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chore: Update configuration and dependencies for enhanced MCP server functionality

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- Add RATE_LIMIT_MAX_AUTH_REQUESTS to .env.example for improved rate limiting
- Update bun.lock and package.json to include new dependencies: @anthropic-ai/sdk, express-rate-limit, and their type definitions
- Modify bunfig.toml for build settings and output configuration
- Refactor src/config.ts to incorporate rate limiting settings
- Implement security middleware for enhanced request validation and sanitization
- Introduce rate limiting middleware for API and authentication endpoints
- Add tests for configuration validation and rate limiting functionality
This commit is contained in:
jango-blockchained
2025-03-23 13:00:02 +01:00
parent 2d5ae034c9
commit febc9bd5b5
20 changed files with 1347 additions and 532 deletions
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125
extra/ha-analyzer-cli.ts
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@@ -1,5 +1,5 @@
import fetch from "node-fetch";
import OpenAI from "openai";
import { Anthropic } from "@anthropic-ai/sdk";
import { DOMParser, Element, Document } from '@xmldom/xmldom';
import dotenv from 'dotenv';
import readline from 'readline';
@@ -9,11 +9,11 @@ import chalk from 'chalk';
dotenv.config();
// Retrieve API keys from environment variables
const openaiApiKey = process.env.OPENAI_API_KEY;
const anthropicApiKey = process.env.ANTHROPIC_API_KEY;
const hassToken = process.env.HASS_TOKEN;
if (!openaiApiKey) {
console.error("Please set the OPENAI_API_KEY environment variable.");
if (!anthropicApiKey) {
console.error("Please set the ANTHROPIC_API_KEY environment variable.");
process.exit(1);
}
@@ -113,13 +113,11 @@ interface ModelConfig {
contextWindow: number;
}
// Update model listing to filter based on API key availability
// Update model listing to use Anthropic's Claude models
const AVAILABLE_MODELS: ModelConfig[] = [
// OpenAI models always available
{ name: 'gpt-4', maxTokens: 8192, contextWindow: 8192 },
{ name: 'gpt-4-turbo-preview', maxTokens: 4096, contextWindow: 128000 },
{ name: 'gpt-3.5-turbo', maxTokens: 4096, contextWindow: 16385 },
{ name: 'gpt-3.5-turbo-16k', maxTokens: 16385, contextWindow: 16385 },
// Anthropic Claude models
{ name: 'claude-3-7-sonnet-20250219', maxTokens: 4096, contextWindow: 200000 },
{ name: 'claude-3-5-haiku-20241022', maxTokens: 4096, contextWindow: 200000 },
// Conditionally include DeepSeek models
...(process.env.DEEPSEEK_API_KEY ? [
@@ -131,7 +129,7 @@ const AVAILABLE_MODELS: ModelConfig[] = [
// Add configuration interface
interface AppConfig {
mcpServer: string;
openaiModel: string;
anthropicModel: string;
maxRetries: number;
analysisTimeout: number;
selectedModel: ModelConfig;
@@ -146,30 +144,31 @@ const logger = {
debug: (msg: string) => process.env.DEBUG && console.log(chalk.gray(`› ${msg}`))
};
// Update default model selection in loadConfig
// Update loadConfig to use Claude models
function loadConfig(): AppConfig {
// Always use gpt-4 for now
const defaultModel = AVAILABLE_MODELS.find(m => m.name === 'gpt-4') || AVAILABLE_MODELS[0];
// Use Claude 3.7 Sonnet as the default model
const defaultModel = AVAILABLE_MODELS.find(m => m.name === 'claude-3-7-sonnet-20250219') || AVAILABLE_MODELS[0];
return {
mcpServer: process.env.MCP_SERVER || 'http://localhost:3000',
openaiModel: defaultModel.name,
anthropicModel: defaultModel.name,
maxRetries: parseInt(process.env.MAX_RETRIES || '3'),
analysisTimeout: parseInt(process.env.ANALYSIS_TIMEOUT || '30000'),
selectedModel: defaultModel
};
}
function getOpenAIClient(): OpenAI {
// Replace OpenAI client with Anthropic client
function getAnthropicClient(): Anthropic {
const config = loadConfig();
return new OpenAI({
apiKey: config.selectedModel.name.startsWith('deepseek')
? process.env.DEEPSEEK_API_KEY
: openaiApiKey,
baseURL: config.selectedModel.name.startsWith('deepseek')
? 'https://api.deepseek.com/v1'
: 'https://api.openai.com/v1'
if (config.selectedModel.name.startsWith('deepseek') && process.env.DEEPSEEK_API_KEY) {
// This is just a stub for DeepSeek - you'd need to implement this properly
throw new Error("DeepSeek models not implemented yet with Anthropic integration");
}
return new Anthropic({
apiKey: anthropicApiKey,
});
}
@@ -463,7 +462,7 @@ function getRelevantDeviceTypes(prompt: string): string[] {
}
/**
* Generates analysis and recommendations using the OpenAI API based on the Home Assistant data
* Generates analysis and recommendations using the Anthropic API based on the Home Assistant data
*/
async function generateAnalysis(haInfo: any): Promise<SystemAnalysis> {
const config = loadConfig();
@@ -520,7 +519,7 @@ async function generateAnalysis(haInfo: any): Promise<SystemAnalysis> {
}
// Original analysis code for non-test mode
const openai = getOpenAIClient();
const anthropic = getAnthropicClient();
const systemSummary = {
total_devices: haInfo.device_summary?.total_devices || 0,
@@ -588,20 +587,21 @@ Generate your response in this EXACT format:
</analysis>`;
try {
const completion = await openai.chat.completions.create({
const completion = await anthropic.messages.create({
model: config.selectedModel.name,
messages: [
{
role: "system",
content: "You are a Home Assistant expert. Analyze the system data and provide detailed insights in the specified XML format. Be specific and actionable in your recommendations."
},
{ role: "user", content: prompt }
role: "user",
content: `<system>You are a Home Assistant expert. Analyze the system data and provide detailed insights in the specified XML format. Be specific and actionable in your recommendations.</system>
${prompt}`
}
],
temperature: 0.7,
max_tokens: Math.min(config.selectedModel.maxTokens, 4000)
});
const result = completion.choices[0].message?.content || "";
const result = completion.content[0]?.type === 'text' ? completion.content[0].text : "";
// Clean the response and parse XML
const cleanedResult = result.replace(/```xml/g, '').replace(/```/g, '').trim();
@@ -673,7 +673,7 @@ Generate your response in this EXACT format:
throw new Error(`Failed to parse analysis response: ${parseError.message}`);
}
} catch (error) {
console.error("Error during OpenAI API call:", error);
console.error("Error during Anthropic API call:", error);
throw new Error("Failed to generate analysis");
}
}
@@ -814,7 +814,7 @@ async function handleAutomationOptimization(haInfo: any): Promise<void> {
}
async function analyzeAutomations(automations: any[]): Promise<string> {
const openai = getOpenAIClient();
const anthropic = getAnthropicClient();
const config = loadConfig();
// Create a more detailed summary of automations
@@ -894,20 +894,21 @@ Focus on:
5. Analyzing the distribution of automation types and suggesting optimizations`;
try {
const completion = await openai.chat.completions.create({
const completion = await anthropic.messages.create({
model: config.selectedModel.name,
messages: [
{
role: "system",
content: "You are a Home Assistant automation expert. Analyze the provided automation summary and respond with specific, actionable suggestions in the required XML format."
},
{ role: "user", content: prompt }
role: "user",
content: `<system>You are a Home Assistant automation expert. Analyze the provided automation summary and respond with specific, actionable suggestions in the required XML format.</system>
${prompt}`
}
],
temperature: 0.2,
max_tokens: Math.min(config.selectedModel.maxTokens, 2048)
});
const response = completion.choices[0].message?.content || "";
const response = completion.content[0]?.type === 'text' ? completion.content[0].text : "";
// Ensure the response is valid XML
if (!response.trim().startsWith('<analysis>')) {
@@ -945,7 +946,7 @@ Focus on:
}
}
// Add new handleCustomPrompt function
// Update handleCustomPrompt function to use Anthropic
async function handleCustomPrompt(haInfo: any, customPrompt: string): Promise<void> {
try {
// Add device metadata
@@ -1027,15 +1028,15 @@ async function handleCustomPrompt(haInfo: any, customPrompt: string): Promise<vo
return;
}
const openai = getOpenAIClient();
const anthropic = getAnthropicClient();
const config = loadConfig();
const completion = await openai.chat.completions.create({
const completion = await anthropic.messages.create({
model: config.selectedModel.name,
messages: [
{
role: "system",
content: `You are a Home Assistant expert. Analyze the following Home Assistant information and respond to the user's prompt.
role: "user",
content: `<system>You are a Home Assistant expert. Analyze the following Home Assistant information and respond to the user's prompt.
Current system has ${totalDevices} devices across ${deviceTypes.length} types.
Device distribution: ${deviceSummary}
@@ -1047,16 +1048,17 @@ async function handleCustomPrompt(haInfo: any, customPrompt: string): Promise<vo
- Service domains used: ${automationSummary.service_domains.join(', ')}
Detailed Automation List:
${JSON.stringify(automationDetails, null, 2)}`
},
{ role: "user", content: customPrompt },
${JSON.stringify(automationDetails, null, 2)}</system>
${customPrompt}`
}
],
max_tokens: Math.min(config.selectedModel.maxTokens, 2048), // Limit token usage
max_tokens: Math.min(config.selectedModel.maxTokens, 2048),
temperature: 0.3,
});
console.log("\nAnalysis Results:\n");
console.log(completion.choices[0].message?.content || "No response generated");
console.log(completion.content[0]?.type === 'text' ? completion.content[0].text : "No response generated");
} catch (error) {
console.error("Error processing custom prompt:", error);
@@ -1075,24 +1077,25 @@ async function handleCustomPrompt(haInfo: any, customPrompt: string): Promise<vo
// Retry with simplified prompt if there's an error
try {
const retryPrompt = "Please provide a simpler analysis of the Home Assistant system.";
const openai = getOpenAIClient();
const anthropic = getAnthropicClient();
const config = loadConfig();
const retryCompletion = await openai.chat.completions.create({
const retryCompletion = await anthropic.messages.create({
model: config.selectedModel.name,
messages: [
{
role: "system",
content: "You are a Home Assistant expert. Provide a simple analysis of the system."
},
{ role: "user", content: retryPrompt },
role: "user",
content: `<system>You are a Home Assistant expert. Provide a simple analysis of the system.</system>
${retryPrompt}`
}
],
max_tokens: Math.min(config.selectedModel.maxTokens, 2048), // Limit token usage
max_tokens: Math.min(config.selectedModel.maxTokens, 2048),
temperature: 0.3,
});
console.log("\nAnalysis Results:\n");
console.log(retryCompletion.choices[0].message?.content || "No response generated");
console.log(retryCompletion.content[0]?.type === 'text' ? retryCompletion.content[0].text : "No response generated");
} catch (retryError) {
console.error("Error during retry:", retryError);
}
@@ -1174,9 +1177,9 @@ function getItems(xmlDoc: Document, path: string): string[] {
.map(item => (item as Element).textContent || "");
}
// Replace the Express server initialization at the bottom with Bun's server
if (process.env.PROCESSOR_TYPE === 'openai') {
// Initialize Bun server for OpenAI
// Replace the Express/Bun server initialization
if (process.env.PROCESSOR_TYPE === 'anthropic') {
// Initialize Bun server for Anthropic
const server = Bun.serve({
port: process.env.PORT || 3000,
async fetch(req) {
@@ -1206,7 +1209,7 @@ if (process.env.PROCESSOR_TYPE === 'openai') {
},
});
console.log(`[OpenAI Server] Running on port ${server.port}`);
console.log(`[Anthropic Server] Running on port ${server.port}`);
} else {
console.log('[Claude Mode] Using stdio communication');
}