Normalize model call output

PIPELINE_TODO.md

@@ -80,7 +80,7 @@
 ## 6. Сделать model_call и tool_loop физически отдельными stages
 
 - [ ] Stage `model_call` должен делать только один model request.
-- [ ] Stage `model_call` должен возвращать normalized model output.
+- [x] Stage `model_call` должен возвращать normalized model output.
 - [ ] Stage `tool_loop` должен решать, есть ли tool calls.
 - [ ] Stage `tool_loop` должен выполнять tools через общий `executeToolBatch`.
 - [ ] Stage `tool_loop` должен добавлять tool results в provider adapter.

src/ai/response-model-output.ts

@@ -0,0 +1,19 @@
+import type {TelegramOutputAttachmentRecord, TelegramToolExecutionRecord} from "./telegram-stream-message.js";
+
+export type NormalizedModelOutput = {
+    text: string;
+    toolExecutions: TelegramToolExecutionRecord[];
+    outputAttachments: TelegramOutputAttachmentRecord[];
+};
+
+export function summarizeModelOutput(params: {
+    text: string;
+    toolExecutions: readonly TelegramToolExecutionRecord[];
+    outputAttachments: readonly TelegramOutputAttachmentRecord[];
+}): NormalizedModelOutput {
+    return {
+        text: params.text.trim(),
+        toolExecutions: [...params.toolExecutions],
+        outputAttachments: [...params.outputAttachments],
+    };
+}

src/ai/unified-ai-response-pipeline.ts

@@ -21,6 +21,7 @@ import {runToolRankStage} from "./tool-rank-stage";
 import {runOpenAi} from "./unified-ai-runner.openai";
 import {runOllama} from "./unified-ai-runner.ollama";
 import {runMistral} from "./unified-ai-runner.mistral";
+import {summarizeModelOutput} from "./response-model-output";
 import {
     resolveTextToSpeechProviderForUser,
     sendSynthesizedSpeech,
@@ -254,6 +255,13 @@ export async function runUnifiedAiResponsePipeline(params: {
                 return {
                     stage: "model_call",
                     status: "succeeded",
+                    details: {
+                        modelOutput: summarizeModelOutput({
+                            text: streamMessage.getText(),
+                            toolExecutions: streamMessage.getToolExecutions(),
+                            outputAttachments: streamMessage.getOutputAttachments(),
+                        }),
+                    },
                 };
             },
         },
@@ -266,6 +274,11 @@ export async function runUnifiedAiResponsePipeline(params: {
                     status: executions.length ? "succeeded" : "skipped",
                     fallbackAction: executions.length ? undefined : "continue_without_stage",
                     details: {
+                        modelOutput: summarizeModelOutput({
+                            text: streamMessage.getText(),
+                            toolExecutions: executions,
+                            outputAttachments: streamMessage.getOutputAttachments(),
+                        }),
                         count: executions.length,
                         tools: executions.map(execution => ({
                             toolName: execution.toolName,

test/response-model-output.test.mjs

@@ -0,0 +1,21 @@
+import test from "node:test";
+import assert from "node:assert/strict";
+
+const {summarizeModelOutput} = await import("../dist/ai/response-model-output.js");
+
+test("model output summary trims text and copies attachment records", () => {
+    const toolExecutions = [{toolName: "read_file", callId: "1", argumentsText: "{}", resultChars: 10}];
+    const outputAttachments = [{artifactKind: "generated_file", fileName: "out.txt", sizeBytes: 12}];
+
+    const summary = summarizeModelOutput({
+        text: "  hello world  ",
+        toolExecutions,
+        outputAttachments,
+    });
+
+    assert.deepEqual(summary, {
+        text: "hello world",
+        toolExecutions,
+        outputAttachments,
+    });
+});