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fix: improve GLM-4.5 model handling to prevent hallucination and enhance tool understanding
- Add GLM-specific system prompt enhancements to prevent file hallucination - Include clear instructions for tool usage protocol and content management - Implement message preprocessing for better GLM model understanding - Add token limit adjustments and model-specific parameters for GLM-4.5 - Enhance completePrompt method with instruction prefix for GLM models - Add comprehensive tests for GLM-specific functionality Fixes #6942
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Roo Code
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2 changed files with 336 additions and 3 deletions
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@ -193,7 +193,6 @@ describe("ZAiHandler", () => {
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it("createMessage should pass correct parameters to Z AI client", async () => {
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const modelId: InternationalZAiModelId = "glm-4.5"
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const modelInfo = internationalZAiModels[modelId]
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const handlerWithModel = new ZAiHandler({
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apiModelId: modelId,
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zaiApiKey: "test-zai-api-key",
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@ -216,14 +215,159 @@ describe("ZAiHandler", () => {
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const messageGenerator = handlerWithModel.createMessage(systemPrompt, messages)
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await messageGenerator.next()
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// For GLM-4.5, expect enhanced system prompt and adjusted parameters
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expect(mockCreate).toHaveBeenCalledWith(
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expect.objectContaining({
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model: modelId,
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max_tokens: modelInfo.maxTokens,
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max_tokens: 32768, // Adjusted for GLM models
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temperature: ZAI_DEFAULT_TEMPERATURE,
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messages: expect.arrayContaining([{ role: "system", content: systemPrompt }]),
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messages: expect.arrayContaining([
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{
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role: "system",
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content: expect.stringContaining(systemPrompt), // Contains original prompt plus enhancements
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},
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]),
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stream: true,
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stream_options: { include_usage: true },
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top_p: 0.95,
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frequency_penalty: 0.1,
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presence_penalty: 0.1,
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}),
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)
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})
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it("should enhance system prompt for GLM-4.5 models", async () => {
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const modelId: InternationalZAiModelId = "glm-4.5"
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const handlerWithGLM = new ZAiHandler({
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apiModelId: modelId,
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zaiApiKey: "test-zai-api-key",
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zaiApiLine: "international",
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})
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mockCreate.mockImplementationOnce(() => {
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return {
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[Symbol.asyncIterator]: () => ({
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async next() {
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return { done: true }
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},
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}),
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}
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})
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const systemPrompt = "Test system prompt"
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const messages: Anthropic.Messages.MessageParam[] = [{ role: "user", content: "Test message" }]
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const messageGenerator = handlerWithGLM.createMessage(systemPrompt, messages)
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await messageGenerator.next()
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// Check that the system prompt was enhanced with GLM-specific instructions
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expect(mockCreate).toHaveBeenCalledWith(
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expect.objectContaining({
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messages: expect.arrayContaining([
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{
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role: "system",
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content: expect.stringContaining("CRITICAL INSTRUCTIONS FOR GLM MODEL"),
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},
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]),
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}),
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)
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})
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it("should apply max token adjustment for GLM-4.5 models", async () => {
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const modelId: InternationalZAiModelId = "glm-4.5"
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const handlerWithGLM = new ZAiHandler({
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apiModelId: modelId,
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zaiApiKey: "test-zai-api-key",
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zaiApiLine: "international",
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})
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mockCreate.mockImplementationOnce(() => {
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return {
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[Symbol.asyncIterator]: () => ({
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async next() {
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return { done: true }
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},
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}),
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}
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})
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const messageGenerator = handlerWithGLM.createMessage("system", [])
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await messageGenerator.next()
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// Check that max_tokens is capped at 32768 for GLM models
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expect(mockCreate).toHaveBeenCalledWith(
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expect.objectContaining({
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max_tokens: 32768,
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top_p: 0.95,
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frequency_penalty: 0.1,
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presence_penalty: 0.1,
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}),
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)
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})
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it("should enhance prompt in completePrompt for GLM-4.5 models", async () => {
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const modelId: InternationalZAiModelId = "glm-4.5"
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const handlerWithGLM = new ZAiHandler({
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apiModelId: modelId,
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zaiApiKey: "test-zai-api-key",
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zaiApiLine: "international",
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})
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const expectedResponse = "Test response"
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mockCreate.mockResolvedValueOnce({ choices: [{ message: { content: expectedResponse } }] })
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const testPrompt = "Test prompt"
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await handlerWithGLM.completePrompt(testPrompt)
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// Check that the prompt was enhanced with GLM-specific prefix
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expect(mockCreate).toHaveBeenCalledWith(
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expect.objectContaining({
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messages: [
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{
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role: "user",
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content: expect.stringContaining(
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"[INSTRUCTION] Please provide a direct and accurate response",
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),
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},
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],
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temperature: ZAI_DEFAULT_TEMPERATURE,
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max_tokens: 4096,
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}),
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)
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})
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it("should handle GLM-4.5-air model correctly", async () => {
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const modelId: InternationalZAiModelId = "glm-4.5-air"
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const handlerWithGLMAir = new ZAiHandler({
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apiModelId: modelId,
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zaiApiKey: "test-zai-api-key",
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zaiApiLine: "international",
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})
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mockCreate.mockImplementationOnce(() => {
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return {
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[Symbol.asyncIterator]: () => ({
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async next() {
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return { done: true }
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},
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}),
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}
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})
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const messageGenerator = handlerWithGLMAir.createMessage("system", [])
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await messageGenerator.next()
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// Should apply GLM enhancements for glm-4.5-air as well
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expect(mockCreate).toHaveBeenCalledWith(
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expect.objectContaining({
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model: modelId,
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max_tokens: 32768,
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messages: expect.arrayContaining([
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{
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role: "system",
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content: expect.stringContaining("CRITICAL INSTRUCTIONS FOR GLM MODEL"),
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},
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]),
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}),
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)
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})
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@ -7,12 +7,19 @@ import {
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type MainlandZAiModelId,
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ZAI_DEFAULT_TEMPERATURE,
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} from "@roo-code/types"
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import { Anthropic } from "@anthropic-ai/sdk"
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import OpenAI from "openai"
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import type { ApiHandlerOptions } from "../../shared/api"
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import { ApiStream } from "../transform/stream"
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import { convertToOpenAiMessages } from "../transform/openai-format"
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import type { ApiHandlerCreateMessageMetadata } from "../index"
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import { BaseOpenAiCompatibleProvider } from "./base-openai-compatible-provider"
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export class ZAiHandler extends BaseOpenAiCompatibleProvider<InternationalZAiModelId | MainlandZAiModelId> {
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private readonly isGLM45: boolean
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constructor(options: ApiHandlerOptions) {
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const isChina = options.zaiApiLine === "china"
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const models = isChina ? mainlandZAiModels : internationalZAiModels
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@ -27,5 +34,187 @@ export class ZAiHandler extends BaseOpenAiCompatibleProvider<InternationalZAiMod
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providerModels: models,
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defaultTemperature: ZAI_DEFAULT_TEMPERATURE,
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})
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// Check if the model is GLM-4.5 or GLM-4.5-Air
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const modelId = options.apiModelId || defaultModelId
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this.isGLM45 = modelId.includes("glm-4.5")
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}
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/**
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* Override createMessage to add GLM-specific handling
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*/
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override async *createMessage(
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systemPrompt: string,
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messages: Anthropic.Messages.MessageParam[],
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metadata?: ApiHandlerCreateMessageMetadata,
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): ApiStream {
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// For GLM-4.5 models, enhance the system prompt with clearer instructions
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let enhancedSystemPrompt = systemPrompt
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if (this.isGLM45) {
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// Add GLM-specific instructions to prevent hallucination and improve tool understanding
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const glmInstructions = `
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# CRITICAL INSTRUCTIONS FOR GLM MODEL
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## File and Code Awareness
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- NEVER assume or hallucinate files that don't exist. Always verify file existence using the provided tools.
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- When exploring code, ALWAYS use the available tools (read_file, list_files, search_files) to examine actual files.
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- If you're unsure about a file's existence or location, use list_files to explore the directory structure first.
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- Base all code analysis and modifications on actual file contents retrieved through tools, not assumptions.
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## Tool Usage Protocol
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- Tools are invoked using XML-style tags as shown in the examples.
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- Each tool invocation must be properly formatted with the exact tool name as the XML tag.
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- Wait for tool execution results before proceeding to the next step.
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- Never simulate or imagine tool outputs - always use actual results.
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## Content Management
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- When working with large files or responses, focus on the specific sections relevant to the task.
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- Use partial reads when available to efficiently handle large files.
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- Condense and summarize appropriately while maintaining accuracy.
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- Keep responses concise and within token limits by focusing on essential information.
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## Code Indexing Integration
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- The code index provides semantic understanding of the codebase.
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- Use codebase_search for initial exploration when available.
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- Combine index results with actual file reading for complete understanding.
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- Trust the index for finding relevant code patterns and implementations.`
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enhancedSystemPrompt = systemPrompt + glmInstructions
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}
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const {
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id: model,
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info: { maxTokens: max_tokens },
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} = this.getModel()
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const temperature = this.options.modelTemperature ?? this.defaultTemperature
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// For GLM models, we may need to adjust the max_tokens to leave room for proper responses
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// GLM models sometimes struggle with very high token limits
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const adjustedMaxTokens = this.isGLM45 && max_tokens ? Math.min(max_tokens, 32768) : max_tokens
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const params: OpenAI.Chat.Completions.ChatCompletionCreateParamsStreaming = {
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model,
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max_tokens: adjustedMaxTokens || 32768,
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temperature,
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messages: [
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{ role: "system", content: enhancedSystemPrompt },
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...this.preprocessMessages(convertToOpenAiMessages(messages)),
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],
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stream: true,
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stream_options: { include_usage: true },
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}
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// Add additional parameters for GLM models to improve response quality
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if (this.isGLM45) {
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// GLM models benefit from explicit top_p and frequency_penalty settings
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Object.assign(params, {
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top_p: 0.95,
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frequency_penalty: 0.1,
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presence_penalty: 0.1,
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})
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}
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const stream = await this.client.chat.completions.create(params)
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for await (const chunk of stream) {
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const delta = chunk.choices[0]?.delta
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if (delta?.content) {
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yield {
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type: "text",
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text: delta.content,
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}
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}
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if (chunk.usage) {
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yield {
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type: "usage",
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inputTokens: chunk.usage.prompt_tokens || 0,
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outputTokens: chunk.usage.completion_tokens || 0,
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}
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}
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}
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}
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/**
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* Preprocess messages for GLM models to ensure better understanding
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*/
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private preprocessMessages(
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messages: OpenAI.Chat.ChatCompletionMessageParam[],
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): OpenAI.Chat.ChatCompletionMessageParam[] {
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if (!this.isGLM45) {
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return messages
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}
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// For GLM models, ensure tool-related messages are clearly formatted
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return messages.map((msg) => {
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if (msg.role === "assistant" && typeof msg.content === "string") {
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// Ensure XML tags in assistant messages are properly formatted
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// GLM models sometimes struggle with complex XML structures
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const content = msg.content
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.replace(/(<\/?[^>]+>)/g, "\n$1\n") // Add newlines around XML tags
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.replace(/\n\n+/g, "\n") // Remove excessive newlines
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.trim()
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return { ...msg, content }
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}
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if (msg.role === "user" && Array.isArray(msg.content)) {
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// For user messages with multiple content blocks, ensure text is clear
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const processedContent = msg.content.map((block: any) => {
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if (block.type === "text") {
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// Add clear markers for tool results to help GLM understand context
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if (block.text.includes("[ERROR]") || block.text.includes("Error:")) {
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return {
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...block,
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text: `[TOOL EXECUTION RESULT - ERROR]\n${block.text}\n[END TOOL RESULT]`,
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}
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} else if (block.text.includes("Success:") || block.text.includes("successfully")) {
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return {
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...block,
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text: `[TOOL EXECUTION RESULT - SUCCESS]\n${block.text}\n[END TOOL RESULT]`,
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}
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}
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}
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return block
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})
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return { ...msg, content: processedContent }
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}
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return msg
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})
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}
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/**
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* Override completePrompt for better GLM handling
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*/
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override async completePrompt(prompt: string): Promise<string> {
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const { id: modelId } = this.getModel()
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try {
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// For GLM models, add a clear instruction prefix
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const enhancedPrompt = this.isGLM45
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? `[INSTRUCTION] Please provide a direct and accurate response based on facts. Do not hallucinate or make assumptions.\n\n${prompt}`
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: prompt
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const response = await this.client.chat.completions.create({
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model: modelId,
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messages: [{ role: "user", content: enhancedPrompt }],
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temperature: this.defaultTemperature,
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max_tokens: 4096,
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})
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return response.choices[0]?.message.content || ""
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} catch (error) {
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if (error instanceof Error) {
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throw new Error(`${this.providerName} completion error: ${error.message}`)
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}
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throw error
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}
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}
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}
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