feat: Added setLlmTokenCountCallback API endpoint to register a callback for calculating token count when none is provided

api.js

@@ -1548,7 +1548,7 @@
  * @param {string} params.traceId Identifier for the feedback event.
  * Obtained from {@link getTraceMetadata}.
  * @param {string} params.category A tag for the event.
  * @param {string} params.rating A indicator of how useful the message was.
  * @param {string} [params.message] The message that triggered the event.
  * @param {object} [params.metadata] Additional key-value pairs to associate
  * with the recorded event.
@@ -1839,7 +1839,7 @@
   )
   metric.incrementCallCount()
 
-  if (!this.shim.isFunction(callback) || this.shim.isPromise(callback)) {
+  if (!this.shim.isFunction(callback) || this.shim.isAsyncFunction(callback)) {
     logger.warn(
       'Error Group callback must be a synchronous function, Error Group attribute will not be added'
     )
@@ -1849,4 +1849,37 @@
   this.agent.errors.errorGroupCallback = callback
 }
 
+/**
+ * Registers a callback which will be used for calculating token counts on Llm events when they are not
+ * available. This function will typically only be used if `ai_monitoring.record_content.enabled` is false
+ * and you want to still capture token counts for Llm events.
+ *
+ * Provided callbacks must return an integer value for the token count for a given piece of content.
+ *
+ * @param {Function} callback - synchronous function called to calculate token count for content.
+ * @example
+ * // @param {string} model - name of model (i.e. gpt-3.5-turbo)
+ * // @param {string} content - prompt or completion response
+ * function tokenCallback(model, content) {
+ *  // calculate tokens based on model and content
+ *  // return token count
+ *  return 40
+ * }
+ */
+API.prototype.setLlmTokenCountCallback = function setLlmTokenCountCallback(callback) {
+  const metric = this.agent.metrics.getOrCreateMetric(
+    NAMES.SUPPORTABILITY.API + '/setLlmTokenCountCallback'
+  )
+  metric.incrementCallCount()
+
+  if (!this.shim.isFunction(callback) || this.shim.isAsyncFunction(callback)) {
+    logger.warn(
+      'Llm token count callback must be a synchronous function, callback will not be registered.'
+    )
+    return
+  }
+
+  this.agent.llm.tokenCountCallback = callback
+}
+
 module.exports = API

lib/instrumentation/restify.js

@@ -93,7 +93,7 @@ function wrapMiddleware(shim, middleware, _name, route) {
   })
 
   const wrappedMw = shim.recordMiddleware(middleware, spec)
-  if (middleware.constructor.name === 'AsyncFunction') {
+  if (shim.isAsyncFunction(middleware)) {
     return async function asyncShim() {
       return wrappedMw.apply(this, arguments)
     }

lib/llm-events/openai/chat-completion-message.js

@@ -20,9 +20,13 @@ module.exports = class LlmChatCompletionMessage extends LlmEvent {
     }
 
     if (this.is_response) {
-      this.token_count = response?.usage?.completion_tokens
+      this.token_count =
+        response?.usage?.completion_tokens ||
+        agent.llm?.tokenCountCallback?.(this['response.model'], message?.content)
     } else {
-      this.token_count = response?.usage?.prompt_tokens
+      this.token_count =
+        response?.usage?.prompt_tokens ||
+        agent.llm?.tokenCountCallback?.(request.model || request.engine, message?.content)
     }
   }
 }

lib/llm-events/openai/embedding.js

@@ -14,6 +14,8 @@ module.exports = class LlmEmbedding extends LlmEvent {
     if (agent.config.ai_monitoring.record_content.enabled === true) {
       this.input = request.input?.toString()
     }
-    this.token_count = response?.usage?.prompt_tokens
+    this.token_count =
+      response?.usage?.prompt_tokens ||
+      agent.llm?.tokenCountCallback?.(this['request.model'], request.input?.toString())
   }
 }

lib/shim/shim.js

@@ -125,6 +125,7 @@ Shim.prototype.getName = getName
 Shim.prototype.isObject = isObject
 Shim.prototype.isFunction = isFunction
 Shim.prototype.isPromise = isPromise
+Shim.prototype.isAsyncFunction = isAsyncFunction
 Shim.prototype.isString = isString
 Shim.prototype.isNumber = isNumber
 Shim.prototype.isBoolean = isBoolean
@@ -1345,6 +1346,20 @@ function isPromise(obj) {
   return obj && typeof obj.then === 'function'
 }
 
+/**
+ * Determines if function is an async function.
+ * Note it does not test if the return value of function is a
+ * promise or async function
+ *
+ * @memberof Shim.prototype
+ * @param fn
+ * @param (function) function to test if async
+ * @returns {boolean} True if the function is an async function
+ */
+function isAsyncFunction(fn) {
+  return fn.constructor.name === 'AsyncFunction'
+}
+
 /**
  * Determines if the given value is null.
  *

test/unit/api/api-llm.test.js

@@ -28,7 +28,8 @@ tap.test('Agent API LLM methods', (t) => {
     loggerMock.warn.reset()
     const agent = helper.loadMockedAgent()
     t.context.api = new API(agent)
-    t.context.api.agent.config.ai_monitoring.enabled = true
+    agent.config.ai_monitoring.enabled = true
+    t.context.agent = agent
   })
 
   t.afterEach((t) => {
@@ -119,4 +120,49 @@ tap.test('Agent API LLM methods', (t) => {
       })
     })
   })
+
+  t.test('setLlmTokenCount should register callback to calculate token counts', async (t) => {
+    const { api, agent } = t.context
+    function callback(model, content) {
+      if (model === 'foo' && content === 'bar') {
+        return 10
+      }
+
+      return 1
+    }
+    api.setLlmTokenCountCallback(callback)
+    t.same(agent.llm.tokenCountCallback, callback)
+  })
+
+  t.test('should not store token count callback if it is async', async (t) => {
+    const { api, agent } = t.context
+    async function callback(model, content) {
+      return await new Promise((resolve) => {
+        if (model === 'foo' && content === 'bar') {
+          resolve(10)
+        }
+      })
+    }
+    api.setLlmTokenCountCallback(callback)
+    t.same(agent.llm.tokenCountCallback, undefined)
+    t.equal(loggerMock.warn.callCount, 1)
+    t.equal(
+      loggerMock.warn.args[0][0],
+      'Llm token count callback must be a synchronous function, callback will not be registered.'
+    )
+  })
+
+  t.test(
+    'should not store token count callback if callback is not actually a function',
+    async (t) => {
+      const { api, agent } = t.context
+      api.setLlmTokenCountCallback({ unit: 'test' })
+      t.same(agent.llm.tokenCountCallback, undefined)
+      t.equal(loggerMock.warn.callCount, 1)
+      t.equal(
+        loggerMock.warn.args[0][0],
+        'Llm token count callback must be a synchronous function, callback will not be registered.'
+      )
+    }
+  )
 })

test/unit/api/api-set-error-group-callback.test.js

@@ -84,8 +84,8 @@ tap.test('Agent API = set Error Group callback', (t) => {
   })
 
   t.test('should not attach the callback when async function', (t) => {
-    function callback() {
-      return new Promise((resolve) => {
+    async function callback() {
+      return await new Promise((resolve) => {
         setTimeout(() => {
           resolve()
         }, 200)

test/unit/api/stub.test.js

@@ -8,7 +8,7 @@
 const tap = require('tap')
 const API = require('../../../stub_api')
 
-const EXPECTED_API_COUNT = 34
+const EXPECTED_API_COUNT = 35
 
 tap.test('Agent API - Stubbed Agent API', (t) => {
   t.autoend()

test/unit/llm-events/openai/chat-completion-message.test.js

@@ -11,8 +11,6 @@ const helper = require('../../../lib/agent_helper')
 const { req, chatRes, getExpectedResult } = require('./common')
 
 tap.test('LlmChatCompletionMessage', (t) => {
-  t.autoend()
-
   let agent
   t.beforeEach(() => {
     agent = helper.loadMockedAgent()
@@ -104,4 +102,115 @@ tap.test('LlmChatCompletionMessage', (t) => {
       t.end()
     })
   })
+
+  t.test('should use token_count from tokenCountCallback for prompt message', (t) => {
+    const api = helper.getAgentApi()
+    const expectedCount = 4
+    function cb(model, content) {
+      t.equal(model, 'gpt-3.5-turbo-0613')
+      t.equal(content, 'What is a woodchuck?')
+      return expectedCount
+    }
+    api.setLlmTokenCountCallback(cb)
+    helper.runInTransaction(agent, () => {
+      api.startSegment('fakeSegment', false, () => {
+        const segment = api.shim.getActiveSegment()
+        const summaryId = 'chat-summary-id'
+        delete chatRes.usage
+        const chatMessageEvent = new LlmChatCompletionMessage({
+          agent,
+          segment,
+          request: req,
+          response: chatRes,
+          completionId: summaryId,
+          message: req.messages[0],
+          index: 0
+        })
+        t.equal(chatMessageEvent.token_count, expectedCount)
+        t.end()
+      })
+    })
+  })
+
+  t.test('should use token_count from tokenCountCallback for completion messages', (t) => {
+    const api = helper.getAgentApi()
+    const expectedCount = 4
+    function cb(model, content) {
+      t.equal(model, 'gpt-3.5-turbo-0613')
+      t.equal(content, 'a lot')
+      return expectedCount
+    }
+    api.setLlmTokenCountCallback(cb)
+    helper.runInTransaction(agent, () => {
+      api.startSegment('fakeSegment', false, () => {
+        const segment = api.shim.getActiveSegment()
+        const summaryId = 'chat-summary-id'
+        delete chatRes.usage
+        const chatMessageEvent = new LlmChatCompletionMessage({
+          agent,
+          segment,
+          request: req,
+          response: chatRes,
+          completionId: summaryId,
+          message: chatRes.choices[0].message,
+          index: 2
+        })
+        t.equal(chatMessageEvent.token_count, expectedCount)
+        t.end()
+      })
+    })
+  })
+
+  t.test('should not set token_count if not set in usage nor a callback registered', (t) => {
+    const api = helper.getAgentApi()
+    helper.runInTransaction(agent, () => {
+      api.startSegment('fakeSegment', false, () => {
+        const segment = api.shim.getActiveSegment()
+        const summaryId = 'chat-summary-id'
+        delete chatRes.usage
+        const chatMessageEvent = new LlmChatCompletionMessage({
+          agent,
+          segment,
+          request: req,
+          response: chatRes,
+          completionId: summaryId,
+          message: chatRes.choices[0].message,
+          index: 2
+        })
+        t.equal(chatMessageEvent.token_count, undefined)
+        t.end()
+      })
+    })
+  })
+
+  t.test(
+    'should not set token_count if not set in usage nor a callback registered returns count',
+    (t) => {
+      const api = helper.getAgentApi()
+      function cb() {
+        // empty cb
+      }
+      api.setLlmTokenCountCallback(cb)
+      helper.runInTransaction(agent, () => {
+        api.startSegment('fakeSegment', false, () => {
+          const segment = api.shim.getActiveSegment()
+          const summaryId = 'chat-summary-id'
+          delete chatRes.usage
+          const chatMessageEvent = new LlmChatCompletionMessage({
+            agent,
+            segment,
+            request: req,
+            response: chatRes,
+            completionId: summaryId,
+            message: chatRes.choices[0].message,
+            index: 2
+          })
+          t.equal(chatMessageEvent.token_count, undefined)
+          t.end()
+        })
+      })
+    }
+  )
+
+  t.end()
 })

test/unit/llm-events/openai/embedding.test.js

@@ -113,4 +113,54 @@ tap.test('LlmEmbedding', (t) => {
       t.end()
     })
   })
+
+  t.test('should calculate token count from tokenCountCallback', (t) => {
+    const req = {
+      input: 'This is my test input',
+      model: 'gpt-3.5-turbo-0613'
+    }
+
+    const api = helper.getAgentApi()
+
+    function cb(model, content) {
+      if (model === req.model) {
+        return content.length
+      }
+    }
+
+    api.setLlmTokenCountCallback(cb)
+    helper.runInTransaction(agent, () => {
+      const segment = api.shim.getActiveSegment()
+      delete res.usage
+      const embeddingEvent = new LlmEmbedding({
+        agent,
+        segment,
+        request: req,
+        response: res
+      })
+      t.equal(embeddingEvent.token_count, 21)
+      t.end()
+    })
+  })
+
+  t.test('should not set token count when not present in usage nor tokenCountCallback', (t) => {
+    const req = {
+      input: 'This is my test input',
+      model: 'gpt-3.5-turbo-0613'
+    }
+
+    const api = helper.getAgentApi()
+    helper.runInTransaction(agent, () => {
+      const segment = api.shim.getActiveSegment()
+      delete res.usage
+      const embeddingEvent = new LlmEmbedding({
+        agent,
+        segment,
+        request: req,
+        response: res
+      })
+      t.equal(embeddingEvent.token_count, undefined)
+      t.end()
+    })
+  })
 })