Track LLM Request ID as llmReqId

README.md

@@ -50,7 +50,7 @@ const openai = new OpenAI({
 });
 
 // Pass the above `openai` object along with your DOKU URL and API key and this will make sure that all OpenAI calls are automatically tracked.
-DokuMetry.init({llm: openai, dokuURL: "YOUR_DOKU_URL", apiKey: "YOUR_DOKU_TOKEN"})
+DokuMetry.init({llm: openai, dokuUrl: "YOUR_DOKU_URL", apiKey: "YOUR_DOKU_TOKEN"})
 
 async function main() {
   const chatCompletion = await openai.chat.completions.create({
@@ -73,7 +73,7 @@ const anthropic = new Anthropic({
 });
 
 // Pass the above `anthropic` object along with your DOKU URL and API key and this will make sure that all Anthropic calls are automatically tracked.
-DokuMetry.init({llm: anthropic, dokuURL: "YOUR_DOKU_URL", apiKey: "YOUR_DOKU_TOKEN"})
+DokuMetry.init({llm: anthropic, dokuUrl: "YOUR_DOKU_URL", apiKey: "YOUR_DOKU_TOKEN"})
 
 async function main() {
   const completion = await anthropic.completions.create({
@@ -97,7 +97,7 @@ const cohere = new CohereClient({
 });
 
 // Pass the above `cohere` object along with your DOKU URL and API key and this will make sure that all Cohere calls are automatically tracked.
-DokuMetry.init({llm: cohere, dokuURL: "YOUR_DOKU_URL", apiKey: "YOUR_DOKU_TOKEN"})
+DokuMetry.init({llm: cohere, dokuUrl: "YOUR_DOKU_URL", apiKey: "YOUR_DOKU_TOKEN"})
 
 (async () => {
     const prediction = await cohere.generate({
@@ -114,7 +114,7 @@ DokuMetry.init({llm: cohere, dokuURL: "YOUR_DOKU_URL", apiKey: "YOUR_DOKU_TOKEN"
 | Parameter         | Description                                               | Required      |
 |-------------------|-----------------------------------------------------------|---------------|
 | llm               | Language Learning Model (LLM) Object to track             | Yes           |
-| dokuURL           | URL of your Doku Instance                                 | Yes           |
+| dokuUrl           | URL of your Doku Instance                                 | Yes           |
 | apiKey            | Your Doku API key                                         | Yes           |
 | environment       | Custom environment tag to include in your metrics         | Optional      |
 | applicationName   | Custom application name tag for your metrics              | Optional      |

src/anthropic.js

@@ -41,6 +41,7 @@ export default function initAnthropic({ llm, dokuUrl, apiKey, environment, appli
     const duration = (end - start) / 1000;
 
     const data = {
+      llmReqId: response.id,
       environment: environment,
       applicationName: applicationName,
       sourceLanguage: 'Javascript',

src/cohere.js

@@ -1,29 +1,5 @@
 import {sendData} from './helpers.js';
 
-/**
- * Counts the number of tokens in the given text.
- *
- * @param {string} text - The input text.
- * @return {number} - The calculated number of tokens.
- *
- * @jsondoc
- * {
- *   "description": "Counts the number of tokens in the given text",
- *   "params": [{"name": "text", "type": "string", "description": "Text"}],
- *   "returns": {"type": "number", "description": "Number of tokens."}
- * }
- */
-function countTokens(text) {
-  const tokensPerWord = 2.5;
-
-  // Split the text into words
-  const words = text.split(/\s+/);
-
-  // Calculate the number of tokens
-  const numTokens = Math.round(words.length * tokensPerWord);
-
-  return numTokens;
-}
 /**
  * Initializes Cohere functionality with performance tracking and data logging.
  *
@@ -72,13 +48,14 @@ export default function initCohere({ llm, dokuUrl, apiKey, environment, applicat
 
     for (const generation of response.generations) {
       const data = {
+        llmReqId: generation.id,
         environment: environment,
         applicationName: applicationName,
         sourceLanguage: 'Javascript',
         endpoint: 'cohere.generate',
         skipResp: skipResp,
-        completionTokens: countTokens(generation.text),
-        promptTokens: countTokens(prompt),
+        completionTokens: response.meta["billedUnits"]["outputTokens"],
+        promptTokens: response.meta["billedUnits"]["inputTokens"],
         requestDuration: duration,
         model: model,
         prompt: prompt,
@@ -89,7 +66,8 @@ export default function initCohere({ llm, dokuUrl, apiKey, environment, applicat
       if (!params.hasOwnProperty('stream') || params.stream !== true) {
         data.finishReason = generation.finish_reason;
       }
-      await sendData(data, dokuUrl, apiKey);
+      console.log(data);
+      //await sendData(data, dokuUrl, apiKey);
     }
 
     return response;
@@ -113,7 +91,7 @@ export default function initCohere({ llm, dokuUrl, apiKey, environment, applicat
       requestDuration: duration,
       model: model,
       prompt: prompt,
-      promptTokens: response.meta["billed_units"]["input_tokens"],
+      promptTokens: response.meta["billedUnits"]["inputTokens"],
     };
 
     await sendData(data, dokuUrl, apiKey);
@@ -131,6 +109,7 @@ export default function initCohere({ llm, dokuUrl, apiKey, environment, applicat
     const prompt = params.message;
 
     const data = {
+      llmReqId: response.response_id,
       environment: environment,
       applicationName: applicationName,
       sourceLanguage: 'Javascript',
@@ -141,7 +120,7 @@ export default function initCohere({ llm, dokuUrl, apiKey, environment, applicat
       prompt: prompt,
       promptTokens: response.meta["billed_units"]["output_tokens"],
       completionTokens: response.meta["billed_units"]["input_tokens"],
-      totalTokens: response.token_count["billed_tokens"],
+      totalTokens: response.token_count["billed_units"],
       response: response.text,
     };
 
@@ -169,12 +148,15 @@ export default function initCohere({ llm, dokuUrl, apiKey, environment, applicat
 
     data.response = ""
     for await (const message of response) {
+      if (message.eventType === "stream-end") {
+        data.llmReqId = message.response.response_id;
+        data.promptTokens = message.response.meta.billed_units["input_tokens"];
+        data.completionTokens = message.response.meta.billed_units["output_tokens"];
+      }
       data.response += message.eventType === "text-generation" ? message.text : "";
       // Pass the message along so it's not consumed
       yield message; // this allows the message to flow back to the original caller
     }
-    data.promptTokens = countTokens(prompt)
-    data.completionTokens = countTokens(data.response)
     data.totalTokens = data.promptTokens + data.completionTokens
 
     const end = performance.now();
@@ -195,14 +177,15 @@ export default function initCohere({ llm, dokuUrl, apiKey, environment, applicat
     const prompt = params.text;
 
     const data = {
+      llmReqId: response.id,
       environment: environment,
       applicationName: applicationName,
       sourceLanguage: 'Javascript',
       endpoint: 'cohere.summarize',
       skipResp: skipResp,
       requestDuration: duration,
-      completionTokens: response.meta["billed_units"]["output_tokens"],
-      promptTokens: response.meta["billed_units"]["input_tokens"],
+      completionTokens: response.meta["billedUnits"]["outputTokens"],
+      promptTokens: response.meta["billedUnits"]["inputTokens"],
       model: model,
       prompt: prompt,
       response: response.summary,

src/openai.js

@@ -64,6 +64,7 @@ export default function initOpenAI({ llm, dokuUrl, apiKey, environment, applicat
             dataResponse += content;
             passThroughStream.push(chunk); // Push chunk to the pass-through stream
           }
+          var responseId = chunk.id;
         }
         passThroughStream.push(null); // Signal end of the pass-through stream
 
@@ -93,6 +94,7 @@ export default function initOpenAI({ llm, dokuUrl, apiKey, environment, applicat
 
         // Prepare the data object for Doku
         const data = {
+          llmReqId: responseId,
           environment: environment,
           applicationName: applicationName,
           sourceLanguage: 'Javascript',
@@ -136,6 +138,7 @@ export default function initOpenAI({ llm, dokuUrl, apiKey, environment, applicat
       }
       let prompt = formattedMessages.join("\n");
       const data = {
+        llmReqId: response.id,
         environment: environment,
         applicationName: applicationName,
         sourceLanguage: 'Javascript',
@@ -169,6 +172,7 @@ export default function initOpenAI({ llm, dokuUrl, apiKey, environment, applicat
           data.promptTokens = response.usage.prompt_tokens;
           data.totalTokens = response.usage.total_tokens;
       }
+
       await sendData(data, dokuUrl, apiKey);
 
       return response;
@@ -198,6 +202,7 @@ export default function initOpenAI({ llm, dokuUrl, apiKey, environment, applicat
             dataResponse += content;
             passThroughStream.push(chunk); // Push chunk to the pass-through stream
           }
+          var responseId = chunk.id;
         }
         passThroughStream.push(null); // Signal end of the pass-through stream
 
@@ -206,6 +211,7 @@ export default function initOpenAI({ llm, dokuUrl, apiKey, environment, applicat
         const duration = (end - start) / 1000;
         // Prepare the data object for Doku
         const data = {
+          llmReqId: responseId,
           environment: environment,
           applicationName: applicationName,
           sourceLanguage: 'Javascript',
@@ -229,6 +235,7 @@ export default function initOpenAI({ llm, dokuUrl, apiKey, environment, applicat
       const duration = (end - start) / 1000;
 
       const data = {
+        llmReqId: response.id,
         environment: environment,
         applicationName: applicationName,
         sourceLanguage: 'Javascript',
@@ -308,7 +315,7 @@ export default function initOpenAI({ llm, dokuUrl, apiKey, environment, applicat
       skipResp: skipResp,
       requestDuration: duration,
       model: params.model,
-      finetuneJobId: response.id,
+      llmReqId: response.id,
       finetuneJobStatus: response.status,
     };
 
@@ -331,9 +338,10 @@ export default function initOpenAI({ llm, dokuUrl, apiKey, environment, applicat
     }
 
     const quality = params.quality ?? 'standard';
-
+    var responseId = response.created;
     for (const item of response.data) {
       const data = {
+        llmReqId: responseId,
         environment: environment,
         applicationName: applicationName,
         sourceLanguage: 'Javascript',
@@ -365,8 +373,10 @@ export default function initOpenAI({ llm, dokuUrl, apiKey, environment, applicat
     if (params.response_format && params.response_format === 'b64_json') {
       imageFormat = 'b64_json';
     }
+    var responseId = response.created;
     for (const item of response.data) {
       const data = {
+        llmReqId: responseId,
         environment: environment,
         applicationName: applicationName,
         sourceLanguage: 'Javascript',

tests/cohere.test.mjs

@@ -6,12 +6,9 @@ describe('Cohere Test', () => {
   const cohere = new CohereClient({
     apiKey: process.env.COHERE_API_TOKEN,
   });
-
-  before(async () => {
-    DokuMetry.init({llm: cohere, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
-  });
 
   it('should return a response with "created" field', async () => {
+    DokuMetry.init({llm: cohere, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
     const text =
     'Ice cream is a sweetened frozen food eaten as a snack or dessert. ' +
     'It may be made from milk or cream and is flavoured with a sweetener, ' +
@@ -48,6 +45,7 @@ describe('Cohere Test', () => {
   }).timeout(10000);
 
   it('should return a response with prompt as "Doku"', async () => {
+    DokuMetry.init({llm: cohere, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
     try {
       const generate = await cohere.generate({
         prompt: 'Doku',
@@ -64,6 +62,7 @@ describe('Cohere Test', () => {
   }).timeout(10000);
 
   it('should return a response with object as "embed"', async () => {
+    DokuMetry.init({llm: cohere, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
     try {
       const embeddings = await cohere.embed({
         texts: ['This is a test'],
@@ -79,6 +78,7 @@ describe('Cohere Test', () => {
   }).timeout(20000);
 
   it('should return a response with object as "chat"', async () => {
+    DokuMetry.init({llm: cohere, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
     try {
       const chatResponse = await cohere.chat({
         message: 'Say this is a test',

tests/openai.test.mjs

@@ -10,10 +10,10 @@ describe('OpenAI Test', () => {
     openai = new OpenAI({
       apiKey: process.env.OPENAI_API_KEY,
     });
-    await DokuMetry.init({llm: openai, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
   });
 
   it('should return a response with object as "chat.completion"', async () => {
+    await DokuMetry.init({llm: openai, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
     const chatCompletion = await openai.chat.completions.create({
       messages: [{role: 'user', content: 'Say this is a test'}],
       model: 'gpt-3.5-turbo',
@@ -23,6 +23,7 @@ describe('OpenAI Test', () => {
   });
 
   it('should return a response with object as "text_completion"', async () => {
+    await DokuMetry.init({llm: openai, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
     const completion = await openai.completions.create({
       model: 'gpt-3.5-turbo-instruct',
       prompt: 'Say this is a test.',
@@ -33,6 +34,7 @@ describe('OpenAI Test', () => {
   });
 
   it('should return a response with object as "embedding"', async () => {
+    await DokuMetry.init({llm: openai, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
     const embeddings = await openai.embeddings.create({
       model: 'text-embedding-ada-002',
       input: 'The quick brown fox jumped over the lazy dog',
@@ -43,6 +45,7 @@ describe('OpenAI Test', () => {
   });
 
   it('should return a response with object as "fine_tuning.job"', async () => {
+    await DokuMetry.init({llm: openai, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
     try {
       const fineTuningJob = await openai.fineTuning.jobs.create({
         training_file: 'file-m36cc45komO83VJKAY1qVgeP',
@@ -59,6 +62,7 @@ describe('OpenAI Test', () => {
   }).timeout(10000);
 
   it('should return a response with "created" field', async () => {
+    await DokuMetry.init({llm: openai, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
     const imageGeneration = await openai.images.generate({
       model: 'dall-e-2',
       prompt: 'Generate an image of a cat.',
@@ -68,6 +72,7 @@ describe('OpenAI Test', () => {
   }).timeout(30000);
 
   it('should return a response with "created" field', async () => {
+    await DokuMetry.init({llm: openai, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
     const imageVariation = await openai.images.createVariation({
       image: fs.createReadStream('tests/test-image-for-openai.png'),
     });
@@ -76,12 +81,12 @@ describe('OpenAI Test', () => {
   }).timeout(30000);
 
   it('should return a response with url as "https://api.openai.com/v1/audio/speech"', async () => {
+    DokuMetry.init({llm: openai, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
     const audioSpeech = await openai.audio.speech.create({
       model: 'tts-1',
       voice: 'alloy',
       input: 'Today is a wonderful day to build something people love!',
     });
-
     expect(audioSpeech.url).to.equal('https://api.openai.com/v1/audio/speech');
-  });
+  }).timeout(30000);
 });