diff --git a/README.md b/README.md
index d2b8f9f..9d3f2d6 100644
--- a/README.md
+++ b/README.md
@@ -123,6 +123,9 @@ DokuMetry.init({llm: cohere, dokuUrl: "YOUR_DOKU_INGESTER_URL", apiKey: "YOUR_DO
 | skipResp          | Skip response from the Doku Ingester for faster execution | Optional      |
 
 
+## Error
+To make it easier to follow the server errors caused by dokumetry, we have logged the errors like `Dokumetry: Error transmitting Data: HTTP status 401`, where `Dokumetry` is the application name in blue and the rest is the error in red.
+
 ## Semantic Versioning
 This package generally follows [SemVer](https://semver.org/spec/v2.0.0.html) conventions, though certain backwards-incompatible changes may be released as minor versions:
 
diff --git a/package-lock.json b/package-lock.json
index 482b614..111a7d0 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "dokumetry",
-  "version": "0.1.1",
+  "version": "0.1.2",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "dokumetry",
-      "version": "0.1.1",
+      "version": "0.1.2",
       "license": "Apache-2.0",
       "dependencies": {
         "stream": "^0.0.2"
diff --git a/package.json b/package.json
index b67bf43..b44eeff 100644
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
   "name": "dokumetry",
-  "version": "0.1.1",
+  "version": "0.1.2",
   "description": "An NPM Package for tracking OpenAI API calls and sending usage metrics to Doku",
   "main": "src/index.js",
   "scripts": {
@@ -27,8 +27,8 @@
     "stream": "^0.0.2"
   },
   "devDependencies": {
-    "@azure/openai": "^1.0.0-beta.11",
     "@anthropic-ai/sdk": "^0.18.0",
+    "@azure/openai": "^1.0.0-beta.11",
     "@mistralai/mistralai": "^0.1.3",
     "chai": "^5.0.3",
     "cohere-ai": "^7.7.3",
diff --git a/src/anthropic.js b/src/anthropic.js
deleted file mode 100644
index 51208ea..0000000
--- a/src/anthropic.js
+++ /dev/null
@@ -1,168 +0,0 @@
-import {sendData} from './helpers.js';
-import { Readable } from 'stream';
-
-/**
- * Initializes Anthropic functionality with performance tracking.
- *
- * @param {Object} llm - The Anthropic function object.
- * @param {string} dokuUrl - The URL for logging data.
- * @param {string} apiKey - The authentication apiKey.
- * @param {string} environment - The environment.
- * @param {string} applicationName - The application name.
- * @param {boolean} skipResp - To skip waiting for API resopnse.
- * @return {void}
- *
- * @jsondoc
- * {
- *   "description": "Initializes Anthropic function and performance tracking",
- *   "params": [
- *     {"name": "dokuUrl", "type": "string", "description": "Doku URL"},
- *     {"name": "apiKey", "type": "string", "description": "Auth apiKey"},
- *     {"name": "llm", "type": "Object", "description": "The Anthropic object"},
- *     {"name": "environment", "type": "string", "description": "Environment"},
- *     {"name": "applicationName", "type": "string", "description": "Application Name"},
- *     {"name": "skipResp", "type": "boolean", "description": "To skip waiting for API resopnse."}
- *   ],
- *   "returns": {"type": "void"},
- *   "example": {
- *     "description": "Example usage of init function.",
- *     "code": "init('https://example.com/log', 'authToken', anthropicFunc);"
- *   }
- * }
- */
-export default function initAnthropic({ llm, dokuUrl, apiKey, environment, applicationName, skipResp }) {
-  const originalMessagesCreate = llm.messages.create;
-
-  // Define wrapped method
-  llm.messages.create = async function(params) {
-    let streaming = params.stream || false;
-    if (streaming) {
-      // Call original method
-      const start = performance.now();
-      const originalResponseStream = await originalMessagesCreate.call(this, params);
-  
-      // Create a pass-through stream
-      const passThroughStream = new Readable({
-        read() {},
-        objectMode: true // Set to true because the chunks are objects
-      });
-  
-      let dataResponse = '';
-      var responseId = '';
-      var promptTokens = 0;
-      var completionTokens = 0;
-  
-      // Immediately-invoked async function to handle streaming
-      (async () => {
-        for await (const chunk of originalResponseStream) {
-          if (chunk.type === 'message_start') {
-            responseId = chunk.message.id;
-            promptTokens = chunk.message.usage.input_tokens;
-            passThroughStream.push(chunk); // Push chunk to the pass-through stream
-          } 
-          else if (chunk.type === 'content_block_delta') {
-            dataResponse += chunk.delta.text;
-            passThroughStream.push(chunk); // Push chunk to the pass-through stream
-          } 
-          else if (chunk.type === 'message_delta') {
-            completionTokens = chunk.usage.output_tokens;
-            passThroughStream.push(chunk); // Push chunk to the pass-through stream
-          }
-        }
-        passThroughStream.push(null); // Signal end of the pass-through stream
-  
-        // Process response data after stream has ended
-        const end = performance.now();
-        const duration = (end - start) / 1000;
-  
-        let formattedMessages = [];
-        for (let message of params.messages) {
-          let role = message.role;
-          let content = message.content;
-
-          if (Array.isArray(content)) {
-            let contentStr = content.map(item => {
-              if (item.type) {
-                return `${item.type}: ${item.text || item.image_url}`;
-              } else {
-                return `text: ${item.text}`;
-              }
-            }).join(", ");
-            formattedMessages.push(`${role}: ${contentStr}`);
-          } else {
-            formattedMessages.push(`${role}: ${content}`);
-          }
-        }
-        let prompt = formattedMessages.join("\n");
-  
-        // Prepare the data object for Doku
-        const data = {
-          llmReqId: responseId,
-          environment: environment,
-          applicationName: applicationName,
-          sourceLanguage: 'Javascript',
-          endpoint: 'anthropic.messages',
-          skipResp: skipResp,
-          requestDuration: duration,
-          model: params.model,
-          prompt: prompt,
-          response: dataResponse,
-          promptTokens: promptTokens,
-          completionTokens: completionTokens,
-        };
-        data.totalTokens = data.promptTokens + data.completionTokens;
-
-        await sendData(data, dokuUrl, apiKey);
-      })();
-      
-      // Return the pass-through stream to the original caller
-      return passThroughStream;
-    }
-    else{
-      const start = performance.now();
-      const response = await originalMessagesCreate.call(this, params);
-      const end = performance.now();
-      const duration = (end - start) / 1000;
-      let formattedMessages = [];
-      for (let message of params.messages) {
-        let role = message.role;
-        let content = message.content;
-
-        if (Array.isArray(content)) {
-          let contentStr = content.map(item => {
-            if (item.type) {
-              return `${item.type}: ${item.text || item.image_url}`;
-            } else {
-              return `text: ${item.text}`;
-            }
-          }).join(", ");
-          formattedMessages.push(`${role}: ${contentStr}`);
-        } else {
-          formattedMessages.push(`${role}: ${content}`);
-        }
-      }
-      let prompt = formattedMessages.join("\n");
-
-      const data = {
-        llmReqId: response.id,
-        environment: environment,
-        applicationName: applicationName,
-        sourceLanguage: 'Javascript',
-        endpoint: 'anthropic.messages',
-        skipResp: skipResp,
-        completionTokens: response.usage.input_tokens,
-        promptTokens: response.usage.output_tokens,
-        requestDuration: duration,
-        model: params.model,
-        prompt: prompt,
-        finishReason: response.stop_reason,
-        response: response.content[0].text,
-      };
-      data.totalTokens = data.promptTokens + data.completionTokens; 
-
-      await sendData(data, dokuUrl, apiKey);
-
-      return response;
-    }
-  };
-}
diff --git a/src/azure_openai.js b/src/azure_openai.js
deleted file mode 100644
index 78776ae..0000000
--- a/src/azure_openai.js
+++ /dev/null
@@ -1,342 +0,0 @@
-import {sendData} from './helpers.js';
-import { Readable } from 'stream';
-
-/**
- * Initializes Azure OpenAI functionality with performance tracking and data logging.
- *
- * @param {Object} llm - The Azure OpenAI function object.
- * @param {string} dokuUrl - The URL for logging data.
- * @param {string} apiKey - The authentication apiKey.
- * @param {string} environment - The environment.
- * @param {string} applicationName - The application name.
- * @param {boolean} skipResp - To skip waiting for API resopnse.
- * @return {void}
- *
- * @jsondoc
- * {
- *   "description": "Performance tracking for Azure OpenAI APIs",
- *   "params": [
- *     {"name": "llm", "type": "Object", "description": "Azure OpenAI function."},
- *     {"name": "dokuUrl", "type": "string", "description": "The URL"},
- *     {"name": "apiKey", "type": "string", "description": "The auth apiKey."},
- *     {"name": "environment", "type": "string", "description": "The environment."},
- *     {"name": "applicationName", "type": "string", "description": "The application name."},
- *     {"name": "skipResp", "type": "boolean", "description": "To skip waiting for API resopnse."}
- *   ],
- *   "returns": {"type": "void"},
- *   "example": {
- *     "description": "Example usage of init function.",
- *     "code": "init(azureOenaiFunc, 'https://example.com/log', 'authToken');"
- *   }
- * }
- */
-export default function initAzureOpenAI({ llm, dokuUrl, apiKey, environment, applicationName, skipResp }) {
-  // Save original method
-  const originalChatCreate = llm.chat.completions.create;
-  const originalCompletionsCreate = llm.completions.create;
-  const originalEmbeddingsCreate = llm.embeddings.create;
-  const originalImagesCreate = llm.images.generate;
-
-  // Define wrapped method
-  llm.chat.completions.create = async function(params) {
-    const start = performance.now();
-    let streaming = params.stream || false;
-    if (streaming) {
-      // Call original method
-      const originalResponseStream = await originalChatCreate.call(this, params);
-  
-      // Create a pass-through stream
-      const passThroughStream = new Readable({
-        read() {},
-        objectMode: true // Set to true because the chunks are objects
-      });
-  
-      let dataResponse = '';
-      let chatModel = '';
-  
-      // Immediately-invoked async function to handle streaming
-      (async () => {
-        for await (const chunk of originalResponseStream) {
-          var content = chunk.choices[0]?.delta?.content;
-          if (content) {
-            dataResponse += content;
-            passThroughStream.push(chunk); // Push chunk to the pass-through stream
-          }
-          var responseId = chunk.id;
-          chatModel = chunk.model;
-        }
-        passThroughStream.push(null); // Signal end of the pass-through stream
-  
-        // Process response data after stream has ended
-        const end = performance.now();
-        const duration = (end - start) / 1000;
-  
-        let formattedMessages = [];
-        for (let message of params.messages) {
-          let role = message.role;
-          let content = message.content;
-
-          if (Array.isArray(content)) {
-            let contentStr = content.map(item => {
-              if (item.type) {
-                return `${item.type}: ${item.text || item.image_url}`;
-              } else {
-                return `text: ${item.text}`;
-              }
-            }).join(", ");
-            formattedMessages.push(`${role}: ${contentStr}`);
-          } else {
-            formattedMessages.push(`${role}: ${content}`);
-          }
-        }
-        let prompt = formattedMessages.join("\n");
-  
-        // Prepare the data object for Doku
-        const data = {
-          llmReqId: responseId,
-          environment: environment,
-          applicationName: applicationName,
-          sourceLanguage: 'Javascript',
-          endpoint: 'azure.chat.completions',
-          skipResp: skipResp,
-          requestDuration: duration,
-          model: "azure_" + chatModel,
-          prompt: prompt,
-          response: dataResponse
-        };
-
-        await sendData(data, dokuUrl, apiKey);
-      })();
-      
-      // Return the pass-through stream to the original caller
-      return passThroughStream;
-    }
-    else {
-      // Call original method
-      const response = await originalChatCreate.call(this, params);
-      const end = performance.now();
-      const duration = (end - start) / 1000;
-
-      let formattedMessages = [];
-      for (let message of params.messages) {
-        let role = message.role;
-        let content = message.content;
-
-        if (Array.isArray(content)) {
-          let contentStr = content.map(item => {
-            if (item.type) {
-              return `${item.type}: ${item.text || item.image_url}`;
-            } else {
-              return `text: ${item.text}`;
-            }
-          }).join(", ");
-          formattedMessages.push(`${role}: ${contentStr}`);
-        } else {
-          formattedMessages.push(`${role}: ${content}`);
-        }
-      }
-      let prompt = formattedMessages.join("\n");
-      const data = {
-        llmReqId: response.id,
-        environment: environment,
-        applicationName: applicationName,
-        sourceLanguage: 'Javascript',
-        endpoint: 'azure.chat.completions',
-        skipResp: skipResp,
-        requestDuration: duration,
-        model: "azure_" + response.model,
-        prompt: prompt,
-      };
-
-      if (!params.hasOwnProperty('tools')) {
-        data.completionTokens = response.usage.completion_tokens;
-        data.promptTokens = response.usage.prompt_tokens;
-        data.totalTokens = response.usage.total_tokens;
-        data.finishReason = response.choices[0].finish_reason;
-
-        if (!params.hasOwnProperty('n') || params.n === 1) {
-          data.response = response.choices[0].message.content;
-        } else {
-          let i = 0;
-          while (i < params.n && i < response.choices.length) {
-            data.response = response.choices[i].message.content;
-            i++;
-            await sendData(data, dokuUrl, apiKey);
-          }
-          return response;
-        }
-      } else if (params.hasOwnProperty('tools')) {
-          data.response = "Function called with tools";
-          data.completionTokens = response.usage.completion_tokens;
-          data.promptTokens = response.usage.prompt_tokens;
-          data.totalTokens = response.usage.total_tokens;
-      }
-
-      await sendData(data, dokuUrl, apiKey);
-
-      return response;
-    }
-  };
-
-  llm.completions.create = async function(params) {
-    const start = performance.now();
-    let streaming = params.stream || false;
-    if (streaming) {
-      // Call original method
-      const originalResponseStream = await originalCompletionsCreate.call(this, params);
-  
-      // Create a pass-through stream
-      const passThroughStream = new Readable({
-        read() {},
-        objectMode: true // Set to true because the chunks are objects
-      });
-  
-      let dataResponse = '';
-      let chatModel = '';
-  
-      // Immediately-invoked async function to handle streaming
-      (async () => {
-        for await (const chunk of originalResponseStream) {
-          if (chunk.choices.length > 0) {
-            dataResponse += chunk.choices[0].text;
-            passThroughStream.push(chunk); // Push chunk to the pass-through stream
-          }
-          var responseId = chunk.id;
-          chatModel = chunk.model;
-        }
-        passThroughStream.push(null); // Signal end of the pass-through stream
-  
-        // Process response data after stream has ended
-        const end = performance.now();
-        const duration = (end - start) / 1000;
-        // Prepare the data object for Doku
-        const data = {
-          llmReqId: responseId,
-          environment: environment,
-          applicationName: applicationName,
-          sourceLanguage: 'Javascript',
-          endpoint: 'azure.completions',
-          skipResp: skipResp,
-          requestDuration: duration,
-          model: "azure_" + chatModel,
-          prompt: params.prompt,
-          response: dataResponse
-        };
-
-        await sendData(data, dokuUrl, apiKey);
-      })();
-      
-      // Return the pass-through stream to the original caller
-      return passThroughStream;
-    }
-    else {
-      const response = await originalCompletionsCreate.call(this, params);
-      const end = performance.now();
-      const duration = (end - start) / 1000;
-
-      const data = {
-        llmReqId: response.id,
-        environment: environment,
-        applicationName: applicationName,
-        sourceLanguage: 'Javascript',
-        endpoint: 'azure.completions',
-        skipResp: skipResp,
-        requestDuration: duration,
-        model: "azure_" + response.model,
-        prompt: params.prompt,
-      };
-
-      if (!params.hasOwnProperty('tools')) {
-        data.completionTokens = response.usage.completion_tokens;
-        data.promptTokens = response.usage.prompt_tokens;
-        data.totalTokens = response.usage.total_tokens;
-        data.finishReason = response.choices[0].finish_reason;
-
-        if (!params.hasOwnProperty('n') || params.n === 1) {
-          data.response = response.choices[0].text;
-        } else {
-          let i = 0;
-          while (i < params.n && i < response.choices.length) {
-            data.response = response.choices[i].text;
-            i++;
-
-            await sendData(data, dokuUrl, apiKey);
-          }
-          return response;
-        }
-      } else if (params.hasOwnProperty('tools')) {
-          data.response = "Function called with tools";
-          data.completionTokens = response.usage.completion_tokens;
-          data.promptTokens = response.usage.prompt_tokens;
-          data.totalTokens = response.usage.total_tokens;
-      }
-      
-      await sendData(data, dokuUrl, apiKey);
-
-      return response;
-    }
-  };
-
-  llm.embeddings.create = async function(params) {
-    const start = performance.now();
-    const response = await originalEmbeddingsCreate.call(this, params);
-    const end = performance.now();
-    const duration = (end - start) / 1000;
-
-    const data = {
-      environment: environment,
-      applicationName: applicationName,
-      sourceLanguage: 'Javascript',
-      endpoint: 'azure.embeddings',
-      skipResp: skipResp,
-      requestDuration: duration,
-      model: "azure_" + response.model,
-      prompt: params.input,
-      promptTokens: response.usage.prompt_tokens,
-      totalTokens: response.usage.total_tokens,
-    };
-
-    await sendData(data, dokuUrl, apiKey);
-
-    return response;
-  };
-
-  llm.images.generate = async function(params) {
-    const start = performance.now();
-    const response = await originalImagesCreate.call(this, params);
-    const end = performance.now();
-    const duration = (end - start) / 1000;
-    const size = params.size || '1024x1024';
-    const model = 'azure_dall-e-3';
-    let imageFormat = 'url';
-
-    if (params.response_format && params.response_format === 'b64_json') {
-      imageFormat = 'b64_json';
-    }
-
-    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',
-        endpoint: 'azure.images.create',
-        skipResp: skipResp,
-        requestDuration: duration,
-        model: model,
-        prompt: params.prompt,
-        imageSize: size,
-        imageQuality: quality,
-        revisedPrompt: item.revised_prompt || null,
-        image: item[imageFormat],
-      };
-
-      await sendData(data, dokuUrl, apiKey);
-    }
-
-    return response;
-  };
-
-}
diff --git a/src/cohere.js b/src/cohere.js
deleted file mode 100644
index 014dbe4..0000000
--- a/src/cohere.js
+++ /dev/null
@@ -1,200 +0,0 @@
-import {sendData} from './helpers.js';
-
-/**
- * Initializes Cohere functionality with performance tracking and data logging.
- *
- * @param {Object} llm - The Cohere function object.
- * @param {string} dokuUrl - The URL for logging data.
- * @param {string} apiKey - The authentication apiKey.
- * @param {string} environment - The environment.
- * @param {string} applicationName - The application name.
- * @param {boolean} skipResp - To skip waiting for API resopnse.
- * @return {void}
- *
- * @jsondoc
- * {
- *   "description": "Initializes Cohere functionality and performance tracking",
- *   "params": [
- *     {"name": "llm", "type": "Object", "description": "Cohere object"},
- *     {"name": "dokuUrl", "type": "string", "description": "URL for Doku"},
- *     {"name": "apiKey", "type": "string", "description": "Auth apiKey."},
- *     {"name": "environment", "type": "string", "description": "Environment."},
- *     {"name": "applicationName", "type": "string", "description": "Application name."},
- *     {"name": "skipResp", "type": "boolean", "description": "To skip waiting for API resopnse."}
- *   ],
- *   "returns": {"type": "void"},
- *   "example": {
- *     "description": "Example usage of init function.",
- *     "code": "init(cohereFunc, 'https://example.com/log', 'authToken');"
- *   }
- * }
- */
-export default function initCohere({ llm, dokuUrl, apiKey, environment, applicationName, skipResp }) {
-  const originalGenerate = llm.generate;
-  const originalEmbed = llm.embed;
-  const originalChat = llm.chat;
-  const originalChatStream = llm.chatStream;
-  const originalSummarize = llm.summarize;
-
-  // Define wrapped methods
-  llm.generate = async function(params) {
-    const start = performance.now();
-    const response = await originalGenerate.call(this, params);
-    const end = performance.now();
-    const duration = (end - start) / 1000;
-
-    const model = params.model || 'command';
-    const prompt = params.prompt;
-
-    for (const generation of response.generations) {
-      const data = {
-        llmReqId: generation.id,
-        environment: environment,
-        applicationName: applicationName,
-        sourceLanguage: 'Javascript',
-        endpoint: 'cohere.generate',
-        skipResp: skipResp,
-        completionTokens: response.meta["billedUnits"]["outputTokens"],
-        promptTokens: response.meta["billedUnits"]["inputTokens"],
-        requestDuration: duration,
-        model: model,
-        prompt: prompt,
-        response: generation.text,
-      };
-      data.totalTokens = data.promptTokens + data.completionTokens;
-
-      if (!params.hasOwnProperty('stream') || params.stream !== true) {
-        data.finishReason = generation.finish_reason;
-      }
-
-      await sendData(data, dokuUrl, apiKey);
-    }
-
-    return response;
-  };
-
-  llm.embed = async function(params) {
-    const start = performance.now();
-    const response = await originalEmbed.call(this, params);
-    const end = performance.now();
-    const duration = (end - start) / 1000;
-
-    const model = params.model || 'embed-english-v2.0';
-    const prompt = params.texts.toString();
-
-    const data = {
-      environment: environment,
-      applicationName: applicationName,
-      sourceLanguage: 'Javascript',
-      endpoint: 'cohere.embed',
-      skipResp: skipResp,
-      requestDuration: duration,
-      model: model,
-      prompt: prompt,
-      promptTokens: response.meta["billedUnits"]["inputTokens"],
-      totalTokens: response.meta["billedUnits"]["inputTokens"],
-    };
-
-    await sendData(data, dokuUrl, apiKey);
-
-    return response;
-  };
-
-  llm.chat = async function(params) {
-    const start = performance.now();
-    const response = await originalChat.call(this, params);
-    const end = performance.now();
-    const duration = (end - start) / 1000;
-
-    const model = params.model || 'command';
-    const prompt = params.message;
-
-    const data = {
-      llmReqId: response.response_id,
-      environment: environment,
-      applicationName: applicationName,
-      sourceLanguage: 'Javascript',
-      endpoint: 'cohere.chat',
-      skipResp: skipResp,
-      requestDuration: duration,
-      model: model,
-      prompt: prompt,
-      promptTokens: response.meta["billed_units"]["output_tokens"],
-      completionTokens: response.meta["billed_units"]["input_tokens"],
-      totalTokens: response.token_count["billed_units"],
-      response: response.text,
-    };
-
-    await sendData(data, dokuUrl, apiKey);
-
-    return response;
-  };
-
-  llm.chatStream = async function* (params) {
-    const start = performance.now();
-    const response = await originalChatStream.call(this, params);
-
-    const model = params.model || 'command';
-    const prompt = params.message;
-
-    const data = {
-      environment: environment,
-      applicationName: applicationName,
-      sourceLanguage: 'Javascript',
-      endpoint: 'cohere.chat',
-      skipResp: skipResp,
-      model: model,
-      prompt: prompt,
-    };
-
-    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.totalTokens = data.promptTokens + data.completionTokens
-
-    const end = performance.now();
-    data.requestDuration = (end - start) / 1000;
-
-    await sendData(data, dokuUrl, apiKey);
-
-    return response;
-  };
-
-  llm.summarize = async function(params) {
-    const start = performance.now();
-    const response = await originalSummarize.call(this, params);
-    const end = performance.now();
-    const duration = (end - start) / 1000;
-
-    const model = params.model || 'command';
-    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["billedUnits"]["outputTokens"],
-      promptTokens: response.meta["billedUnits"]["inputTokens"],
-      model: model,
-      prompt: prompt,
-      response: response.summary,
-    };
-    data.totalTokens = data.promptTokens + data.completionTokens;
-
-    await sendData(data, dokuUrl, apiKey);
-
-    return response;
-  };
-}
diff --git a/src/helpers.js b/src/helpers/api.js
similarity index 65%
rename from src/helpers.js
rename to src/helpers/api.js
index 03927c5..f905cdf 100644
--- a/src/helpers.js
+++ b/src/helpers/api.js
@@ -1,3 +1,5 @@
+import { logError } from "./logger.js";
+
 /**
  * Sends data to the specified Doku URL using the provided authentication apiKey.
  *
@@ -25,24 +27,23 @@
  * }
  */
 export async function sendData(data, dokuUrl, authToken) {
-  // Remove trailing slash if present
-  const url = dokuUrl.endsWith('/') ? dokuUrl.slice(0, -1) : dokuUrl;
-  try {
-    const response = await fetch(`${url}/api/push`, {
-      method: 'post',
-      headers: {
-        'Content-Type': 'application/json',
-        'Authorization': authToken,
-      },
-      body: JSON.stringify(data),
-    });
-    
-    if (!response.ok) {
-      throw new Error(`Error sending Data: HTTP status ${response.status}`);
-    }
-    return response;
-    
-  } catch (err) {
-    throw new Error(`Error sending Metrics: ${err}`);
-  }
+	// Remove trailing slash if present
+	const url = dokuUrl.endsWith("/") ? dokuUrl.slice(0, -1) : dokuUrl;
+	try {
+		const response = await fetch(`${url}/api/push`, {
+			method: "post",
+			headers: {
+				"Content-Type": "application/json",
+				Authorization: authToken,
+			},
+			body: JSON.stringify(data),
+		});
+
+		if (!response.ok) {
+			logError(`Error sending Data: HTTP status ${response.status}`);
+		}
+		return response;
+	} catch (err) {
+		logError(`Error sending Metrics: ${err}`);
+	}
 }
diff --git a/src/helpers/logger.js b/src/helpers/logger.js
new file mode 100644
index 0000000..12a01bc
--- /dev/null
+++ b/src/helpers/logger.js
@@ -0,0 +1,12 @@
+/**
+ * Logs data on console with prefix `Dokumetry:`
+ *
+ * @param {Error} error - Error to be logged.
+ */
+export async function logError(error) {
+	const APPLICATION_NAME = "Dokumetry";
+	const formattedError = "\x1b[31m" + error + "\x1b[0m"; // Red color for error
+	const formattedAppName = "\x1b[34m" + APPLICATION_NAME + "\x1b[0m"; // Blue color for application name
+
+	console.log(formattedAppName + ": " + formattedError);
+}
diff --git a/src/index.js b/src/index.js
index f26ebe4..92aa73b 100644
--- a/src/index.js
+++ b/src/index.js
@@ -1,20 +1,20 @@
-import initOpenAI from './openai.js';
-import initCohere from './cohere.js';
-import initAnthropic from './anthropic.js';
-import initMistral from './mistral.js';
-import initAzureOpenAI from './azure_openai.js';
+import initOpenAI from "./providers/openai.js";
+import initCohere from "./providers/cohere.js";
+import initAnthropic from "./providers/anthropic.js";
+import initMistral from "./providers/mistral.js";
+import initAzureOpenAI from "./providers/azure_openai.js";
 
 /**
  * Represents the configuration for Doku.
  * @class DokuConfig
  */
 class DokuConfig {
-  static dokuUrl = null;
-  static apiKey = null;
-  static llm = null;
-  static environment = null;
-  static applicationName = null;
-  static skipResp = null;
+	static dokuUrl = null;
+	static apiKey = null;
+	static llm = null;
+	static environment = null;
+	static applicationName = null;
+	static skipResp = null;
 }
 
 /**
@@ -46,30 +46,80 @@ class DokuConfig {
  *   }
  * }
  */
-function init({ llm, dokuUrl, apiKey, environment="default", applicationName="default", skipResp=false }) {
-  DokuConfig.dokuUrl = dokuUrl;
-  DokuConfig.apiKey = apiKey;
-  DokuConfig.llm = llm;
-  DokuConfig.environment = environment;
-  DokuConfig.applicationName = applicationName;
-  DokuConfig.skipResp = skipResp;
+function init({
+	llm,
+	dokuUrl,
+	apiKey,
+	environment = "default",
+	applicationName = "default",
+	skipResp = false,
+}) {
+	DokuConfig.dokuUrl = dokuUrl;
+	DokuConfig.apiKey = apiKey;
+	DokuConfig.llm = llm;
+	DokuConfig.environment = environment;
+	DokuConfig.applicationName = applicationName;
+	DokuConfig.skipResp = skipResp;
 
-  if (llm.fineTuning && typeof llm.completions.create === 'function' && !(llm.baseURL.includes('azure.com'))) {
-    initOpenAI({ llm, dokuUrl, apiKey, environment, applicationName, skipResp });
-  } else  if (llm.fineTuning && typeof llm.completions.create === 'function' && llm.baseURL.includes('azure.com')) {
-    initAzureOpenAI({ llm, dokuUrl, apiKey, environment, applicationName, skipResp });
-  } else if (llm.generate && typeof llm.rerank === 'function') {
-    initCohere({ llm, dokuUrl, apiKey, environment, applicationName, skipResp });
-  } else if (llm.messages && typeof llm.messages.create === 'function') {
-    initAnthropic({ llm, dokuUrl, apiKey, environment, applicationName, skipResp });
-  } else if (llm.listModels && typeof llm.chatStream === 'function') {
-    initMistral({ llm, dokuUrl, apiKey, environment, applicationName, skipResp });
-  }
+	if (
+		llm.fineTuning &&
+		typeof llm.completions.create === "function" &&
+		!llm.baseURL.includes("azure.com")
+	) {
+		initOpenAI({
+			llm,
+			dokuUrl,
+			apiKey,
+			environment,
+			applicationName,
+			skipResp,
+		});
+	} else if (
+		llm.fineTuning &&
+		typeof llm.completions.create === "function" &&
+		llm.baseURL.includes("azure.com")
+	) {
+		initAzureOpenAI({
+			llm,
+			dokuUrl,
+			apiKey,
+			environment,
+			applicationName,
+			skipResp,
+		});
+	} else if (llm.generate && typeof llm.rerank === "function") {
+		initCohere({
+			llm,
+			dokuUrl,
+			apiKey,
+			environment,
+			applicationName,
+			skipResp,
+		});
+	} else if (llm.messages && typeof llm.messages.create === "function") {
+		initAnthropic({
+			llm,
+			dokuUrl,
+			apiKey,
+			environment,
+			applicationName,
+			skipResp,
+		});
+	} else if (llm.listModels && typeof llm.chatStream === "function") {
+		initMistral({
+			llm,
+			dokuUrl,
+			apiKey,
+			environment,
+			applicationName,
+			skipResp,
+		});
+	}
 }
 
 // Setting up the dokumetry namespace object
 const DokuMetry = {
-  init: init,
+	init: init,
 };
 
-export default DokuMetry;
\ No newline at end of file
+export default DokuMetry;
diff --git a/src/mistral.js b/src/mistral.js
deleted file mode 100644
index aed7be0..0000000
--- a/src/mistral.js
+++ /dev/null
@@ -1,171 +0,0 @@
-import {sendData} from './helpers.js';
-
-/**
- * Initializes Mistral functionality with performance tracking.
- *
- * @param {Object} llm - The Mistral function object.
- * @param {string} dokuUrl - The URL for logging data.
- * @param {string} apiKey - The authentication apiKey.
- * @param {string} environment - The environment.
- * @param {string} applicationName - The application name.
- * @param {boolean} skipResp - To skip waiting for API resopnse.
- * @return {void}
- *
- * @jsondoc
- * {
- *   "description": "Initializes Mistral function and performance tracking",
- *   "params": [
- *     {"name": "dokuUrl", "type": "string", "description": "Doku URL"},
- *     {"name": "apiKey", "type": "string", "description": "Auth apiKey"},
- *     {"name": "llm", "type": "Object", "description": "The Mistral object"},
- *     {"name": "environment", "type": "string", "description": "Environment"},
- *     {"name": "applicationName", "type": "string", "description": "Application Name"},
- *     {"name": "skipResp", "type": "boolean", "description": "To skip waiting for API resopnse."}
- *   ],
- *   "returns": {"type": "void"},
- *   "example": {
- *     "description": "Example usage of init function.",
- *     "code": "init('https://example.com/log', 'authToken', mistralFunc);"
- *   }
- * }
- */
-export default function initMistral({ llm, dokuUrl, apiKey, environment, applicationName, skipResp }) {
-  const origianlMistralChat = llm.chat;
-  const origianlMistralChatStream = llm.chatStream;
-  const originalMistralEmbedding = llm.embeddings;
-
-  // Define wrapped method
-  llm.chat = async function(params) {
-    const start = performance.now();
-    const response = await origianlMistralChat.call(this, params);
-    const end = performance.now();
-    const duration = (end - start) / 1000;
-    let formattedMessages = [];
-    for (let message of params.messages) {
-    let role = message.role;
-    let content = message.content;
-
-    if (Array.isArray(content)) {
-        let contentStr = content.map(item => {
-        if (item.type) {
-            return `${item.type}: ${item.text || item.image_url}`;
-        } else {
-            return `text: ${item.text}`;
-        }
-        }).join(", ");
-        formattedMessages.push(`${role}: ${contentStr}`);
-    } else {
-        formattedMessages.push(`${role}: ${content}`);
-    }
-    }
-    let prompt = formattedMessages.join("\n");
-
-    const data = {
-        llmReqId: response.id,
-        environment: environment,
-        applicationName: applicationName,
-        sourceLanguage: 'Javascript',
-        endpoint: 'mistral.chat',
-        skipResp: skipResp,
-        completionTokens: response.usage.prompt_tokens,
-        promptTokens: response.usage.completion_tokens,
-        totalTokens: response.usage.total_tokens,
-        requestDuration: duration,
-        model: params.model,
-        prompt: String(prompt),
-        finishReason: response.choices[0].finish_reason,
-        response: String(response.choices[0].message.content),
-    };
-
-    await sendData(data, dokuUrl, apiKey);
-
-    return response;
-  };
-
-  llm.chatStream = async function* (params) {
-    const start = performance.now();
-    const response = await origianlMistralChatStream.call(this, params);
-
-    const model = params.model || 'mistral-large-latest';
-    let formattedMessages = [];
-    for (let message of params.messages) {
-        let role = message.role;
-        let content = message.content;
-
-        if (Array.isArray(content)) {
-            let contentStr = content.map(item => {
-            if (item.type) {
-                return `${item.type}: ${item.text || item.image_url}`;
-            } else {
-                return `text: ${item.text}`;
-            }
-            }).join(", ");
-            formattedMessages.push(`${role}: ${contentStr}`);
-        } else {
-            formattedMessages.push(`${role}: ${content}`);
-        }
-    }
-    let prompt = formattedMessages.join("\n");
-
-    const data = {
-      environment: environment,
-      applicationName: applicationName,
-      sourceLanguage: 'Javascript',
-      endpoint: 'mistral.chat',
-      skipResp: skipResp,
-      model: model,
-      prompt: prompt,
-    };
-
-    data.response = ""
-    for await (const message of response) {
-        data.llmReqId = message.id;
-        data.response += message.choices[0].delta.content
-        if (message.choices[0].finish_reason != null) {
-            data.promptTokens = message.usage.prompt_tokens;
-            data.completionTokens = message.usage.completion_tokens;
-            data.totalTokens = message.usage.total_tokens;
-            data.finishReason = message.choices[0].finish_reason;
-        }
-        // Pass the message along so it's not consumed
-        yield message; // this allows the message to flow back to the original caller
-    }
-
-    const end = performance.now();
-    data.requestDuration = (end - start) / 1000;
-
-    await sendData(data, dokuUrl, apiKey);
-
-    return response;
-  };
-
-  llm.embeddings = async function(params) {
-    const start = performance.now();
-    const response = await originalMistralEmbedding.call(this, params);
-    const end = performance.now();
-    const duration = (end - start) / 1000;
-
-    const model = params.model || 'mistral-embed';
-    const prompt = params.input.toString();
-
-    const data = {
-      llmReqId: response.id,
-      environment: environment,
-      applicationName: applicationName,
-      sourceLanguage: 'Javascript',
-      endpoint: 'mistral.embeddings',
-      skipResp: skipResp,
-      requestDuration: duration,
-      model: model,
-      prompt: prompt,
-      promptTokens: response.usage.prompt_tokens,
-      completionTokens: response.usage.completion_tokens,
-      totalTokens: response.usage.total_tokens,
-    };
-
-    await sendData(data, dokuUrl, apiKey);
-
-    return response;
-  };
-
-}
diff --git a/src/openai.js b/src/openai.js
deleted file mode 100644
index 534d579..0000000
--- a/src/openai.js
+++ /dev/null
@@ -1,420 +0,0 @@
-import {sendData} from './helpers.js';
-import { Readable } from 'stream';
-
-/**
- * Initializes OpenAI functionality with performance tracking and data logging.
- *
- * @param {Object} llm - The OpenAI function object.
- * @param {string} dokuUrl - The URL for logging data.
- * @param {string} apiKey - The authentication apiKey.
- * @param {string} environment - The environment.
- * @param {string} applicationName - The application name.
- * @param {boolean} skipResp - To skip waiting for API resopnse.
- * @return {void}
- *
- * @jsondoc
- * {
- *   "description": "Performance tracking for OpenAI APIs",
- *   "params": [
- *     {"name": "llm", "type": "Object", "description": "OpenAI function."},
- *     {"name": "dokuUrl", "type": "string", "description": "The URL"},
- *     {"name": "apiKey", "type": "string", "description": "The auth apiKey."},
- *     {"name": "environment", "type": "string", "description": "The environment."},
- *     {"name": "applicationName", "type": "string", "description": "The application name."},
- *     {"name": "skipResp", "type": "boolean", "description": "To skip waiting for API resopnse."}
- *   ],
- *   "returns": {"type": "void"},
- *   "example": {
- *     "description": "Example usage of init function.",
- *     "code": "init(openaiFunc, 'https://example.com/log', 'authToken');"
- *   }
- * }
- */
-export default function initOpenAI({ llm, dokuUrl, apiKey, environment, applicationName, skipResp }) {
-  // Save original method
-  const originalChatCreate = llm.chat.completions.create;
-  const originalCompletionsCreate = llm.completions.create;
-  const originalEmbeddingsCreate = llm.embeddings.create;
-  const originalFineTuningJobsCreate = llm.fineTuning.jobs.create;
-  const originalImagesCreate = llm.images.generate;
-  const originalImagesCreateVariation = llm.images.createVariation;
-  const originalAudioSpeechCreate = llm.audio.speech.create;
-
-  // Define wrapped method
-  llm.chat.completions.create = async function(params) {
-    const start = performance.now();
-    let streaming = params.stream || false;
-    if (streaming) {
-      // Call original method
-      const originalResponseStream = await originalChatCreate.call(this, params);
-  
-      // Create a pass-through stream
-      const passThroughStream = new Readable({
-        read() {},
-        objectMode: true // Set to true because the chunks are objects
-      });
-  
-      let dataResponse = '';
-  
-      // Immediately-invoked async function to handle streaming
-      (async () => {
-        for await (const chunk of originalResponseStream) {
-          var content = chunk.choices[0]?.delta?.content;
-          if (content) {
-            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
-  
-        // Process response data after stream has ended
-        const end = performance.now();
-        const duration = (end - start) / 1000;
-  
-        let formattedMessages = [];
-        for (let message of params.messages) {
-          let role = message.role;
-          let content = message.content;
-
-          if (Array.isArray(content)) {
-            let contentStr = content.map(item => {
-              if (item.type) {
-                return `${item.type}: ${item.text || item.image_url}`;
-              } else {
-                return `text: ${item.text}`;
-              }
-            }).join(", ");
-            formattedMessages.push(`${role}: ${contentStr}`);
-          } else {
-            formattedMessages.push(`${role}: ${content}`);
-          }
-        }
-        let prompt = formattedMessages.join("\n");
-  
-        // Prepare the data object for Doku
-        const data = {
-          llmReqId: responseId,
-          environment: environment,
-          applicationName: applicationName,
-          sourceLanguage: 'Javascript',
-          endpoint: 'openai.chat.completions',
-          skipResp: skipResp,
-          requestDuration: duration,
-          model: params.model,
-          prompt: prompt,
-          response: dataResponse
-        };
-
-        await sendData(data, dokuUrl, apiKey);
-      })();
-      
-      // Return the pass-through stream to the original caller
-      return passThroughStream;
-    }
-    else {
-      // Call original method
-      const response = await originalChatCreate.call(this, params);
-      const end = performance.now();
-      const duration = (end - start) / 1000;
-
-      let formattedMessages = [];
-      for (let message of params.messages) {
-        let role = message.role;
-        let content = message.content;
-
-        if (Array.isArray(content)) {
-          let contentStr = content.map(item => {
-            if (item.type) {
-              return `${item.type}: ${item.text || item.image_url}`;
-            } else {
-              return `text: ${item.text}`;
-            }
-          }).join(", ");
-          formattedMessages.push(`${role}: ${contentStr}`);
-        } else {
-          formattedMessages.push(`${role}: ${content}`);
-        }
-      }
-      let prompt = formattedMessages.join("\n");
-      const data = {
-        llmReqId: response.id,
-        environment: environment,
-        applicationName: applicationName,
-        sourceLanguage: 'Javascript',
-        endpoint: 'openai.chat.completions',
-        skipResp: skipResp,
-        requestDuration: duration,
-        model: params.model,
-        prompt: prompt,
-      };
-
-      if (!params.hasOwnProperty('tools')) {
-        data.completionTokens = response.usage.completion_tokens;
-        data.promptTokens = response.usage.prompt_tokens;
-        data.totalTokens = response.usage.total_tokens;
-        data.finishReason = response.choices[0].finish_reason;
-
-        if (!params.hasOwnProperty('n') || params.n === 1) {
-          data.response = response.choices[0].message.content;
-        } else {
-          let i = 0;
-          while (i < params.n && i < response.choices.length) {
-            data.response = response.choices[i].message.content;
-            i++;
-            await sendData(data, dokuUrl, apiKey);
-          }
-          return response;
-        }
-      } else if (params.hasOwnProperty('tools')) {
-          data.response = "Function called with tools";
-          data.completionTokens = response.usage.completion_tokens;
-          data.promptTokens = response.usage.prompt_tokens;
-          data.totalTokens = response.usage.total_tokens;
-      }
-
-      await sendData(data, dokuUrl, apiKey);
-
-      return response;
-    }
-  };
-
-  llm.completions.create = async function(params) {
-    const start = performance.now();
-    let streaming = params.stream || false;
-    if (streaming) {
-      // Call original method
-      const originalResponseStream = await originalCompletionsCreate.call(this, params);
-  
-      // Create a pass-through stream
-      const passThroughStream = new Readable({
-        read() {},
-        objectMode: true // Set to true because the chunks are objects
-      });
-  
-      let dataResponse = '';
-  
-      // Immediately-invoked async function to handle streaming
-      (async () => {
-        for await (const chunk of originalResponseStream) {
-          var content = chunk.choices[0].text;
-          if (content) {
-            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
-  
-        // Process response data after stream has ended
-        const end = performance.now();
-        const duration = (end - start) / 1000;
-        // Prepare the data object for Doku
-        const data = {
-          llmReqId: responseId,
-          environment: environment,
-          applicationName: applicationName,
-          sourceLanguage: 'Javascript',
-          endpoint: 'openai.completions',
-          skipResp: skipResp,
-          requestDuration: duration,
-          model: params.model,
-          prompt: params.prompt,
-          response: dataResponse
-        };
-
-        await sendData(data, dokuUrl, apiKey);
-      })();
-      
-      // Return the pass-through stream to the original caller
-      return passThroughStream;
-    }
-    else {
-      const response = await originalCompletionsCreate.call(this, params);
-      const end = performance.now();
-      const duration = (end - start) / 1000;
-
-      const data = {
-        llmReqId: response.id,
-        environment: environment,
-        applicationName: applicationName,
-        sourceLanguage: 'Javascript',
-        endpoint: 'openai.completions',
-        skipResp: skipResp,
-        requestDuration: duration,
-        model: params.model,
-        prompt: params.prompt,
-      };
-
-      if (!params.hasOwnProperty('tools')) {
-        data.completionTokens = response.usage.completion_tokens;
-        data.promptTokens = response.usage.prompt_tokens;
-        data.totalTokens = response.usage.total_tokens;
-        data.finishReason = response.choices[0].finish_reason;
-
-        if (!params.hasOwnProperty('n') || params.n === 1) {
-          data.response = response.choices[0].text;
-        } else {
-          let i = 0;
-          while (i < params.n && i < response.choices.length) {
-            data.response = response.choices[i].text;
-            i++;
-
-            await sendData(data, dokuUrl, apiKey);
-          }
-          return response;
-        }
-      } else if (params.hasOwnProperty('tools')) {
-          data.response = "Function called with tools";
-          data.completionTokens = response.usage.completion_tokens;
-          data.promptTokens = response.usage.prompt_tokens;
-          data.totalTokens = response.usage.total_tokens;
-      }
-      
-      await sendData(data, dokuUrl, apiKey);
-
-      return response;
-    }
-  };
-
-  llm.embeddings.create = async function(params) {
-    const start = performance.now();
-    const response = await originalEmbeddingsCreate.call(this, params);
-    const end = performance.now();
-    const duration = (end - start) / 1000;
-
-    const data = {
-      environment: environment,
-      applicationName: applicationName,
-      sourceLanguage: 'Javascript',
-      endpoint: 'openai.embeddings',
-      skipResp: skipResp,
-      requestDuration: duration,
-      model: params.model,
-      prompt: params.input,
-      promptTokens: response.usage.prompt_tokens,
-      totalTokens: response.usage.total_tokens,
-    };
-
-    await sendData(data, dokuUrl, apiKey);
-
-    return response;
-  };
-
-  llm.fineTuning.jobs.create = async function(params) {
-    const start = performance.now();
-    const response = await originalFineTuningJobsCreate.call(this, params);
-    const end = performance.now();
-    const duration = (end - start) / 1000;
-
-    const data = {
-      environment: environment,
-      applicationName: applicationName,
-      sourceLanguage: 'Javascript',
-      endpoint: 'openai.fine_tuning',
-      skipResp: skipResp,
-      requestDuration: duration,
-      model: params.model,
-      llmReqId: response.id,
-      finetuneJobStatus: response.status,
-    };
-
-    await sendData(data, dokuUrl, apiKey);
-
-    return response;
-  };
-
-  llm.images.generate = async function(params) {
-    const start = performance.now();
-    const response = await originalImagesCreate.call(this, params);
-    const end = performance.now();
-    const duration = (end - start) / 1000;
-    const size = params.size || '1024x1024';
-    const model = params.model || 'dall-e-2';
-    let imageFormat = 'url';
-
-    if (params.response_format && params.response_format === 'b64_json') {
-      imageFormat = 'b64_json';
-    }
-
-    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',
-        endpoint: 'openai.images.create',
-        skipResp: skipResp,
-        requestDuration: duration,
-        model: model,
-        prompt: params.prompt,
-        imageSize: size,
-        imageQuality: quality,
-        revisedPrompt: item.revised_prompt || null,
-        image: item[imageFormat],
-      };
-
-      await sendData(data, dokuUrl, apiKey);
-    }
-
-    return response;
-  };
-
-  llm.images.createVariation = async function(params) {
-    const start = performance.now();
-    const response = await originalImagesCreateVariation.call(this, params);
-    const end = performance.now();
-    const duration = (end - start) / 1000;
-    const size = params.size || '1024x1024'; // Default size if not provided
-    const model = params.model || 'dall-e-2';
-    let imageFormat = 'url';
-    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',
-        endpoint: 'openai.images.create.variations',
-        skipResp: skipResp,
-        requestDuration: duration,
-        model: model,
-        imageSize: size,
-        imageQuality: "standard",
-        image: item[imageFormat],
-      };
-
-      await sendData(data, dokuUrl, apiKey);
-    }
-
-    return response;
-  };
-
-  llm.audio.speech.create = async function(params) {
-    const start = performance.now();
-    const response = await originalAudioSpeechCreate.call(this, params);
-    const end = performance.now();
-    const duration = (end - start) / 1000;
-
-    const data = {
-      environment: environment,
-      applicationName: applicationName,
-      sourceLanguage: 'Javascript',
-      endpoint: 'openai.audio.speech.create',
-      skipResp: skipResp,
-      requestDuration: duration,
-      model: params.model,
-      prompt: params.input,
-      audioVoice: params.voice,
-    };
-
-    await sendData(data, dokuUrl, apiKey);
-
-    return response;
-  };
-}
diff --git a/src/providers/anthropic.js b/src/providers/anthropic.js
new file mode 100644
index 0000000..cb18cab
--- /dev/null
+++ b/src/providers/anthropic.js
@@ -0,0 +1,179 @@
+import { sendData } from "../helpers/api.js";
+import { Readable } from "stream";
+
+/**
+ * Initializes Anthropic functionality with performance tracking.
+ *
+ * @param {Object} llm - The Anthropic function object.
+ * @param {string} dokuUrl - The URL for logging data.
+ * @param {string} apiKey - The authentication apiKey.
+ * @param {string} environment - The environment.
+ * @param {string} applicationName - The application name.
+ * @param {boolean} skipResp - To skip waiting for API resopnse.
+ * @return {void}
+ *
+ * @jsondoc
+ * {
+ *   "description": "Initializes Anthropic function and performance tracking",
+ *   "params": [
+ *     {"name": "dokuUrl", "type": "string", "description": "Doku URL"},
+ *     {"name": "apiKey", "type": "string", "description": "Auth apiKey"},
+ *     {"name": "llm", "type": "Object", "description": "The Anthropic object"},
+ *     {"name": "environment", "type": "string", "description": "Environment"},
+ *     {"name": "applicationName", "type": "string", "description": "Application Name"},
+ *     {"name": "skipResp", "type": "boolean", "description": "To skip waiting for API resopnse."}
+ *   ],
+ *   "returns": {"type": "void"},
+ *   "example": {
+ *     "description": "Example usage of init function.",
+ *     "code": "init('https://example.com/log', 'authToken', anthropicFunc);"
+ *   }
+ * }
+ */
+export default function initAnthropic({
+	llm,
+	dokuUrl,
+	apiKey,
+	environment,
+	applicationName,
+	skipResp,
+}) {
+	const originalMessagesCreate = llm.messages.create;
+
+	// Define wrapped method
+	llm.messages.create = async function (params) {
+		let streaming = params.stream || false;
+		if (streaming) {
+			// Call original method
+			const start = performance.now();
+			const originalResponseStream = await originalMessagesCreate.call(
+				this,
+				params
+			);
+
+			// Create a pass-through stream
+			const passThroughStream = new Readable({
+				read() {},
+				objectMode: true, // Set to true because the chunks are objects
+			});
+
+			let dataResponse = "";
+			var responseId = "";
+			var promptTokens = 0;
+			var completionTokens = 0;
+
+			// Immediately-invoked async function to handle streaming
+			(async () => {
+				for await (const chunk of originalResponseStream) {
+					if (chunk.type === "message_start") {
+						responseId = chunk.message.id;
+						promptTokens = chunk.message.usage.input_tokens;
+						passThroughStream.push(chunk); // Push chunk to the pass-through stream
+					} else if (chunk.type === "content_block_delta") {
+						dataResponse += chunk.delta.text;
+						passThroughStream.push(chunk); // Push chunk to the pass-through stream
+					} else if (chunk.type === "message_delta") {
+						completionTokens = chunk.usage.output_tokens;
+						passThroughStream.push(chunk); // Push chunk to the pass-through stream
+					}
+				}
+				passThroughStream.push(null); // Signal end of the pass-through stream
+
+				// Process response data after stream has ended
+				const end = performance.now();
+				const duration = (end - start) / 1000;
+
+				let formattedMessages = [];
+				for (let message of params.messages) {
+					let role = message.role;
+					let content = message.content;
+
+					if (Array.isArray(content)) {
+						let contentStr = content
+							.map((item) => {
+								if (item.type) {
+									return `${item.type}: ${item.text || item.image_url}`;
+								} else {
+									return `text: ${item.text}`;
+								}
+							})
+							.join(", ");
+						formattedMessages.push(`${role}: ${contentStr}`);
+					} else {
+						formattedMessages.push(`${role}: ${content}`);
+					}
+				}
+				let prompt = formattedMessages.join("\n");
+
+				// Prepare the data object for Doku
+				const data = {
+					llmReqId: responseId,
+					environment: environment,
+					applicationName: applicationName,
+					sourceLanguage: "Javascript",
+					endpoint: "anthropic.messages",
+					skipResp: skipResp,
+					requestDuration: duration,
+					model: params.model,
+					prompt: prompt,
+					response: dataResponse,
+					promptTokens: promptTokens,
+					completionTokens: completionTokens,
+				};
+				data.totalTokens = data.promptTokens + data.completionTokens;
+
+				await sendData(data, dokuUrl, apiKey);
+			})();
+
+			// Return the pass-through stream to the original caller
+			return passThroughStream;
+		} else {
+			const start = performance.now();
+			const response = await originalMessagesCreate.call(this, params);
+			const end = performance.now();
+			const duration = (end - start) / 1000;
+			let formattedMessages = [];
+			for (let message of params.messages) {
+				let role = message.role;
+				let content = message.content;
+
+				if (Array.isArray(content)) {
+					let contentStr = content
+						.map((item) => {
+							if (item.type) {
+								return `${item.type}: ${item.text || item.image_url}`;
+							} else {
+								return `text: ${item.text}`;
+							}
+						})
+						.join(", ");
+					formattedMessages.push(`${role}: ${contentStr}`);
+				} else {
+					formattedMessages.push(`${role}: ${content}`);
+				}
+			}
+			let prompt = formattedMessages.join("\n");
+
+			const data = {
+				llmReqId: response.id,
+				environment: environment,
+				applicationName: applicationName,
+				sourceLanguage: "Javascript",
+				endpoint: "anthropic.messages",
+				skipResp: skipResp,
+				completionTokens: response.usage.input_tokens,
+				promptTokens: response.usage.output_tokens,
+				requestDuration: duration,
+				model: params.model,
+				prompt: prompt,
+				finishReason: response.stop_reason,
+				response: response.content[0].text,
+			};
+			data.totalTokens = data.promptTokens + data.completionTokens;
+
+			await sendData(data, dokuUrl, apiKey);
+
+			return response;
+		}
+	};
+}
diff --git a/src/providers/azure_openai.js b/src/providers/azure_openai.js
new file mode 100644
index 0000000..09331d8
--- /dev/null
+++ b/src/providers/azure_openai.js
@@ -0,0 +1,356 @@
+import { sendData } from "../helpers/api.js";
+import { Readable } from "stream";
+
+/**
+ * Initializes Azure OpenAI functionality with performance tracking and data logging.
+ *
+ * @param {Object} llm - The Azure OpenAI function object.
+ * @param {string} dokuUrl - The URL for logging data.
+ * @param {string} apiKey - The authentication apiKey.
+ * @param {string} environment - The environment.
+ * @param {string} applicationName - The application name.
+ * @param {boolean} skipResp - To skip waiting for API resopnse.
+ * @return {void}
+ *
+ * @jsondoc
+ * {
+ *   "description": "Performance tracking for Azure OpenAI APIs",
+ *   "params": [
+ *     {"name": "llm", "type": "Object", "description": "Azure OpenAI function."},
+ *     {"name": "dokuUrl", "type": "string", "description": "The URL"},
+ *     {"name": "apiKey", "type": "string", "description": "The auth apiKey."},
+ *     {"name": "environment", "type": "string", "description": "The environment."},
+ *     {"name": "applicationName", "type": "string", "description": "The application name."},
+ *     {"name": "skipResp", "type": "boolean", "description": "To skip waiting for API resopnse."}
+ *   ],
+ *   "returns": {"type": "void"},
+ *   "example": {
+ *     "description": "Example usage of init function.",
+ *     "code": "init(azureOenaiFunc, 'https://example.com/log', 'authToken');"
+ *   }
+ * }
+ */
+export default function initAzureOpenAI({
+	llm,
+	dokuUrl,
+	apiKey,
+	environment,
+	applicationName,
+	skipResp,
+}) {
+	// Save original method
+	const originalChatCreate = llm.chat.completions.create;
+	const originalCompletionsCreate = llm.completions.create;
+	const originalEmbeddingsCreate = llm.embeddings.create;
+	const originalImagesCreate = llm.images.generate;
+
+	// Define wrapped method
+	llm.chat.completions.create = async function (params) {
+		const start = performance.now();
+		let streaming = params.stream || false;
+		if (streaming) {
+			// Call original method
+			const originalResponseStream = await originalChatCreate.call(
+				this,
+				params
+			);
+
+			// Create a pass-through stream
+			const passThroughStream = new Readable({
+				read() {},
+				objectMode: true, // Set to true because the chunks are objects
+			});
+
+			let dataResponse = "";
+			let chatModel = "";
+
+			// Immediately-invoked async function to handle streaming
+			(async () => {
+				for await (const chunk of originalResponseStream) {
+					var content = chunk.choices[0]?.delta?.content;
+					if (content) {
+						dataResponse += content;
+						passThroughStream.push(chunk); // Push chunk to the pass-through stream
+					}
+					var responseId = chunk.id;
+					chatModel = chunk.model;
+				}
+				passThroughStream.push(null); // Signal end of the pass-through stream
+
+				// Process response data after stream has ended
+				const end = performance.now();
+				const duration = (end - start) / 1000;
+
+				let formattedMessages = [];
+				for (let message of params.messages) {
+					let role = message.role;
+					let content = message.content;
+
+					if (Array.isArray(content)) {
+						let contentStr = content
+							.map((item) => {
+								if (item.type) {
+									return `${item.type}: ${item.text || item.image_url}`;
+								} else {
+									return `text: ${item.text}`;
+								}
+							})
+							.join(", ");
+						formattedMessages.push(`${role}: ${contentStr}`);
+					} else {
+						formattedMessages.push(`${role}: ${content}`);
+					}
+				}
+				let prompt = formattedMessages.join("\n");
+
+				// Prepare the data object for Doku
+				const data = {
+					llmReqId: responseId,
+					environment: environment,
+					applicationName: applicationName,
+					sourceLanguage: "Javascript",
+					endpoint: "azure.chat.completions",
+					skipResp: skipResp,
+					requestDuration: duration,
+					model: "azure_" + chatModel,
+					prompt: prompt,
+					response: dataResponse,
+				};
+
+				await sendData(data, dokuUrl, apiKey);
+			})();
+
+			// Return the pass-through stream to the original caller
+			return passThroughStream;
+		} else {
+			// Call original method
+			const response = await originalChatCreate.call(this, params);
+			const end = performance.now();
+			const duration = (end - start) / 1000;
+
+			let formattedMessages = [];
+			for (let message of params.messages) {
+				let role = message.role;
+				let content = message.content;
+
+				if (Array.isArray(content)) {
+					let contentStr = content
+						.map((item) => {
+							if (item.type) {
+								return `${item.type}: ${item.text || item.image_url}`;
+							} else {
+								return `text: ${item.text}`;
+							}
+						})
+						.join(", ");
+					formattedMessages.push(`${role}: ${contentStr}`);
+				} else {
+					formattedMessages.push(`${role}: ${content}`);
+				}
+			}
+			let prompt = formattedMessages.join("\n");
+			const data = {
+				llmReqId: response.id,
+				environment: environment,
+				applicationName: applicationName,
+				sourceLanguage: "Javascript",
+				endpoint: "azure.chat.completions",
+				skipResp: skipResp,
+				requestDuration: duration,
+				model: "azure_" + response.model,
+				prompt: prompt,
+			};
+
+			if (!params.hasOwnProperty("tools")) {
+				data.completionTokens = response.usage.completion_tokens;
+				data.promptTokens = response.usage.prompt_tokens;
+				data.totalTokens = response.usage.total_tokens;
+				data.finishReason = response.choices[0].finish_reason;
+
+				if (!params.hasOwnProperty("n") || params.n === 1) {
+					data.response = response.choices[0].message.content;
+				} else {
+					let i = 0;
+					while (i < params.n && i < response.choices.length) {
+						data.response = response.choices[i].message.content;
+						i++;
+						await sendData(data, dokuUrl, apiKey);
+					}
+					return response;
+				}
+			} else if (params.hasOwnProperty("tools")) {
+				data.response = "Function called with tools";
+				data.completionTokens = response.usage.completion_tokens;
+				data.promptTokens = response.usage.prompt_tokens;
+				data.totalTokens = response.usage.total_tokens;
+			}
+
+			await sendData(data, dokuUrl, apiKey);
+
+			return response;
+		}
+	};
+
+	llm.completions.create = async function (params) {
+		const start = performance.now();
+		let streaming = params.stream || false;
+		if (streaming) {
+			// Call original method
+			const originalResponseStream = await originalCompletionsCreate.call(
+				this,
+				params
+			);
+
+			// Create a pass-through stream
+			const passThroughStream = new Readable({
+				read() {},
+				objectMode: true, // Set to true because the chunks are objects
+			});
+
+			let dataResponse = "";
+			let chatModel = "";
+
+			// Immediately-invoked async function to handle streaming
+			(async () => {
+				for await (const chunk of originalResponseStream) {
+					if (chunk.choices.length > 0) {
+						dataResponse += chunk.choices[0].text;
+						passThroughStream.push(chunk); // Push chunk to the pass-through stream
+					}
+					var responseId = chunk.id;
+					chatModel = chunk.model;
+				}
+				passThroughStream.push(null); // Signal end of the pass-through stream
+
+				// Process response data after stream has ended
+				const end = performance.now();
+				const duration = (end - start) / 1000;
+				// Prepare the data object for Doku
+				const data = {
+					llmReqId: responseId,
+					environment: environment,
+					applicationName: applicationName,
+					sourceLanguage: "Javascript",
+					endpoint: "azure.completions",
+					skipResp: skipResp,
+					requestDuration: duration,
+					model: "azure_" + chatModel,
+					prompt: params.prompt,
+					response: dataResponse,
+				};
+
+				await sendData(data, dokuUrl, apiKey);
+			})();
+
+			// Return the pass-through stream to the original caller
+			return passThroughStream;
+		} else {
+			const response = await originalCompletionsCreate.call(this, params);
+			const end = performance.now();
+			const duration = (end - start) / 1000;
+
+			const data = {
+				llmReqId: response.id,
+				environment: environment,
+				applicationName: applicationName,
+				sourceLanguage: "Javascript",
+				endpoint: "azure.completions",
+				skipResp: skipResp,
+				requestDuration: duration,
+				model: "azure_" + response.model,
+				prompt: params.prompt,
+			};
+
+			if (!params.hasOwnProperty("tools")) {
+				data.completionTokens = response.usage.completion_tokens;
+				data.promptTokens = response.usage.prompt_tokens;
+				data.totalTokens = response.usage.total_tokens;
+				data.finishReason = response.choices[0].finish_reason;
+
+				if (!params.hasOwnProperty("n") || params.n === 1) {
+					data.response = response.choices[0].text;
+				} else {
+					let i = 0;
+					while (i < params.n && i < response.choices.length) {
+						data.response = response.choices[i].text;
+						i++;
+
+						await sendData(data, dokuUrl, apiKey);
+					}
+					return response;
+				}
+			} else if (params.hasOwnProperty("tools")) {
+				data.response = "Function called with tools";
+				data.completionTokens = response.usage.completion_tokens;
+				data.promptTokens = response.usage.prompt_tokens;
+				data.totalTokens = response.usage.total_tokens;
+			}
+
+			await sendData(data, dokuUrl, apiKey);
+
+			return response;
+		}
+	};
+
+	llm.embeddings.create = async function (params) {
+		const start = performance.now();
+		const response = await originalEmbeddingsCreate.call(this, params);
+		const end = performance.now();
+		const duration = (end - start) / 1000;
+
+		const data = {
+			environment: environment,
+			applicationName: applicationName,
+			sourceLanguage: "Javascript",
+			endpoint: "azure.embeddings",
+			skipResp: skipResp,
+			requestDuration: duration,
+			model: "azure_" + response.model,
+			prompt: params.input,
+			promptTokens: response.usage.prompt_tokens,
+			totalTokens: response.usage.total_tokens,
+		};
+
+		await sendData(data, dokuUrl, apiKey);
+
+		return response;
+	};
+
+	llm.images.generate = async function (params) {
+		const start = performance.now();
+		const response = await originalImagesCreate.call(this, params);
+		const end = performance.now();
+		const duration = (end - start) / 1000;
+		const size = params.size || "1024x1024";
+		const model = "azure_dall-e-3";
+		let imageFormat = "url";
+
+		if (params.response_format && params.response_format === "b64_json") {
+			imageFormat = "b64_json";
+		}
+
+		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",
+				endpoint: "azure.images.create",
+				skipResp: skipResp,
+				requestDuration: duration,
+				model: model,
+				prompt: params.prompt,
+				imageSize: size,
+				imageQuality: quality,
+				revisedPrompt: item.revised_prompt || null,
+				image: item[imageFormat],
+			};
+
+			await sendData(data, dokuUrl, apiKey);
+		}
+
+		return response;
+	};
+}
diff --git a/src/providers/cohere.js b/src/providers/cohere.js
new file mode 100644
index 0000000..851027b
--- /dev/null
+++ b/src/providers/cohere.js
@@ -0,0 +1,209 @@
+import { sendData } from "../helpers/api.js";
+
+/**
+ * Initializes Cohere functionality with performance tracking and data logging.
+ *
+ * @param {Object} llm - The Cohere function object.
+ * @param {string} dokuUrl - The URL for logging data.
+ * @param {string} apiKey - The authentication apiKey.
+ * @param {string} environment - The environment.
+ * @param {string} applicationName - The application name.
+ * @param {boolean} skipResp - To skip waiting for API resopnse.
+ * @return {void}
+ *
+ * @jsondoc
+ * {
+ *   "description": "Initializes Cohere functionality and performance tracking",
+ *   "params": [
+ *     {"name": "llm", "type": "Object", "description": "Cohere object"},
+ *     {"name": "dokuUrl", "type": "string", "description": "URL for Doku"},
+ *     {"name": "apiKey", "type": "string", "description": "Auth apiKey."},
+ *     {"name": "environment", "type": "string", "description": "Environment."},
+ *     {"name": "applicationName", "type": "string", "description": "Application name."},
+ *     {"name": "skipResp", "type": "boolean", "description": "To skip waiting for API resopnse."}
+ *   ],
+ *   "returns": {"type": "void"},
+ *   "example": {
+ *     "description": "Example usage of init function.",
+ *     "code": "init(cohereFunc, 'https://example.com/log', 'authToken');"
+ *   }
+ * }
+ */
+export default function initCohere({
+	llm,
+	dokuUrl,
+	apiKey,
+	environment,
+	applicationName,
+	skipResp,
+}) {
+	const originalGenerate = llm.generate;
+	const originalEmbed = llm.embed;
+	const originalChat = llm.chat;
+	const originalChatStream = llm.chatStream;
+	const originalSummarize = llm.summarize;
+
+	// Define wrapped methods
+	llm.generate = async function (params) {
+		const start = performance.now();
+		const response = await originalGenerate.call(this, params);
+		const end = performance.now();
+		const duration = (end - start) / 1000;
+
+		const model = params.model || "command";
+		const prompt = params.prompt;
+
+		for (const generation of response.generations) {
+			const data = {
+				llmReqId: generation.id,
+				environment: environment,
+				applicationName: applicationName,
+				sourceLanguage: "Javascript",
+				endpoint: "cohere.generate",
+				skipResp: skipResp,
+				completionTokens: response.meta["billedUnits"]["outputTokens"],
+				promptTokens: response.meta["billedUnits"]["inputTokens"],
+				requestDuration: duration,
+				model: model,
+				prompt: prompt,
+				response: generation.text,
+			};
+			data.totalTokens = data.promptTokens + data.completionTokens;
+
+			if (!params.hasOwnProperty("stream") || params.stream !== true) {
+				data.finishReason = generation.finish_reason;
+			}
+
+			await sendData(data, dokuUrl, apiKey);
+		}
+
+		return response;
+	};
+
+	llm.embed = async function (params) {
+		const start = performance.now();
+		const response = await originalEmbed.call(this, params);
+		const end = performance.now();
+		const duration = (end - start) / 1000;
+
+		const model = params.model || "embed-english-v2.0";
+		const prompt = params.texts.toString();
+
+		const data = {
+			environment: environment,
+			applicationName: applicationName,
+			sourceLanguage: "Javascript",
+			endpoint: "cohere.embed",
+			skipResp: skipResp,
+			requestDuration: duration,
+			model: model,
+			prompt: prompt,
+			promptTokens: response.meta["billedUnits"]["inputTokens"],
+			totalTokens: response.meta["billedUnits"]["inputTokens"],
+		};
+
+		await sendData(data, dokuUrl, apiKey);
+
+		return response;
+	};
+
+	llm.chat = async function (params) {
+		const start = performance.now();
+		const response = await originalChat.call(this, params);
+		const end = performance.now();
+		const duration = (end - start) / 1000;
+
+		const model = params.model || "command";
+		const prompt = params.message;
+
+		const data = {
+			llmReqId: response.response_id,
+			environment: environment,
+			applicationName: applicationName,
+			sourceLanguage: "Javascript",
+			endpoint: "cohere.chat",
+			skipResp: skipResp,
+			requestDuration: duration,
+			model: model,
+			prompt: prompt,
+			promptTokens: response.meta["billed_units"]["output_tokens"],
+			completionTokens: response.meta["billed_units"]["input_tokens"],
+			totalTokens: response.token_count["billed_tokens"],
+			response: response.text,
+		};
+
+		await sendData(data, dokuUrl, apiKey);
+
+		return response;
+	};
+
+	llm.chatStream = async function* (params) {
+		const start = performance.now();
+		const response = await originalChatStream.call(this, params);
+
+		const model = params.model || "command";
+		const prompt = params.message;
+
+		const data = {
+			environment: environment,
+			applicationName: applicationName,
+			sourceLanguage: "Javascript",
+			endpoint: "cohere.chat",
+			skipResp: skipResp,
+			model: model,
+			prompt: prompt,
+		};
+
+		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.totalTokens = data.promptTokens + data.completionTokens;
+
+		const end = performance.now();
+		data.requestDuration = (end - start) / 1000;
+
+		await sendData(data, dokuUrl, apiKey);
+
+		return response;
+	};
+
+	llm.summarize = async function (params) {
+		const start = performance.now();
+		const response = await originalSummarize.call(this, params);
+		const end = performance.now();
+		const duration = (end - start) / 1000;
+
+		const model = params.model || "command";
+		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["billedUnits"]["outputTokens"],
+			promptTokens: response.meta["billedUnits"]["inputTokens"],
+			model: model,
+			prompt: prompt,
+			response: response.summary,
+		};
+		data.totalTokens = data.promptTokens + data.completionTokens;
+
+		await sendData(data, dokuUrl, apiKey);
+
+		return response;
+	};
+}
diff --git a/src/providers/mistral.js b/src/providers/mistral.js
new file mode 100644
index 0000000..69fe9a9
--- /dev/null
+++ b/src/providers/mistral.js
@@ -0,0 +1,181 @@
+import { sendData } from "../helpers/api.js";
+
+/**
+ * Initializes Mistral functionality with performance tracking.
+ *
+ * @param {Object} llm - The Mistral function object.
+ * @param {string} dokuUrl - The URL for logging data.
+ * @param {string} apiKey - The authentication apiKey.
+ * @param {string} environment - The environment.
+ * @param {string} applicationName - The application name.
+ * @param {boolean} skipResp - To skip waiting for API resopnse.
+ * @return {void}
+ *
+ * @jsondoc
+ * {
+ *   "description": "Initializes Mistral function and performance tracking",
+ *   "params": [
+ *     {"name": "dokuUrl", "type": "string", "description": "Doku URL"},
+ *     {"name": "apiKey", "type": "string", "description": "Auth apiKey"},
+ *     {"name": "llm", "type": "Object", "description": "The Mistral object"},
+ *     {"name": "environment", "type": "string", "description": "Environment"},
+ *     {"name": "applicationName", "type": "string", "description": "Application Name"},
+ *     {"name": "skipResp", "type": "boolean", "description": "To skip waiting for API resopnse."}
+ *   ],
+ *   "returns": {"type": "void"},
+ *   "example": {
+ *     "description": "Example usage of init function.",
+ *     "code": "init('https://example.com/log', 'authToken', mistralFunc);"
+ *   }
+ * }
+ */
+export default function initMistral({
+	llm,
+	dokuUrl,
+	apiKey,
+	environment,
+	applicationName,
+	skipResp,
+}) {
+	const origianlMistralChat = llm.chat;
+	const origianlMistralChatStream = llm.chatStream;
+	const originalMistralEmbedding = llm.embeddings;
+
+	// Define wrapped method
+	llm.chat = async function (params) {
+		const start = performance.now();
+		const response = await origianlMistralChat.call(this, params);
+		const end = performance.now();
+		const duration = (end - start) / 1000;
+		let formattedMessages = [];
+		for (let message of params.messages) {
+			let role = message.role;
+			let content = message.content;
+
+			if (Array.isArray(content)) {
+				let contentStr = content
+					.map((item) => {
+						if (item.type) {
+							return `${item.type}: ${item.text || item.image_url}`;
+						} else {
+							return `text: ${item.text}`;
+						}
+					})
+					.join(", ");
+				formattedMessages.push(`${role}: ${contentStr}`);
+			} else {
+				formattedMessages.push(`${role}: ${content}`);
+			}
+		}
+		let prompt = formattedMessages.join("\n");
+
+		const data = {
+			llmReqId: response.id,
+			environment: environment,
+			applicationName: applicationName,
+			sourceLanguage: "Javascript",
+			endpoint: "mistral.chat",
+			skipResp: skipResp,
+			completionTokens: response.usage.prompt_tokens,
+			promptTokens: response.usage.completion_tokens,
+			totalTokens: response.usage.total_tokens,
+			requestDuration: duration,
+			model: params.model,
+			prompt: String(prompt),
+			finishReason: response.choices[0].finish_reason,
+			response: String(response.choices[0].message.content),
+		};
+
+		await sendData(data, dokuUrl, apiKey);
+
+		return response;
+	};
+
+	llm.chatStream = async function* (params) {
+		const start = performance.now();
+		const response = await origianlMistralChatStream.call(this, params);
+
+		const model = params.model || "mistral-large-latest";
+		let formattedMessages = [];
+		for (let message of params.messages) {
+			let role = message.role;
+			let content = message.content;
+
+			if (Array.isArray(content)) {
+				let contentStr = content
+					.map((item) => {
+						if (item.type) {
+							return `${item.type}: ${item.text || item.image_url}`;
+						} else {
+							return `text: ${item.text}`;
+						}
+					})
+					.join(", ");
+				formattedMessages.push(`${role}: ${contentStr}`);
+			} else {
+				formattedMessages.push(`${role}: ${content}`);
+			}
+		}
+		let prompt = formattedMessages.join("\n");
+
+		const data = {
+			environment: environment,
+			applicationName: applicationName,
+			sourceLanguage: "Javascript",
+			endpoint: "mistral.chat",
+			skipResp: skipResp,
+			model: model,
+			prompt: prompt,
+		};
+
+		data.response = "";
+		for await (const message of response) {
+			data.llmReqId = message.id;
+			data.response += message.choices[0].delta.content;
+			if (message.choices[0].finish_reason != null) {
+				data.promptTokens = message.usage.prompt_tokens;
+				data.completionTokens = message.usage.completion_tokens;
+				data.totalTokens = message.usage.total_tokens;
+				data.finishReason = message.choices[0].finish_reason;
+			}
+			// Pass the message along so it's not consumed
+			yield message; // this allows the message to flow back to the original caller
+		}
+
+		const end = performance.now();
+		data.requestDuration = (end - start) / 1000;
+
+		await sendData(data, dokuUrl, apiKey);
+
+		return response;
+	};
+
+	llm.embeddings = async function (params) {
+		const start = performance.now();
+		const response = await originalMistralEmbedding.call(this, params);
+		const end = performance.now();
+		const duration = (end - start) / 1000;
+
+		const model = params.model || "mistral-embed";
+		const prompt = params.input.toString();
+
+		const data = {
+			llmReqId: response.id,
+			environment: environment,
+			applicationName: applicationName,
+			sourceLanguage: "Javascript",
+			endpoint: "mistral.embeddings",
+			skipResp: skipResp,
+			requestDuration: duration,
+			model: model,
+			prompt: prompt,
+			promptTokens: response.usage.prompt_tokens,
+			completionTokens: response.usage.completion_tokens,
+			totalTokens: response.usage.total_tokens,
+		};
+
+		await sendData(data, dokuUrl, apiKey);
+
+		return response;
+	};
+}
diff --git a/src/providers/openai.js b/src/providers/openai.js
new file mode 100644
index 0000000..1179577
--- /dev/null
+++ b/src/providers/openai.js
@@ -0,0 +1,435 @@
+import { sendData } from "../helpers/api.js";
+import { Readable } from "stream";
+
+/**
+ * Initializes OpenAI functionality with performance tracking and data logging.
+ *
+ * @param {Object} llm - The OpenAI function object.
+ * @param {string} dokuUrl - The URL for logging data.
+ * @param {string} apiKey - The authentication apiKey.
+ * @param {string} environment - The environment.
+ * @param {string} applicationName - The application name.
+ * @param {boolean} skipResp - To skip waiting for API resopnse.
+ * @return {void}
+ *
+ * @jsondoc
+ * {
+ *   "description": "Performance tracking for OpenAI APIs",
+ *   "params": [
+ *     {"name": "llm", "type": "Object", "description": "OpenAI function."},
+ *     {"name": "dokuUrl", "type": "string", "description": "The URL"},
+ *     {"name": "apiKey", "type": "string", "description": "The auth apiKey."},
+ *     {"name": "environment", "type": "string", "description": "The environment."},
+ *     {"name": "applicationName", "type": "string", "description": "The application name."},
+ *     {"name": "skipResp", "type": "boolean", "description": "To skip waiting for API resopnse."}
+ *   ],
+ *   "returns": {"type": "void"},
+ *   "example": {
+ *     "description": "Example usage of init function.",
+ *     "code": "init(openaiFunc, 'https://example.com/log', 'authToken');"
+ *   }
+ * }
+ */
+export default function initOpenAI({
+	llm,
+	dokuUrl,
+	apiKey,
+	environment,
+	applicationName,
+	skipResp,
+}) {
+	// Save original method
+	const originalChatCreate = llm.chat.completions.create;
+	const originalCompletionsCreate = llm.completions.create;
+	const originalEmbeddingsCreate = llm.embeddings.create;
+	const originalFineTuningJobsCreate = llm.fineTuning.jobs.create;
+	const originalImagesCreate = llm.images.generate;
+	const originalImagesCreateVariation = llm.images.createVariation;
+	const originalAudioSpeechCreate = llm.audio.speech.create;
+
+	// Define wrapped method
+	llm.chat.completions.create = async function (params) {
+		const start = performance.now();
+		let streaming = params.stream || false;
+		if (streaming) {
+			// Call original method
+			const originalResponseStream = await originalChatCreate.call(
+				this,
+				params
+			);
+
+			// Create a pass-through stream
+			const passThroughStream = new Readable({
+				read() {},
+				objectMode: true, // Set to true because the chunks are objects
+			});
+
+			let dataResponse = "";
+
+			// Immediately-invoked async function to handle streaming
+			(async () => {
+				for await (const chunk of originalResponseStream) {
+					var content = chunk.choices[0]?.delta?.content;
+					if (content) {
+						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
+
+				// Process response data after stream has ended
+				const end = performance.now();
+				const duration = (end - start) / 1000;
+
+				let formattedMessages = [];
+				for (let message of params.messages) {
+					let role = message.role;
+					let content = message.content;
+
+					if (Array.isArray(content)) {
+						let contentStr = content
+							.map((item) => {
+								if (item.type) {
+									return `${item.type}: ${item.text || item.image_url}`;
+								} else {
+									return `text: ${item.text}`;
+								}
+							})
+							.join(", ");
+						formattedMessages.push(`${role}: ${contentStr}`);
+					} else {
+						formattedMessages.push(`${role}: ${content}`);
+					}
+				}
+				let prompt = formattedMessages.join("\n");
+
+				// Prepare the data object for Doku
+				const data = {
+					llmReqId: responseId,
+					environment: environment,
+					applicationName: applicationName,
+					sourceLanguage: "Javascript",
+					endpoint: "openai.chat.completions",
+					skipResp: skipResp,
+					requestDuration: duration,
+					model: params.model,
+					prompt: prompt,
+					response: dataResponse,
+				};
+
+				await sendData(data, dokuUrl, apiKey);
+			})();
+
+			// Return the pass-through stream to the original caller
+			return passThroughStream;
+		} else {
+			// Call original method
+			const response = await originalChatCreate.call(this, params);
+			const end = performance.now();
+			const duration = (end - start) / 1000;
+
+			let formattedMessages = [];
+			for (let message of params.messages) {
+				let role = message.role;
+				let content = message.content;
+
+				if (Array.isArray(content)) {
+					let contentStr = content
+						.map((item) => {
+							if (item.type) {
+								return `${item.type}: ${item.text || item.image_url}`;
+							} else {
+								return `text: ${item.text}`;
+							}
+						})
+						.join(", ");
+					formattedMessages.push(`${role}: ${contentStr}`);
+				} else {
+					formattedMessages.push(`${role}: ${content}`);
+				}
+			}
+			let prompt = formattedMessages.join("\n");
+			const data = {
+				llmReqId: response.id,
+				environment: environment,
+				applicationName: applicationName,
+				sourceLanguage: "Javascript",
+				endpoint: "openai.chat.completions",
+				skipResp: skipResp,
+				requestDuration: duration,
+				model: params.model,
+				prompt: prompt,
+			};
+
+			if (!params.hasOwnProperty("tools")) {
+				data.completionTokens = response.usage.completion_tokens;
+				data.promptTokens = response.usage.prompt_tokens;
+				data.totalTokens = response.usage.total_tokens;
+				data.finishReason = response.choices[0].finish_reason;
+
+				if (!params.hasOwnProperty("n") || params.n === 1) {
+					data.response = response.choices[0].message.content;
+				} else {
+					let i = 0;
+					while (i < params.n && i < response.choices.length) {
+						data.response = response.choices[i].message.content;
+						i++;
+						await sendData(data, dokuUrl, apiKey);
+					}
+					return response;
+				}
+			} else if (params.hasOwnProperty("tools")) {
+				data.response = "Function called with tools";
+				data.completionTokens = response.usage.completion_tokens;
+				data.promptTokens = response.usage.prompt_tokens;
+				data.totalTokens = response.usage.total_tokens;
+			}
+
+			await sendData(data, dokuUrl, apiKey);
+
+			return response;
+		}
+	};
+
+	llm.completions.create = async function (params) {
+		const start = performance.now();
+		let streaming = params.stream || false;
+		if (streaming) {
+			// Call original method
+			const originalResponseStream = await originalCompletionsCreate.call(
+				this,
+				params
+			);
+
+			// Create a pass-through stream
+			const passThroughStream = new Readable({
+				read() {},
+				objectMode: true, // Set to true because the chunks are objects
+			});
+
+			let dataResponse = "";
+
+			// Immediately-invoked async function to handle streaming
+			(async () => {
+				for await (const chunk of originalResponseStream) {
+					var content = chunk.choices[0].text;
+					if (content) {
+						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
+
+				// Process response data after stream has ended
+				const end = performance.now();
+				const duration = (end - start) / 1000;
+				// Prepare the data object for Doku
+				const data = {
+					llmReqId: responseId,
+					environment: environment,
+					applicationName: applicationName,
+					sourceLanguage: "Javascript",
+					endpoint: "openai.completions",
+					skipResp: skipResp,
+					requestDuration: duration,
+					model: params.model,
+					prompt: params.prompt,
+					response: dataResponse,
+				};
+
+				await sendData(data, dokuUrl, apiKey);
+			})();
+
+			// Return the pass-through stream to the original caller
+			return passThroughStream;
+		} else {
+			const response = await originalCompletionsCreate.call(this, params);
+			const end = performance.now();
+			const duration = (end - start) / 1000;
+
+			const data = {
+				llmReqId: response.id,
+				environment: environment,
+				applicationName: applicationName,
+				sourceLanguage: "Javascript",
+				endpoint: "openai.completions",
+				skipResp: skipResp,
+				requestDuration: duration,
+				model: params.model,
+				prompt: params.prompt,
+			};
+
+			if (!params.hasOwnProperty("tools")) {
+				data.completionTokens = response.usage.completion_tokens;
+				data.promptTokens = response.usage.prompt_tokens;
+				data.totalTokens = response.usage.total_tokens;
+				data.finishReason = response.choices[0].finish_reason;
+
+				if (!params.hasOwnProperty("n") || params.n === 1) {
+					data.response = response.choices[0].text;
+				} else {
+					let i = 0;
+					while (i < params.n && i < response.choices.length) {
+						data.response = response.choices[i].text;
+						i++;
+
+						await sendData(data, dokuUrl, apiKey);
+					}
+					return response;
+				}
+			} else if (params.hasOwnProperty("tools")) {
+				data.response = "Function called with tools";
+				data.completionTokens = response.usage.completion_tokens;
+				data.promptTokens = response.usage.prompt_tokens;
+				data.totalTokens = response.usage.total_tokens;
+			}
+
+			await sendData(data, dokuUrl, apiKey);
+
+			return response;
+		}
+	};
+
+	llm.embeddings.create = async function (params) {
+		const start = performance.now();
+		const response = await originalEmbeddingsCreate.call(this, params);
+		const end = performance.now();
+		const duration = (end - start) / 1000;
+
+		const data = {
+			environment: environment,
+			applicationName: applicationName,
+			sourceLanguage: "Javascript",
+			endpoint: "openai.embeddings",
+			skipResp: skipResp,
+			requestDuration: duration,
+			model: params.model,
+			prompt: params.input,
+			promptTokens: response.usage.prompt_tokens,
+			totalTokens: response.usage.total_tokens,
+		};
+
+		await sendData(data, dokuUrl, apiKey);
+
+		return response;
+	};
+
+	llm.fineTuning.jobs.create = async function (params) {
+		const start = performance.now();
+		const response = await originalFineTuningJobsCreate.call(this, params);
+		const end = performance.now();
+		const duration = (end - start) / 1000;
+
+		const data = {
+			environment: environment,
+			applicationName: applicationName,
+			sourceLanguage: "Javascript",
+			endpoint: "openai.fine_tuning",
+			skipResp: skipResp,
+			requestDuration: duration,
+			model: params.model,
+			llmReqId: response.id,
+			finetuneJobStatus: response.status,
+		};
+
+		await sendData(data, dokuUrl, apiKey);
+
+		return response;
+	};
+
+	llm.images.generate = async function (params) {
+		const start = performance.now();
+		const response = await originalImagesCreate.call(this, params);
+		const end = performance.now();
+		const duration = (end - start) / 1000;
+		const size = params.size || "1024x1024";
+		const model = params.model || "dall-e-2";
+		let imageFormat = "url";
+
+		if (params.response_format && params.response_format === "b64_json") {
+			imageFormat = "b64_json";
+		}
+
+		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",
+				endpoint: "openai.images.create",
+				skipResp: skipResp,
+				requestDuration: duration,
+				model: model,
+				prompt: params.prompt,
+				imageSize: size,
+				imageQuality: quality,
+				revisedPrompt: item.revised_prompt || null,
+				image: item[imageFormat],
+			};
+
+			await sendData(data, dokuUrl, apiKey);
+		}
+
+		return response;
+	};
+
+	llm.images.createVariation = async function (params) {
+		const start = performance.now();
+		const response = await originalImagesCreateVariation.call(this, params);
+		const end = performance.now();
+		const duration = (end - start) / 1000;
+		const size = params.size || "1024x1024"; // Default size if not provided
+		const model = params.model || "dall-e-2";
+		let imageFormat = "url";
+		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",
+				endpoint: "openai.images.create.variations",
+				skipResp: skipResp,
+				requestDuration: duration,
+				model: model,
+				imageSize: size,
+				imageQuality: "standard",
+				image: item[imageFormat],
+			};
+
+			await sendData(data, dokuUrl, apiKey);
+		}
+
+		return response;
+	};
+
+	llm.audio.speech.create = async function (params) {
+		const start = performance.now();
+		const response = await originalAudioSpeechCreate.call(this, params);
+		const end = performance.now();
+		const duration = (end - start) / 1000;
+
+		const data = {
+			environment: environment,
+			applicationName: applicationName,
+			sourceLanguage: "Javascript",
+			endpoint: "openai.audio.speech.create",
+			skipResp: skipResp,
+			requestDuration: duration,
+			model: params.model,
+			prompt: params.input,
+			audioVoice: params.voice,
+		};
+
+		await sendData(data, dokuUrl, apiKey);
+
+		return response;
+	};
+}
diff --git a/tests/anthropic.test.mjs b/tests/anthropic.test.mjs
new file mode 100644
index 0000000..6d45d55
--- /dev/null
+++ b/tests/anthropic.test.mjs
@@ -0,0 +1,50 @@
+import Anthropic from '@anthropic-ai/sdk';
+import {expect} from 'chai';
+import DokuMetry from '../src/index.js';
+
+describe('Anthropic Test', () => {
+  const anthropic = new Anthropic({
+    apiKey: process.env.ANTHROPIC_API_TOKEN,
+  });
+
+  // Non-streaming messages
+  it('should return a response with type as "message"', async () => {
+    DokuMetry.init({llm: anthropic, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
+    try {
+      const message = await anthropic.messages.create({
+        model: "claude-3-haiku-20240307",
+        max_tokens: 1,
+        messages: [{ role: "user", content: "How to monitor LLM Applications in one sentence?" }],
+      });
+      expect(message.type).to.equal('message');
+    } catch (error) {
+        if (typeof error.message === 'string' && error.message.includes('rate limit')) {
+          console.error("Daily Rate limit Reached");
+        }
+    }
+  }).timeout(30000);
+
+  it('should return a response with type as "message"', async () => {
+    DokuMetry.init({llm: anthropic, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
+    try {
+      var stream = await anthropic.messages.create({
+        max_tokens: 1,
+        messages: [{ role: 'user', content: 'How to monitor LLM Applications in one sentence?' }],
+        model: 'claude-3-haiku-20240307',
+        stream: true,
+      });
+      for await (const messageStreamEvent of stream) {
+        if (messageStreamEvent.type === 'message_start') {
+          expect(messageStreamEvent.type).to.equal('message_start');
+        }
+        if (messageStreamEvent.type === 'content_block_delta') {
+          expect(messageStreamEvent.type).to.equal('content_block_delta');
+        }
+      }
+    } catch (error) {
+        if (typeof error.message === 'string' && error.message.includes('rate limit')) {
+          console.error("Daily Rate limit Reached");
+        }
+    }
+  }).timeout(30000);
+});
\ No newline at end of file
diff --git a/tests/anthropic.test.mjs.hold b/tests/anthropic.test.mjs.hold
deleted file mode 100644
index f09a3c7..0000000
--- a/tests/anthropic.test.mjs.hold
+++ /dev/null
@@ -1,39 +0,0 @@
-import Anthropic from '@anthropic-ai/sdk';
-import {expect} from 'chai';
-import DokuMetry from '../src/index.js';
-
-describe('Anthropic Test', () => {
-  const anthropic = new Anthropic({
-    apiKey: process.env.ANTHROPIC_API_TOKEN,
-  });
-
-  // Non-streaming messages
-  it('should return a response with type as "message"', async () => {
-    DokuMetry.init({llm: anthropic, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
-    const message = await anthropic.messages.create({
-      model: "claude-3-opus-20240229",
-      max_tokens: 100,
-      messages: [{ role: "user", content: "How to monitor LLM Applications in one sentence?" }],
-    });
-
-    expect(message.type).to.equal('message');
-  }).timeout(30000);
-
-  it('should return a response with type as "message"', async () => {
-    DokuMetry.init({llm: anthropic, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
-    var stream = await anthropic.messages.create({
-      max_tokens: 100,
-      messages: [{ role: 'user', content: 'How to monitor LLM Applications in one sentence?' }],
-      model: 'claude-3-opus-20240229',
-      stream: true,
-    });
-    for await (const messageStreamEvent of stream) {
-      if (messageStreamEvent.type === 'message_start') {
-        expect(messageStreamEvent.type).to.equal('message_start');
-      }
-      if (messageStreamEvent.type === 'content_block_delta') {
-        expect(messageStreamEvent.type).to.equal('content_block_delta');
-      }
-    }
-  }).timeout(30000);
-});
\ No newline at end of file
diff --git a/tests/mistral.test.mjs.hold b/tests/mistral.test.mjs
similarity index 95%
rename from tests/mistral.test.mjs.hold
rename to tests/mistral.test.mjs
index 669da28..1f51204 100644
--- a/tests/mistral.test.mjs.hold
+++ b/tests/mistral.test.mjs
@@ -23,8 +23,9 @@ describe('Mistral Test', () => {
   it('should return a response with object as "chat.completion"', async () => {
     DokuMetry.init({llm: client, dokuUrl: process.env.DOKU_URL, apiKey: process.env.DOKU_TOKEN, environment: "dokumetry-testing", applicationName: "dokumetry-node-test", skipResp: false});
     const message = await client.chat({
-      model: 'mistral-large-latest',
+      model: 'open-mistral-7b',
       messages: [{role: 'user', content: 'What is LLM Observability?'}],
+      maxTokens: 1,
     });
 
     expect(message.object).to.equal('chat.completion');