Files
bolt-diy/app/routes/api.chat.ts
Stijnus b5d9055851 🔧 Fix Token Limits & Invalid JSON Response Errors (#1934)
ISSUES FIXED:
-  Invalid JSON response errors during streaming
-  Incorrect token limits causing API rejections
-  Outdated hardcoded model configurations
-  Poor error messages for API failures

SOLUTIONS IMPLEMENTED:

🎯 ACCURATE TOKEN LIMITS & CONTEXT SIZES
- OpenAI GPT-4o: 128k context (was 8k)
- OpenAI GPT-3.5-turbo: 16k context (was 8k)
- Anthropic Claude 3.5 Sonnet: 200k context (was 8k)
- Anthropic Claude 3 Haiku: 200k context (was 8k)
- Google Gemini 1.5 Pro: 2M context (was 8k)
- Google Gemini 1.5 Flash: 1M context (was 8k)
- Groq Llama models: 128k context (was 8k)
- Together models: Updated with accurate limits

�� DYNAMIC MODEL FETCHING ENHANCED
- Smart context detection from provider APIs
- Automatic fallback to known limits when API unavailable
- Safety caps to prevent token overflow (100k max)
- Intelligent model filtering and deduplication

🛡️ IMPROVED ERROR HANDLING
- Specific error messages for Invalid JSON responses
- Token limit exceeded warnings with solutions
- API key validation with clear guidance
- Rate limiting detection and user guidance
- Network timeout handling

 PERFORMANCE OPTIMIZATIONS
- Reduced static models from 40+ to 12 essential
- Enhanced streaming error detection
- Better API response validation
- Improved context window display (shows M/k units)

🔧 TECHNICAL IMPROVEMENTS
- Dynamic model context detection from APIs
- Enhanced streaming reliability
- Better token limit enforcement
- Comprehensive error categorization
- Smart model validation before API calls

IMPACT:
 Eliminates Invalid JSON response errors
 Prevents token limit API rejections
 Provides accurate model capabilities
 Improves user experience with clear errors
 Enables full utilization of modern LLM context windows
2025-08-29 20:53:57 +02:00

449 lines
16 KiB
TypeScript

import { type ActionFunctionArgs } from '@remix-run/cloudflare';
import { createDataStream, generateId } from 'ai';
import { MAX_RESPONSE_SEGMENTS, MAX_TOKENS, type FileMap } from '~/lib/.server/llm/constants';
import { CONTINUE_PROMPT } from '~/lib/common/prompts/prompts';
import { streamText, type Messages, type StreamingOptions } from '~/lib/.server/llm/stream-text';
import SwitchableStream from '~/lib/.server/llm/switchable-stream';
import type { IProviderSetting } from '~/types/model';
import { createScopedLogger } from '~/utils/logger';
import { getFilePaths, selectContext } from '~/lib/.server/llm/select-context';
import type { ContextAnnotation, ProgressAnnotation } from '~/types/context';
import { WORK_DIR } from '~/utils/constants';
import { createSummary } from '~/lib/.server/llm/create-summary';
import { extractPropertiesFromMessage } from '~/lib/.server/llm/utils';
import type { DesignScheme } from '~/types/design-scheme';
import { MCPService } from '~/lib/services/mcpService';
export async function action(args: ActionFunctionArgs) {
return chatAction(args);
}
const logger = createScopedLogger('api.chat');
function parseCookies(cookieHeader: string): Record<string, string> {
const cookies: Record<string, string> = {};
const items = cookieHeader.split(';').map((cookie) => cookie.trim());
items.forEach((item) => {
const [name, ...rest] = item.split('=');
if (name && rest) {
const decodedName = decodeURIComponent(name.trim());
const decodedValue = decodeURIComponent(rest.join('=').trim());
cookies[decodedName] = decodedValue;
}
});
return cookies;
}
async function chatAction({ context, request }: ActionFunctionArgs) {
const { messages, files, promptId, contextOptimization, supabase, chatMode, designScheme, maxLLMSteps } =
await request.json<{
messages: Messages;
files: any;
promptId?: string;
contextOptimization: boolean;
chatMode: 'discuss' | 'build';
designScheme?: DesignScheme;
supabase?: {
isConnected: boolean;
hasSelectedProject: boolean;
credentials?: {
anonKey?: string;
supabaseUrl?: string;
};
};
maxLLMSteps: number;
}>();
const cookieHeader = request.headers.get('Cookie');
const apiKeys = JSON.parse(parseCookies(cookieHeader || '').apiKeys || '{}');
const providerSettings: Record<string, IProviderSetting> = JSON.parse(
parseCookies(cookieHeader || '').providers || '{}',
);
const stream = new SwitchableStream();
const cumulativeUsage = {
completionTokens: 0,
promptTokens: 0,
totalTokens: 0,
};
const encoder: TextEncoder = new TextEncoder();
let progressCounter: number = 1;
try {
const mcpService = MCPService.getInstance();
const totalMessageContent = messages.reduce((acc, message) => acc + message.content, '');
logger.debug(`Total message length: ${totalMessageContent.split(' ').length}, words`);
let lastChunk: string | undefined = undefined;
const dataStream = createDataStream({
async execute(dataStream) {
const filePaths = getFilePaths(files || {});
let filteredFiles: FileMap | undefined = undefined;
let summary: string | undefined = undefined;
let messageSliceId = 0;
const processedMessages = await mcpService.processToolInvocations(messages, dataStream);
if (processedMessages.length > 3) {
messageSliceId = processedMessages.length - 3;
}
if (filePaths.length > 0 && contextOptimization) {
logger.debug('Generating Chat Summary');
dataStream.writeData({
type: 'progress',
label: 'summary',
status: 'in-progress',
order: progressCounter++,
message: 'Analysing Request',
} satisfies ProgressAnnotation);
// Create a summary of the chat
console.log(`Messages count: ${processedMessages.length}`);
summary = await createSummary({
messages: [...processedMessages],
env: context.cloudflare?.env,
apiKeys,
providerSettings,
promptId,
contextOptimization,
onFinish(resp) {
if (resp.usage) {
logger.debug('createSummary token usage', JSON.stringify(resp.usage));
cumulativeUsage.completionTokens += resp.usage.completionTokens || 0;
cumulativeUsage.promptTokens += resp.usage.promptTokens || 0;
cumulativeUsage.totalTokens += resp.usage.totalTokens || 0;
}
},
});
dataStream.writeData({
type: 'progress',
label: 'summary',
status: 'complete',
order: progressCounter++,
message: 'Analysis Complete',
} satisfies ProgressAnnotation);
dataStream.writeMessageAnnotation({
type: 'chatSummary',
summary,
chatId: processedMessages.slice(-1)?.[0]?.id,
} as ContextAnnotation);
// Update context buffer
logger.debug('Updating Context Buffer');
dataStream.writeData({
type: 'progress',
label: 'context',
status: 'in-progress',
order: progressCounter++,
message: 'Determining Files to Read',
} satisfies ProgressAnnotation);
// Select context files
console.log(`Messages count: ${processedMessages.length}`);
filteredFiles = await selectContext({
messages: [...processedMessages],
env: context.cloudflare?.env,
apiKeys,
files,
providerSettings,
promptId,
contextOptimization,
summary,
onFinish(resp) {
if (resp.usage) {
logger.debug('selectContext token usage', JSON.stringify(resp.usage));
cumulativeUsage.completionTokens += resp.usage.completionTokens || 0;
cumulativeUsage.promptTokens += resp.usage.promptTokens || 0;
cumulativeUsage.totalTokens += resp.usage.totalTokens || 0;
}
},
});
if (filteredFiles) {
logger.debug(`files in context : ${JSON.stringify(Object.keys(filteredFiles))}`);
}
dataStream.writeMessageAnnotation({
type: 'codeContext',
files: Object.keys(filteredFiles).map((key) => {
let path = key;
if (path.startsWith(WORK_DIR)) {
path = path.replace(WORK_DIR, '');
}
return path;
}),
} as ContextAnnotation);
dataStream.writeData({
type: 'progress',
label: 'context',
status: 'complete',
order: progressCounter++,
message: 'Code Files Selected',
} satisfies ProgressAnnotation);
// logger.debug('Code Files Selected');
}
const options: StreamingOptions = {
supabaseConnection: supabase,
toolChoice: 'auto',
tools: mcpService.toolsWithoutExecute,
maxSteps: maxLLMSteps,
onStepFinish: ({ toolCalls }) => {
// add tool call annotations for frontend processing
toolCalls.forEach((toolCall) => {
mcpService.processToolCall(toolCall, dataStream);
});
},
onFinish: async ({ text: content, finishReason, usage }) => {
logger.debug('usage', JSON.stringify(usage));
if (usage) {
cumulativeUsage.completionTokens += usage.completionTokens || 0;
cumulativeUsage.promptTokens += usage.promptTokens || 0;
cumulativeUsage.totalTokens += usage.totalTokens || 0;
}
if (finishReason !== 'length') {
dataStream.writeMessageAnnotation({
type: 'usage',
value: {
completionTokens: cumulativeUsage.completionTokens,
promptTokens: cumulativeUsage.promptTokens,
totalTokens: cumulativeUsage.totalTokens,
},
});
dataStream.writeData({
type: 'progress',
label: 'response',
status: 'complete',
order: progressCounter++,
message: 'Response Generated',
} satisfies ProgressAnnotation);
await new Promise((resolve) => setTimeout(resolve, 0));
// stream.close();
return;
}
if (stream.switches >= MAX_RESPONSE_SEGMENTS) {
throw Error('Cannot continue message: Maximum segments reached');
}
const switchesLeft = MAX_RESPONSE_SEGMENTS - stream.switches;
logger.info(`Reached max token limit (${MAX_TOKENS}): Continuing message (${switchesLeft} switches left)`);
const lastUserMessage = processedMessages.filter((x) => x.role == 'user').slice(-1)[0];
const { model, provider } = extractPropertiesFromMessage(lastUserMessage);
processedMessages.push({ id: generateId(), role: 'assistant', content });
processedMessages.push({
id: generateId(),
role: 'user',
content: `[Model: ${model}]\n\n[Provider: ${provider}]\n\n${CONTINUE_PROMPT}`,
});
const result = await streamText({
messages: [...processedMessages],
env: context.cloudflare?.env,
options,
apiKeys,
files,
providerSettings,
promptId,
contextOptimization,
contextFiles: filteredFiles,
chatMode,
designScheme,
summary,
messageSliceId,
});
result.mergeIntoDataStream(dataStream);
(async () => {
for await (const part of result.fullStream) {
if (part.type === 'error') {
const error: any = part.error;
logger.error(`${error}`);
return;
}
}
})();
return;
},
};
dataStream.writeData({
type: 'progress',
label: 'response',
status: 'in-progress',
order: progressCounter++,
message: 'Generating Response',
} satisfies ProgressAnnotation);
const result = await streamText({
messages: [...processedMessages],
env: context.cloudflare?.env,
options,
apiKeys,
files,
providerSettings,
promptId,
contextOptimization,
contextFiles: filteredFiles,
chatMode,
designScheme,
summary,
messageSliceId,
});
(async () => {
for await (const part of result.fullStream) {
if (part.type === 'error') {
const error: any = part.error;
logger.error('Streaming error:', error);
// Enhanced error handling for common streaming issues
if (error.message?.includes('Invalid JSON response')) {
logger.error('Invalid JSON response detected - likely malformed API response');
} else if (error.message?.includes('token')) {
logger.error('Token-related error detected - possible token limit exceeded');
}
return;
}
}
})();
result.mergeIntoDataStream(dataStream);
},
onError: (error: any) => {
// Provide more specific error messages for common issues
const errorMessage = error.message || 'Unknown error';
if (errorMessage.includes('model') && errorMessage.includes('not found')) {
return 'Custom error: Invalid model selected. Please check that the model name is correct and available.';
}
if (errorMessage.includes('Invalid JSON response')) {
return 'Custom error: The AI service returned an invalid response. This may be due to an invalid model name, API rate limiting, or server issues. Try selecting a different model or check your API key.';
}
if (
errorMessage.includes('API key') ||
errorMessage.includes('unauthorized') ||
errorMessage.includes('authentication')
) {
return 'Custom error: Invalid or missing API key. Please check your API key configuration.';
}
if (errorMessage.includes('token') && errorMessage.includes('limit')) {
return 'Custom error: Token limit exceeded. The conversation is too long for the selected model. Try using a model with larger context window or start a new conversation.';
}
if (errorMessage.includes('rate limit') || errorMessage.includes('429')) {
return 'Custom error: API rate limit exceeded. Please wait a moment before trying again.';
}
if (errorMessage.includes('network') || errorMessage.includes('timeout')) {
return 'Custom error: Network error. Please check your internet connection and try again.';
}
return `Custom error: ${errorMessage}`;
},
}).pipeThrough(
new TransformStream({
transform: (chunk, controller) => {
if (!lastChunk) {
lastChunk = ' ';
}
if (typeof chunk === 'string') {
if (chunk.startsWith('g') && !lastChunk.startsWith('g')) {
controller.enqueue(encoder.encode(`0: "<div class=\\"__boltThought__\\">"\n`));
}
if (lastChunk.startsWith('g') && !chunk.startsWith('g')) {
controller.enqueue(encoder.encode(`0: "</div>\\n"\n`));
}
}
lastChunk = chunk;
let transformedChunk = chunk;
if (typeof chunk === 'string' && chunk.startsWith('g')) {
let content = chunk.split(':').slice(1).join(':');
if (content.endsWith('\n')) {
content = content.slice(0, content.length - 1);
}
transformedChunk = `0:${content}\n`;
}
// Convert the string stream to a byte stream
const str = typeof transformedChunk === 'string' ? transformedChunk : JSON.stringify(transformedChunk);
controller.enqueue(encoder.encode(str));
},
}),
);
return new Response(dataStream, {
status: 200,
headers: {
'Content-Type': 'text/event-stream; charset=utf-8',
Connection: 'keep-alive',
'Cache-Control': 'no-cache',
'Text-Encoding': 'chunked',
},
});
} catch (error: any) {
logger.error(error);
const errorResponse = {
error: true,
message: error.message || 'An unexpected error occurred',
statusCode: error.statusCode || 500,
isRetryable: error.isRetryable !== false, // Default to retryable unless explicitly false
provider: error.provider || 'unknown',
};
if (error.message?.includes('API key')) {
return new Response(
JSON.stringify({
...errorResponse,
message: 'Invalid or missing API key',
statusCode: 401,
isRetryable: false,
}),
{
status: 401,
headers: { 'Content-Type': 'application/json' },
statusText: 'Unauthorized',
},
);
}
return new Response(JSON.stringify(errorResponse), {
status: errorResponse.statusCode,
headers: { 'Content-Type': 'application/json' },
statusText: 'Error',
});
}
}