Release 5.3.2

CHANGELOG.md

@@ -1,5 +1,12 @@
 # @langtrase/typescript-sdk
 
+
+## 5.3.2
+
+### Patch Changes
+
+- Add Vertex AI tools and funcition tracing support
+
 ## 5.3.1
 
 ### Patch Changes

package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langtrase/typescript-sdk",
-  "version": "5.3.1",
+  "version": "5.3.2",
   "description": "A typescript SDK for Langtrace",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",

src/examples/vertexai/basic.ts

@@ -1,6 +1,6 @@
 import { init } from '@langtrace-init/init'
 import dotenv from 'dotenv'
-import { VertexAI } from '@google-cloud/vertexai'
+import { VertexAI, FunctionDeclarationSchemaType } from '@google-cloud/vertexai'
 
 dotenv.config()
 init({ batch: false, write_spans_to_console: true })
@@ -13,6 +13,38 @@ const vertexAI = new VertexAI({ project, location })
 
 const generativeModel = vertexAI.getGenerativeModel({ model: textModel })
 
+const functionDeclarations = [
+  {
+    functionDeclarations: [
+      {
+        name: 'get_current_weather',
+        description: 'get weather in a given location',
+        parameters: {
+          type: FunctionDeclarationSchemaType.OBJECT,
+          properties: {
+            location: { type: FunctionDeclarationSchemaType.STRING },
+            unit: {
+              type: FunctionDeclarationSchemaType.STRING,
+              enum: ['celsius', 'fahrenheit']
+            }
+          },
+          required: ['location']
+        }
+      }
+    ]
+  }
+]
+
+const functionResponseParts = [
+  {
+    functionResponse: {
+      name: 'get_current_weather',
+      response:
+        { name: 'get_current_weather', content: { weather: 'super nice' } }
+    }
+  }
+]
+
 export const basicVertexAIChat = async (): Promise<void> => {
   const request = { contents: [{ role: 'user', parts: [{ text: 'How are you doing today?' }] }] }
   const result = await generativeModel.generateContent(request)
@@ -65,11 +97,59 @@ export const basicVertexAIStartChatStream = async (): Promise<void> => {
   for await (const item of result.stream) {
     const text = item.candidates?.[0]?.content?.parts?.[0]?.text
     if (text === undefined || text === null) {
-      console.log('Stream chunk: ', text)
-    } else {
       console.log('Stream chunk: No text available')
+    } else {
+      console.log('Stream chunk: ', text)
     }
   }
   const aggregatedResponse = await result.response
   console.log('Aggregated response: ', JSON.stringify(aggregatedResponse))
 }
+
+export const basicVertexAIStartChatWithToolRequest = async (): Promise<void> => {
+  const request = {
+    contents: [
+      { role: 'user', parts: [{ text: 'What is the weather in Boston?' }] },
+      { role: 'model', parts: [{ functionCall: { name: 'get_current_weather', args: { location: 'Boston' } } }] },
+      { role: 'user', parts: functionResponseParts }
+    ],
+    tools: functionDeclarations
+  }
+  const streamingResult =
+    await generativeModel.generateContentStream(request)
+  for await (const item of streamingResult.stream) {
+    if (item?.candidates !== undefined) {
+      console.log(item.candidates[0])
+    }
+  }
+}
+
+export const basicVertexAIStartChatWithToolResponse = async (): Promise<void> => {
+  // Create a chat session and pass your function declarations
+  const chat = generativeModel.startChat({ tools: functionDeclarations })
+
+  const chatInput1 = 'What is the weather in Boston?'
+
+  // This should include a functionCall response from the model
+  const streamingResult1 = await chat.sendMessageStream(chatInput1)
+  for await (const item of streamingResult1.stream) {
+    if (item?.candidates !== undefined) {
+      console.log(item.candidates[0])
+    }
+  }
+  const response1 = await streamingResult1.response
+  console.log('first aggregated response: ', JSON.stringify(response1))
+
+  // Send a follow up message with a FunctionResponse
+  const streamingResult2 = await chat.sendMessageStream(functionResponseParts)
+  for await (const item of streamingResult2.stream) {
+    if (item?.candidates !== undefined) {
+      console.log(item.candidates[0])
+    }
+  }
+
+  // This should include a text response from the model using the response content
+  // provided above
+  const response2 = await streamingResult2.response
+  console.log('second aggregated response: ', JSON.stringify(response2))
+}

src/instrumentation/vertexai/patch.ts

@@ -53,29 +53,83 @@ export function generateContentPatch (
     const serviceProvider = Vendors.VERTEXAI
     const customAttributes = context.active().getValue(LANGTRACE_ADDITIONAL_SPAN_ATTRIBUTES_KEY) ?? {}
 
-    const prompts = args.flatMap((arg: string | { contents: CandidateContent[] }) => {
-      if (typeof arg === 'string') {
+    let argTools: any[] = []
+    const prompts = args.flatMap((arg: string | { contents?: CandidateContent[], tools?: any, functionResponse?: any }) => {
+      if (Array.isArray(arg)) {
+        // Handle the case where `arg` is an array (like [ { functionResponse: ... } ])
+        return arg.flatMap(innerArg => {
+          if (Array.isArray(innerArg.tools)) argTools = argTools.concat(innerArg.tools)
+          if (innerArg.functionResponse != null) {
+            return [{ role: 'model', content: JSON.stringify(innerArg.functionResponse) }]
+          } else if (innerArg.contents != null) {
+            return innerArg.contents.map((content: CandidateContent) => ({
+              role: content.role,
+              content: content.parts.map((part: CandidateContentPart) => {
+                if (typeof part.text === 'string') {
+                  return part.text
+                } else if ('functionCall' in part) {
+                  return JSON.stringify((part as any).functionCall)
+                } else if (typeof part === 'object') {
+                  return JSON.stringify(part)
+                } else {
+                  return ''
+                }
+              }).join('')
+            }))
+          } else {
+            return []
+          }
+        })
+      } else if (typeof arg === 'string') {
         // Handle the case where `arg` is a string
         return [{ role: 'user', content: arg }]
-      } else {
+      } else if (arg.contents != null) {
+        if (Array.isArray(arg.tools)) argTools = argTools.concat(arg.tools)
         // Handle the case where `arg` has the `contents` structure
         return arg.contents.map(content => ({
           role: content.role,
-          content: content.parts.map(part => part.text).join('')
+          content: content.parts.map((part: CandidateContentPart) => {
+            if (typeof part.text === 'string') {
+              return part.text
+            } else if ('functionCall' in part) {
+              return JSON.stringify((part as any).functionCall)
+            } else if (typeof part === 'object') {
+              return JSON.stringify(part)
+            } else {
+              return ''
+            }
+          }).join('')
         }))
+      } else if (arg.functionResponse != null) {
+        // Handle the case where `arg` has a `functionResponse` structure
+        return [{ role: 'model', content: JSON.stringify(arg.functionResponse) }]
+      } else {
+        return []
       }
     })
 
+    const allTools = argTools.concat(this?.tools ?? [])
     const attributes: LLMSpanAttributes = {
       'langtrace.sdk.name': sdkName,
       'langtrace.service.name': serviceProvider,
       'langtrace.service.type': 'llm',
       'gen_ai.operation.name': 'chat',
       'langtrace.service.version': version,
       'langtrace.version': langtraceVersion,
-      'url.full': '',
-      'url.path': this?.publisherModelEndpoint,
-      'gen_ai.request.model': this?.model,
+      'url.full': this?.apiEndpoint,
+      'url.path': this?.publisherModelEndpoint ?? this?.resourcePath ?? undefined,
+      'gen_ai.request.model': (() => {
+        if (this?.model !== undefined && this.model !== null) {
+          return this.model
+        }
+        if (typeof this?.resourcePath === 'string') {
+          return this.resourcePath.split('/').pop()
+        }
+        if (typeof this?.publisherModelEndpoint === 'string') {
+          return this.publisherModelEndpoint.split('/').pop()
+        }
+        return undefined
+      })(),
       'http.max.retries': this?._client?.maxRetries,
       'http.timeout': this?._client?.timeout,
       'gen_ai.request.temperature': this?.generationConfig?.temperature,
@@ -86,6 +140,7 @@ export function generateContentPatch (
       'gen_ai.request.frequency_penalty': this?.generationConfig?.frequencyPenalty,
       'gen_ai.request.presence_penalty': this?.generationConfig?.presencePenalty,
       'gen_ai.request.seed': this?.generationConfig?.seed,
+      'gen_ai.request.tools': allTools.length > 0 ? JSON.stringify(allTools) : undefined,
       ...customAttributes
     }
 
@@ -179,7 +234,17 @@ async function * handleStreamResponse (
       const { content } = chunk.candidates.map((candidate: Candidate) => {
         return {
           role: candidate.content.role,
-          content: candidate.content.parts.map((part: CandidateContentPart) => part.text).join('')
+          content: candidate.content.parts.map((part: CandidateContentPart) => {
+            if (typeof part.text === 'string') {
+              return part.text
+            } else if ('functionCall' in part) {
+              return JSON.stringify(part.functionCall)
+            } else if (typeof part === 'object') {
+              return JSON.stringify(part)
+            } else {
+              return ''
+            }
+          }).join('')
         }
       })[0]
       const tokenCount = estimateTokens(content)

src/instrumentation/vertexai/types.ts

@@ -1,5 +1,6 @@
 export interface CandidateContentPart {
   text: string
+  functionCall: any
 }
 
 export interface CandidateContent {