{"version":3,"file":"index.cjs","sources":["../../../../src/llm/anthropic/index.ts"],"sourcesContent":["import { AIMessageChunk } from '@langchain/core/messages';\nimport { ChatAnthropicMessages } from '@langchain/anthropic';\nimport { ChatGenerationChunk } from '@langchain/core/outputs';\nimport type { BaseChatModelParams } from '@langchain/core/language_models/chat_models';\nimport type {\n BaseMessage,\n UsageMetadata,\n MessageContentComplex,\n} from '@langchain/core/messages';\nimport type { CallbackManagerForLLMRun } from '@langchain/core/callbacks/manager';\nimport type { AnthropicInput } from '@langchain/anthropic';\nimport type { Anthropic } from '@anthropic-ai/sdk';\nimport type {\n AnthropicMessageCreateParams,\n AnthropicStreamingMessageCreateParams,\n AnthropicStreamUsage,\n AnthropicMessageStartEvent,\n AnthropicMessageDeltaEvent,\n AnthropicOutputConfig,\n} from '@/llm/anthropic/types';\nimport { _makeMessageChunkFromAnthropicEvent } from './utils/message_outputs';\nimport { _convertMessagesToAnthropicPayload } from './utils/message_inputs';\nimport { handleToolChoice } from './utils/tools';\nimport { TextStream } from '@/llm/text';\n\nfunction _toolsInParams(\n params: AnthropicMessageCreateParams | AnthropicStreamingMessageCreateParams\n): boolean {\n return !!(params.tools && params.tools.length > 0);\n}\nfunction _documentsInParams(\n params: AnthropicMessageCreateParams | AnthropicStreamingMessageCreateParams\n): boolean {\n for (const message of params.messages ?? []) {\n if (typeof message.content === 'string') {\n continue;\n }\n for (const block of message.content ?? []) {\n if (\n typeof block === 'object' &&\n block != null &&\n block.type === 'document' &&\n block.citations != null &&\n typeof block.citations === 'object' &&\n block.citations.enabled\n ) {\n return true;\n }\n }\n }\n return false;\n}\n\nfunction _thinkingInParams(\n params: AnthropicMessageCreateParams | AnthropicStreamingMessageCreateParams\n): boolean {\n return !!(\n params.thinking &&\n (params.thinking.type === 'enabled' || params.thinking.type === 'adaptive')\n );\n}\n\nfunction _compactionInParams(\n params: AnthropicMessageCreateParams | AnthropicStreamingMessageCreateParams\n): boolean {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n const cm = (params as any).context_management;\n return !!cm?.edits?.some(\n (e: { type: string }) => e.type === 'compact_20260112'\n );\n}\n\nfunction extractToken(\n chunk: AIMessageChunk\n): [string, 'string' | 'input' | 'content'] | [undefined] {\n if (typeof chunk.content === 'string') {\n return [chunk.content, 'string'];\n } else if (\n Array.isArray(chunk.content) &&\n chunk.content.length >= 1 &&\n 'input' in chunk.content[0]\n ) {\n return typeof chunk.content[0].input === 'string'\n ? [chunk.content[0].input, 'input']\n : [JSON.stringify(chunk.content[0].input), 'input'];\n } else if (\n Array.isArray(chunk.content) &&\n chunk.content.length >= 1 &&\n 'text' in chunk.content[0]\n ) {\n return [chunk.content[0].text, 'content'];\n } else if (\n Array.isArray(chunk.content) &&\n chunk.content.length >= 1 &&\n 'thinking' in chunk.content[0]\n ) {\n return [chunk.content[0].thinking, 'content'];\n }\n return [undefined];\n}\n\nfunction cloneChunk(\n text: string,\n tokenType: string,\n chunk: AIMessageChunk\n): AIMessageChunk {\n if (tokenType === 'string') {\n return new AIMessageChunk(Object.assign({}, chunk, { content: text }));\n } else if (tokenType === 'input') {\n return chunk;\n }\n const content = chunk.content[0] as MessageContentComplex;\n if (tokenType === 'content' && content.type === 'text') {\n return new AIMessageChunk(\n Object.assign({}, chunk, {\n content: [Object.assign({}, content, { text })],\n })\n );\n } else if (tokenType === 'content' && content.type === 'text_delta') {\n return new AIMessageChunk(\n Object.assign({}, chunk, {\n content: [Object.assign({}, content, { text })],\n })\n );\n } else if (tokenType === 'content' && content.type?.startsWith('thinking')) {\n return new AIMessageChunk(\n Object.assign({}, chunk, {\n content: [Object.assign({}, content, { thinking: text })],\n })\n );\n }\n\n return chunk;\n}\n\nexport type CustomAnthropicInput = AnthropicInput & {\n _lc_stream_delay?: number;\n outputConfig?: AnthropicOutputConfig;\n inferenceGeo?: string;\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n contextManagement?: any;\n} & BaseChatModelParams;\n\n/**\n * A type representing additional parameters that can be passed to the\n * Anthropic API.\n */\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\ntype Kwargs = Record;\n\nexport class CustomAnthropic extends ChatAnthropicMessages {\n _lc_stream_delay: number;\n private message_start: AnthropicMessageStartEvent | undefined;\n private message_delta: AnthropicMessageDeltaEvent | undefined;\n private tools_in_params?: boolean;\n private emitted_usage?: boolean;\n top_k: number | undefined;\n outputConfig?: AnthropicOutputConfig;\n inferenceGeo?: string;\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n contextManagement?: any;\n constructor(fields?: CustomAnthropicInput) {\n super(fields);\n this.resetTokenEvents();\n this.setDirectFields(fields);\n this._lc_stream_delay = fields?._lc_stream_delay ?? 25;\n this.outputConfig = fields?.outputConfig;\n this.inferenceGeo = fields?.inferenceGeo;\n this.contextManagement = fields?.contextManagement;\n }\n\n static lc_name(): 'IllumaAnthropic' {\n return 'IllumaAnthropic';\n }\n\n /**\n * Get the parameters used to invoke the model\n */\n override invocationParams(\n options?: this['ParsedCallOptions']\n ): Omit<\n AnthropicMessageCreateParams | AnthropicStreamingMessageCreateParams,\n 'messages'\n > &\n Kwargs {\n const tool_choice:\n | Anthropic.Messages.ToolChoiceAuto\n | Anthropic.Messages.ToolChoiceAny\n | Anthropic.Messages.ToolChoiceTool\n | undefined = handleToolChoice(options?.tool_choice);\n\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n const callOptions = options as Record | undefined;\n const mergedOutputConfig: AnthropicOutputConfig | undefined = (():\n | AnthropicOutputConfig\n | undefined => {\n const base = {\n ...this.outputConfig,\n ...callOptions?.outputConfig,\n };\n if (callOptions?.outputFormat && !base.format) {\n base.format = callOptions.outputFormat;\n }\n return Object.keys(base).length > 0 ? base : undefined;\n })();\n\n const inferenceGeo = callOptions?.inferenceGeo ?? this.inferenceGeo;\n\n const contextManagement = this.contextManagement;\n\n const sharedParams = {\n tools: this.formatStructuredToolToAnthropic(options?.tools),\n tool_choice,\n thinking: this.thinking,\n ...(mergedOutputConfig ? { output_config: mergedOutputConfig } : {}),\n ...(inferenceGeo ? { inference_geo: inferenceGeo } : {}),\n ...(contextManagement ? { context_management: contextManagement } : {}),\n ...this.invocationKwargs,\n };\n\n if (this.thinking.type === 'enabled' || this.thinking.type === 'adaptive') {\n if (this.top_k !== -1 && (this.top_k as number | undefined) != null) {\n throw new Error('topK is not supported when thinking is enabled');\n }\n if (this.topP !== -1 && (this.topP as number | undefined) != null) {\n throw new Error('topP is not supported when thinking is enabled');\n }\n if (\n this.temperature !== 1 &&\n (this.temperature as number | undefined) != null\n ) {\n throw new Error(\n 'temperature is not supported when thinking is enabled'\n );\n }\n\n return {\n model: this.model,\n stop_sequences: options?.stop ?? this.stopSequences,\n stream: this.streaming,\n max_tokens: this.maxTokens,\n ...sharedParams,\n };\n }\n return {\n model: this.model,\n temperature: this.temperature,\n top_k: this.top_k,\n top_p: this.topP,\n stop_sequences: options?.stop ?? this.stopSequences,\n stream: this.streaming,\n max_tokens: this.maxTokens,\n ...sharedParams,\n };\n }\n\n /**\n * Get stream usage as returned by this client's API response.\n * @returns The stream usage object.\n */\n getStreamUsage(): UsageMetadata | undefined {\n if (this.emitted_usage === true) {\n return;\n }\n const inputUsage = this.message_start?.message.usage as\n | undefined\n | AnthropicStreamUsage;\n const outputUsage = this.message_delta?.usage as\n | undefined\n | Partial;\n if (!outputUsage) {\n return;\n }\n const totalUsage: UsageMetadata = {\n input_tokens: inputUsage?.input_tokens ?? 0,\n output_tokens: outputUsage.output_tokens ?? 0,\n total_tokens:\n (inputUsage?.input_tokens ?? 0) + (outputUsage.output_tokens ?? 0),\n };\n\n if (\n inputUsage?.cache_creation_input_tokens != null ||\n inputUsage?.cache_read_input_tokens != null\n ) {\n totalUsage.input_token_details = {\n cache_creation: inputUsage.cache_creation_input_tokens ?? 0,\n cache_read: inputUsage.cache_read_input_tokens ?? 0,\n };\n }\n\n this.emitted_usage = true;\n return totalUsage;\n }\n\n resetTokenEvents(): void {\n this.message_start = undefined;\n this.message_delta = undefined;\n this.emitted_usage = undefined;\n this.tools_in_params = undefined;\n }\n\n setDirectFields(fields?: CustomAnthropicInput): void {\n this.temperature = fields?.temperature ?? undefined;\n this.topP = fields?.topP ?? undefined;\n this.top_k = fields?.topK;\n if (this.temperature === -1 || this.temperature === 1) {\n this.temperature = undefined;\n }\n if (this.topP === -1) {\n this.topP = undefined;\n }\n if (this.top_k === -1) {\n this.top_k = undefined;\n }\n }\n\n private createGenerationChunk({\n token,\n chunk,\n usageMetadata,\n shouldStreamUsage,\n }: {\n token?: string;\n chunk: AIMessageChunk;\n shouldStreamUsage: boolean;\n usageMetadata?: UsageMetadata;\n }): ChatGenerationChunk {\n const usage_metadata = shouldStreamUsage\n ? (usageMetadata ?? chunk.usage_metadata)\n : undefined;\n return new ChatGenerationChunk({\n message: new AIMessageChunk({\n // Just yield chunk as it is and tool_use will be concat by BaseChatModel._generateUncached().\n content: chunk.content,\n additional_kwargs: chunk.additional_kwargs,\n tool_call_chunks: chunk.tool_call_chunks,\n response_metadata: chunk.response_metadata,\n usage_metadata,\n id: chunk.id,\n }),\n text: token ?? '',\n });\n }\n\n async *_streamResponseChunks(\n messages: BaseMessage[],\n options: this['ParsedCallOptions'],\n runManager?: CallbackManagerForLLMRun\n ): AsyncGenerator {\n this.resetTokenEvents();\n const params = this.invocationParams(options);\n const formattedMessages = _convertMessagesToAnthropicPayload(messages);\n const payload = {\n ...params,\n ...formattedMessages,\n stream: true,\n } as const;\n const coerceContentToString =\n !_toolsInParams(payload) &&\n !_documentsInParams(payload) &&\n !_thinkingInParams(payload) &&\n !_compactionInParams(payload);\n\n const stream = await this.createStreamWithRetry(payload, {\n headers: options.headers,\n });\n\n const shouldStreamUsage = this.streamUsage ?? options.streamUsage;\n\n for await (const data of stream) {\n if (options.signal?.aborted === true) {\n stream.controller.abort();\n throw new Error('AbortError: User aborted the request.');\n }\n\n if (data.type === 'message_start') {\n this.message_start = data as AnthropicMessageStartEvent;\n } else if (data.type === 'message_delta') {\n this.message_delta = data as AnthropicMessageDeltaEvent;\n }\n\n let usageMetadata: UsageMetadata | undefined;\n if (this.tools_in_params !== true && this.emitted_usage !== true) {\n usageMetadata = this.getStreamUsage();\n }\n\n const result = _makeMessageChunkFromAnthropicEvent(\n data as Anthropic.Beta.Messages.BetaRawMessageStreamEvent,\n {\n streamUsage: shouldStreamUsage,\n coerceContentToString,\n }\n );\n if (!result) continue;\n\n const { chunk } = result;\n const [token = '', tokenType] = extractToken(chunk);\n\n if (\n !tokenType ||\n tokenType === 'input' ||\n (token === '' && (usageMetadata != null || chunk.id != null))\n ) {\n const generationChunk = this.createGenerationChunk({\n token,\n chunk,\n usageMetadata,\n shouldStreamUsage,\n });\n yield generationChunk;\n await runManager?.handleLLMNewToken(\n token,\n undefined,\n undefined,\n undefined,\n undefined,\n { chunk: generationChunk }\n );\n continue;\n }\n\n const textStream = new TextStream(token, {\n delay: this._lc_stream_delay,\n firstWordChunk: true,\n minChunkSize: 4,\n maxChunkSize: 8,\n });\n\n const generator = textStream.generateText(options.signal);\n try {\n let emittedUsage = false;\n for await (const currentToken of generator) {\n if ((options.signal as AbortSignal | undefined)?.aborted === true) {\n break;\n }\n const newChunk = cloneChunk(currentToken, tokenType, chunk);\n\n const generationChunk = this.createGenerationChunk({\n token: currentToken,\n chunk: newChunk,\n usageMetadata: emittedUsage ? undefined : usageMetadata,\n shouldStreamUsage,\n });\n\n if (usageMetadata && !emittedUsage) {\n emittedUsage = true;\n }\n yield generationChunk;\n\n await runManager?.handleLLMNewToken(\n currentToken,\n undefined,\n undefined,\n undefined,\n undefined,\n { chunk: generationChunk }\n );\n }\n } finally {\n await generator.return();\n }\n }\n\n this.resetTokenEvents();\n 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