✅ Fix chat interface - restore continuous conversation flow

🎯 Major improvements to MissionControl component:
- Always keep input field visible and functional after AI responses
- Auto-clear input after submitting questions for better UX
- Add dynamic visual indicators (first question vs follow-up)
- Improve response layout with clear separation and hints
- Enable proper chat-like experience for continuous learning

🌟 Additional enhancements:
- Better language-specific messaging throughout interface
- Clearer visual hierarchy between input and response areas
- Intuitive flow that guides users to ask follow-up questions
- Maintains responsive design and accessibility

🔧 Technical changes:
- Enhanced MissionControl state management
- Improved component layout and styling
- Better TypeScript integration across components
- Updated tsconfig for stricter type checking

node_modules/openai/resources/embeddings.mjs

@@ -1,6 +1,6 @@
 // File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
-import { APIResource } from "../core/resource.mjs";
-import { loggerFor, toFloat32Array } from "../internal/utils.mjs";
+import { APIResource } from "../resource.mjs";
+import * as Core from "../core.mjs";
 export class Embeddings extends APIResource {
     /**
      * Creates an embedding vector representing the input text.
@@ -20,7 +20,7 @@ export class Embeddings extends APIResource {
         // See https://github.com/openai/openai-node/pull/1312
         let encoding_format = hasUserProvidedEncodingFormat ? body.encoding_format : 'base64';
         if (hasUserProvidedEncodingFormat) {
-            loggerFor(this._client).debug('embeddings/user defined encoding_format:', body.encoding_format);
+            Core.debug('Request', 'User defined encoding_format:', body.encoding_format);
         }
         const response = this._client.post('/embeddings', {
             body: {
@@ -37,12 +37,12 @@ export class Embeddings extends APIResource {
         // and we defaulted to base64 for performance reasons
         // we are sure then that the response is base64 encoded, let's decode it
         // the returned result will be a float32 array since this is OpenAI API's default encoding
-        loggerFor(this._client).debug('embeddings/decoding base64 embeddings from base64');
+        Core.debug('response', 'Decoding base64 embeddings to float32 array');
         return response._thenUnwrap((response) => {
             if (response && response.data) {
                 response.data.forEach((embeddingBase64Obj) => {
                     const embeddingBase64Str = embeddingBase64Obj.embedding;
-                    embeddingBase64Obj.embedding = toFloat32Array(embeddingBase64Str);
+                    embeddingBase64Obj.embedding = Core.toFloat32Array(embeddingBase64Str);
                 });
             }
             return response;