АРЧ - UI и domain loop: синхронизировать локальную LLM-модель через shared config + КВИН 3

docs/orchestration/active_domain_contract.json

@@ -41,8 +41,8 @@
     "buyer_candidate": "Департамент капитального ремонта города Москвы"
   },
   "question_pool": {
-    "total_questions": 30,
+    "total_questions": 31,
-    "core_questions_total": 21,
+    "core_questions_total": 22,
     "followup_checkpoints_total": 9,
     "questions": [
       {
@@ -279,6 +279,15 @@
         "wording_family": "ui_selected_object_colloquial",
         "semantic_goal": "проверить разговорный selected-object follow-up про поставщика без слова `поставщик`"
       },
+      {
+        "question_id": "Q31",
+        "text": "По выбранному объекту \"...\": у кого куплено",
+        "layer": "selected_item_provenance",
+        "node_id": "N03_selected_item_supplier",
+        "role": "critical_child",
+        "wording_family": "ui_selected_object_colloquial",
+        "semantic_goal": "проверить selected-object follow-up с краткой страдательной формой `у кого куплено`, который должен оставаться в supplier provenance и наследовать историческую дату root-среза"
+      },
       {
         "question_id": "Q29",
         "text": "По выбранному объекту \"...\": где мы купили это",
@@ -1032,6 +1041,51 @@
               "organization_scope"
             ]
           },
+          {
+            "step_id": "step_03bb_selected_item_supplier_u_kogo_kupleno",
+            "question_id": "Q31",
+            "node_id": "N03_selected_item_supplier",
+            "node_role": "critical_child",
+            "paraphrase_family": "ui_selected_object_colloquial",
+            "title": "Selected item supplier passive colloquial wording",
+            "question": "По выбранному объекту \"{{bindings.focus_item_historical}}\": у кого куплено",
+            "depends_on": [
+              "step_01_snapshot_historical"
+            ],
+            "analysis_context": {
+              "as_of_date": "2019-03-31",
+              "source": "binding_target_date_historical"
+            },
+            "expected_capability": "inventory_purchase_provenance_for_item",
+            "forbidden_capabilities": [
+              "confirmed_inventory_on_hand_as_of_date"
+            ],
+            "forbidden_recipes": [
+              "address_inventory_on_hand_as_of_date_v1"
+            ],
+            "required_state_objects": [
+              "focus_object"
+            ],
+            "required_carryover_invariants": [
+              "selected_object",
+              "focus_object",
+              "date_scope",
+              "warehouse_scope",
+              "organization_scope"
+            ],
+            "required_filters": {
+              "as_of_date": "2019-03-31",
+              "period_from": "2019-03-01",
+              "period_to": "2019-03-31"
+            },
+            "invariant_severity": {
+              "wrong_as_of_date": "P0",
+              "wrong_period_from": "P0",
+              "wrong_period_to": "P0",
+              "forbidden_route_selected": "P0",
+              "focus_object_missing": "P0"
+            }
+          },
           {
             "step_id": "step_03c_selected_item_supplier_gde_my_kupili",
             "question_id": "Q29",
@@ -1852,7 +1906,7 @@
     },
     {
       "pattern_id": "F12b_selected_object_supplier_u_kogo_kupili_misroute",
-      "symptom": "selected-object follow-up such as `у кого купили` stays on the root stock snapshot instead of switching to selected-item provenance",
+      "symptom": "selected-object follow-up such as `у кого купили` or `у кого куплено` stays on the root stock snapshot instead of switching to selected-item provenance",
       "defect_class": "followup_action_resolution_gap"
     },
     {

llm_normalizer/backend/dist/config.js

@@ -4,7 +4,7 @@ var __importDefault = (this && this.__importDefault) || function (mod) {
 };
 Object.defineProperty(exports, "__esModule", { value: true });
 exports.TRACES_DIR = exports.DATA_DIR = exports.VAT_PAYABLE_19_PREFIXES = exports.VAT_PAYABLE_68_PREFIXES = exports.ASSISTANT_MCP_LIVE_LIMIT = exports.ASSISTANT_MCP_TIMEOUT_MS = exports.ASSISTANT_MCP_CHANNEL = exports.ASSISTANT_MCP_PROXY_URL = exports.FEATURE_ASSISTANT_LIVING_CHAT_ROUTER_V1 = exports.FEATURE_ASSISTANT_ROUTE_EXPECTATION_HARD_GUARD_V1 = exports.FEATURE_ASSISTANT_ROUTE_EXPECTATION_AUDIT_V1 = exports.FEATURE_ASSISTANT_ROUTE_SHADOW_PAYABLES_EXACT_V1 = exports.FEATURE_ASSISTANT_ROUTE_RECEIVABLES_HEURISTIC_V1 = exports.FEATURE_ASSISTANT_ROUTE_PAYABLES_HEURISTIC_V1 = exports.FEATURE_ASSISTANT_ROUTE_RECEIVABLES_CONFIRMED_V1 = exports.FEATURE_ASSISTANT_ROUTE_PAYABLES_CONFIRMED_V1 = exports.FEATURE_ASSISTANT_ROUTE_BALANCE_EXACT_V1 = exports.FEATURE_ASSISTANT_ROUTE_DRILLDOWN_V1 = exports.FEATURE_ASSISTANT_ROUTE_ADDRESS_GENERIC_V1 = exports.FEATURE_ASSISTANT_CAPABILITY_ROUTE_GUARD_V1 = exports.FEATURE_ASSISTANT_ADDRESS_NAVIGATION_STATE_V1 = exports.FEATURE_ASSISTANT_ADDRESS_QUERY_LIVE_V1 = exports.FEATURE_ASSISTANT_ADDRESS_QUERY_LLM_PREDECOMPOSE_V1 = exports.FEATURE_ASSISTANT_ADDRESS_QUERY_V1 = exports.FEATURE_ASSISTANT_MCP_RUNTIME_V1 = exports.FEATURE_ASSISTANT_GRAPH_RUNTIME_V1 = exports.FEATURE_ASSISTANT_LIFECYCLE_ANSWER_V1 = exports.FEATURE_ASSISTANT_LIFECYCLE_RUNTIME_V1 = exports.FEATURE_ASSISTANT_STAGE2_EVAL_V1 = exports.FEATURE_ASSISTANT_PROBLEM_UNIT_CONTINUITY_V1 = exports.FEATURE_ASSISTANT_PROBLEM_CENTRIC_ANSWER_V1 = exports.FEATURE_ASSISTANT_PROBLEM_UNITS_V1 = exports.FEATURE_ASSISTANT_ACCOUNTANT_EVAL_V1 = exports.FEATURE_ASSISTANT_ANSWER_POLICY_V11 = exports.FEATURE_ASSISTANT_ANTI_GENERIC_RANKING_GUARD_V1 = exports.FEATURE_ASSISTANT_MIN_EVIDENCE_GATE_V1 = exports.FEATURE_ASSISTANT_BROAD_GUARD_V1 = exports.FEATURE_ASSISTANT_EVIDENCE_ENRICHMENT_V1 = exports.FEATURE_ASSISTANT_STATE_FOLLOWUP_BINDING_V1 = exports.FEATURE_ASSISTANT_CONTRACTS_V11 = exports.FEATURE_ASSISTANT_INVESTIGATION_STATE_V1 = exports.DEFAULT_PROMPT_VERSION = exports.DEFAULT_MAX_OUTPUT_TOKENS = exports.DEFAULT_TEMPERATURE = exports.DEFAULT_MODEL = exports.DEFAULT_OPENAI_BASE_URL = exports.TIMEZONE = exports.PORT = exports.MODULE_ROOT = exports.BACKEND_ROOT = void 0;
-exports.MANUAL_CASE_DECISION_SCHEMA_FILE = exports.ASSISTANT_CAPABILITIES_REGISTRY_FILE = exports.ASSISTANT_CANON_FILE = exports.ARCH_EXPORT_2020_DIR = exports.SCHEMAS_DIR = exports.EVAL_DATASETS_DIR = exports.REPORTS_DIR = exports.PROMPTS_DIR = exports.AUTORUN_GENERATOR_HISTORY_FILE = exports.AUTORUN_GENERATOR_DIR = exports.AUTORUN_ANNOTATIONS_FILE = exports.AUTORUN_ANNOTATIONS_DIR = exports.ASSISTANT_ANNOTATIONS_FILE = exports.ASSISTANT_ANNOTATIONS_DIR = exports.ASSISTANT_SESSIONS_DIR = exports.EVAL_CASES_DIR = exports.PRESETS_DIR = void 0;
+exports.MANUAL_CASE_DECISION_SCHEMA_FILE = exports.ASSISTANT_CAPABILITIES_REGISTRY_FILE = exports.ASSISTANT_CANON_FILE = exports.ARCH_EXPORT_2020_DIR = exports.SCHEMAS_DIR = exports.EVAL_DATASETS_DIR = exports.REPORTS_DIR = exports.PROMPTS_DIR = exports.SHARED_LLM_CONNECTION_FILE = exports.AUTORUN_GENERATOR_HISTORY_FILE = exports.AUTORUN_GENERATOR_DIR = exports.AUTORUN_ANNOTATIONS_FILE = exports.AUTORUN_ANNOTATIONS_DIR = exports.ASSISTANT_ANNOTATIONS_FILE = exports.ASSISTANT_ANNOTATIONS_DIR = exports.ASSISTANT_SESSIONS_DIR = exports.EVAL_CASES_DIR = exports.PRESETS_DIR = void 0;
 const path_1 = __importDefault(require("path"));
 exports.BACKEND_ROOT = path_1.default.resolve(__dirname, "..");
 exports.MODULE_ROOT = path_1.default.resolve(exports.BACKEND_ROOT, "..");
@@ -90,6 +90,7 @@ exports.AUTORUN_ANNOTATIONS_DIR = path_1.default.resolve(exports.DATA_DIR, "auto
 exports.AUTORUN_ANNOTATIONS_FILE = path_1.default.resolve(exports.AUTORUN_ANNOTATIONS_DIR, "annotations.json");
 exports.AUTORUN_GENERATOR_DIR = path_1.default.resolve(exports.DATA_DIR, "autorun_generators");
 exports.AUTORUN_GENERATOR_HISTORY_FILE = path_1.default.resolve(exports.AUTORUN_GENERATOR_DIR, "history.json");
+exports.SHARED_LLM_CONNECTION_FILE = path_1.default.resolve(exports.DATA_DIR, "shared_llm_connection.json");
 exports.PROMPTS_DIR = path_1.default.resolve(exports.MODULE_ROOT, "prompts");
 exports.REPORTS_DIR = path_1.default.resolve(exports.MODULE_ROOT, "reports");
 exports.EVAL_DATASETS_DIR = path_1.default.resolve(exports.MODULE_ROOT, "eval_cases");

llm_normalizer/backend/dist/routes/sharedLlmConfig.js

@@ -0,0 +1,107 @@
+"use strict";
+var __importDefault = (this && this.__importDefault) || function (mod) {
+    return (mod && mod.__esModule) ? mod : { "default": mod };
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.buildSharedLlmConfigRouter = buildSharedLlmConfigRouter;
+const express_1 = require("express");
+const config_1 = require("../config");
+const http_1 = require("../utils/http");
+const files_1 = require("../utils/files");
+const path_1 = __importDefault(require("path"));
+function sanitizeString(value, fallback) {
+    if (typeof value !== "string") {
+        return fallback;
+    }
+    const trimmed = value.trim();
+    return trimmed.length > 0 ? trimmed : fallback;
+}
+function sanitizeNumber(value, fallback) {
+    if (typeof value === "number" && Number.isFinite(value)) {
+        return value;
+    }
+    if (typeof value === "string" && value.trim().length > 0) {
+        const parsed = Number(value);
+        if (Number.isFinite(parsed)) {
+            return parsed;
+        }
+    }
+    return fallback;
+}
+function buildFallbackRecord() {
+    return {
+        schema_version: "shared_llm_connection_v1",
+        updated_at: "",
+        connection: {
+            llmProvider: "local",
+            model: "qwen2.5-14b-instruct-1m",
+            baseUrl: "http://127.0.0.1:1234/v1",
+            temperature: 0,
+            maxOutputTokens: 900
+        }
+    };
+}
+function loadSharedRecord() {
+    const fallback = buildFallbackRecord();
+    const parsed = (0, files_1.readJsonFile)(config_1.SHARED_LLM_CONNECTION_FILE, null);
+    if (!parsed || typeof parsed !== "object" || !("connection" in parsed) || !parsed.connection) {
+        return null;
+    }
+    const connection = parsed.connection;
+    return {
+        schema_version: "shared_llm_connection_v1",
+        updated_at: sanitizeString(parsed.updated_at, ""),
+        connection: {
+            llmProvider: connection.llmProvider === "local" ? "local" : "openai",
+            model: sanitizeString(connection.model, fallback.connection.model),
+            baseUrl: sanitizeString(connection.baseUrl, fallback.connection.baseUrl),
+            temperature: sanitizeNumber(connection.temperature, fallback.connection.temperature),
+            maxOutputTokens: Math.max(1, Math.trunc(sanitizeNumber(connection.maxOutputTokens, fallback.connection.maxOutputTokens)))
+        }
+    };
+}
+function buildSharedLlmConfigRouter() {
+    const router = (0, express_1.Router)();
+    router.get("/api/llm/shared-connection", (_req, res, next) => {
+        try {
+            const record = loadSharedRecord();
+            (0, http_1.ok)(res, {
+                ok: true,
+                connection: record?.connection ?? null,
+                updated_at: record?.updated_at ?? null,
+                exists: Boolean(record)
+            });
+        }
+        catch (error) {
+            next(error);
+        }
+    });
+    router.post("/api/llm/shared-connection", (req, res, next) => {
+        try {
+            const body = (req.body ?? {});
+            const fallback = buildFallbackRecord();
+            const record = {
+                schema_version: "shared_llm_connection_v1",
+                updated_at: new Date().toISOString(),
+                connection: {
+                    llmProvider: body.llmProvider === "local" ? "local" : "openai",
+                    model: sanitizeString(body.model, fallback.connection.model),
+                    baseUrl: sanitizeString(body.baseUrl, fallback.connection.baseUrl),
+                    temperature: sanitizeNumber(body.temperature, fallback.connection.temperature),
+                    maxOutputTokens: Math.max(1, Math.trunc(sanitizeNumber(body.maxOutputTokens, fallback.connection.maxOutputTokens)))
+                }
+            };
+            (0, files_1.ensureDir)(path_1.default.dirname(config_1.SHARED_LLM_CONNECTION_FILE));
+            (0, files_1.writeJsonFile)(config_1.SHARED_LLM_CONNECTION_FILE, record);
+            (0, http_1.ok)(res, {
+                ok: true,
+                connection: record.connection,
+                updated_at: record.updated_at
+            });
+        }
+        catch (error) {
+            next(error);
+        }
+    });
+    return router;
+}

llm_normalizer/backend/dist/server.js

@@ -15,6 +15,7 @@ const eval_1 = require("./routes/eval");
 const history_1 = require("./routes/history");
 const normalize_1 = require("./routes/normalize");
 const presets_1 = require("./routes/presets");
+const sharedLlmConfig_1 = require("./routes/sharedLlmConfig");
 const testConnection_1 = require("./routes/testConnection");
 const inMemoryRuntimeAdapter_1 = require("./runtime/inMemoryRuntimeAdapter");
 const assistantService_1 = require("./services/assistantService");
@@ -59,6 +60,7 @@ function createApp() {
         });
     });
     app.use((0, testConnection_1.buildTestConnectionRouter)(openaiClient));
+    app.use((0, sharedLlmConfig_1.buildSharedLlmConfigRouter)());
     app.use((0, normalize_1.buildNormalizeRouter)(services));
     app.use((0, eval_1.buildEvalRouter)(services));
     app.use((0, assistant_1.buildAssistantRouter)(services));

llm_normalizer/backend/dist/services/addressIntentResolver.js

@@ -1345,7 +1345,7 @@ function hasSelectedObjectInventoryCue(text) {
 }
 function hasSelectedObjectInventoryProvenanceSignal(text) {
     return (hasSelectedObjectInventoryCue(text) &&
-        /(?:кто\s+(?:(?:это|этот\s+товар|эту\s+позицию)\s+)?(?:нам\s+)?поставил|кто\s+(?:нам\s+)?поставил\s+(?:это|этот\s+товар|эту\s+позицию)|от\s+какого\s+поставщика|у\s+какого\s+поставщика|от\s+кого\s+куплен|у\s+кого\s+купили|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|этот\s+товар|эту\s+позицию))?|где\s+куплено|supplier|vendor|поставщик)/iu.test(text));
+        /(?:кто\s+(?:(?:это|этот\s+товар|эту\s+позицию)\s+)?(?:нам\s+)?поставил|кто\s+(?:нам\s+)?поставил\s+(?:это|этот\s+товар|эту\s+позицию)|от\s+какого\s+поставщика|у\s+какого\s+поставщика|от\s+кого\s+куплен|у\s+кого\s+купили|у\s+кого\s+куплено|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|этот\s+товар|эту\s+позицию))?|где\s+куплено|supplier|vendor|поставщик)/iu.test(text));
 }
 function hasSelectedObjectInventoryPurchaseDocumentsSignal(text) {
     return (hasSelectedObjectInventoryCue(text) &&
@@ -1353,7 +1353,7 @@ function hasSelectedObjectInventoryPurchaseDocumentsSignal(text) {
 }
 function hasInventoryProvenanceSignalV2(text) {
     const hasItemCue = /(?:товар|номенклатур|sku|item|product|остат(?:ок|ки)|склад)/iu.test(text);
-    const hasSupplierCue = /(?:от\s+какого\s+поставщика|у\s+какого\s+поставщика|от\s+кого\s+куплен|у\s+кого\s+купили|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|товар|позицию))?|где\s+куплено|кто\s+(?:нам\s+)?поставил|кем\s+поставлен|поставщик|supplier|vendor)/iu.test(text);
+    const hasSupplierCue = /(?:от\s+какого\s+поставщика|у\s+какого\s+поставщика|от\s+кого\s+куплен|у\s+кого\s+купили|у\s+кого\s+куплено|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|товар|позицию))?|где\s+куплено|кто\s+(?:нам\s+)?поставил|кем\s+поставлен|поставщик|supplier|vendor)/iu.test(text);
     const hasPurchaseCue = /(?:куплен(?:ы|а|о)?|закупк|происхождени|откуда|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|товар|позицию))?|где\s+куплено|когда\s+был\s+куплен|когда\s+куплен|дата\s+закупк|кто\s+(?:нам\s+)?поставил|кем\s+поставлен|поставлен(?:ы|а)?|purchase\s+provenance|purchase\s+date)/iu.test(text);
     return hasItemCue && hasSupplierCue && hasPurchaseCue;
 }

llm_normalizer/backend/dist/services/addressQueryClassifier.js

@@ -269,7 +269,7 @@ function hasSelectedObjectInventoryFollowupSignal(text) {
     if (!/(?:по\s+выбранному\s+объекту|по\s+выбранной\s+позиции)/iu.test(text)) {
         return false;
     }
-    return /(?:у\s+кого\s+купили|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|товар|позицию))?|где\s+куплено|кто\s+(?:поставил|продал)|кому\s+(?:продали|реализовали)|когда\s+(?:примерно\s+)?купили|по\s+каким\s+документам\s+.*купили)/iu.test(text);
+    return /(?:у\s+кого\s+купили|у\s+кого\s+куплено|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|товар|позицию))?|где\s+куплено|кто\s+(?:поставил|продал)|кому\s+(?:продали|реализовали)|когда\s+(?:примерно\s+)?купили|по\s+каким\s+документам\s+.*купили)/iu.test(text);
 }
 function hasDocsOrBankSignal(text) {
     return /(?:док(?:и|умент|ументы|ументов)|docs?|documents?|банк|выписк|платеж|платёж|оплат|поступлен|списан|транзак|transactions?|bank\s+ops|bank\s+operations?)/iu.test(text);

llm_normalizer/backend/dist/services/address_runtime/decomposeStage.js

@@ -25,7 +25,7 @@ function hasSameDateHint(text) {
     return /(?:на\s+ту\s+же\s+дат[ауеы]|на\s+эту\s+же\s+дат[ауеы]|на\s+эту\s+дат[ауеы]|эту\s+дат[ауеы]|та\s+же\s+дата|same\s+date|as\s+of\s+same\s+date|the\s+same\s+date)/iu.test(String(text ?? ""));
 }
 function hasExplicitPeriodLiteral(text) {
-    return /\b(?:19|20)\d{2}(?:[./-](?:0?[1-9]|1[0-2]))?\b/.test(String(text ?? ""));
+    return /(?:^|[^\d*×xх])((?:19|20)\d{2}(?:[./-](?:0?[1-9]|1[0-2]))?)(?=$|[^\d*×xх])/iu.test(String(text ?? ""));
 }
 function hasExplicitCurrentDateHint(text) {
     return /(?:на\s+текущ(?:ую|ая|ий|ее|ей|ем|его)\s+дат(?:у|а|е|ой|ою)|на\s+сегодняшн(?:юю|ий|ей|ем|его)\s+дат(?:у|а|е|ой|ою)|на\s+сегодня|сегодня|на\s+текущ(?:ий|ую)\s+момент|today|as\s+of\s+today|current\s+date|as\s+of\s+current\s+date)/iu.test(String(text ?? ""));
@@ -257,7 +257,7 @@ function hasSelectedObjectInventorySignal(text) {
     return /(?:по\s+выбранному\s+объекту|for\s+selected\s+object)/iu.test(String(text ?? ""));
 }
 function hasInventorySupplierFollowupCue(text) {
-    return /(?:кто\s+(?:(?:это|этот\s+товар|эту\s+позицию)\s+)?(?:нам\s+)?поставил|кто\s+(?:нам\s+)?поставил\s+(?:это|этот\s+товар|эту\s+позицию)|от\s+какого\s+поставщика|у\s+какого\s+поставщика|от\s+кого\s+куплен|у\s+кого\s+купили|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|этот\s+товар|эту\s+позицию))?|где\s+куплено|supplier|vendor|поставщик)/iu.test(String(text ?? ""));
+    return /(?:кто\s+(?:(?:это|этот\s+товар|эту\s+позицию)\s+)?(?:нам\s+)?поставил|кто\s+(?:нам\s+)?поставил\s+(?:это|этот\s+товар|эту\s+позицию)|от\s+какого\s+поставщика|у\s+какого\s+поставщика|от\s+кого\s+куплен|у\s+кого\s+купили|у\s+кого\s+куплено|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|этот\s+товар|эту\s+позицию))?|где\s+куплено|supplier|vendor|поставщик)/iu.test(String(text ?? ""));
 }
 function hasInventoryPurchaseDocumentsFollowupCue(text) {
     return /(?:по\s+каким\s+документам\s+(?:это|его|этот\s+товар|эту\s+позицию)\s+купили|по\s+каким\s+документам\s+(?:был\s+)?куплен|какими\s+документами\s+(?:это|его|этот\s+товар|эту\s+позицию)\s+купили|какими\s+документами\s+(?:был\s+)?куплен|покажи\s+документы\s+по\s+(?:этой\s+позиции|этому\s+товару|ней|нему)|документы\s+по\s+(?:этой\s+позиции|этому\s+товару|ней|нему)|purchase\s+documents|documents\s+of\s+purchase|through\s+which\s+documents)/iu.test(String(text ?? ""));

llm_normalizer/backend/src/config.ts

@@ -188,6 +188,7 @@ export const AUTORUN_ANNOTATIONS_DIR = path.resolve(DATA_DIR, "autorun_annotatio
 export const AUTORUN_ANNOTATIONS_FILE = path.resolve(AUTORUN_ANNOTATIONS_DIR, "annotations.json");
 export const AUTORUN_GENERATOR_DIR = path.resolve(DATA_DIR, "autorun_generators");
 export const AUTORUN_GENERATOR_HISTORY_FILE = path.resolve(AUTORUN_GENERATOR_DIR, "history.json");
+export const SHARED_LLM_CONNECTION_FILE = path.resolve(DATA_DIR, "shared_llm_connection.json");
 
 export const PROMPTS_DIR = path.resolve(MODULE_ROOT, "prompts");
 export const REPORTS_DIR = path.resolve(MODULE_ROOT, "reports");

llm_normalizer/backend/src/routes/sharedLlmConfig.ts

@@ -0,0 +1,124 @@
+import { NextFunction, Request, Response, Router } from "express";
+import { SHARED_LLM_CONNECTION_FILE } from "../config";
+import { ok } from "../utils/http";
+import { ensureDir, readJsonFile, writeJsonFile } from "../utils/files";
+import path from "path";
+
+type LlmProvider = "openai" | "local";
+
+interface SharedLlmConnectionRecord {
+  schema_version: "shared_llm_connection_v1";
+  updated_at: string;
+  connection: {
+    llmProvider: LlmProvider;
+    model: string;
+    baseUrl: string;
+    temperature: number;
+    maxOutputTokens: number;
+  };
+}
+
+function sanitizeString(value: unknown, fallback: string): string {
+  if (typeof value !== "string") {
+    return fallback;
+  }
+  const trimmed = value.trim();
+  return trimmed.length > 0 ? trimmed : fallback;
+}
+
+function sanitizeNumber(value: unknown, fallback: number): number {
+  if (typeof value === "number" && Number.isFinite(value)) {
+    return value;
+  }
+  if (typeof value === "string" && value.trim().length > 0) {
+    const parsed = Number(value);
+    if (Number.isFinite(parsed)) {
+      return parsed;
+    }
+  }
+  return fallback;
+}
+
+function buildFallbackRecord(): SharedLlmConnectionRecord {
+  return {
+    schema_version: "shared_llm_connection_v1",
+    updated_at: "",
+    connection: {
+      llmProvider: "local",
+      model: "qwen2.5-14b-instruct-1m",
+      baseUrl: "http://127.0.0.1:1234/v1",
+      temperature: 0,
+      maxOutputTokens: 900
+    }
+  };
+}
+
+function loadSharedRecord(): SharedLlmConnectionRecord | null {
+  const fallback = buildFallbackRecord();
+  const parsed = readJsonFile<SharedLlmConnectionRecord | null>(SHARED_LLM_CONNECTION_FILE, null);
+  if (!parsed || typeof parsed !== "object" || !("connection" in parsed) || !parsed.connection) {
+    return null;
+  }
+  const connection = parsed.connection;
+  return {
+    schema_version: "shared_llm_connection_v1",
+    updated_at: sanitizeString(parsed.updated_at, ""),
+    connection: {
+      llmProvider: connection.llmProvider === "local" ? "local" : "openai",
+      model: sanitizeString(connection.model, fallback.connection.model),
+      baseUrl: sanitizeString(connection.baseUrl, fallback.connection.baseUrl),
+      temperature: sanitizeNumber(connection.temperature, fallback.connection.temperature),
+      maxOutputTokens: Math.max(1, Math.trunc(sanitizeNumber(connection.maxOutputTokens, fallback.connection.maxOutputTokens)))
+    }
+  };
+}
+
+export function buildSharedLlmConfigRouter(): Router {
+  const router = Router();
+
+  router.get("/api/llm/shared-connection", (_req: Request, res: Response, next: NextFunction) => {
+    try {
+      const record = loadSharedRecord();
+      ok(res, {
+        ok: true,
+        connection: record?.connection ?? null,
+        updated_at: record?.updated_at ?? null,
+        exists: Boolean(record)
+      });
+    } catch (error) {
+      next(error);
+    }
+  });
+
+  router.post("/api/llm/shared-connection", (req: Request, res: Response, next: NextFunction) => {
+    try {
+      const body = (req.body ?? {}) as Record<string, unknown>;
+      const fallback = buildFallbackRecord();
+      const record: SharedLlmConnectionRecord = {
+        schema_version: "shared_llm_connection_v1",
+        updated_at: new Date().toISOString(),
+        connection: {
+          llmProvider: body.llmProvider === "local" ? "local" : "openai",
+          model: sanitizeString(body.model, fallback.connection.model),
+          baseUrl: sanitizeString(body.baseUrl, fallback.connection.baseUrl),
+          temperature: sanitizeNumber(body.temperature, fallback.connection.temperature),
+          maxOutputTokens: Math.max(
+            1,
+            Math.trunc(sanitizeNumber(body.maxOutputTokens, fallback.connection.maxOutputTokens))
+          )
+        }
+      };
+      ensureDir(path.dirname(SHARED_LLM_CONNECTION_FILE));
+      writeJsonFile(SHARED_LLM_CONNECTION_FILE, record);
+      ok(res, {
+        ok: true,
+        connection: record.connection,
+        updated_at: record.updated_at
+      });
+    } catch (error) {
+      next(error);
+    }
+  });
+
+  return router;
+}

llm_normalizer/backend/src/server.ts

@@ -20,6 +20,7 @@ import { buildEvalRouter } from "./routes/eval";
 import { buildHistoryRouter } from "./routes/history";
 import { buildNormalizeRouter } from "./routes/normalize";
 import { buildPresetsRouter } from "./routes/presets";
+import { buildSharedLlmConfigRouter } from "./routes/sharedLlmConfig";
 import { buildTestConnectionRouter } from "./routes/testConnection";
 import { InMemoryRuntimeAdapter } from "./runtime/inMemoryRuntimeAdapter";
 import { AssistantService } from "./services/assistantService";
@@ -71,6 +72,7 @@ export function createApp(): express.Express {
   });
 
   app.use(buildTestConnectionRouter(openaiClient));
+  app.use(buildSharedLlmConfigRouter());
   app.use(buildNormalizeRouter(services));
   app.use(buildEvalRouter(services));
   app.use(buildAssistantRouter(services));

llm_normalizer/backend/src/services/addressIntentResolver.ts

@@ -1612,7 +1612,7 @@ function hasSelectedObjectInventoryCue(text: string): boolean {
 function hasSelectedObjectInventoryProvenanceSignal(text: string): boolean {
   return (
     hasSelectedObjectInventoryCue(text) &&
-    /(?:кто\s+(?:(?:это|этот\s+товар|эту\s+позицию)\s+)?(?:нам\s+)?поставил|кто\s+(?:нам\s+)?поставил\s+(?:это|этот\s+товар|эту\s+позицию)|от\s+какого\s+поставщика|у\s+какого\s+поставщика|от\s+кого\s+куплен|у\s+кого\s+купили|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|этот\s+товар|эту\s+позицию))?|где\s+куплено|supplier|vendor|поставщик)/iu.test(
+    /(?:кто\s+(?:(?:это|этот\s+товар|эту\s+позицию)\s+)?(?:нам\s+)?поставил|кто\s+(?:нам\s+)?поставил\s+(?:это|этот\s+товар|эту\s+позицию)|от\s+какого\s+поставщика|у\s+какого\s+поставщика|от\s+кого\s+куплен|у\s+кого\s+купили|у\s+кого\s+куплено|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|этот\s+товар|эту\s+позицию))?|где\s+куплено|supplier|vendor|поставщик)/iu.test(
       text
     )
   );
@@ -1630,7 +1630,7 @@ function hasSelectedObjectInventoryPurchaseDocumentsSignal(text: string): boolea
 function hasInventoryProvenanceSignalV2(text: string): boolean {
   const hasItemCue = /(?:товар|номенклатур|sku|item|product|остат(?:ок|ки)|склад)/iu.test(text);
   const hasSupplierCue =
-    /(?:от\s+какого\s+поставщика|у\s+какого\s+поставщика|от\s+кого\s+куплен|у\s+кого\s+купили|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|товар|позицию))?|где\s+куплено|кто\s+(?:нам\s+)?поставил|кем\s+поставлен|поставщик|supplier|vendor)/iu.test(
+    /(?:от\s+какого\s+поставщика|у\s+какого\s+поставщика|от\s+кого\s+куплен|у\s+кого\s+купили|у\s+кого\s+куплено|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|товар|позицию))?|где\s+куплено|кто\s+(?:нам\s+)?поставил|кем\s+поставлен|поставщик|supplier|vendor)/iu.test(
       text
     );
   const hasPurchaseCue =

llm_normalizer/backend/src/services/addressQueryClassifier.ts

@@ -278,7 +278,7 @@ function hasSelectedObjectInventoryFollowupSignal(text: string): boolean {
   if (!/(?:по\s+выбранному\s+объекту|по\s+выбранной\s+позиции)/iu.test(text)) {
     return false;
   }
-  return /(?:у\s+кого\s+купили|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|товар|позицию))?|где\s+куплено|кто\s+(?:поставил|продал)|кому\s+(?:продали|реализовали)|когда\s+(?:примерно\s+)?купили|по\s+каким\s+документам\s+.*купили)/iu.test(
+  return /(?:у\s+кого\s+купили|у\s+кого\s+куплено|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|товар|позицию))?|где\s+куплено|кто\s+(?:поставил|продал)|кому\s+(?:продали|реализовали)|когда\s+(?:примерно\s+)?купили|по\s+каким\s+документам\s+.*купили)/iu.test(
     text
   );
 }

llm_normalizer/backend/src/services/address_runtime/decomposeStage.ts

@@ -59,7 +59,7 @@ function hasSameDateHint(text: string): boolean {
 }
 
 function hasExplicitPeriodLiteral(text: string): boolean {
-  return /\b(?:19|20)\d{2}(?:[./-](?:0?[1-9]|1[0-2]))?\b/.test(String(text ?? ""));
+  return /(?:^|[^\d*×xх])((?:19|20)\d{2}(?:[./-](?:0?[1-9]|1[0-2]))?)(?=$|[^\d*×xх])/iu.test(String(text ?? ""));
 }
 
 function hasExplicitCurrentDateHint(text: string): boolean {
@@ -323,7 +323,7 @@ function hasSelectedObjectInventorySignal(text: string): boolean {
 }
 
 function hasInventorySupplierFollowupCue(text: string): boolean {
-  return /(?:кто\s+(?:(?:это|этот\s+товар|эту\s+позицию)\s+)?(?:нам\s+)?поставил|кто\s+(?:нам\s+)?поставил\s+(?:это|этот\s+товар|эту\s+позицию)|от\s+какого\s+поставщика|у\s+какого\s+поставщика|от\s+кого\s+куплен|у\s+кого\s+купили|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|этот\s+товар|эту\s+позицию))?|где\s+куплено|supplier|vendor|поставщик)/iu.test(
+  return /(?:кто\s+(?:(?:это|этот\s+товар|эту\s+позицию)\s+)?(?:нам\s+)?поставил|кто\s+(?:нам\s+)?поставил\s+(?:это|этот\s+товар|эту\s+позицию)|от\s+какого\s+поставщика|у\s+какого\s+поставщика|от\s+кого\s+куплен|у\s+кого\s+купили|у\s+кого\s+куплено|где\s+(?:мы\s+)?купили(?:\s+(?:это|его|этот\s+товар|эту\s+позицию))?|где\s+куплено|supplier|vendor|поставщик)/iu.test(
     String(text ?? "")
   );
 }

llm_normalizer/backend/tests/addressInventorySelectedObjectFollowup.test.ts

@@ -245,6 +245,60 @@ describe("inventory selected-object follow-up", () => {
     expect(String(result?.reply_text ?? "")).toContain("ООО \\Производство мебели\\");
   });
 
+  it("handles selected-object colloquial supplier wording 'у кого куплено' as provenance follow-up", async () => {
+    executeAddressMcpQueryMock.mockResolvedValueOnce({
+      fetched_rows: 1,
+      matched_rows: 1,
+      raw_rows: [
+        {
+          Period: "2020-06-18T00:00:00Z",
+          Registrator: "Поступление товаров и услуг 00000000101 от 18.06.2020 0:00:00",
+          AccountDt: "41.01",
+          AccountKt: "60.01",
+          Amount: 498472.5,
+          SubcontoDt1: "Конструкция трансформер рабочей станции 1300*900*2000",
+          SubcontoDt3: "Основной склад",
+          SubcontoKt1: "ООО \\Гамма-мебель\\",
+          SubcontoKt2: "Договор поставки № 11 от 15.06.2020",
+          Organization: "ООО \\Альтернатива Плюс\\"
+        }
+      ],
+      rows: [],
+      error: null
+    });
+
+    const service = new AddressQueryService();
+    const result = await service.tryHandle(
+      'По выбранному объекту "Конструкция трансформер рабочей станции 1300*900*2000": у кого куплено',
+      {
+        followupContext: {
+          previous_intent: "inventory_on_hand_as_of_date",
+          previous_filters: {
+            as_of_date: "2020-06-30",
+            period_from: "2020-06-01",
+            period_to: "2020-06-30",
+            warehouse: "Основной склад",
+            organization: "ООО \\Альтернатива Плюс\\"
+          },
+          previous_anchor_type: "unknown",
+          previous_anchor_value: null
+        }
+      }
+    );
+
+    expect(result?.handled).toBe(true);
+    expect(result?.response_type).toBe("FACTUAL_SUMMARY");
+    expect(result?.debug.detected_intent).toBe("inventory_purchase_provenance_for_item");
+    expect(result?.debug.selected_recipe).toBe("address_inventory_purchase_provenance_for_item_v1");
+    expect(result?.debug.extracted_filters?.item).toBe("Конструкция трансформер рабочей станции 1300*900*2000");
+    expect(result?.debug.extracted_filters?.as_of_date).toBe("2020-06-30");
+    expect(result?.debug.extracted_filters?.period_from).toBe("2020-06-01");
+    expect(result?.debug.extracted_filters?.period_to).toBe("2020-06-30");
+    expect(result?.debug.capability_id).toBe("inventory_inventory_purchase_provenance_for_item");
+    expect(result?.debug.capability_route_mode).toBe("exact");
+    expect(String(result?.reply_text ?? "")).toContain("ООО \\Гамма-мебель\\");
+  });
+
   it("handles selected-object wording 'где мы купили это' as provenance follow-up", async () => {
     executeAddressMcpQueryMock.mockResolvedValueOnce({
       fetched_rows: 1,

llm_normalizer/backend/tests/assistantAddressFollowupContext.test.ts

@@ -373,6 +373,112 @@ describe("assistant address follow-up carryover", () => {
     expect(normalizerService.normalize).not.toHaveBeenCalled();
   });
 
+  it("keeps historical stock date window for selected-object supplier wording 'у кого куплено'", async () => {
+    const calls: Array<{ message: string; options?: any }> = [];
+    const rootMessage = 'какие у нас остатки на складе на июнь 2020';
+    const followupMessage = 'По выбранному объекту "Конструкция трансформер рабочей станции 1300*900*2000": у кого куплено';
+
+    const addressQueryService = {
+      tryHandle: vi.fn(async (message: string, options?: any) => {
+        calls.push({ message, options });
+        if (message === rootMessage) {
+          return {
+            handled: true,
+            reply_text:
+              "На 30.06.2020 на складе подтверждено 12 позиций с остатком на 755.392,33 ₽.\n1. Конструкция трансформер рабочей станции 1300*900*2000 | склад: Основной склад",
+            reply_type: "factual",
+            response_type: "FACTUAL_LIST",
+            debug: {
+              detected_mode: "address_query",
+              detected_intent: "inventory_on_hand_as_of_date",
+              detected_intent_confidence: "high",
+              extracted_filters: {
+                as_of_date: "2020-06-30",
+                period_from: "2020-06-01",
+                period_to: "2020-06-30",
+                warehouse: "Основной склад",
+                organization: "ООО \\Альтернатива Плюс\\"
+              },
+              selected_recipe: "address_inventory_on_hand_as_of_date_v1",
+              capability_id: "confirmed_inventory_on_hand_as_of_date",
+              capability_route_mode: "exact",
+              anchor_type: "unknown",
+              anchor_value_raw: null,
+              anchor_value_resolved: null,
+              reasons: ["address_action_detected", "address_entity_detected"]
+            }
+          };
+        }
+        return {
+          handled: true,
+          reply_text:
+            "Поставщик по позиции Конструкция трансформер рабочей станции 1300*900*2000: ООО \\Гамма-мебель\\.",
+          reply_type: "factual",
+          response_type: "FACTUAL_SUMMARY",
+          debug: {
+            detected_mode: "address_query",
+            detected_intent: "inventory_purchase_provenance_for_item",
+            detected_intent_confidence: "medium",
+            extracted_filters: {
+              item: "Конструкция трансформер рабочей станции 1300*900*2000",
+              as_of_date: "2020-06-30",
+              period_from: "2020-06-01",
+              period_to: "2020-06-30"
+            },
+            selected_recipe: "address_inventory_purchase_provenance_for_item_v1",
+            capability_id: "inventory_inventory_purchase_provenance_for_item",
+            capability_route_mode: "exact",
+            reasons: ["address_action_detected", "address_entity_detected", "address_followup_context_applied"]
+          },
+          ...(options?.followupContext ? {} : {})
+        };
+      })
+    } as any;
+
+    const normalizerService = {
+      normalize: vi.fn(async () => ({
+        assistant_reply: "normalizer_fallback_should_not_be_used",
+        reply_type: "partial_coverage",
+        debug: {}
+      }))
+    } as any;
+
+    const sessions = new AssistantSessionStore();
+    const service = new AssistantService(
+      normalizerService,
+      sessions as any,
+      {} as any,
+      { persistSession: vi.fn() } as any,
+      addressQueryService
+    );
+
+    const sessionId = `asst-address-followup-u-kogo-kupleno-${Date.now()}`;
+    const first = await service.handleMessage({
+      session_id: sessionId,
+      user_message: rootMessage,
+      useMock: true
+    } as any);
+    expect(first.ok).toBe(true);
+    expect(first.reply_type).toBe("factual");
+
+    const second = await service.handleMessage({
+      session_id: sessionId,
+      user_message: followupMessage,
+      useMock: true
+    } as any);
+
+    expect(second.ok).toBe(true);
+    expect(second.reply_type).toBe("factual");
+    expect(calls).toHaveLength(2);
+    expect(calls[1].message).toBe(followupMessage);
+    expect(calls[1].options?.followupContext?.previous_intent).toBe("inventory_on_hand_as_of_date");
+    expect(calls[1].options?.followupContext?.previous_filters?.as_of_date).toBe("2020-06-30");
+    expect(calls[1].options?.followupContext?.previous_filters?.period_from).toBe("2020-06-01");
+    expect(calls[1].options?.followupContext?.previous_filters?.period_to).toBe("2020-06-30");
+    expect(calls[1].options?.followupContext?.previous_filters?.warehouse).toBe("Основной склад");
+    expect(normalizerService.normalize).not.toHaveBeenCalled();
+  });
+
   it("treats typo imperative 'показывыай' as implicit continuation and switches to suggested follow-up intent", async () => {
     const calls: Array<{ message: string; options?: any }> = [];
     const firstMessage = "покажи документы по свк за 2020";

llm_normalizer/data/shared_llm_connection.json

@@ -0,0 +1,11 @@
+{
+  "schema_version": "shared_llm_connection_v1",
+  "updated_at": "2026-04-15T06:12:46.714Z",
+  "connection": {
+    "llmProvider": "local",
+    "model": "unsloth/qwen3-30b-a3b-instruct-2507",
+    "baseUrl": "http://127.0.0.1:1234/v1",
+    "temperature": 0.8,
+    "maxOutputTokens": 900
+  }
+}

llm_normalizer/frontend/dist/index.html

@@ -4,7 +4,7 @@
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>NDC AI Normalizer Playground</title>
-    <script type="module" crossorigin src="/assets/index-B9mz4jx4.js"></script>
+    <script type="module" crossorigin src="/assets/index-Qq5WpuqR.js"></script>
     <link rel="stylesheet" crossorigin href="/assets/index-Dcuz1nX5.css">
   </head>
   <body>

llm_normalizer/frontend/src/App.tsx

@@ -153,6 +153,7 @@ export default function App() {
   });
   const presetAutoloadDoneRef = useRef(false);
   const skipPresetAutoloadRef = useRef(false);
+  const sharedConnectionSyncReadyRef = useRef(false);
 
   useEffect(() => {
     const root = document.documentElement;
@@ -192,22 +193,45 @@ export default function App() {
   }
 
   useEffect(() => {
-    const cached = localStorage.getItem(SESSION_CONFIG_KEY);
+    const bootstrapSharedConnection = async () => {
-    if (cached) {
+      const cached = localStorage.getItem(SESSION_CONFIG_KEY);
-      try {
+      if (cached) {
-        const parsed = JSON.parse(cached) as Partial<ConnectionState>;
+        try {
-        setConnection((prev) => ({
+          const parsed = JSON.parse(cached) as Partial<ConnectionState>;
-          ...prev,
+          setConnection((prev) => ({
-          llmProvider: parsed.llmProvider === "local" ? "local" : "openai",
+            ...prev,
-          model: parsed.model ?? prev.model,
+            llmProvider: parsed.llmProvider === "local" ? "local" : "openai",
-          baseUrl: parsed.baseUrl ?? prev.baseUrl,
+            model: parsed.model ?? prev.model,
-          temperature: parsed.temperature ?? prev.temperature,
+            baseUrl: parsed.baseUrl ?? prev.baseUrl,
-          maxOutputTokens: parsed.maxOutputTokens ?? prev.maxOutputTokens
+            temperature: parsed.temperature ?? prev.temperature,
-        }));
+            maxOutputTokens: parsed.maxOutputTokens ?? prev.maxOutputTokens
-      } catch {
+          }));
-        // ignore broken local cache
+        } catch {
+          // ignore broken local cache
+        }
       }
-    }
+
+      try {
+        const payload = await apiClient.loadSharedConnectionConfig();
+        if (payload.connection && payload.connection.llmProvider === "local") {
+          setConnection((prev) => ({
+            ...prev,
+            llmProvider: "local",
+            model: payload.connection?.model ?? prev.model,
+            baseUrl: payload.connection?.baseUrl ?? prev.baseUrl,
+            temperature: payload.connection?.temperature ?? prev.temperature,
+            maxOutputTokens: payload.connection?.maxOutputTokens ?? prev.maxOutputTokens
+          }));
+          log(`Shared local LLM config loaded: ${payload.connection.model}`);
+        }
+      } catch (error) {
+        log(`Shared local config load error: ${error instanceof Error ? error.message : String(error)}`);
+      } finally {
+        sharedConnectionSyncReadyRef.current = true;
+      }
+    };
+
+    void bootstrapSharedConnection();
 
     const cachedAutorunsLayout = localStorage.getItem(AUTORUNS_LAYOUT_CONFIG_KEY);
     if (cachedAutorunsLayout) {
@@ -304,6 +328,23 @@ export default function App() {
     void refreshRuns();
   }, []);
 
+  useEffect(() => {
+    if (!sharedConnectionSyncReadyRef.current) {
+      return;
+    }
+    if (connection.llmProvider !== "local") {
+      return;
+    }
+    const timer = window.setTimeout(() => {
+      void apiClient
+        .saveSharedConnectionConfig(connection)
+        .catch((error) =>
+          log(`Shared local config sync error: ${error instanceof Error ? error.message : String(error)}`)
+        );
+    }, 250);
+    return () => window.clearTimeout(timer);
+  }, [connection.baseUrl, connection.llmProvider, connection.maxOutputTokens, connection.model, connection.temperature]);
+
   async function refreshHistory() {
     try {
       const payload = await apiClient.loadHistory();
@@ -369,6 +410,17 @@ export default function App() {
         maxOutputTokens: connection.maxOutputTokens
       })
     );
+    if (connection.llmProvider === "local") {
+      void apiClient
+        .saveSharedConnectionConfig(connection)
+        .then(() => {
+          log("Local config saved and synced to shared agent config (without API key).");
+        })
+        .catch((error) => {
+          log(`Local config saved, but shared sync failed: ${error instanceof Error ? error.message : String(error)}`);
+        });
+      return;
+    }
     log("Local config saved (without API key).");
   }
 

llm_normalizer/frontend/src/api/client.ts

@@ -41,6 +41,32 @@ async function request<T>(path: string, init?: RequestInit): Promise<T> {
 }
 
 export const apiClient = {
+  async loadSharedConnectionConfig(): Promise<{
+    ok: boolean;
+    connection: Omit<ConnectionState, "apiKey"> | null;
+    updated_at: string | null;
+    exists: boolean;
+  }> {
+    return request("/llm/shared-connection");
+  },
+
+  async saveSharedConnectionConfig(connection: ConnectionState): Promise<{
+    ok: boolean;
+    connection: Omit<ConnectionState, "apiKey">;
+    updated_at: string;
+  }> {
+    return request("/llm/shared-connection", {
+      method: "POST",
+      body: JSON.stringify({
+        llmProvider: connection.llmProvider,
+        model: connection.model,
+        baseUrl: connection.baseUrl,
+        temperature: connection.temperature,
+        maxOutputTokens: connection.maxOutputTokens
+      })
+    });
+  },
+
   async listModels(connection: ConnectionState): Promise<{ ok: boolean; models: string[]; count: number; timestamp: string }> {
     return request("/llm/models", {
       method: "POST",

llm_normalizer/frontend/src/components/ConnectionPanel.tsx

@@ -2,6 +2,57 @@ import { useEffect, useState } from "react";
 import { PanelFrame } from "./PanelFrame";
 import type { ConnectionState } from "../state/types";
 
+const LOCAL_BASE_URL = "http://127.0.0.1:1234/v1";
+const OPENAI_BASE_URL = "https://api.openai.com/v1";
+const LOCAL_QWEN25_MODEL = "qwen2.5-14b-instruct-1m";
+const LOCAL_QWEN3_MODEL = "unsloth/qwen3-30b-a3b-instruct-2507";
+
+type ProviderPreset = "openai" | "local_qwen25" | "local_qwen3" | "local_custom";
+
+interface ModelOptionDescriptor {
+  value: string;
+  label: string;
+}
+
+const BUILTIN_LOCAL_MODEL_OPTIONS: ModelOptionDescriptor[] = [
+  {
+    value: LOCAL_QWEN25_MODEL,
+    label: "Qwen2.5 14B Instruct 1M"
+  },
+  {
+    value: LOCAL_QWEN3_MODEL,
+    label: "Qwen3 30B A3B Instruct 2507"
+  }
+];
+
+function deriveProviderPreset(value: ConnectionState): ProviderPreset {
+  if (value.llmProvider !== "local") {
+    return "openai";
+  }
+  if (value.model === LOCAL_QWEN3_MODEL) {
+    return "local_qwen3";
+  }
+  if (value.model === LOCAL_QWEN25_MODEL) {
+    return "local_qwen25";
+  }
+  return "local_custom";
+}
+
+function buildModelOptions(modelOptions: string[], isLocal: boolean): ModelOptionDescriptor[] {
+  const merged = new Map<string, ModelOptionDescriptor>();
+  if (isLocal) {
+    for (const option of BUILTIN_LOCAL_MODEL_OPTIONS) {
+      merged.set(option.value, option);
+    }
+  }
+  for (const modelId of modelOptions) {
+    if (!merged.has(modelId)) {
+      merged.set(modelId, { value: modelId, label: modelId });
+    }
+  }
+  return Array.from(merged.values());
+}
+
 interface ConnectionPanelProps {
   value: ConnectionState;
   modelOptions: string[];
@@ -26,7 +77,9 @@ export function ConnectionPanel({
   busy
 }: ConnectionPanelProps) {
   const isLocal = value.llmProvider === "local";
-  const modelInCatalog = modelOptions.includes(value.model);
+  const providerPreset = deriveProviderPreset(value);
+  const availableModelOptions = buildModelOptions(modelOptions, isLocal);
+  const modelInCatalog = availableModelOptions.some((option) => option.value === value.model);
   const [temperatureInput, setTemperatureInput] = useState(String(value.temperature));
   const [maxOutputTokensInput, setMaxOutputTokensInput] = useState(String(value.maxOutputTokens));
 
@@ -78,18 +131,47 @@ export function ConnectionPanel({
         <label>
           Provider
           <select
-            value={value.llmProvider}
+            value={providerPreset}
             onChange={(event) => {
-              const nextProvider = event.target.value === "local" ? "local" : "openai";
+              const selected = event.target.value as ProviderPreset;
+              if (selected === "openai") {
+                onChange({
+                  ...value,
+                  llmProvider: "openai",
+                  baseUrl: OPENAI_BASE_URL
+                });
+                return;
+              }
+              if (selected === "local_qwen25") {
+                onChange({
+                  ...value,
+                  llmProvider: "local",
+                  model: LOCAL_QWEN25_MODEL,
+                  baseUrl: LOCAL_BASE_URL
+                });
+                return;
+              }
+              if (selected === "local_qwen3") {
+                onChange({
+                  ...value,
+                  llmProvider: "local",
+                  model: LOCAL_QWEN3_MODEL,
+                  baseUrl: LOCAL_BASE_URL
+                });
+                return;
+              }
               onChange({
                 ...value,
-                llmProvider: nextProvider,
+                llmProvider: "local",
-                baseUrl: nextProvider === "local" ? "http://127.0.0.1:1234/v1" : "https://api.openai.com/v1"
+                model: value.llmProvider === "local" ? value.model : LOCAL_QWEN25_MODEL,
+                baseUrl: LOCAL_BASE_URL
               });
             }}
           >
             <option value="openai">OpenAI (token)</option>
-            <option value="local">Local (LM Studio / OpenAI-compatible)</option>
+            <option value="local_qwen25">Qwen2.5 14B Instruct 1M (Local LM Studio)</option>
+            <option value="local_qwen3">Qwen3 30B A3B Instruct 2507 (Local LM Studio)</option>
+            <option value="local_custom">Local custom (LM Studio / OpenAI-compatible)</option>
           </select>
         </label>
 
@@ -106,20 +188,20 @@ export function ConnectionPanel({
             }}
           >
             <option value="__manual__">Manual input</option>
-            {modelOptions.map((modelId) => (
+            {availableModelOptions.map((option) => (
-              <option key={modelId} value={modelId}>
+              <option key={option.value} value={option.value}>
-                {modelId}
+                {option.label}
               </option>
             ))}
           </select>
         </label>
 
         <label>
-          Model ID (manual)
+          Model ID (manual / current)
           <input
             value={value.model}
             onChange={(event) => onChange({ ...value, model: event.target.value })}
-            placeholder="qwen2.5-14b-instruct or lmstudio loaded model id"
+            placeholder="qwen2.5-14b-instruct-1m or unsloth/qwen3-30b-a3b-instruct-2507"
           />
         </label>
 
@@ -140,7 +222,7 @@ export function ConnectionPanel({
           <input
             value={value.baseUrl}
             onChange={(event) => onChange({ ...value, baseUrl: event.target.value })}
-            placeholder={isLocal ? "http://127.0.0.1:1234/v1" : "https://api.openai.com/v1"}
+            placeholder={isLocal ? LOCAL_BASE_URL : OPENAI_BASE_URL}
           />
         </label>
 

scripts/domain_case_loop.py

@@ -19,14 +19,15 @@ DEFAULT_ARTIFACTS_ROOT = REPO_ROOT / "artifacts" / "domain_runs"
 DEFAULT_SESSIONS_DIR = REPO_ROOT / "llm_normalizer" / "data" / "assistant_sessions"
 DEFAULT_REPORTS_DIR = REPO_ROOT / "llm_normalizer" / "reports"
 DEFAULT_LOOP_SCHEMA_DIR = REPO_ROOT / "docs" / "orchestration" / "schemas"
+SHARED_LLM_CONNECTION_CONFIG = REPO_ROOT / "llm_normalizer" / "data" / "shared_llm_connection.json"
 DEFAULT_BACKEND_URL = "http://127.0.0.1:8787"
 DEFAULT_PROMPT_VERSION = "address_query_runtime_v1"
-DEFAULT_LLM_PROVIDER = "local"
+BASE_DEFAULT_LLM_PROVIDER = "local"
-DEFAULT_LLM_MODEL = "qwen2.5-14b-instruct-1m"
+BASE_DEFAULT_LLM_MODEL = "qwen2.5-14b-instruct-1m"
-DEFAULT_LLM_BASE_URL = "http://127.0.0.1:1234/v1"
+BASE_DEFAULT_LLM_BASE_URL = "http://127.0.0.1:1234/v1"
 DEFAULT_LLM_API_KEY = ""
-DEFAULT_TEMPERATURE = 0.0
+BASE_DEFAULT_TEMPERATURE = 0.0
-DEFAULT_MAX_OUTPUT_TOKENS = 900
+BASE_DEFAULT_MAX_OUTPUT_TOKENS = 900
 TECH_SECTION_HEADER = "### technical_debug_payload_json"
 SCENARIO_MANIFEST_SCHEMA_VERSION = "domain_scenario_manifest_v1"
 SCENARIO_STATE_SCHEMA_VERSION = "domain_scenario_state_v1"
@@ -35,6 +36,51 @@ SCENARIO_PACK_SCHEMA_VERSION = "domain_scenario_pack_v1"
 ACTIVE_DOMAIN_CONTRACT_SCHEMA_VERSION = "active_domain_contract_v1"
 AUTONOMOUS_LOOP_SCHEMA_VERSION = "domain_autonomous_loop_v1"
 
+
+def load_shared_local_llm_defaults(config_path: Path | None = None) -> dict[str, Any]:
+    defaults: dict[str, Any] = {
+        "llm_provider": BASE_DEFAULT_LLM_PROVIDER,
+        "llm_model": BASE_DEFAULT_LLM_MODEL,
+        "llm_base_url": BASE_DEFAULT_LLM_BASE_URL,
+        "temperature": BASE_DEFAULT_TEMPERATURE,
+        "max_output_tokens": BASE_DEFAULT_MAX_OUTPUT_TOKENS,
+    }
+    target = config_path or SHARED_LLM_CONNECTION_CONFIG
+    if not target.exists():
+        return defaults
+    try:
+        raw = json.loads(target.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError):
+        return defaults
+    connection = raw.get("connection")
+    if not isinstance(connection, dict):
+        return defaults
+    if str(connection.get("llmProvider") or "").strip().lower() != "local":
+        return defaults
+
+    model = str(connection.get("model") or "").strip()
+    base_url = str(connection.get("baseUrl") or "").strip()
+    temperature = connection.get("temperature")
+    max_output_tokens = connection.get("maxOutputTokens")
+
+    if model:
+        defaults["llm_model"] = model
+    if base_url:
+        defaults["llm_base_url"] = base_url
+    if isinstance(temperature, (int, float)) and not isinstance(temperature, bool):
+        defaults["temperature"] = float(temperature)
+    if isinstance(max_output_tokens, (int, float)) and not isinstance(max_output_tokens, bool):
+        defaults["max_output_tokens"] = max(1, int(max_output_tokens))
+    return defaults
+
+
+SHARED_LLM_DEFAULTS = load_shared_local_llm_defaults()
+DEFAULT_LLM_PROVIDER = str(SHARED_LLM_DEFAULTS["llm_provider"])
+DEFAULT_LLM_MODEL = str(SHARED_LLM_DEFAULTS["llm_model"])
+DEFAULT_LLM_BASE_URL = str(SHARED_LLM_DEFAULTS["llm_base_url"])
+DEFAULT_TEMPERATURE = float(SHARED_LLM_DEFAULTS["temperature"])
+DEFAULT_MAX_OUTPUT_TOKENS = int(SHARED_LLM_DEFAULTS["max_output_tokens"])
+
 TOP_LEVEL_NOISE_PATTERNS = (
     re.compile(r"^(?:status|статус(?: результата)?)\b", re.IGNORECASE),
     re.compile(r"^(?:что учтено|сводка)\b", re.IGNORECASE),

tests/test_domain_case_loop.py

@@ -17,6 +17,7 @@ from scripts.domain_case_loop import (
     evaluate_analyst_gate,
     evaluate_deterministic_loop_gate,
     load_scenario_pack,
+    load_shared_local_llm_defaults,
     merge_scenario_date_scope,
     select_primary_repair_focus,
     restore_line_collapsed_files_from_snapshot,
@@ -66,6 +67,65 @@ def test_merge_scenario_date_scope_preserves_historical_anchor_on_followup() ->
     assert merged["source"] == "current_analysis"
 
 
+def test_load_shared_local_llm_defaults_uses_ui_selected_local_model(tmp_path) -> None:
+    config_path = tmp_path / "shared_llm_connection.json"
+    config_path.write_text(
+        json.dumps(
+            {
+                "schema_version": "shared_llm_connection_v1",
+                "updated_at": "2026-04-15T06:00:00Z",
+                "connection": {
+                    "llmProvider": "local",
+                    "model": "unsloth/qwen3-30b-a3b-instruct-2507",
+                    "baseUrl": "http://127.0.0.1:1234/v1",
+                    "temperature": 0.2,
+                    "maxOutputTokens": 1200,
+                },
+            },
+            ensure_ascii=False,
+            indent=2,
+        )
+        + "\n",
+        encoding="utf-8",
+    )
+
+    defaults = load_shared_local_llm_defaults(config_path)
+
+    assert defaults["llm_provider"] == "local"
+    assert defaults["llm_model"] == "unsloth/qwen3-30b-a3b-instruct-2507"
+    assert defaults["llm_base_url"] == "http://127.0.0.1:1234/v1"
+    assert defaults["temperature"] == 0.2
+    assert defaults["max_output_tokens"] == 1200
+
+
+def test_load_shared_local_llm_defaults_ignores_non_local_provider(tmp_path) -> None:
+    config_path = tmp_path / "shared_llm_connection.json"
+    config_path.write_text(
+        json.dumps(
+            {
+                "schema_version": "shared_llm_connection_v1",
+                "updated_at": "2026-04-15T06:00:00Z",
+                "connection": {
+                    "llmProvider": "openai",
+                    "model": "gpt-4o-mini",
+                    "baseUrl": "https://api.openai.com/v1",
+                    "temperature": 0,
+                    "maxOutputTokens": 700,
+                },
+            },
+            ensure_ascii=False,
+            indent=2,
+        )
+        + "\n",
+        encoding="utf-8",
+    )
+
+    defaults = load_shared_local_llm_defaults(config_path)
+
+    assert defaults["llm_provider"] == "local"
+    assert defaults["llm_model"] == "qwen2.5-14b-instruct-1m"
+
+
 def test_load_scenario_pack_accepts_active_domain_contract(tmp_path) -> None:
     manifest_path = tmp_path / "active_domain_contract.json"
     manifest_path.write_text(