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* Copyright The OpenTelemetry Authors\n * SPDX-License-Identifier: Apache-2.0\n */\nimport {\n Attributes,\n DiagLogger,\n diag,\n Histogram,\n HrTime,\n Meter,\n Span,\n Tracer,\n ValueType,\n} from '@opentelemetry/api';\nimport { RequestMetadata, ServiceExtension } from './ServiceExtension';\nimport {\n ATTR_GEN_AI_SYSTEM,\n ATTR_GEN_AI_OPERATION_NAME,\n ATTR_GEN_AI_REQUEST_MODEL,\n ATTR_GEN_AI_REQUEST_MAX_TOKENS,\n ATTR_GEN_AI_REQUEST_TEMPERATURE,\n ATTR_GEN_AI_REQUEST_TOP_P,\n ATTR_GEN_AI_REQUEST_STOP_SEQUENCES,\n ATTR_GEN_AI_TOKEN_TYPE,\n ATTR_GEN_AI_USAGE_INPUT_TOKENS,\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n ATTR_GEN_AI_RESPONSE_FINISH_REASONS,\n GEN_AI_OPERATION_NAME_VALUE_CHAT,\n GEN_AI_SYSTEM_VALUE_AWS_BEDROCK,\n GEN_AI_TOKEN_TYPE_VALUE_INPUT,\n GEN_AI_TOKEN_TYPE_VALUE_OUTPUT,\n METRIC_GEN_AI_CLIENT_OPERATION_DURATION,\n METRIC_GEN_AI_CLIENT_TOKEN_USAGE,\n} from '../semconv';\nimport {\n AwsSdkInstrumentationConfig,\n NormalizedRequest,\n NormalizedResponse,\n} from '../types';\nimport type {\n ConverseStreamOutput,\n TokenUsage,\n} from '@aws-sdk/client-bedrock-runtime';\nimport {\n hrTime,\n hrTimeDuration,\n hrTimeToMilliseconds,\n} from '@opentelemetry/core';\n\nexport class BedrockRuntimeServiceExtension implements ServiceExtension {\n private tokenUsage!: Histogram;\n private operationDuration!: Histogram;\n private _diag: DiagLogger = diag;\n\n updateMetricInstruments(meter: Meter) {\n // https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-metrics/#metric-gen_aiclienttokenusage\n this.tokenUsage = meter.createHistogram(METRIC_GEN_AI_CLIENT_TOKEN_USAGE, {\n unit: '{token}',\n description: 'Measures number of input and output tokens used',\n valueType: ValueType.INT,\n advice: {\n explicitBucketBoundaries: [\n 1, 4, 16, 64, 256, 1024, 4096, 16384, 65536, 262144, 1048576, 4194304,\n 16777216, 67108864,\n ],\n },\n });\n\n // https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-metrics/#metric-gen_aiclientoperationduration\n this.operationDuration = meter.createHistogram(\n METRIC_GEN_AI_CLIENT_OPERATION_DURATION,\n {\n unit: 's',\n description: 'GenAI operation duration',\n advice: {\n explicitBucketBoundaries: [\n 0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.64, 1.28, 2.56, 5.12, 10.24,\n 20.48, 40.96, 81.92,\n ],\n },\n }\n );\n }\n\n requestPreSpanHook(\n request: NormalizedRequest,\n config: AwsSdkInstrumentationConfig,\n diag: DiagLogger\n ): RequestMetadata {\n switch (request.commandName) {\n case 'Converse':\n return this.requestPreSpanHookConverse(request, config, diag, false);\n case 'ConverseStream':\n return this.requestPreSpanHookConverse(request, config, diag, true);\n case 'InvokeModel':\n return this.requestPreSpanHookInvokeModel(request, config, diag, false);\n case 'InvokeModelWithResponseStream':\n return this.requestPreSpanHookInvokeModel(request, config, diag, true);\n }\n\n return {\n isIncoming: false,\n };\n }\n\n private requestPreSpanHookConverse(\n request: NormalizedRequest,\n config: AwsSdkInstrumentationConfig,\n diag: DiagLogger,\n isStream: boolean\n ): RequestMetadata {\n let spanName = GEN_AI_OPERATION_NAME_VALUE_CHAT;\n const spanAttributes: Attributes = {\n [ATTR_GEN_AI_SYSTEM]: GEN_AI_SYSTEM_VALUE_AWS_BEDROCK,\n [ATTR_GEN_AI_OPERATION_NAME]: GEN_AI_OPERATION_NAME_VALUE_CHAT,\n };\n\n const modelId = request.commandInput.modelId;\n if (modelId) {\n spanAttributes[ATTR_GEN_AI_REQUEST_MODEL] = modelId;\n if (spanName) {\n spanName += ` ${modelId}`;\n }\n }\n\n const inferenceConfig = request.commandInput.inferenceConfig;\n if (inferenceConfig) {\n const { maxTokens, temperature, topP, stopSequences } = inferenceConfig;\n if (maxTokens !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_MAX_TOKENS] = maxTokens;\n }\n if (temperature !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TEMPERATURE] = temperature;\n }\n if (topP !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TOP_P] = topP;\n }\n if (stopSequences !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_STOP_SEQUENCES] = stopSequences;\n }\n }\n\n return {\n spanName,\n isIncoming: false,\n isStream,\n spanAttributes,\n };\n }\n\n private requestPreSpanHookInvokeModel(\n request: NormalizedRequest,\n config: AwsSdkInstrumentationConfig,\n diag: DiagLogger,\n isStream: boolean\n ): RequestMetadata {\n let spanName: string | undefined;\n const spanAttributes: Attributes = {\n [ATTR_GEN_AI_SYSTEM]: GEN_AI_SYSTEM_VALUE_AWS_BEDROCK,\n // add operation name for InvokeModel API\n };\n\n const modelId = request.commandInput?.modelId;\n if (modelId) {\n spanAttributes[ATTR_GEN_AI_REQUEST_MODEL] = modelId;\n }\n\n if (request.commandInput?.body) {\n const requestBody = JSON.parse(request.commandInput.body);\n if (modelId.includes('amazon.titan')) {\n if (requestBody.textGenerationConfig?.temperature !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TEMPERATURE] =\n requestBody.textGenerationConfig.temperature;\n }\n if (requestBody.textGenerationConfig?.topP !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TOP_P] =\n requestBody.textGenerationConfig.topP;\n }\n if (requestBody.textGenerationConfig?.maxTokenCount !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_MAX_TOKENS] =\n requestBody.textGenerationConfig.maxTokenCount;\n }\n if (requestBody.textGenerationConfig?.stopSequences !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_STOP_SEQUENCES] =\n requestBody.textGenerationConfig.stopSequences;\n }\n } else if (modelId.includes('amazon.nova')) {\n if (requestBody.inferenceConfig?.temperature !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TEMPERATURE] =\n requestBody.inferenceConfig.temperature;\n }\n if (requestBody.inferenceConfig?.top_p !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TOP_P] =\n requestBody.inferenceConfig.top_p;\n }\n if (requestBody.inferenceConfig?.max_new_tokens !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_MAX_TOKENS] =\n requestBody.inferenceConfig.max_new_tokens;\n }\n if (requestBody.inferenceConfig?.stopSequences !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_STOP_SEQUENCES] =\n requestBody.inferenceConfig.stopSequences;\n }\n } else if (modelId.includes('anthropic.claude')) {\n if (requestBody.max_tokens !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_MAX_TOKENS] =\n requestBody.max_tokens;\n }\n if (requestBody.temperature !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TEMPERATURE] =\n requestBody.temperature;\n }\n if (requestBody.top_p !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TOP_P] = requestBody.top_p;\n }\n if (requestBody.stop_sequences !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_STOP_SEQUENCES] =\n requestBody.stop_sequences;\n }\n } else if (modelId.includes('meta.llama')) {\n if (requestBody.max_gen_len !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_MAX_TOKENS] =\n requestBody.max_gen_len;\n }\n if (requestBody.temperature !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TEMPERATURE] =\n requestBody.temperature;\n }\n if (requestBody.top_p !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TOP_P] = requestBody.top_p;\n }\n // request for meta llama models does not contain stop_sequences field\n } else if (modelId.includes('cohere.command-r')) {\n if (requestBody.max_tokens !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_MAX_TOKENS] =\n requestBody.max_tokens;\n }\n if (requestBody.temperature !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TEMPERATURE] =\n requestBody.temperature;\n }\n if (requestBody.p !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TOP_P] = requestBody.p;\n }\n if (requestBody.message !== undefined) {\n // NOTE: We approximate the token count since this value is not directly available in the body\n // According to Bedrock docs they use (total_chars / 6) to approximate token count for pricing.\n // https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-prepare.html\n spanAttributes[ATTR_GEN_AI_USAGE_INPUT_TOKENS] = Math.ceil(\n requestBody.message.length / 6\n );\n }\n if (requestBody.stop_sequences !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_STOP_SEQUENCES] =\n requestBody.stop_sequences;\n }\n } else if (modelId.includes('cohere.command')) {\n if (requestBody.max_tokens !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_MAX_TOKENS] =\n requestBody.max_tokens;\n }\n if (requestBody.temperature !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TEMPERATURE] =\n requestBody.temperature;\n }\n if (requestBody.p !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TOP_P] = requestBody.p;\n }\n if (requestBody.prompt !== undefined) {\n // NOTE: We approximate the token count since this value is not directly available in the body\n // According to Bedrock docs they use (total_chars / 6) to approximate token count for pricing.\n // https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-prepare.html\n spanAttributes[ATTR_GEN_AI_USAGE_INPUT_TOKENS] = Math.ceil(\n requestBody.prompt.length / 6\n );\n }\n if (requestBody.stop_sequences !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_STOP_SEQUENCES] =\n requestBody.stop_sequences;\n }\n } else if (modelId.includes('mistral')) {\n if (requestBody.prompt !== undefined) {\n // NOTE: We approximate the token count since this value is not directly available in the body\n // According to Bedrock docs they use (total_chars / 6) to approximate token count for pricing.\n // https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-prepare.html\n spanAttributes[ATTR_GEN_AI_USAGE_INPUT_TOKENS] = Math.ceil(\n requestBody.prompt.length / 6\n );\n }\n if (requestBody.max_tokens !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_MAX_TOKENS] =\n requestBody.max_tokens;\n }\n if (requestBody.temperature !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TEMPERATURE] =\n requestBody.temperature;\n }\n if (requestBody.top_p !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_TOP_P] = requestBody.top_p;\n }\n if (requestBody.stop !== undefined) {\n spanAttributes[ATTR_GEN_AI_REQUEST_STOP_SEQUENCES] = requestBody.stop;\n }\n }\n }\n\n return {\n spanName,\n isIncoming: false,\n isStream,\n spanAttributes,\n };\n }\n\n responseHook(\n response: NormalizedResponse,\n span: Span,\n tracer: Tracer,\n config: AwsSdkInstrumentationConfig,\n startTime: HrTime\n ) {\n if (!span.isRecording()) {\n return;\n }\n\n switch (response.request.commandName) {\n case 'Converse':\n return this.responseHookConverse(\n response,\n span,\n tracer,\n config,\n startTime\n );\n case 'ConverseStream':\n return this.responseHookConverseStream(\n response,\n span,\n tracer,\n config,\n startTime\n );\n case 'InvokeModel':\n return this.responseHookInvokeModel(response, span, tracer, config);\n case 'InvokeModelWithResponseStream':\n return this.responseHookInvokeModelWithResponseStream(\n response,\n span,\n tracer,\n config\n );\n }\n }\n\n private responseHookConverse(\n response: NormalizedResponse,\n span: Span,\n tracer: Tracer,\n config: AwsSdkInstrumentationConfig,\n startTime: HrTime\n ) {\n const { stopReason, usage } = response.data;\n\n BedrockRuntimeServiceExtension.setStopReason(span, stopReason);\n this.setUsage(response, span, usage, startTime);\n }\n\n private responseHookConverseStream(\n response: NormalizedResponse,\n span: Span,\n tracer: Tracer,\n config: AwsSdkInstrumentationConfig,\n startTime: HrTime\n ) {\n return {\n ...response.data,\n // Wrap and replace the response stream to allow processing events to telemetry\n // before yielding to the user.\n stream: this.wrapConverseStreamResponse(\n response,\n response.data.stream,\n span,\n startTime\n ),\n };\n }\n\n private async *wrapConverseStreamResponse(\n response: NormalizedResponse,\n stream: AsyncIterable,\n span: Span,\n startTime: HrTime\n ) {\n try {\n let usage: TokenUsage | undefined;\n for await (const item of stream) {\n BedrockRuntimeServiceExtension.setStopReason(\n span,\n item.messageStop?.stopReason\n );\n usage = item.metadata?.usage;\n yield item;\n }\n this.setUsage(response, span, usage, startTime);\n } finally {\n span.end();\n }\n }\n\n private static setStopReason(span: Span, stopReason: string | undefined) {\n if (stopReason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [stopReason]);\n }\n }\n\n private setUsage(\n response: NormalizedResponse,\n span: Span,\n usage: TokenUsage | undefined,\n startTime: HrTime\n ) {\n const sharedMetricAttrs: Attributes = {\n [ATTR_GEN_AI_SYSTEM]: GEN_AI_SYSTEM_VALUE_AWS_BEDROCK,\n [ATTR_GEN_AI_OPERATION_NAME]: GEN_AI_OPERATION_NAME_VALUE_CHAT,\n [ATTR_GEN_AI_REQUEST_MODEL]: response.request.commandInput.modelId,\n };\n\n const durationSecs =\n hrTimeToMilliseconds(hrTimeDuration(startTime, hrTime())) / 1000;\n this.operationDuration.record(durationSecs, sharedMetricAttrs);\n\n if (usage) {\n const { inputTokens, outputTokens } = usage;\n if (inputTokens !== undefined) {\n span.setAttribute(ATTR_GEN_AI_USAGE_INPUT_TOKENS, inputTokens);\n\n this.tokenUsage.record(inputTokens, {\n ...sharedMetricAttrs,\n [ATTR_GEN_AI_TOKEN_TYPE]: GEN_AI_TOKEN_TYPE_VALUE_INPUT,\n });\n }\n if (outputTokens !== undefined) {\n span.setAttribute(ATTR_GEN_AI_USAGE_OUTPUT_TOKENS, outputTokens);\n\n this.tokenUsage.record(outputTokens, {\n ...sharedMetricAttrs,\n [ATTR_GEN_AI_TOKEN_TYPE]: GEN_AI_TOKEN_TYPE_VALUE_OUTPUT,\n });\n }\n }\n }\n\n private responseHookInvokeModel(\n response: NormalizedResponse,\n span: Span,\n tracer: Tracer,\n config: AwsSdkInstrumentationConfig\n ) {\n const currentModelId = response.request.commandInput?.modelId;\n if (response.data?.body) {\n const decodedResponseBody = new TextDecoder().decode(response.data.body);\n const responseBody = JSON.parse(decodedResponseBody);\n if (currentModelId.includes('amazon.titan')) {\n if (responseBody.inputTextTokenCount !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_INPUT_TOKENS,\n responseBody.inputTextTokenCount\n );\n }\n if (responseBody.results?.[0]?.tokenCount !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n responseBody.results[0].tokenCount\n );\n }\n if (responseBody.results?.[0]?.completionReason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [\n responseBody.results[0].completionReason,\n ]);\n }\n } else if (currentModelId.includes('amazon.nova')) {\n if (responseBody.usage !== undefined) {\n if (responseBody.usage.inputTokens !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_INPUT_TOKENS,\n responseBody.usage.inputTokens\n );\n }\n if (responseBody.usage.outputTokens !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n responseBody.usage.outputTokens\n );\n }\n }\n if (responseBody.stopReason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [\n responseBody.stopReason,\n ]);\n }\n } else if (currentModelId.includes('anthropic.claude')) {\n if (responseBody.usage?.input_tokens !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_INPUT_TOKENS,\n responseBody.usage.input_tokens\n );\n }\n if (responseBody.usage?.output_tokens !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n responseBody.usage.output_tokens\n );\n }\n if (responseBody.stop_reason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [\n responseBody.stop_reason,\n ]);\n }\n } else if (currentModelId.includes('meta.llama')) {\n if (responseBody.prompt_token_count !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_INPUT_TOKENS,\n responseBody.prompt_token_count\n );\n }\n if (responseBody.generation_token_count !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n responseBody.generation_token_count\n );\n }\n if (responseBody.stop_reason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [\n responseBody.stop_reason,\n ]);\n }\n } else if (currentModelId.includes('cohere.command-r')) {\n if (responseBody.text !== undefined) {\n // NOTE: We approximate the token count since this value is not directly available in the body\n // According to Bedrock docs they use (total_chars / 6) to approximate token count for pricing.\n // https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-prepare.html\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n Math.ceil(responseBody.text.length / 6)\n );\n }\n if (responseBody.finish_reason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [\n responseBody.finish_reason,\n ]);\n }\n } else if (currentModelId.includes('cohere.command')) {\n if (responseBody.generations?.[0]?.text !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n // NOTE: We approximate the token count since this value is not directly available in the body\n // According to Bedrock docs they use (total_chars / 6) to approximate token count for pricing.\n // https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-prepare.html\n Math.ceil(responseBody.generations[0].text.length / 6)\n );\n }\n if (responseBody.generations?.[0]?.finish_reason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [\n responseBody.generations[0].finish_reason,\n ]);\n }\n } else if (currentModelId.includes('mistral')) {\n if (responseBody.outputs?.[0]?.text !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n // NOTE: We approximate the token count since this value is not directly available in the body\n // According to Bedrock docs they use (total_chars / 6) to approximate token count for pricing.\n // https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-prepare.html\n Math.ceil(responseBody.outputs[0].text.length / 6)\n );\n }\n if (responseBody.outputs?.[0]?.stop_reason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [\n responseBody.outputs[0].stop_reason,\n ]);\n }\n }\n }\n }\n\n private async responseHookInvokeModelWithResponseStream(\n response: NormalizedResponse,\n span: Span,\n tracer: Tracer,\n config: AwsSdkInstrumentationConfig\n ): Promise {\n const stream = response.data?.body;\n const modelId = response.request.commandInput?.modelId;\n if (!stream || !modelId) return;\n\n // Replace the original response body with our instrumented stream.\n // - Defers span.end() until the entire stream is consumed\n // This ensures downstream consumers still receive the full stream correctly,\n // while OpenTelemetry can record span attributes from streamed data.\n response.data.body = async function* (\n this: BedrockRuntimeServiceExtension\n ) {\n try {\n for await (const chunk of stream) {\n const parsedChunk = this.parseChunk(chunk?.chunk?.bytes);\n\n if (!parsedChunk) {\n // pass through\n } else if (modelId.includes('amazon.titan')) {\n BedrockRuntimeServiceExtension.recordTitanAttributes(\n parsedChunk,\n span\n );\n } else if (modelId.includes('anthropic.claude')) {\n BedrockRuntimeServiceExtension.recordClaudeAttributes(\n parsedChunk,\n span\n );\n } else if (modelId.includes('amazon.nova')) {\n BedrockRuntimeServiceExtension.recordNovaAttributes(\n parsedChunk,\n span\n );\n } else if (modelId.includes('meta.llama')) {\n BedrockRuntimeServiceExtension.recordLlamaAttributes(\n parsedChunk,\n span\n );\n } else if (modelId.includes('cohere.command-r')) {\n BedrockRuntimeServiceExtension.recordCohereRAttributes(\n parsedChunk,\n span\n );\n } else if (modelId.includes('cohere.command')) {\n BedrockRuntimeServiceExtension.recordCohereAttributes(\n parsedChunk,\n span\n );\n } else if (modelId.includes('mistral')) {\n BedrockRuntimeServiceExtension.recordMistralAttributes(\n parsedChunk,\n span\n );\n }\n yield chunk;\n }\n } finally {\n span.end();\n }\n }.bind(this)();\n return response.data;\n }\n\n private parseChunk(bytes?: Uint8Array): any {\n if (!bytes || !(bytes instanceof Uint8Array)) return null;\n try {\n const str = Buffer.from(bytes).toString('utf-8');\n return JSON.parse(str);\n } catch (err) {\n this._diag.warn('Failed to parse streamed chunk', err);\n return null;\n }\n }\n\n private static recordNovaAttributes(parsedChunk: any, span: Span) {\n if (parsedChunk.metadata?.usage !== undefined) {\n if (parsedChunk.metadata?.usage.inputTokens !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_INPUT_TOKENS,\n parsedChunk.metadata.usage.inputTokens\n );\n }\n if (parsedChunk.metadata?.usage.outputTokens !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n parsedChunk.metadata.usage.outputTokens\n );\n }\n }\n if (parsedChunk.messageStop?.stopReason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [\n parsedChunk.messageStop.stopReason,\n ]);\n }\n }\n\n private static recordClaudeAttributes(parsedChunk: any, span: Span) {\n if (parsedChunk.message?.usage?.input_tokens !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_INPUT_TOKENS,\n parsedChunk.message.usage.input_tokens\n );\n }\n if (parsedChunk.message?.usage?.output_tokens !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n parsedChunk.message.usage.output_tokens\n );\n }\n if (parsedChunk.delta?.stop_reason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [\n parsedChunk.delta.stop_reason,\n ]);\n }\n }\n\n private static recordTitanAttributes(parsedChunk: any, span: Span) {\n if (parsedChunk.inputTextTokenCount !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_INPUT_TOKENS,\n parsedChunk.inputTextTokenCount\n );\n }\n if (parsedChunk.totalOutputTextTokenCount !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n parsedChunk.totalOutputTextTokenCount\n );\n }\n if (parsedChunk.completionReason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [\n parsedChunk.completionReason,\n ]);\n }\n }\n private static recordLlamaAttributes(parsedChunk: any, span: Span) {\n if (parsedChunk.prompt_token_count !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_INPUT_TOKENS,\n parsedChunk.prompt_token_count\n );\n }\n if (parsedChunk.generation_token_count !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n parsedChunk.generation_token_count\n );\n }\n if (parsedChunk.stop_reason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [\n parsedChunk.stop_reason,\n ]);\n }\n }\n\n private static recordMistralAttributes(parsedChunk: any, span: Span) {\n if (parsedChunk.outputs?.[0]?.text !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n // NOTE: We approximate the token count since this value is not directly available in the body\n // According to Bedrock docs they use (total_chars / 6) to approximate token count for pricing.\n // https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-prepare.html\n Math.ceil(parsedChunk.outputs[0].text.length / 6)\n );\n }\n if (parsedChunk.outputs?.[0]?.stop_reason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [\n parsedChunk.outputs[0].stop_reason,\n ]);\n }\n }\n\n private static recordCohereAttributes(parsedChunk: any, span: Span) {\n if (parsedChunk.generations?.[0]?.text !== undefined) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n // NOTE: We approximate the token count since this value is not directly available in the body\n // According to Bedrock docs they use (total_chars / 6) to approximate token count for pricing.\n // https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-prepare.html\n Math.ceil(parsedChunk.generations[0].text.length / 6)\n );\n }\n if (parsedChunk.generations?.[0]?.finish_reason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [\n parsedChunk.generations[0].finish_reason,\n ]);\n }\n }\n\n private static recordCohereRAttributes(parsedChunk: any, span: Span) {\n if (parsedChunk.text !== undefined) {\n // NOTE: We approximate the token count since this value is not directly available in the body\n // According to Bedrock docs they use (total_chars / 6) to approximate token count for pricing.\n // https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-prepare.html\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n Math.ceil(parsedChunk.text.length / 6)\n );\n }\n if (parsedChunk.finish_reason !== undefined) {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [\n parsedChunk.finish_reason,\n ]);\n }\n }\n}\n"]}