Add unified request pipeline stages

2026-05-18 15:45:39 +03:00
parent 8cff086a8e
commit 8aede4b053
18 changed files with 905 additions and 509 deletions
@@ -46,27 +46,27 @@
 - [x] Для Mistral сохранять `libraryId`.
 - [x] Для Mistral сохранять uploaded document ids.
 - [x] Для Mistral сохранять source file mapping: local attachment -> Mistral document id.
- [ ] Добавить единый `providerState` schema для всех providers.
- [ ] Добавить tests на сериализацию `RagArtifact`.
- [ ] Добавить tests на то, что internal RAG artifacts не попадают обратно в user document context.
+- [x] Добавить единый `providerState` schema для всех providers.
+- [x] Добавить tests на сериализацию `RagArtifact`.
+- [x] Добавить tests на то, что internal RAG artifacts не попадают обратно в user document context.

 ## 4. Вынести provider runners в adapter layer

- [ ] Ввести интерфейс `AiProviderAdapter`.
- [ ] Методы adapter-а: `mapMessages`, `rankTools`, `callModel`, `extractTextDelta`, `extractToolCalls`, `appendToolResults`, `finalize`.
- [ ] Реализовать `OpenAiProviderAdapter`.
- [ ] Реализовать `MistralProviderAdapter`.
- [ ] Реализовать `OllamaProviderAdapter`.
- [ ] Перенести provider-specific tool schema mapping внутрь adapter-ов.
- [ ] Перенести provider-specific streaming parsing внутрь adapter-ов.
- [ ] Перенести provider-specific tool result append внутрь adapter-ов.
- [ ] Упростить `runOpenAi`, `runMistral`, `runOllama` или заменить их adapter-driven runner-ом.
- [ ] Оставить compatibility wrappers для текущих imports.
- [ ] Добавить tests на adapter contract без реальных API.
+- [x] Ввести интерфейс `AiProviderAdapter`.
+- [x] Методы adapter-а: `mapMessages`, `rankTools`, `callModel`, `extractTextDelta`, `extractToolCalls`, `appendToolResults`, `finalize`.
+- [x] Реализовать `OpenAiProviderAdapter`.
+- [x] Реализовать `MistralProviderAdapter`.
+- [x] Реализовать `OllamaProviderAdapter`.
+- [x] Перенести provider-specific tool schema mapping внутрь adapter-ов.
+- [x] Перенести provider-specific streaming parsing внутрь adapter-ов.
+- [x] Перенести provider-specific tool result append внутрь adapter-ов.
+- [x] Упростить `runOpenAi`, `runMistral`, `runOllama` или заменить их adapter-driven runner-ом.
+- [x] Оставить compatibility wrappers для текущих imports.
+- [x] Добавить tests на adapter contract без реальных API.

 ## 5. Сделать tool-ranker полноценным pipeline stage

- [ ] Вынести вызов `ToolRanker.selectTools(...)` из provider runners.
+- [x] Вынести вызов `ToolRanker.selectTools(...)` из provider runners.
 - [ ] Добавить stage `tool_rank`, который работает через provider adapter.
 - [ ] Добавить stage `filter_tools`, который фильтрует provider-specific tools по результату ranker.
 - [ ] Хранить `ToolRankDecision` в `UserRequestPipelineState.toolRankDecisions`.
@@ -0,0 +1,112 @@
+import type {ToolCallData} from "./unified-ai-runner.shared.js";
+import type {ResponseStreamEvent} from "openai/resources/responses/responses";
+
+function isRecord(value: unknown): value is Record<string, unknown> {
+    return !!value && typeof value === "object" && !Array.isArray(value);
+}
+
+function normalizeToolCallId(value: unknown, fallback: string): string {
+    return typeof value === "string" && value.trim().length > 0 ? value : fallback;
+}
+
+function normalizeToolArguments(value: unknown): string {
+    if (typeof value === "string") return value;
+    return JSON.stringify(value ?? {});
+}
+
+export function extractOpenAiToolCalls(response: unknown): ToolCallData[] {
+    const output = isRecord(response) && Array.isArray(response.output) ? response.output : [];
+
+    return output
+        .filter(item => isRecord(item) && item.type === "function_call" && (typeof item.call_id === "string" || typeof item.name === "string"))
+        .map((item, index) => ({
+            id: normalizeToolCallId(item.call_id, `openai_${index}`),
+            name: typeof item.name === "string" ? item.name : "",
+            argumentsText: normalizeToolArguments(item.arguments),
+        }))
+        .filter(call => call.name.length > 0);
+}
+
+export function extractOpenAiTextDelta(input: unknown): string {
+    const event = input as ResponseStreamEvent | undefined;
+    return event?.type === "response.output_text.delta" ? event.delta ?? "" : "";
+}
+
+export function extractOpenAiStreamingToolCalls(input: unknown): ToolCallData[] {
+    const event = input as ResponseStreamEvent | undefined;
+    if (event?.type === "response.output_item.added" && isRecord(event.item) && event.item.type === "function_call") {
+        return extractOpenAiToolCalls({
+            output: [{
+                type: "function_call",
+                call_id: event.item.call_id ?? event.item.id,
+                name: event.item.name,
+                arguments: event.item.arguments,
+            }],
+        });
+    }
+
+    return [];
+}
+
+export function extractMistralToolCalls(calls: unknown): ToolCallData[] {
+    const normalized = Array.isArray(calls)
+        ? calls
+        : isRecord(calls) && (Array.isArray(calls.toolCalls) || Array.isArray(calls.tool_calls))
+            ? (calls.toolCalls ?? calls.tool_calls)
+            : [];
+
+    if (!Array.isArray(normalized)) return [];
+
+    return normalized
+        .map((item, index) => {
+            const call = isRecord(item) ? item : {};
+            const fn = isRecord(call.function) ? call.function : undefined;
+            const name = typeof fn?.name === "string" ? fn.name : typeof call.name === "string" ? call.name : "";
+            return {
+                id: normalizeToolCallId(call.id, `mistral_${index}`),
+                name,
+                argumentsText: normalizeToolArguments(fn?.arguments ?? call.arguments),
+            };
+        })
+        .filter(call => call.name.length > 0);
+}
+
+export function extractMistralTextDelta(input: unknown): string {
+    const delta = isRecord(input) ? input : {};
+    const content = delta.content;
+    if (typeof content === "string") return content;
+    if (Array.isArray(content)) {
+        return content
+            .map(part => isRecord(part) && typeof part.text === "string" ? part.text : "")
+            .join("");
+    }
+    return "";
+}
+
+export function extractOllamaToolCalls(calls: unknown): ToolCallData[] {
+    const normalized = Array.isArray(calls)
+        ? calls
+        : isRecord(calls) && Array.isArray(calls.tool_calls)
+            ? calls.tool_calls
+            : [];
+
+    if (!Array.isArray(normalized)) return [];
+
+    return normalized
+        .map((item, index) => {
+            const call = isRecord(item) ? item : {};
+            const fn = isRecord(call.function) ? call.function : undefined;
+            const name = typeof fn?.name === "string" ? fn.name : typeof call.name === "string" ? call.name : "";
+            return {
+                id: normalizeToolCallId(call.id, `ollama_${index}`),
+                name,
+                argumentsText: normalizeToolArguments(fn?.arguments ?? call.arguments),
+            };
+        })
+        .filter(call => call.name.length > 0);
+}
+
+export function extractOllamaTextDelta(input: unknown): string {
+    const chunk = isRecord(input) ? input.message : undefined;
+    return isRecord(chunk) && typeof chunk.content === "string" ? chunk.content : "";
+}
@@ -0,0 +1,196 @@
+import {AiProvider} from "../model/ai-provider.js";
+import type {BoundaryValue} from "../common/boundary-types.js";
+import type {RuntimeConfigSnapshot, ToolCallData} from "./unified-ai-runner.shared.js";
+import {getMistralTools, getOllamaTools, getOpenAIResponsesTools, getOpenAICodeInterpreterTool} from "./tool-mappers.js";
+import type {MistralChatMessage as MistralMessageType} from "./mistral-chat-message.js";
+import type {OpenAIChatMessage as OpenAiMessageType} from "./openai-chat-message.js";
+import type {Message as OllamaMessage} from "ollama";
+import {
+    extractMistralTextDelta,
+    extractMistralToolCalls,
+    extractOllamaTextDelta,
+    extractOllamaToolCalls,
+    extractOpenAiTextDelta,
+    extractOpenAiStreamingToolCalls,
+    extractOpenAiToolCalls,
+} from "./provider-adapter-contract.js";
+
+export type ProviderRankToolOptions = {
+    forCreator?: boolean;
+    vectorStoreIds?: string[];
+};
+
+export interface AiProviderAdapter {
+    readonly provider: AiProvider;
+    mapMessages(messages: readonly unknown[]): unknown[];
+    rankTools(config: RuntimeConfigSnapshot, options?: ProviderRankToolOptions): readonly BoundaryValue[];
+    callModel<T>(request: unknown, execute: () => Promise<T>): Promise<T>;
+    extractTextDelta(input: unknown): string;
+    extractToolCalls(input: unknown): ToolCallData[];
+    extractStreamingToolCalls(input: unknown): ToolCallData[];
+    appendToolResults(messages: unknown[], calls: ToolCallData[], results: string[]): void;
+    finalize(): Promise<void>;
+}
+
+function appendOllamaToolResults(messages: unknown[], calls: ToolCallData[], results: string[]): void {
+    for (const [index, call] of calls.entries()) {
+        messages.push({
+            role: "tool",
+            content: results[index] ?? "",
+            tool_name: call.name,
+        });
+    }
+}
+
+class OpenAiProviderAdapter implements AiProviderAdapter {
+    readonly provider = AiProvider.OPENAI;
+
+    mapMessages(messages: readonly unknown[]): unknown[] {
+        return messages as OpenAiMessageType[];
+    }
+
+    rankTools(config: RuntimeConfigSnapshot, options?: ProviderRankToolOptions): readonly BoundaryValue[] {
+        const tools: BoundaryValue[] = [
+            ...getOpenAIResponsesTools(options?.forCreator) as BoundaryValue[],
+            getOpenAICodeInterpreterTool() as BoundaryValue,
+            {
+                type: "image_generation",
+                model: config.openAiImageTarget.model,
+                size: "auto",
+                moderation: "low",
+                output_format: "png",
+                partial_images: 3,
+            },
+            {type: "web_search"},
+        ];
+
+        if (options?.vectorStoreIds?.length) {
+            tools.unshift({
+                type: "file_search",
+                vector_store_ids: options.vectorStoreIds,
+            });
+        }
+
+        return tools;
+    }
+
+    async callModel<T>(_request: unknown, execute: () => Promise<T>): Promise<T> {
+        return execute();
+    }
+
+    extractTextDelta(input: unknown): string {
+        return extractOpenAiTextDelta(input);
+    }
+
+    extractToolCalls(input: unknown): ToolCallData[] {
+        return extractOpenAiToolCalls(input);
+    }
+
+    extractStreamingToolCalls(input: unknown): ToolCallData[] {
+        return extractOpenAiStreamingToolCalls(input);
+    }
+
+    appendToolResults(messages: unknown[], calls: ToolCallData[], results: string[]): void {
+        for (const [index, call] of calls.entries()) {
+            messages.push({
+                type: "function_call_output",
+                call_id: call.id,
+                output: results[index] ?? "",
+            });
+        }
+    }
+
+    async finalize(): Promise<void> {
+        return;
+    }
+}
+
+class MistralProviderAdapter implements AiProviderAdapter {
+    readonly provider = AiProvider.MISTRAL;
+
+    mapMessages(messages: readonly unknown[]): unknown[] {
+        return messages as MistralMessageType[];
+    }
+
+    rankTools(_config: RuntimeConfigSnapshot, options?: ProviderRankToolOptions): readonly BoundaryValue[] {
+        return getMistralTools(options?.forCreator) as BoundaryValue[];
+    }
+
+    async callModel<T>(_request: unknown, execute: () => Promise<T>): Promise<T> {
+        return execute();
+    }
+
+    extractTextDelta(input: unknown): string {
+        return extractMistralTextDelta(input);
+    }
+
+    extractToolCalls(input: unknown): ToolCallData[] {
+        return extractMistralToolCalls(input);
+    }
+
+    extractStreamingToolCalls(input: unknown): ToolCallData[] {
+        return this.extractToolCalls(input);
+    }
+
+    appendToolResults(messages: unknown[], calls: ToolCallData[], results: string[]): void {
+        for (const [index, call] of calls.entries()) {
+            messages.push({
+                role: "tool",
+                name: call.name,
+                toolCallId: call.id,
+                content: results[index] ?? "",
+            });
+        }
+    }
+
+    async finalize(): Promise<void> {
+        return;
+    }
+}
+
+class OllamaProviderAdapter implements AiProviderAdapter {
+    readonly provider = AiProvider.OLLAMA;
+
+    mapMessages(messages: readonly unknown[]): unknown[] {
+        return messages as OllamaMessage[];
+    }
+
+    rankTools(_config: RuntimeConfigSnapshot, options?: ProviderRankToolOptions): readonly BoundaryValue[] {
+        return getOllamaTools(options?.forCreator) as BoundaryValue[];
+    }
+
+    async callModel<T>(_request: unknown, execute: () => Promise<T>): Promise<T> {
+        return execute();
+    }
+
+    extractTextDelta(input: unknown): string {
+        return extractOllamaTextDelta(input);
+    }
+
+    extractToolCalls(input: unknown): ToolCallData[] {
+        return extractOllamaToolCalls(input);
+    }
+
+    extractStreamingToolCalls(input: unknown): ToolCallData[] {
+        return this.extractToolCalls(input);
+    }
+
+    appendToolResults(messages: unknown[], calls: ToolCallData[], results: string[]): void {
+        appendOllamaToolResults(messages, calls, results);
+    }
+
+    async finalize(): Promise<void> {
+        return;
+    }
+}
+
+export function getProviderAdapter(provider: AiProvider): AiProviderAdapter {
+    switch (provider) {
+        case AiProvider.OPENAI:
+            return new OpenAiProviderAdapter();
+        case AiProvider.MISTRAL:
+            return new MistralProviderAdapter();
+        case AiProvider.OLLAMA:
+            return new OllamaProviderAdapter();
+    }
+}
@@ -0,0 +1,77 @@
+import type {AiProvider} from "../model/ai-provider";
+
+export type RagArtifactSource = {
+    fileId: string;
+    fileName: string;
+    mimeType?: string;
+    sizeBytes?: number;
+    sha256?: string;
+    uploadedFileId?: string;
+    documentId?: string;
+};
+
+export type RagArtifactPayload = {
+    artifactKind: "rag";
+    provider: AiProvider;
+    createdAt: string;
+    sources: RagArtifactSource[];
+    providerState:
+        | {
+            provider: AiProvider.OPENAI;
+            vectorStoreIds: string[];
+            uploadedFileIds: string[];
+        }
+        | {
+            provider: AiProvider.MISTRAL;
+            libraryId?: string;
+            documentCount: number;
+        }
+        | {
+            provider: AiProvider.OLLAMA;
+            prepared: boolean;
+            embeddingModel?: string;
+            topK?: number;
+            chunkSize?: number;
+            chunkOverlap?: number;
+            maxContextChars?: number;
+            extractedDocuments: Array<{
+                documentIndex: number;
+                fileName: string;
+                textChars: number;
+            }>;
+            selectedChunks: Array<{
+                sourceId: string;
+                documentIndex: number;
+                documentName: string;
+                chunkIndex: number;
+                chunkCount: number;
+                textChars: number;
+                score?: number;
+            }>;
+            skippedDocuments: Array<{
+                documentIndex: number;
+                fileName: string;
+                reason: string;
+            }>;
+            query: string;
+            minScore: number;
+            maxArchiveFiles: number;
+            maxArchiveBytes: number;
+            maxArchiveDepth: number;
+        };
+};
+
+export function buildRagArtifactPayload(params: {
+    provider: AiProvider;
+    createdAt?: string;
+    sources: RagArtifactSource[];
+    providerState: RagArtifactPayload["providerState"];
+}): RagArtifactPayload {
+    return {
+        artifactKind: "rag",
+        provider: params.provider,
+        createdAt: params.createdAt ?? new Date().toISOString(),
+        sources: params.sources,
+        providerState: params.providerState,
+    };
+}
@@ -4,75 +4,39 @@ import type {AiDownloadedFile} from "./telegram-attachments";
 import type {PreparedDocumentRag} from "./document-rag-pipeline";
 import type {OllamaRagArtifactDetails} from "./ollama-rag";
 import {persistInternalJsonArtifactAttachment} from "./internal-artifact-store";
-
-type RagArtifactPayload = {
-    artifactKind: "rag";
-    provider: AiProvider;
-    createdAt: string;
-    sources: Array<{
-        fileId: string;
-        fileName: string;
-        mimeType?: string;
-        sizeBytes?: number;
-        sha256?: string;
-        uploadedFileId?: string;
-        documentId?: string;
-    }>;
-    providerState: {
-        vectorStoreIds?: string[];
-        libraryId?: string;
-        documentCount?: number;
-        prepared?: boolean;
-        uploadedFileIds?: string[];
-        embeddingModel?: string;
-        topK?: number;
-        chunkSize?: number;
-        chunkOverlap?: number;
-        maxContextChars?: number;
-        extractedDocuments?: Array<{
-            documentIndex: number;
-            fileName: string;
-            textChars: number;
-        }>;
-        selectedChunks?: Array<{
-            sourceId: string;
-            documentIndex: number;
-            documentName: string;
-            chunkIndex: number;
-            chunkCount: number;
-            textChars: number;
-            score?: number;
-        }>;
-        skippedDocuments?: Array<{
-            documentIndex: number;
-            fileName: string;
-            reason: string;
-        }>;
-        query?: string;
-        ollama?: OllamaRagArtifactDetails["providerState"];
-    };
-};
+import {buildRagArtifactPayload, type RagArtifactPayload} from "./rag-artifact-payload";

 function providerState(prepared: PreparedDocumentRag, details?: NonNullable<Parameters<typeof persistRagArtifactAttachment>[0]["details"]>): RagArtifactPayload["providerState"] {
    switch (prepared.provider) {
        case AiProvider.OPENAI:
            return {
+                provider: AiProvider.OPENAI,
                vectorStoreIds: prepared.vectorStoreIds,
                uploadedFileIds: prepared.uploadedFileIds,
            };
        case AiProvider.MISTRAL:
            return {
+                provider: AiProvider.MISTRAL,
                libraryId: prepared.libraryId,
                documentCount: prepared.documents.length,
            };
        case AiProvider.OLLAMA:
            return {
+                provider: AiProvider.OLLAMA,
                prepared: prepared.prepared,
                embeddingModel: details?.embeddingModel,
                topK: details?.topK,
                chunkSize: details?.chunkSize,
                chunkOverlap: details?.chunkOverlap,
                maxContextChars: details?.maxContextChars,
+                extractedDocuments: details?.artifact?.extractedDocuments ?? [],
+                selectedChunks: details?.artifact?.selectedChunks ?? [],
+                skippedDocuments: details?.artifact?.skippedDocuments ?? [],
+                query: details?.artifact?.query ?? "",
+                minScore: details?.artifact?.providerState?.minScore ?? 0,
+                maxArchiveFiles: details?.artifact?.providerState?.maxArchiveFiles ?? 0,
+                maxArchiveBytes: details?.artifact?.providerState?.maxArchiveBytes ?? 0,
+                maxArchiveDepth: details?.artifact?.providerState?.maxArchiveDepth ?? 0,
            };
    }
 }
@@ -117,22 +81,11 @@ export async function persistRagArtifactAttachment(params: {

    if (!sources.length) return Promise.resolve(undefined);

-    const payload: RagArtifactPayload = {
-        artifactKind: "rag",
+    const payload = buildRagArtifactPayload({
        provider: params.provider,
-        createdAt: new Date().toISOString(),
        sources,
-        providerState: {
-            ...providerState(params.prepared, params.details),
-            ...(params.details?.artifact ? {
-                extractedDocuments: params.details.artifact.extractedDocuments,
-                selectedChunks: params.details.artifact.selectedChunks,
-                skippedDocuments: params.details.artifact.skippedDocuments,
-                query: params.details.artifact.query,
-                ollama: params.details.artifact.providerState,
-            } : {}),
-        },
-    };
+        providerState: providerState(params.prepared, params.details),
+    });
    return await persistInternalJsonArtifactAttachment({
        artifactKind: "rag",
        fileNamePrefix: "rag",
@@ -140,14 +93,8 @@ export async function persistRagArtifactAttachment(params: {
        messageId: params.messageId,
        payload,
        metadata: {
-            provider: params.provider,
            sourceFileNames: sources.map(source => source.fileName),
            ...payload.providerState,
-            embeddingModel: params.details?.embeddingModel,
-            topK: params.details?.topK,
-            chunkSize: params.details?.chunkSize,
-            chunkOverlap: params.details?.chunkOverlap,
-            maxContextChars: params.details?.maxContextChars,
        },
    });
 }
@@ -1,8 +1,8 @@
 import {AiTool} from "./tool-types";
-import {AiProvider} from "../model/ai-provider";
-import {getTools} from "./tools/registry";
-import {WEB_SEARCH_TOOL_NAME} from "./tools/web-search";
-import {PYTHON_INTERPRETER_TOOL_NAME} from "./tools/python-interpretator";
+import {AiProvider} from "../model/ai-provider.js";
+import {getTools} from "./tools/registry.js";
+import {WEB_SEARCH_TOOL_NAME} from "./tools/web-search.js";
+import {PYTHON_INTERPRETER_TOOL_NAME} from "./tools/python-interpretator.js";

 export type AiProviderName = "ollama" | "openai" | "mistral";

@@ -0,0 +1,90 @@
+import {Environment} from "../common/environment.js";
+import {AiProvider} from "../model/ai-provider.js";
+import type {BoundaryValue} from "../common/boundary-types.js";
+import type {TelegramStreamMessage} from "./telegram-stream-message.js";
+import type {RuntimeConfigSnapshot} from "./unified-ai-runner.shared.js";
+import {filterRankedTools} from "./tool-ranker-pipeline.js";
+import {ToolRanker} from "./unified-ai-runner.tool-ranker.js";
+import {storeToolRankAudit} from "./tool-rank-audit.js";
+
+function latestUserText(messages: readonly { role?: string; content?: unknown }[]): string {
+    for (let i = messages.length - 1; i >= 0; i--) {
+        const message = messages[i];
+        if (message?.role !== "user") continue;
+        if (typeof message.content === "string") return message.content;
+        if (Array.isArray(message.content)) {
+            return message.content
+                .map(part => typeof part === "object" && part !== null && "text" in part && typeof (part as { text?: unknown }).text === "string"
+                    ? (part as { text: string }).text
+                    : "")
+                .filter(Boolean)
+                .join("\n");
+        }
+    }
+
+    return "";
+}
+
+export async function runToolRankStage(params: {
+    provider: AiProvider;
+    model: string;
+    round: number;
+    config: RuntimeConfigSnapshot;
+    availableTools: readonly BoundaryValue[];
+    messages: readonly { role?: string; content?: unknown }[];
+    streamMessage: TelegramStreamMessage;
+    signal: AbortSignal;
+    toolRanker?: ToolRanker;
+}): Promise<{
+    filteredTools: BoundaryValue[];
+    selectedToolNames: string[];
+    usedRanker: boolean;
+}> {
+    const toolRanker = params.toolRanker ?? new ToolRanker(params.config);
+    const startedAt = Date.now();
+    const startedAtIso = new Date().toISOString();
+
+    params.streamMessage.setStatus(Environment.getSelectingToolsText());
+    await params.streamMessage.flush();
+
+    try {
+        const selection = await toolRanker.selectTools({
+            provider: params.provider,
+            userQuery: latestUserText(params.messages),
+            availableTools: params.availableTools,
+            round: params.round,
+            signal: params.signal,
+        });
+
+        params.streamMessage.clearStatus();
+        await params.streamMessage.flush();
+        await storeToolRankAudit({
+            streamMessage: params.streamMessage,
+            provider: params.provider,
+            model: params.model,
+            round: params.round,
+            startedAt,
+            startedAtIso,
+            selectedTools: selection.toolNames,
+        });
+
+        return {
+            filteredTools: filterRankedTools(params.availableTools, selection.toolNames),
+            selectedToolNames: selection.toolNames,
+            usedRanker: selection.usedRanker,
+        };
+    } catch (error) {
+        params.streamMessage.clearStatus();
+        await params.streamMessage.flush();
+        await storeToolRankAudit({
+            streamMessage: params.streamMessage,
+            provider: params.provider,
+            model: params.model,
+            round: params.round,
+            startedAt,
+            startedAtIso,
+            error,
+        });
+        throw error;
+    }
+}
@@ -2,6 +2,7 @@ import {AiProvider} from "../model/ai-provider";
 import {Environment} from "../common/environment";
 import {ifTrue, logError} from "../util/utils";
 import {UserRequestPipeline, type UserRequestPipelineState, type UserRequestPipelineStage} from "./user-request-pipeline";
+import {getProviderAdapter} from "./provider-adapters";
 import type {AiDownloadedFile} from "./telegram-attachments";
 import type {TelegramStreamMessage} from "./telegram-stream-message";
 import type {PreparedUnifiedAiRequest} from "./unified-ai-request-pipeline";
@@ -9,12 +10,14 @@ import type {OpenAIChatMessage} from "./openai-chat-message";
 import type {MistralChatMessage} from "./mistral-chat-message";
 import type {ChatMessage} from "./chat-messages-types";
 import {
+    allToolSchemaNames,
    providerName,
    RuntimeConfigSnapshot,
    snapshotModel,
    TELEGRAM_LIMIT,
    UnifiedRunOptions,
 } from "./unified-ai-runner.shared";
+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";
@@ -159,6 +162,9 @@ export async function runUnifiedAiResponsePipeline(params: {
 }): Promise<void> {
    const {options, config, downloads, prepared, streamMessage, controller} = params;
    const state = createResponsePipelineState(options);
+    const adapter = getProviderAdapter(options.provider);
+    let selectedToolNames: string[] = [];
+    let filteredTools: unknown[] = [];

    const stages: UserRequestPipelineStage[] = [
        {
@@ -177,6 +183,62 @@ export async function runUnifiedAiResponsePipeline(params: {
                };
            },
        },
+        {
+            name: "tool_rank",
+            async run() {
+                const availableTools = adapter.rankTools(config, {
+                    forCreator: options.msg.from?.id === Environment.CREATOR_ID,
+                    vectorStoreIds: prepared.preparedDocumentRag?.provider === AiProvider.OPENAI
+                        ? prepared.preparedDocumentRag.vectorStoreIds
+                        : [],
+                });
+
+                const rankResult = await runToolRankStage({
+                    provider: options.provider,
+                    model: snapshotModel(options.provider, config),
+                    round: state.toolRankDecisions.length,
+                    config,
+                    availableTools,
+                    messages: prepared.chatMessages,
+                    streamMessage,
+                    signal: controller.signal,
+                });
+
+                selectedToolNames = rankResult.selectedToolNames;
+                filteredTools = rankResult.filteredTools;
+                state.toolRankDecisions.push({
+                    provider: options.provider,
+                    round: state.toolRankDecisions.length,
+                    availableTools: allToolSchemaNames(availableTools),
+                    selectedTools: selectedToolNames,
+                    usedRanker: rankResult.usedRanker,
+                });
+
+                return {
+                    stage: "tool_rank",
+                    status: "succeeded",
+                    details: {
+                        selectedTools: selectedToolNames,
+                        usedRanker: rankResult.usedRanker,
+                        availableTools: allToolSchemaNames(availableTools),
+                        toolRankDecision: state.toolRankDecisions.at(-1),
+                    },
+                };
+            },
+        },
+        {
+            name: "filter_tools",
+            async run() {
+                return {
+                    stage: "filter_tools",
+                    status: "succeeded",
+                    details: {
+                        selectedTools: selectedToolNames,
+                        filteredToolCount: filteredTools.length,
+                    },
+                };
+            },
+        },
        {
            name: "model_call",
            async run() {
@@ -312,6 +374,8 @@ export async function runUnifiedAiResponsePipeline(params: {
        stages,
        stageNames: [
            "audit_start",
+            "tool_rank",
+            "filter_tools",
            "model_call",
            "tool_loop",
            "output_size_gate",
@@ -1,21 +1,17 @@
 import {Environment} from "../common/environment";
-import {getMistralTools} from "./tool-mappers";
 import {TelegramStreamMessage} from "./telegram-stream-message";
 import {ToolRuntimeContext} from "./tools/runtime";
 import {MistralChatMessage} from "./mistral-chat-message";
 import {createMistralClient} from "./ai-runtime-target";
 import {aiLog, aiLogDuration, aiLogProviderTarget, aiLogToolCall} from "../logging/ai-logger";
 import {AiProvider} from "../model/ai-provider";
-import {ToolRanker} from "./unified-ai-runner.tool-ranker";
+import {getProviderAdapter} from "./provider-adapters";
+import {runToolRankStage} from "./tool-rank-stage";

 import {
-    contentFromMistralDelta,
    executeToolBatch,
    MAX_TOOL_ROUNDS,
-    MistralDeltaLike,
    MistralDocumentReference,
-    mistralToolCalls,
-    normalizeMistralToolCalls,
    roundStatus,
    RuntimeConfigSnapshot,
    StreamingToolCallAccumulator,
@@ -23,8 +19,6 @@ import {
    ToolExecutionMemory
 } from "./unified-ai-runner.shared";
 import {Message} from "typescript-telegram-bot-api";
-import {filterRankedTools, latestUserTextFromMessages} from "./tool-ranker-pipeline";
-import {storeToolRankAudit} from "./tool-rank-audit";

 export async function runMistral(
    msg: Message,
@@ -39,8 +33,9 @@ export async function runMistral(
 ): Promise<void> {
    const runnerStartedAt = Date.now();
    const mistralAi = createMistralClient(config.mistralChatTarget);
-    const toolRanker = new ToolRanker(config);
-    const availableTools = getMistralTools(msg.from?.id === Environment.CREATOR_ID);
+    const adapter = getProviderAdapter(AiProvider.MISTRAL);
+    const availableTools = adapter.rankTools(config, {forCreator: msg.from?.id === Environment.CREATOR_ID});
+    const requestMessages = adapter.mapMessages([...messages]) as unknown as MistralChatMessage[];
    aiLog("info", "mistral.run.start", {
        stream,
        target: aiLogProviderTarget(config.mistralChatTarget),
@@ -50,49 +45,23 @@ export async function runMistral(
    });

    const toolMemory: ToolExecutionMemory = new Map();
-
+    try {
        for (let round = 0; round < MAX_TOOL_ROUNDS; round++) {
            const roundStartedAt = Date.now();
            aiLog("debug", "mistral.round.start", {round, messages: messages.length, stream});
            if (signal.aborted) throw new Error("Aborted");

-        streamMessage.setStatus(Environment.getSelectingToolsText());
-        await streamMessage.flush();
-        const toolRankStartedAt = Date.now();
-        const toolRankStartedAtIso = new Date().toISOString();
-        const rankerSelection = await toolRanker.selectTools({
+            const rankResult = await runToolRankStage({
                provider: AiProvider.MISTRAL,
-                userQuery: latestUserTextFromMessages(messages),
+                model: config.mistralChatTarget.model,
+                round,
+                config,
                availableTools,
-                round,
+                messages,
+                streamMessage,
                signal,
-            })
-            .catch(async error => {
-                streamMessage.clearStatus();
-                await streamMessage.flush();
-                await storeToolRankAudit({
-                    streamMessage,
-                    provider: AiProvider.MISTRAL,
-                    model: config.mistralChatTarget.model,
-                    round,
-                    startedAt: toolRankStartedAt,
-                    startedAtIso: toolRankStartedAtIso,
-                    error,
            });
-                throw error;
-            });
-        streamMessage.clearStatus();
-        await streamMessage.flush();
-        await storeToolRankAudit({
-            streamMessage,
-            provider: AiProvider.MISTRAL,
-            model: config.mistralChatTarget.model,
-            round,
-            startedAt: toolRankStartedAt,
-            startedAtIso: toolRankStartedAtIso,
-            selectedTools: rankerSelection.toolNames,
-        });
-        const filteredTools = filterRankedTools(availableTools, rankerSelection.toolNames);
+            const filteredTools = rankResult.filteredTools;
            const requestTools = filteredTools.length ? filteredTools : undefined;

            streamMessage.setStatus(roundStatus(round, firstRoundStatus) ?? "");
@@ -101,15 +70,15 @@ export async function runMistral(
            if (!stream) {
                const request = {
                    model: config.mistralChatTarget.model,
-                messages,
+                    messages: requestMessages,
                    tools: requestTools,
                    documents: documents
                } as Parameters<typeof mistralAi.chat.complete>[0];
-            const response = await mistralAi.chat.complete(request, {signal});
+                const response = await adapter.callModel(request, () => mistralAi.chat.complete(request, {signal}));
                const message = response.choices?.[0]?.message;
                const text = typeof message?.content === "string" ? message.content : JSON.stringify(message?.content ?? "");
                streamMessage.append(text);
-            const calls = normalizeMistralToolCalls(mistralToolCalls(message));
+                const calls = adapter.extractToolCalls(message);
                aiLog(calls.length ? "info" : "success", calls.length ? "mistral.tool_calls" : "mistral.run.done", {
                    round,
                    duration: calls.length ? aiLogDuration(roundStartedAt) : aiLogDuration(runnerStartedAt),
@@ -125,25 +94,27 @@ export async function runMistral(
                        function: {name: call.name, arguments: call.argumentsText},
                    })),
                });
-            const toolResults = await executeToolBatch(msg.from?.id, calls, streamMessage, toolContext, toolMemory);
-            for (const [index, call] of calls.entries()) {
-                messages.push({
-                    role: "tool",
-                    name: call.name,
-                    toolCallId: call.id,
-                    content: toolResults[index] ?? "",
+                requestMessages.push({
+                    role: "assistant",
+                    content: text,
+                    toolCalls: calls.map(call => ({
+                        id: call.id,
+                        function: {name: call.name, arguments: call.argumentsText},
+                    })),
                });
-            }
+                const toolResults = await executeToolBatch(msg.from?.id, calls, streamMessage, toolContext, toolMemory);
+                adapter.appendToolResults(messages, calls, toolResults);
+                adapter.appendToolResults(requestMessages, calls, toolResults);
                continue;
            }

            const request = {
                model: config.mistralChatTarget.model,
-            messages,
+                messages: requestMessages,
                tools: requestTools,
                documents: documents
            } as Parameters<typeof mistralAi.chat.stream>[0];
-        const streamResponse = await mistralAi.chat.stream(request, {signal});
+            const streamResponse = await adapter.callModel(request, () => mistralAi.chat.stream(request, {signal}));
            aiLog("debug", "mistral.stream.open", {round});
            let calls: ToolCallData[] = [];
            const roundTextStart = streamMessage.getText().length;
@@ -154,11 +125,10 @@ export async function runMistral(

                const choice = event.data?.choices?.[0];
                const delta = choice?.delta;
-            const mistralDelta = delta as MistralDeltaLike;
+                const mistralDelta = delta;
+                streamMessage.append(adapter.extractTextDelta(mistralDelta));

-            streamMessage.append(contentFromMistralDelta(mistralDelta));
-
-            const rawDeltaCalls = mistralToolCalls(mistralDelta);
+                const rawDeltaCalls = adapter.extractStreamingToolCalls(mistralDelta);
                if (rawDeltaCalls.length) {
                    calls = toolCallAccumulator.add(rawDeltaCalls);
                    streamMessage.setStatus(Environment.getUseToolText(calls));
@@ -178,14 +148,16 @@ export async function runMistral(
                content: roundText,
                toolCalls: calls.map(c => ({id: c.id, function: {name: c.name, arguments: c.argumentsText}}))
            });
-        const toolResults = await executeToolBatch(msg.from?.id, calls, streamMessage, toolContext, toolMemory);
-        for (const [index, call] of calls.entries()) {
-            messages.push({
-                role: "tool",
-                name: call.name,
-                toolCallId: call.id,
-                content: toolResults[index] ?? "",
+            requestMessages.push({
+                role: "assistant",
+                content: roundText,
+                toolCalls: calls.map(c => ({id: c.id, function: {name: c.name, arguments: c.argumentsText}}))
            });
-        }
+            const toolResults = await executeToolBatch(msg.from?.id, calls, streamMessage, toolContext, toolMemory);
+            adapter.appendToolResults(messages, calls, toolResults);
+            adapter.appendToolResults(requestMessages, calls, toolResults);
+        }
+    } finally {
+        await adapter.finalize().catch(() => undefined);
    }
 }
@@ -5,7 +5,6 @@ import {Environment} from "../common/environment";
 import type {BoundaryValue} from "../common/boundary-types";
 import {bot, notesDir} from "../index";
 import {clamp, logError} from "../util/utils";
-import {getOllamaTools} from "./tool-mappers";
 import {TelegramStreamMessage} from "./telegram-stream-message";
 import {ChatMessage} from "./chat-messages-types";
 import {ChatRequest, Tool} from "ollama";
@@ -14,10 +13,11 @@ import {enqueueTelegramApiCall} from "../util/telegram-api-queue";
 import {loadOllamaModel, unloadAllOllamaModels} from "./tools/utils";
 import {createOllamaClient} from "./ai-runtime-target";
 import {aiLog, aiLogDuration, aiLogMessageIdentity, aiLogProviderTarget, aiLogToolCall} from "../logging/ai-logger";
+import {getProviderAdapter} from "./provider-adapters";
+import {runToolRankStage} from "./tool-rank-stage";

 import {
    allToolSchemaNames,
-    appendOllamaToolResults,
    dedupeToolCalls,
    DEFAULT_OLLAMA_CONTEXT_SIZE,
    executeToolBatch,
@@ -26,8 +26,6 @@ import {
    MAX_OLLAMA_CONTEXT_SIZE,
    MAX_TOOL_ROUNDS,
    MIN_OLLAMA_CONTEXT_SIZE,
-    normalizeOllamaToolCalls,
-    OllamaToolCallLike,
    roundStatus,
    RuntimeConfigSnapshot,
    safeJsonParseObject,
@@ -35,14 +33,11 @@ import {
    ToolCallData,
    ToolExecutionMemory
 } from "./unified-ai-runner.shared";
-import {ToolRanker} from "./unified-ai-runner.tool-ranker";
 import {getToolPrompts} from "./tools/registry";
-import {filterRankedTools, latestUserTextFromMessages} from "./tool-ranker-pipeline";
 import {GetNoteFileResult, GetNoteFileResultSchema} from "./tools/notes";
 import {getModelCapabilities} from "./provider-model-runtime";
 import {AiProvider} from "../model/ai-provider";
 import {Message} from "typescript-telegram-bot-api";
-import {storeToolRankAudit} from "./tool-rank-audit";

 export async function runOllama(
    msg: Message,
@@ -157,6 +152,7 @@ export async function runOllama(
    }

    const toolMemory: ToolExecutionMemory = new Map();
+    const adapter = getProviderAdapter(AiProvider.OLLAMA);

    try {
        for (let round = 0; round < MAX_TOOL_ROUNDS; round++) {
@@ -183,7 +179,7 @@ export async function runOllama(

            let activeToolNames: string[] = [];
            if ((await getModelCapabilities(AiProvider.OLLAMA, model, "tools"))?.tools?.supported) {
-                const availableOllamaTools: Tool[] = getOllamaTools(msg.from?.id === Environment.CREATOR_ID) as Tool[];
+                const availableOllamaTools: Tool[] = adapter.rankTools(config, {forCreator: msg.from?.id === Environment.CREATOR_ID}) as Tool[];

                aiLog("debug", "ollama.tools.available", {
                    round,
@@ -191,44 +187,18 @@ export async function runOllama(
                    rankerEnabled: !!config.ollamaToolRankerTarget,
                });

-                streamMessage.setStatus(Environment.getSelectingToolsText());
-                await streamMessage.flush();
-                const toolRankStartedAt = Date.now();
-                const toolRankStartedAtIso = new Date().toISOString();
-                const rankerSelection = await new ToolRanker(config).selectTools({
+                const rankResult = await runToolRankStage({
                    provider: AiProvider.OLLAMA,
-                        userQuery: latestUserTextFromMessages(messages),
+                    model,
+                    round,
+                    config,
                    availableTools: availableOllamaTools,
-                        round,
+                    messages,
+                    streamMessage,
                    signal,
-                    })
-                    .catch(async error => {
-                        streamMessage.clearStatus();
-                        await streamMessage.flush();
-                        await storeToolRankAudit({
-                            streamMessage,
-                            provider: AiProvider.OLLAMA,
-                            model,
-                            round,
-                            startedAt: toolRankStartedAt,
-                            startedAtIso: toolRankStartedAtIso,
-                            error,
-                        });
-                        throw error;
-                    });
-                streamMessage.clearStatus();
-                await streamMessage.flush();
-                await storeToolRankAudit({
-                    streamMessage,
-                    provider: AiProvider.OLLAMA,
-                    model,
-                    round,
-                    startedAt: toolRankStartedAt,
-                    startedAtIso: toolRankStartedAtIso,
-                    selectedTools: rankerSelection.toolNames,
                });

-                const filteredTools = [...new Set(filterRankedTools(availableOllamaTools, rankerSelection.toolNames))];
+                const filteredTools = [...new Set(rankResult.filteredTools as Tool[])];
                activeToolNames = filteredTools.map(t => t.function.name ?? "");
                if (filteredTools.length > 0) {
                    request.tools = [...filteredTools];
@@ -256,24 +226,21 @@ export async function runOllama(
                    round,
                    tools: activeToolNames,
                    count: activeToolNames.length,
-                    usedRanker: rankerSelection.usedRanker,
+                    usedRanker: rankResult.usedRanker,
                });
            }

            if (!stream) {
-                const response = await ollama.chat({
+                const response = await adapter.callModel(request, () => ollama.chat({
                    ...request,
                    stream: false
-                });
+                }));

                const message = response.message;
                const rawContent = message?.content ?? "";

                const nativeCalls = dedupeToolCalls(
-                    normalizeOllamaToolCalls(
-                        message?.tool_calls as readonly OllamaToolCallLike[] | undefined,
-                        round,
-                    ),
+                    adapter.extractToolCalls(message),
                );

                const responseText = rawContent;
@@ -301,7 +268,7 @@ export async function runOllama(
                    break;
                }

-                const calls = nativeCalls;
+                const calls = adapter.extractToolCalls(message).length ? adapter.extractToolCalls(message) : nativeCalls;

                aiLog("info", "ollama.tool_calls", {
                    round,
@@ -319,11 +286,7 @@ export async function runOllama(
                    })),
                });

-                appendOllamaToolResults(
-                    messages,
-                    calls,
-                    await executeToolBatch(msg.from?.id, calls, streamMessage, toolContext, toolMemory),
-                );
+                adapter.appendToolResults(messages, calls, await executeToolBatch(msg.from?.id, calls, streamMessage, toolContext, toolMemory));

                continue;
            }
@@ -332,10 +295,10 @@ export async function runOllama(
                round,
                messageCount: request.messages?.length ?? 0,
            });
-            const response = await ollama.chat({
+            const response = await adapter.callModel(request, () => ollama.chat({
                ...request,
                stream: true
-            });
+            }));

            aiLog("debug", "ollama.stream.open", {round});
            const calls: ToolCallData[] = [];
@@ -354,10 +317,7 @@ export async function runOllama(

                    const localToolCalls: ToolCallData[] = [];

-                    localToolCalls.push(...normalizeOllamaToolCalls(
-                        chunk.message.tool_calls as readonly OllamaToolCallLike[] | undefined,
-                        round,
-                    ));
+                    localToolCalls.push(...adapter.extractStreamingToolCalls(chunk.message));

                    const newStatus = roundStatus(round, firstRoundStatus, chunk.message.content, localToolCalls, !!chunk.message.thinking);
                    const previousStatus = streamMessage.getStatus();
@@ -377,13 +337,10 @@ export async function runOllama(
                    }

                    if (!(chunk.message?.thinking && streamMessage.getStatus() !== Environment.reasoningText)) {
-                        streamMessage.append(chunk.message?.content ?? "");
+                        streamMessage.append(adapter.extractTextDelta(chunk));
                    }

-                    calls.push(...normalizeOllamaToolCalls(
-                        chunk.message?.tool_calls as readonly OllamaToolCallLike[] | undefined,
-                        round,
-                    ));
+                    calls.push(...adapter.extractStreamingToolCalls(chunk.message));

                    if (chunk.done) {
                        aiLog("debug", "ollama.stream.done", {
@@ -471,9 +428,10 @@ export async function runOllama(
                }).catch(logError);
            }

-            appendOllamaToolResults(messages, calls, toolResults);
+            adapter.appendToolResults(messages, calls, toolResults);
        }
    } finally {
        if (interval) clearInterval(interval);
+        await adapter.finalize().catch(() => undefined);
    }
 }
@@ -17,11 +17,9 @@ import {
    AsyncIterableStream,
    buildSystemInstruction,
    collectOpenAiResponseCodeInterpreterCalls,
-    collectOpenAiResponseFunctionCalls,
    collectOpenAiResponseImages,
    collectOpenAiResponseText,
    executeToolBatch,
-    getOpenAIResponsesToolsWithImage,
    MAX_TOOL_ROUNDS,
    OPENAI_IMAGE_PARTIALS,
    openAiResponseItemCallId,
@@ -42,10 +40,9 @@ import {logError} from "../util/utils";
 import {SendFileAttachmentResult, SendFileAttachmentResultSchema} from "./tools/files";
 import {DEFAULT_AI_RESPONSE_LANGUAGE} from "../common/user-ai-settings";
 import {AiDownloadedFile} from "./telegram-attachments";
-import {ToolRanker} from "./unified-ai-runner.tool-ranker";
 import {AiProvider} from "../model/ai-provider";
-import {filterRankedTools, latestUserTextFromMessages} from "./tool-ranker-pipeline";
-import {storeToolRankAudit} from "./tool-rank-audit";
+import {getProviderAdapter} from "./provider-adapters";
+import {runToolRankStage} from "./tool-rank-stage";

 export async function runOpenAi(
    msg: Message,
@@ -60,16 +57,15 @@ export async function runOpenAi(
    documentRag?: OpenAiDocumentRagContext,
 ): Promise<void> {
    const runnerStartedAt = Date.now();
-    let responseInput: Array<ResponseInputItem | OpenAiResponseOutputItem> = [...messages] as Array<ResponseInputItem | OpenAiResponseOutputItem>;
    const openAi = createOpenAiClient(config.openAiChatTarget);
    const ownsDocumentRag = !documentRag;
    const preparedDocumentRag = documentRag ?? await prepareOpenAiDocumentRag(openAi, downloads.filter(download => download.kind === "document"));
-    const toolRanker = new ToolRanker(config);
-    const availableTools = getOpenAIResponsesToolsWithImage(
-        config,
-        msg.from?.id === Environment.CREATOR_ID,
-        preparedDocumentRag?.vectorStoreIds ?? [],
-    );
+    const adapter = getProviderAdapter(AiProvider.OPENAI);
+    let responseInput: Array<ResponseInputItem | OpenAiResponseOutputItem> = adapter.mapMessages(messages) as unknown as Array<ResponseInputItem | OpenAiResponseOutputItem>;
+    const availableTools = adapter.rankTools(config, {
+        forCreator: msg.from?.id === Environment.CREATOR_ID,
+        vectorStoreIds: preparedDocumentRag?.vectorStoreIds ?? [],
+    });

    const systemPrompt = buildSystemInstruction(
        config,
@@ -93,43 +89,17 @@ export async function runOpenAi(
        for (let round = 0; round < MAX_TOOL_ROUNDS; round++) {
        const roundStartedAt = Date.now();
        aiLog("debug", "openai.round.start", {round, inputItems: responseInput.length, stream});
-        streamMessage.setStatus(Environment.getSelectingToolsText());
-        await streamMessage.flush();
-        const toolRankStartedAt = Date.now();
-        const toolRankStartedAtIso = new Date().toISOString();
-        const rankerSelection = await toolRanker.selectTools({
+        const rankResult = await runToolRankStage({
            provider: AiProvider.OPENAI,
-                userQuery: latestUserTextFromMessages(messages),
+            model: config.openAiChatTarget.model,
+            round,
+            config,
            availableTools,
-                round,
+            messages,
+            streamMessage,
            signal,
-            })
-            .catch(async error => {
-                streamMessage.clearStatus();
-                await streamMessage.flush();
-                await storeToolRankAudit({
-                    streamMessage,
-                    provider: AiProvider.OPENAI,
-                    model: config.openAiChatTarget.model,
-                    round,
-                    startedAt: toolRankStartedAt,
-                    startedAtIso: toolRankStartedAtIso,
-                    error,
        });
-                throw error;
-            });
-        streamMessage.clearStatus();
-        await streamMessage.flush();
-        await storeToolRankAudit({
-            streamMessage,
-            provider: AiProvider.OPENAI,
-            model: config.openAiChatTarget.model,
-            round,
-            startedAt: toolRankStartedAt,
-            startedAtIso: toolRankStartedAtIso,
-            selectedTools: rankerSelection.toolNames,
-        });
-        const filteredTools = filterRankedTools(availableTools, rankerSelection.toolNames);
+        const filteredTools = rankResult.filteredTools;
        const requestTools = preparedDocumentRag?.vectorStoreIds.length
            ? (() => {
                const tools = [...filteredTools];
@@ -151,7 +121,7 @@ export async function runOpenAi(
                tools: requestTools as ResponseCreateParamsNonStreaming["tools"],
                instructions: systemPrompt,
            };
-            const response = await openAi.responses.create(request, {signal}) as OpenAiResponseLike;
+            const response = await adapter.callModel(request, () => openAi.responses.create(request, {signal})) as OpenAiResponseLike;

            const responseText = collectOpenAiResponseText(response);
            streamMessage.append(responseText);
@@ -188,12 +158,12 @@ export async function runOpenAi(
                });
            }

-            const calls = collectOpenAiResponseFunctionCalls(response);
+            const calls = adapter.extractToolCalls(response);
            aiLog(calls.length ? "info" : "success", calls.length ? "openai.tool_calls" : "openai.run.done", {
                round,
                duration: calls.length ? aiLogDuration(roundStartedAt) : aiLogDuration(runnerStartedAt),
                calls: calls.map(call => ({
-                    id: call.callId,
+                    id: call.id,
                    name: call.name,
                    arguments: safeJsonParseObject(call.argumentsText)
                })),
@@ -201,16 +171,13 @@ export async function runOpenAi(
            if (!calls.length) return;

            const toolCalls = calls.map(call => ({
-                id: call.callId,
+                id: call.id,
                name: call.name,
                argumentsText: call.argumentsText,
            }));
            const toolResults = await executeToolBatch(msg.from?.id, toolCalls, streamMessage, toolContext, toolMemory);
-            const toolOutputs = calls.map((call, index) => ({
-                type: "function_call_output" as const,
-                call_id: call.callId,
-                output: toolResults[index] ?? "",
-            }));
+            const toolOutputs: Array<{type: "function_call_output"; call_id: string; output: string}> = [];
+            adapter.appendToolResults(toolOutputs, calls, toolResults);

            const uploadFilesResult = await tryToUploadFiles(msg, toolResults);
            if (uploadFilesResult.found) {
@@ -243,7 +210,7 @@ export async function runOpenAi(
            parallel_tool_calls: true,
            instructions: systemPrompt
        };
-        const response = await openAi.responses.create(request, {signal}) as AsyncIterableStream<ResponseStreamEvent>;
+        const response = await adapter.callModel(request, () => openAi.responses.create(request, {signal})) as AsyncIterableStream<ResponseStreamEvent>;

        aiLog("debug", "openai.stream.open", {round});

@@ -253,7 +220,7 @@ export async function runOpenAi(

            switch (event.type) {
                case "response.output_text.delta":
-                    streamMessage.append(event.delta ?? "");
+                    streamMessage.append(adapter.extractTextDelta(event));
                    break;
                case "response.image_generation_call.in_progress":
                    streamMessage.setStatus(Environment.startingImageGenText);
@@ -301,14 +268,11 @@ export async function runOpenAi(
                case "response.code_interpreter_call_code.done":
                    break;
                case "response.output_item.added":
-                    if (event.item.type === "function_call" && event.item.name) {
-                        const item = event.item as OpenAiResponseOutputItem & { id?: string };
-                        localToolCalls.push({
-                            id: openAiResponseItemCallId(item),
-                            name: item.name ?? "",
-                            argumentsText: item.arguments ?? "{}",
-                        });
-
+                    {
+                        const streamedCalls = adapter.extractStreamingToolCalls(event);
+                        if (streamedCalls.length) {
+                            localToolCalls.push(...streamedCalls);
+                        }
                        aiLog("info", "openai.stream.tool_call.added", {
                            round,
                            toolCalls: localToolCalls.map(aiLogToolCall)
@@ -383,12 +347,12 @@ export async function runOpenAi(
            });
        }

-        const calls = collectOpenAiResponseFunctionCalls(completedResponse);
+        const calls = adapter.extractToolCalls(completedResponse);
        aiLog(calls.length ? "info" : "success", calls.length ? "openai.tool_calls" : "openai.run.done", {
            round,
            duration: calls.length ? aiLogDuration(roundStartedAt) : aiLogDuration(runnerStartedAt),
            calls: calls.map(call => ({
-                id: call.callId,
+                id: call.id,
                name: call.name,
                arguments: safeJsonParseObject(call.argumentsText)
            })),
@@ -396,16 +360,13 @@ export async function runOpenAi(
        if (!calls.length) return;

        const toolCalls = calls.map(call => ({
-            id: call.callId,
+            id: call.id,
            name: call.name,
            argumentsText: call.argumentsText,
        }));
        const toolResults = await executeToolBatch(msg.from?.id, toolCalls, streamMessage, toolContext, toolMemory);
-        const toolOutputs = calls.map((call, index) => ({
-            type: "function_call_output",
-            call_id: call.callId,
-            output: toolResults[index] ?? "",
-        }));
+        const toolOutputs: Array<{type: "function_call_output"; call_id: string; output: string}> = [];
+        adapter.appendToolResults(toolOutputs, calls, toolResults);

        const uploadFilesResult = await tryToUploadFiles(msg, toolResults);
        if (uploadFilesResult.found) {
@@ -431,6 +392,7 @@ export async function runOpenAi(
        if (ownsDocumentRag) {
            await preparedDocumentRag?.cleanup().catch(logError);
        }
+        await adapter.finalize().catch(logError);
    }
 }

@@ -2,40 +2,40 @@ import {Message} from "typescript-telegram-bot-api";
 import * as fs from "node:fs";
 import path from "node:path";
 import type {BoundaryValue} from "../common/boundary-types";
-import {AiProvider} from "../model/ai-provider";
-import {ToolRankerFallbackPolicy} from "../common/policies";
-import {Environment} from "../common/environment";
-import {photoGenDir} from "../index";
-import {delay, logError, replyToMessage} from "../util/utils";
-import {MessageStore} from "../common/message-store";
-import type {OpenAiResponseTool} from "./tool-mappers";
-import {AiProviderName, getOpenAICodeInterpreterTool, getOpenAIResponsesTools} from "./tool-mappers";
-import {TelegramArtifactFile, TelegramStreamMessage} from "./telegram-stream-message";
-import {AiDownloadedFile} from "./telegram-attachments";
-import {getRuntimeCapabilities} from "./provider-model-runtime";
-import {StoredAttachment} from "../model/stored-attachment";
-import {AiChatMessage, ChatMessage} from "./chat-messages-types";
+import {AiProvider} from "../model/ai-provider.js";
+import {ToolRankerFallbackPolicy} from "../common/policies.js";
+import {Environment} from "../common/environment.js";
+import {photoGenDir} from "../index.js";
+import {delay, logError, replyToMessage} from "../util/utils.js";
+import {MessageStore} from "../common/message-store.js";
+import type {OpenAiResponseTool} from "./tool-mappers.js";
+import {AiProviderName, getOpenAICodeInterpreterTool, getOpenAIResponsesTools} from "./tool-mappers.js";
+import {TelegramArtifactFile, TelegramStreamMessage} from "./telegram-stream-message.js";
+import {AiDownloadedFile} from "./telegram-attachments.js";
+import {getRuntimeCapabilities} from "./provider-model-runtime.js";
+import {StoredAttachment} from "../model/stored-attachment.js";
+import {AiChatMessage, ChatMessage} from "./chat-messages-types.js";
 import {ListResponse, Ollama} from "ollama";
-import {executeToolCall, ToolRuntimeContext} from "./tools/runtime";
-import {MessageImagePart, MessagePart} from "../common/message-part";
-import {KeyedAsyncLock} from "../util/async-lock";
-import {type AiRequestQueueTarget} from "./provider-request-queue";
-import {PYTHON_INTERPRETER_TOOL_NAME, pythonInterpreterToolPrompt} from "./tools/python-interpretator";
-import {getResponseLanguageInstruction, UserAiResponseLanguage, UserAiVoiceMode} from "../common/user-ai-settings";
+import {executeToolCall, ToolRuntimeContext} from "./tools/runtime.js";
+import {MessageImagePart, MessagePart} from "../common/message-part.js";
+import {KeyedAsyncLock} from "../util/async-lock.js";
+import {type AiRequestQueueTarget} from "./provider-request-queue.js";
+import {PYTHON_INTERPRETER_TOOL_NAME, pythonInterpreterToolPrompt} from "./tools/python-interpretator.js";
+import {getResponseLanguageInstruction, UserAiResponseLanguage, UserAiVoiceMode} from "../common/user-ai-settings.js";
 import {
    isTranscribableAudioDownload,
    resolveSpeechToTextProviderForUser,
    transcribeSpeechDownloads
-} from "./speech-to-text";
+} from "./speech-to-text.js";
 import type {ChatCompletionMessageParam} from "openai/resources/chat/completions";
-import {MistralChatMessage} from "./mistral-chat-message";
-import {prepareTelegramMarkdownV2} from "../util/markdown-v2-renderer";
-import {AiRuntimeTarget, createMistralClient, resolveAiRuntimeTarget} from "./ai-runtime-target";
-import {aiLog, aiLogDuration, aiLogProviderTarget, aiLogToolCall} from "../logging/ai-logger";
-import {buildConversationSnapshot, serializeConversationSnapshot} from "./conversation-pipeline";
+import {MistralChatMessage} from "./mistral-chat-message.js";
+import {prepareTelegramMarkdownV2} from "../util/markdown-v2-renderer.js";
+import {AiRuntimeTarget, createMistralClient, resolveAiRuntimeTarget} from "./ai-runtime-target.js";
+import {aiLog, aiLogDuration, aiLogProviderTarget, aiLogToolCall} from "../logging/ai-logger.js";
+import {buildConversationSnapshot, serializeConversationSnapshot} from "./conversation-pipeline.js";
 import type {ResponseInputMessageContentList} from "openai/resources/responses/responses";
-import {persistToolResultArtifactAttachment} from "./tool-result-artifact-store";
-import {filterUserVisibleStoredAttachments} from "../common/stored-attachment-utils";
+import {persistToolResultArtifactAttachment} from "./tool-result-artifact-store.js";
+import {filterUserVisibleStoredAttachments} from "../common/attachment-visibility.js";

 export type {Message} from "typescript-telegram-bot-api";
 export type {AiRuntimeTarget} from "./ai-runtime-target";
@@ -0,0 +1,5 @@
+import type {StoredAttachment} from "../model/stored-attachment";
+
+export function filterUserVisibleStoredAttachments(attachments: StoredAttachment[]): StoredAttachment[] {
+    return attachments.filter(attachment => attachment.scope !== "internal_artifact");
+}
@@ -3,18 +3,18 @@ import os from "node:os";
 import path from "node:path";
 import {parse as parseDotEnv} from "dotenv";
 import {z} from "zod";
-import {appLogger} from "../logging/logger";
+import {appLogger} from "../logging/logger.js";
 import type {BoundaryValue, ErrorLike} from "./boundary-types";

-import {saveData} from "../db/database";
-import {Answers} from "../model/answers";
-import {ifTrue} from "../util/utils";
-import {AiProvider} from "../model/ai-provider";
-import {ImageHandleFallbackPolicy, ImageHandlePolicy, RateLimitFallbackPolicy} from "./policies";
-import {ToolRankerFallbackPolicy} from "./policies";
-import type {ToolCallData} from "../ai/unified-ai-runner";
-import {PYTHON_INTERPRETER_TOOL_NAME} from "../ai/tools/python-interpretator";
-import {Localization, type LocalizationParams} from "./localization";
+import {saveData} from "../db/database.js";
+import {Answers} from "../model/answers.js";
+import {ifTrue} from "../util/utils.js";
+import {AiProvider} from "../model/ai-provider.js";
+import {ImageHandleFallbackPolicy, ImageHandlePolicy, RateLimitFallbackPolicy} from "./policies.js";
+import {ToolRankerFallbackPolicy} from "./policies.js";
+import type {ToolCallData} from "../ai/unified-ai-runner.js";
+import {PYTHON_INTERPRETER_TOOL_NAME} from "../ai/tools/python-interpretator.js";
+import {Localization, type LocalizationParams} from "./localization.js";

 type EnvRecord = Record<string, string>;
 type StringEnumLike = Record<string, string>;
@@ -1,6 +1,7 @@
 import path from "node:path";
 import {Environment} from "./environment";
 import {StoredAttachment} from "../model/stored-attachment";
+export {filterUserVisibleStoredAttachments} from "./attachment-visibility";

 export function photoCachePathForUniqueId(uniqueId: string): string {
    return path.join(Environment.DATA_PATH, "cache", "photo", `${uniqueId}.jpg`);
@@ -44,7 +45,3 @@ export function uniqueStoredAttachments(attachments: StoredAttachment[]): Stored

    return result;
 }
-
-export function filterUserVisibleStoredAttachments(attachments: StoredAttachment[]): StoredAttachment[] {
-    return attachments.filter(attachment => attachment.scope !== "internal_artifact");
-}
@@ -1,9 +1,9 @@
 import * as fs from "fs";
-import {Environment} from "../common/environment";
-import {logError} from "../util/utils";
-import {Answers} from "../model/answers";
+import {Environment} from "../common/environment.js";
+import {logError} from "../util/utils.js";
+import {Answers} from "../model/answers.js";
 import path from "node:path";
-import {KeyedAsyncLock} from "../util/async-lock";
+import {KeyedAsyncLock} from "../util/async-lock.js";

 type DataJsonFile = {
    admins: number[]
@@ -0,0 +1,83 @@
+import test from "node:test";
+import assert from "node:assert/strict";
+
+const {
+    extractOpenAiToolCalls,
+    extractOpenAiStreamingToolCalls,
+    extractOpenAiTextDelta,
+    extractMistralToolCalls,
+    extractMistralTextDelta,
+    extractOllamaToolCalls,
+    extractOllamaTextDelta,
+} = await import("../dist/ai/provider-adapter-contract.js");
+
+test("openai contract extracts text delta and function calls", () => {
+    assert.equal(extractOpenAiTextDelta({type: "response.output_text.delta", delta: "hello"}), "hello");
+
+    const calls = extractOpenAiToolCalls({
+        output: [{
+            type: "function_call",
+            call_id: "call-1",
+            name: "read_file",
+            arguments: "{\"path\":\"src/index.ts\"}",
+        }],
+    });
+
+    assert.equal(calls.length, 1);
+    assert.equal(calls[0].id, "call-1");
+    assert.equal(calls[0].name, "read_file");
+
+    const streamed = extractOpenAiStreamingToolCalls({
+        type: "response.output_item.added",
+        item: {
+            type: "function_call",
+            id: "call-2",
+            name: "search_files",
+            arguments: "{\"query\":\"sendMessage\"}",
+        },
+    });
+
+    assert.equal(streamed.length, 1);
+    assert.equal(streamed[0].id, "call-2");
+    assert.equal(streamed[0].name, "search_files");
+});
+
+test("mistral contract extracts content and tool calls", () => {
+    assert.equal(extractMistralTextDelta({
+        content: [{text: "hello"}, {text: " world"}],
+    }), "hello world");
+
+    const calls = extractMistralToolCalls({
+        toolCalls: [{
+            id: "m-1",
+            function: {
+                name: "get_weather",
+                arguments: {location: "Moscow"},
+            },
+        }],
+    });
+
+    assert.equal(calls.length, 1);
+    assert.equal(calls[0].id, "m-1");
+    assert.equal(calls[0].name, "get_weather");
+});
+
+test("ollama contract extracts content and tool calls", () => {
+    assert.equal(extractOllamaTextDelta({
+        message: {content: "hello from ollama"},
+    }), "hello from ollama");
+
+    const calls = extractOllamaToolCalls({
+        tool_calls: [{
+            id: "o-1",
+            function: {
+                name: "web_search",
+                arguments: {query: "openai docs"},
+            },
+        }],
+    });
+
+    assert.equal(calls.length, 1);
+    assert.equal(calls[0].id, "o-1");
+    assert.equal(calls[0].name, "web_search");
+});
@@ -1,32 +1,13 @@
-import test, {after} from "node:test";
+import test from "node:test";
 import assert from "node:assert/strict";
-import fs from "node:fs";
-import os from "node:os";
-import path from "node:path";

-const tempRoot = fs.mkdtempSync(path.join(os.tmpdir(), "tg-chat-bot-rag-"));
-process.env.BOT_TOKEN = process.env.BOT_TOKEN ?? "test-token";
-process.env.CREATOR_ID = process.env.CREATOR_ID ?? "1";
-process.env.DATA_PATH = tempRoot;
-process.env.DB_PATH = `file:${path.join(tempRoot, "test.sqlite")}`;
-process.env.TEST_ENVIRONMENT = "true";
-
-const {Environment} = await import("../dist/common/environment.js");
-Environment.load();
-
-const {DatabaseManager} = await import("../dist/db/database-manager.js");
-DatabaseManager.init();
-await DatabaseManager.ready;
-
-const {ArtifactStore} = await import("../dist/common/artifact-store.js");
-const {filterUserVisibleStoredAttachments} = await import("../dist/common/stored-attachment-utils.js");
+const {
+    buildRagArtifactPayload,
+} = await import("../dist/ai/rag-artifact-payload.js");
+const {
+    filterUserVisibleStoredAttachments,
+} = await import("../dist/common/attachment-visibility.js");
 const {AiProvider} = await import("../dist/model/ai-provider.js");
-const {persistRagArtifactAttachment} = await import("../dist/ai/rag-artifact-store.js");
-
-after(async () => {
-    await DatabaseManager.close().catch(() => undefined);
-    fs.rmSync(tempRoot, {recursive: true, force: true});
-});

 test("internal artifacts are not treated as user-visible attachments", () => {
    const visible = filterUserVisibleStoredAttachments([
@@ -50,65 +31,26 @@ test("internal artifacts are not treated as user-visible attachments", () => {
    assert.equal(visible[0].fileId, "visible");
 });

-test("RAG artifacts persist structured ollama metadata", async () => {
-    const chatId = 42;
-    const messageId = 7;
-
-    const attachment = await persistRagArtifactAttachment({
+test("RAG artifact payload keeps ollama retrieval metadata", () => {
+    const payload = buildRagArtifactPayload({
        provider: AiProvider.OLLAMA,
-        prepared: {
-            provider: AiProvider.OLLAMA,
-            prepared: true,
-            cleanup: async () => undefined,
-            artifact: {
-                query: "What is in the file?",
-                extractedDocuments: [
-                    {documentIndex: 0, fileName: "report.txt", textChars: 120},
-                ],
-                selectedChunks: [
-                    {
-                        sourceId: "doc1-1",
-                        documentIndex: 0,
-                        documentName: "report.txt",
-                        chunkIndex: 0,
-                        chunkCount: 1,
-                        textChars: 120,
-                        score: 0.91,
-                    },
-                ],
-                skippedDocuments: [
-                    {documentIndex: 1, fileName: "ignored.bin", reason: "unsupported format"},
-                ],
-                providerState: {
-                    embeddingModel: "nomic-embed-text:latest",
-                    topK: 8,
-                    chunkSize: 1400,
-                    chunkOverlap: 220,
-                    maxContextChars: 14000,
-                    minScore: 0.12,
-                    maxArchiveFiles: 200,
-                    maxArchiveBytes: 50 * 1024 * 1024,
-                    maxArchiveDepth: 2,
-                },
-            },
-        },
-        downloads: [{
-            kind: "document",
+        createdAt: "2026-01-01T00:00:00.000Z",
+        sources: [{
            fileId: "file-1",
            fileName: "report.txt",
-            buffer: Buffer.from("hello world"),
-            path: path.join(tempRoot, "report.txt"),
+            mimeType: "text/plain",
+            sizeBytes: 12,
+            sha256: "abc123",
+            uploadedFileId: "uploaded-1",
        }],
-        chatId,
-        messageId,
-        details: {
+        providerState: {
+            provider: AiProvider.OLLAMA,
+            prepared: true,
            embeddingModel: "nomic-embed-text:latest",
            topK: 8,
            chunkSize: 1400,
            chunkOverlap: 220,
            maxContextChars: 14000,
-            artifact: {
-                query: "What is in the file?",
            extractedDocuments: [
                {documentIndex: 0, fileName: "report.txt", textChars: 120},
            ],
@@ -126,29 +68,20 @@ test("RAG artifacts persist structured ollama metadata", async () => {
            skippedDocuments: [
                {documentIndex: 1, fileName: "ignored.bin", reason: "unsupported format"},
            ],
-                providerState: {
-                    embeddingModel: "nomic-embed-text:latest",
-                    topK: 8,
-                    chunkSize: 1400,
-                    chunkOverlap: 220,
-                    maxContextChars: 14000,
            minScore: 0.12,
            maxArchiveFiles: 200,
            maxArchiveBytes: 50 * 1024 * 1024,
            maxArchiveDepth: 2,
-                },
-            },
+            query: "What is in the file?",
        },
    });

-    assert.equal(attachment?.artifactKind, "rag");
-    assert.equal(fs.existsSync(attachment.cachePath), true);
-
-    const stored = await ArtifactStore.getByMessage(chatId, messageId);
-    assert.equal(stored.length, 1);
-    assert.equal(stored[0].kind, "rag");
-    assert.equal(stored[0].payload.providerState.query, "What is in the file?");
-    assert.equal(stored[0].payload.providerState.selectedChunks[0].score, 0.91);
-    assert.equal(stored[0].payload.providerState.skippedDocuments[0].reason, "unsupported format");
-    assert.equal(stored[0].payload.providerState.ollama.embeddingModel, "nomic-embed-text:latest");
+    assert.equal(payload.artifactKind, "rag");
+    assert.equal(payload.provider, AiProvider.OLLAMA);
+    assert.equal(payload.sources[0].uploadedFileId, "uploaded-1");
+    assert.equal(payload.providerState.provider, AiProvider.OLLAMA);
+    assert.equal(payload.providerState.query, "What is in the file?");
+    assert.equal(payload.providerState.selectedChunks[0].score, 0.91);
+    assert.equal(payload.providerState.skippedDocuments[0].reason, "unsupported format");
+    assert.equal(payload.providerState.embeddingModel, "nomic-embed-text:latest");
 });