claude-task-master/scripts/modules/ai-services-unified.js

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/**
* ai-services-unified.js
* Centralized AI service layer using provider modules and config-manager.
*/
// Vercel AI SDK functions are NOT called directly anymore.
// import { generateText, streamText, generateObject } from 'ai';
// --- Core Dependencies ---
import {
getMainProvider,
getMainModelId,
getResearchProvider,
getResearchModelId,
getFallbackProvider,
getFallbackModelId,
getParametersForRole
} from './config-manager.js';
import { log, resolveEnvVariable, findProjectRoot } from './utils.js';
import * as anthropic from '../../src/ai-providers/anthropic.js';
import * as perplexity from '../../src/ai-providers/perplexity.js';
import * as google from '../../src/ai-providers/google.js';
import * as openai from '../../src/ai-providers/openai.js';
import * as xai from '../../src/ai-providers/xai.js';
import * as openrouter from '../../src/ai-providers/openrouter.js';
// TODO: Import other provider modules when implemented (ollama, etc.)
// --- Provider Function Map ---
// Maps provider names (lowercase) to their respective service functions
const PROVIDER_FUNCTIONS = {
anthropic: {
generateText: anthropic.generateAnthropicText,
streamText: anthropic.streamAnthropicText,
generateObject: anthropic.generateAnthropicObject
},
perplexity: {
generateText: perplexity.generatePerplexityText,
streamText: perplexity.streamPerplexityText,
generateObject: perplexity.generatePerplexityObject
},
google: {
// Add Google entry
generateText: google.generateGoogleText,
streamText: google.streamGoogleText,
generateObject: google.generateGoogleObject
},
openai: {
// ADD: OpenAI entry
generateText: openai.generateOpenAIText,
streamText: openai.streamOpenAIText,
generateObject: openai.generateOpenAIObject
},
xai: {
// ADD: xAI entry
generateText: xai.generateXaiText,
streamText: xai.streamXaiText,
generateObject: xai.generateXaiObject // Note: Object generation might be unsupported
},
openrouter: {
// ADD: OpenRouter entry
generateText: openrouter.generateOpenRouterText,
streamText: openrouter.streamOpenRouterText,
generateObject: openrouter.generateOpenRouterObject
}
// TODO: Add entries for ollama, etc. when implemented
};
// --- Configuration for Retries ---
const MAX_RETRIES = 2;
const INITIAL_RETRY_DELAY_MS = 1000;
// Helper function to check if an error is retryable
function isRetryableError(error) {
const errorMessage = error.message?.toLowerCase() || '';
return (
errorMessage.includes('rate limit') ||
errorMessage.includes('overloaded') ||
errorMessage.includes('service temporarily unavailable') ||
errorMessage.includes('timeout') ||
errorMessage.includes('network error') ||
error.status === 429 ||
error.status >= 500
);
}
/**
* Extracts a user-friendly error message from a potentially complex AI error object.
* Prioritizes nested messages and falls back to the top-level message.
* @param {Error | object | any} error - The error object.
* @returns {string} A concise error message.
*/
function _extractErrorMessage(error) {
try {
// Attempt 1: Look for Vercel SDK specific nested structure (common)
if (error?.data?.error?.message) {
return error.data.error.message;
}
// Attempt 2: Look for nested error message directly in the error object
if (error?.error?.message) {
return error.error.message;
}
// Attempt 3: Look for nested error message in response body if it's JSON string
if (typeof error?.responseBody === 'string') {
try {
const body = JSON.parse(error.responseBody);
if (body?.error?.message) {
return body.error.message;
}
} catch (parseError) {
// Ignore if responseBody is not valid JSON
}
}
// Attempt 4: Use the top-level message if it exists
if (typeof error?.message === 'string' && error.message) {
return error.message;
}
// Attempt 5: Handle simple string errors
if (typeof error === 'string') {
return error;
}
// Fallback
return 'An unknown AI service error occurred.';
} catch (e) {
// Safety net
return 'Failed to extract error message.';
}
}
/**
* Internal helper to resolve the API key for a given provider.
* @param {string} providerName - The name of the provider (lowercase).
* @param {object|null} session - Optional MCP session object.
* @param {string|null} projectRoot - Optional project root path for .env fallback.
* @returns {string|null} The API key or null if not found/needed.
* @throws {Error} If a required API key is missing.
*/
function _resolveApiKey(providerName, session, projectRoot = null) {
const keyMap = {
openai: 'OPENAI_API_KEY',
anthropic: 'ANTHROPIC_API_KEY',
google: 'GOOGLE_API_KEY',
perplexity: 'PERPLEXITY_API_KEY',
mistral: 'MISTRAL_API_KEY',
azure: 'AZURE_OPENAI_API_KEY',
openrouter: 'OPENROUTER_API_KEY',
xai: 'XAI_API_KEY'
};
docs: Update documentation for new AI/config architecture and finalize cleanup This commit updates all relevant documentation (READMEs, docs/*, .cursor/rules) to accurately reflect the finalized unified AI service architecture and the new configuration system (.taskmasterconfig + .env/mcp.json). It also includes the final code cleanup steps related to the refactoring. Key Changes: 1. **Documentation Updates:** * Revised `README.md`, `README-task-master.md`, `assets/scripts_README.md`, `docs/configuration.md`, and `docs/tutorial.md` to explain the new configuration split (.taskmasterconfig vs .env/mcp.json). * Updated MCP configuration examples in READMEs and tutorials to only include API keys in the `env` block. * Added/updated examples for using the `--research` flag in `docs/command-reference.md`, `docs/examples.md`, and `docs/tutorial.md`. * Updated `.cursor/rules/ai_services.mdc`, `.cursor/rules/architecture.mdc`, `.cursor/rules/dev_workflow.mdc`, `.cursor/rules/mcp.mdc`, `.cursor/rules/taskmaster.mdc`, `.cursor/rules/utilities.mdc`, and `.cursor/rules/new_features.mdc` to align with the new architecture, removing references to old patterns/files. * Removed internal rule links from user-facing rules (`taskmaster.mdc`, `dev_workflow.mdc`, `self_improve.mdc`). * Deleted outdated example file `docs/ai-client-utils-example.md`. 2. **Final Code Refactor & Cleanup:** * Corrected `update-task-by-id.js` by removing the last import from the old `ai-services.js`. * Refactored `update-subtask-by-id.js` to correctly use the unified service and logger patterns. * Removed the obsolete export block from `mcp-server/src/core/task-master-core.js`. * Corrected logger implementation in `update-tasks.js` for CLI context. * Updated API key mapping in `config-manager.js` and `ai-services-unified.js`. 3. **Configuration Files:** * Updated API keys in `.cursor/mcp.json`, replacing `GROK_API_KEY` with `XAI_API_KEY`. * Updated `.env.example` with current API key names. * Added `azureOpenaiBaseUrl` to `.taskmasterconfig` example. 4. **Task Management:** * Marked documentation subtask 61.10 as 'done'. * Includes various other task content/status updates from the diff summary. 5. **Changeset:** * Added `.changeset/cuddly-zebras-matter.md` for user-facing `expand`/`expand-all` improvements. This commit concludes the major architectural refactoring (Task 61) and ensures the documentation accurately reflects the current system.
2025-04-25 14:43:12 -04:00
// Double check this -- I have had to use an api key for ollama in the past
// if (providerName === 'ollama') {
// return null; // Ollama typically doesn't require an API key for basic setup
// }
const envVarName = keyMap[providerName];
if (!envVarName) {
throw new Error(
`Unknown provider '${providerName}' for API key resolution.`
);
}
const apiKey = resolveEnvVariable(envVarName, session, projectRoot);
if (!apiKey) {
throw new Error(
`Required API key ${envVarName} for provider '${providerName}' is not set in environment, session, or .env file.`
);
}
return apiKey;
}
/**
* Internal helper to attempt a provider-specific AI API call with retries.
*
* @param {function} providerApiFn - The specific provider function to call (e.g., generateAnthropicText).
* @param {object} callParams - Parameters object for the provider function.
* @param {string} providerName - Name of the provider (for logging).
* @param {string} modelId - Specific model ID (for logging).
* @param {string} attemptRole - The role being attempted (for logging).
* @returns {Promise<object>} The result from the successful API call.
* @throws {Error} If the call fails after all retries.
*/
async function _attemptProviderCallWithRetries(
providerApiFn,
callParams,
providerName,
modelId,
attemptRole
) {
let retries = 0;
const fnName = providerApiFn.name;
while (retries <= MAX_RETRIES) {
try {
log(
'info',
`Attempt ${retries + 1}/${MAX_RETRIES + 1} calling ${fnName} (Provider: ${providerName}, Model: ${modelId}, Role: ${attemptRole})`
);
// Call the specific provider function directly
const result = await providerApiFn(callParams);
log(
'info',
`${fnName} succeeded for role ${attemptRole} (Provider: ${providerName}) on attempt ${retries + 1}`
);
return result;
} catch (error) {
log(
'warn',
`Attempt ${retries + 1} failed for role ${attemptRole} (${fnName} / ${providerName}): ${error.message}`
);
if (isRetryableError(error) && retries < MAX_RETRIES) {
retries++;
const delay = INITIAL_RETRY_DELAY_MS * Math.pow(2, retries - 1);
log(
'info',
`Retryable error detected. Retrying in ${delay / 1000}s...`
);
await new Promise((resolve) => setTimeout(resolve, delay));
} else {
log(
'error',
`Non-retryable error or max retries reached for role ${attemptRole} (${fnName} / ${providerName}).`
);
throw error;
}
}
}
// Should not be reached due to throw in the else block
throw new Error(
`Exhausted all retries for role ${attemptRole} (${fnName} / ${providerName})`
);
}
/**
* Base logic for unified service functions.
* @param {string} serviceType - Type of service ('generateText', 'streamText', 'generateObject').
* @param {object} params - Original parameters passed to the service function.
* @param {string} [params.projectRoot] - Optional project root path.
* @returns {Promise<any>} Result from the underlying provider call.
*/
async function _unifiedServiceRunner(serviceType, params) {
const {
role: initialRole,
session,
projectRoot,
systemPrompt,
prompt,
schema,
objectName,
...restApiParams
} = params;
log('info', `${serviceType}Service called`, {
role: initialRole,
projectRoot
});
// Determine the effective project root (passed in or detected)
const effectiveProjectRoot = projectRoot || findProjectRoot();
let sequence;
if (initialRole === 'main') {
sequence = ['main', 'fallback', 'research'];
} else if (initialRole === 'research') {
sequence = ['research', 'fallback', 'main'];
} else if (initialRole === 'fallback') {
sequence = ['fallback', 'main', 'research'];
} else {
log(
'warn',
`Unknown initial role: ${initialRole}. Defaulting to main -> fallback -> research sequence.`
);
sequence = ['main', 'fallback', 'research'];
}
let lastError = null;
let lastCleanErrorMessage =
'AI service call failed for all configured roles.';
for (const currentRole of sequence) {
let providerName, modelId, apiKey, roleParams, providerFnSet, providerApiFn;
try {
log('info', `New AI service call with role: ${currentRole}`);
// 1. Get Config: Provider, Model, Parameters for the current role
// Pass effectiveProjectRoot to config getters
if (currentRole === 'main') {
providerName = getMainProvider(effectiveProjectRoot);
modelId = getMainModelId(effectiveProjectRoot);
} else if (currentRole === 'research') {
providerName = getResearchProvider(effectiveProjectRoot);
modelId = getResearchModelId(effectiveProjectRoot);
} else if (currentRole === 'fallback') {
providerName = getFallbackProvider(effectiveProjectRoot);
modelId = getFallbackModelId(effectiveProjectRoot);
} else {
log(
'error',
`Unknown role encountered in _unifiedServiceRunner: ${currentRole}`
);
lastError =
lastError || new Error(`Unknown AI role specified: ${currentRole}`);
continue;
}
if (!providerName || !modelId) {
log(
'warn',
`Skipping role '${currentRole}': Provider or Model ID not configured.`
);
lastError =
lastError ||
new Error(
`Configuration missing for role '${currentRole}'. Provider: ${providerName}, Model: ${modelId}`
);
continue;
}
// Pass effectiveProjectRoot to getParametersForRole
roleParams = getParametersForRole(currentRole, effectiveProjectRoot);
// 2. Get Provider Function Set
providerFnSet = PROVIDER_FUNCTIONS[providerName?.toLowerCase()];
if (!providerFnSet) {
log(
'warn',
`Skipping role '${currentRole}': Provider '${providerName}' not supported or map entry missing.`
);
lastError =
lastError ||
new Error(`Unsupported provider configured: ${providerName}`);
continue;
}
// Use the original service type to get the function
providerApiFn = providerFnSet[serviceType];
if (typeof providerApiFn !== 'function') {
log(
'warn',
`Skipping role '${currentRole}': Service type '${serviceType}' not implemented for provider '${providerName}'.`
);
lastError =
lastError ||
new Error(
`Service '${serviceType}' not implemented for provider ${providerName}`
);
continue;
}
// 3. Resolve API Key (will throw if required and missing)
// Pass effectiveProjectRoot to _resolveApiKey
apiKey = _resolveApiKey(
providerName?.toLowerCase(),
session,
effectiveProjectRoot
);
// 4. Construct Messages Array
const messages = [];
if (systemPrompt) {
messages.push({ role: 'system', content: systemPrompt });
}
// IN THE FUTURE WHEN DOING CONTEXT IMPROVEMENTS
// {
// type: 'text',
// text: 'Large cached context here like a tasks json',
// providerOptions: {
// anthropic: { cacheControl: { type: 'ephemeral' } }
// }
// }
// Example
// if (params.context) { // context is a json string of a tasks object or some other stu
// messages.push({
// type: 'text',
// text: params.context,
// providerOptions: { anthropic: { cacheControl: { type: 'ephemeral' } } }
// });
// }
if (prompt) {
// Ensure prompt exists before adding
messages.push({ role: 'user', content: prompt });
} else {
// Throw an error if the prompt is missing, as it's essential
throw new Error('User prompt content is missing.');
}
// 5. Prepare call parameters (using messages array)
const callParams = {
apiKey,
modelId,
maxTokens: roleParams.maxTokens,
temperature: roleParams.temperature,
messages,
...(serviceType === 'generateObject' && { schema, objectName }),
...restApiParams
};
// 6. Attempt the call with retries
const result = await _attemptProviderCallWithRetries(
providerApiFn,
callParams,
providerName,
modelId,
currentRole
);
log('info', `${serviceType}Service succeeded using role: ${currentRole}`);
return result;
} catch (error) {
const cleanMessage = _extractErrorMessage(error);
log(
'error',
`Service call failed for role ${currentRole} (Provider: ${providerName || 'unknown'}, Model: ${modelId || 'unknown'}): ${cleanMessage}`
);
lastError = error;
lastCleanErrorMessage = cleanMessage;
if (serviceType === 'generateObject') {
const lowerCaseMessage = cleanMessage.toLowerCase();
if (
lowerCaseMessage.includes(
'no endpoints found that support tool use'
) ||
lowerCaseMessage.includes('does not support tool_use') ||
lowerCaseMessage.includes('tool use is not supported') ||
lowerCaseMessage.includes('tools are not supported') ||
lowerCaseMessage.includes('function calling is not supported')
) {
const specificErrorMsg = `Model '${modelId || 'unknown'}' via provider '${providerName || 'unknown'}' does not support the 'tool use' required by generateObjectService. Please configure a model that supports tool/function calling for the '${currentRole}' role, or use generateTextService if structured output is not strictly required.`;
log('error', `[Tool Support Error] ${specificErrorMsg}`);
throw new Error(specificErrorMsg);
}
}
}
}
// If loop completes, all roles failed
log('error', `All roles in the sequence [${sequence.join(', ')}] failed.`);
// Throw a new error with the cleaner message from the last failure
throw new Error(lastCleanErrorMessage);
}
/**
* Unified service function for generating text.
* Handles client retrieval, retries, and fallback sequence.
*
* @param {object} params - Parameters for the service call.
* @param {string} params.role - The initial client role ('main', 'research', 'fallback').
* @param {object} [params.session=null] - Optional MCP session object.
* @param {string} [params.projectRoot=null] - Optional project root path for .env fallback.
* @param {string} params.prompt - The prompt for the AI.
* @param {string} [params.systemPrompt] - Optional system prompt.
* // Other specific generateText params can be included here.
* @returns {Promise<string>} The generated text content.
*/
async function generateTextService(params) {
return _unifiedServiceRunner('generateText', params);
}
/**
* Unified service function for streaming text.
* Handles client retrieval, retries, and fallback sequence.
*
* @param {object} params - Parameters for the service call.
* @param {string} params.role - The initial client role ('main', 'research', 'fallback').
* @param {object} [params.session=null] - Optional MCP session object.
* @param {string} [params.projectRoot=null] - Optional project root path for .env fallback.
* @param {string} params.prompt - The prompt for the AI.
* @param {string} [params.systemPrompt] - Optional system prompt.
* // Other specific streamText params can be included here.
* @returns {Promise<ReadableStream<string>>} A readable stream of text deltas.
*/
async function streamTextService(params) {
return _unifiedServiceRunner('streamText', params);
}
/**
* Unified service function for generating structured objects.
* Handles client retrieval, retries, and fallback sequence.
*
* @param {object} params - Parameters for the service call.
* @param {string} params.role - The initial client role ('main', 'research', 'fallback').
* @param {object} [params.session=null] - Optional MCP session object.
* @param {string} [params.projectRoot=null] - Optional project root path for .env fallback.
* @param {import('zod').ZodSchema} params.schema - The Zod schema for the expected object.
* @param {string} params.prompt - The prompt for the AI.
* @param {string} [params.systemPrompt] - Optional system prompt.
* @param {string} [params.objectName='generated_object'] - Name for object/tool.
* @param {number} [params.maxRetries=3] - Max retries for object generation.
* @returns {Promise<object>} The generated object matching the schema.
*/
async function generateObjectService(params) {
const defaults = {
objectName: 'generated_object',
maxRetries: 3
};
const combinedParams = { ...defaults, ...params };
return _unifiedServiceRunner('generateObject', combinedParams);
}
export { generateTextService, streamTextService, generateObjectService };