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feat: add support for new cohere command r models #118

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feat: add support for new cohere command r models #118

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a3355ea
feat: add support for new cohere command r models
yiyuan-he Nov 6, 2024
c25d7de
fix: mistral ai model conditional
yiyuan-he Nov 8, 2024
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30 changes: 27 additions & 3 deletions aws-distro-opentelemetry-node-autoinstrumentation/src/patches/aws/services/bedrock.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -245,6 +245,22 @@ export class BedrockRuntimeServiceExtension implements ServiceExtension {
if (requestBody.top_p !== undefined) {
spanAttributes[AwsSpanProcessingUtil.GEN_AI_REQUEST_TOP_P] = requestBody.top_p;
}
} else if (modelId.includes('cohere.command-r')) {
if (requestBody.max_tokens !== undefined) {
spanAttributes[AwsSpanProcessingUtil.GEN_AI_REQUEST_MAX_TOKENS] = requestBody.max_tokens;
}
if (requestBody.temperature !== undefined) {
spanAttributes[AwsSpanProcessingUtil.GEN_AI_REQUEST_TEMPERATURE] = requestBody.temperature;
}
if (requestBody.p !== undefined) {
spanAttributes[AwsSpanProcessingUtil.GEN_AI_REQUEST_TOP_P] = requestBody.p;
}
if (requestBody.message !== undefined) {
// NOTE: We approximate the token count since this value is not directly available in the body
// According to Bedrock docs they use (total_chars / 6) to approximate token count for pricing.
// https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-prepare.html
spanAttributes[AwsSpanProcessingUtil.GEN_AI_USAGE_INPUT_TOKENS] = Math.ceil(requestBody.message.length / 6);
}
Comment on lines +258 to +263
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According to the docs, this data should be available in the response body. However when logging the response body in the implementation it seems the data is not actually there. As a result, I decided to stay with this token approximation approach.

Screenshot 2024-11-06 at 9 38 29 AM

} else if (modelId.includes('cohere.command')) {
if (requestBody.max_tokens !== undefined) {
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I'm not sure if we should go ahead and remove support for the old Cohere Command model.

According to the docs, EOL should not be until 2025 but we are already getting 404s to this model.

spanAttributes[AwsSpanProcessingUtil.GEN_AI_REQUEST_MAX_TOKENS] = requestBody.max_tokens;
Expand All @@ -255,6 +271,9 @@ export class BedrockRuntimeServiceExtension implements ServiceExtension {
if (requestBody.p !== undefined) {
spanAttributes[AwsSpanProcessingUtil.GEN_AI_REQUEST_TOP_P] = requestBody.p;
}
if (requestBody.prompt !== undefined) {
spanAttributes[AwsSpanProcessingUtil.GEN_AI_USAGE_INPUT_TOKENS] = Math.ceil(requestBody.prompt.length / 6);
}
} else if (modelId.includes('ai21.jamba')) {
if (requestBody.max_tokens !== undefined) {
spanAttributes[AwsSpanProcessingUtil.GEN_AI_REQUEST_MAX_TOKENS] = requestBody.max_tokens;
Expand Down Expand Up @@ -329,13 +348,18 @@ export class BedrockRuntimeServiceExtension implements ServiceExtension {
if (responseBody.stop_reason !== undefined) {
span.setAttribute(AwsSpanProcessingUtil.GEN_AI_RESPONSE_FINISH_REASONS, [responseBody.stop_reason]);
}
} else if (currentModelId.includes('cohere.command')) {
if (responseBody.prompt !== undefined) {
} else if (currentModelId.includes('cohere.command-r')) {
console.log('Response Body:', responseBody);
if (responseBody.text !== undefined) {
// NOTE: We approximate the token count since this value is not directly available in the body
// According to Bedrock docs they use (total_chars / 6) to approximate token count for pricing.
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The prompt is only available in the JavaScript implementation because of special data model defined in an upstream Otel package. This makes it possible to approximate the input token usage from the response body.

However, this is not possible in the Java implementation as there is no special data model wrapping the inputs into the response body. As a result, I decided to move this approximation logic strictly to the request body to keep the implementation logic consistent between languages.

Screenshot 2024-11-06 at 9 42 25 AM

// https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-prepare.html
span.setAttribute(AwsSpanProcessingUtil.GEN_AI_USAGE_INPUT_TOKENS, Math.ceil(responseBody.prompt.length / 6));
span.setAttribute(AwsSpanProcessingUtil.GEN_AI_USAGE_OUTPUT_TOKENS, Math.ceil(responseBody.text.length / 6));
}
if (responseBody.finish_reason !== undefined) {
span.setAttribute(AwsSpanProcessingUtil.GEN_AI_RESPONSE_FINISH_REASONS, [responseBody.finish_reason]);
}
} else if (currentModelId.includes('cohere.command')) {
if (responseBody.generations?.[0]?.text !== undefined) {
span.setAttribute(
AwsSpanProcessingUtil.GEN_AI_USAGE_OUTPUT_TOKENS,
Expand Down
54 changes: 54 additions & 0 deletions aws-distro-opentelemetry-node-autoinstrumentation/test/patches/aws/services/bedrock.test.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -517,6 +517,60 @@ describe('BedrockRuntime', () => {
expect(invokeModelSpan.kind).toBe(SpanKind.CLIENT);
});

it('Add Cohere Command R model attributes to span', async () => {
const modelId: string = 'cohere.command-r-v1:0"';
const prompt: string = "Describe the purpose of a 'hello world' program in one line";
const nativeRequest: any = {
message: prompt,
max_tokens: 512,
temperature: 0.5,
p: 0.65,
};
const mockRequestBody: string = JSON.stringify(nativeRequest);
const mockResponseBody: any = {
finish_reason: 'COMPLETE',
text: 'test-generation-text',
prompt: prompt,
request: {
commandInput: {
modelId: modelId,
},
},
};

nock(`https://bedrock-runtime.${region}.amazonaws.com`)
.post(`/model/${encodeURIComponent(modelId)}/invoke`)
.reply(200, mockResponseBody);

await bedrock
.invokeModel({
modelId: modelId,
body: mockRequestBody,
})
.catch((err: any) => {
console.log('error', err);
});

const testSpans: ReadableSpan[] = getTestSpans();
const invokeModelSpans: ReadableSpan[] = testSpans.filter((s: ReadableSpan) => {
return s.name === 'BedrockRuntime.InvokeModel';
});
expect(invokeModelSpans.length).toBe(1);
const invokeModelSpan = invokeModelSpans[0];
expect(invokeModelSpan.attributes[AWS_ATTRIBUTE_KEYS.AWS_BEDROCK_AGENT_ID]).toBeUndefined();
expect(invokeModelSpan.attributes[AWS_ATTRIBUTE_KEYS.AWS_BEDROCK_KNOWLEDGE_BASE_ID]).toBeUndefined();
expect(invokeModelSpan.attributes[AWS_ATTRIBUTE_KEYS.AWS_BEDROCK_DATA_SOURCE_ID]).toBeUndefined();
expect(invokeModelSpan.attributes[AwsSpanProcessingUtil.GEN_AI_SYSTEM]).toBe('aws_bedrock');
expect(invokeModelSpan.attributes[AwsSpanProcessingUtil.GEN_AI_REQUEST_MODEL]).toBe(modelId);
expect(invokeModelSpan.attributes[AwsSpanProcessingUtil.GEN_AI_REQUEST_MAX_TOKENS]).toBe(512);
expect(invokeModelSpan.attributes[AwsSpanProcessingUtil.GEN_AI_REQUEST_TEMPERATURE]).toBe(0.5);
expect(invokeModelSpan.attributes[AwsSpanProcessingUtil.GEN_AI_REQUEST_TOP_P]).toBe(0.65);
expect(invokeModelSpan.attributes[AwsSpanProcessingUtil.GEN_AI_USAGE_INPUT_TOKENS]).toBe(10);
expect(invokeModelSpan.attributes[AwsSpanProcessingUtil.GEN_AI_USAGE_OUTPUT_TOKENS]).toBe(4);
expect(invokeModelSpan.attributes[AwsSpanProcessingUtil.GEN_AI_RESPONSE_FINISH_REASONS]).toEqual(['COMPLETE']);
expect(invokeModelSpan.kind).toBe(SpanKind.CLIENT);
});

it('Add Meta Llama model attributes to span', async () => {
const modelId: string = 'meta.llama2-13b-chat-v1';
const prompt: string = 'Describe the purpose of an interpreter program in one line.';
Expand Down
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