@c8y/ngx-components/fesm2022/c8y-ngx-components-ai.mjs.map

Angular modules for Cumulocity IoT applications

{"version":3,"file":"c8y-ngx-components-ai.mjs","sources":["../../ai/ai.service.ts","../../ai/c8y-ngx-components-ai.ts"],"sourcesContent":["import { inject, Injectable } from '@angular/core';\nimport { FetchClient } from '@c8y/client';\nimport { Observable, Subscriber } from 'rxjs';\nimport {\n  AgentDefinition,\n  AgentHealthCheckResponse,\n  AIAssistantMessage,\n  AIMessage,\n  AIMessagePart,\n  AIStreamResponse,\n  ClientAgentDefinition,\n  ObjectAgentResponse,\n  ToolCallPart\n} from './ai.model';\nimport { gettext } from '@c8y/ngx-components/gettext';\n\n// Internal implementation detail; maybe we don't even need this enum since\nexport enum DataStreamType {\n  TEXT_DELTA = 'text-delta',\n  TOOL_CALL = 'tool-call',\n  TOOL_INPUT_START = 'tool-input-start',\n  TOOL_INPUT_DELTA = 'tool-input-delta',\n  TOOL_CALL_STREAMING = 'tool-call-streaming',\n  TOOL_CALL_DELTA = 'tool-call-delta',\n  TOOL_RESULT = 'tool-result',\n  REASONING = 'reasoning',\n  REASONING_DELTA = 'reasoning-delta',\n  REDACTED_REASONING = 'redacted-reasoning',\n  REASONING_SIGNATURE = 'reasoning-signature',\n  FINISH_REASONING = 'finish-reasoning',\n  FINISH = 'finish',\n  FINISH_STEP = 'finish-step',\n  ERROR = 'error',\n  DATA = 'data',\n  MESSAGE_ANNOTATIONS = 'message-annotations',\n  SOURCE = 'source',\n  FILE = 'file',\n  STEP_START = 'start-step',\n  STEP_FINISH = 'finish-step'\n}\n\n/** This service manages communication with the AI Agent Manager microservice, supporting\n * management of agents, and sending requests to the agents.\n */\n@Injectable({\n  providedIn: 'root'\n})\nexport class AIService {\n  private baseUrl = '/service/ai';\n  private client: FetchClient;\n\n  // nb: allowing the client to be passed in the constructor allows efficient unit testing without setting up the Angular TestBed\n  constructor(client?: FetchClient) {\n    this.client = client ?? inject(FetchClient);\n  }\n\n  /**\n   * Creates or updates an agent.\n   * @param def The agent definition\n   */\n  async createOrUpdateAgent(def: AgentDefinition): Promise<void> {\n    const health = await this.getAgentHealth(def.name);\n    let resource = `${this.baseUrl}/agent/${def.type}`;\n    let method = 'POST';\n    if (health.exists) {\n      resource = `${this.baseUrl}/agent/${def.type}/${def.name}`;\n      method = 'PUT';\n    }\n    const response = await this.client.fetch(resource, {\n      body: JSON.stringify(def),\n      method,\n      headers: { 'Content-Type': 'application/json' }\n    });\n\n    if (!response.ok) {\n      throw new Error(`Failed to create agent: ${response.statusText}`);\n    }\n  }\n\n  /**\n   * Check if an agent exists and is ready for use.\n   * @param name Agent name (optional)\n   * @param fromApp The app context path to check for an agent. This can be used, if the agent should get distributed by the plugin (optional).\n   * @returns Agent health check response.\n   */\n  async getAgentHealth(name = '', fromApp?: string): Promise<AgentHealthCheckResponse> {\n    const response = await this.client.fetch(\n      `${this.baseUrl}/agent/test/${name || crypto.randomUUID()}${fromApp ? `?fromApp=${fromApp}` : ''}`,\n      {\n        method: 'GET',\n        headers: { 'Content-Type': 'application/json' }\n      }\n    );\n\n    if (!response.ok) {\n      return {\n        status: response.status === 403 ? 'missing-permissions' : 'missing-microservice',\n        exists: false,\n        canCreate: false,\n        isProviderConfigured: false,\n        messages: response.status === 403 ? [] : [response.statusText]\n      };\n    }\n    const json: AgentHealthCheckResponse = await response.json();\n    if (!json.isProviderConfigured) {\n      json.status = 'missing-provider';\n    } else if (!json.exists) {\n      json.status = 'missing-agent';\n    } else {\n      json.status = 'ready';\n    }\n    return json;\n  }\n\n  /**\n   * Send a text message to the agent.\n   * @param name Agent name\n   * @param messages The message to send, including any previous message history.\n   * Typically you should use `defaultPruneMessagesForAgent` or a custom function to prepare the message history\n   * by removing unnecessary content such as large/old tool inputs/outputs.\n   * @param variables Variables to include\n   * @param fromApp The app context path to check for an agent. This can be used, if the agent should get distributed by the plugin (optional).\n   * @returns Text response from the agent.\n   */\n  async text(\n    name: string,\n    messages: AIMessage[],\n    variables: Record<string, unknown>,\n    fromApp?: string\n  ): Promise<string> {\n    const parsedMessages = this.convertToAgentMessageFormat(messages);\n\n    const data = this.client.fetch(\n      `${this.baseUrl}/agent/text/${name}${fromApp ? `?fromApp=${fromApp}` : ''}`,\n      {\n        body: JSON.stringify({ messages: parsedMessages, variables }),\n        method: 'POST',\n        headers: { 'Content-Type': 'application/json' }\n      }\n    );\n\n    const response = await data;\n    if (!response.ok) {\n      throw new Error(`Failed to talk with agent: ${response.statusText}`);\n    }\n    const text = await response.text();\n    return text;\n  }\n\n  /**\n   * Send messages to an object-type agent via the non-streaming test endpoint.\n   * Object agents return a single structured JSON response rather than a stream.\n   *\n   * @param agent A ClientAgentDefinition with `snapshot: true` (required for test endpoint).\n   * @param messages Messages to send\n   * @param variables Variables to include\n   * @param abortController An AbortController to cancel the request.\n   * @returns The full response including the structured object and usage information.\n   */\n  async callObjectAgent(\n    agent: string | ClientAgentDefinition,\n    messages: AIMessage[],\n    variables: Record<string, unknown>,\n    abortController: AbortController\n  ): Promise<ObjectAgentResponse> {\n    const parsedMessages = this.convertToAgentMessageFormat(messages);\n    const agentName = typeof agent === 'string' ? agent : agent.definition.name;\n    const useTest = typeof agent !== 'string' && agent.snapshot;\n    const baseUrl = useTest\n      ? `${this.baseUrl}/agent/test/${typeof agent !== 'string' ? agent.definition.type : 'object'}`\n      : `${this.baseUrl}/agent/${typeof agent !== 'string' ? agent.definition.type : 'object'}/${agentName}`;\n    let body = JSON.stringify({ messages: parsedMessages, variables });\n    if (useTest) {\n      const agentCopy = {\n        ...(agent as ClientAgentDefinition).definition\n      };\n      agentCopy.agent.messages = parsedMessages;\n      agentCopy.agent.variables = variables;\n      body = JSON.stringify(agentCopy);\n    }\n\n    const response = await this.client.fetch(`${baseUrl}?fullResponse=true`, {\n      method: 'POST',\n      body,\n      headers: {\n        ...this.client.defaultHeaders,\n        'content-type': 'application/json'\n      },\n      signal: abortController.signal\n    });\n\n    if (response.status > 300) {\n      const data = await response.json();\n      throw new Error(JSON.stringify(data, null, 2));\n    }\n\n    const data = await response.json();\n\n    if (!('object' in data)) {\n      throw new Error('Invalid response: missing required \"object\" field');\n    }\n\n    if (typeof data.object !== 'object' || data.object === null) {\n      throw new Error('Invalid response: \"object\" field must be an object');\n    }\n\n    // Validate optional totalUsage field if present\n    if (data.totalUsage !== undefined) {\n      if (typeof data.totalUsage !== 'object' || data.totalUsage === null) {\n        throw new Error('Invalid response: \"totalUsage\" must be an object');\n      }\n    }\n\n    return data;\n  }\n\n  /**\n   * Stream a text message to the agent.\n   * @param agent Agent name or an agent definition.\n   * @param messages The message to send, including any previous message history.\n   * Typically you should use `defaultPruneMessagesForAgent` or a custom function to prepare the message history\n   * by removing unnecessary content such as large/old tool inputs/outputs.\n   * @param variables Variables to include\n   * @param abortController An AbortController to cancel the request.\n   * @param fromApp The app context path to check for an agent. This can be used, if the agent should get distributed by the plugin (optional).\n   * @returns An observable that emits AIStreamResponse including partial `AIMessage` objects as they are received.\n   * When additional useful metadata is available, this will be streamed with type indicating it is metadata.\n   * The observable can be cancelled using the provided AbortController.\n   * The observable will emit an error if the request fails or is aborted.\n   * The observable will complete when the stream is finished.\n   *\n   * The messages sent to the agent can include special options:\n   * - `hiddenContent`: If set, this content will be sent to the agent instead of the `content` field.\n   * - `skipToLLM`: If set to true, this message will be skipped when sending to the agent.\n   *\n   * Example usage:\n   * ```typescript\n   * const abortController = new AbortController();\n   * const messages: AIMessage[] = [\n   *   { role: 'user', content: 'Hello' },\n   *   { role: 'assistant', content: 'Hi there!' },\n   *   { role: 'user', content: 'Tell me a joke.', options: { hiddenContent: 'Tell me a joke about cats.' } }\n   * ];\n   * const observable = aiService.stream$('my-agent', messages, {}, abortController);\n   * const subscription = observable.subscribe({\n   *   next: (response) => { console.log('Received message update:', response.message); },\n   *   error: (err) => console.error('Error:', err),\n   *   complete: () => console.log('Stream complete')\n   * });\n   *\n   * // To cancel the request:\n   * abortController.abort();\n   * subscription.unsubscribe();\n   * ```\n   */\n  async stream$(\n    agent: string | ClientAgentDefinition,\n    messages: AIMessage[],\n    variables: { [key: string]: any },\n    abortController: AbortController,\n    fromApp?: string\n  ): Promise<Observable<AIStreamResponse>> {\n    const parsedMessages = this.convertToAgentMessageFormat(messages);\n\n    const agentName = typeof agent === 'string' ? agent : agent.definition.name;\n    const useTest = typeof agent !== 'string' && agent.snapshot;\n    const baseUrl = useTest\n      ? `${this.baseUrl}/agent/test/text`\n      : `${this.baseUrl}/agent/text/${agentName}`;\n    let body = JSON.stringify({ messages: parsedMessages, variables });\n    if (useTest) {\n      const agentCopy = {\n        ...agent.definition\n      };\n      agentCopy.agent['messages'] = parsedMessages;\n      agentCopy.agent['variables'] = variables;\n      body = JSON.stringify(agentCopy);\n    }\n\n    const response = await this.client.fetch(\n      `${baseUrl}?fullResponse=true${fromApp ? `&fromApp=${fromApp}` : ''}`,\n      {\n        method: 'POST',\n        body,\n        headers: {\n          ...this.client.defaultHeaders,\n          'content-type': 'application/json',\n          accept: 'text/event-stream'\n        },\n        signal: abortController.signal\n      }\n    );\n\n    const stream = response.body;\n    const decoder = new TextDecoder();\n\n    return new Observable<AIStreamResponse>(observer => {\n      if (response.status > 300) {\n        response\n          .json()\n          .then(data => {\n            observer.error(`Error communicating with server: ${JSON.stringify(data, null, 2)}`);\n          })\n          .catch(err => {\n            observer.error(\n              `Error communicating with server: HTTP ${response.status}: ${response.statusText}; ${err}`\n            );\n          });\n        return;\n      }\n\n      if (!stream) {\n        observer.error('Server error - no response body');\n        return;\n      }\n\n      const reader = stream.getReader();\n      const message: AIAssistantMessage = {\n        role: 'assistant',\n        content: []\n      };\n\n      const abortHandler = (e: Event) => {\n        const reason = (e?.target as AbortSignal)?.reason || gettext('Aborted');\n        reader.cancel();\n        observer.error(new DOMException(reason, 'AbortError'));\n      };\n\n      abortController.signal.addEventListener('abort', abortHandler);\n\n      let buffer = '';\n\n      const read = () => {\n        reader.read().then(({ done, value }) => {\n          if (done) {\n            if (buffer.trim()) this.processLine(buffer, observer, message); // process any remaining data\n            observer.complete();\n            return;\n          }\n          buffer += decoder.decode(value, { stream: true });\n          const lines = buffer.split('\\n\\n');\n          buffer = lines.pop() || '';\n          for (const line of lines) {\n            if (line.trim()) this.processLine(line, observer, message);\n          }\n          read();\n        });\n      };\n\n      read();\n      return () => {\n        abortController.signal.removeEventListener('abort', abortHandler);\n        reader.cancel();\n      };\n    });\n  }\n\n  protected convertToAgentMessageFormat(messages: AIMessage[]): object[] {\n    // We will need to rewrite this to include tool outputs soon (effectively this means targetting LanguageModelV*ToolResultOutput).\n    // Once we have more fields than just role and content, we will need to convert between our format and\n    // the Vercel format expected by the agent manager (e.g. steps->content parts)\n    return messages.map(m => {\n      if (m.role !== 'assistant') {\n        return {\n          role: m.role,\n          content: m.content\n        };\n      }\n\n      // For assistant messages, check if there's an object part\n      const objectPart = m.content.find(\n        (part): part is { type: 'object'; jsonContent: string } => part.type === 'object'\n      );\n\n      if (objectPart) {\n        // If there's an object part, parse and send the JSON object as content\n        return {\n          role: m.role,\n          content: objectPart.jsonContent\n        };\n      }\n\n      // Otherwise, send text parts as joined string\n      return {\n        role: m.role,\n        content: m.content\n          .filter((part): part is { type: 'text'; text: string } => part.type === 'text')\n          .map(part => part.text)\n          .join('\\n\\n')\n      };\n    });\n  }\n\n  /**\n   * Convert a tool output from the MCP/wire format (which is different/more compact than LanguageModelV3ToolResultOutput)\n   * into our own standard representation, which can be roundtripped to the format we need to provide when making future requests.\n   *\n   * Handles different data types from Vercel's streaming API:\n   * - Text: stored as simple string in output field\n   * - JSON objects: stored as-is in output field\n   * - Content arrays: stored in output field with outputType=\"content\" to enable roundtripping\n   * - Single text in content array: unwrapped to simple string for simpler handling (and easier truncation)\n   * - Errors: error flag set to true\n   */\n  protected convertStreamingToolOutputToToolCallPart(output: any, target: ToolCallPart) {\n    if (!output) {\n      return;\n    }\n\n    if (output.isError || output.type === 'error-text' || output.type === 'error-json')\n      target.error = true;\n\n    // Handle content array format from streaming API\n    if (output.content && Array.isArray(output.content)) {\n      // Single text element - unwrap to simple string for simplicity\n      if (output.content.length === 1 && output.content[0].type === 'text') {\n        target.output = output.content[0].text;\n        return;\n      }\n\n      // Multiple elements or mixed types - preserve as content array\n      target.output = output.content;\n      target.outputType = 'content'; // Mark as content to enable roundtripping\n      return;\n    }\n\n    // Plain string or structured (JSON-serializable) object - store as-is\n    target.output = output;\n  }\n\n  /** Add the specified tool call info to the current step. If this toolCallId is already known,\n   * updates it rather than adding a new one, since for UI rendering and compactness of data it's\n   * easiest to just have one item per tool call.\n   *\n   * Returns the changed item.\n   */\n  private addToolCallInfo(\n    type: ToolCallPart['type'],\n    rawInfo: any,\n    addTo: AIMessagePart[]\n  ): ToolCallPart {\n    // Vercel v4 seems to set this to \"id\" not \"toolCallId\" in some places despite doc to the contrary, so normalize it here\n    if (!rawInfo.toolCallId) rawInfo.toolCallId = rawInfo.id;\n\n    let existing: ToolCallPart | undefined = addTo.find(\n      (tc): tc is ToolCallPart =>\n        tc.type.startsWith('tool-') && (tc as ToolCallPart).toolCallId === rawInfo.toolCallId\n    );\n\n    if (existing) {\n      existing.type = type;\n    } else if (!existing) {\n      existing = { type: type, toolName: rawInfo.toolName, toolCallId: rawInfo.toolCallId };\n      addTo.push(existing);\n    }\n    if (rawInfo?.input) existing.input = rawInfo.input;\n    if (rawInfo?.output) this.convertStreamingToolOutputToToolCallPart(rawInfo.output, existing);\n\n    return existing;\n  }\n\n  /** Unpacks data from the Vercel Data Stream Protocol (sent by the Agent Manager over SSE) to update the message\n   *\n   * See streamText().fullStream() from https://ai-sdk.dev/docs/ai-sdk-ui/stream-protocol#data-stream-protocol\n   *\n   * This is similar to what the Vercel `toUIMessageStreamResponse` API does (though it'd need the agent manager to publish the\n   * UIMessageStream which has a bit more - useful - data than what is included here).\n   *\n   * We always use the same parts object, to make it easier for consumers (eg. agent-chat)\n   * to add items to the list and have them still there on the next update from this service.\n   */\n  private processLine(\n    line: string,\n    observer: Subscriber<AIStreamResponse>,\n    message: AIAssistantMessage\n  ) {\n    if (!line.trim()) {\n      return;\n    }\n\n    try {\n      let data: any = {};\n      let type = '';\n      try {\n        data = JSON.parse(line.replace('data: ', ''));\n        type = data.type;\n      } catch (e) {\n        console.error('Error parsing line from AI response: ', line, e);\n        return;\n      }\n\n      let lastPart = !message.content ? undefined : message.content[message.content.length - 1];\n\n      switch (type) {\n        case DataStreamType.STEP_START:\n          if (data.request && data.request.body) {\n            observer.next({\n              message: message,\n              changedPart: {\n                type: 'response-metadata',\n                model: data.request.body.model,\n                systemPrompt: data.request.body.systemPrompt\n              }\n            });\n          }\n          // Don't add consecutive duplicate step bounaries\n          if (!lastPart || lastPart.type !== 'step-start') {\n            message.content.push({ type: 'step-start' });\n            observer.next({ message, changedPart: message.content[message.content.length - 1] });\n          }\n\n          return;\n\n        case DataStreamType.REASONING_DELTA:\n          const reasoning = data.text;\n          if (!reasoning) return;\n          if (lastPart?.type === 'reasoning') {\n            lastPart.text += reasoning;\n          } else {\n            lastPart = { type: 'reasoning', text: reasoning };\n            message.content.push(lastPart);\n          }\n          observer.next({ message, changedPart: lastPart });\n          return;\n\n        case DataStreamType.TEXT_DELTA:\n          const text = data.textDelta || data.text;\n          if (!text) return;\n          if (lastPart?.type === 'text') {\n            lastPart.text += text;\n          } else {\n            lastPart = { type: 'text', text: text };\n            message.content.push(lastPart);\n          }\n          observer.next({ message, changedPart: lastPart });\n          return;\n\n        case DataStreamType.TOOL_INPUT_START:\n          observer.next({\n            message,\n            changedPart: this.addToolCallInfo('tool-input-streaming', data, message.content)\n          });\n          return;\n\n        case DataStreamType.TOOL_CALL:\n          observer.next({\n            message,\n            changedPart: this.addToolCallInfo('tool-executing', data, message.content)\n          });\n          return;\n\n        case DataStreamType.TOOL_RESULT:\n          observer.next({\n            message,\n            changedPart: this.addToolCallInfo('tool-result', data, message.content)\n          });\n          return;\n\n        case DataStreamType.FINISH:\n          message.finishReason = data.finishReason; // FUTURE could be anything, may not match the options in AIMessage\n          message.usage = data.totalUsage;\n          observer.next({ message, changedPart: undefined });\n          observer.complete();\n          return;\n\n        case DataStreamType.ERROR:\n          message.finishReason = 'error';\n          observer.next({ message, changedPart: undefined });\n          observer.error(data?.message || data?.errorText || data);\n          // Error is instead of completing the stream\n          return;\n\n        // Should we also handle other types such as \"abort\"?\n      }\n    } catch (e) {\n      observer.error(e);\n    }\n  }\n}\n\n/**\n * The default implementation of for reducing the size of the message history so it is ready to be sent to the LLM agent.\n *\n * This is important since old tool inputs and outputs can be very large and fill up the agent's context window.\n *\n * Implements `pruneMessagesFunction`.\n *\n * When sending an AI request you can provide your own function instead of using this one,\n * or you can write your own function that calls this and adds extra logic,\n * for example to suppress specific tools from the AIMessage, or\n * retain only the final step text in multi-step responses, etc.\n *\n * The current default implementation preserves text content, but does not include any tool calls.\n * This may change in future releases.\n */\nexport function defaultPruneMessagesForAgent(messages: AIMessage[]): AIMessage[] {\n  // When we change this to include tool calls, we should remove large inputs and truncate large and/or old outputs\n  // to avoid too many tokens and context window consumption.\n  // Some applications will want to entirely remove specific tool calls that have only transient use,\n  // which can be done by providing their own pruneMessagesForAgent function.\n  return messages.map(m => {\n    if (m.role !== 'assistant') {\n      return {\n        role: m.role,\n        content: m.content\n      };\n    }\n\n    const textParts = m.content.filter(\n      (part): part is { type: 'text'; text: string } => part.type === 'text'\n    );\n    const objectParts = m.content.filter(\n      (part): part is { type: 'object'; jsonContent: string } => part.type === 'object'\n    );\n\n    const content: AIMessagePart[] = [];\n\n    // Only add text part if there are text parts\n    if (textParts.length > 0) {\n      content.push({\n        type: 'text',\n        text: textParts.map(part => part.text).join('\\n\\n')\n      });\n    }\n\n    // Preserve object parts for object agents\n    content.push(...objectParts);\n\n    return {\n      role: m.role,\n      content\n    };\n  });\n}\n","/**\n * Generated bundle index. Do not edit.\n */\n\nexport * from 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