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Refactor smart routing configuration and async database handling (#519)

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samanhappy
2025-12-20 12:16:09 +08:00
committed by GitHub
parent eb1a965e45
commit 33eae50bd3
5 changed files with 122 additions and 104 deletions
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95
src/services/vectorSearchService.ts
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@@ -6,8 +6,8 @@ import { getSmartRoutingConfig } from '../utils/smartRouting.js';
import OpenAI from 'openai';
// Get OpenAI configuration from smartRouting settings or fallback to environment variables
const getOpenAIConfig = () => {
const smartRoutingConfig = getSmartRoutingConfig();
const getOpenAIConfig = async () => {
const smartRoutingConfig = await getSmartRoutingConfig();
return {
apiKey: smartRoutingConfig.openaiApiKey,
baseURL: smartRoutingConfig.openaiApiBaseUrl,
@@ -34,8 +34,8 @@ const getDimensionsForModel = (model: string): number => {
};
// Initialize the OpenAI client with smartRouting configuration
const getOpenAIClient = () => {
const config = getOpenAIConfig();
const getOpenAIClient = async () => {
const config = await getOpenAIConfig();
return new OpenAI({
apiKey: config.apiKey, // Get API key from smartRouting settings or environment variables
baseURL: config.baseURL, // Get base URL from smartRouting settings or fallback to default
@@ -53,32 +53,26 @@ const getOpenAIClient = () => {
* @returns Promise with vector embedding as number array
*/
async function generateEmbedding(text: string): Promise<number[]> {
try {
const config = getOpenAIConfig();
const openai = getOpenAIClient();
const config = await getOpenAIConfig();
const openai = await getOpenAIClient();
// Check if API key is configured
if (!openai.apiKey) {
console.warn('OpenAI API key is not configured. Using fallback embedding method.');
return generateFallbackEmbedding(text);
}
// Truncate text if it's too long (OpenAI has token limits)
const truncatedText = text.length > 8000 ? text.substring(0, 8000) : text;
// Call OpenAI's embeddings API
const response = await openai.embeddings.create({
model: config.embeddingModel, // Modern model with better performance
input: truncatedText,
});
// Return the embedding
return response.data[0].embedding;
} catch (error) {
console.error('Error generating embedding:', error);
console.warn('Falling back to simple embedding method');
// Check if API key is configured
if (!openai.apiKey) {
console.warn('OpenAI API key is not configured. Using fallback embedding method.');
return generateFallbackEmbedding(text);
}
// Truncate text if it's too long (OpenAI has token limits)
const truncatedText = text.length > 8000 ? text.substring(0, 8000) : text;
// Call OpenAI's embeddings API
const response = await openai.embeddings.create({
model: config.embeddingModel, // Modern model with better performance
input: truncatedText,
});
// Return the embedding
return response.data[0].embedding;
}
/**
@@ -198,12 +192,12 @@ export const saveToolsAsVectorEmbeddings = async (
return;
}
const smartRoutingConfig = getSmartRoutingConfig();
const smartRoutingConfig = await getSmartRoutingConfig();
if (!smartRoutingConfig.enabled) {
return;
}
const config = getOpenAIConfig();
const config = await getOpenAIConfig();
const vectorRepository = getRepositoryFactory(
'vectorEmbeddings',
)() as VectorEmbeddingRepository;
@@ -227,31 +221,26 @@ export const saveToolsAsVectorEmbeddings = async (
.filter(Boolean)
.join(' ');
try {
// Generate embedding
const embedding = await generateEmbedding(searchableText);
// Generate embedding
const embedding = await generateEmbedding(searchableText);
// Check database compatibility before saving
await checkDatabaseVectorDimensions(embedding.length);
// Check database compatibility before saving
await checkDatabaseVectorDimensions(embedding.length);
// Save embedding
await vectorRepository.saveEmbedding(
'tool',
`${serverName}:${tool.name}`,
searchableText,
embedding,
{
serverName,
toolName: tool.name,
description: tool.description,
inputSchema: tool.inputSchema,
},
config.embeddingModel, // Store the model used for this embedding
);
} catch (toolError) {
console.error(`Error processing tool ${tool.name} for server ${serverName}:`, toolError);
// Continue with the next tool rather than failing the whole batch
}
// Save embedding
await vectorRepository.saveEmbedding(
'tool',
`${serverName}:${tool.name}`,
searchableText,
embedding,
{
serverName,
toolName: tool.name,
description: tool.description,
inputSchema: tool.inputSchema,
},
config.embeddingModel, // Store the model used for this embedding
);
}
console.log(`Saved ${tools.length} tool embeddings for server: ${serverName}`);
@@ -381,7 +370,7 @@ export const getAllVectorizedTools = async (
}>
> => {
try {
const config = getOpenAIConfig();
const config = await getOpenAIConfig();
const vectorRepository = getRepositoryFactory(
'vectorEmbeddings',
)() as VectorEmbeddingRepository;