feat: Added support for Claude 3+ Chat API in Bedrock

lib/instrumentation/aws-sdk/v3/bedrock.js

@@ -118,27 +118,34 @@ function recordChatCompletionMessages({
     isError: err !== null
   })
 
-  const msg = new LlmChatCompletionMessage({
-    agent,
-    segment,
-    bedrockCommand,
-    bedrockResponse,
-    transaction,
-    index: 0,
-    completionId: summary.id
+  // Record context message(s)
+  const promptContextMessages = bedrockCommand.prompt
+  promptContextMessages.forEach((contextMessage, promptIndex) => {
+    const msg = new LlmChatCompletionMessage({
+      agent,
+      segment,
+      transaction,
+      bedrockCommand,
+      content: contextMessage.content,
+      role: contextMessage.role,
+      bedrockResponse,
+      index: promptIndex,
+      completionId: summary.id
+    })
+    recordEvent({ agent, type: 'LlmChatCompletionMessage', msg })
   })
-  recordEvent({ agent, type: 'LlmChatCompletionMessage', msg })
 
-  bedrockResponse.completions.forEach((content, index) => {
+  bedrockResponse.completions.forEach((content, completionIndex) => {
     const chatCompletionMessage = new LlmChatCompletionMessage({
       agent,
       segment,
       transaction,
       bedrockCommand,
       bedrockResponse,
       isResponse: true,
-      index: index + 1,
+      index: promptContextMessages.length + completionIndex,
       content,
+      role: 'assistant',
       completionId: summary.id
     })
     recordEvent({ agent, type: 'LlmChatCompletionMessage', msg: chatCompletionMessage })
@@ -179,18 +186,22 @@ function recordEmbeddingMessage({
     return
   }
 
-  const embedding = new LlmEmbedding({
+  const embeddings = bedrockCommand.prompt.map(prompt => new LlmEmbedding({
     agent,
     segment,
     transaction,
     bedrockCommand,
+    input: prompt.content,
     bedrockResponse,
     isError: err !== null
+  }))
+
+  embeddings.forEach(embedding => {
+    recordEvent({ agent, type: 'LlmEmbedding', msg: embedding })
   })
 
-  recordEvent({ agent, type: 'LlmEmbedding', msg: embedding })
   if (err) {
-    const llmError = new LlmError({ bedrockResponse, err, embedding })
+    const llmError = new LlmError({ bedrockResponse, err, embedding: embeddings.length === 1 ? embeddings[0] : undefined })
     agent.errors.add(transaction, err, llmError)
   }
 }

lib/llm-events/aws-bedrock/bedrock-command.js

@@ -5,6 +5,8 @@
 
 'use strict'
 
+const { stringifyClaudeChunkedMessage } = require('./utils')
+
 /**
  * Parses an AWS invoke command instance into a re-usable entity.
  */
@@ -68,37 +70,34 @@
   /**
    * The question posed to the LLM.
    *
-   * @returns {string|string[]|undefined}
+   * @returns {object[]} The array of context messages passed to the LLM (or a single user prompt for legacy "non-chat" models)
    */
   get prompt() {
-    let result
     if (this.isTitan() === true || this.isTitanEmbed() === true) {
-      result = this.#body.inputText
+      return [
+        {
+          role: 'user',
+          content: this.#body.inputText
+        }
+      ]
     } else if (this.isCohereEmbed() === true) {
-      result = this.#body.texts.join(' ')
+      return [
+        {
+          role: 'user',
+          content: this.#body.texts.join(' ')
+        }
+      ]
     } else if (
-      this.isClaude() === true ||
+      this.isClaudeTextCompletionApi() === true ||
       this.isAi21() === true ||
       this.isCohere() === true ||
       this.isLlama() === true
     ) {
-      result = this.#body.prompt
-    } else if (this.isClaude3() === true) {
-      const collected = []
-      for (const message of this.#body?.messages) {
-        if (message?.role === 'assistant') {
-          continue
-        }
-        if (typeof message?.content === 'string') {
-          collected.push(message?.content)
-          continue
-        }
-        const mappedMsgObj = message?.content.map((msgContent) => msgContent.text)
-        collected.push(mappedMsgObj)
-      }
-      result = collected.join(' ')
+      return [{ role: 'user', content: this.#body.prompt }]
+    } else if (this.isClaudeMessagesApi() === true) {
+      return normalizeClaude3Messages(this.#body?.messages)
     }
-    return result
+    return []
   }
 
   /**
@@ -151,6 +150,41 @@
   isTitanEmbed() {
     return this.#modelId.startsWith('amazon.titan-embed')
   }
+
+  isClaudeMessagesApi() {
+    return (this.isClaude3() === true || this.isClaude() === true) && 'messages' in this.#body
+  }
+
+  isClaudeTextCompletionApi() {
+    return this.isClaude() === true && 'prompt' in this.#body
+  }
+}
+
+/**
+ * Claude v3 requests in Bedrock can have two different "chat" flavors. This function normalizes them into a consistent
+ * format per the AIM agent spec
+ *
+ * @param messages - The raw array of messages passed to the invoke API
+ * @returns {number|undefined} - The normalized messages
+ */
+function normalizeClaude3Messages(messages) {
+  const result = []
+  for (const message of messages ?? []) {
+    if (message == null) {
+      continue
+    }
+    if (typeof message.content === 'string') {
+      // Messages can be specified with plain string content
+      result.push({ role: message.role, content: message.content })
+    } else if (Array.isArray(message.content)) {
+      // Or in a "chunked" format for multi-modal support
+      result.push({
+        role: message.role,
+        content: stringifyClaudeChunkedMessage(message.content)
+      })
+    }
+  }
+  return result
 }
 
 module.exports = BedrockCommand

lib/llm-events/aws-bedrock/bedrock-response.js

@@ -5,6 +5,8 @@
 
 'use strict'
 
+const { stringifyClaudeChunkedMessage } = require('./utils')
+
 /**
  * @typedef {object} AwsBedrockMiddlewareResponse
  * @property {object} response Has a `body` property that is an IncomingMessage,
@@ -63,7 +65,7 @@ class BedrockResponse {
         // Streamed response
         this.#completions = body.completions
       } else {
-        this.#completions = body?.content?.map((c) => c.text)
+        this.#completions = [stringifyClaudeChunkedMessage(body?.content)]
       }
       this.#id = body.id
     } else if (cmd.isCohere() === true) {

lib/llm-events/aws-bedrock/chat-completion-message.js

@@ -39,28 +39,19 @@ class LlmChatCompletionMessage extends LlmEvent {
     params = Object.assign({}, defaultParams, params)
     super(params)
 
-    const { agent, content, isResponse, index, completionId } = params
+    const { agent, content, isResponse, index, completionId, role } = params
     const recordContent = agent.config?.ai_monitoring?.record_content?.enabled
     const tokenCB = agent?.llm?.tokenCountCallback
 
     this.is_response = isResponse
     this.completion_id = completionId
     this.sequence = index
     this.content = recordContent === true ? content : undefined
-    this.role = ''
+    this.role = role
 
     this.#setId(index)
-    if (this.is_response === true) {
-      this.role = 'assistant'
-      if (typeof tokenCB === 'function') {
-        this.token_count = tokenCB(this.bedrockCommand.modelId, content)
-      }
-    } else {
-      this.role = 'user'
-      this.content = recordContent === true ? this.bedrockCommand.prompt : undefined
-      if (typeof tokenCB === 'function') {
-        this.token_count = tokenCB(this.bedrockCommand.modelId, this.bedrockCommand.prompt)
-      }
+    if (typeof tokenCB === 'function') {
+      this.token_count = tokenCB(this.bedrockCommand.modelId, content)
     }
   }
 

lib/llm-events/aws-bedrock/chat-completion-summary.js

@@ -36,7 +36,7 @@ class LlmChatCompletionSummary extends LlmEvent {
     const cmd = this.bedrockCommand
     this[cfr] = this.bedrockResponse.finishReason
     this[rt] = cmd.temperature
-    this[nm] = 1 + this.bedrockResponse.completions.length
+    this[nm] = (this.bedrockCommand.prompt.length) + this.bedrockResponse.completions.length
   }
 }
 

lib/llm-events/aws-bedrock/embedding.js

@@ -10,7 +10,7 @@ const LlmEvent = require('./event')
 /**
  * @typedef {object} LlmEmbeddingParams
  * @augments LlmEventParams
- * @property
+ * @property {string} input - The input message for the embedding call
  */
 /**
  * @type {LlmEmbeddingParams}
@@ -20,16 +20,18 @@ const defaultParams = {}
 class LlmEmbedding extends LlmEvent {
   constructor(params = defaultParams) {
     super(params)
-    const { agent } = params
+    const { agent, input } = params
     const tokenCb = agent?.llm?.tokenCountCallback
 
     this.input = agent.config?.ai_monitoring?.record_content?.enabled
-      ? this.bedrockCommand.prompt
+      ? input
       : undefined
     this.error = params.isError
     this.duration = params.segment.getDurationInMillis()
+
+    // Even if not recording content, we should use the local token counting callback to record token usage
     if (typeof tokenCb === 'function') {
-      this.token_count = tokenCb(this.bedrockCommand.modelId, this.bedrockCommand.prompt)
+      this.token_count = tokenCb(this.bedrockCommand.modelId, input)
     }
   }
 }

lib/llm-events/aws-bedrock/utils.js

@@ -0,0 +1,36 @@
+/*
+ * Copyright 2024 New Relic Corporation. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+'use strict'
+
+/**
+ *
+ * @param {object[]} chunks - The "chunks" that make up a single conceptual message. In a multi-modal scenario, a single message
+ * might have a number of different-typed chunks interspersed
+ * @returns {string} - A stringified version of the message. We make a best-effort effort attempt to represent non-text chunks. In the future
+ * we may want to extend the agent to support these non-text chunks in a richer way. Placeholders are represented in an XML-like format but
+ * are NOT intended to be parsed as valid XML
+ */
+function stringifyClaudeChunkedMessage(chunks) {
+  const stringifiedChunks = chunks.map((msgContent) => {
+    switch (msgContent.type) {
+      case 'text':
+        return msgContent.text
+      case 'image':
+        return '<image>'
+      case 'tool_use':
+        return `<tool_use>${msgContent.name}</tool_use>`
+      case 'tool_result':
+        return `<tool_result>${msgContent.content}</tool_result>`
+      default:
+        return '<unknown_chunk>'
+    }
+  })
+  return stringifiedChunks.join('\n\n')
+}
+
+module.exports = {
+  stringifyClaudeChunkedMessage
+}

test/lib/aws-server-stubs/ai-server/index.js

@@ -101,6 +101,12 @@ function handler(req, res) {
         break
       }
 
+      // Chunked claude model
+      case 'anthropic.claude-3-5-sonnet-20240620-v1:0': {
+        response = responses.claude3.get(payload?.messages?.[0]?.content?.[0].text)
+        break
+      }
+
       case 'cohere.command-text-v14':
       case 'cohere.command-light-text-v14': {
         response = responses.cohere.get(payload.prompt)

test/lib/aws-server-stubs/ai-server/responses/claude3.js

@@ -34,6 +34,40 @@ responses.set('text claude3 ultimate question', {
   }
 })
 
+responses.set('text claude3 ultimate question chunked', {
+  headers: {
+    'content-type': contentType,
+    'x-amzn-requestid': reqId,
+    'x-amzn-bedrock-invocation-latency': '926',
+    'x-amzn-bedrock-output-token-count': '36',
+    'x-amzn-bedrock-input-token-count': '14'
+  },
+  statusCode: 200,
+  body: {
+    id: 'msg_bdrk_019V7ABaw8ZZZYuRDSTWK7VE',
+    type: 'message',
+    role: 'assistant',
+    model: 'claude-3-haiku-20240307',
+    stop_sequence: null,
+    usage: { input_tokens: 30, output_tokens: 265 },
+    content: [
+      {
+        type: 'text',
+        text: "Here's a nice picture of a 42"
+      },
+      {
+        type: 'image',
+        source: {
+          type: 'base64',
+          media_type: 'image/jpeg',
+          data: 'U2hoLiBUaGlzIGlzbid0IHJlYWxseSBhbiBpbWFnZQ=='
+        }
+      }
+    ],
+    stop_reason: 'endoftext'
+  }
+})
+
 responses.set('text claude3 ultimate question streamed', {
   headers: {
     'content-type': 'application/vnd.amazon.eventstream',