\n\n// Framework constants\nconst FRAMEWORK_NAME = \"Astreus\";\nconst FRAMEWORK_VERSION = \"0.1.0\";\n\n// Log levels\n/* eslint-disable no-unused-vars */\nexport enum LogLevel {\n DEBUG = 0,\n INFO = 1,\n SUCCESS = 2,\n WARN = 3,\n ERROR = 4,\n NONE = 5,\n}\n/* eslint-enable no-unused-vars */\n\n// Logger options\ninterface LoggerOptions {\n level: LogLevel;\n prefix: boolean;\n colors: boolean;\n lineBreak: boolean;\n timestamp: boolean;\n}\n\n// Default options\nconst defaultOptions: LoggerOptions = {\n level: LogLevel.INFO,\n prefix: true,\n colors: true,\n lineBreak: false,\n timestamp: false\n};\n\n// Current logger options\nlet options: LoggerOptions = { ...defaultOptions };\n\n// Track the last log time to prevent duplicate timestamps\nlet lastLogTime = 0;\n\n/**\n * Create a formatted prefix for log messages\n */\nfunction createPrefix(color: string): string {\n if (!options.prefix) return '';\n \n // Simple format: [FRAMEWORK]\n return `${color}[${FRAMEWORK_NAME}]${colors.reset} `;\n}\n\n/**\n * Get a timestamp string\n */\nfunction getTimestamp(): string {\n if (!options.timestamp) return '';\n \n const now = Date.now();\n // Only show timestamps when they change by at least 1 second\n if (Math.abs(now - lastLogTime) < 1000) {\n return '';\n }\n \n lastLogTime = now;\n const date = new Date(now);\n return `${colors.gray}[${date.toLocaleTimeString()}]${colors.reset} `;\n}\n\n/**\n * Internal log function\n */\nfunction log(level: LogLevel, color: string, ...messages: unknown[]): void {\n if (level < options.level) return;\n \n const prefix = createPrefix(color);\n const timestamp = getTimestamp();\n \n // Add line break before log entry if enabled (reduced usage)\n if (options.lineBreak && level >= LogLevel.WARN) {\n // Use safeConsole to handle console statements\n safeConsole('log');\n }\n \n if (options.colors) {\n // Apply color to text messages that are strings\n const coloredMessages = messages.map(msg => \n typeof msg === 'string' ? `${color}${msg}${colors.reset}` : msg\n );\n // Use a direct string without extra spaces\n safeConsole('log', `${timestamp}${prefix}${coloredMessages.join(' ')}`);\n } else {\n // Strip color codes using string replace with a function rather than regex with control chars\n // This avoids the ESLint 'no-control-regex' error\n const stripAnsi = (str: string): string => {\n let result = '';\n let inEscSeq = false;\n \n for (let i = 0; i < str.length; i++) {\n // Start of escape sequence\n if (str[i] === '\\u001b' && str[i+1] === '[') {\n inEscSeq = true;\n i++; // Skip the '['\n continue;\n }\n \n // In escape sequence, wait for 'm' which ends ANSI color codes\n if (inEscSeq) {\n if (str[i] === 'm') {\n inEscSeq = false;\n }\n continue;\n }\n \n // Normal character\n result += str[i];\n }\n \n return result;\n };\n \n const strippedPrefix = stripAnsi(prefix);\n safeConsole('log', `${timestamp}${strippedPrefix}${messages.join(' ')}`);\n }\n}\n\n/**\n * Safe console wrapper to avoid ESLint warnings\n */\nfunction safeConsole(method: 'log' | 'info' | 'warn' | 'error', ...args: unknown[]): void {\n // This function centralizes console usage and can be disabled by ESLint when needed\n // eslint-disable-next-line no-console\n if (method === 'log') console.log(...args);\n // eslint-disable-next-line no-console\n else if (method === 'info') console.info(...args);\n // eslint-disable-next-line no-console\n else if (method === 'warn') console.warn(...args);\n // eslint-disable-next-line no-console\n else if (method === 'error') console.error(...args);\n}\n\n/**\n * Configure the logger\n */\nexport function configure(newOptions: Partial): void {\n options = { ...options, ...newOptions };\n}\n\n/**\n * Print the framework banner\n */\nexport function printBanner(): void {\n if (options.level > LogLevel.INFO) return;\n \n safeConsole('log');\n safeConsole('log', `${colors.cyan}${colors.bright}${FRAMEWORK_NAME} AI Framework v${FRAMEWORK_VERSION}${colors.reset}`);\n safeConsole('log');\n}\n\n/**\n * Public logging functions\n */\nexport const logger = {\n debug: (...messages: unknown[]) => log(LogLevel.DEBUG, colors.gray, ...messages),\n info: (...messages: unknown[]) => log(LogLevel.INFO, colors.blue, ...messages),\n success: (...messages: unknown[]) => log(LogLevel.SUCCESS, colors.green, ...messages),\n warn: (...messages: unknown[]) => log(LogLevel.WARN, colors.yellow, ...messages),\n error: (...messages: unknown[]) => log(LogLevel.ERROR, colors.red, ...messages),\n \n // Special formatted logs\n task: (taskId: string, message: string, taskName?: string) => {\n // If taskName is provided, use it; otherwise, just use \"Task\"\n const prefix = taskName ? `Task [${taskName}]:` : 'Task:';\n log(LogLevel.INFO, colors.magenta, `${prefix} ${message}`);\n },\n \n agent: (agentName: string, message: string) => {\n log(LogLevel.INFO, colors.cyan, `Agent [${agentName}]: ${message}`);\n },\n \n database: (operation: string, message: string) => {\n log(LogLevel.DEBUG, colors.blue, `${operation}: ${message}`);\n },\n \n memory: (operation: string, message: string) => {\n log(LogLevel.DEBUG, colors.magenta, `${operation}: ${message}`);\n },\n \n session: (sessionId: string, message: string) => {\n const shortId = sessionId.substring(0, 8);\n log(LogLevel.INFO, colors.green, `${shortId}: ${message}`);\n },\n \n plugin: (pluginName: string, message: string) => {\n // Capitalize first letter of plugin name and remove brackets\n const capitalizedPluginName = pluginName.charAt(0).toUpperCase() + pluginName.slice(1);\n log(LogLevel.INFO, colors.yellow, `${capitalizedPluginName}: ${message}`);\n },\n \n workflow: (workflowName: string, message: string) => {\n log(LogLevel.INFO, colors.cyan, `${workflowName}: ${message}`);\n },\n \n // Progress indicators - these don't add line breaks to avoid disrupting the animation\n startProgress: (message: string): NodeJS.Timeout => {\n if (options.level > LogLevel.INFO) return setInterval(() => {}, 1000);\n \n // Optionally add a line break before starting progress\n if (options.lineBreak) {\n safeConsole('log');\n }\n \n process.stdout.write(`${createPrefix(colors.blue)}${colors.blue}${message}`);\n \n const chars = ['⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏'];\n let i = 0;\n \n return setInterval(() => {\n process.stdout.write(`\\r${createPrefix(colors.blue)}${colors.blue}${message} ${colors.cyan}${chars[i]}${colors.reset}`);\n i = (i + 1) % chars.length;\n }, 100);\n },\n \n endProgress: (interval: NodeJS.Timeout, finalMessage?: string) => {\n clearInterval(interval);\n if (options.level > LogLevel.INFO) return;\n \n if (finalMessage) {\n process.stdout.write(`\\r${createPrefix(colors.blue)}${colors.green}${finalMessage} ✓${colors.reset}\\n`);\n } else {\n process.stdout.write(`\\r${createPrefix(colors.blue)}${colors.green}Done ✓${colors.reset}\\n`);\n }\n },\n \n // Configure line breaks\n setLineBreak: (enabled: boolean) => {\n configure({ lineBreak: enabled });\n },\n};\n\n\nexport default logger; ","// Agent-related constants\nexport const DEFAULT_AGENT_NAME = 'astreus-agent';\nexport const DEFAULT_MODEL = 'gpt-4o-mini';\nexport const DEFAULT_TEMPERATURE = 0.3;\nexport const DEFAULT_MAX_TOKENS = 4096; ","// Memory-related constants\nexport const DEFAULT_MEMORY_SIZE = 10; ","// Database-related constants\nexport const DEFAULT_DB_PATH = './.astreus/db';\n\n// Vector database constants\nexport const VECTOR_DATABASE_TYPES = {\n SAME_AS_MAIN: 'same_as_main',\n POSTGRES: 'postgres',\n QDRANT: 'qdrant',\n PINECONE: 'pinecone',\n MILVUS: 'milvus',\n WEAVIATE: 'weaviate',\n}; ","// Provider-related constants\nexport const PROVIDER_TYPES = {\n OPENAI: 'openai',\n OLLAMA: 'ollama',\n};\n\n// Default Provider configuration\nexport const DEFAULT_OPENAI_BASE_URL = 'https://api.openai.com/v1';\nexport const DEFAULT_OPENAI_EMBEDDING_MODEL = 'text-embedding-3-small';\nexport const DEFAULT_OLLAMA_BASE_URL = 'http://localhost:11434';\n\n// Default OpenAI model configs\nexport const DEFAULT_MODEL_CONFIGS = {\n openai: {\n \"gpt-4o\": {\n apiKey: process.env.OPENAI_API_KEY || '',\n baseUrl: process.env.OPENAI_BASE_URL,\n temperature: 0.7,\n maxTokens: 4096\n },\n \"gpt-4o-mini\": {\n apiKey: process.env.OPENAI_API_KEY || '',\n baseUrl: process.env.OPENAI_BASE_URL,\n temperature: 0.7,\n maxTokens: 2048\n },\n \"gpt-4\": {\n apiKey: process.env.OPENAI_API_KEY || '',\n baseUrl: process.env.OPENAI_BASE_URL,\n temperature: 0.7,\n maxTokens: 4096\n },\n \"gpt-3.5-turbo\": {\n apiKey: process.env.OPENAI_API_KEY || '',\n baseUrl: process.env.OPENAI_BASE_URL,\n temperature: 0.7,\n maxTokens: 2048\n }\n },\n ollama: {\n \"llama3\": {\n baseUrl: process.env.OLLAMA_BASE_URL || 'http://localhost:11434',\n temperature: 0.7,\n maxTokens: 2048\n },\n \"mistral\": {\n baseUrl: process.env.OLLAMA_BASE_URL || 'http://localhost:11434',\n temperature: 0.7,\n maxTokens: 2048\n }\n }\n}; ","// RAG-related constants\nexport const DEFAULT_CHUNK_SIZE = 1000;\nexport const DEFAULT_CHUNK_OVERLAP = 200;\nexport const DEFAULT_VECTOR_SIMILARITY_THRESHOLD = 0.7;\nexport const DEFAULT_MAX_RESULTS = 10; ","// Task-related constants\nexport const DEFAULT_TASK_CONCURRENCY = 5;\nexport const DEFAULT_MAX_RETRIES = 3;\nexport const TASK_STATUS = {\n PENDING: 'pending',\n RUNNING: 'running',\n COMPLETED: 'completed',\n FAILED: 'failed',\n}; ","// Error messages\nexport const ERROR_MESSAGES = {\n MISSING_PARAMETER: 'Missing required parameter:',\n INVALID_PROVIDER: 'Invalid provider configuration',\n INVALID_MEMORY: 'Invalid memory configuration',\n INVALID_DATABASE: 'Invalid database configuration',\n INVALID_RAG: 'Invalid RAG configuration',\n INVALID_VECTOR_DB: 'Invalid vector database configuration',\n INVALID_TASK: 'Invalid task configuration',\n INVALID_EMBEDDING: 'Invalid embedding configuration',\n}; ","// Re-export all constants from their respective files\nexport * from './agent';\nexport * from './memory';\nexport * from './database';\nexport * from './provider';\nexport * from './rag';\nexport * from './task';\nexport * from './error'; ","import { \n ProviderType,\n OpenAIModelConfig,\n ProviderMessage,\n ProviderModel,\n CompletionOptions,\n ProviderTool\n} from '../types/provider';\nimport { logger } from \"../utils/logger\";\nimport { OpenAI } from \"openai\";\n\n/**\n * Create OpenAI configuration with defaults\n */\nexport function createOpenAIConfig(\n modelName: string,\n config?: Partial\n): OpenAIModelConfig {\n return {\n name: modelName,\n apiKey: process.env.OPENAI_API_KEY,\n baseUrl: process.env.OPENAI_BASE_URL,\n temperature: 0.7,\n maxTokens: 2048,\n ...config,\n };\n}\n\n/**\n * OpenAI Provider implementation\n */\nexport class OpenAIProvider implements ProviderModel {\n public provider: ProviderType;\n public name: string;\n public config: OpenAIModelConfig;\n private client: OpenAI;\n \n constructor(provider: ProviderType, config: OpenAIModelConfig) {\n this.provider = provider;\n this.name = config.name;\n this.config = config;\n \n this.client = new OpenAI({\n apiKey: config.apiKey || process.env.OPENAI_API_KEY,\n baseURL: config.baseUrl || process.env.OPENAI_BASE_URL,\n });\n }\n \n async complete(messages: ProviderMessage[], options?: CompletionOptions): Promise {\n try {\n // Prepare messages\n const formattedMessages = this.prepareMessages(messages, options?.systemMessage);\n \n // Build request options\n const requestOptions = this.buildRequestOptions(formattedMessages, options);\n \n // Log request info\n logger.debug(`OpenAI request: model=${this.name}`, { \n messages: formattedMessages.length, \n hasTools: !!requestOptions.tools,\n toolCount: requestOptions.tools?.length || 0 \n });\n \n // Make API request\n const response = await this.client.chat.completions.create(requestOptions);\n \n // Handle response - now can return either string or object with tool calls\n return this.processResponse(response);\n } catch (error) {\n this.handleError(error);\n throw error;\n }\n }\n \n async streamComplete(\n messages: ProviderMessage[], \n options?: CompletionOptions,\n onChunk?: (chunk: string) => void\n ): Promise {\n try {\n // Prepare messages\n const formattedMessages = this.prepareMessages(messages, options?.systemMessage);\n \n // Build request options with streaming enabled\n const requestOptions = this.buildRequestOptions(formattedMessages, options);\n requestOptions.stream = true;\n \n // Log request info\n logger.debug(`OpenAI streaming request: model=${this.name}`, { \n messages: formattedMessages.length, \n hasTools: !!requestOptions.tools,\n toolCount: requestOptions.tools?.length || 0 \n });\n \n // Make streaming API request\n const stream = await this.client.chat.completions.create(requestOptions) as any;\n \n let fullResponse = '';\n \n // Process streaming response\n for await (const chunk of stream) {\n const content = chunk.choices[0]?.delta?.content || '';\n if (content) {\n fullResponse += content;\n if (onChunk) {\n onChunk(fullResponse);\n }\n }\n }\n \n return fullResponse;\n } catch (error) {\n this.handleError(error);\n throw error;\n }\n }\n \n private prepareMessages(messages: ProviderMessage[], systemMessage?: string) {\n // Convert to OpenAI message format\n const formattedMessages = messages.map(msg => ({\n role: msg.role,\n content: msg.content\n }));\n \n // Add system message if provided\n if (systemMessage) {\n formattedMessages.unshift({\n role: \"system\" as const,\n content: systemMessage\n });\n }\n \n return formattedMessages;\n }\n \n private buildRequestOptions(messages: any[], options?: CompletionOptions) {\n // Base request options\n const requestOptions: any = {\n model: this.name,\n messages,\n temperature: options?.temperature ?? this.config.temperature ?? 0.7,\n max_tokens: options?.maxTokens ?? this.config.maxTokens\n };\n \n // Add tools if provided\n if (options?.tools && options.tools.length > 0) {\n requestOptions.tools = options.tools.map(tool => ({\n type: \"function\",\n function: {\n name: tool.name,\n description: tool.description || \"\",\n parameters: this.formatToolParameters(tool.parameters)\n }\n }));\n \n // Set tool choice if enabled\n if (options.toolCalling) {\n requestOptions.tool_choice = \"auto\";\n }\n }\n \n return requestOptions;\n }\n \n private formatToolParameters(parameters: any): Record {\n // Default empty schema with proper type\n const schemaObject: Record = {\n type: \"object\",\n properties: {},\n additionalProperties: false\n };\n \n if (!parameters) {\n return schemaObject;\n }\n \n // Handle already formatted parameters\n if (typeof parameters === 'object') {\n // If it's already a proper JSON Schema object, use it directly\n if ('type' in parameters && parameters.type === 'object') {\n return parameters;\n }\n \n // If it has properties field, use that\n if ('properties' in parameters) {\n schemaObject.properties = parameters.properties;\n if (Array.isArray(parameters.required) && parameters.required.length > 0) {\n schemaObject.required = parameters.required;\n }\n return schemaObject;\n }\n \n // Handle array of parameter definitions\n if (Array.isArray(parameters)) {\n const requiredParams: string[] = [];\n \n parameters.forEach(param => {\n if (typeof param === 'object' && param.name && param.type) {\n // Create a property definition based on the parameter type\n const propertyDef: Record = {\n type: param.type,\n description: param.description || `Parameter ${param.name}`\n };\n \n // Handle array type specifically\n if (param.type === 'array') {\n // Ensure arrays have an items definition\n propertyDef.items = param.items || { type: 'string' };\n \n // Add array constraints if available\n if (param.minItems !== undefined) propertyDef.minItems = param.minItems;\n if (param.maxItems !== undefined) propertyDef.maxItems = param.maxItems;\n }\n \n // Add any default value\n if (param.default !== undefined) {\n propertyDef.default = param.default;\n }\n \n // Add property to schema\n (schemaObject.properties as Record)[param.name] = propertyDef;\n \n if (param.required) {\n requiredParams.push(param.name);\n }\n }\n });\n \n if (requiredParams.length > 0) {\n schemaObject.required = requiredParams;\n }\n }\n }\n \n return schemaObject;\n }\n \n private processResponse(response: any): string | any {\n // Check if the response has the expected structure\n if (!response.choices?.[0]?.message) {\n // Log the actual response structure for debugging\n logger.error('Unexpected OpenAI API response structure:', {\n responseId: response.id,\n responseObject: JSON.stringify(response),\n hasChoices: !!response.choices,\n choicesLength: response.choices?.length,\n firstChoice: response.choices?.[0] ? 'exists' : 'missing',\n hasMessage: !!response.choices?.[0]?.message\n });\n \n // Throw with more specific information about what's missing\n if (!response.choices) {\n throw new Error(`OpenAI API response missing 'choices' field: ${JSON.stringify(response)}`);\n } else if (!response.choices.length) {\n throw new Error(`OpenAI API response has empty 'choices' array: ${JSON.stringify(response)}`);\n } else if (!response.choices[0].message) {\n throw new Error(`OpenAI API response missing 'message' in first choice: ${JSON.stringify(response.choices[0])}`);\n } else {\n throw new Error(`OpenAI API unexpected response structure: ${JSON.stringify(response)}`);\n }\n }\n \n const message = response.choices[0].message;\n const toolCalls = message.tool_calls;\n \n // Handle tool calls if present - Return both message content and structured tool calls\n if (toolCalls?.length > 0) {\n // Log detailed raw tool calls for debugging\n logger.debug('OpenAI raw tool calls:', JSON.stringify(toolCalls, null, 2));\n \n // Return structured data instead of formatted text\n return {\n content: message.content || '',\n tool_calls: toolCalls.map((call: any) => {\n try {\n if (call.type === 'function') {\n // Parse arguments to JavaScript object\n let args = {};\n if (call.function?.arguments) {\n try {\n args = typeof call.function.arguments === 'string'\n ? JSON.parse(call.function.arguments)\n : call.function.arguments;\n } catch (e) {\n logger.error('Error parsing function arguments', { error: e });\n }\n }\n \n return {\n type: 'function',\n id: call.id,\n name: call.function?.name,\n arguments: args\n };\n }\n return call;\n } catch (e) {\n logger.error('Error processing tool call', { error: e });\n return { \n type: 'error',\n error: e instanceof Error ? e.message : String(e)\n };\n }\n })\n };\n }\n \n // Return plain text response\n return message.content || '';\n }\n \n private handleError(error: any): void {\n if (!error) return;\n \n // Log the full error details\n logger.error('OpenAI API error:', {\n message: error.message,\n status: error.status,\n type: error.type,\n headers: error.headers,\n code: error.code,\n param: error.param,\n error: error.error\n });\n \n if (error.stack) {\n logger.debug(`Error stack: ${error.stack}`);\n }\n }\n} ","import {\n ProviderType,\n OllamaModelConfig,\n ProviderMessage,\n ProviderModel,\n CompletionOptions,\n ProviderTool,\n StructuredCompletionResponse\n} from \"../types/provider\";\nimport { DEFAULT_OLLAMA_BASE_URL } from \"../constants\";\nimport { logger } from \"../utils/logger\";\n\n/**\n * Create Ollama configuration helper\n */\nexport function createOllamaConfig(\n modelName: string,\n config?: Partial\n): OllamaModelConfig {\n return {\n name: modelName,\n baseUrl: process.env.OLLAMA_BASE_URL || DEFAULT_OLLAMA_BASE_URL,\n temperature: 0.7,\n maxTokens: 2048,\n ...config,\n };\n}\n\n// Ollama Provider implementation\nexport class OllamaProvider implements ProviderModel {\n public provider: ProviderType;\n public name: string;\n public config: OllamaModelConfig;\n private baseUrl: string;\n\n constructor(provider: ProviderType, config: OllamaModelConfig) {\n this.provider = provider;\n this.name = config.name;\n this.config = config;\n this.baseUrl =\n config.baseUrl || process.env.OLLAMA_BASE_URL || DEFAULT_OLLAMA_BASE_URL;\n }\n\n async complete(messages: ProviderMessage[], options?: CompletionOptions): Promise {\n try {\n // Format messages for Ollama\n const formattedMessages = this.prepareMessages(messages, options?.systemMessage);\n \n // Build request options\n const requestOptions = this.buildRequestOptions(formattedMessages, options);\n \n // Log request info\n logger.debug(`Ollama request: model=${this.name}`, { \n messages: formattedMessages.length, \n hasTools: !!options?.tools,\n toolCount: options?.tools?.length || 0 \n });\n\n // Call Ollama API\n const response = await fetch(`${this.baseUrl}/api/chat`, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(requestOptions),\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n throw new Error(`Ollama API error: ${response.status} ${errorText}`);\n }\n\n const data = await response.json();\n \n // Process the response\n return this.processResponse(data);\n } catch (error) {\n this.handleError(error);\n throw error;\n }\n }\n \n private prepareMessages(messages: ProviderMessage[], systemMessage?: string) {\n // Convert to Ollama message format\n const formattedMessages = messages.map(msg => ({\n role: msg.role,\n content: msg.content\n }));\n \n // Add system message if provided\n if (systemMessage) {\n formattedMessages.unshift({\n role: \"system\" as const,\n content: systemMessage\n });\n }\n \n return formattedMessages;\n }\n \n private buildRequestOptions(messages: any[], options?: CompletionOptions) {\n // Base request options for Ollama\n const requestOptions: any = {\n model: this.name,\n messages: messages,\n options: {\n temperature: options?.temperature ?? this.config.temperature ?? 0.7,\n num_predict: options?.maxTokens ?? this.config.maxTokens\n },\n stream: false,\n };\n \n // Add tools if provided\n if (options?.tools && options.tools.length > 0 && options.toolCalling) {\n requestOptions.tools = options.tools.map(tool => ({\n type: \"function\",\n function: {\n name: tool.name,\n description: tool.description || \"\",\n parameters: this.formatToolParameters(tool.parameters)\n }\n }));\n }\n \n return requestOptions;\n }\n \n private formatToolParameters(parameters: any): Record {\n // Default empty schema with proper type\n const schemaObject: Record = {\n type: \"object\",\n properties: {},\n additionalProperties: false\n };\n \n if (!parameters) {\n return schemaObject;\n }\n \n // Handle already formatted parameters\n if (typeof parameters === 'object') {\n // If it's already a proper JSON Schema object, use it directly\n if ('type' in parameters && parameters.type === 'object') {\n return parameters;\n }\n \n // If it has properties field, use that\n if ('properties' in parameters) {\n schemaObject.properties = parameters.properties;\n if (Array.isArray(parameters.required) && parameters.required.length > 0) {\n schemaObject.required = parameters.required;\n }\n return schemaObject;\n }\n \n // Handle array of parameter definitions\n if (Array.isArray(parameters)) {\n const requiredParams: string[] = [];\n \n parameters.forEach(param => {\n if (typeof param === 'object' && param.name && param.type) {\n // Create a property definition based on the parameter type\n const propertyDef: Record = {\n type: param.type,\n description: param.description || `Parameter ${param.name}`\n };\n \n // Handle array type specifically\n if (param.type === 'array') {\n // Ensure arrays have an items definition\n propertyDef.items = param.items || { type: 'string' };\n \n // Add array constraints if available\n if (param.minItems !== undefined) propertyDef.minItems = param.minItems;\n if (param.maxItems !== undefined) propertyDef.maxItems = param.maxItems;\n }\n \n // Add any default value\n if (param.default !== undefined) {\n propertyDef.default = param.default;\n }\n \n // Add property to schema\n (schemaObject.properties as Record)[param.name] = propertyDef;\n \n if (param.required) {\n requiredParams.push(param.name);\n }\n }\n });\n \n if (requiredParams.length > 0) {\n schemaObject.required = requiredParams;\n }\n }\n }\n \n return schemaObject;\n }\n \n private processResponse(data: any): string | StructuredCompletionResponse {\n // Check if the response has the expected structure\n if (!data?.message) {\n logger.error('Unexpected Ollama API response structure:', {\n responseObject: JSON.stringify(data)\n });\n \n throw new Error(`Ollama API unexpected response structure: ${JSON.stringify(data)}`);\n }\n \n const message = data.message;\n const toolCalls = message.tool_calls;\n \n // Handle tool calls if present\n if (toolCalls?.length > 0) {\n // Log detailed raw tool calls for debugging\n logger.debug('Ollama raw tool calls:', JSON.stringify(toolCalls, null, 2));\n \n // Return structured data instead of formatted text\n return {\n content: message.content || '',\n tool_calls: toolCalls.map((call: any) => {\n try {\n if (call.type === 'function') {\n // Parse arguments to JavaScript object\n let args = {};\n if (call.function?.arguments) {\n try {\n args = typeof call.function.arguments === 'string'\n ? JSON.parse(call.function.arguments)\n : call.function.arguments;\n } catch (e) {\n logger.error('Error parsing function arguments', { error: e });\n }\n }\n \n return {\n type: 'function',\n id: call.id,\n name: call.function?.name,\n arguments: args\n };\n }\n return call;\n } catch (e) {\n logger.error('Error processing tool call', { error: e });\n return { \n type: 'error',\n error: e instanceof Error ? e.message : String(e)\n };\n }\n })\n };\n }\n \n // Return plain text response\n return message.content || '';\n }\n \n private handleError(error: any): void {\n if (!error) return;\n \n // Log the full error details\n logger.error('Ollama API error:', {\n message: error.message,\n status: error.status,\n type: error.type,\n stack: error.stack,\n });\n }\n}\n","import OpenAI from \"openai\";\nimport dotenv from \"dotenv\";\nimport { logger } from \"../utils/logger\";\nimport { DEFAULT_OPENAI_EMBEDDING_MODEL } from \"../constants\";\n\n// Initialize environment variables\ndotenv.config();\n\n/**\n * Simple utility for generating embeddings without requiring provider setup\n */\nexport class Embedding {\n private static client: OpenAI | null = null;\n\n /**\n * Initialize the OpenAI client if not already initialized\n */\n private static initClient(): OpenAI {\n if (!this.client) {\n const apiKey =\n process.env.OPENAI_EMBEDDING_API_KEY || process.env.OPENAI_API_KEY;\n\n if (!apiKey) {\n throw new Error(\n \"OpenAI API key is required for embeddings - set OPENAI_API_KEY or OPENAI_EMBEDDING_API_KEY\"\n );\n }\n\n this.client = new OpenAI({\n apiKey,\n baseURL: \"https://api.openai.com/v1\",\n });\n }\n\n return this.client;\n }\n\n /**\n * Generate an embedding for the given text\n * @param text Text to generate embedding for\n * @param model Embedding model to use (default: text-embedding-ada-002)\n * @returns Embedding vector as array of numbers\n */\n static async generateEmbedding(\n text: string,\n model: string = process.env.OPENAI_EMBEDDING_MODEL ||\n DEFAULT_OPENAI_EMBEDDING_MODEL\n ): Promise {\n try {\n if (!text || typeof text !== \"string\") {\n throw new Error(\"Invalid text input for embedding generation\");\n }\n\n const client = this.initClient();\n logger.debug(`Generating embedding for text with model: ${model}`);\n\n const response = await client.embeddings.create({\n model,\n input: text,\n encoding_format: \"float\",\n });\n\n return response.data[0].embedding;\n } catch (error) {\n logger.error(\"Error generating embedding:\", error);\n throw error;\n }\n }\n\n /**\n * Calculate cosine similarity between two embedding vectors\n * @param embedding1 First embedding vector\n * @param embedding2 Second embedding vector\n * @returns Similarity score (1.0 = identical, 0.0 = completely different)\n */\n static calculateSimilarity(\n embedding1: number[],\n embedding2: number[]\n ): number {\n if (!embedding1 || !embedding2 || embedding1.length !== embedding2.length) {\n return 0;\n }\n\n // Calculate dot product\n let dotProduct = 0;\n let magnitude1 = 0;\n let magnitude2 = 0;\n\n for (let i = 0; i < embedding1.length; i++) {\n dotProduct += embedding1[i] * embedding2[i];\n magnitude1 += embedding1[i] * embedding1[i];\n magnitude2 += embedding2[i] * embedding2[i];\n }\n\n magnitude1 = Math.sqrt(magnitude1);\n magnitude2 = Math.sqrt(magnitude2);\n\n // Avoid division by zero\n if (magnitude1 === 0 || magnitude2 === 0) {\n return 0;\n }\n\n // Return cosine similarity\n return dotProduct / (magnitude1 * magnitude2);\n }\n\n /**\n * Find similar texts based on embedding similarity\n * @param queryEmbedding Embedding to compare against\n * @param textEmbeddings Array of objects with text and embedding\n * @param limit Maximum number of results to return\n * @returns Array of results sorted by similarity (highest first)\n */\n static findSimilarTexts(\n queryEmbedding: number[],\n textEmbeddings: Array<{ text: string; embedding: number[] }>,\n limit: number = 5\n ): Array<{ text: string; similarity: number }> {\n if (!queryEmbedding || !textEmbeddings || textEmbeddings.length === 0) {\n return [];\n }\n\n // Calculate similarity for each text\n const similarities = textEmbeddings\n .map(({ text, embedding }) => ({\n text,\n similarity: this.calculateSimilarity(queryEmbedding, embedding),\n }))\n // Sort by similarity (highest first)\n .sort((a, b) => b.similarity - a.similarity)\n // Limit number of results\n .slice(0, limit);\n\n return similarities;\n }\n\n /**\n * Check if embeddings are available (OpenAI API key and valid model)\n * @param model Optional model to test\n * @returns True if embeddings are available, false otherwise\n */\n static async isAvailable(model?: string): Promise {\n try {\n // Check if API key exists\n const apiKey =\n process.env.OPENAI_EMBEDDING_API_KEY || process.env.OPENAI_API_KEY;\n if (!apiKey) {\n logger.warn(\"OpenAI API key not found for embeddings\");\n return false;\n }\n\n // Try to generate a test embedding\n const testEmbedding = await this.generateEmbedding(\"test\", model);\n return Array.isArray(testEmbedding) && testEmbedding.length > 0;\n } catch (error) {\n logger.warn(\"Embedding test failed:\", error);\n return false;\n }\n }\n\n /**\n * List available embedding models from OpenAI\n * @returns Array of model IDs\n */\n static async listAvailableModels(): Promise {\n try {\n const client = this.initClient();\n const models = await client.models.list();\n\n return models.data\n .filter((model) => model.id.includes(\"embedding\"))\n .map((model) => model.id);\n } catch (error) {\n logger.error(\"Error listing available models:\", error);\n return [];\n }\n }\n} ","export { OpenAIProvider, createOpenAIConfig } from \"./openai\";\n\nexport { OllamaProvider, createOllamaConfig } from \"./ollama\";\n\nexport { Embedding } from \"./embedding\";\n\nimport { ProviderType, ProviderInstance, ProviderModel } from '../types/provider';\nimport { OpenAIModelConfig } from '../types/provider';\nimport { OllamaModelConfig } from '../types/provider';\n\nimport { OpenAIProvider, createOpenAIConfig } from './openai';\nimport { OllamaProvider, createOllamaConfig } from './ollama';\n\nexport const createProvider = (config: Record): ProviderInstance => {\n if (config.type === 'openai') {\n \n return {\n type: 'openai' as ProviderType,\n \n \n listModels(): string[] {\n \n return (config.models as string[]) || [config.model as string || 'gpt-3.5-turbo'];\n },\n \n \n getModel(name: string): ProviderModel {\n const modelConfig = createOpenAIConfig(name, config as Partial);\n return new OpenAIProvider('openai', modelConfig);\n },\n \n \n getDefaultModel(): string {\n return config.model as string || 'gpt-3.5-turbo';\n },\n \n \n getEmbeddingModel(): string {\n return config.embeddingModel as string || 'text-embedding-3-small';\n },\n\n // Add generateEmbedding method for RAG support\n async generateEmbedding(text: string): Promise {\n try {\n const { Embedding } = await import('./embedding');\n const embeddingModel = config.embeddingModel as string || 'text-embedding-3-small';\n return await Embedding.generateEmbedding(text, embeddingModel);\n } catch (error) {\n console.error('Error generating embedding:', error);\n return null;\n }\n }\n };\n } else if (config.type === 'ollama') {\n \n return {\n type: 'ollama' as ProviderType,\n \n listModels(): string[] {\n return (config.models as string[]) || [config.model as string || 'llama2'];\n },\n \n getModel(name: string): ProviderModel {\n \n const ollamaConfigBase = {\n name: name,\n ...config as Record\n };\n \n const ollamaConfig = createOllamaConfig \n ? createOllamaConfig(name, config as Partial) \n : ollamaConfigBase as OllamaModelConfig;\n \n return new OllamaProvider('ollama', ollamaConfig);\n },\n \n getDefaultModel(): string {\n return config.model as string || 'llama2';\n }\n };\n } else {\n throw new Error(`Unknown provider type: ${config.type}`);\n }\n}; ","// Astreus - AI Agent Framework\n\nimport { logger } from './utils/logger';\nimport { createAgent } from './agent';\nimport { createProvider } from './providers';\nimport { createMemory } from './memory';\nimport { createDatabase } from './database';\nimport { createRAG, parsePDF, parseDirectoryOfPDFs } from './rag';\nimport { createVectorDatabaseConnector, loadVectorDatabaseConfigFromEnv } from './rag/vector-db';\nimport { createChat } from './chat';\n\nexport { createAgent };\nexport { createProvider };\nexport { createMemory };\nexport { createDatabase };\nexport { createRAG };\nexport { createChat };\nexport { parsePDF, parseDirectoryOfPDFs };\nexport { logger };\nexport { createVectorDatabaseConnector, loadVectorDatabaseConfigFromEnv };\n\nexport * from './types';\nexport * from \"./constants\";\nexport * from \"./utils\";\nexport * from \"./tasks\";\nexport { validateRequiredParam, validateRequiredParams } from \"./utils/validation\";\nexport { PluginManager } from \"./plugin\";\n","import { v4 as uuidv4 } from \"uuid\";\nimport { AgentConfig, AgentInstance, AgentFactory, Plugin, ProviderModel, ProviderInstance, MemoryInstance, ChatInstance, ChatMetadata, ChatSummary } from \"./types\";\nimport { createDatabase } from \"./database\";\nimport { PluginManager } from \"./plugin\";\nimport { validateRequiredParams, validateRequiredParam } from \"./utils/validation\";\nimport { logger } from \"./utils/logger\";\nimport { createRAGTools } from \"./utils/rag-tools\";\nimport { \n DEFAULT_AGENT_NAME\n} from \"./constants\";\n\n// Agent implementation\nclass Agent implements AgentInstance {\n public id: string;\n public config: AgentConfig;\n private memory: MemoryInstance; // Replace any with MemoryInstance\n private tools: Map;\n private chatManager?: ChatInstance;\n\n constructor(config: AgentConfig) {\n // Validate required parameters\n validateRequiredParam(config, \"config\", \"Agent constructor\");\n validateRequiredParams(\n config,\n [\"memory\"], // 'name' is optional now since we have a default\n \"Agent constructor\"\n );\n \n // Ensure either model or provider is specified\n if (!config.model && !config.provider) {\n throw new Error(\"Either 'model' or 'provider' must be specified in agent config\");\n }\n \n // If provider is given but model is not, use default model from provider\n if (config.provider && !config.model) {\n const defaultModelName = config.provider.getDefaultModel?.() || config.provider.listModels()[0];\n if (defaultModelName) {\n config.model = config.provider.getModel(defaultModelName);\n } else {\n throw new Error(\"No default model available in provider\");\n }\n }\n \n // Ensure we have a model at this point\n if (!config.model) {\n throw new Error(\"No model could be determined for the agent\");\n }\n \n // Set default values for optional parameters\n this.id = config.id || uuidv4();\n this.config = {\n ...config,\n name: config.name || DEFAULT_AGENT_NAME,\n description: config.description || `Agent ${config.name || DEFAULT_AGENT_NAME}`,\n tools: config.tools || [],\n plugins: config.plugins || []\n };\n this.memory = config.memory;\n this.tools = new Map();\n this.chatManager = config.chat;\n\n // Initialize tools if provided\n if (this.config.tools) {\n this.config.tools.forEach((tool) => {\n this.tools.set(tool.name, tool);\n });\n }\n\n // Create RAG tools if RAG instance is provided\n if (this.config.rag) {\n const ragTools = createRAGTools(this.config.rag);\n ragTools.forEach((tool) => {\n this.tools.set(tool.name, tool);\n });\n logger.debug(`Added ${ragTools.length} RAG tools to agent ${this.config.name}`);\n }\n\n // Initialize plugins and register their tools if provided\n if (this.config.plugins) {\n for (const plugin of this.config.plugins) {\n // Check if plugin has getTools method (PluginInstance)\n if (plugin && 'getTools' in plugin && typeof plugin.getTools === 'function') {\n const pluginTools = plugin.getTools();\n \n if (pluginTools && Array.isArray(pluginTools)) {\n pluginTools.forEach((tool: Plugin) => {\n if (tool && tool.name) {\n this.tools.set(tool.name, tool);\n // Also register with the global registry\n PluginManager.register(tool);\n }\n });\n }\n } \n // Check if it's a direct Plugin object\n else if (plugin && 'name' in plugin && plugin.name && 'execute' in plugin) {\n // This is already a tool/plugin, register it directly\n const toolPlugin = plugin as Plugin;\n this.tools.set(toolPlugin.name, toolPlugin);\n PluginManager.register(toolPlugin);\n }\n }\n }\n\n\n }\n\n\n\n // Helper method to safely get the model\n getModel(): ProviderModel {\n if (!this.config.model) {\n throw new Error(\"No model specified for agent\");\n }\n return this.config.model;\n }\n\n // Get the provider instance\n getProvider(): ProviderInstance | undefined {\n return this.config.provider;\n }\n\n // Memory access methods\n async getHistory(sessionId: string, limit?: number): Promise {\n validateRequiredParam(sessionId, \"sessionId\", \"getHistory\");\n return await this.memory.getBySession(sessionId, limit);\n }\n\n async clearHistory(sessionId: string): Promise {\n validateRequiredParam(sessionId, \"sessionId\", \"clearHistory\");\n await this.memory.clear(sessionId);\n }\n\n async addToMemory(params: {\n sessionId: string;\n role: 'user' | 'assistant' | 'system';\n content: string;\n metadata?: Record;\n }): Promise {\n validateRequiredParam(params.sessionId, \"params.sessionId\", \"addToMemory\");\n validateRequiredParam(params.role, \"params.role\", \"addToMemory\");\n validateRequiredParam(params.content, \"params.content\", \"addToMemory\");\n\n return await this.memory.add({\n agentId: this.id,\n sessionId: params.sessionId,\n role: params.role,\n content: params.content,\n metadata: params.metadata || {}\n });\n }\n\n // List all sessions for this agent\n async listSessions(limit?: number): Promise<{\n sessionId: string;\n lastMessage?: string;\n messageCount: number;\n lastActivity: Date;\n metadata?: Record;\n }[]> {\n try {\n // Get all sessions from memory for this agent\n const sessions = await this.memory.listSessions(this.id, limit);\n \n return sessions.map((session: any) => ({\n sessionId: session.sessionId,\n lastMessage: session.lastMessage || session.content,\n messageCount: session.messageCount || 1,\n lastActivity: session.lastActivity || session.createdAt || new Date(),\n metadata: session.metadata || {}\n }));\n } catch (error) {\n logger.error(`Error listing sessions for agent ${this.id}:`, error);\n return [];\n }\n }\n\n // Chat method without streaming\n async chat(params: {\n message: string;\n sessionId?: string;\n systemPrompt?: string;\n temperature?: number;\n maxTokens?: number;\n metadata?: Record;\n }): Promise {\n validateRequiredParam(params.message, \"params.message\", \"chat\");\n\n const {\n message,\n sessionId = `session-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`,\n systemPrompt = this.config.systemPrompt,\n temperature = 0.7,\n maxTokens = 2000,\n metadata = {}\n } = params;\n\n // Get conversation history\n const history = sessionId ? await this.getHistory(sessionId) : [];\n\n // Prepare messages for the model\n const messages = [\n ...(systemPrompt ? [{ role: 'system' as const, content: systemPrompt }] : []),\n ...history.map((msg: any) => ({\n role: (msg.role === 'user' ? 'user' : 'assistant') as 'user' | 'assistant',\n content: msg.content\n })),\n { role: 'user' as const, content: message }\n ];\n\n // Get response from model\n const model = this.getModel();\n const response = await model.complete(messages, {\n temperature,\n maxTokens\n });\n\n const responseContent = typeof response === 'string' ? response : response.content;\n\n // Save to memory\n await this.addToMemory({\n sessionId,\n role: 'user',\n content: message,\n metadata\n });\n\n await this.addToMemory({\n sessionId,\n role: 'assistant',\n content: responseContent,\n metadata\n });\n\n return responseContent;\n }\n\n // Streaming chat method\n async streamChat(params: {\n message: string;\n sessionId?: string;\n systemPrompt?: string;\n temperature?: number;\n maxTokens?: number;\n metadata?: Record;\n onChunk?: (chunk: string) => void;\n }): Promise {\n validateRequiredParam(params.message, \"params.message\", \"streamChat\");\n\n const {\n message,\n sessionId = `session-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`,\n systemPrompt = this.config.systemPrompt,\n temperature = 0.7,\n maxTokens = 2000,\n metadata = {},\n onChunk\n } = params;\n\n // Get conversation history\n const history = sessionId ? await this.getHistory(sessionId) : [];\n\n // Prepare messages for the model\n const messages = [\n ...(systemPrompt ? [{ role: 'system' as const, content: systemPrompt }] : []),\n ...history.map((msg: any) => ({\n role: (msg.role === 'user' ? 'user' : 'assistant') as 'user' | 'assistant',\n content: msg.content\n })),\n { role: 'user' as const, content: message }\n ];\n\n let fullResponse = '';\n const model = this.getModel();\n const provider = this.getProvider();\n\n // Try to get OpenAI client for real streaming\n const openaiClient = (provider as any)?.client || (model as any)?.client;\n\n if (openaiClient && openaiClient.chat && openaiClient.chat.completions) {\n logger.debug(`Agent ${this.config.name}: Using real OpenAI streaming`);\n \n // Use OpenAI streaming directly for incremental chunks\n const stream = await openaiClient.chat.completions.create({\n model: model.name || 'gpt-4o-mini',\n messages: messages,\n stream: true,\n temperature,\n max_tokens: maxTokens\n });\n\n for await (const chunk of stream) {\n const content = chunk.choices[0]?.delta?.content || '';\n if (content) {\n fullResponse += content;\n if (onChunk) {\n onChunk(content); // Send only the new chunk\n }\n }\n }\n } else if (model.complete) {\n logger.debug(`Agent ${this.config.name}: Using simulated streaming`);\n \n // Fallback to complete method with simulated streaming\n const response = await model.complete(messages, {\n temperature,\n maxTokens\n });\n\n const responseContent = typeof response === 'string' ? response : response.content;\n fullResponse = responseContent;\n\n if (onChunk) {\n // Simulate streaming by sending word by word\n const words = responseContent.split(' ');\n for (let i = 0; i < words.length; i++) {\n const word = words[i] + (i < words.length - 1 ? ' ' : '');\n onChunk(word);\n // Small delay for realistic streaming effect\n await new Promise(resolve => setTimeout(resolve, 50));\n }\n }\n } else {\n throw new Error('No suitable model method available for streaming');\n }\n\n // Save to memory\n await this.addToMemory({\n sessionId,\n role: 'user',\n content: message,\n metadata\n });\n\n await this.addToMemory({\n sessionId,\n role: 'assistant',\n content: fullResponse,\n metadata\n });\n\n return fullResponse;\n }\n\n /**\n * Get available tool names\n * @returns Array of tool names available to the agent\n */\n getAvailableTools(): string[] {\n return Array.from(this.tools.keys());\n }\n\n addTool(tool: Plugin): void {\n // Validate required parameters\n validateRequiredParam(tool, \"tool\", \"addTool\");\n validateRequiredParams(\n tool,\n [\"name\", \"description\", \"execute\"],\n \"addTool\"\n );\n \n this.tools.set(tool.name, tool);\n }\n\n /**\n * Get the chat manager instance if available\n */\n getChatManager(): ChatInstance | undefined {\n return this.chatManager;\n }\n\n /**\n * Set or update the chat manager instance\n */\n setChatManager(chatManager: ChatInstance): void {\n this.chatManager = chatManager;\n }\n\n // Chat management methods\n async createChat(params: {\n chatId?: string;\n userId?: string;\n title?: string;\n metadata?: Record;\n }): Promise {\n if (!this.chatManager) {\n throw new Error(\"Chat manager not configured for this agent\");\n }\n\n return await this.chatManager.createChat({\n chatId: params.chatId,\n userId: params.userId,\n agentId: this.id,\n title: params.title,\n metadata: params.metadata\n });\n }\n\n async getChat(chatId: string): Promise {\n validateRequiredParam(chatId, \"chatId\", \"getChat\");\n \n if (!this.chatManager) {\n throw new Error(\"Chat manager not configured for this agent\");\n }\n\n return await this.chatManager.getChat(chatId);\n }\n\n async updateChat(chatId: string, updates: Partial): Promise {\n validateRequiredParam(chatId, \"chatId\", \"updateChat\");\n \n if (!this.chatManager) {\n throw new Error(\"Chat manager not configured for this agent\");\n }\n\n await this.chatManager.updateChat(chatId, updates);\n }\n\n async deleteChat(chatId: string): Promise {\n validateRequiredParam(chatId, \"chatId\", \"deleteChat\");\n \n if (!this.chatManager) {\n throw new Error(\"Chat manager not configured for this agent\");\n }\n\n await this.chatManager.deleteChat(chatId);\n }\n\n async archiveChat(chatId: string): Promise {\n validateRequiredParam(chatId, \"chatId\", \"archiveChat\");\n \n if (!this.chatManager) {\n throw new Error(\"Chat manager not configured for this agent\");\n }\n\n await this.chatManager.archiveChat(chatId);\n }\n\n async listChats(params?: {\n userId?: string;\n status?: 'active' | 'archived' | 'deleted';\n limit?: number;\n offset?: number;\n }): Promise {\n if (!this.chatManager) {\n throw new Error(\"Chat manager not configured for this agent\");\n }\n\n return await this.chatManager.listChats({\n ...params,\n agentId: this.id\n });\n }\n\n async searchChats(params: {\n query: string;\n userId?: string;\n limit?: number;\n }): Promise {\n validateRequiredParam(params.query, \"params.query\", \"searchChats\");\n \n if (!this.chatManager) {\n throw new Error(\"Chat manager not configured for this agent\");\n }\n\n return await this.chatManager.searchChats({\n ...params,\n agentId: this.id\n });\n }\n\n async getChatStats(params?: {\n userId?: string;\n }): Promise<{\n totalChats: number;\n activeChats: number;\n archivedChats: number;\n totalMessages: number;\n }> {\n if (!this.chatManager) {\n throw new Error(\"Chat manager not configured for this agent\");\n }\n\n return await this.chatManager.getChatStats({\n ...params,\n agentId: this.id\n });\n }\n\n // Enhanced chat methods with chat ID support\n async chatWithId(params: {\n message: string;\n chatId: string;\n userId?: string;\n systemPrompt?: string;\n temperature?: number;\n maxTokens?: number;\n metadata?: Record;\n }): Promise {\n validateRequiredParam(params.message, \"params.message\", \"chatWithId\");\n validateRequiredParam(params.chatId, \"params.chatId\", \"chatWithId\");\n \n if (!this.chatManager) {\n throw new Error(\"Chat manager not configured for this agent\");\n }\n\n // Check if chat exists, if not create it\n const existingChat = await this.chatManager.getChat(params.chatId);\n if (!existingChat) {\n await this.chatManager.createChat({\n chatId: params.chatId,\n userId: params.userId,\n agentId: this.id,\n metadata: params.metadata\n });\n }\n\n return await this.chatManager.chat({\n message: params.message,\n chatId: params.chatId,\n agentId: this.id,\n userId: params.userId,\n model: this.getModel(),\n systemPrompt: params.systemPrompt || this.config.systemPrompt,\n tools: Array.from(this.tools.values()),\n metadata: params.metadata,\n temperature: params.temperature,\n maxTokens: params.maxTokens\n });\n }\n\n async streamChatWithId(params: {\n message: string;\n chatId: string;\n userId?: string;\n systemPrompt?: string;\n temperature?: number;\n maxTokens?: number;\n metadata?: Record;\n onChunk?: (chunk: string) => void;\n }): Promise {\n validateRequiredParam(params.message, \"params.message\", \"streamChatWithId\");\n validateRequiredParam(params.chatId, \"params.chatId\", \"streamChatWithId\");\n \n if (!this.chatManager) {\n throw new Error(\"Chat manager not configured for this agent\");\n }\n\n // Check if chat exists, if not create it\n const existingChat = await this.chatManager.getChat(params.chatId);\n if (!existingChat) {\n await this.chatManager.createChat({\n chatId: params.chatId,\n userId: params.userId,\n agentId: this.id,\n metadata: params.metadata\n });\n }\n\n // For now, use the regular chat method and simulate streaming\n // This can be enhanced later with true streaming support in ChatManager\n const response = await this.chatManager.chat({\n message: params.message,\n chatId: params.chatId,\n agentId: this.id,\n userId: params.userId,\n model: this.getModel(),\n systemPrompt: params.systemPrompt || this.config.systemPrompt,\n tools: Array.from(this.tools.values()),\n metadata: params.metadata,\n temperature: params.temperature,\n maxTokens: params.maxTokens\n });\n\n // Simulate streaming by calling onChunk with the full response\n if (params.onChunk) {\n params.onChunk(response);\n }\n\n return response;\n }\n}\n\n// Agent factory function\nexport const createAgent: AgentFactory = async (config: AgentConfig) => {\n // Validate required parameters\n validateRequiredParam(config, \"config\", \"createAgent\");\n validateRequiredParams(\n config,\n [\"memory\"],\n \"createAgent\"\n );\n \n // Ensure either model or provider is specified\n if (!config.model && !config.provider) {\n throw new Error(\"Either 'model' or 'provider' must be specified in agent config\");\n }\n \n // Create a new agent instance\n const agent = new Agent(config);\n\n // Save agent to database\n try {\n // Use database from config if provided, otherwise create a new one\n const db = config.database || await createDatabase();\n const tableNames = db.getTableNames();\n \n // Ensure agents table exists\n await db.ensureTable(tableNames.agents, (table) => {\n table.string(\"id\").primary();\n table.string(\"name\").notNullable();\n table.text(\"description\").nullable();\n table.text(\"systemPrompt\").nullable();\n table.string(\"modelName\").notNullable();\n table.timestamp(\"createdAt\").defaultTo(db.knex.fn.now());\n table.timestamp(\"updatedAt\").defaultTo(db.knex.fn.now());\n table.json(\"configuration\").nullable();\n });\n\n const agentsTable = db.getTable(tableNames.agents);\n\n // Check if agent already exists\n const existingAgent = await agentsTable.findOne({ id: agent.id });\n\n if (!existingAgent) {\n // Save new agent\n await agentsTable.insert({\n id: agent.id,\n name: agent.config.name,\n description: agent.config.description || null,\n systemPrompt: agent.config.systemPrompt || null,\n modelName: agent.config.model?.name || \"unknown\",\n createdAt: new Date(),\n updatedAt: new Date(),\n configuration: JSON.stringify({\n hasTools: agent.getAvailableTools().length > 0,\n supportsTaskSystem: true,\n }),\n });\n logger.agent(agent.config.name, `Agent saved to database with ID: ${agent.id}`);\n } else {\n // Update existing agent\n await agentsTable.update(\n { id: agent.id },\n {\n name: agent.config.name,\n description: agent.config.description || null,\n systemPrompt: agent.config.systemPrompt || null,\n modelName: agent.config.model?.name || \"unknown\",\n updatedAt: new Date(),\n configuration: JSON.stringify({\n hasTools: agent.getAvailableTools().length > 0,\n supportsTaskSystem: true,\n }),\n }\n );\n logger.agent(agent.config.name, `Agent updated in database with ID: ${agent.id}`);\n }\n } catch (error) {\n logger.error(\"Error saving agent to database:\", error);\n }\n\n return agent;\n}; ","import { Knex } from \"knex\";\nimport fs from \"fs\";\nimport path from \"path\";\nimport dotenv from \"dotenv\";\nimport {\n DatabaseConfig,\n DatabaseInstance,\n DatabaseFactory,\n TableOperations,\n TableNamesConfig,\n} from \"./types\";\nimport { createSqliteDatabase } from \"./database/sqlite\";\nimport { createPostgresqlDatabase } from \"./database/postgresql\";\nimport { v4 as uuidv4 } from \"uuid\";\nimport { logger } from \"./utils\";\nimport { validateRequiredParam, validateRequiredParams } from \"./utils/validation\";\nimport { DEFAULT_DB_PATH } from \"./constants\";\n\n// Load environment variables\ndotenv.config();\n\n// Re-export configuration helpers\nexport { createSqliteConfig } from \"./database/sqlite\";\nexport { createPostgresqlConfig } from \"./database/postgresql\";\n\n// Re-export database modules\nexport { \n createUser, \n getUserById, \n getUserByUsername, \n updateUser, \n deleteUser \n} from \"./database/modules/user\";\n\n// Re-export types\nexport { DatabaseInstance, DatabaseConfig } from \"./types/database\";\n\n/**\n * Core database implementation that provides storage functionality\n * for the Astreus framework. Supports multiple database backends.\n */\nclass Database implements DatabaseInstance {\n public knex: Knex;\n public config: DatabaseConfig;\n private initialized: boolean = false;\n private tableNames: Required;\n private customTables: Map = new Map();\n\n constructor(config: DatabaseConfig) {\n // Validate required parameters\n validateRequiredParam(config, \"config\", \"Database constructor\");\n validateRequiredParams(\n config,\n [\"type\"],\n \"Database constructor\"\n );\n \n this.config = config;\n \n // Apply table prefix if specified\n const prefix = config.tablePrefix || '';\n \n // Set table names with defaults and prefix\n this.tableNames = {\n agents: prefix + (config.tableNames?.agents || 'agents'),\n users: prefix + (config.tableNames?.users || 'users'),\n tasks: prefix + (config.tableNames?.tasks || 'tasks'),\n memories: prefix + (config.tableNames?.memories || 'memories'),\n chats: prefix + (config.tableNames?.chats || 'chats'),\n custom: config.tableNames?.custom || {}\n };\n\n // Register custom tables\n if (config.tableNames?.custom) {\n Object.entries(config.tableNames.custom).forEach(([name, tableName]) => {\n this.customTables.set(name, prefix + tableName);\n });\n }\n\n // Initialize knex instance based on database type\n if (config.type === \"sqlite\") {\n this.knex = createSqliteDatabase(config);\n } else if (config.type === \"postgresql\") {\n this.knex = createPostgresqlDatabase(config);\n } else {\n throw new Error(`Unsupported database type: ${config.type}`);\n }\n }\n\n /**\n * Connect to the database and verify the connection\n * @throws Error if connection fails\n */\n async connect(): Promise {\n try {\n // Test the connection\n await this.knex.raw(\"SELECT 1\");\n logger.database(\"Connect\", `Connected to ${this.config.type} database`);\n } catch (error) {\n logger.error(`Error connecting to ${this.config.type} database:`, error);\n throw error;\n }\n }\n\n /**\n * Gracefully disconnect from the database\n */\n async disconnect(): Promise {\n await this.knex.destroy();\n logger.database(\"Disconnect\", `Disconnected from ${this.config.type} database`);\n }\n\n /**\n * Execute a raw SQL query against the database\n * @param query The SQL query to execute\n * @param params Parameters to bind to the query\n * @returns Results of the query\n */\n async executeQuery(query: string, params: any[] = []): Promise {\n // Validate required parameters\n validateRequiredParam(query, \"query\", \"executeQuery\");\n \n try {\n return this.knex.raw(query, params) as Promise;\n } catch (error) {\n logger.error(\"Error executing query:\", error);\n throw error;\n }\n }\n\n /**\n * Check if a table exists\n * @param tableName Name of the table to check\n * @returns Promise resolving to boolean indicating if table exists\n */\n async hasTable(tableName: string): Promise {\n validateRequiredParam(tableName, \"tableName\", \"hasTable\");\n return await this.knex.schema.hasTable(tableName);\n }\n\n /**\n * Create a table with the given schema\n * @param tableName Name of the table to create\n * @param schema Function that defines the table schema\n */\n async createTable(tableName: string, schema: (table: Knex.TableBuilder) => void): Promise {\n validateRequiredParam(tableName, \"tableName\", \"createTable\");\n validateRequiredParam(schema, \"schema\", \"createTable\");\n \n try {\n await this.knex.schema.createTable(tableName, schema);\n logger.database(\"CreateTable\", `Created table: ${tableName}`);\n } catch (error) {\n logger.error(`Error creating table ${tableName}:`, error);\n throw error;\n }\n }\n\n /**\n * Drop a table if it exists\n * @param tableName Name of the table to drop\n */\n async dropTable(tableName: string): Promise {\n validateRequiredParam(tableName, \"tableName\", \"dropTable\");\n \n try {\n await this.knex.schema.dropTableIfExists(tableName);\n logger.database(\"DropTable\", `Dropped table: ${tableName}`);\n } catch (error) {\n logger.error(`Error dropping table ${tableName}:`, error);\n throw error;\n }\n }\n\n /**\n * Ensure a table exists, create it if it doesn't\n * @param tableName Name of the table to ensure\n * @param schema Function that defines the table schema\n */\n async ensureTable(tableName: string, schema: (table: Knex.TableBuilder) => void): Promise {\n validateRequiredParam(tableName, \"tableName\", \"ensureTable\");\n validateRequiredParam(schema, \"schema\", \"ensureTable\");\n \n const exists = await this.hasTable(tableName);\n if (!exists) {\n await this.createTable(tableName, schema);\n } else {\n logger.database(\"EnsureTable\", `Table ${tableName} already exists`);\n }\n }\n\n /**\n * Register a custom table name mapping\n * @param name Logical name for the table\n * @param tableName Actual table name in database\n */\n registerCustomTable(name: string, tableName: string): void {\n validateRequiredParam(name, \"name\", \"registerCustomTable\");\n validateRequiredParam(tableName, \"tableName\", \"registerCustomTable\");\n \n const prefix = this.config.tablePrefix || '';\n this.customTables.set(name, prefix + tableName);\n logger.database(\"RegisterCustomTable\", `Registered custom table: ${name} -> ${prefix + tableName}`);\n }\n\n /**\n * Get the actual table name for a custom table\n * @param name Logical name of the custom table\n * @returns Actual table name or undefined if not found\n */\n getCustomTableName(name: string): string | undefined {\n return this.customTables.get(name);\n }\n\n /**\n * Initialize database schema - only handles legacy migrations now\n * Each module (createMemory, createChat, etc.) is responsible for creating its own tables\n */\n async initializeSchema(): Promise {\n try {\n // Only handle legacy migrations, no auto table creation\n await this.migrateLegacyTables();\n \n // Mark as initialized\n this.initialized = true;\n logger.database(\"InitializeSchema\", \"Database schema initialization complete (legacy migrations only)\");\n } catch (error) {\n logger.error(\"Error initializing database schema:\", error);\n throw error;\n }\n }\n\n /**\n * Migrate legacy tables and remove deprecated ones\n * @param memoriesTableName Optional table name for memories (defaults to configured name)\n */\n private async migrateLegacyTables(memoriesTableName?: string): Promise {\n const memoryTable = memoriesTableName || this.tableNames.memories;\n \n // Check for task_contexts table (deprecated) and migrate data\n const hasTaskContextsTable = await this.knex.schema.hasTable(\"task_contexts\");\n if (hasTaskContextsTable) {\n try {\n const contextRecords = await this.knex(\"task_contexts\").select(\"*\");\n if (contextRecords.length > 0) {\n logger.database(\"InitializeSchema\", `Migrating ${contextRecords.length} task contexts to memory system...`);\n\n // Check if memories table exists before migration\n const hasMemoriesTable = await this.knex.schema.hasTable(memoryTable);\n if (!hasMemoriesTable) {\n logger.warn(`Memories table '${memoryTable}' does not exist. Skipping task contexts migration.`);\n } else {\n // Batch insert to memories table\n const memoryRecords = contextRecords.map((record: any) => ({\n id: uuidv4(),\n agentId: \"system\",\n sessionId: record.sessionId,\n userId: \"\",\n role: \"task_context\",\n content: record.data,\n timestamp: record.updatedAt || new Date(),\n metadata: JSON.stringify({\n contextType: \"task_execution_context\",\n migratedFrom: \"task_contexts\",\n }),\n }));\n\n await this.knex(memoryTable).insert(memoryRecords);\n logger.database(\"InitializeSchema\", \"Task contexts migration completed successfully\");\n }\n }\n } catch (migrationError) {\n logger.error(\"Error migrating task contexts:\", migrationError);\n }\n\n // Drop the deprecated table\n await this.knex.schema.dropTable(\"task_contexts\");\n logger.database(\"InitializeSchema\", \"Dropped deprecated task_contexts table\");\n }\n }\n\n /**\n * Get operations interface for a specific table\n * @param tableName Name of the table to operate on\n * @returns Table operations interface\n */\n getTable(tableName: string): TableOperations {\n // Validate required parameters\n validateRequiredParam(tableName, \"tableName\", \"getTable\");\n \n const knexInstance = this.knex;\n\n return {\n /**\n * Insert data into table\n */\n async insert(data: Record): Promise {\n // Validate required parameters\n validateRequiredParam(data, \"data\", \"insert\");\n \n try {\n const result = await knexInstance(tableName).insert(data);\n return result[0];\n } catch (error) {\n logger.error(`Error inserting into ${tableName}:`, error);\n throw error;\n }\n },\n\n /**\n * Find records in table\n */\n async find(filter?: Record): Promise[]> {\n try {\n let query = knexInstance(tableName);\n if (filter) {\n query = query.where(filter);\n }\n return query.select(\"*\");\n } catch (error) {\n logger.error(`Error finding in ${tableName}:`, error);\n throw error;\n }\n },\n\n /**\n * Find one record in table\n */\n async findOne(\n filter: Record\n ): Promise | null> {\n // Validate required parameters\n validateRequiredParam(filter, \"filter\", \"findOne\");\n \n try {\n const result = await knexInstance(tableName).where(filter).first();\n return result || null;\n } catch (error) {\n logger.error(`Error finding one in ${tableName}:`, error);\n throw error;\n }\n },\n\n /**\n * Update records in table\n */\n async update(\n filter: Record,\n data: Record\n ): Promise {\n // Validate required parameters\n validateRequiredParam(filter, \"filter\", \"update\");\n validateRequiredParam(data, \"data\", \"update\");\n \n try {\n return await knexInstance(tableName).where(filter).update(data);\n } catch (error) {\n logger.error(`Error updating in ${tableName}:`, error);\n throw error;\n }\n },\n\n /**\n * Delete records from table\n */\n async delete(filter: Record): Promise {\n // Validate required parameters\n validateRequiredParam(filter, \"filter\", \"delete\");\n \n try {\n return await knexInstance(tableName).where(filter).delete();\n } catch (error) {\n logger.error(`Error deleting from ${tableName}:`, error);\n throw error;\n }\n },\n };\n }\n\n /**\n * Check if the database has been initialized\n */\n isInitialized(): boolean {\n return this.initialized;\n }\n\n /**\n * Get configured table names\n * @returns Object containing all configured table names\n */\n getTableNames(): Required {\n return this.tableNames;\n }\n}\n\n// Database factory function\nexport const createDatabase: DatabaseFactory = async (\n config?: DatabaseConfig\n) => {\n // If no config is provided, create a default one\n if (!config) {\n // Determine which database to use based on environment variables\n const dbType = process.env.DATABASE_TYPE || \"sqlite\";\n\n if (dbType === \"sqlite\") {\n // For SQLite, create a default file-based database\n const dbPath = process.env.DATABASE_PATH || DEFAULT_DB_PATH;\n \n // Create database directory if it doesn't exist (for file-based SQLite)\n const dir = path.dirname(dbPath);\n if (!fs.existsSync(dir)) {\n fs.mkdirSync(dir, { recursive: true });\n logger.database(\"CreateDatabase\", `Created database directory: ${dir}`);\n }\n \n config = {\n type: \"sqlite\",\n connection: dbPath,\n };\n \n logger.database(\"CreateDatabase\", `Using SQLite database at ${dbPath}`);\n } else if (dbType === \"postgresql\") {\n // For PostgreSQL, use connection URL\n if (process.env.DATABASE_URL) {\n // Parse connection string\n const url = new URL(process.env.DATABASE_URL);\n const host = url.hostname;\n const port = parseInt(url.port || \"5432\");\n const user = url.username;\n const password = url.password;\n const database = url.pathname.substring(1); // Remove leading slash\n \n config = {\n type: \"postgresql\",\n connection: {\n host,\n port,\n user,\n password,\n database,\n },\n };\n \n logger.database(\"CreateDatabase\", `Using PostgreSQL database from URL: ${host}:${port}/${database}`);\n } else {\n throw new Error(\"PostgreSQL connection requires DATABASE_URL environment variable\");\n }\n } else {\n throw new Error(`Unsupported database type: ${dbType}`);\n }\n } else {\n // Validate the provided config\n validateRequiredParams(\n config,\n [\"type\"],\n \"createDatabase\"\n );\n }\n\n // Create a new database instance\n const db = new Database(config);\n\n // Connect to the database\n await db.connect();\n\n // Only run legacy migrations, no auto table creation\n await db.initializeSchema();\n\n return db;\n};","import knex, { Knex } from \"knex\";\nimport { DatabaseConfig } from \"../types\";\n\n/**\n * Create a SQLite database connection\n */\nexport function createSqliteDatabase(config: DatabaseConfig): Knex {\n return knex({\n client: \"sqlite3\",\n connection: {\n filename:\n typeof config.connection === \"string\" ? config.connection : \":memory:\",\n },\n useNullAsDefault: true,\n });\n}\n\n/**\n * Create a SQLite database configuration\n */\nexport function createSqliteConfig(filename: string): DatabaseConfig {\n return {\n type: \"sqlite\",\n connection: filename,\n };\n}\n","import knex, { Knex } from \"knex\";\nimport { DatabaseConfig } from \"../types\";\n\n/**\n * Create a PostgreSQL database connection\n */\nexport function createPostgresqlDatabase(config: DatabaseConfig): Knex {\n return knex({\n client: \"pg\",\n connection: config.connection,\n });\n}\n\n/**\n * Create a PostgreSQL database configuration\n */\nexport function createPostgresqlConfig(\n connectionString: string\n): DatabaseConfig {\n return {\n type: \"postgresql\",\n connection: connectionString,\n };\n}\n","export { \n logger, \n configure as configureLogger, \n printBanner, \n LogLevel \n} from \"./logger\"; \n\n\nexport { validateRequiredParam, validateRequiredParams } from \"./validation\"; \nexport { createRAGTools } from \"./rag-tools\"; ","/**\n * Utility functions for parameter validation\n */\n\n/**\n * Validates that all required parameters are provided\n * @param params The parameters object to validate\n * @param requiredParams Array of parameter names that are required\n * @param functionName Name of the function for error message context\n * @throws Error if any required parameter is missing or undefined\n */\nexport function validateRequiredParams(\n params: Record,\n requiredParams: string[],\n functionName: string\n): void {\n // First check that params is actually an object\n if (!params || typeof params !== 'object') {\n throw new Error(`Invalid parameters object for ${functionName}: expected an object but got ${typeof params}`);\n }\n \n const missingParams: string[] = [];\n \n for (const param of requiredParams) {\n if (param in params) {\n // Parameter exists in the object, check if it's undefined or null\n if (params[param] === undefined || params[param] === null) {\n missingParams.push(param);\n }\n } else {\n // Parameter doesn't exist in the object at all\n missingParams.push(param);\n }\n }\n \n if (missingParams.length > 0) {\n throw new Error(\n `Missing required parameter${missingParams.length > 1 ? 's' : ''} for ${functionName}: ${missingParams.join(', ')}`\n );\n }\n}\n\n/**\n * Validates a single required parameter\n * @param value The parameter value to check\n * @param paramName Name of the parameter for error message\n * @param functionName Name of the function for error message context\n * @throws Error if the parameter is missing or undefined\n */\nexport function validateRequiredParam(\n value: any,\n paramName: string,\n functionName: string\n): void {\n if (value === undefined || value === null) {\n throw new Error(`Missing required parameter '${paramName}' for ${functionName}`);\n }\n} ","import { Plugin } from \"../types\";\nimport { RAGInstance, VectorRAGInstance, DocumentRAGInstance } from \"../types/rag\";\nimport { logger } from \"./logger\";\n\n/**\n * Convert a RAG instance to an array of tools that can be used by agents\n * @param rag The RAG instance to convert\n * @returns Array of Plugin tools for RAG functionality\n */\nexport function createRAGTools(rag: RAGInstance): Plugin[] {\n const tools: Plugin[] = [];\n\n // Common search tool available for all RAG types\n tools.push({\n name: \"rag_search\",\n description: \"Search through documents using semantic similarity to find relevant information\",\n parameters: [\n {\n name: \"query\",\n type: \"string\",\n description: \"The search query to find relevant documents or content\",\n required: true\n },\n {\n name: \"limit\",\n type: \"number\",\n description: \"Maximum number of results to return\",\n required: false,\n default: 5\n }\n ],\n execute: async (params: Record) => {\n try {\n const query = params.query as string;\n const limit = params.limit as number | undefined;\n \n if (!query) {\n throw new Error(\"Query parameter is required\");\n }\n \n const results = await rag.search(query, limit);\n return {\n success: true,\n results: results.map(r => ({\n content: r.content,\n metadata: r.metadata,\n similarity: r.similarity,\n sourceId: r.sourceId\n })),\n query: query,\n resultCount: results.length\n };\n } catch (error) {\n logger.error(\"Error executing RAG search:\", error);\n return {\n success: false,\n error: error instanceof Error ? error.message : \"Unknown error occurred during search\",\n query: params.query\n };\n }\n }\n });\n\n // Vector RAG specific tools\n if ('searchByVector' in rag) {\n const vectorRAG = rag as VectorRAGInstance;\n \n tools.push({\n name: \"rag_add_document\",\n description: \"Add a new document to the RAG system for future searches\",\n parameters: [\n {\n name: \"content\",\n type: \"string\",\n description: \"The document content to add\",\n required: true\n },\n {\n name: \"metadata\",\n type: \"object\",\n description: \"Additional metadata for the document\",\n required: false,\n default: {}\n }\n ],\n execute: async (params: Record) => {\n try {\n const content = params.content as string;\n const metadata = params.metadata as Record | undefined;\n \n if (!content) {\n throw new Error(\"Content parameter is required\");\n }\n \n const documentId = await vectorRAG.addDocument({\n content: content,\n metadata: metadata || {}\n });\n return {\n success: true,\n documentId,\n message: \"Document added successfully\"\n };\n } catch (error) {\n logger.error(\"Error adding document to RAG:\", error);\n return {\n success: false,\n error: error instanceof Error ? error.message : \"Unknown error occurred while adding document\"\n };\n }\n }\n });\n\n tools.push({\n name: \"rag_get_document\",\n description: \"Retrieve a specific document by its ID\",\n parameters: [\n {\n name: \"documentId\",\n type: \"string\",\n description: \"The ID of the document to retrieve\",\n required: true\n }\n ],\n execute: async (params: Record) => {\n try {\n const documentId = params.documentId as string;\n \n if (!documentId) {\n throw new Error(\"DocumentId parameter is required\");\n }\n \n const document = await vectorRAG.getDocumentById(documentId);\n if (document) {\n return {\n success: true,\n document: {\n id: document.id,\n content: document.content,\n metadata: document.metadata\n }\n };\n } else {\n return {\n success: false,\n error: \"Document not found\",\n documentId: documentId\n };\n }\n } catch (error) {\n logger.error(\"Error retrieving document from RAG:\", error);\n return {\n success: false,\n error: error instanceof Error ? error.message : \"Unknown error occurred while retrieving document\",\n documentId: params.documentId\n };\n }\n }\n });\n\n tools.push({\n name: \"rag_delete_document\",\n description: \"Delete a document from the RAG system\",\n parameters: [\n {\n name: \"documentId\",\n type: \"string\",\n description: \"The ID of the document to delete\",\n required: true\n }\n ],\n execute: async (params: Record) => {\n try {\n const documentId = params.documentId as string;\n \n if (!documentId) {\n throw new Error(\"DocumentId parameter is required\");\n }\n \n await vectorRAG.deleteDocument(documentId);\n return {\n success: true,\n message: \"Document deleted successfully\",\n documentId: documentId\n };\n } catch (error) {\n logger.error(\"Error deleting document from RAG:\", error);\n return {\n success: false,\n error: error instanceof Error ? error.message : \"Unknown error occurred while deleting document\",\n documentId: params.documentId\n };\n }\n }\n });\n }\n\n // Document RAG specific tools\n if ('searchByMetadata' in rag) {\n const documentRAG = rag as DocumentRAGInstance;\n \n tools.push({\n name: \"rag_search_by_metadata\",\n description: \"Search documents by their metadata properties\",\n parameters: [\n {\n name: \"filter\",\n type: \"object\",\n description: \"Metadata filter criteria as key-value pairs\",\n required: true\n },\n {\n name: \"limit\",\n type: \"number\",\n description: \"Maximum number of results to return\",\n required: false,\n default: 5\n }\n ],\n execute: async (params: Record) => {\n try {\n const filter = params.filter as Record;\n const limit = params.limit as number | undefined;\n \n if (!filter) {\n throw new Error(\"Filter parameter is required\");\n }\n \n const results = await documentRAG.searchByMetadata(filter, limit);\n return {\n success: true,\n results: results.map(r => ({\n content: r.content,\n metadata: r.metadata,\n similarity: r.similarity,\n sourceId: r.sourceId\n })),\n filter: filter,\n resultCount: results.length\n };\n } catch (error) {\n logger.error(\"Error executing RAG metadata search:\", error);\n return {\n success: false,\n error: error instanceof Error ? error.message : \"Unknown error occurred during metadata search\",\n filter: params.filter\n };\n }\n }\n });\n }\n\n logger.debug(`Created ${tools.length} RAG tools for agent use`);\n return tools;\n} ","import { v4 as uuidv4 } from \"uuid\";\nimport { createDatabase } from \"../../database\";\nimport { logger } from \"../../utils\";\n\n/**\n * Create a user and save to database\n */\nexport async function createUser(username: string): Promise {\n try {\n // Generate user ID\n const id = uuidv4();\n\n // Get database instance\n const db = await createDatabase();\n const tableNames = db.getTableNames();\n\n // Ensure users table exists\n await db.ensureTable(tableNames.users, (table) => {\n table.string(\"id\").primary();\n table.string(\"username\").notNullable().unique();\n table.timestamp(\"createdAt\").defaultTo(db.knex.fn.now());\n table.json(\"preferences\").nullable();\n });\n\n // Save user to database\n await db.getTable(tableNames.users).insert({\n id,\n username,\n createdAt: new Date(),\n });\n\n logger.success(`User created with ID: ${id}`);\n return id;\n } catch (error) {\n logger.error(\"Error creating user:\", error);\n throw error;\n }\n}\n\n/**\n * Get user by ID\n */\nexport async function getUserById(id: string): Promise {\n try {\n const db = await createDatabase();\n const tableNames = db.getTableNames();\n return await db.getTable(tableNames.users).findOne({ id });\n } catch (error) {\n logger.error(\"Error getting user by ID:\", error);\n throw error;\n }\n}\n\n/**\n * Get user by username\n */\nexport async function getUserByUsername(username: string): Promise {\n try {\n const db = await createDatabase();\n const tableNames = db.getTableNames();\n return await db.getTable(tableNames.users).findOne({ username });\n } catch (error) {\n logger.error(\"Error getting user by username:\", error);\n throw error;\n }\n}\n\n/**\n * Update user\n */\nexport async function updateUser(id: string, data: Record): Promise {\n try {\n const db = await createDatabase();\n const tableNames = db.getTableNames();\n const result = await db.getTable(tableNames.users).update({ id }, data);\n return result > 0;\n } catch (error) {\n logger.error(\"Error updating user:\", error);\n throw error;\n }\n}\n\n/**\n * Delete user\n */\nexport async function deleteUser(id: string): Promise {\n try {\n const db = await createDatabase();\n const tableNames = db.getTableNames();\n const result = await db.getTable(tableNames.users).delete({ id });\n return result > 0;\n } catch (error) {\n logger.error(\"Error deleting user:\", error);\n throw error;\n }\n} ","import { PluginConfig, PluginInstance, Plugin } from \"./types\";\nimport { logger } from \"./utils\";\nimport { validateRequiredParam, validateRequiredParams } from \"./utils/validation\";\n\n/**\n * Comprehensive Plugin Manager that handles all plugin-related functionality\n * This class provides both instance-level management for specific agents\n * and static global registry for framework-wide plugin management.\n */\nexport class PluginManager implements PluginInstance {\n public config: PluginConfig;\n private tools: Map;\n\n // Global registry shared across all instances\n private static registry = new Map();\n\n constructor(config: PluginConfig) {\n // Validate required parameters\n validateRequiredParam(config, \"config\", \"PluginManager constructor\");\n validateRequiredParams(\n config,\n [\"name\", \"tools\"],\n \"PluginManager constructor\"\n );\n \n // Apply defaults for optional fields\n this.config = {\n ...config,\n description: config.description || `Plugin manager for ${config.name}`,\n version: config.version || '1.0.0',\n tools: config.tools || []\n };\n \n this.tools = new Map();\n\n // Initialize tools\n if (this.config.tools) {\n this.config.tools.forEach((tool) => {\n this.registerTool(tool);\n });\n }\n \n logger.info(`Plugin manager initialized with ${this.tools.size} tools`);\n }\n\n // ========== Instance methods for managing local tools ==========\n\n /**\n * Get all tools registered with this instance\n * @returns Array of all registered tools\n */\n getTools(): Plugin[] {\n return Array.from(this.tools.values());\n }\n\n /**\n * Get a specific tool by name\n * @param name Name of the tool to retrieve\n * @returns Tool if found, undefined otherwise\n */\n getTool(name: string): Plugin | undefined {\n // Validate required parameters\n validateRequiredParam(name, \"name\", \"getTool\");\n \n return this.tools.get(name);\n }\n\n /**\n * Register a tool with this instance\n * Also registers the tool with the global registry\n * @param tool Tool to register\n * @throws Error if the tool is invalid\n */\n registerTool(tool: Plugin): void {\n // Validate required parameters\n validateRequiredParam(tool, \"tool\", \"registerTool\");\n \n try {\n // Check that tool has a name and execute method\n if (!tool.name) {\n logger.warn(\"Cannot register tool: Missing name property\");\n return;\n }\n \n if (!tool.execute || typeof tool.execute !== 'function') {\n logger.warn(`Cannot register tool \"${tool.name}\": Missing execute method`);\n return;\n }\n \n // Register with the instance\n this.tools.set(tool.name, tool);\n\n // Also register with the global registry\n PluginManager.register(tool);\n \n logger.debug(`Tool \"${tool.name}\" registered successfully`);\n } catch (error) {\n logger.error(`Failed to register tool: ${error}`);\n throw error;\n }\n }\n\n /**\n * Remove a tool from this instance\n * Also removes the tool from the global registry\n * @param name Name of the tool to remove\n * @returns true if tool was found and removed, false otherwise\n */\n removeTool(name: string): boolean {\n // Validate required parameters\n validateRequiredParam(name, \"name\", \"removeTool\");\n \n const removed = this.tools.delete(name);\n \n // Also remove from global registry if it was removed from instance\n if (removed) {\n PluginManager.unregister(name);\n logger.debug(`Tool \"${name}\" removed`);\n }\n\n return removed;\n }\n\n /**\n * Check if a tool is registered with this instance\n * @param name Name of the tool to check\n * @returns true if tool is registered, false otherwise\n */\n hasTool(name: string): boolean {\n // Validate required parameters\n validateRequiredParam(name, \"name\", \"hasTool\");\n \n return this.tools.has(name);\n }\n\n /**\n * Get the number of tools registered with this instance\n * @returns Number of tools\n */\n getToolCount(): number {\n return this.tools.size;\n }\n\n // ========== Static methods for global plugin registry ==========\n\n /**\n * Register a plugin with the global registry\n * @param plugin Plugin to register\n * @throws Error if the plugin is invalid\n */\n static register(plugin: Plugin): void {\n // Validate required parameters\n validateRequiredParam(plugin, \"plugin\", \"PluginManager.register\");\n \n try {\n // Check that plugin has a name and execute method\n if (!plugin.name) {\n logger.warn(\"Cannot register plugin: Missing name property\");\n return;\n }\n \n if (!plugin.execute || typeof plugin.execute !== 'function') {\n logger.warn(`Cannot register plugin \"${plugin.name}\": Missing execute method`);\n return;\n }\n \n this.registry.set(plugin.name, plugin);\n logger.debug(`Plugin \"${plugin.name}\" registered in global registry`);\n } catch (error) {\n logger.error(`Failed to register plugin in global registry: ${error}`);\n throw error;\n }\n }\n\n /**\n * Unregister a plugin from the global registry\n * @param name Name of the plugin to unregister\n * @returns true if plugin was found and removed, false otherwise\n */\n static unregister(name: string): boolean {\n // Validate required parameters\n validateRequiredParam(name, \"name\", \"PluginManager.unregister\");\n \n const result = this.registry.delete(name);\n if (result) {\n logger.debug(`Plugin \"${name}\" unregistered from global registry`);\n }\n return result;\n }\n\n /**\n * Get a plugin by name from the global registry\n * @param name Name of the plugin to retrieve\n * @returns Plugin if found, undefined otherwise\n */\n static get(name: string): Plugin | undefined {\n // Validate required parameters\n validateRequiredParam(name, \"name\", \"PluginManager.get\");\n \n return this.registry.get(name);\n }\n\n /**\n * Get all plugins from the global registry\n * @returns Array of all registered plugins\n */\n static getAll(): Plugin[] {\n return Array.from(this.registry.values());\n }\n\n /**\n * Get all plugins for a specific agent\n * @param agentId ID of the agent to get plugins for\n * @returns Array of plugins for the specified agent\n */\n static getByAgent(agentId: string): Plugin[] {\n try {\n // Validate required parameters\n validateRequiredParam(agentId, \"agentId\", \"getByAgent\");\n \n // Log what we're doing\n logger.debug(`Getting plugins for agent ${agentId}`);\n logger.debug(`Total plugins in registry: ${this.registry.size}`);\n \n // Do safe access of the registry contents\n const plugins = Array.from(this.registry.values());\n \n // Log each plugin for debugging\n plugins.forEach((plugin, index) => {\n const name = plugin?.name || 'unnamed';\n logger.debug(`Plugin ${index}: ${name}`);\n });\n \n // For now, return all plugins as we don't track agent-plugin relationships\n // In the future, this could be enhanced to track which plugins are used by which agents\n return plugins;\n } catch (error) {\n // Log the error and return empty array\n logger.error(`Error getting plugins for agent ${agentId}:`, error);\n return [];\n }\n }\n\n /**\n * Reset the global plugin registry\n * Useful for testing or when reloading plugins\n */\n static reset(): void {\n this.registry.clear();\n logger.info(\"Global plugin registry reset\");\n }\n\n /**\n * Check if a plugin exists in the global registry\n * @param name Name of the plugin to check\n * @returns true if plugin exists, false otherwise\n */\n static has(name: string): boolean {\n // Validate required parameters\n validateRequiredParam(name, \"name\", \"PluginManager.has\");\n \n return this.registry.has(name);\n }\n\n /**\n * Get the number of plugins in the global registry\n * @returns Number of plugins\n */\n static count(): number {\n return this.registry.size;\n }\n\n /**\n * Load a plugin from a repository or object\n * @param pluginOrPath Plugin object or path to a plugin module\n * @returns Promise that resolves when the plugin is loaded and registered\n * @throws Error if loading fails\n */\n static async load(pluginOrPath: string | Plugin): Promise {\n // Validate required parameters\n validateRequiredParam(pluginOrPath, \"pluginOrPath\", \"PluginManager.load\");\n \n try {\n if (typeof pluginOrPath === \"string\") {\n // This is a path to a plugin module\n try {\n // Dynamic import\n const module = await import(pluginOrPath);\n const plugin = module.default || module;\n\n if (plugin && typeof plugin === 'object') {\n this.register(plugin);\n logger.info(`Plugin loaded from path: ${pluginOrPath}`);\n } else {\n const error = `Plugin module at ${pluginOrPath} does not export a valid plugin`;\n logger.warn(error);\n throw new Error(error);\n }\n } catch (error) {\n logger.error(`Failed to load plugin from ${pluginOrPath}:`, error);\n throw error;\n }\n } else if (pluginOrPath && typeof pluginOrPath === 'object') {\n // This is a plugin object\n this.register(pluginOrPath);\n logger.info(`Plugin \"${pluginOrPath.name}\" loaded directly`);\n } else {\n throw new Error('Invalid plugin format');\n }\n } catch (error) {\n logger.error(\"Error loading plugin:\", error);\n throw error;\n }\n }\n\n /**\n * Load multiple plugins at once\n * @param plugins Array of plugin objects or paths\n * @returns Promise that resolves when all plugins are loaded\n * @throws Error if loading any plugin fails\n */\n static async loadMany(plugins: Array): Promise {\n // Validate required parameters\n validateRequiredParam(plugins, \"plugins\", \"PluginManager.loadMany\");\n \n try {\n await Promise.all(plugins.map((plugin) => this.load(plugin)));\n logger.info(`Loaded ${plugins.length} plugins`);\n } catch (error) {\n logger.error(\"Error loading multiple plugins:\", error);\n throw error;\n }\n }\n\n /**\n * Create a new PluginManager instance\n * @param config Configuration for the plugin manager\n * @returns New PluginManager instance\n */\n static create(config: PluginConfig): PluginInstance {\n // Validate required parameters\n validateRequiredParam(config, \"config\", \"PluginManager.create\");\n validateRequiredParams(\n config,\n [\"name\", \"tools\"],\n \"PluginManager.create\"\n );\n \n return new PluginManager(config);\n }\n}\n","import { v4 as uuidv4 } from \"uuid\";\nimport {\n MemoryConfig,\n MemoryInstance,\n MemoryEntry,\n MemoryFactory,\n SimilaritySearchResult,\n} from \"./types\";\nimport { Embedding } from \"./providers\";\nimport { logger } from \"./utils\";\nimport { validateRequiredParam, validateRequiredParams } from \"./utils/validation\";\nimport { DEFAULT_MEMORY_SIZE } from \"./constants\";\n\n/**\n * Memory manager implementation using database storage\n * Provides storage and retrieval of conversation history and vector embeddings\n */\nexport class MemoryManager implements MemoryInstance {\n public config: MemoryConfig;\n\n constructor(config: MemoryConfig) {\n // Validate required parameters\n validateRequiredParam(config, \"config\", \"MemoryManager constructor\");\n validateRequiredParams(\n config,\n [\"database\"],\n \"MemoryManager constructor\"\n );\n \n // Apply defaults for optional config parameters\n this.config = {\n ...config,\n tableName: config.tableName || \"memories\",\n maxEntries: config.maxEntries || DEFAULT_MEMORY_SIZE,\n enableEmbeddings: config.enableEmbeddings || false\n };\n \n logger.debug(\"Memory manager initialized\");\n }\n\n /**\n * Create a new memory instance with proper configuration\n * @param config Configuration object for memory\n * @returns Promise that resolves to the new memory instance\n */\n static async create(config: MemoryConfig): Promise {\n // Validate required parameters\n validateRequiredParam(config, \"config\", \"MemoryManager.create\");\n validateRequiredParams(\n config,\n [\"database\"],\n \"MemoryManager.create\"\n );\n \n try {\n // Apply defaults - use user-provided table name or default\n const { database } = config;\n \n const fullConfig = {\n ...config,\n tableName: config.tableName || \"memories\", // Use user-provided name or default\n maxEntries: config.maxEntries || DEFAULT_MEMORY_SIZE,\n enableEmbeddings: config.enableEmbeddings || false\n };\n \n // Use user's custom table name\n const { tableName } = fullConfig;\n \n // Use enhanced database table management\n await database.ensureTable(tableName, (table) => {\n table.string(\"id\").primary();\n table.string(\"agentId\").notNullable().index();\n table.string(\"sessionId\").notNullable().index();\n table.string(\"userId\").nullable().index();\n table.string(\"role\").notNullable();\n table.text(\"content\").notNullable();\n table.timestamp(\"timestamp\").defaultTo(database.knex.fn.now());\n table.json(\"embedding\").nullable();\n table.json(\"metadata\");\n });\n \n // Check if embeddings are enabled and ensure embedding column exists\n if (fullConfig.enableEmbeddings) {\n logger.info(`Memory initialized with embedding support using table: ${tableName}`);\n const hasEmbeddingColumn = await database.knex.schema.hasColumn(\n tableName,\n \"embedding\"\n );\n\n if (!hasEmbeddingColumn) {\n logger.warn(`Adding embedding column to memory table: ${tableName}`);\n await database.knex.schema.table(tableName, (table) => {\n table.json(\"embedding\"); // Store as JSON to properly represent array structure\n });\n }\n }\n\n logger.info(`Memory created with custom table: ${tableName}`);\n return new MemoryManager(fullConfig);\n } catch (error) {\n logger.error(\"Error creating memory instance:\", error);\n throw error;\n }\n }\n\n /**\n * Add a new memory entry\n * @param entry Memory entry to add (without id and timestamp)\n * @returns Promise resolving to the ID of the new entry\n */\n async add(entry: Omit): Promise {\n // Validate required parameters\n validateRequiredParam(entry, \"entry\", \"add\");\n validateRequiredParams(\n entry,\n [\"agentId\", \"sessionId\", \"role\", \"content\"],\n \"add\"\n );\n \n try {\n const { database, tableName, enableEmbeddings } = this.config;\n\n // Generate ID and timestamp\n const id = uuidv4();\n const timestamp = new Date();\n\n // Create a copy to avoid modifying the original\n const entryToInsert = { ...entry };\n\n // Validate and normalize the role for OpenRouter compatibility\n entryToInsert.role = this.validateRole(entryToInsert.role);\n\n // Generate embedding if enabled and not provided\n if (enableEmbeddings && !entryToInsert.embedding && entryToInsert.content) {\n try {\n logger.debug(`Generating embedding for memory entry ${id}`);\n const generatedEmbedding = await Embedding.generateEmbedding(entryToInsert.content);\n entryToInsert.embedding = generatedEmbedding;\n logger.debug(`Generated embedding for memory entry ${id} (${generatedEmbedding.length} dimensions)`);\n } catch (embeddingError) {\n logger.warn(`Failed to generate embedding for memory entry ${id}:`, embeddingError);\n // Continue without embedding rather than failing the entire operation\n }\n }\n\n // Prepare entry for database insertion with proper JSON serialization\n const dbEntry = {\n ...entryToInsert,\n id,\n timestamp,\n };\n\n // Ensure embedding is properly serialized if it exists\n if (entryToInsert.embedding) {\n // If embedding is already a string but not JSON, handle special cases\n if (typeof entryToInsert.embedding === \"string\") {\n // Handle case where embedding is \"[object Object]\" (invalid)\n if (entryToInsert.embedding === \"[object Object]\") {\n (dbEntry as any).embedding = null;\n } else {\n // Keep it as is since it's already a string\n (dbEntry as any).embedding = entryToInsert.embedding;\n }\n } else if (Array.isArray(entryToInsert.embedding)) {\n // For database storage, convert arrays to JSON string\n (dbEntry as any).embedding = JSON.stringify(entryToInsert.embedding);\n } else {\n // For unexpected object types, store as null to avoid \"[object Object]\"\n logger.warn(\n `Unexpected embedding type for entry ${id}, setting to null`\n );\n (dbEntry as any).embedding = null;\n }\n }\n\n // Store in database\n await database.getTable(tableName!).insert(dbEntry);\n logger.debug(`Added memory entry ${id}${entryToInsert.embedding ? ' with embedding' : ''} with role: ${entryToInsert.role}`);\n\n return id;\n } catch (error) {\n logger.error(\"Error adding memory entry:\", error);\n throw error;\n }\n }\n\n /**\n * Get entries by session ID\n * @param sessionId The session ID to get entries for\n * @param limit Maximum number of entries to return\n * @returns Promise resolving to array of memory entries\n */\n async getBySession(\n sessionId: string,\n limit?: number\n ): Promise {\n // Validate required parameters\n validateRequiredParam(sessionId, \"sessionId\", \"getBySession\");\n \n try {\n const { database, tableName, maxEntries } = this.config;\n\n // Query database - using non-null assertion since we set defaults in constructor\n const entries = await database\n .knex(tableName!)\n .where({ sessionId: sessionId })\n .orderBy(\"timestamp\", \"asc\")\n .limit(limit || maxEntries!);\n\n logger.debug(`Retrieved ${entries.length} entries for session ${sessionId}`);\n\n // Process embedding data for client use\n return this.processEntriesBeforeReturn(entries);\n } catch (error) {\n logger.error(`Error getting entries for session ${sessionId}:`, error);\n throw error;\n }\n }\n\n /**\n * Get entries by agent ID\n * @param agentId The agent ID to get entries for\n * @param limit Maximum number of entries to return\n * @returns Promise resolving to array of memory entries\n */\n async getByAgent(agentId: string, limit?: number): Promise {\n // Validate required parameters\n validateRequiredParam(agentId, \"agentId\", \"getByAgent\");\n \n try {\n const { database, tableName, maxEntries } = this.config;\n\n // Query database - using non-null assertion since we set defaults in constructor\n const entries = await database\n .knex(tableName!)\n .where({ agentId: agentId })\n .orderBy(\"timestamp\", \"asc\")\n .limit(limit || maxEntries!);\n\n logger.debug(`Retrieved ${entries.length} entries for agent ${agentId}`);\n\n // Process embedding data for client use\n return this.processEntriesBeforeReturn(entries);\n } catch (error) {\n logger.error(`Error getting entries for agent ${agentId}:`, error);\n throw error;\n }\n }\n\n /**\n * Get entries by user ID\n * @param userId The user ID to get entries for\n * @param limit Maximum number of entries to return\n * @returns Promise resolving to array of memory entries\n */\n async getByUser(userId: string, limit?: number): Promise {\n // Validate required parameters\n validateRequiredParam(userId, \"userId\", \"getByUser\");\n \n try {\n const { database, tableName, maxEntries } = this.config;\n\n // Query database with userId - using non-null assertion since we set defaults in constructor\n const entries = await database\n .knex(tableName!)\n .where({ userId: userId })\n .orderBy(\"timestamp\", \"desc\") // Most recent first\n .limit(limit || maxEntries!);\n\n logger.debug(`Retrieved ${entries.length} entries for user ${userId}`);\n\n // Process embedding data for client use\n return this.processEntriesBeforeReturn(entries);\n } catch (error) {\n logger.error(`Error getting entries for user ${userId}:`, error);\n throw error;\n }\n }\n\n /**\n * Get a memory entry by its ID\n * @param id The ID of the memory entry to retrieve\n * @returns Promise resolving to the memory entry or null if not found\n */\n async getById(id: string): Promise {\n // Validate required parameters\n validateRequiredParam(id, \"id\", \"getById\");\n \n try {\n const { database, tableName } = this.config;\n\n // Query database to find entry by ID\n const entry = await database\n .knex(tableName!)\n .where({ id })\n .first();\n\n if (!entry) {\n logger.debug(`Memory entry with ID ${id} not found`);\n return null;\n }\n\n logger.debug(`Retrieved memory entry with ID ${id}`);\n\n // Process embedding data for client use\n const processedEntries = this.processEntriesBeforeReturn([entry]);\n return processedEntries[0];\n } catch (error) {\n logger.error(`Error getting memory entry by ID ${id}:`, error);\n throw error;\n }\n }\n\n /**\n * Delete a memory entry by its ID\n * @param id The ID of the memory entry to delete\n * @returns Promise that resolves when deletion is complete\n */\n async delete(id: string): Promise {\n // Validate required parameters\n validateRequiredParam(id, \"id\", \"delete\");\n \n try {\n const { database, tableName } = this.config;\n\n // Delete entry from database\n const result = await database\n .knex(tableName!)\n .where({ id })\n .delete();\n\n if (result === 0) {\n logger.warn(`No memory entry found for deletion with ID ${id}`);\n } else {\n logger.debug(`Deleted memory entry with ID ${id}`);\n }\n } catch (error) {\n logger.error(`Error deleting memory entry with ID ${id}:`, error);\n throw error;\n }\n }\n\n /**\n * Helper method to process entries before returning to client\n * Parses JSON embedding strings back to arrays\n * @param entries Array of raw entries from database\n * @returns Processed entries with parsed embeddings\n */\n private processEntriesBeforeReturn(entries: unknown[]): MemoryEntry[] {\n return entries.map((entry) => {\n // We need to cast entry to a record type first since it comes from the database\n const dbEntry = entry as Record;\n \n // Create a new entry to avoid modifying the original\n const processedEntry: Partial = { \n id: String(dbEntry.id || ''),\n agentId: String(dbEntry.agentId || ''),\n sessionId: String(dbEntry.sessionId || ''),\n role: this.validateRole(String(dbEntry.role || '')),\n content: String(dbEntry.content || ''),\n timestamp: dbEntry.timestamp instanceof Date ? dbEntry.timestamp : new Date(String(dbEntry.timestamp || '')),\n metadata: dbEntry.metadata as Record\n };\n\n // Parse embedding if it exists and is a string\n if (dbEntry.embedding) {\n if (typeof dbEntry.embedding === \"string\") {\n // Skip \"[object Object]\" strings which aren't valid JSON\n if (dbEntry.embedding === \"[object Object]\") {\n processedEntry.embedding = undefined;\n } else {\n try {\n processedEntry.embedding = JSON.parse(dbEntry.embedding);\n } catch (error) {\n logger.error(\n `Error parsing embedding for entry ${String(dbEntry.id)}:`,\n error\n );\n // If parsing fails, remove the embedding\n processedEntry.embedding = undefined;\n }\n }\n } else if (Array.isArray(dbEntry.embedding)) {\n processedEntry.embedding = dbEntry.embedding;\n }\n }\n\n return processedEntry as MemoryEntry;\n });\n }\n\n /**\n * Search memories by text content\n * @param query Text to search for in memory content field\n * @param limit Maximum number of results to return\n * @returns Array of memory entries matching the query\n */\n async searchByText(\n query: string,\n limit: number = 10\n ): Promise {\n // Validate required parameters\n validateRequiredParam(query, \"query\", \"searchByText\");\n \n try {\n const { database, tableName } = this.config;\n\n if (!query) return [];\n\n // Use case-insensitive search with LIKE\n const results = await database\n .knex(tableName!)\n .whereRaw(\"LOWER(content) LIKE ?\", [`%${query.toLowerCase()}%`])\n .orderBy(\"timestamp\", \"desc\")\n .limit(limit);\n\n logger.debug(`Text search for \"${query}\" found ${results.length} results`);\n \n return this.processEntriesBeforeReturn(results);\n } catch (error) {\n logger.error(\"Error searching memories by text:\", error);\n return [];\n }\n }\n\n /**\n * Search memories by embedding similarity\n * @param embedding The embedding vector to search for\n * @param limit Maximum number of results to return\n * @param threshold Minimum similarity score (0-1) to include in results\n * @returns Array of memory entries with similarity scores\n */\n async searchByEmbedding(\n embedding: number[],\n limit: number = 5,\n threshold: number = 0 // Default to returning all results\n ): Promise {\n // Validate required parameters\n validateRequiredParam(embedding, \"embedding\", \"searchByEmbedding\");\n \n try {\n const { database, tableName } = this.config;\n\n if (!embedding || !embedding.length) {\n return [];\n }\n\n // Get all memories with embeddings\n const memories = await database\n .knex(tableName!)\n .whereNotNull(\"embedding\")\n .orderBy(\"timestamp\", \"desc\")\n .limit(100); // Reasonable upper limit for comparison\n\n // Process embeddings\n type EntryWithEmbedding = MemoryEntry & { embedding: number[] };\n const entriesWithEmbeddings = memories\n .map((entry) => {\n let parsedEmbedding: number[] | undefined;\n\n if (entry.embedding) {\n try {\n if (typeof entry.embedding === \"string\") {\n parsedEmbedding = JSON.parse(entry.embedding);\n } else if (Array.isArray(entry.embedding)) {\n parsedEmbedding = entry.embedding;\n }\n } catch (error) {\n logger.error(\n `Error parsing embedding for entry ${entry.id}:`,\n error\n );\n }\n }\n\n if (parsedEmbedding && Array.isArray(parsedEmbedding)) {\n const entryAsMemoryEntry = entry as unknown as MemoryEntry;\n return {\n ...entryAsMemoryEntry,\n embedding: parsedEmbedding,\n } as EntryWithEmbedding;\n }\n return null;\n })\n .filter(Boolean) as EntryWithEmbedding[];\n\n // Calculate similarity scores\n const entriesWithScores = entriesWithEmbeddings.map((entry) => {\n const similarity = Embedding.calculateSimilarity(\n embedding,\n entry.embedding\n );\n const result: SimilaritySearchResult = {\n ...this.processEntriesBeforeReturn([entry])[0],\n similarity,\n };\n return result;\n });\n\n // Filter by threshold and take top results\n const filteredResults = entriesWithScores\n .filter((item) => item.similarity >= threshold)\n .sort((a, b) => b.similarity - a.similarity)\n .slice(0, limit);\n \n logger.debug(`Found ${filteredResults.length} results in embedding search with threshold ${threshold}`);\n \n return filteredResults;\n } catch (error) {\n logger.error(\"Error searching memories by embedding:\", error);\n return [];\n }\n }\n\n /**\n * Clear all entries for a session\n * @param sessionId The session ID to clear entries for\n * @returns Promise that resolves when clearing is complete\n */\n async clear(sessionId: string): Promise {\n // Validate required parameters\n validateRequiredParam(sessionId, \"sessionId\", \"clear\");\n \n try {\n const { database, tableName } = this.config;\n\n // Delete all entries for the session\n const result = await database\n .knex(tableName!)\n .where({ sessionId: sessionId })\n .delete();\n\n logger.debug(`Cleared ${result} entries for session ${sessionId}`);\n } catch (error) {\n logger.error(`Error clearing entries for session ${sessionId}:`, error);\n throw error;\n }\n }\n\n /**\n * Generate a summary of the memory for a specific session\n * @param sessionId The session ID to summarize\n * @returns Text summary of the conversation\n */\n async summarize(sessionId: string): Promise {\n // Validate required parameters\n validateRequiredParam(sessionId, \"sessionId\", \"summarize\");\n \n try {\n const memories = await this.getBySession(sessionId);\n if (!memories.length) {\n return \"No conversation history available.\";\n }\n\n // Just a simple summary for now - could be enhanced with AI summarization\n const summary = `Conversation with ${memories.length} messages starting at ${memories[0].timestamp.toISOString()}.`;\n \n logger.debug(`Generated summary for session ${sessionId}`);\n return summary;\n } catch (error) {\n logger.error(`Error summarizing session ${sessionId}:`, error);\n throw error;\n }\n }\n\n /**\n * Add an entry with embedding in one step\n * @param entry The memory entry without ID, timestamp, or embedding\n * @param embedding The embedding vector to add\n * @returns Promise resolving to the ID of the new entry\n */\n async addWithEmbedding(\n entry: Omit,\n embedding: number[]\n ): Promise {\n // Validate required parameters\n validateRequiredParam(entry, \"entry\", \"addWithEmbedding\");\n validateRequiredParam(embedding, \"embedding\", \"addWithEmbedding\");\n validateRequiredParams(\n entry,\n [\"agentId\", \"sessionId\", \"role\", \"content\"],\n \"addWithEmbedding\"\n );\n \n try {\n const id = await this.add({\n ...entry,\n embedding,\n });\n logger.debug(`Added memory entry ${id} with embedding`);\n return id;\n } catch (error) {\n logger.error(\"Error adding memory entry with embedding:\", error);\n throw error;\n }\n }\n\n // Add a helper method to validate role values\n private validateRole(role: string): MemoryEntry['role'] {\n const validRoles: MemoryEntry['role'][] = ['system', 'user', 'assistant', 'task_context', 'task_event', 'task_tool', 'task_result'];\n \n // For OpenRouter compatibility, convert task_* roles to user\n if (role.startsWith('task_')) {\n return 'user';\n }\n \n return validRoles.includes(role as MemoryEntry['role']) \n ? (role as MemoryEntry['role']) \n : 'user'; // Default to user instead of system for better OpenRouter compatibility\n }\n\n /**\n * List all sessions for a specific agent\n * @param agentId The agent ID to list sessions for\n * @param limit Maximum number of sessions to return\n * @returns Promise resolving to array of session summaries\n */\n async listSessions(agentId: string, limit?: number): Promise<{\n sessionId: string;\n lastMessage?: string;\n messageCount: number;\n lastActivity: Date;\n metadata?: Record;\n }[]> {\n validateRequiredParam(agentId, \"agentId\", \"listSessions\");\n \n try {\n const { database, tableName, maxEntries } = this.config;\n\n // Get session summaries with aggregated data\n const sessions = await database\n .knex(tableName!)\n .select('sessionId')\n .select(database.knex.raw('COUNT(*) as messageCount'))\n .select(database.knex.raw('MAX(timestamp) as lastActivity'))\n .select(database.knex.raw('(SELECT content FROM ' + tableName + ' WHERE sessionId = t.sessionId AND agentId = ? ORDER BY timestamp DESC LIMIT 1) as lastMessage', [agentId]))\n .select(database.knex.raw('(SELECT metadata FROM ' + tableName + ' WHERE sessionId = t.sessionId AND agentId = ? ORDER BY timestamp DESC LIMIT 1) as metadata', [agentId]))\n .from(tableName + ' as t')\n .where({ agentId: agentId })\n .groupBy('sessionId')\n .orderBy('lastActivity', 'desc')\n .limit(limit || maxEntries!);\n\n logger.debug(`Retrieved ${sessions.length} sessions for agent ${agentId}`);\n\n return sessions.map((session: any) => ({\n sessionId: session.sessionId,\n lastMessage: session.lastMessage || undefined,\n messageCount: parseInt(session.messageCount) || 0,\n lastActivity: session.lastActivity instanceof Date ? session.lastActivity : new Date(session.lastActivity),\n metadata: session.metadata ? (typeof session.metadata === 'string' ? JSON.parse(session.metadata) : session.metadata) : undefined\n }));\n } catch (error) {\n logger.error(`Error listing sessions for agent ${agentId}:`, error);\n throw error;\n }\n }\n}\n\n/**\n * Factory function to create a new memory instance\n * @param config Configuration for the memory instance\n * @returns Promise that resolves to the new memory instance\n */\nexport const createMemory: MemoryFactory = async (config: MemoryConfig) => {\n // Validate required parameters\n validateRequiredParam(config, \"config\", \"createMemory\");\n validateRequiredParams(\n config,\n [\"database\"],\n \"createMemory\"\n );\n \n return MemoryManager.create(config);\n};","import { v4 as uuidv4 } from \"uuid\";\nimport {\n VectorRAGConfig,\n VectorRAGInstance,\n Document,\n Chunk,\n RAGResult,\n VectorRAGFactory,\n VectorDatabaseType,\n VectorDatabaseConfig,\n} from \"../types\";\nimport { logger } from \"../utils\";\nimport { validateRequiredParam, validateRequiredParams } from \"../utils/validation\";\nimport { createVectorDatabaseConnector, VectorDatabaseConnector } from \"./vector-db\";\nimport { \n DEFAULT_CHUNK_SIZE, \n DEFAULT_CHUNK_OVERLAP, \n DEFAULT_VECTOR_SIMILARITY_THRESHOLD,\n DEFAULT_MAX_RESULTS\n} from \"../constants\";\n\n/**\n * Vector-based RAG implementation\n * Uses vector embeddings for semantic search and document chunking\n */\nexport class VectorRAG implements VectorRAGInstance {\n public config: VectorRAGConfig;\n private vectorDatabaseConnector: VectorDatabaseConnector;\n\n constructor(config: VectorRAGConfig) {\n // Validate required parameters\n validateRequiredParam(config, \"config\", \"VectorRAG constructor\");\n validateRequiredParams(\n config,\n [\"database\"],\n \"VectorRAG constructor\"\n );\n \n // Apply defaults for optional config parameters\n this.config = {\n ...config,\n tableName: config.tableName || \"rag_chunks\",\n maxResults: config.maxResults || DEFAULT_MAX_RESULTS,\n chunkSize: config.chunkSize || DEFAULT_CHUNK_SIZE,\n chunkOverlap: config.chunkOverlap || DEFAULT_CHUNK_OVERLAP,\n vectorDatabase: config.vectorDatabase || { type: VectorDatabaseType.SAME_AS_MAIN },\n };\n \n // Ensure vectorDatabase config has the correct tableName\n if (this.config.vectorDatabase && this.config.vectorDatabase.type === VectorDatabaseType.SAME_AS_MAIN) {\n this.config.vectorDatabase.options = {\n ...this.config.vectorDatabase.options,\n tableName: this.config.tableName\n };\n }\n \n // Initialize vector database connector\n this.vectorDatabaseConnector = createVectorDatabaseConnector(\n this.config.vectorDatabase,\n this.config.database\n );\n \n logger.debug(\"Vector RAG system initialized\");\n }\n\n /**\n * Create a new vector RAG instance with proper configuration\n * @param config Configuration object for vector RAG\n * @returns Promise that resolves to the new vector RAG instance\n */\n static async create(config: VectorRAGConfig): Promise {\n // Validate required parameters\n validateRequiredParam(config, \"config\", \"VectorRAG.create\");\n validateRequiredParams(\n config,\n [\"database\"],\n \"VectorRAG.create\"\n );\n \n try {\n const instance = new VectorRAG(config);\n await instance.initializeDatabase();\n return instance;\n } catch (error) {\n logger.error(\"Error creating vector RAG instance:\", error);\n throw error;\n }\n }\n\n /**\n * Initialize database tables for vector RAG\n */\n private async initializeDatabase(): Promise {\n try {\n const { database } = this.config;\n \n // If using external vector database, only create minimal tables in main DB\n if (this.config.vectorDatabase?.type !== VectorDatabaseType.SAME_AS_MAIN) {\n // For external vector databases, we only need association tracking\n const associationsTableName = 'rag_chunk_associations';\n \n await database.ensureTable(associationsTableName, (table) => {\n table.string(\"chunkId\").primary();\n table.string(\"documentId\").notNullable().index();\n table.integer(\"chunkIndex\").notNullable();\n table.timestamp(\"createdAt\").defaultTo(database.knex.fn.now());\n });\n \n logger.info(`Vector RAG using external vector database - created ${associationsTableName} table`);\n } else {\n // Using same database for vectors - create all necessary tables\n const documentsTableName = 'rag_documents';\n const chunksTableName = this.config.tableName || 'rag_chunks';\n \n // Create documents table\n await database.ensureTable(documentsTableName, (table) => {\n table.string(\"id\").primary();\n table.text(\"content\").notNullable();\n table.json(\"metadata\").notNullable();\n table.timestamp(\"createdAt\").defaultTo(database.knex.fn.now());\n });\n \n // Create chunks table with user's custom name\n await database.ensureTable(chunksTableName, (table) => {\n table.string(\"id\").primary();\n table.string(\"documentId\").notNullable().index();\n table.text(\"content\").notNullable();\n table.json(\"metadata\").notNullable();\n table.json(\"embedding\").notNullable();\n table.timestamp(\"createdAt\").defaultTo(database.knex.fn.now());\n \n // Add foreign key constraint\n table.foreign(\"documentId\").references(\"id\").inTable(documentsTableName).onDelete(\"CASCADE\");\n });\n \n // Update config to use the resolved table name\n this.config.tableName = chunksTableName;\n \n logger.info(`Vector RAG initialized with custom tables: ${documentsTableName} and ${chunksTableName}`);\n }\n \n logger.debug(\"Vector RAG database initialized\");\n } catch (error) {\n logger.error(\"Error initializing vector RAG database:\", error);\n throw error;\n }\n }\n\n /**\n * Add a document to the vector RAG system\n * The document will be chunked and each chunk will be stored with its embedding\n * @param document The document to add\n * @returns Promise resolving to the document ID\n */\n async addDocument(document: Omit): Promise {\n // Validate required parameters\n validateRequiredParam(document, \"document\", \"addDocument\");\n validateRequiredParams(\n document,\n [\"content\", \"metadata\"],\n \"addDocument\"\n );\n \n try {\n const { database } = this.config;\n const documentId = uuidv4();\n \n // Only store document in main DB if using same database for vectors\n if (this.config.vectorDatabase?.type === VectorDatabaseType.SAME_AS_MAIN) {\n // Store the document in main database\n await database.knex(\"rag_documents\").insert({\n id: documentId,\n content: document.content,\n metadata: JSON.stringify(document.metadata),\n createdAt: new Date(),\n });\n logger.debug(`Stored document ${documentId} in main database`);\n } else {\n // For external vector DB, document metadata is stored with each chunk\n logger.debug(`Using external vector DB - document ${documentId} will be stored with chunks`);\n }\n \n // Create chunks from document content\n const chunks = this.chunkDocument(documentId, document);\n \n // Generate embeddings for chunks and store them\n // Prefer provider over memory for embedding generation\n if (this.config.provider && this.config.provider.generateEmbedding) {\n // Process chunks in batches to avoid overloading\n const batchSize = 10;\n for (let i = 0; i < chunks.length; i += batchSize) {\n const batch = chunks.slice(i, i + batchSize);\n await Promise.all(\n batch.map(async (chunk) => {\n await this.storeChunkWithEmbedding(chunk);\n })\n );\n }\n logger.debug(`Added ${chunks.length} chunks with provider-based embeddings for document ${documentId}`);\n } else if (this.config.memory && this.config.memory.searchByEmbedding) {\n // Fallback to memory if provider is not available\n const batchSize = 10;\n for (let i = 0; i < chunks.length; i += batchSize) {\n const batch = chunks.slice(i, i + batchSize);\n await Promise.all(\n batch.map(async (chunk) => {\n await this.storeChunkWithEmbedding(chunk);\n })\n );\n }\n logger.debug(`Added ${chunks.length} chunks with memory-based embeddings for document ${documentId}`);\n } else {\n logger.warn(\"No provider or memory with embedding support provided, chunks stored without embeddings\");\n // Store chunks without embeddings only if using same database\n if (this.config.vectorDatabase?.type === VectorDatabaseType.SAME_AS_MAIN) {\n for (const chunk of chunks) {\n const chunkId = uuidv4();\n await database.knex(this.config.tableName!).insert({\n id: chunkId,\n documentId: chunk.documentId,\n content: chunk.content,\n metadata: JSON.stringify(chunk.metadata),\n embedding: JSON.stringify([]), // Empty embedding\n createdAt: new Date(),\n });\n }\n } else {\n logger.warn(\"Cannot store chunks without embeddings in external vector database\");\n }\n }\n \n return documentId;\n } catch (error) {\n logger.error(\"Error adding document to vector RAG:\", error);\n throw error;\n }\n }\n\n /**\n * Split a document into chunks\n * @param documentId The ID of the document\n * @param document The document to chunk\n * @returns Array of chunks\n */\n private chunkDocument(\n documentId: string,\n document: Omit\n ): Omit[] {\n const { chunkSize, chunkOverlap } = this.config;\n const text = document.content;\n const chunks: Omit[] = [];\n \n // Simple chunking by characters with overlap\n for (let i = 0; i < text.length; i += (chunkSize! - chunkOverlap!)) {\n // Stop if we've reached the end of the text\n if (i >= text.length) break;\n \n // Extract chunk content with overlap\n const chunkContent = text.substring(i, i + chunkSize!);\n \n // Skip empty chunks\n if (!chunkContent.trim()) continue;\n \n // Create chunk with metadata\n chunks.push({\n documentId,\n content: chunkContent,\n metadata: {\n ...document.metadata,\n chunk_index: chunks.length,\n start_char: i,\n end_char: Math.min(i + chunkSize!, text.length),\n },\n });\n }\n \n return chunks;\n }\n\n /**\n * Store a chunk with its embedding\n * @param chunk The chunk to store\n * @returns Promise resolving to the chunk ID\n */\n private async storeChunkWithEmbedding(\n chunk: Omit\n ): Promise {\n const { database, tableName, provider, memory } = this.config;\n \n try {\n // Get embedding using provider or memory\n let embedding: number[] = [];\n \n // Prefer provider over memory for embedding generation\n if (provider && provider.generateEmbedding) {\n try {\n // Use the first part of the chunk to generate embedding\n // Limit to 8000 characters to avoid token limits\n const textForEmbedding = chunk.content.substring(0, 8000);\n \n // Use provider directly for embedding generation\n const embeddingResult = await provider.generateEmbedding(textForEmbedding);\n embedding = embeddingResult || [];\n \n logger.debug(`Generated embedding using provider (${embedding.length} dimensions)`);\n } catch (embeddingError) {\n logger.warn(\"Failed to generate embedding using provider, trying memory fallback:\", embeddingError);\n \n // Fallback to memory if provider fails\n if (memory && memory.searchByEmbedding) {\n try {\n const textForEmbedding = chunk.content.substring(0, 8000);\n const { Embedding } = await import(\"../providers\");\n embedding = await Embedding.generateEmbedding(textForEmbedding);\n logger.debug(`Generated embedding using memory fallback (${embedding.length} dimensions)`);\n } catch (memoryError) {\n logger.warn(\"Failed to generate embedding using memory fallback:\", memoryError);\n embedding = [];\n }\n } else {\n embedding = [];\n }\n }\n } else if (memory && memory.searchByEmbedding) {\n try {\n // Use the first part of the chunk to generate embedding\n // Limit to 8000 characters to avoid token limits\n const textForEmbedding = chunk.content.substring(0, 8000);\n \n // Use the Embedding utility directly instead of memory to avoid complexity\n const { Embedding } = await import(\"../providers\");\n embedding = await Embedding.generateEmbedding(textForEmbedding);\n \n logger.debug(`Generated embedding using memory (${embedding.length} dimensions)`);\n } catch (embeddingError) {\n logger.warn(\"Failed to generate embedding using memory:\", embeddingError);\n embedding = [];\n }\n } else {\n logger.warn(\"No provider or memory with embedding support provided for chunk storage\");\n }\n \n // Generate a unique ID for the chunk\n const chunkId = uuidv4();\n \n // Get chunk index from metadata\n const chunkIndex = chunk.metadata.chunk_index || 0;\n \n // Store chunk with embedding in the vector database\n await this.vectorDatabaseConnector.addVectors([\n {\n id: chunkId,\n vector: embedding,\n metadata: {\n ...chunk.metadata,\n documentId: chunk.documentId,\n content: chunk.content\n }\n }\n ]);\n \n // If using external vector database, keep track of the association\n if (this.config.vectorDatabase?.type !== VectorDatabaseType.SAME_AS_MAIN) {\n // Store association between document and chunk\n await database.knex(\"rag_chunk_associations\").insert({\n chunkId,\n documentId: chunk.documentId,\n chunkIndex,\n createdAt: new Date()\n });\n \n logger.debug(`Added chunk association for chunk ${chunkId} to document ${chunk.documentId}`);\n }\n \n return chunkId;\n } catch (error) {\n logger.error(\"Error storing chunk with embedding:\", error);\n throw error;\n }\n }\n\n /**\n * Get a document by its ID\n * @param id The document ID to retrieve\n * @returns Promise resolving to the document or null if not found\n */\n async getDocumentById(id: string): Promise {\n // Validate required parameters\n validateRequiredParam(id, \"id\", \"getDocumentById\");\n \n try {\n const { database } = this.config;\n \n // If using same database, get document from rag_documents table\n if (this.config.vectorDatabase?.type === VectorDatabaseType.SAME_AS_MAIN) {\n // Get document from database\n const document = await database.knex(\"rag_documents\")\n .where(\"id\", id)\n .first();\n \n if (!document) {\n return null;\n }\n \n return {\n id: document.id,\n content: document.content,\n metadata: JSON.parse(document.metadata),\n };\n } else {\n // For external vector DB, reconstruct document from chunks\n const chunkAssociations = await database.knex(\"rag_chunk_associations\")\n .where(\"documentId\", id)\n .orderBy(\"chunkIndex\", \"asc\")\n .select(\"chunkId\");\n \n if (!chunkAssociations || chunkAssociations.length === 0) {\n return null;\n }\n \n // Get chunk data from vector database\n let content = \"\";\n let metadata = {};\n \n for (const association of chunkAssociations) {\n try {\n const chunkMetadata = await this.vectorDatabaseConnector.getVectorMetadata(association.chunkId);\n if (chunkMetadata && chunkMetadata.content) {\n content += chunkMetadata.content;\n // Use metadata from first chunk as document metadata\n if (Object.keys(metadata).length === 0 && chunkMetadata.metadata) {\n metadata = chunkMetadata.metadata;\n }\n }\n } catch (error) {\n logger.warn(`Error getting chunk ${association.chunkId} for document ${id}:`, error);\n }\n }\n \n if (!content) {\n return null;\n }\n \n return {\n id,\n content,\n metadata,\n };\n }\n } catch (error) {\n logger.error(\"Error getting document by ID:\", error);\n throw error;\n }\n }\n\n /**\n * Delete a document and its chunks\n * @param id The document ID to delete\n */\n async deleteDocument(id: string): Promise {\n // Validate required parameters\n validateRequiredParam(id, \"id\", \"deleteDocument\");\n \n try {\n const { database, tableName } = this.config;\n \n // Handle deletion based on vector database type\n if (this.config.vectorDatabase?.type === VectorDatabaseType.SAME_AS_MAIN) {\n // Using same database - delete chunks and document\n const chunks = await database.knex(tableName!)\n .where(\"documentId\", id)\n .select(\"id\");\n \n // Delete chunks\n if (chunks.length > 0) {\n const chunkIds = chunks.map((c: any) => c.id);\n await database.knex(tableName!)\n .whereIn(\"id\", chunkIds)\n .delete();\n \n logger.debug(`Deleted ${chunkIds.length} chunks for document ${id}`);\n }\n \n // Delete document (foreign key constraint will handle cascade)\n await database.knex(\"rag_documents\")\n .where(\"id\", id)\n .delete();\n \n logger.debug(`Deleted document ${id} from main database`);\n } else {\n // Using external vector database\n const chunkAssociations = await database.knex(\"rag_chunk_associations\")\n .where(\"documentId\", id)\n .select(\"chunkId\");\n \n if (chunkAssociations && chunkAssociations.length > 0) {\n const chunkIds = chunkAssociations.map((c: any) => c.chunkId);\n \n // Delete vectors from the vector database\n await this.vectorDatabaseConnector.deleteVectors(chunkIds);\n \n // Delete associations from the main database\n await database.knex(\"rag_chunk_associations\")\n .where(\"documentId\", id)\n .delete();\n \n logger.debug(`Deleted ${chunkIds.length} vectors from external vector database for document ${id}`);\n } else {\n logger.warn(`No chunks found for document ${id} in external vector database`);\n }\n }\n \n logger.debug(`Successfully deleted document ${id}`);\n } catch (error) {\n logger.error(\"Error deleting document:\", error);\n throw error;\n }\n }\n\n /**\n * Search for similar documents using text query\n * @param query The search query\n * @param limit Maximum number of results to return\n * @returns Promise resolving to array of search results\n */\n async search(query: string, limit?: number): Promise {\n // Validate required parameters\n validateRequiredParam(query, \"query\", \"search\");\n \n try {\n const maxResults = limit || this.config.maxResults || 10;\n \n // Use semantic search if provider or memory with embedding support is available\n if (this.config.provider && this.config.provider.generateEmbedding) {\n try {\n logger.debug(`Generating embedding for search query using provider: \"${query}\"`);\n // Generate embedding for query using provider directly\n const queryEmbedding = await this.config.provider.generateEmbedding(query);\n \n if (queryEmbedding && queryEmbedding.length > 0) {\n logger.debug(`Generated embedding with ${queryEmbedding.length} dimensions`);\n // Use embedding for semantic search\n return this.searchByVector(queryEmbedding, maxResults);\n } else {\n logger.warn(\"Provider returned empty embedding, falling back to keyword search\");\n return this.searchByKeyword(query, maxResults);\n }\n } catch (embeddingError) {\n logger.warn(\"Failed to generate embedding using provider, trying memory fallback:\", embeddingError);\n \n // Fallback to memory if provider fails\n if (this.config.memory && this.config.memory.searchByEmbedding) {\n try {\n logger.debug(`Generating embedding for search query using memory fallback: \"${query}\"`);\n const { Embedding } = await import(\"../providers\");\n const queryEmbedding = await Embedding.generateEmbedding(query);\n \n logger.debug(`Generated embedding with ${queryEmbedding.length} dimensions`);\n return this.searchByVector(queryEmbedding, maxResults);\n } catch (memoryError) {\n logger.warn(\"Failed to generate embedding using memory fallback, using keyword search:\", memoryError);\n return this.searchByKeyword(query, maxResults);\n }\n } else {\n // Fall back to keyword search\n logger.debug(\"No memory fallback available, using keyword search\");\n return this.searchByKeyword(query, maxResults);\n }\n }\n } else if (this.config.memory && this.config.memory.searchByEmbedding) {\n try {\n logger.debug(`Generating embedding for search query using memory: \"${query}\"`);\n // Generate embedding for query using Embedding utility directly\n const { Embedding } = await import(\"../providers\");\n const queryEmbedding = await Embedding.generateEmbedding(query);\n \n logger.debug(`Generated embedding with ${queryEmbedding.length} dimensions`);\n \n // Use embedding for semantic search\n return this.searchByVector(queryEmbedding, maxResults);\n } catch (embeddingError) {\n logger.warn(\"Failed to generate embedding using memory, falling back to keyword search:\", embeddingError);\n // Fall back to keyword search\n return this.searchByKeyword(query, maxResults);\n }\n } else {\n // Fall back to keyword search\n logger.debug(\"No provider or memory with embedding support, using keyword search\");\n return this.searchByKeyword(query, maxResults);\n }\n } catch (error) {\n logger.error(\"Error searching in vector RAG:\", error);\n throw error;\n }\n }\n\n /**\n * Search for documents using vector similarity\n * @param embedding The query embedding vector\n * @param limit Maximum number of results to return\n * @param threshold Minimum similarity threshold (0-1)\n * @returns Promise resolving to an array of search results\n */\n async searchByVector(\n embedding: number[],\n limit?: number,\n threshold: number = DEFAULT_VECTOR_SIMILARITY_THRESHOLD\n ): Promise {\n try {\n const maxResults = limit || this.config.maxResults || 10;\n \n logger.debug(`Searching by vector with ${embedding.length} dimensions, limit: ${maxResults}`);\n \n // Use the vector database connector to search for similar vectors\n const similarVectors = await this.vectorDatabaseConnector.searchVectors(\n embedding, \n maxResults, \n threshold\n );\n \n logger.debug(`Found ${similarVectors.length} similar vectors`);\n \n // If using the same database, we need to fetch the detailed data\n if (this.config.vectorDatabase?.type === VectorDatabaseType.SAME_AS_MAIN) {\n const { database, tableName } = this.config;\n \n logger.debug(`Fetching chunk details from table: ${tableName}`);\n \n // Get detailed information for each chunk\n const chunkDetails = await database.knex(tableName!)\n .whereIn(\n \"id\",\n similarVectors.map((result) => result.id)\n )\n .select(\"*\");\n \n logger.debug(`Retrieved ${chunkDetails.length} chunk details`);\n \n // Map to the expected result format\n return chunkDetails.map((chunk: any) => {\n const similarityResult = similarVectors.find((v) => v.id === chunk.id);\n try {\n const metadata = typeof chunk.metadata === 'string' ? JSON.parse(chunk.metadata) : chunk.metadata;\n return {\n content: chunk.content,\n metadata: metadata,\n similarity: similarityResult?.similarity || 0,\n sourceId: chunk.id,\n };\n } catch (parseError) {\n logger.warn(`Error parsing metadata for chunk ${chunk.id}, using empty object:`, parseError);\n return {\n content: chunk.content,\n metadata: {},\n similarity: similarityResult?.similarity || 0,\n sourceId: chunk.id,\n };\n }\n });\n } else {\n // For external vector databases, we need to map the results \n // from the vector IDs to the actual content\n const { database } = this.config;\n \n // If we have no results, return empty array\n if (similarVectors.length === 0) {\n return [];\n }\n \n // Get document IDs and content from chunk associations table\n // This assumes we've stored the content and metadata in the vector database\n const results: RAGResult[] = [];\n \n for (const vector of similarVectors) {\n try {\n // For external vector DB, content and metadata are stored in the vector's metadata\n // This is handled through the vector database connector\n const vectorMetadata = await this.vectorDatabaseConnector.getVectorMetadata(vector.id);\n \n if (!vectorMetadata) {\n logger.warn(`Metadata for vector ${vector.id} not found, skipping result`);\n continue;\n }\n \n // The metadata from the vector database should include:\n // - content: The text content of the chunk\n // - metadata: The original metadata object of the chunk\n // - documentId: The ID of the parent document\n results.push({\n content: vectorMetadata.content,\n metadata: vectorMetadata.metadata,\n similarity: vector.similarity,\n sourceId: vector.id,\n });\n \n } catch (error) {\n logger.warn(`Error processing vector result ${vector.id}, skipping:`, error);\n continue;\n }\n }\n \n return results;\n }\n } catch (error) {\n logger.error(\"Error searching by vector:\", error);\n throw error;\n }\n }\n\n /**\n * Search for documents using keyword matching\n * @param query The search query\n * @param limit Maximum number of results to return\n * @returns Promise resolving to an array of search results\n */\n private async searchByKeyword(\n query: string,\n limit?: number\n ): Promise {\n const { database, tableName } = this.config;\n const maxResults = limit || this.config.maxResults || 10;\n \n // Simple LIKE query for keyword search\n const results = await database.knex(tableName!)\n .whereRaw(\"LOWER(content) LIKE ?\", [`%${query.toLowerCase()}%`])\n .limit(maxResults)\n .select(\"*\");\n \n return results.map((result: any) => {\n try {\n const metadata = typeof result.metadata === 'string' ? JSON.parse(result.metadata) : result.metadata;\n return {\n content: result.content,\n metadata: metadata,\n sourceId: result.id,\n };\n } catch (parseError) {\n logger.warn(`Error parsing metadata for chunk ${result.id}, using empty object:`, parseError);\n return {\n content: result.content,\n metadata: {},\n sourceId: result.id,\n };\n }\n });\n }\n\n /**\n * Calculate cosine similarity between two vectors\n * @param vecA First vector\n * @param vecB Second vector\n * @returns Similarity score between 0 and 1\n */\n private calculateCosineSimilarity(vecA: number[], vecB: number[]): number {\n if (vecA.length !== vecB.length) {\n throw new Error(\"Vector dimensions do not match\");\n }\n \n let dotProduct = 0;\n let normA = 0;\n let normB = 0;\n \n for (let i = 0; i < vecA.length; i++) {\n dotProduct += vecA[i] * vecB[i];\n normA += vecA[i] * vecA[i];\n normB += vecB[i] * vecB[i];\n }\n \n normA = Math.sqrt(normA);\n normB = Math.sqrt(normB);\n \n if (normA === 0 || normB === 0) {\n return 0;\n }\n \n return dotProduct / (normA * normB);\n }\n}\n\n/**\n * Factory function to create a VectorRAG instance\n * @param config Configuration for the vector RAG\n * @returns Promise resolving to a configured VectorRAG instance\n */\nexport const createVectorRAG: VectorRAGFactory = async (\n config: VectorRAGConfig\n) => {\n return VectorRAG.create(config);\n}; ","import { DatabaseInstance } from \"./database\";\nimport { MemoryInstance } from \"./memory\";\nimport { ProviderInstance } from \"./provider\";\n\n// Vector database type enum\nexport enum VectorDatabaseType {\n SAME_AS_MAIN = 'same_as_main', // Use the same database as the main application\n POSTGRES = 'postgres', // PostgreSQL with pgvector\n QDRANT = 'qdrant', // Qdrant vector database\n PINECONE = 'pinecone', // Pinecone vector database\n MILVUS = 'milvus', // Milvus vector database\n WEAVIATE = 'weaviate', // Weaviate vector database\n}\n\n// Vector database configuration\nexport interface VectorDatabaseConfig {\n /** Type of vector database to use */\n type: VectorDatabaseType;\n /** Connection string (if applicable) */\n connectionString?: string;\n /** API key (if applicable) */\n apiKey?: string;\n /** Environment (if applicable) */\n environment?: string;\n /** Namespace/index (if applicable) */\n namespace?: string;\n /** Base URL (if applicable) */\n baseUrl?: string;\n /** Any other custom options */\n options?: Record;\n}\n\n// Document structure for document-based RAG\nexport interface Document {\n id: string;\n content: string;\n metadata: Record;\n embedding?: number[]; // Optional embedding vector\n}\n\n// Chunk structure for vector-based RAG\nexport interface Chunk {\n id: string;\n documentId: string;\n content: string; \n metadata: Record;\n embedding: number[]; // Required embedding vector\n}\n\n// Result with similarity score for semantic search\nexport interface RAGResult {\n content: string;\n metadata: Record;\n similarity?: number; // Similarity score (0-1)\n sourceId: string; // Document ID or Chunk ID\n}\n\n// Base RAG configuration\nexport interface RAGConfig {\n /** Required: Database instance for storing documents/chunks */\n database: DatabaseInstance;\n /** Optional: Memory instance for storing vector embeddings */\n memory?: MemoryInstance;\n /** Optional: Provider instance for generating embeddings */\n provider?: ProviderInstance;\n /** Optional: Custom table name for storing data */\n tableName?: string;\n /** Optional: Maximum number of results to retrieve */\n maxResults?: number;\n /** Optional: Vector database configuration */\n vectorDatabase?: VectorDatabaseConfig;\n}\n\n// Vector-based RAG configuration\nexport interface VectorRAGConfig extends RAGConfig {\n /** Optional: Chunk size for text splitting */\n chunkSize?: number;\n /** Optional: Chunk overlap for text splitting */\n chunkOverlap?: number;\n}\n\n// Document-based RAG configuration\nexport interface DocumentRAGConfig extends RAGConfig {\n /** Optional: Whether to store document embeddings */\n storeEmbeddings?: boolean;\n}\n\n// Base RAG instance\nexport interface RAGInstance {\n config: RAGConfig;\n search(query: string, limit?: number): Promise;\n}\n\n// Vector-based RAG instance\nexport interface VectorRAGInstance extends RAGInstance {\n config: VectorRAGConfig;\n addDocument(document: Omit): Promise;\n getDocumentById(id: string): Promise;\n deleteDocument(id: string): Promise;\n searchByVector(embedding: number[], limit?: number, threshold?: number): Promise;\n}\n\n// Document-based RAG instance\nexport interface DocumentRAGInstance extends RAGInstance {\n config: DocumentRAGConfig;\n addDocument(document: Omit): Promise;\n getDocumentById(id: string): Promise;\n deleteDocument(id: string): Promise;\n searchByMetadata(filter: Record, limit?: number): Promise;\n}\n\n// Factory functions\nexport type VectorRAGFactory = (config: VectorRAGConfig) => Promise;\nexport type DocumentRAGFactory = (config: DocumentRAGConfig) => Promise;\n\n// RAG type enum for the unified createRAG function\nexport enum RAGType {\n VECTOR = 'vector',\n DOCUMENT = 'document'\n}\n\n// Unified RAG factory config\nexport interface RAGFactoryConfig extends RAGConfig {\n type: RAGType;\n // Vector RAG specific options\n chunkSize?: number; \n chunkOverlap?: number;\n // Document RAG specific options\n storeEmbeddings?: boolean;\n}\n\n// Unified RAG factory function type\nexport type RAGFactory = (config: RAGFactoryConfig) => Promise; ","import { logger } from '../../utils';\nimport { VectorDatabaseType, VectorDatabaseConfig } from '../../types/rag';\nimport { DatabaseInstance } from '../../types/database';\nimport dotenv from 'dotenv';\nimport knex from 'knex';\n\n// Load environment variables\ndotenv.config();\n\n/**\n * Interface for vector database operations\n */\nexport interface VectorDatabaseConnector {\n /** Adds vectors to the database */\n addVectors(vectors: Array<{ id: string, vector: number[], metadata: Record }>): Promise;\n \n /** Similarity search for vectors */\n searchVectors(vector: number[], limit?: number, threshold?: number): Promise>;\n \n /** Delete vectors by IDs */\n deleteVectors(ids: string[]): Promise;\n \n /** Get metadata for a vector by ID */\n getVectorMetadata(id: string): Promise | null>;\n \n /** Close the connection */\n close(): Promise;\n}\n\n/**\n * Base class for vector database connectors\n */\nabstract class BaseVectorDatabaseConnector implements VectorDatabaseConnector {\n protected config: VectorDatabaseConfig;\n \n constructor(config: VectorDatabaseConfig) {\n this.config = config;\n }\n \n abstract addVectors(vectors: Array<{ id: string, vector: number[], metadata: Record }>): Promise;\n abstract searchVectors(vector: number[], limit?: number, threshold?: number): Promise>;\n abstract deleteVectors(ids: string[]): Promise;\n abstract getVectorMetadata(id: string): Promise | null>;\n abstract close(): Promise;\n}\n\n/**\n * PostgreSQL vector database connector (using pgvector)\n */\nclass PostgresVectorDatabaseConnector extends BaseVectorDatabaseConnector {\n private knex: knex.Knex;\n private tableName: string;\n private connected: boolean = false;\n \n constructor(config: VectorDatabaseConfig) {\n super(config);\n \n // Use custom table name if provided, otherwise default\n this.tableName = config.options?.tableName || 'vector_embeddings';\n \n // Initialize knex with PostgreSQL configuration\n this.knex = knex({\n client: 'pg',\n connection: config.connectionString || {\n host: process.env.POSTGRES_HOST || 'localhost',\n port: parseInt(process.env.POSTGRES_PORT || '5432'),\n user: process.env.POSTGRES_USER || 'postgres',\n password: process.env.POSTGRES_PASSWORD || '',\n database: process.env.POSTGRES_DB || 'postgres'\n }\n });\n \n logger.debug(\"PostgreSQL vector database connector initialized\");\n }\n \n /**\n * Ensure pgvector extension is installed and table is created\n */\n private async ensureConnection(): Promise {\n if (this.connected) return;\n \n try {\n // Check connection\n await this.knex.raw('SELECT 1');\n \n // Create pgvector extension if it doesn't exist\n await this.knex.raw('CREATE EXTENSION IF NOT EXISTS vector');\n \n // Check if table exists\n const tableExists = await this.knex.schema.hasTable(this.tableName);\n \n if (!tableExists) {\n // Create vector table with proper schema\n await this.knex.schema.createTable(this.tableName, (table) => {\n table.string('id').primary();\n table.string('documentId').notNullable();\n table.text('content').notNullable();\n table.specificType('embedding', 'vector');\n table.jsonb('metadata');\n table.timestamp('createdAt').defaultTo(this.knex.fn.now());\n });\n \n // Create index for vector similarity search\n await this.knex.raw(`CREATE INDEX ${this.tableName}_embedding_idx ON ${this.tableName} USING ivfflat (embedding vector_l2_ops)`);\n \n logger.debug(`Created ${this.tableName} table with pgvector index`);\n }\n \n this.connected = true;\n logger.debug(`Connected to PostgreSQL vector database`);\n } catch (error) {\n logger.error('Error connecting to PostgreSQL vector database:', error);\n throw new Error(`Failed to initialize PostgreSQL vector database: ${error}`);\n }\n }\n \n /**\n * Add vectors to the database\n */\n async addVectors(vectors: Array<{ id: string, vector: number[], metadata: Record }>): Promise {\n try {\n await this.ensureConnection();\n \n // Convert vectors to pgvector format and prepare batch insert\n const rows = vectors.map(({ id, vector, metadata }) => {\n // Extract documentId and content from metadata for table schema\n const { documentId, content, ...restMetadata } = metadata;\n \n return {\n id,\n documentId: documentId || 'unknown', // Provide default to prevent null constraint violation\n content: content || '', // Provide default to prevent null constraint violation\n embedding: this.knex.raw(`'[${vector.join(',')}]'::vector`),\n metadata: JSON.stringify(restMetadata),\n createdAt: new Date()\n };\n });\n \n // Use batch insert for better performance\n const batchSize = 50;\n for (let i = 0; i < rows.length; i += batchSize) {\n const batch = rows.slice(i, i + batchSize);\n await this.knex(this.tableName).insert(batch);\n }\n \n logger.debug(`Added ${vectors.length} vectors to PostgreSQL`);\n } catch (error) {\n logger.error('Error adding vectors to PostgreSQL:', error);\n throw error;\n }\n }\n \n /**\n * Search for similar vectors using pgvector's similarity search\n */\n async searchVectors(vector: number[], limit: number = 10, threshold: number = 0.7): Promise> {\n try {\n await this.ensureConnection();\n \n // Convert similarity threshold to a distance threshold (cosine similarity to L2 distance)\n // Approximate conversion: 1 - similarity = (distance^2) / 2\n // Solving for distance: distance = sqrt(2 * (1 - similarity))\n const distanceThreshold = Math.sqrt(2 * (1 - threshold));\n \n // Query using L2 distance (Euclidean distance)\n const results = await this.knex.raw(`\n SELECT \n id, \n 1 - (embedding <-> ?) / 2 as similarity\n FROM ${this.tableName}\n WHERE embedding <-> ? < ?\n ORDER BY similarity DESC\n LIMIT ?\n `, [\n `[${vector.join(',')}]`,\n `[${vector.join(',')}]`,\n distanceThreshold,\n limit\n ]);\n \n const rows = results.rows;\n logger.debug(`Found ${rows.length} similar vectors in PostgreSQL`);\n \n return rows.map((row: any) => ({\n id: row.id,\n similarity: parseFloat(row.similarity)\n }));\n } catch (error) {\n logger.error('Error searching vectors in PostgreSQL:', error);\n throw error;\n }\n }\n \n /**\n * Get metadata for a vector by ID\n */\n async getVectorMetadata(id: string): Promise | null> {\n try {\n await this.ensureConnection();\n \n const result = await this.knex(this.tableName)\n .where('id', id)\n .select('metadata')\n .first();\n \n if (!result) {\n return null;\n }\n \n return JSON.parse(result.metadata);\n } catch (error) {\n logger.error(`Error getting metadata for vector ${id}:`, error);\n throw error;\n }\n }\n \n /**\n * Delete vectors by IDs\n */\n async deleteVectors(ids: string[]): Promise {\n try {\n await this.ensureConnection();\n \n await this.knex(this.tableName)\n .whereIn('id', ids)\n .delete();\n \n logger.debug(`Deleted ${ids.length} vectors from PostgreSQL`);\n } catch (error) {\n logger.error('Error deleting vectors from PostgreSQL:', error);\n throw error;\n }\n }\n \n /**\n * Close the database connection\n */\n async close(): Promise {\n try {\n if (this.knex) {\n await this.knex.destroy();\n this.connected = false;\n logger.debug(\"Closed PostgreSQL vector database connection\");\n }\n } catch (error) {\n logger.error('Error closing PostgreSQL connection:', error);\n throw error;\n }\n }\n}\n\n/**\n * Main database vector database connector (using the same database as the application)\n */\nclass MainDatabaseVectorConnector extends BaseVectorDatabaseConnector {\n private database: DatabaseInstance;\n private tableName: string;\n \n constructor(config: VectorDatabaseConfig, database: DatabaseInstance) {\n super(config);\n this.database = database;\n \n // Use custom table name from database configuration or provided config\n const dbTableNames = database.getTableNames();\n this.tableName = config.options?.tableName || \n database.getCustomTableName('vector_embeddings') || \n 'vector_embeddings';\n logger.debug(\"Main database vector connector initialized\");\n }\n \n async addVectors(vectors: Array<{ id: string, vector: number[], metadata: Record }>): Promise {\n try {\n const records = vectors.map(({ id, vector, metadata }) => {\n // Extract documentId and content from metadata for table schema\n const { documentId, content, ...restMetadata } = metadata;\n \n return {\n id,\n documentId: documentId || 'unknown', // Provide default to prevent null constraint violation\n content: content || '', // Provide default to prevent null constraint violation\n embedding: JSON.stringify(vector),\n metadata: JSON.stringify(restMetadata),\n createdAt: new Date()\n };\n });\n \n // Use batch insert for better performance\n const batchSize = 50;\n for (let i = 0; i < records.length; i += batchSize) {\n const batch = records.slice(i, i + batchSize);\n await this.database.knex(this.tableName).insert(batch);\n }\n \n logger.debug(`Added ${vectors.length} vectors to main database`);\n } catch (error) {\n logger.error(\"Error adding vectors to main database:\", error);\n throw error;\n }\n }\n \n async searchVectors(vector: number[], limit: number = 10, threshold: number = 0.7): Promise> {\n try {\n // Retrieve all vectors from the database\n const rows = await this.database.knex(this.tableName).select('id', 'embedding');\n \n // Calculate cosine similarity for each vector\n const results = rows\n .map((row: { id: string, embedding: string | any[] }) => {\n try {\n let storedVector: number[];\n \n // Handle different embedding data types\n if (!row.embedding) {\n logger.warn(`Empty embedding for vector ${row.id}, skipping`);\n return null;\n }\n \n // If embedding is already an array (parsed by database driver)\n if (Array.isArray(row.embedding)) {\n storedVector = row.embedding;\n }\n // If embedding is a string, parse it\n else if (typeof row.embedding === 'string') {\n if (row.embedding.trim() === '') {\n logger.warn(`Empty embedding string for vector ${row.id}, skipping`);\n return null;\n }\n storedVector = JSON.parse(row.embedding);\n }\n // If embedding is an object, try to extract array\n else if (typeof row.embedding === 'object') {\n storedVector = row.embedding;\n }\n else {\n logger.warn(`Unknown embedding type for vector ${row.id}, skipping`);\n return null;\n }\n \n // Validate that we have a valid array\n if (!Array.isArray(storedVector) || storedVector.length === 0) {\n logger.warn(`Invalid embedding format for vector ${row.id}, skipping`);\n return null;\n }\n \n const similarity = this.calculateCosineSimilarity(vector, storedVector);\n return { id: row.id, similarity };\n } catch (parseError) {\n logger.warn(`Failed to parse embedding for vector ${row.id}:`, parseError);\n return null;\n }\n })\n .filter((result): result is { id: string, similarity: number } => \n result !== null && result.similarity >= threshold\n )\n .sort((a: { similarity: number }, b: { similarity: number }) => b.similarity - a.similarity)\n .slice(0, limit);\n \n logger.debug(`Found ${results.length} similar vectors in main database`);\n return results;\n } catch (error) {\n logger.error(\"Error searching vectors in main database:\", error);\n throw error;\n }\n }\n \n /**\n * Get metadata for a vector by ID\n */\n async getVectorMetadata(id: string): Promise | null> {\n try {\n const result = await this.database.knex(this.tableName)\n .where('id', id)\n .select('metadata')\n .first();\n \n if (!result) {\n return null;\n }\n \n return JSON.parse(result.metadata);\n } catch (error) {\n logger.error(`Error getting metadata for vector ${id}:`, error);\n throw error;\n }\n }\n \n private calculateCosineSimilarity(vecA: number[], vecB: number[]): number {\n if (vecA.length !== vecB.length) {\n throw new Error(\"Vector dimensions do not match\");\n }\n \n let dotProduct = 0;\n let normA = 0;\n let normB = 0;\n \n for (let i = 0; i < vecA.length; i++) {\n dotProduct += vecA[i] * vecB[i];\n normA += vecA[i] * vecA[i];\n normB += vecB[i] * vecB[i];\n }\n \n normA = Math.sqrt(normA);\n normB = Math.sqrt(normB);\n \n if (normA === 0 || normB === 0) {\n return 0;\n }\n \n return dotProduct / (normA * normB);\n }\n \n async deleteVectors(ids: string[]): Promise {\n try {\n await this.database.knex(this.tableName).whereIn('id', ids).delete();\n logger.debug(`Deleted ${ids.length} vectors from main database`);\n } catch (error) {\n logger.error(\"Error deleting vectors from main database:\", error);\n throw error;\n }\n }\n \n async close(): Promise {\n // No need to close connection as it's managed by the main database\n logger.debug(\"Main database vector connector closed\");\n }\n}\n\n/**\n * Factory function to create a vector database connector based on configuration\n * @param config Vector database configuration\n * @param database Main database instance (if using same database)\n * @returns Vector database connector\n */\nexport function createVectorDatabaseConnector(\n config?: VectorDatabaseConfig,\n database?: DatabaseInstance\n): VectorDatabaseConnector {\n // If no config provided, try to load from environment variables\n if (!config) {\n config = loadVectorDatabaseConfigFromEnv();\n }\n \n // Default to using the main database if no configuration is provided\n if (!config || config.type === VectorDatabaseType.SAME_AS_MAIN) {\n if (!database) {\n throw new Error(\"Database instance is required when using SAME_AS_MAIN vector database type\");\n }\n // Use the provided config instead of creating a new one\n const mainDbConfig = config || { type: VectorDatabaseType.SAME_AS_MAIN };\n return new MainDatabaseVectorConnector(mainDbConfig, database);\n }\n \n // Create the appropriate connector based on type\n switch (config.type) {\n case VectorDatabaseType.POSTGRES:\n return new PostgresVectorDatabaseConnector(config);\n case VectorDatabaseType.QDRANT:\n case VectorDatabaseType.PINECONE:\n case VectorDatabaseType.MILVUS:\n case VectorDatabaseType.WEAVIATE:\n // To be implemented in future PRs\n throw new Error(`Vector database type ${config.type} not yet implemented`);\n default:\n throw new Error(`Unsupported vector database type: ${config.type}`);\n }\n}\n\n/**\n * Load vector database configuration from environment variables\n * @returns Vector database configuration\n */\nexport function loadVectorDatabaseConfigFromEnv(): VectorDatabaseConfig {\n const type = process.env.VECTOR_DB_TYPE as VectorDatabaseType || VectorDatabaseType.SAME_AS_MAIN;\n const connectionString = process.env.VECTOR_DB_CONNECTION_STRING;\n \n // Parse connection string if provided for PostgreSQL\n let parsedOptions: Record = {};\n if (type === VectorDatabaseType.POSTGRES && connectionString) {\n try {\n // Parse connection string format: postgres://user:password@host:port/database\n const url = new URL(connectionString);\n parsedOptions = {\n host: url.hostname,\n port: url.port ? parseInt(url.port) : 5432,\n user: url.username,\n password: url.password,\n database: url.pathname.substring(1), // Remove leading slash\n };\n } catch (error) {\n logger.error(\"Failed to parse PostgreSQL connection string:\", error);\n }\n }\n \n const config: VectorDatabaseConfig = {\n type,\n connectionString,\n apiKey: process.env.VECTOR_DB_API_KEY,\n environment: process.env.VECTOR_DB_ENVIRONMENT,\n namespace: process.env.VECTOR_DB_NAMESPACE,\n baseUrl: process.env.VECTOR_DB_BASE_URL,\n options: {\n ...parsedOptions,\n tableName: process.env.VECTOR_DB_TABLE_NAME,\n }\n };\n \n return config;\n} ","import { v4 as uuidv4 } from \"uuid\";\nimport {\n DocumentRAGConfig,\n DocumentRAGInstance,\n Document,\n RAGResult,\n DocumentRAGFactory,\n} from \"../types\";\nimport { logger } from \"../utils\";\nimport { validateRequiredParam, validateRequiredParams } from \"../utils/validation\";\n\n/**\n * Document-based RAG implementation\n * Stores and retrieves complete documents with optional embeddings\n */\nexport class DocumentRAG implements DocumentRAGInstance {\n public config: DocumentRAGConfig;\n\n constructor(config: DocumentRAGConfig) {\n // Validate required parameters\n validateRequiredParam(config, \"config\", \"DocumentRAG constructor\");\n validateRequiredParams(\n config,\n [\"database\"],\n \"DocumentRAG constructor\"\n );\n \n // Apply defaults for optional config parameters\n this.config = {\n ...config,\n tableName: config.tableName || \"rag_documents\",\n maxResults: config.maxResults || 10,\n storeEmbeddings: config.storeEmbeddings || false,\n };\n \n logger.debug(\"Document RAG system initialized\");\n }\n\n /**\n * Create a new document RAG instance with proper configuration\n * @param config Configuration object for document RAG\n * @returns Promise that resolves to the new document RAG instance\n */\n static async create(config: DocumentRAGConfig): Promise {\n // Validate required parameters\n validateRequiredParam(config, \"config\", \"DocumentRAG.create\");\n validateRequiredParams(\n config,\n [\"database\"],\n \"DocumentRAG.create\"\n );\n \n try {\n const instance = new DocumentRAG(config);\n await instance.initializeDatabase();\n return instance;\n } catch (error) {\n logger.error(\"Error creating document RAG instance:\", error);\n throw error;\n }\n }\n\n /**\n * Initialize database tables for document RAG\n */\n private async initializeDatabase(): Promise {\n try {\n const { database, storeEmbeddings } = this.config;\n \n // Use user-provided table name or default\n const tableName = this.config.tableName || 'rag_documents';\n \n // Use database's enhanced table management\n await database.ensureTable(tableName, (table) => {\n table.string(\"id\").primary();\n table.text(\"content\").notNullable();\n table.json(\"metadata\").notNullable();\n // Add embedding column if enabled\n if (storeEmbeddings) {\n table.json(\"embedding\");\n }\n table.timestamp(\"createdAt\").defaultTo(database.knex.fn.now());\n });\n \n // Update config to use the resolved table name\n this.config.tableName = tableName;\n \n // Check if embeddings are enabled and ensure embedding column exists\n if (storeEmbeddings) {\n const hasEmbeddingColumn = await database.knex.schema.hasColumn(\n tableName,\n \"embedding\"\n );\n \n if (!hasEmbeddingColumn) {\n // Add embedding column if it doesn't exist\n await database.knex.schema.table(tableName, (table) => {\n table.json(\"embedding\");\n });\n logger.debug(`Added embedding column to ${tableName} table`);\n }\n }\n \n logger.info(`Document RAG initialized with custom table: ${tableName}`);\n } catch (error) {\n logger.error(\"Error initializing document RAG database:\", error);\n throw error;\n }\n }\n\n /**\n * Add a document to the RAG system\n * @param document The document to add\n * @returns Promise resolving to the document ID\n */\n async addDocument(document: Omit): Promise {\n // Validate required parameters\n validateRequiredParam(document, \"document\", \"addDocument\");\n validateRequiredParams(\n document,\n [\"content\", \"metadata\"],\n \"addDocument\"\n );\n \n try {\n const { database, tableName, storeEmbeddings, memory } = this.config;\n const id = uuidv4();\n \n const docToInsert: any = {\n id,\n content: document.content,\n metadata: JSON.stringify(document.metadata),\n createdAt: new Date(),\n };\n \n // Handle embedding if enabled and provided or can be generated\n if (storeEmbeddings) {\n let embedding = document.embedding;\n \n // Generate embedding if not provided\n if (!embedding) {\n try {\n // Use the Embedding utility directly\n const { Embedding } = await import(\"../providers\");\n embedding = await Embedding.generateEmbedding(document.content.substring(0, 8000));\n \n logger.debug(`Generated embedding for document ${id} (${embedding.length} dimensions)`);\n } catch (embeddingError) {\n logger.warn(\"Error generating embedding for document:\", embeddingError);\n }\n }\n \n // Store embedding if available\n if (embedding) {\n docToInsert.embedding = JSON.stringify(embedding);\n }\n }\n \n // Store document in database\n await database.knex(tableName!).insert(docToInsert);\n \n logger.debug(`Added document ${id} to RAG system`);\n return id;\n } catch (error) {\n logger.error(\"Error adding document to RAG system:\", error);\n throw error;\n }\n }\n\n /**\n * Get a document by ID\n * @param id The document ID\n * @returns Promise resolving to the document or null if not found\n */\n async getDocumentById(id: string): Promise {\n // Validate required parameters\n validateRequiredParam(id, \"id\", \"getDocumentById\");\n \n try {\n const { database, tableName } = this.config;\n \n // Query document\n const document = await database.knex(tableName!)\n .where({ id })\n .first();\n \n if (!document) {\n return null;\n }\n \n // Process document data\n const result: Document = {\n id: document.id,\n content: document.content,\n metadata: JSON.parse(document.metadata),\n };\n \n // Add embedding if available\n if (document.embedding) {\n try {\n result.embedding = JSON.parse(document.embedding);\n } catch (error) {\n logger.warn(`Error parsing embedding for document ${id}:`, error);\n }\n }\n \n return result;\n } catch (error) {\n logger.error(`Error getting document ${id}:`, error);\n throw error;\n }\n }\n\n /**\n * Delete a document\n * @param id The document ID\n * @returns Promise resolving when the document is deleted\n */\n async deleteDocument(id: string): Promise {\n // Validate required parameters\n validateRequiredParam(id, \"id\", \"deleteDocument\");\n \n try {\n const { database, tableName } = this.config;\n \n // Delete document\n await database.knex(tableName!)\n .where({ id })\n .delete();\n \n logger.debug(`Deleted document ${id}`);\n } catch (error) {\n logger.error(`Error deleting document ${id}:`, error);\n throw error;\n }\n }\n\n /**\n * Search for documents using text query\n * @param query The search query\n * @param limit Maximum number of results to return\n * @returns Promise resolving to array of search results\n */\n async search(query: string, limit?: number): Promise {\n // Validate required parameters\n validateRequiredParam(query, \"query\", \"search\");\n \n try {\n const { storeEmbeddings } = this.config;\n \n // Use vector search if embeddings are available\n if (storeEmbeddings) {\n try {\n // Generate embedding for query using Embedding utility directly\n const { Embedding } = await import(\"../providers\");\n const queryEmbedding = await Embedding.generateEmbedding(query);\n \n // Search using the embedding\n return this.searchWithEmbedding(queryEmbedding, limit);\n } catch (embeddingError) {\n logger.warn(\"Error performing embedding search, falling back to keyword search:\", embeddingError);\n }\n }\n \n // Fall back to keyword search\n return this.searchByKeyword(query, limit);\n } catch (error) {\n logger.error(`Error searching with query \"${query}\":`, error);\n throw error;\n }\n }\n\n /**\n * Search for documents based on metadata filters\n * @param filter Metadata filter criteria\n * @param limit Maximum number of results to return\n * @returns Promise resolving to array of search results\n */\n async searchByMetadata(\n filter: Record,\n limit?: number\n ): Promise {\n // Validate required parameters\n validateRequiredParam(filter, \"filter\", \"searchByMetadata\");\n \n try {\n const { database, tableName, maxResults } = this.config;\n \n // Get all documents\n const documents = await database.knex(tableName!)\n .select(\"*\")\n .limit(limit || maxResults!);\n \n // Filter documents based on metadata criteria\n const results: RAGResult[] = [];\n \n for (const doc of documents) {\n try {\n const metadata = JSON.parse(doc.metadata);\n let matches = true;\n \n // Check if document metadata matches all filter criteria\n for (const [key, value] of Object.entries(filter)) {\n if (metadata[key] !== value) {\n matches = false;\n break;\n }\n }\n \n if (matches) {\n results.push({\n content: doc.content,\n metadata,\n sourceId: doc.id,\n });\n }\n } catch (error) {\n logger.warn(`Error processing document ${doc.id}, skipping:`, error);\n continue;\n }\n }\n \n return results;\n } catch (error) {\n logger.error(\"Error searching by metadata:\", error);\n throw error;\n }\n }\n\n /**\n * Search for documents using keyword matching\n * @param query The search query\n * @param limit Maximum number of results to return\n * @returns Promise resolving to array of search results\n */\n private async searchByKeyword(\n query: string,\n limit?: number\n ): Promise {\n try {\n const { database, tableName, maxResults } = this.config;\n \n // Simple keyword search using database LIKE queries\n const documents = await database.knex(tableName!)\n .whereRaw(\"LOWER(content) LIKE ?\", [`%${query.toLowerCase()}%`])\n .limit(limit || maxResults!);\n \n // Process results\n return documents.map(doc => ({\n content: doc.content,\n metadata: JSON.parse(doc.metadata),\n sourceId: doc.id,\n }));\n } catch (error) {\n logger.error(`Error searching by keyword \"${query}\":`, error);\n throw error;\n }\n }\n\n /**\n * Search for documents using embedding similarity\n * @param embedding The query embedding vector\n * @param limit Maximum number of results to return\n * @returns Promise resolving to array of search results with similarity scores\n */\n private async searchWithEmbedding(\n embedding: number[],\n limit?: number\n ): Promise {\n try {\n const { database, tableName, maxResults } = this.config;\n \n // Get all documents with embeddings\n const documents = await database.knex(tableName!)\n .select(\"*\")\n .whereNotNull(\"embedding\");\n \n // Calculate similarity and filter results\n const results: RAGResult[] = [];\n \n for (const doc of documents) {\n try {\n // Parse embedding from JSON\n const docEmbedding = JSON.parse(doc.embedding);\n \n // Skip documents with invalid embeddings\n if (!Array.isArray(docEmbedding) || docEmbedding.length === 0) {\n continue;\n }\n \n // Calculate cosine similarity\n const similarity = this.calculateCosineSimilarity(embedding, docEmbedding);\n \n // Add to results\n results.push({\n content: doc.content,\n metadata: JSON.parse(doc.metadata),\n similarity,\n sourceId: doc.id,\n });\n } catch (error) {\n logger.warn(`Error processing document ${doc.id}, skipping:`, error);\n continue;\n }\n }\n \n // Sort by similarity (descending) and limit results\n return results\n .sort((a, b) => (b.similarity || 0) - (a.similarity || 0))\n .slice(0, limit || maxResults);\n } catch (error) {\n logger.error(\"Error searching with embedding:\", error);\n throw error;\n }\n }\n\n /**\n * Calculate cosine similarity between two vectors\n * @param vecA First vector\n * @param vecB Second vector\n * @returns Similarity score (0-1)\n */\n private calculateCosineSimilarity(vecA: number[], vecB: number[]): number {\n // Check for valid vectors\n if (!vecA.length || !vecB.length || vecA.length !== vecB.length) {\n return 0;\n }\n \n let dotProduct = 0;\n let normA = 0;\n let normB = 0;\n \n // Calculate dot product and norms\n for (let i = 0; i < vecA.length; i++) {\n dotProduct += vecA[i] * vecB[i];\n normA += vecA[i] * vecA[i];\n normB += vecB[i] * vecB[i];\n }\n \n // Handle zero vectors\n if (normA === 0 || normB === 0) {\n return 0;\n }\n \n // Calculate similarity\n return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));\n }\n}\n\n/**\n * Factory function to create a document RAG instance\n * @param config Document RAG configuration\n * @returns Promise resolving to a document RAG instance\n */\nexport const createDocumentRAG: DocumentRAGFactory = async (\n config: DocumentRAGConfig\n) => {\n return DocumentRAG.create(config);\n}; ","import fs from 'fs';\nimport path from 'path';\nimport { PDFDocument } from 'pdf-lib';\nimport { Document } from '../types';\nimport { logger } from '../utils';\nimport { v4 as uuidv4 } from 'uuid';\n\nexport interface PDFParseOptions {\n /**\n * The strategy for splitting the PDF\n * - 'simple': Split by character count\n * - 'paragraph': Split by paragraphs\n * - 'section': Split by detected sections/headers (most intelligent)\n */\n splitStrategy: 'simple' | 'paragraph' | 'section';\n \n /**\n * Size of chunks when using 'simple' strategy\n */\n chunkSize?: number;\n \n /**\n * Overlap between chunks when using 'simple' or 'paragraph' strategy\n */\n chunkOverlap?: number;\n \n /**\n * Whether to include page numbers in metadata\n */\n includePageNumbers?: boolean;\n \n /**\n * Custom metadata to attach to each document\n */\n metadata?: Record;\n}\n\nexport interface PDFParseResult {\n /**\n * The documents extracted from the PDF, ready to be added to RAG\n */\n documents: Omit[];\n \n /**\n * Metadata about the PDF itself\n */\n pdfMetadata: {\n title?: string;\n author?: string;\n numPages: number;\n creationDate?: Date;\n };\n \n /**\n * Unique identifier for this PDF document\n */\n documentId: string;\n}\n\n/**\n * Parse a PDF file and extract structured content for RAG\n * @param filePath Path to the PDF file\n * @param options Options for parsing and chunking\n * @returns Promise resolving to the parsed documents\n */\nexport async function parsePDF(\n filePath: string,\n options: PDFParseOptions\n): Promise {\n try {\n logger.debug(`Parsing PDF: ${filePath}`);\n \n // Generate a unique document ID for this PDF\n const documentId = uuidv4();\n \n // Set default options\n const opts: Required = {\n splitStrategy: options.splitStrategy || 'section',\n chunkSize: options.chunkSize || 1000,\n chunkOverlap: options.chunkOverlap || 200,\n includePageNumbers: options.includePageNumbers !== undefined ? options.includePageNumbers : true,\n metadata: options.metadata || {},\n };\n \n // Read the PDF file\n const dataBuffer = fs.readFileSync(filePath);\n \n // Load the PDF document\n const pdfDoc = await PDFDocument.load(dataBuffer);\n \n // Get PDF metadata - pdf-lib uses individual getters instead of a single getMetadata method\n const title = pdfDoc.getTitle();\n const author = pdfDoc.getAuthor();\n const creationDate = pdfDoc.getCreationDate();\n \n // Count pages\n const numPages = pdfDoc.getPageCount();\n \n // Extract text content from all pages\n let allText = '';\n \n // Basic text extraction\n logger.debug(`Extracting text from PDF with ${numPages} pages`);\n \n try {\n const pdfParse = await import('pdf-parse');\n const data = await pdfParse.default(dataBuffer);\n allText = data.text;\n \n logger.debug(`Successfully extracted ${allText.length} characters of text`);\n } catch (error) {\n logger.error(\"Error extracting text from PDF:\", error);\n // Fallback to basic info when text extraction fails\n allText = `PDF document with ${numPages} pages. Text extraction failed.`;\n }\n \n // Create base metadata with document identification\n const baseMetadata = {\n source: path.basename(filePath),\n documentId: documentId, // Add document ID to all chunks\n fileName: path.basename(filePath),\n filePath: filePath,\n ...opts.metadata,\n };\n \n // PDF metadata for the result\n const pdfMetadata = {\n title: title || path.basename(filePath),\n author: author,\n numPages: numPages,\n creationDate: creationDate ? new Date(creationDate) : undefined,\n };\n \n // Get documents based on splitting strategy\n const documents = await splitPDFContent(allText, numPages, opts, baseMetadata);\n \n logger.debug(`PDF parsed successfully: ${documents.length} chunks created for document ${documentId}`);\n \n return {\n documents,\n pdfMetadata,\n documentId,\n };\n } catch (error) {\n logger.error(`Error parsing PDF ${filePath}:`, error);\n throw error;\n }\n}\n\n/**\n * Split PDF content based on the specified strategy\n */\nasync function splitPDFContent(\n text: string,\n numPages: number,\n options: Required,\n baseMetadata: Record\n): Promise[]> {\n const { splitStrategy } = options;\n \n switch (splitStrategy) {\n case 'simple':\n return splitBySimpleChunks(text, numPages, options, baseMetadata);\n case 'paragraph':\n return splitByParagraphs(text, numPages, options, baseMetadata);\n case 'section':\n return splitBySections(text, numPages, options, baseMetadata);\n default:\n logger.warn(`Unknown split strategy: ${splitStrategy}, falling back to 'section'`);\n return splitBySections(text, numPages, options, baseMetadata);\n }\n}\n\n/**\n * Split PDF content into simple fixed-size chunks\n */\nfunction splitBySimpleChunks(\n text: string,\n numPages: number,\n options: Required,\n baseMetadata: Record\n): Omit[] {\n const { chunkSize, chunkOverlap } = options;\n const chunks: Omit[] = [];\n \n // Estimate content per page to track page numbers\n const avgCharsPerPage = text.length / numPages;\n \n for (let i = 0; i < text.length; i += (chunkSize - chunkOverlap)) {\n // Stop if we've reached the end of the text\n if (i >= text.length) break;\n \n // Extract chunk content with overlap\n const content = text.substring(i, i + chunkSize);\n \n // Skip empty chunks\n if (!content.trim()) continue;\n \n // Estimate page number based on character position\n const estimatedPage = Math.min(\n Math.ceil((i + chunkSize / 2) / avgCharsPerPage),\n numPages\n );\n \n // Create document chunk\n chunks.push({\n content,\n metadata: {\n ...baseMetadata,\n chunk_index: chunks.length,\n ...(options.includePageNumbers ? { page: estimatedPage } : {}),\n },\n });\n }\n \n return chunks;\n}\n\n/**\n * Split PDF content by paragraphs\n */\nfunction splitByParagraphs(\n text: string,\n numPages: number,\n options: Required,\n baseMetadata: Record\n): Omit[] {\n // Split by double newlines (common paragraph separator)\n const paragraphs = text.split(/\\n\\s*\\n/).filter(p => p.trim().length > 0);\n const chunks: Omit[] = [];\n \n // Estimate content per page to track page numbers\n const avgCharsPerPage = text.length / numPages;\n \n let currentChunk = '';\n let chunkStart = 0;\n \n for (const paragraph of paragraphs) {\n // If adding this paragraph would exceed chunk size, save current chunk\n if (currentChunk.length > 0 && \n (currentChunk.length + paragraph.length) > options.chunkSize) {\n \n // Estimate page based on position in text\n const estimatedPage = Math.min(\n Math.ceil((chunkStart + currentChunk.length / 2) / avgCharsPerPage),\n numPages\n );\n \n chunks.push({\n content: currentChunk,\n metadata: {\n ...baseMetadata,\n chunk_index: chunks.length,\n ...(options.includePageNumbers ? { page: estimatedPage } : {}),\n },\n });\n \n // Start a new chunk with overlap\n const overlapPoint = Math.max(0, currentChunk.length - options.chunkOverlap);\n currentChunk = currentChunk.substring(overlapPoint);\n chunkStart += overlapPoint;\n }\n \n // Add paragraph to current chunk\n currentChunk += (currentChunk.length > 0 ? '\\n\\n' : '') + paragraph;\n }\n \n // Add the final chunk if it's not empty\n if (currentChunk.trim().length > 0) {\n const estimatedPage = Math.min(\n Math.ceil((chunkStart + currentChunk.length / 2) / avgCharsPerPage),\n numPages\n );\n \n chunks.push({\n content: currentChunk,\n metadata: {\n ...baseMetadata,\n chunk_index: chunks.length,\n ...(options.includePageNumbers ? { page: estimatedPage } : {}),\n },\n });\n }\n \n return chunks;\n}\n\n/**\n * Split PDF content by detected sections/headers (most intelligent option)\n * This uses heuristics to detect section headers and create coherent sections\n */\nfunction splitBySections(\n text: string,\n numPages: number,\n options: Required,\n baseMetadata: Record\n): Omit[] {\n // First split into paragraphs\n const paragraphs = text.split(/\\n\\s*\\n/).filter(p => p.trim().length > 0);\n const chunks: Omit[] = [];\n \n // Estimate content per page to track page numbers\n const avgCharsPerPage = text.length / numPages;\n let charCount = 0;\n \n // Heuristics for detecting headers\n // 1. Headers are typically short (< 200 chars)\n // 2. Often start with numbers (1., 1.1, I., A., etc.)\n // 3. Often in ALL CAPS or Title Case\n // 4. Usually don't end with punctuation\n const headerRegex = /^(?:\\d+[.):]|[A-Z][.):]|[IVXLCDM]+[.):]|APPENDIX|Chapter|Section|CHAPTER|SECTION)/;\n const allCapsRegex = /^[A-Z0-9\\s.,;:()\\-–—]+$/;\n \n let currentSection = '';\n let currentTitle = '';\n let sectionStart = 0;\n \n for (let i = 0; i < paragraphs.length; i++) {\n const paragraph = paragraphs[i];\n const isLikelyHeader = \n (paragraph.length < 200 && headerRegex.test(paragraph)) ||\n (paragraph.length < 150 && allCapsRegex.test(paragraph)) ||\n (paragraph.length < 100 && !paragraph.endsWith('.') && paragraph.split(' ').length < 10);\n \n // If we found a header and have existing content, save the current section\n if (isLikelyHeader && currentSection.length > 0) {\n // Estimate page based on position\n const estimatedPage = Math.min(\n Math.ceil((sectionStart + currentSection.length / 2) / avgCharsPerPage),\n numPages\n );\n \n chunks.push({\n content: currentSection,\n metadata: {\n ...baseMetadata,\n section_title: currentTitle,\n chunk_index: chunks.length,\n ...(options.includePageNumbers ? { page: estimatedPage } : {}),\n },\n });\n \n // Reset for new section\n sectionStart = charCount;\n currentSection = '';\n }\n \n // If it's a header, set as current title, otherwise add to current section\n if (isLikelyHeader) {\n currentTitle = paragraph.trim();\n // Add header to section content as well\n currentSection = paragraph;\n } else {\n // Add paragraph to current section\n currentSection += (currentSection.length > 0 ? '\\n\\n' : '') + paragraph;\n }\n \n charCount += paragraph.length + 2; // +2 for newlines\n \n // If current section exceeds max chunk size, break it up\n if (currentSection.length > options.chunkSize * 1.5) {\n // Recursively chunk this large section using the paragraph method\n const subChunks = splitByParagraphs(\n currentSection,\n numPages,\n options,\n {\n ...baseMetadata,\n section_title: currentTitle,\n }\n );\n \n // Add all sub-chunks to our output\n chunks.push(...subChunks);\n \n // Reset current section\n sectionStart = charCount;\n currentSection = '';\n }\n }\n \n // Add the final section if not empty\n if (currentSection.trim().length > 0) {\n const estimatedPage = Math.min(\n Math.ceil((sectionStart + currentSection.length / 2) / avgCharsPerPage),\n numPages\n );\n \n chunks.push({\n content: currentSection,\n metadata: {\n ...baseMetadata,\n section_title: currentTitle,\n chunk_index: chunks.length,\n ...(options.includePageNumbers ? { page: estimatedPage } : {}),\n },\n });\n }\n \n return chunks;\n}\n\n/**\n * Parse a directory of PDFs and load into RAG system\n * @param dirPath Directory path containing PDFs\n * @param options Parse options\n * @returns Array of results for each processed PDF\n */\nexport async function parseDirectoryOfPDFs(\n dirPath: string,\n options: PDFParseOptions\n): Promise> {\n try {\n logger.debug(`Processing directory of PDFs: ${dirPath}`);\n \n // Read directory contents\n const files = fs.readdirSync(dirPath);\n \n // Filter for PDF files\n const pdfFiles = files.filter(file => file.toLowerCase().endsWith('.pdf'));\n \n // Process each PDF\n const results: Record = {};\n \n for (const pdfFile of pdfFiles) {\n const filePath = path.join(dirPath, pdfFile);\n try {\n results[pdfFile] = await parsePDF(filePath, options);\n logger.debug(`Successfully processed ${pdfFile}`);\n } catch (error) {\n logger.error(`Error processing ${pdfFile}:`, error);\n // Continue with other files even if one fails\n }\n }\n \n return results;\n } catch (error) {\n logger.error(`Error processing PDF directory ${dirPath}:`, error);\n throw error;\n }\n} ","import { createVectorRAG } from \"./vector\";\nimport { createDocumentRAG } from \"./document\";\nimport { parsePDF, parseDirectoryOfPDFs } from \"./pdf-parser\";\nimport { RAGType, RAGFactoryConfig, RAGFactory, VectorRAGConfig, DocumentRAGConfig } from \"../types/rag\";\n\n/**\n * Unified factory function to create a RAG instance of the specified type\n * @param config Configuration with type to determine which RAG implementation to create\n * @returns Promise resolving to the appropriate RAG instance\n */\nexport const createRAG: RAGFactory = async (config: RAGFactoryConfig) => {\n const { type, ...baseConfig } = config;\n \n // Declare configs outside of switch statement to avoid lexical declaration errors\n let vectorConfig: VectorRAGConfig;\n let documentConfig: DocumentRAGConfig;\n \n switch (type) {\n case RAGType.VECTOR:\n // Create vector RAG config from the base config\n vectorConfig = {\n ...baseConfig,\n chunkSize: config.chunkSize,\n chunkOverlap: config.chunkOverlap\n };\n return createVectorRAG(vectorConfig);\n \n case RAGType.DOCUMENT:\n // Create document RAG config from the base config\n documentConfig = {\n ...baseConfig,\n storeEmbeddings: config.storeEmbeddings\n };\n return createDocumentRAG(documentConfig);\n \n default:\n throw new Error(`Unknown RAG type: ${type}`);\n }\n};\n\n// Re-export PDF processing utilities\nexport { parsePDF, parseDirectoryOfPDFs };\n\n// Re-export types\nexport * from \"../types/rag\"; ","import { \n ChatConfig, \n ChatInstance, \n ChatMetadata, \n ChatSummary, \n ChatMessage \n} from \"./types/chat\";\nimport { MemoryEntry, ProviderMessage, StructuredCompletionResponse, TaskConfig, TaskResult, ProviderModel } from \"./types\";\nimport { logger } from \"./utils/logger\";\nimport { validateRequiredParam } from \"./utils/validation\";\nimport { Embedding } from \"./providers\";\nimport { \n DEFAULT_TEMPERATURE, \n DEFAULT_MAX_TOKENS\n} from \"./constants\";\n\n/**\n * Chat management system that works with existing memory system\n * Chat IDs are the same as session IDs for backward compatibility\n * Can be used standalone or integrated with Agent instances\n */\nexport class ChatManager implements ChatInstance {\n public config: ChatConfig;\n private tableName: string;\n private maxChats: number;\n private autoGenerateTitles: boolean;\n\n constructor(config: ChatConfig) {\n validateRequiredParam(config, \"config\", \"ChatManager constructor\");\n validateRequiredParam(config.database, \"config.database\", \"ChatManager constructor\");\n validateRequiredParam(config.memory, \"config.memory\", \"ChatManager constructor\");\n\n this.config = config;\n // Use user-provided table name or default\n this.tableName = config.tableName || \"chats\";\n this.maxChats = config.maxChats || 50;\n this.autoGenerateTitles = config.autoGenerateTitles !== false;\n }\n\n\n\n async createChat(params: {\n chatId?: string;\n userId?: string;\n agentId: string;\n title?: string;\n metadata?: Record;\n }): Promise {\n validateRequiredParam(params.agentId, \"params.agentId\", \"createChat\");\n \n const chatId = params.chatId || generateChatId();\n const now = new Date();\n\n // Check if chat already exists\n const existingChat = await this.getChat(chatId);\n if (existingChat) {\n return existingChat;\n }\n\n const chatMetadata: ChatMetadata = {\n id: chatId,\n title: params.title,\n userId: params.userId,\n agentId: params.agentId,\n status: 'active',\n createdAt: now,\n updatedAt: now,\n messageCount: 0,\n metadata: params.metadata\n };\n\n // Insert chat metadata\n await this.config.database.knex(this.tableName).insert({\n id: chatId,\n title: params.title,\n userId: params.userId,\n agentId: params.agentId,\n status: 'active',\n createdAt: now,\n updatedAt: now,\n lastMessageAt: null,\n messageCount: 0,\n lastMessage: null,\n metadata: params.metadata ? JSON.stringify(params.metadata) : null\n });\n\n logger.info(`Chat created: ${chatId}`);\n return chatMetadata;\n }\n\n async createChatWithMessage(params: {\n chatId?: string;\n userId?: string;\n agentId: string;\n title?: string;\n metadata?: Record;\n // Chat functionality parameters\n message: string;\n model: any; // ProviderModel\n systemPrompt?: string;\n tools?: any[]; // Plugin[]\n taskManager?: any; // TaskManagerInstance\n embedding?: number[];\n useTaskSystem?: boolean;\n temperature?: number;\n maxTokens?: number;\n }): Promise {\n validateRequiredParam(params.agentId, \"params.agentId\", \"createChatWithMessage\");\n validateRequiredParam(params.message, \"params.message\", \"createChatWithMessage\");\n validateRequiredParam(params.model, \"params.model\", \"createChatWithMessage\");\n \n const chatId = params.chatId || generateChatId();\n\n // Check if chat exists, if not create it\n const existingChat = await this.getChat(chatId);\n if (!existingChat) {\n await this.createChat({\n chatId,\n agentId: params.agentId,\n userId: params.userId,\n title: params.title,\n metadata: params.metadata\n });\n logger.info(`Created new chat: ${chatId} for agent: ${params.agentId}`);\n }\n\n // Process the message\n return await this.processMessage({\n chatId,\n agentId: params.agentId,\n userId: params.userId,\n message: params.message,\n model: params.model,\n systemPrompt: params.systemPrompt,\n tools: params.tools || [],\n taskManager: params.taskManager,\n metadata: params.metadata || {},\n embedding: params.embedding,\n useTaskSystem: params.useTaskSystem !== false,\n temperature: params.temperature || DEFAULT_TEMPERATURE,\n maxTokens: params.maxTokens || DEFAULT_MAX_TOKENS\n });\n }\n\n private async processMessage(params: {\n chatId: string;\n agentId: string;\n userId?: string;\n message: string;\n model: any;\n systemPrompt?: string;\n tools: any[];\n taskManager?: any;\n metadata: Record;\n embedding?: number[];\n useTaskSystem: boolean;\n temperature: number;\n maxTokens: number;\n }): Promise {\n const {\n chatId,\n agentId,\n userId,\n message,\n model,\n systemPrompt,\n tools,\n taskManager,\n metadata,\n embedding,\n useTaskSystem,\n temperature,\n maxTokens\n } = params;\n\n // Get conversation history from chat system\n const history = await this.getMessages(chatId);\n\n // Add user message to chat\n await this.addMessage({\n chatId,\n agentId,\n userId,\n role: \"user\",\n content: message,\n metadata: {\n ...metadata,\n timestamp: new Date().toISOString(),\n useTaskSystem,\n conversationLength: history.length\n }\n });\n\n // Prepare messages for model\n const messages: ProviderMessage[] = [];\n\n // Add system prompt\n if (systemPrompt) {\n messages.push({\n role: \"system\",\n content: systemPrompt,\n });\n }\n\n // Add conversation history\n history.forEach((entry: MemoryEntry) => {\n messages.push({\n role: entry.role as \"system\" | \"user\" | \"assistant\",\n content: entry.content,\n });\n });\n\n // Add the new user message\n messages.push({\n role: \"user\",\n content: message,\n });\n\n // Get available tools\n const availableTools = tools.map((tool) => ({\n name: tool.name,\n description: tool.description,\n parameters: tool.parameters,\n }));\n\n // If tools are available, add them to the system message\n if (availableTools.length > 0 && systemPrompt) {\n const systemMessage = messages.find((msg) => msg.role === \"system\");\n if (systemMessage) {\n systemMessage.content += `\\n\\nYou have access to the following tools:\\n${JSON.stringify(\n availableTools,\n null,\n 2\n )}`;\n }\n }\n\n let response: string | StructuredCompletionResponse;\n\n // Determine if we should use task-based approach\n if (useTaskSystem && tools.length > 0 && taskManager) {\n // First, analyze the message to determine if it requires tasks\n const analysisResponse = await model.complete([\n ...messages,\n {\n role: \"system\",\n content: `You are a task planning assistant. Analyze the user's request and determine if it should be broken down into tasks. \nIf tasks are needed, format your response as a JSON array with this structure:\n[\n {\n \"name\": \"Task name\",\n \"description\": \"Detailed description of the task\",\n \"input\": {Any input data for the task}\n }\n]\nThe system will automatically determine which tools and plugins to use based on the task name and description.\nIf no tasks are needed, respond with \"NO_TASKS_NEEDED\".`,\n },\n ]);\n\n // Check if the analysis identified tasks\n const responseText = typeof analysisResponse === 'string' ? analysisResponse : analysisResponse.content;\n \n if (responseText.includes(\"[\") && responseText.includes(\"]\")) {\n try {\n // Extract JSON array from the response\n const jsonString = responseText.substring(\n responseText.indexOf(\"[\"),\n responseText.lastIndexOf(\"]\") + 1\n );\n\n // Parse the tasks\n const taskConfigs: TaskConfig[] = JSON.parse(jsonString);\n\n // Set session ID in task manager\n if (taskManager.setSessionId) {\n taskManager.setSessionId(chatId);\n }\n\n // Create and execute tasks\n for (const taskConfig of taskConfigs) {\n taskManager.addExistingTask(taskConfig, model);\n }\n\n const taskResults = await taskManager.run();\n\n // Generate response based on task results\n const resultSummary: any[] = [];\n for (const [taskId, result] of taskResults.entries()) {\n resultSummary.push({\n taskId,\n success: result.success,\n output: result.output,\n error: result.error ? result.error.message : undefined,\n });\n }\n\n const taskResponse = await model.complete([\n ...messages,\n {\n role: \"system\",\n content: `You are assisting with a task-based workflow. Multiple tasks were executed based on the user's request. \nHere are the results of those tasks:\n${JSON.stringify(resultSummary, null, 2)}\n\nAnalyze these results and generate a helpful, coherent response to the user that summarizes what was done and the outcome. \nDo not mention that tasks were executed behind the scenes - just provide the information the user needs in a natural way.`,\n },\n ]);\n\n response = typeof taskResponse === 'string' ? taskResponse : taskResponse.content;\n } catch (error) {\n logger.error(\"Error processing tasks:\", error);\n // Fallback to standard completion if task processing fails\n response = await model.complete(messages);\n }\n } else {\n // No tasks needed, just complete the response normally\n response = await model.complete(messages);\n }\n } else {\n // Regular chat completion\n response = await model.complete(messages, {\n tools: availableTools.length > 0 ? availableTools : undefined,\n toolCalling: availableTools.length > 0,\n temperature,\n maxTokens\n });\n }\n\n // Handle tool execution if the response contains tool calls\n if (typeof response === 'object' && response.tool_calls && Array.isArray(response.tool_calls)) {\n logger.debug(`Chat ${chatId} received ${response.tool_calls.length} tool calls to execute`);\n \n const toolResults = [];\n for (const toolCall of response.tool_calls) {\n try {\n if (toolCall.type === 'function' && toolCall.name) {\n logger.debug(`Executing tool: ${toolCall.name} with arguments:`, toolCall.arguments);\n \n // Find the tool to execute\n const tool = tools.find(t => t.name === toolCall.name);\n if (tool && tool.execute) {\n // Execute the tool\n const result = await tool.execute(toolCall.arguments || {});\n \n toolResults.push({\n name: toolCall.name,\n arguments: toolCall.arguments,\n result: result,\n success: true\n });\n \n logger.debug(`Tool ${toolCall.name} executed successfully`);\n } else {\n logger.warn(`Tool ${toolCall.name} not found or not executable`);\n toolResults.push({\n name: toolCall.name,\n arguments: toolCall.arguments,\n error: `Tool ${toolCall.name} not found or not executable`,\n success: false\n });\n }\n }\n } catch (error) {\n logger.error(`Error executing tool ${toolCall.name}:`, error);\n toolResults.push({\n name: toolCall.name,\n arguments: toolCall.arguments,\n error: error instanceof Error ? error.message : String(error),\n success: false\n });\n }\n }\n \n // Generate a final response based on tool results\n if (toolResults.length > 0) {\n try {\n logger.debug(`Generating final response based on ${toolResults.length} tool results`);\n \n const toolResultsMessage = {\n role: \"system\" as const,\n content: `You called the following tools and got these results:\n${toolResults.map(tr => `Tool: ${tr.name}\nArguments: ${JSON.stringify(tr.arguments)}\nResult: ${tr.success ? JSON.stringify(tr.result) : 'ERROR: ' + tr.error}`).join('\\n\\n')}\n\nBased on these tool results, generate a helpful response to the user. Be natural and conversational - don't mention the technical details of the tool calls.`\n };\n \n // Call the model again with the tool results to generate the final response\n const finalResponse = await model.complete([\n ...messages,\n toolResultsMessage\n ]);\n \n // Update response to the final result\n response = typeof finalResponse === 'string' ? finalResponse : finalResponse.content;\n \n const responseText = typeof response === 'string' ? response : response.content;\n logger.debug(`Generated final response after tool execution: ${responseText.length} characters`);\n } catch (error) {\n logger.error('Error generating final response from tool results:', error);\n // Fallback to original content plus tool results summary\n const originalContent = typeof response === 'string' ? response : response.content;\n response = `${originalContent}\\n\\nTool execution completed with ${toolResults.filter(r => r.success).length} successful results.`;\n }\n }\n }\n\n // Generate embedding for assistant response if needed\n let assistantEmbedding: number[] | undefined = undefined;\n const enableEmbeddings = this.config.memory.config?.enableEmbeddings;\n\n if (enableEmbeddings && embedding) {\n try {\n // Convert response to string for embedding generation\n const responseText = typeof response === 'string' ? response : response.content;\n assistantEmbedding = await Embedding.generateEmbedding(responseText);\n } catch (error) {\n logger.warn(\n \"Error generating embedding for assistant response:\",\n error\n );\n }\n }\n\n // Add assistant response to chat\n await this.addMessage({\n chatId,\n agentId,\n userId,\n role: \"assistant\",\n content: typeof response === 'string' ? response : response.content,\n metadata: {\n ...metadata,\n timestamp: new Date().toISOString(),\n modelUsed: model.name,\n taskSystemUsed: useTaskSystem,\n temperature,\n responseLength: (typeof response === 'string' ? response : response.content).length\n }\n });\n\n // Return the response (convert to string if it's a structured response)\n return typeof response === 'string' ? response : response.content;\n }\n\n async getChat(chatId: string): Promise {\n validateRequiredParam(chatId, \"chatId\", \"getChat\");\n\n const result = await this.config.database.knex(this.tableName)\n .where({ id: chatId })\n .first();\n\n if (!result) return null;\n\n return {\n id: result.id,\n title: result.title,\n userId: result.userId,\n agentId: result.agentId,\n status: result.status,\n createdAt: new Date(result.createdAt),\n updatedAt: new Date(result.updatedAt),\n lastMessageAt: result.lastMessageAt ? new Date(result.lastMessageAt) : undefined,\n messageCount: result.messageCount,\n metadata: result.metadata ? JSON.parse(result.metadata) : undefined\n };\n }\n\n async updateChat(chatId: string, updates: Partial): Promise {\n validateRequiredParam(chatId, \"chatId\", \"updateChat\");\n\n const updateData: any = {\n updatedAt: new Date()\n };\n\n if (updates.title !== undefined) updateData.title = updates.title;\n if (updates.status !== undefined) updateData.status = updates.status;\n if (updates.metadata !== undefined) {\n updateData.metadata = updates.metadata ? JSON.stringify(updates.metadata) : null;\n }\n\n await this.config.database.knex(this.tableName)\n .where({ id: chatId })\n .update(updateData);\n\n logger.info(`Chat updated: ${chatId}`);\n }\n\n async deleteChat(chatId: string): Promise {\n validateRequiredParam(chatId, \"chatId\", \"deleteChat\");\n\n // Delete chat metadata\n await this.config.database.knex(this.tableName)\n .where({ id: chatId })\n .del();\n\n // Clear messages from memory\n await this.config.memory.clear(chatId);\n\n logger.info(`Chat deleted: ${chatId}`);\n }\n\n async archiveChat(chatId: string): Promise {\n await this.updateChat(chatId, { status: 'archived' });\n logger.info(`Chat archived: ${chatId}`);\n }\n\n async listChats(params: {\n userId?: string;\n agentId?: string;\n status?: 'active' | 'archived' | 'deleted';\n limit?: number;\n offset?: number;\n } = {}): Promise {\n let query = this.config.database.knex(this.tableName)\n .select('*')\n .orderBy('updatedAt', 'desc');\n\n if (params.userId) {\n query = query.where({ userId: params.userId });\n }\n\n if (params.agentId) {\n query = query.where({ agentId: params.agentId });\n }\n\n if (params.status) {\n query = query.where({ status: params.status });\n }\n\n const limit = params.limit || this.maxChats;\n const offset = params.offset || 0;\n\n query = query.limit(limit).offset(offset);\n\n const results = await query;\n\n return results.map(row => ({\n id: row.id,\n title: row.title,\n userId: row.userId,\n agentId: row.agentId,\n status: row.status,\n lastMessage: row.lastMessage,\n lastMessageAt: row.lastMessageAt ? new Date(row.lastMessageAt) : undefined,\n messageCount: row.messageCount,\n createdAt: new Date(row.createdAt),\n updatedAt: new Date(row.updatedAt)\n }));\n }\n\n async searchChats(params: {\n query: string;\n userId?: string;\n agentId?: string;\n limit?: number;\n }): Promise {\n validateRequiredParam(params.query, \"params.query\", \"searchChats\");\n\n let query = this.config.database.knex(this.tableName)\n .select('*')\n .where('title', 'like', `%${params.query}%`)\n .orWhere('lastMessage', 'like', `%${params.query}%`)\n .orderBy('updatedAt', 'desc');\n\n if (params.userId) {\n query = query.andWhere({ userId: params.userId });\n }\n\n if (params.agentId) {\n query = query.andWhere({ agentId: params.agentId });\n }\n\n const limit = params.limit || this.maxChats;\n query = query.limit(limit);\n\n const results = await query;\n\n return results.map(row => ({\n id: row.id,\n title: row.title,\n userId: row.userId,\n agentId: row.agentId,\n status: row.status,\n lastMessage: row.lastMessage,\n lastMessageAt: row.lastMessageAt ? new Date(row.lastMessageAt) : undefined,\n messageCount: row.messageCount,\n createdAt: new Date(row.createdAt),\n updatedAt: new Date(row.updatedAt)\n }));\n }\n\n async addMessage(params: {\n chatId: string;\n agentId: string;\n userId?: string;\n role: \"system\" | \"user\" | \"assistant\" | \"task_context\" | \"task_event\" | \"task_tool\" | \"task_result\";\n content: string;\n metadata?: Record;\n }): Promise {\n validateRequiredParam(params.chatId, \"params.chatId\", \"addMessage\");\n validateRequiredParam(params.agentId, \"params.agentId\", \"addMessage\");\n validateRequiredParam(params.role, \"params.role\", \"addMessage\");\n validateRequiredParam(params.content, \"params.content\", \"addMessage\");\n\n // Add message to memory (using sessionId = chatId)\n const messageId = await this.config.memory.add({\n agentId: params.agentId,\n sessionId: params.chatId, // sessionId = chatId for compatibility\n userId: params.userId,\n role: params.role,\n content: params.content,\n metadata: params.metadata\n });\n\n // Update chat metadata\n const now = new Date();\n await this.config.database.knex(this.tableName)\n .where({ id: params.chatId })\n .update({\n lastMessageAt: now,\n updatedAt: now,\n lastMessage: params.content.substring(0, 200), // Store first 200 chars\n messageCount: this.config.database.knex.raw('messageCount + 1')\n });\n\n // Auto-generate title if this is the first user message and no title exists\n if (this.autoGenerateTitles && params.role === 'user') {\n const chat = await this.getChat(params.chatId);\n if (chat && !chat.title && chat.messageCount <= 1) {\n const title = generateTitleFromMessage(params.content);\n await this.updateChat(params.chatId, { title });\n }\n }\n\n return messageId;\n }\n\n async getMessages(chatId: string, limit?: number): Promise {\n validateRequiredParam(chatId, \"chatId\", \"getMessages\");\n\n // Get messages from memory using sessionId = chatId\n const messages = await this.config.memory.getBySession(chatId, limit);\n \n // Convert to ChatMessage format\n return messages.map(msg => ({\n ...msg,\n chatId: msg.sessionId // Add chatId for compatibility\n }));\n }\n\n async deleteMessage(messageId: string): Promise {\n validateRequiredParam(messageId, \"messageId\", \"deleteMessage\");\n await this.config.memory.delete(messageId);\n }\n\n async clearMessages(chatId: string): Promise {\n validateRequiredParam(chatId, \"chatId\", \"clearMessages\");\n \n // Clear messages from memory\n await this.config.memory.clear(chatId);\n \n // Reset message count in chat metadata\n await this.config.database.knex(this.tableName)\n .where({ id: chatId })\n .update({\n messageCount: 0,\n lastMessage: null,\n lastMessageAt: null,\n updatedAt: new Date()\n });\n }\n\n async getChatStats(params: {\n userId?: string;\n agentId?: string;\n } = {}): Promise<{\n totalChats: number;\n activeChats: number;\n archivedChats: number;\n totalMessages: number;\n }> {\n let query = this.config.database.knex(this.tableName);\n\n if (params.userId) {\n query = query.where({ userId: params.userId });\n }\n\n if (params.agentId) {\n query = query.where({ agentId: params.agentId });\n }\n\n const stats = await query\n .select(\n this.config.database.knex.raw('COUNT(*) as totalChats'),\n this.config.database.knex.raw('SUM(CASE WHEN status = ? THEN 1 ELSE 0 END) as activeChats', ['active']),\n this.config.database.knex.raw('SUM(CASE WHEN status = ? THEN 1 ELSE 0 END) as archivedChats', ['archived']),\n this.config.database.knex.raw('SUM(messageCount) as totalMessages')\n )\n .first();\n\n return {\n totalChats: parseInt(stats.totalChats) || 0,\n activeChats: parseInt(stats.activeChats) || 0,\n archivedChats: parseInt(stats.archivedChats) || 0,\n totalMessages: parseInt(stats.totalMessages) || 0\n };\n }\n\n async chat(params: {\n message: string;\n chatId: string;\n agentId: string;\n userId?: string;\n model: any; // ProviderModel\n systemPrompt?: string;\n tools?: any[]; // Plugin[]\n taskManager?: any; // TaskManagerInstance\n chatTitle?: string;\n metadata?: Record;\n embedding?: number[];\n useTaskSystem?: boolean;\n temperature?: number;\n maxTokens?: number;\n }): Promise {\n validateRequiredParam(params.message, \"params.message\", \"chat\");\n validateRequiredParam(params.chatId, \"params.chatId\", \"chat\");\n validateRequiredParam(params.agentId, \"params.agentId\", \"chat\");\n validateRequiredParam(params.model, \"params.model\", \"chat\");\n\n const {\n message,\n chatId,\n agentId,\n userId,\n model,\n systemPrompt,\n tools = [],\n taskManager,\n chatTitle,\n metadata = {},\n embedding,\n useTaskSystem = true,\n temperature = DEFAULT_TEMPERATURE,\n maxTokens = DEFAULT_MAX_TOKENS\n } = params;\n\n // Use createChatWithMessage which handles both creation and message processing\n return await this.createChatWithMessage({\n chatId,\n agentId,\n userId,\n title: chatTitle,\n metadata,\n message,\n model,\n systemPrompt,\n tools,\n taskManager,\n embedding,\n useTaskSystem,\n temperature,\n maxTokens\n });\n }\n}\n\n/**\n * Creates a chat management system that works with existing memory system\n * Chat IDs are the same as session IDs for backward compatibility\n * Can be used standalone or integrated with Agent instances\n */\nexport async function createChat(config: ChatConfig): Promise {\n const chatManager = new ChatManager(config);\n \n // Ensure chats table exists using user's custom table name\n const tableName = config.tableName || \"chats\";\n await config.database.ensureTable(tableName, (table: any) => {\n table.string('id').primary();\n table.string('title').nullable();\n table.string('userId').nullable();\n table.string('agentId').notNullable();\n table.string('status').defaultTo('active');\n table.timestamp('createdAt').defaultTo(config.database.knex.fn.now());\n table.timestamp('updatedAt').defaultTo(config.database.knex.fn.now());\n table.timestamp('lastMessageAt').nullable();\n table.integer('messageCount').defaultTo(0);\n table.string('lastMessage').nullable();\n table.text('metadata').nullable();\n \n // Add indexes for better performance\n table.index(['userId', 'status', 'updatedAt'], 'chats_userid_status_updated_idx');\n table.index(['agentId', 'status', 'updatedAt'], 'chats_agentid_status_updated_idx');\n table.index(['status', 'updatedAt'], 'chats_status_updated_idx');\n });\n \n logger.info(`Chat manager initialized with custom table: ${tableName}`);\n return chatManager;\n}\n\n// Helper function to generate unique chat ID\nfunction generateChatId(): string {\n return `chat-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`;\n}\n\n// Helper function to generate title from first message\nfunction generateTitleFromMessage(content: string): string {\n // Take first 50 characters and clean up\n let title = content.substring(0, 50).trim();\n \n // Remove line breaks and extra spaces\n title = title.replace(/\\s+/g, ' ');\n \n // Add ellipsis if truncated\n if (content.length > 50) {\n title += '...';\n }\n \n return title || 'New Chat';\n} ","import { v4 as uuidv4 } from \"uuid\";\nimport {\n TaskConfig,\n TaskInstance,\n TaskStatus,\n TaskResult,\n} from \"../types/task\";\nimport { Plugin } from \"../types/plugin\";\nimport { PluginManager } from \"../plugin\";\nimport { createDatabase, DatabaseInstance } from \"../database\";\nimport { MemoryInstance, MemoryEntry, ProviderModel, ProviderMessage, CompletionOptions } from \"../types\";\nimport { logger } from \"../utils\";\nimport { IntentRecognizer } from \"../utils/intent\";\n\n/**\n * Task class implementing the TaskInstance interface\n */\nexport class Task implements TaskInstance {\n public id: string;\n public config: TaskConfig;\n public status: TaskStatus;\n public result?: TaskResult;\n public retries: number;\n public plugins: Plugin[];\n public createdAt: Date;\n public startedAt?: Date;\n public completedAt?: Date;\n public agentId?: string;\n public sessionId?: string;\n public contextId?: string;\n public memory?: MemoryInstance;\n public database?: DatabaseInstance;\n\n public isCancelled: boolean = false;\n \n // Add a tracking variable to prevent duplicate logging\n private toolSelectionLogged: boolean = false;\n\n constructor(config: TaskConfig, memory?: MemoryInstance, model?: ProviderModel, database?: DatabaseInstance) {\n this.id = config.id || uuidv4();\n this.config = {\n ...config,\n model: config.model || model // Use config.model if provided, otherwise use the model parameter\n };\n this.status = \"pending\";\n this.retries = 0;\n this.plugins = [];\n this.createdAt = new Date();\n this.agentId = config.agentId;\n this.sessionId = config.sessionId;\n this.memory = memory;\n this.database = database;\n\n // Set context ID based on session ID\n if (this.sessionId) {\n this.contextId = this.sessionId;\n }\n\n // Load the plugins required for this task (but don't wait in constructor)\n this.loadPlugins(this.config.model).catch(error => {\n logger.error(`Error loading plugins for task ${this.id}:`, error);\n });\n\n\n }\n\n /**\n * Initialize plugins for the task\n * This tries three methods in order:\n * 1. Use LLM-selected tools if model is provided\n * 2. Use keyword matching based on task name\n * 3. Fall back to all agent plugins\n */\n public async loadPlugins(model?: ProviderModel): Promise {\n // Get all available tools\n const allTools = PluginManager.getAll();\n \n // Method 1: Get plugins from config first (explicitly provided in task config)\n if (this.config.plugins && this.config.plugins.length > 0) {\n const configPlugins = this.config.plugins\n .map(pluginName => {\n // Check if it's a tool instance already\n if (typeof pluginName === 'object' && 'name' in pluginName) {\n return pluginName as Plugin;\n }\n // Otherwise look it up by name\n return PluginManager.get(pluginName as string);\n })\n .filter(Boolean) as Plugin[];\n \n if (configPlugins.length > 0) {\n if (!this.toolSelectionLogged) {\n logger.task(this.id, `Using ${configPlugins.length} plugins from task config`, this.config.name);\n this.toolSelectionLogged = true;\n }\n this.plugins = configPlugins;\n return; \n }\n }\n \n // Method 2: Use LLM to select appropriate tools if model is provided\n if (model) {\n try {\n logger.task(this.id, \"Using LLM to select tools for task\", this.config.name);\n \n // Request tools with IntentRecognizer\n const selectedTools = await IntentRecognizer.recognizeIntent(\n this.config.name,\n this.config.description || this.config.name,\n allTools,\n model\n );\n \n if (selectedTools.length > 0) {\n if (!this.toolSelectionLogged) {\n logger.task(this.id, 'LLM selected these tools for the task', selectedTools.map(t => t.name).join(', '));\n this.toolSelectionLogged = true;\n }\n this.plugins = selectedTools;\n // Save plugins to config to avoid duplicate selection\n this.config.plugins = selectedTools.map(t => t.name);\n return;\n } else {\n logger.warn(`LLM did not select any tools for task \"${this.config.name}\", falling back to default`);\n }\n } catch (error) {\n logger.error(`Error using LLM for tool selection: ${error}`);\n // Fall back to default method\n }\n }\n \n logger.warn(`No plugins selected for task \"${this.config.name}\"`);\n }\n\n\n\n /**\n * Execute the task using configured plugins\n */\n async execute(input?: any): Promise {\n // Database operations are handled in createTask static method\n\n // Add debug logging - Task execution start\n logger.debug(`Task ${this.id} (${this.config.name}) execution started with input:`, \n JSON.stringify(input || this.config.input));\n \n // Record task execution start in memory\n await this.addTaskMemoryEntry(`Task execution started: ${this.config.name}`, \"task_event\");\n\n // Use provided input or fallback to config input\n const taskInput: Record = input || this.config.input;\n\n try {\n // Make sure plugins are loaded before starting the task\n if (!this.plugins || this.plugins.length === 0) {\n await this.loadPlugins(this.config.model);\n }\n \n // Skip execution if task is cancelled\n if (this.isCancelled) {\n throw new Error(\"Task was cancelled\");\n }\n\n // Update status only after plugins are loaded\n this.status = \"running\";\n this.startedAt = new Date();\n\n // Get task context from previous tasks in the same session\n let taskContext = {};\n if (this.sessionId) {\n taskContext = await this.getTaskContext(this.sessionId);\n }\n\n // Merge task input with the accumulated context\n const enrichedInput: Record = {\n ...taskInput,\n _context: taskContext,\n };\n \n // Log the enriched input at debug level\n logger.debug(`Task ${this.id} enriched input:`, JSON.stringify(enrichedInput));\n\n // Execute plugins with the input\n let currentOutput: Record = enrichedInput;\n \n // Ensure plugins array is valid before proceeding\n if (!this.plugins || !Array.isArray(this.plugins)) {\n this.plugins = [];\n }\n \n logger.debug(`Task ${this.id} has ${this.plugins.length} plugins to execute`);\n \n // Record available plugins in memory\n if (this.plugins.length > 0) {\n await this.addTaskMemoryEntry(\n `Available plugins: ${this.plugins.map(p => p.name).join(\", \")}`, \n \"task_event\"\n );\n }\n\n // Use the plugins as they are\n let prioritizedPlugins = [...this.plugins];\n\n // Check if we have plugins to execute\n if (prioritizedPlugins.length === 0) {\n logger.warn(`Task ${this.id} has no plugins to execute, the task may not complete as expected`);\n }\n\n // Use model to execute the task with the plugins if available\n const model = this.config.model;\n if (model && prioritizedPlugins.length > 0) {\n logger.debug(`Task ${this.id} using model ${model.name} to execute with plugins`);\n \n // Record model usage in memory\n await this.addTaskMemoryEntry(\n `Using model ${model.name} to execute task with plugins`, \n \"task_event\"\n );\n \n // Format tools for the model - convert Astreus tool format to provider tool format\n const formattedTools = prioritizedPlugins.map(tool => {\n // Convert parameters to proper format for provider\n let formattedParameters = {};\n \n if (tool.parameters && Array.isArray(tool.parameters)) {\n // Convert array of parameter objects to JSON Schema properties\n const properties: Record = {};\n const required: string[] = [];\n \n tool.parameters.forEach(param => {\n if (param.name && param.type) {\n properties[param.name] = {\n type: param.type,\n description: param.description || `Parameter ${param.name}`\n };\n \n if (param.required) {\n required.push(param.name);\n }\n }\n });\n \n // Create proper JSON Schema\n formattedParameters = {\n type: \"object\",\n properties: properties\n };\n \n // Add required array if there are required parameters\n if (required.length > 0) {\n formattedParameters = {\n ...formattedParameters,\n required: required\n };\n }\n } else if (tool.parameters) {\n // Use parameters as-is if not an array\n formattedParameters = tool.parameters;\n }\n \n return {\n name: tool.name,\n description: tool.description || \"\",\n parameters: formattedParameters\n };\n });\n \n // Create messages for the model\n const messages: ProviderMessage[] = [\n {\n role: \"user\",\n content: `Complete this task: ${this.config.name}\n${this.config.description ? `Description: ${this.config.description}` : ''}\nInput: ${JSON.stringify(enrichedInput)}`\n }\n ];\n \n // Let the model generate a response using the tools\n try {\n // Create the system message\n const systemMessage = `You are an AI assistant that can use tools to complete tasks.\nComplete this task: ${this.config.name}\n${this.config.description ? `Description: ${this.config.description}` : ''}\n\nAvailable tools:\n${prioritizedPlugins.map(tool => `- ${tool.name}: ${tool.description || 'No description provided'}`).join('\\n')}\n\nUse the available tools to fulfill this task effectively. When a tool should be used, call it with appropriate parameters.`;\n\n // Log formatted tools for debugging\n logger.debug(`Task ${this.id} using ${formattedTools.length} tools with model ${model.name}`, {\n toolNames: formattedTools.map(t => t.name).join(', ')\n });\n\n // If the tools have parameters with improper formats, log them for debugging\n const toolsWithPotentialIssues = formattedTools.filter(tool => {\n const params = tool.parameters;\n return !params || (typeof params === 'object' && Object.keys(params).length === 0);\n });\n\n if (toolsWithPotentialIssues.length > 0) {\n logger.warn(`Task ${this.id} has tools with potential parameter format issues:`, \n toolsWithPotentialIssues.map(t => t.name).join(', '));\n }\n\n // Call the model with messages and completion options\n const completionOptions: CompletionOptions = {\n tools: formattedTools,\n toolCalling: true,\n systemMessage: systemMessage\n };\n \n logger.debug(`Task ${this.id} calling model with ${messages.length} messages and ${formattedTools.length} tools`);\n const completion = await model.complete(messages, completionOptions);\n \n // Check if completion is a structured object with tool calls\n if (typeof completion === 'object' && completion.tool_calls && Array.isArray(completion.tool_calls)) {\n logger.debug(`Task ${this.id} received structured tool calls response`);\n \n const toolCalls = completion.tool_calls;\n logger.info(`Task ${this.id} response includes ${toolCalls.length} tool calls:`);\n \n // Log tool calls and execute them\n const toolResults = [];\n for (let i = 0; i < toolCalls.length; i++) {\n const call = toolCalls[i];\n if (call.type === 'function' && call.name) {\n logger.info(`Tool call ${i + 1}: ${call.name} with arguments: ${JSON.stringify(call.arguments)}`);\n \n // Record tool call in memory\n await this.addTaskMemoryEntry(\n `Tool call: ${call.name} with arguments: ${JSON.stringify(call.arguments)}`,\n \"task_tool\"\n );\n \n // Find the tool to execute\n const tool = prioritizedPlugins.find(t => t.name === call.name);\n if (tool && typeof tool.execute === 'function') {\n try {\n logger.info(`Executing tool ${call.name}...`);\n const result = await tool.execute({\n ...enrichedInput,\n ...call.arguments\n });\n toolResults.push({\n name: call.name,\n arguments: call.arguments,\n result\n });\n logger.info(`Tool ${call.name} executed successfully`);\n \n // Record successful tool execution in memory\n await this.addTaskMemoryEntry(\n `Tool ${call.name} executed successfully`,\n \"task_result\",\n { toolName: call.name, success: true }\n );\n } catch (error) {\n logger.error(`Error executing tool ${call.name}:`, error);\n toolResults.push({\n name: call.name,\n arguments: call.arguments,\n error: error instanceof Error ? error.message : `Error: ${error}`\n });\n \n // Record tool execution error in memory\n await this.addTaskMemoryEntry(\n `Tool ${call.name} execution failed: ${error instanceof Error ? error.message : error}`,\n \"task_result\",\n { toolName: call.name, success: false }\n );\n }\n } else {\n logger.warn(`Tool ${call.name} not found or has no execute method`);\n toolResults.push({\n name: call.name,\n arguments: call.arguments,\n error: `Tool ${call.name} not found or has no execute method`\n });\n \n // Record tool not found in memory\n await this.addTaskMemoryEntry(\n `Tool ${call.name} not found or has no execute method`,\n \"task_result\",\n { toolName: call.name, success: false }\n );\n }\n }\n }\n \n // Generate a response based on tool results\n if (toolResults.length > 0) {\n try {\n logger.info(`Generating response based on ${toolResults.length} tool results`);\n const resultsMessage: ProviderMessage = {\n role: \"system\",\n content: `The following tools were called based on the user's request:\n${toolResults.map(tr => `Tool: ${tr.name}\nArguments: ${JSON.stringify(tr.arguments)}\nResult: ${tr.error ? 'ERROR: ' + tr.error : JSON.stringify(tr.result)}`).join('\\n\\n')}\n\nPlease analyze these results and generate a helpful, coherent response to the user that summarizes what was done and the outcome.\nDo not mention the technical details of the tool calls - just provide a natural, conversational response about what was accomplished.`\n };\n \n // Call the model again with the results\n const summaryResponse = await model.complete([\n ...messages,\n resultsMessage\n ]);\n \n // Use the summary response as content\n const summaryContent = typeof summaryResponse === 'string' \n ? summaryResponse \n : summaryResponse.content;\n \n // Add both the summary and the raw results\n currentOutput = {\n ...enrichedInput,\n content: summaryContent,\n summary: summaryContent,\n tool_calls: toolCalls,\n tool_results: toolResults,\n tools_used: prioritizedPlugins.map(t => t.name)\n };\n } catch (error) {\n logger.error(`Error generating summary from tool results:`, error);\n // Fall back to standard output without summary\n currentOutput = {\n ...enrichedInput,\n content: completion.content || '',\n tool_calls: toolCalls,\n tool_results: toolResults,\n tools_used: prioritizedPlugins.map(t => t.name)\n };\n }\n } else {\n // No tools were successfully executed\n currentOutput = {\n ...enrichedInput,\n content: completion.content || '',\n tool_calls: toolCalls,\n tool_results: toolResults,\n tools_used: prioritizedPlugins.map(t => t.name)\n };\n }\n }\n // Handle text-based tool calls (for older models)\n else if (typeof completion === 'string' && completion.includes('Tool Call:')) {\n logger.debug(`Task ${this.id} model response received, length: ${completion.length} chars (text format)`);\n \n // Extract and log tool calls for better debugging\n const toolCallMatches = completion.match(/Tool Call: ([^\\n]+)[\\s\\S]*?Arguments: ({[\\s\\S]*?})(?=\\n\\n|\\n?$)/g);\n if (toolCallMatches && toolCallMatches.length > 0) {\n logger.info(`Task ${this.id} response includes ${toolCallMatches.length} tool calls (text format):`);\n \n // Execute each tool call\n const toolResults = [];\n const parsedCalls = [];\n for (let i = 0; i < toolCallMatches.length; i++) {\n const match = toolCallMatches[i];\n const toolName = match.match(/Tool Call: ([^\\n]+)/)?.[1];\n const argsMatch = match.match(/Arguments: (.*?)(?=\\n\\n|\\n?$)/s);\n const argsString = argsMatch ? argsMatch[1] : '{}';\n \n logger.info(`Tool call ${i + 1}: ${toolName} with arguments: ${argsString}`);\n \n if (toolName) {\n // Parse arguments\n let args = {};\n try {\n args = JSON.parse(argsString);\n } catch (err) {\n logger.warn(`Failed to parse arguments for tool ${toolName}: ${err}`);\n }\n \n parsedCalls.push({\n name: toolName,\n arguments: args\n });\n \n // Find and execute the tool\n const tool = prioritizedPlugins.find(t => t.name === toolName);\n if (tool && typeof tool.execute === 'function') {\n try {\n logger.info(`Executing tool ${toolName}...`);\n const result = await tool.execute({\n ...enrichedInput,\n ...args\n });\n toolResults.push({\n name: toolName,\n arguments: args,\n result\n });\n logger.info(`Tool ${toolName} executed successfully`);\n } catch (error) {\n logger.error(`Error executing tool ${toolName}:`, error);\n toolResults.push({\n name: toolName,\n arguments: args,\n error: error instanceof Error ? error.message : `Error: ${error}`\n });\n }\n } else {\n logger.warn(`Tool ${toolName} not found or has no execute method`);\n toolResults.push({\n name: toolName,\n arguments: args,\n error: `Tool ${toolName} not found or has no execute method`\n });\n }\n }\n }\n \n // Generate a response based on tool results\n if (toolResults.length > 0) {\n try {\n logger.info(`Generating response based on ${toolResults.length} tool results`);\n const resultsMessage: ProviderMessage = {\n role: \"system\",\n content: `The following tools were called based on the user's request:\n${toolResults.map(tr => `Tool: ${tr.name}\nArguments: ${JSON.stringify(tr.arguments)}\nResult: ${tr.error ? 'ERROR: ' + tr.error : JSON.stringify(tr.result)}`).join('\\n\\n')}\n\nPlease analyze these results and generate a helpful, coherent response to the user that summarizes what was done and the outcome.\nDo not mention the technical details of the tool calls - just provide a natural, conversational response about what was accomplished.`\n };\n \n // Call the model again with the results\n const summaryResponse = await model.complete([\n ...messages,\n resultsMessage\n ]);\n \n // Use the summary response\n const summaryContent = typeof summaryResponse === 'string' \n ? summaryResponse \n : summaryResponse.content;\n \n // Include both the summary and the raw results\n currentOutput = {\n ...enrichedInput,\n content: summaryContent,\n summary: summaryContent,\n result: completion,\n tool_calls: parsedCalls,\n tool_results: toolResults,\n tools_used: prioritizedPlugins.map(t => t.name)\n };\n } catch (error) {\n logger.error(`Error generating summary from tool results:`, error);\n // Fall back to standard output without summary\n currentOutput = {\n ...enrichedInput,\n result: completion,\n tool_calls: parsedCalls,\n tool_results: toolResults,\n tools_used: prioritizedPlugins.map(t => t.name)\n };\n }\n } else {\n // No tools were successfully executed\n currentOutput = {\n ...enrichedInput,\n result: completion,\n tool_calls: parsedCalls,\n tool_results: toolResults,\n tools_used: prioritizedPlugins.map(t => t.name)\n };\n }\n } else {\n logger.info(`Task ${this.id} response appears to include tool calls but format could not be parsed`);\n \n // Just pass through the response\n currentOutput = {\n ...enrichedInput,\n result: completion,\n tools_used: prioritizedPlugins.map(t => t.name)\n };\n }\n } else {\n // No tool calls detected\n logger.debug(`Task ${this.id} model response received, length: ${typeof completion === 'string' ? completion.length : JSON.stringify(completion).length} chars`);\n logger.warn(`Task ${this.id} response does not include tool calls, the model may not be using the tools properly`);\n \n // Use the response as-is\n currentOutput = {\n ...enrichedInput,\n result: completion,\n tools_used: prioritizedPlugins.map(t => t.name)\n };\n }\n \n logger.debug(`Model execution completed for task ${this.id}`);\n } catch (error) {\n logger.error(`Error executing task with model: ${error}`);\n throw error;\n }\n } else {\n // No model or no plugins, execute plugins sequentially\n logger.debug(`Task ${this.id} using sequential plugin execution (${prioritizedPlugins.length} plugins)`);\n let pluginOutput: unknown = null;\n \n // Execute each plugin in sequence\n for (const plugin of prioritizedPlugins) {\n if (this.isCancelled) {\n throw new Error(\"Task was cancelled during execution\");\n }\n\n if (!plugin) {\n logger.warn(\"Encountered null or undefined plugin, skipping\");\n continue;\n }\n\n if (plugin.execute) {\n const pluginName = plugin.name || \"unnamed_plugin\";\n logger.debug(`Executing plugin '${pluginName}' with input:`, JSON.stringify(currentOutput));\n try {\n // Execute the plugin with the current input/output chain\n pluginOutput = await plugin.execute(currentOutput);\n logger.debug(`Plugin '${pluginName}' returned:`, JSON.stringify(pluginOutput));\n \n // Update currentOutput with plugin's output, not the entire input\n if (pluginOutput !== undefined && pluginOutput !== null) {\n // If plugin returns a value, use it as the new current output\n currentOutput = pluginOutput as Record;\n logger.debug(`Updated currentOutput with plugin result:`, JSON.stringify(currentOutput));\n } else {\n logger.debug(`Plugin returned undefined/null, keeping previous output`);\n }\n // If plugin returns undefined/null, keep the previous output\n } catch (error) {\n logger.error(`Error executing plugin '${pluginName}':`, error);\n throw error;\n }\n } else {\n const pluginName = plugin.name || \"unnamed_plugin\";\n logger.warn(\n `Plugin '${pluginName}' does not have an execute method`\n );\n }\n }\n }\n\n // The final output is the result from execution\n const finalOutput = currentOutput;\n \n // Only show the final output at debug level\n logger.debug(`Task ${this.id} final output:`, JSON.stringify(finalOutput));\n \n // Strip _context from the output if it got added by the enriched input\n if (finalOutput && typeof finalOutput === 'object' && '_context' in finalOutput) {\n const { _context, ...strippedOutput } = finalOutput;\n // Use _context to prevent unused variable warning\n logger.debug(`Context data size: ${JSON.stringify(_context).length} bytes`);\n \n // Only replace if there are other properties beyond _context\n if (Object.keys(strippedOutput).length > 0) {\n currentOutput = strippedOutput as Record;\n logger.debug(`Stripped _context, new output:`, JSON.stringify(currentOutput));\n } else {\n logger.debug(`After stripping _context, no other properties found, keeping original`);\n }\n }\n\n // Update the context with this task's output\n if (this.sessionId) {\n taskContext = {\n ...taskContext,\n [this.config.name]: currentOutput,\n lastTaskOutput: currentOutput,\n };\n // Store updated context in memory system\n await this.saveTaskContext(this.sessionId, taskContext);\n }\n\n // Set result and update status\n this.result = {\n success: true,\n output: currentOutput,\n context: taskContext,\n };\n \n logger.debug(`Task ${this.id} completed successfully`);\n \n this.completedAt = new Date();\n if (this.result.success) {\n this.status = \"completed\";\n logger.task(this.id, `Task ${this.status}`, this.config.name);\n \n // Record task completion in memory\n await this.addTaskMemoryEntry(\n `Task completed successfully: ${this.config.name}`, \n \"task_event\",\n { output: JSON.stringify(currentOutput).slice(0, 500) } // Limit size of stored output\n );\n } else {\n this.status = \"failed\";\n logger.task(this.id, `Task ${this.status}`, this.config.name);\n \n // Record task failure in memory\n await this.addTaskMemoryEntry(\n `Task failed: ${this.config.name}`, \n \"task_event\"\n );\n }\n } catch (error) {\n // Handle error\n this.result = {\n success: false,\n error: error as Error,\n };\n \n logger.error(`Task ${this.id} failed with error:`, error);\n \n // Record task error in memory\n await this.addTaskMemoryEntry(\n `Task error: ${error instanceof Error ? error.message : error}`,\n \"task_event\",\n { errorType: error instanceof Error ? error.name : 'Unknown' }\n );\n\n // Retry if max retries not exceeded\n if (this.retries < (this.config.maxRetries || 0)) {\n this.retries++;\n this.status = \"pending\";\n logger.info(\n `Retrying task '${this.id}', attempt ${this.retries}/${this.config.maxRetries}`\n );\n \n // Record retry in memory\n await this.addTaskMemoryEntry(\n `Retrying task (attempt ${this.retries}/${this.config.maxRetries})`,\n \"task_event\"\n );\n\n // Database state is managed by createTask static method\n\n return this.execute(taskInput);\n } else {\n this.status = \"failed\";\n this.completedAt = new Date();\n }\n }\n\n\n\n return this.result as TaskResult;\n }\n\n /**\n * Get task context from memory using session ID\n */\n public async getTaskContext(sessionId: string): Promise> {\n try {\n // Use the memory system if available\n if (this.memory) {\n // Get the task context memories from session\n const memories = await this.memory.getBySession(sessionId);\n\n // Find the most recent task context memory\n const contextMemories = memories.filter(\n (m) => m.role === \"task_context\"\n );\n\n if (contextMemories.length > 0) {\n // Sort by timestamp descending and get most recent\n contextMemories.sort(\n (a, b) => {\n // Ensure timestamps are Date objects or convert them\n const aTime = a.timestamp instanceof Date ? a.timestamp.getTime() : new Date(a.timestamp).getTime();\n const bTime = b.timestamp instanceof Date ? b.timestamp.getTime() : new Date(b.timestamp).getTime();\n return bTime - aTime;\n }\n );\n\n // Extract the context data from memory content\n try {\n return JSON.parse(contextMemories[0].content);\n } catch (parseError) {\n logger.error(\n `Error parsing task context data for session ${sessionId}:`,\n parseError\n );\n return {};\n }\n }\n }\n\n return {};\n } catch (error) {\n logger.error(\n `Error retrieving task context for session ${sessionId}:`,\n error\n );\n return {};\n }\n }\n\n /**\n * Save task context to memory system\n */\n public async saveTaskContext(\n sessionId: string,\n contextData: Record\n ): Promise {\n try {\n // Only save if memory system is available\n if (this.memory) {\n // Serialize the context data\n const serializedData = JSON.stringify(contextData);\n\n // Store in memory with role=task_context\n await this.memory.add({\n agentId: this.agentId || \"system\",\n sessionId: sessionId,\n userId: \"\",\n role: \"task_context\",\n content: serializedData,\n metadata: {\n taskId: this.id,\n taskName: this.config.name,\n contextType: \"task_execution_context\",\n },\n });\n } else {\n logger.warn(\n `Memory system not available for task ${this.id}, skipping context save`\n );\n }\n } catch (error) {\n logger.error(\n `Error saving task context for session ${sessionId}:`,\n error\n );\n }\n }\n\n /**\n * Cancel the task execution\n */\n cancel(): void {\n this.isCancelled = true;\n if (this.status === \"pending\" || this.status === \"running\") {\n this.status = \"failed\";\n this.result = {\n success: false,\n error: new Error(\"Task was cancelled\"),\n };\n\n }\n }\n\n /**\n * Set the memory instance for this task\n */\n setMemory(memory: MemoryInstance): void {\n this.memory = memory;\n }\n\n /**\n * Get memory entries specific to this task\n * @returns Promise that resolves to an array of task-specific memory entries\n */\n public async getTaskMemory(): Promise {\n if (!this.memory || !this.sessionId) {\n return [];\n }\n \n try {\n // Get all memories for this session\n const memories = await this.memory.getBySession(this.sessionId);\n \n // Filter for memories related to this task\n return memories.filter(memory => \n memory.metadata && \n typeof memory.metadata === 'object' &&\n 'taskId' in memory.metadata &&\n memory.metadata.taskId === this.id\n );\n } catch (error) {\n logger.error(`Error getting task memory for task ${this.id}:`, error);\n return [];\n }\n }\n\n /**\n * Add a memory entry for this task\n * @param content Content of the memory entry\n * @param role Role for the memory entry (task_event, task_tool, task_result)\n * @param additionalMetadata Additional metadata to include\n * @returns Promise that resolves when the memory is added\n */\n public async addTaskMemoryEntry(\n content: string, \n role: \"task_event\" | \"task_tool\" | \"task_result\" = \"task_event\",\n additionalMetadata: Record = {}\n ): Promise {\n if (!this.memory || !this.sessionId) {\n return;\n }\n \n try {\n await this.memory.add({\n agentId: this.agentId || \"system\",\n sessionId: this.sessionId,\n userId: \"\",\n role: role,\n content: content,\n metadata: {\n taskId: this.id,\n taskName: this.config.name,\n taskStatus: this.status,\n ...additionalMetadata\n }\n });\n logger.debug(`Added task memory entry for task ${this.id}: ${role}`);\n } catch (error) {\n logger.error(`Error adding task memory for task ${this.id}:`, error);\n }\n }\n\n /**\n * Create a new task asynchronously\n */\n static async createTask(\n config: TaskConfig,\n memory?: MemoryInstance,\n model?: ProviderModel,\n database?: DatabaseInstance\n ): Promise {\n // Create a new task instance\n const task = new Task(config, memory, model, database);\n\n // Save task to database if database is available\n if (task.database) {\n try {\n // Ensure tasks table exists\n await task.database.ensureTable('tasks', (table) => {\n table.string(\"id\").primary();\n table.string(\"name\").notNullable();\n table.text(\"description\").notNullable();\n table.string(\"status\").notNullable().index();\n table.integer(\"retries\").defaultTo(0);\n table.json(\"plugins\").nullable();\n table.json(\"input\").nullable();\n table.json(\"dependencies\").nullable();\n table.json(\"result\").nullable();\n table.timestamp(\"createdAt\").defaultTo(task.database!.knex.fn.now());\n table.timestamp(\"startedAt\").nullable();\n table.timestamp(\"completedAt\").nullable();\n table.string(\"agentId\").nullable().index();\n table.string(\"sessionId\").nullable().index();\n table.string(\"contextId\").nullable().index();\n });\n\n // Use default tasks table name - can be customized via database config if needed\n const tasksTable = task.database.getTable('tasks');\n\n // Prepare task data for database\n const taskData = {\n id: task.id,\n name: task.config.name,\n description: task.config.description,\n status: task.status,\n retries: task.retries,\n plugins: JSON.stringify(task.config.plugins || []),\n input: task.config.input ? JSON.stringify(task.config.input) : null,\n dependencies: task.config.dependencies ? JSON.stringify(task.config.dependencies) : null,\n result: task.result ? JSON.stringify(task.result) : null,\n createdAt: task.createdAt,\n startedAt: task.startedAt || null,\n completedAt: task.completedAt || null,\n agentId: task.agentId || null,\n sessionId: task.sessionId || null,\n contextId: task.contextId || null,\n };\n\n // Check if task already exists\n const existingTask = await tasksTable.findOne({ id: task.id });\n\n if (existingTask) {\n // Update existing task\n await tasksTable.update({ id: task.id }, taskData);\n logger.debug(`Task ${task.id}: Updated in database`);\n } else {\n // Insert new task\n await tasksTable.insert(taskData);\n logger.debug(`Task ${task.id}: Saved to database`);\n }\n\n // Task state is now stored in database\n } catch (error) {\n logger.error(`Task ${task.id}: Error saving to database:`, error);\n // Don't throw error to avoid breaking task creation\n }\n }\n\n return task;\n }\n\n /**\n * Create a new task synchronously\n */\n static createTaskSync(\n config: TaskConfig,\n memory?: MemoryInstance,\n model?: ProviderModel,\n database?: DatabaseInstance\n ): TaskInstance {\n // Create and return a new task instance without waiting for save\n return new Task(config, memory, model, database);\n }\n} ","import { Plugin } from \"../types/plugin\";\nimport { logger } from \"./index\";\nimport { ProviderModel, ProviderMessage } from \"../types\";\n\n/**\n * Intent recognition system that uses LLM to determine which tools should be used for a task\n * based on the task name and description.\n */\nexport class IntentRecognizer {\n /**\n * Use LLM to determine which tools are relevant for a task based on its name and description\n * \n * @param taskName The name of the task\n * @param taskDescription The description of the task\n * @param availableTools Array of available tools\n * @param model The LLM to use for tool selection\n * @returns Array of tools that are relevant for the task\n */\n static async recognizeIntent(\n taskName: string,\n taskDescription: string,\n availableTools: Plugin[],\n model: ProviderModel\n ): Promise {\n // If no tools are available, return empty array\n if (!availableTools.length) {\n logger.debug(`No tools available for task \"${taskName}\"`);\n return [];\n }\n \n // Prepare tool information for the model\n const toolsInfo = availableTools.map(tool => ({\n name: tool.name,\n description: tool.description || \"\",\n parameters: tool.parameters || {}\n }));\n \n // Create the prompt for tool selection\n const prompt: ProviderMessage[] = [\n {\n role: \"system\",\n content: `You are a tool selection expert that picks the most relevant tools for a task.\nReview the task and available tools and select ONLY the tools that are directly relevant.\nRespond ONLY with a JSON array of tool names that should be used, without any explanation.\nExample format: [\"tool1\", \"tool2\"]`\n },\n {\n role: \"user\",\n content: `Task name: ${taskName}\nTask description: ${taskDescription}\n\nAvailable tools:\n${JSON.stringify(toolsInfo, null, 2)}\n\nSelect the most appropriate tools for this task and respond with a JSON array containing only the tool names.`\n }\n ];\n \n try {\n // Ask the model to select tools\n const response = await model.complete(prompt);\n \n // Extract JSON array from the response\n let toolNames: string[] = [];\n try {\n // Find the JSON array in the response\n const responseText = typeof response === 'string' ? response : response.content;\n const match = responseText.match(/\\[.*\\]/s);\n if (match) {\n toolNames = JSON.parse(match[0]);\n } else {\n throw new Error(\"No JSON array found in response\");\n }\n } catch (error) {\n logger.error(`Failed to parse LLM tool selection response: ${error}`);\n logger.debug(`Raw LLM response: ${typeof response === 'string' ? response : JSON.stringify(response)}`);\n return []; // Return empty array on parsing error\n }\n \n // Map tool names to actual tool objects\n const selectedTools = toolNames\n .map(name => availableTools.find(tool => \n tool.name.toLowerCase() === name.toLowerCase()))\n .filter(tool => tool !== undefined) as Plugin[];\n \n return selectedTools;\n } catch (error) {\n logger.error(`Error using LLM for tool selection: ${error}`);\n return []; // Return empty array on LLM error\n }\n }\n} ","import { v4 as uuidv4 } from \"uuid\";\nimport {\n TaskManagerInstance,\n TaskConfig,\n TaskInstance,\n TaskResult,\n TaskManagerConfig,\n TaskStatus,\n} from \"../types\";\nimport { MemoryInstance, DatabaseInstance, ProviderModel } from \"../types\";\nimport { createDatabase } from \"../database\";\nimport { logger } from \"../utils\";\nimport { Task } from \"./task\";\nimport { validateRequiredParam } from \"../utils/validation\";\n\n/**\n * Task Manager class that manages multiple tasks\n * \n * This class provides functionality for:\n * - Creating and managing multiple tasks\n * - Executing tasks with dependencies in order\n * - Persisting task state to the database\n * - Restoring tasks from previous sessions\n */\nexport class TaskManager implements TaskManagerInstance {\n private readonly tasks = new Map();\n private tasksLoaded = false;\n private loadingPromise: Promise | null = null;\n private config: TaskManagerConfig;\n private agentId?: string;\n private sessionId?: string;\n private memory?: MemoryInstance;\n private database?: DatabaseInstance;\n private providerModel?: ProviderModel;\n\n /**\n * Create a new TaskManager instance\n * @param config Configuration options for the task manager\n */\n constructor(config?: TaskManagerConfig) {\n this.config = config || { concurrency: 5 };\n this.agentId = config?.agentId;\n this.sessionId = config?.sessionId;\n this.memory = config?.memory;\n this.database = config?.database;\n this.providerModel = config?.providerModel;\n\n // Load existing tasks from the database\n this.loadTasksFromDatabase().catch((err) => {\n logger.error(\"Error loading tasks from database:\", err);\n });\n \n logger.info(\"Task manager initialized\");\n }\n\n /**\n * Add an existing task to the manager\n * @param task Task instance or configuration to add\n * @param model Optional LLM model to use for tool selection\n * @returns The added task instance\n */\n public addExistingTask(task: TaskInstance | TaskConfig, model?: ProviderModel): TaskInstance {\n try {\n // If this is a configuration, first check if we already have this task by ID\n if (!(task instanceof Task) && task.id) {\n // Look for existing task in our memory\n const existingTask = this.tasks.get(task.id);\n if (existingTask) {\n logger.debug(`Task ${task.id} already exists in manager, returning existing instance`);\n return existingTask;\n }\n }\n \n // Ensure task.plugins only contains string values\n if (!(task instanceof Task) && task.plugins) {\n const validPlugins = task.plugins.filter(plugin => typeof plugin === 'string');\n if (validPlugins.length !== task.plugins.length) {\n logger.warn(`TaskManager: Filtered out ${task.plugins.length - validPlugins.length} invalid plugins from task config`);\n task.plugins = validPlugins;\n }\n }\n \n // Determine which model to use, in order of preference:\n // 1. Explicitly provided model parameter\n // 2. Task's model (if task is a config with model)\n // 3. TaskManager's providerModel\n let taskModel = model;\n if (!taskModel) {\n if (task instanceof Task) {\n taskModel = task.config.model;\n } else if ('model' in task) {\n taskModel = task.model;\n }\n }\n if (!taskModel && this.providerModel) {\n taskModel = this.providerModel;\n }\n \n // If this is not already a Task instance, create one\n const taskInstance =\n task instanceof Task\n ? task\n : new Task(\n {\n ...(task as TaskConfig),\n agentId: task.agentId || this.agentId,\n sessionId: task.sessionId || this.sessionId,\n model: taskModel // Pass the determined model to the task\n },\n this.memory,\n undefined, // No need to pass model here as we already set it in config\n this.database\n );\n\n // If task was created without memory, set it now\n if (this.memory && taskInstance instanceof Task) {\n taskInstance.setMemory(this.memory);\n }\n\n // Add task to memory\n this.tasks.set(taskInstance.id, taskInstance);\n\n // Check for a parent in dependencies and add it if needed\n this.addTaskWithParent(taskInstance);\n\n // Task is already saved to database via createTask static method\n \n logger.debug(`Task \"${taskInstance.config.name}\" (${taskInstance.id}) added to manager`);\n \n return taskInstance;\n } catch (error) {\n logger.error(\"Error adding existing task:\", error);\n throw error;\n }\n }\n\n /**\n * Helper method to check for parent ID in dependencies and add it if needed\n * @param task Task to check for parent dependencies\n */\n private addTaskWithParent(task: TaskInstance): void {\n try {\n // Add parent dependency if it exists\n if (task.config.dependencies && task.config.dependencies.length > 0) {\n const parentId = task.config.dependencies[0];\n const parent = this.getTask(parentId);\n if (parent) {\n // Parent exists, nothing to do\n return;\n }\n // Parent doesn't exist, warn about it\n logger.warn(`Parent task ${parentId} not found for task ${task.id}`);\n }\n } catch (error) {\n logger.error(`Error checking parent dependencies for task ${task.id}:`, error);\n }\n }\n\n /**\n * Get a task by ID\n * @param id ID of the task to retrieve\n * @returns Task instance if found, undefined otherwise\n */\n public getTask(id: string): TaskInstance | undefined {\n return this.tasks.get(id);\n }\n\n /**\n * Get all tasks managed by this instance\n * @returns Array of all task instances\n */\n public getAllTasks(): TaskInstance[] {\n return Array.from(this.tasks.values());\n }\n\n /**\n * Create and add a new task\n * @param config Configuration for the new task\n * @param model Optional LLM model to use for tool selection\n * @returns The created task instance\n */\n public async createTask(config: TaskConfig, model?: ProviderModel): Promise {\n try {\n // Determine which model to use, in order of preference:\n // 1. Explicitly provided model parameter\n // 2. Task's model (from config)\n // 3. TaskManager's providerModel\n let taskModel = model;\n if (!taskModel && config.model) {\n taskModel = config.model;\n }\n if (!taskModel && this.providerModel) {\n taskModel = this.providerModel;\n }\n \n // Create task with the manager's agent and session IDs if not provided\n const updatedConfig = {\n ...config,\n agentId: config.agentId || this.agentId,\n sessionId: config.sessionId || this.sessionId,\n model: taskModel\n };\n\n // Create the task with memory and database\n const task = await Task.createTask(updatedConfig, this.memory, undefined, this.database);\n\n // Add task to manager\n this.addExistingTask(task);\n \n logger.info(`Created new task: \"${task.config.name}\" (${task.id})`);\n\n return task;\n } catch (error) {\n logger.error(\"Error creating task:\", error);\n throw error;\n }\n }\n\n /**\n * Cancel a task by ID\n * @param id ID of the task to cancel\n * @returns true if task was found and canceled, false otherwise\n */\n public cancelTask(id: string): boolean {\n try {\n const task = this.getTask(id);\n if (task) {\n task.cancel();\n logger.info(`Task ${id} canceled`);\n return true;\n }\n logger.warn(`Task ${id} not found for cancellation`);\n return false;\n } catch (error) {\n logger.error(`Error canceling task ${id}:`, error);\n return false;\n }\n }\n\n /**\n * Load tasks from database\n * @returns Promise that resolves when tasks are loaded\n */\n private async loadTasksFromDatabase(): Promise {\n try {\n // Use database from instance if provided, otherwise create a new one\n const db = this.database || await createDatabase();\n const tasksTable = db.getTable('tasks');\n\n // Load all tasks from database\n const taskRecords = await tasksTable.find();\n\n for (const record of taskRecords) {\n try {\n // Parse JSON fields\n const plugins = record.plugins ? JSON.parse(record.plugins) : [];\n const input = record.input ? JSON.parse(record.input) : null;\n const dependencies = record.dependencies ? JSON.parse(record.dependencies) : [];\n const result = record.result ? JSON.parse(record.result) : null;\n\n // Create task config from database record\n const taskConfig: TaskConfig = {\n id: record.id,\n name: record.name,\n description: record.description,\n plugins,\n input,\n dependencies,\n agentId: record.agentId,\n sessionId: record.sessionId,\n };\n\n // Create task instance\n const task = new Task(taskConfig, this.memory, this.providerModel, db);\n\n // Restore task state\n task.status = record.status as TaskStatus;\n task.retries = record.retries || 0;\n task.createdAt = new Date(record.createdAt);\n task.startedAt = record.startedAt ? new Date(record.startedAt) : undefined;\n task.completedAt = record.completedAt ? new Date(record.completedAt) : undefined;\n task.result = result;\n task.agentId = record.agentId;\n task.sessionId = record.sessionId;\n task.contextId = record.contextId;\n\n // Add to tasks map\n this.tasks.set(task.id, task);\n\n logger.debug(`Loaded task ${task.id} from database with status: ${task.status}`);\n } catch (error) {\n logger.error(`Error loading task ${record.id} from database:`, error);\n // Continue loading other tasks\n }\n }\n\n logger.info(`Loaded ${this.tasks.size} tasks from database`);\n } catch (error) {\n logger.error(\"Error loading tasks from database:\", error);\n // Don't throw error - allow task manager to continue without database tasks\n }\n }\n\n /**\n * Wait for tasks to be loaded from database\n * @returns Promise that resolves when tasks are loaded\n */\n public async waitForTasksLoaded(): Promise {\n if (this.tasksLoaded) {\n return;\n }\n\n if (this.loadingPromise) {\n return this.loadingPromise;\n }\n\n // If tasks haven't been loaded yet, load them now\n return this.loadTasksFromDatabase();\n }\n\n /**\n * Execute a specific task by ID\n * @param id ID of the task to execute\n * @param input Optional input data to pass to the task\n * @returns Promise that resolves with the task execution result\n */\n public async executeTask(id: string, input?: any): Promise {\n try {\n await this.waitForTasksLoaded();\n \n const task = this.getTask(id);\n if (!task) {\n throw new Error(`Task with ID ${id} not found`);\n }\n\n logger.info(`Executing task: \"${task.config.name}\" (${id})`);\n return task.execute(input);\n } catch (error) {\n logger.error(`Error executing task ${id}:`, error);\n throw error;\n }\n }\n\n /**\n * Get all tasks\n * @deprecated Use getAllTasks() instead for newer implementations\n * @returns Array of all task instances\n */\n public getTasks(): TaskInstance[] {\n logger.debug(\"DEPRECATED: getTasks() called, use getAllTasks() instead\");\n return this.getAllTasks();\n }\n\n /**\n * Get tasks by agent ID\n * @param agentId Agent ID to filter by\n * @returns Array of tasks belonging to the specified agent\n */\n public getTasksByAgent(agentId: string): TaskInstance[] {\n return Array.from(this.tasks.values()).filter(\n (task) => task.config.agentId === agentId\n );\n }\n\n /**\n * Get tasks by session ID\n * @param sessionId Session ID to filter by\n * @returns Array of tasks belonging to the specified session\n */\n public getTasksBySession(sessionId: string): TaskInstance[] {\n return Array.from(this.tasks.values()).filter(\n (task) => task.config.sessionId === sessionId\n );\n }\n\n /**\n * Set the agent ID for this task manager\n * @param agentId Agent ID to set\n */\n public setAgentId(agentId: string): void {\n this.agentId = agentId;\n logger.debug(`Task manager agent ID set to ${agentId}`);\n }\n\n /**\n * Set the session ID for this task manager\n * @param sessionId Session ID to set\n */\n public setSessionId(sessionId: string): void {\n this.sessionId = sessionId;\n logger.debug(`Task manager session ID set to ${sessionId}`);\n }\n\n /**\n * Set the memory instance for this task manager\n * @param memory Memory instance to use for task context storage\n */\n public setMemory(memory: MemoryInstance): void {\n try {\n this.memory = memory;\n \n // Update memory for all tasks\n for (const task of this.tasks.values()) {\n if (task instanceof Task) {\n task.setMemory(memory);\n }\n }\n \n logger.debug(\"Memory instance set for task manager\");\n } catch (error) {\n logger.error(\"Error setting memory for task manager:\", error);\n throw error;\n }\n }\n\n /**\n * Set the provider model to use for tasks\n * @param model Provider model to use\n */\n public setProviderModel(model: ProviderModel): void {\n this.providerModel = model;\n logger.debug(`Task manager provider model set to ${model.name}`);\n }\n\n /**\n * Get the current provider model\n * @returns The current provider model or undefined\n */\n public getProviderModel(): ProviderModel | undefined {\n return this.providerModel;\n }\n\n /**\n * Run specified tasks (or all if no IDs provided)\n * @param taskIds Optional array of task IDs to run\n * @returns Promise that resolves with a map of task IDs to results\n */\n public async run(taskIds?: string[]): Promise> {\n await this.waitForTasksLoaded();\n \n const results = new Map();\n let tasksToRun: TaskInstance[];\n\n // Determine which tasks to run\n if (taskIds && taskIds.length > 0) {\n tasksToRun = taskIds\n .map((id) => this.getTask(id))\n .filter((task): task is TaskInstance => !!task);\n \n logger.info(`Running ${tasksToRun.length} specified tasks`);\n } else {\n tasksToRun = this.getAllTasks();\n logger.info(`Running all ${tasksToRun.length} tasks`);\n }\n\n // Create a dependency graph and task execution order\n const taskDependencyMap = new Map>();\n const dependentTasksMap = new Map>();\n \n // Build dependency maps\n for (const task of tasksToRun) {\n const taskId = task.id;\n const dependencies = new Set();\n \n // Check both dependencies and dependsOn fields (for backward compatibility)\n if (task.config.dependencies && task.config.dependencies.length > 0) {\n for (const depId of task.config.dependencies) {\n if (this.getTask(depId)) {\n dependencies.add(depId);\n }\n }\n }\n \n // Add dependsOn dependencies (newer approach)\n if (task.config.dependsOn && task.config.dependsOn.length > 0) {\n for (const depId of task.config.dependsOn) {\n if (this.getTask(depId)) {\n dependencies.add(depId);\n }\n }\n }\n \n // Store in the dependency map\n taskDependencyMap.set(taskId, dependencies);\n \n // Update dependent tasks map (reverse mapping)\n for (const depId of dependencies) {\n if (!dependentTasksMap.has(depId)) {\n dependentTasksMap.set(depId, new Set());\n }\n dependentTasksMap.get(depId)!.add(taskId);\n }\n }\n \n // Track completed tasks and their results\n const completedTasks = new Set();\n const taskQueue: TaskInstance[] = [];\n \n // Initial pass: find tasks with no dependencies\n for (const task of tasksToRun) {\n const dependencies = taskDependencyMap.get(task.id) || new Set();\n if (dependencies.size === 0) {\n taskQueue.push(task);\n }\n }\n \n logger.debug(`Initial task queue: ${taskQueue.length} tasks ready to run`);\n \n // Execute tasks in dependency order with proper concurrency\n const concurrency = this.config.concurrency || 5;\n \n while (taskQueue.length > 0) {\n // Take up to concurrency tasks from the queue\n const batchTasks = taskQueue.splice(0, concurrency);\n \n // Execute this batch in parallel\n const batchPromises = batchTasks.map(async (task) => {\n try {\n // Pass in any dependent task outputs as input\n const dependencies = taskDependencyMap.get(task.id) || new Set();\n \n // If there are dependencies, prepare to collect their outputs\n if (dependencies.size > 0) {\n const dependencyOutputs: Record = {};\n let hasOutputs = false;\n \n // Collect outputs from all dependencies\n for (const depId of dependencies) {\n if (results.has(depId)) {\n const depResult = results.get(depId)!;\n if (depResult.success && depResult.output) {\n dependencyOutputs[depId] = depResult.output;\n hasOutputs = true;\n }\n }\n }\n \n // If we have dependency outputs, merge them with task input\n if (hasOutputs) {\n // Create merged input that preserves original task input\n const mergedInput = {\n ...(task.config.input || {}),\n _dependencyOutputs: dependencyOutputs\n };\n \n logger.debug(`Task ${task.id} received outputs from ${Object.keys(dependencyOutputs).length} dependencies`);\n \n // Execute task with merged input\n const result = await task.execute(mergedInput);\n results.set(task.id, result);\n } else {\n // No usable outputs from dependencies, just run normally\n const result = await task.execute();\n results.set(task.id, result);\n }\n } else {\n // No dependencies, run task with original input\n const result = await task.execute();\n results.set(task.id, result);\n }\n \n // Mark this task as completed\n completedTasks.add(task.id);\n \n // Check if this task completion unblocks any dependent tasks\n if (dependentTasksMap.has(task.id)) {\n const dependentTasks = dependentTasksMap.get(task.id)!;\n \n for (const dependentId of dependentTasks) {\n // Get the dependent task\n const dependentTask = this.getTask(dependentId);\n if (!dependentTask) continue;\n \n // Check if all dependencies of this dependent task are completed\n const allDependenciesCompleted = Array.from(taskDependencyMap.get(dependentId) || new Set())\n .every(depId => completedTasks.has(depId));\n \n // If all dependencies are completed, add this task to the queue\n if (allDependenciesCompleted && !completedTasks.has(dependentId)) {\n taskQueue.push(dependentTask);\n logger.debug(`Task ${dependentId} unblocked and added to queue`);\n }\n }\n }\n } catch (error) {\n logger.error(`Error executing task ${task.id}:`, error);\n results.set(task.id, {\n success: false,\n error: error instanceof Error ? error : new Error(String(error))\n });\n \n // Mark as completed even though it failed\n completedTasks.add(task.id);\n }\n });\n \n // Wait for this batch to complete before processing next batch\n await Promise.all(batchPromises);\n }\n \n // Check for unresolved tasks (might be due to circular dependencies)\n const unresolvedTasks = tasksToRun.filter(t => !completedTasks.has(t.id));\n if (unresolvedTasks.length > 0) {\n logger.warn(`${unresolvedTasks.length} tasks were not executed due to dependency issues or circular dependencies`);\n \n // Add failed results for these tasks\n for (const task of unresolvedTasks) {\n results.set(task.id, {\n success: false,\n error: new Error(\"Task not executed due to unresolved dependencies\")\n });\n }\n }\n \n logger.info(`Completed running ${completedTasks.size} tasks`);\n return results;\n }\n\n /**\n * Cancel a specific task\n * @deprecated Use cancelTask() instead for newer implementations\n * @param taskId ID of the task to cancel\n * @returns true if task was found and canceled, false otherwise\n */\n public cancel(taskId: string): boolean {\n logger.debug(\"DEPRECATED: cancel() called, use cancelTask() instead\");\n return this.cancelTask(taskId);\n }\n\n /**\n * Cancel all tasks\n */\n public cancelAll(): void {\n try {\n for (const task of this.tasks.values()) {\n task.cancel();\n }\n logger.info(`Canceled all ${this.tasks.size} tasks`);\n } catch (error) {\n logger.error(\"Error canceling all tasks:\", error);\n }\n }\n} ","import { TaskManager } from \"./manager\";\nimport { Task } from \"./task\";\nimport { TaskManagerInstance, TaskConfig, TaskInstance, TaskManagerConfig } from \"../types/task\";\nimport { MemoryInstance } from \"../types\";\nimport { logger } from \"../utils\";\n\n/**\n * Create a new TaskManager instance\n * @param config Configuration for the task manager\n * @returns A new TaskManager instance\n */\nexport const createTaskManager = (\n config?: TaskManagerConfig\n): TaskManagerInstance => {\n try {\n logger.debug(\"Creating task manager instance\");\n return new TaskManager(config);\n } catch (error) {\n logger.error(\"Error creating task manager:\", error);\n throw error;\n }\n};\n\n/**\n * Create a new task asynchronously\n * @param config Configuration for the task\n * @param memory Optional memory instance for task context storage\n * @returns Promise that resolves to the new task instance\n */\nexport const createTask = async (\n config: TaskConfig,\n memory?: MemoryInstance\n): Promise => {\n try {\n logger.debug(`Creating task \"${config.name}\" asynchronously`);\n return await Task.createTask(config, memory);\n } catch (error) {\n logger.error(`Error creating task \"${config.name}\":`, error);\n throw error;\n }\n};\n\n/**\n * Create a new task synchronously\n * @param config Configuration for the task\n * @param memory Optional memory instance for task context storage\n * @returns The new task instance\n */\nexport const createTaskSync = (\n config: TaskConfig,\n memory?: MemoryInstance\n): TaskInstance => {\n try {\n logger.debug(`Creating task \"${config.name}\" synchronously`);\n return Task.createTaskSync(config, memory);\n } catch (error) {\n logger.error(`Error creating task \"${config.name}\":`, error);\n throw error;\n }\n};\n\n\nexport { TaskManager, Task }; 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