Updated Telemetry DB (cosmos) & Packaging fix

.github/workflows/build-package-shell.yml

@@ -23,6 +23,7 @@ permissions:
   contents: read
 
 env:
+
   ELECTRON_CACHE: ${{ github.workspace }}/.cache/electron
   ELECTRON_BUILDER_CACHE: ${{ github.workspace }}/.cache/electron-builder
 

ts/examples/chat/package.json

@@ -41,6 +41,7 @@
     "knowpro-test": "workspace:*",
     "memory-providers": "workspace:*",
     "memory-storage": "workspace:*",
+    "telemetry": "workspace:*",
     "textpro": "workspace:*",
     "typeagent": "workspace:*",
     "typechat": "^0.1.1",

ts/examples/chat/src/codeChat/codeChat.ts

@@ -38,6 +38,9 @@ import {
     sampleFiles,
 } from "./common.js";
 import { createCommandTransformer } from "./commandTransformer.js";
+import { createPromptLogger } from "telemetry";
+
+const promptLogger = createPromptLogger();
 
 export async function runCodeChat(): Promise<void> {
     const model = openai.createChatModelDefault("codeChat");
@@ -381,7 +384,12 @@ export async function runCodeChat(): Promise<void> {
     async function regex(args: string[], io: InteractiveIo): Promise<void> {
         if (args.length > 0) {
             const prompt = `Return a Typescript regular expression for the following:\n ${args.join(" ")}`;
-            const result = await codeReviewer.model.complete(prompt);
+            const result = await codeReviewer.model.complete(
+                prompt,
+                undefined,
+                undefined,
+                promptLogger.logModelRequest,
+            );
             if (result.success) {
                 io.writer.writeLine(result.data);
             } else {

ts/examples/playground/package.json

@@ -25,6 +25,7 @@
     "copyfiles": "^2.4.1",
     "dotenv": "^16.3.1",
     "interactive-app": "workspace:*",
+    "telemetry": "workspace:*",
     "typeagent": "workspace:*",
     "typechat": "^0.1.1",
     "typescript": "~5.4.5"

ts/examples/playground/src/main.ts

@@ -25,6 +25,9 @@ import {
 } from "typeagent";
 import { PromptSection } from "typechat";
 import * as fs from "fs";
+import { createPromptLogger } from "telemetry";
+
+const promptLogger = createPromptLogger();
 
 const envPath = new URL("../../../.env", import.meta.url);
 dotenv.config({ path: envPath });
@@ -188,7 +191,12 @@ async function runPlayground(): Promise<void> {
             ),
             userMessage,
         ];
-        const chatResponse = await chatModel.complete(context);
+        const chatResponse = await chatModel.complete(
+            context,
+            undefined,
+            undefined,
+            promptLogger.logModelRequest,
+        );
         if (chatResponse.success) {
             const responseText = chatResponse.data;
             io.writer.writeLine(responseText);

ts/examples/vscodeSchemaGen/package.json

@@ -28,6 +28,7 @@
     "debug": "^4.4.0",
     "dotenv": "^16.3.1",
     "fastest-levenshtein": "^1.0.16",
+    "telemetry": "workspace:*",
     "typeagent": "workspace:*",
     "typechat": "^0.1.1"
   },

ts/examples/vscodeSchemaGen/src/schemaGen.ts

@@ -5,6 +5,9 @@ import * as path from "path";
 import dotenv from "dotenv";
 import * as fs from "fs";
 import { finished } from "stream/promises";
+import { createPromptLogger } from "telemetry";
+
+const promptLogger = createPromptLogger();
 
 import {
     ChatModel,
@@ -23,7 +26,12 @@ async function getModelCompletionResponse(
     prompt: string,
     jsonNode: any,
 ): Promise<string | undefined> {
-    const chatResponse = await chatModel.complete(prompt);
+    const chatResponse = await chatModel.complete(
+        prompt,
+        undefined,
+        undefined,
+        promptLogger.logModelRequest,
+    );
     if (chatResponse.success) {
         const responseText = chatResponse.data;
         return responseText;

ts/package.json

@@ -58,7 +58,7 @@
     "prettier": "^3.5.3",
     "shx": "^0.4.0"
   },
-  "packageManager": "pnpm@10.28.0+sha512.05df71d1421f21399e053fde567cea34d446fa02c76571441bfc1c7956e98e363088982d940465fd34480d4d90a0668bc12362f8aa88000a64e83d0b0e47be48",
+  "packageManager": "pnpm@10.24.0+sha512.01ff8ae71b4419903b65c60fb2dc9d34cf8bb6e06d03bde112ef38f7a34d6904c424ba66bea5cdcf12890230bf39f9580473140ed9c946fef328b6e5238a345a",
   "engines": {
     "node": ">=20",
     "pnpm": ">=10"

ts/packages/agents/code/package.json

@@ -28,6 +28,7 @@
     "better-sqlite3": "12.2.0",
     "chalk": "^5.4.1",
     "debug": "^4.4.0",
+    "telemetry": "workspace:*",
     "websocket-utils": "workspace:*",
     "ws": "^8.17.1"
   },

ts/packages/agents/markdown/package.json

@@ -50,6 +50,7 @@
     "prosemirror-model": "^1.19.0",
     "prosemirror-state": "^1.4.2",
     "prosemirror-view": "^1.31.0",
+    "telemetry": "workspace:*",
     "typechat": "^0.1.1",
     "unist-util-visit": "^4.1.2",
     "ws": "^8.14.2",

ts/packages/agents/markdown/src/agent/translator.ts

@@ -12,6 +12,9 @@ import fs from "node:fs";
 import { fileURLToPath } from "node:url";
 import path from "node:path";
 import registerDebug from "debug";
+import { createPromptLogger } from "telemetry";
+
+const promptLogger = createPromptLogger();
 
 import { MarkdownUpdateResult } from "./markdownOperationSchema.js";
 
@@ -174,7 +177,12 @@ export class MarkdownAgent<T extends object> {
             let accumulatedContent = "";
 
             // Use the ChatModel's complete method with proper parameters
-            const response = await this.model.complete(streamingPrompt);
+            const response = await this.model.complete(
+                streamingPrompt,
+                undefined,
+                undefined,
+                promptLogger.logModelRequest,
+            );
 
             // Extract content from response
             let content = "";

ts/packages/aiclient/package.json

@@ -35,7 +35,6 @@
     "@azure/identity": "^4.10.0",
     "async": "^3.2.5",
     "debug": "^4.4.0",
-    "telemetry": "workspace:*",
     "typechat": "^0.1.1"
   },
   "devDependencies": {

ts/packages/aiclient/src/models.ts

@@ -67,6 +67,7 @@ export interface ChatModel extends TypeChatLanguageModel {
         prompt: string | PromptSection[],
         usageCallback?: CompleteUsageStatsCallback,
         jsonSchema?: CompletionJsonSchema,
+        promptLogFn?: (msg: any) => void,
     ): Promise<Result<string>>;
 }
 
@@ -80,6 +81,7 @@ export interface ChatModelWithStreaming extends ChatModel {
         prompt: string | PromptSection[],
         usageCallback?: CompleteUsageStatsCallback,
         jsonSchema?: CompletionJsonSchema,
+        promptLogFn?: (msg: any) => void,
     ): Promise<Result<AsyncIterableIterator<string>>>;
 }
 

ts/packages/aiclient/src/openai.ts

@@ -29,7 +29,6 @@ import { readServerEventStream } from "./serverEvents.js";
 import { priorityQueue } from "async";
 import registerDebug from "debug";
 import { TokenCounter } from "./tokenCounter.js";
-import { PromptLogger } from "./promptLogger.js";
 import {
     createOllamaChatModel,
     OllamaApiSettings,
@@ -485,6 +484,7 @@ function createAzureOpenAIChatModel(
         prompt: string | PromptSection[],
         usageCallback?: CompleteUsageStatsCallback,
         jsonSchema?: CompletionJsonSchema,
+        logFn?: (msg: any) => void,
     ): Promise<Result<string>> {
         verifyPromptLength(settings, prompt);
 
@@ -521,9 +521,9 @@ function createAzureOpenAIChatModel(
         }
 
         try {
-            if (settings.enableModelRequestLogging) {
+            if (settings.enableModelRequestLogging && logFn) {
                 // Log request
-                PromptLogger.getInstance().logModelRequest({
+                logFn({
                     prompt: messages as PromptSection[],
                     response: data.choices[0].message?.content ?? "",
                     tokenUsage: data.usage,
@@ -561,6 +561,7 @@ function createAzureOpenAIChatModel(
         prompt: string | PromptSection[],
         usageCallback?: CompleteUsageStatsCallback,
         jsonSchema?: CompletionJsonSchema,
+        logFn?: (msg: any) => void,
     ): Promise<Result<AsyncIterableIterator<string>>> {
         verifyPromptLength(settings, prompt);
 
@@ -612,9 +613,9 @@ function createAzureOpenAIChatModel(
                 for await (const evt of readServerEventStream(result.data)) {
                     if (evt.data === "[DONE]") {
                         try {
-                            if (settings.enableModelRequestLogging) {
+                            if (settings.enableModelRequestLogging && logFn) {
                                 // Log request.
-                                PromptLogger.getInstance().logModelRequest({
+                                logFn({
                                     prompt: messages as PromptSection[],
                                     response: fullResponseText,
                                     tokenUsageData: tokenUsage,

ts/packages/cache/src/explanation/v5/explanationV5.ts

@@ -81,6 +81,9 @@ import {
     PolitenessGeneralizer,
 } from "./politenessGeneralizationV5.js";
 import { PolitenessGeneralization } from "./politenessGeneralizationSchemaV5.js";
+import { createPromptLogger } from "telemetry";
+
+const promptLogger = createPromptLogger();
 
 type Explanation = PropertyExplanation &
     SubPhraseExplanation &
@@ -334,7 +337,12 @@ variations: ParameterVariation[];
 For every value V that is within the range of ${paramRange.min} to ${paramRange.max} by step ${paramRange.step}, generate a phrase P that only changes the phrase '${subPhrase}' enough to change the value to V.
 Emit the generated phrases and values as a JSON object of type ParameterVariationResult with 2 spaces of indentation and no properties with the value undefined:
 `;
-                    const result = await model.complete(prompt);
+                    const result = await model.complete(
+                        prompt,
+                        undefined,
+                        undefined,
+                        promptLogger.logModelRequest,
+                    );
                     if (result.success) {
                         const generatedAlternatives = JSON.parse(
                             result.data,

ts/packages/cli/package.json

@@ -64,6 +64,7 @@
     "marked": "^15.0.0",
     "marked-terminal": "^7.3.0",
     "open": "^10.1.0",
+    "telemetry": "workspace:*",
     "ts-node": "^10.9.1",
     "typechat": "^0.1.1",
     "typechat-utils": "workspace:*"

ts/packages/cli/src/commands/prompt.ts

@@ -5,6 +5,9 @@ import { Args, Command, Flags } from "@oclif/core";
 import { openai, getChatModelNames } from "aiclient";
 import fs from "node:fs";
 import chalk from "chalk";
+import { createPromptLogger } from "telemetry";
+
+const promptLogger = createPromptLogger();
 
 const modelNames = await getChatModelNames();
 export default class Prompt extends Command {
@@ -83,7 +86,12 @@ export default class Prompt extends Command {
                 time = end - start;
             } else {
                 const start = performance.now();
-                const complete = await model.complete(request);
+                const complete = await model.complete(
+                    request,
+                    undefined,
+                    undefined,
+                    promptLogger.logModelRequest,
+                );
                 const end = performance.now();
                 if (complete.success) {
                     responseText = complete.data;

ts/packages/defaultAgentProvider/package.json

@@ -76,6 +76,7 @@
     "typechat": "^0.1.1",
     "typechat-utils": "workspace:*",
     "video-agent": "workspace:*",
+    "weather-agent": "workspace:*",
     "ws": "^8.17.1",
     "zod": "^4.1.13"
   },

ts/packages/dispatcher/dispatcher/package.json

@@ -41,6 +41,7 @@
     "@anthropic-ai/claude-agent-sdk": "^0.2.12",
     "@azure/ai-agents": "^1.0.0-beta.3",
     "@azure/ai-projects": "^1.0.0-beta.8",
+    "@azure/cosmos": "^4.2.1",
     "@azure/identity": "^4.10.0",
     "@typeagent/action-schema": "workspace:*",
     "@typeagent/agent-rpc": "workspace:*",