mirror of
https://github.com/simstudioai/sim.git
synced 2026-01-09 23:17:59 -05:00
Remove dead code
This commit is contained in:
Siddharth Ganesan
@@ -1,360 +0,0 @@
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import { sql } from 'drizzle-orm'
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import { type NextRequest, NextResponse } from 'next/server'
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import { z } from 'zod'
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import { getCopilotConfig, getCopilotModel } from '@/lib/copilot/config'
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import { createLogger } from '@/lib/logs/console-logger'
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import { generateEmbeddings } from '@/app/api/knowledge/utils'
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import { db } from '@/db'
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import { docsEmbeddings } from '@/db/schema'
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import { executeProviderRequest } from '@/providers'
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import { getApiKey } from '@/providers/utils'
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const logger = createLogger('DocsRAG')
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const DocsQuerySchema = z.object({
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query: z.string().min(1, 'Query is required'),
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topK: z.number().min(1).max(20).default(10),
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provider: z.string().optional(), // Allow override of provider per request
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model: z.string().optional(), // Allow override of model per request
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stream: z.boolean().optional().default(false), // Enable streaming responses
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})
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/**
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* Generate embedding for search query
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*/
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async function generateSearchEmbedding(query: string): Promise<number[]> {
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try {
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const embeddings = await generateEmbeddings([query])
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return embeddings[0] || []
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} catch (error) {
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logger.error('Failed to generate search embedding:', error)
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throw new Error('Failed to generate search embedding')
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}
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}
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/**
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* Search docs embeddings using vector similarity
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*/
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async function searchDocs(queryEmbedding: number[], topK: number) {
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try {
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const results = await db
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.select({
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chunkId: docsEmbeddings.chunkId,
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chunkText: docsEmbeddings.chunkText,
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sourceDocument: docsEmbeddings.sourceDocument,
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sourceLink: docsEmbeddings.sourceLink,
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headerText: docsEmbeddings.headerText,
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headerLevel: docsEmbeddings.headerLevel,
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similarity: sql<number>`1 - (${docsEmbeddings.embedding} <=> ${JSON.stringify(queryEmbedding)}::vector)`,
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})
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.from(docsEmbeddings)
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.orderBy(sql`${docsEmbeddings.embedding} <=> ${JSON.stringify(queryEmbedding)}::vector`)
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.limit(topK)
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return results
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} catch (error) {
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logger.error('Failed to search docs:', error)
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throw new Error('Failed to search docs')
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}
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}
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/**
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* Generate response using LLM with retrieved context
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*/
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async function generateResponse(
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query: string,
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chunks: any[],
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provider?: string,
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model?: string,
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stream = false,
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conversationHistory: any[] = []
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): Promise<string | ReadableStream> {
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const config = getCopilotConfig()
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// Determine which provider and model to use - allow overrides
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const selectedProvider = provider || config.rag.defaultProvider
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const selectedModel = model || config.rag.defaultModel
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// Get API key using the provider utils
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let apiKey: string
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try {
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// Use rotating key directly for hosted providers
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if (selectedProvider === 'openai' || selectedProvider === 'anthropic') {
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const { getRotatingApiKey } = require('@/lib/utils')
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apiKey = getRotatingApiKey(selectedProvider)
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} else {
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apiKey = getApiKey(selectedProvider, selectedModel)
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}
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} catch (error) {
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logger.error(`Failed to get API key for ${selectedProvider} ${selectedModel}:`, error)
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throw new Error(
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`API key not configured for ${selectedProvider}. Please set up API keys for this provider or use a different one.`
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)
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}
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// Format chunks as context with numbered sources
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const context = chunks
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.map((chunk, index) => {
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// Ensure all chunk properties are strings to avoid object serialization
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const headerText =
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typeof chunk.headerText === 'string'
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? chunk.headerText
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: String(chunk.headerText || 'Untitled Section')
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const sourceDocument =
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typeof chunk.sourceDocument === 'string'
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? chunk.sourceDocument
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: String(chunk.sourceDocument || 'Unknown Document')
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const sourceLink =
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typeof chunk.sourceLink === 'string' ? chunk.sourceLink : String(chunk.sourceLink || '#')
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const chunkText =
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typeof chunk.chunkText === 'string' ? chunk.chunkText : String(chunk.chunkText || '')
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return `[${index + 1}] ${headerText}
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Document: ${sourceDocument}
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URL: ${sourceLink}
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Content: ${chunkText}`
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})
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.join('\n\n')
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// Build conversation context if we have history
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let conversationContext = ''
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if (conversationHistory.length > 0) {
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conversationContext = '\n\nConversation History:\n'
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conversationHistory.slice(-config.general.maxConversationHistory).forEach((msg: any) => {
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// Use config for conversation history limit
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const role = msg.role === 'user' ? 'Human' : 'Assistant'
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conversationContext += `${role}: ${msg.content}\n`
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})
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conversationContext += '\n'
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}
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const systemPrompt = `You are a helpful assistant that answers questions about Sim Studio documentation. You are having a conversation with the user, so refer to the conversation history when relevant.
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IMPORTANT: Use inline citations strategically and sparingly. When referencing information from the sources, include the citation number in curly braces like {cite:1}, {cite:2}, etc.
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Citation Guidelines:
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- Cite each source only ONCE at the specific header or topic that relates to that source
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- Do NOT repeatedly cite the same source throughout your response
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- Place citations directly after the header or concept that the source specifically addresses
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- If multiple sources support the same specific topic, cite them together like {cite:1}{cite:2}{cite:3}
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- Each citation should be placed at the relevant header/topic it supports, not grouped at the beginning
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- Avoid cluttering the text with excessive citations
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Content Guidelines:
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- Answer the user's question accurately using the provided documentation
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- Consider the conversation history and refer to previous messages when relevant
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- Format your response in clean, readable markdown
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- Use bullet points, code blocks, and headers where appropriate
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- If the question cannot be answered from the context, say so clearly
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- Be conversational but precise
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- NEVER include object representations like "[object Object]" - always use proper text
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- When mentioning tool names, use their actual names from the documentation
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The sources are numbered [1] through [${chunks.length}] in the context below.`
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const userPrompt = `${conversationContext}Current Question: ${query}
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Documentation Context:
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${context}`
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try {
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logger.info(`Generating response using provider: ${selectedProvider}, model: ${selectedModel}`)
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const providerRequest = {
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model: selectedModel,
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systemPrompt,
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context: userPrompt,
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temperature: config.rag.temperature,
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maxTokens: config.rag.maxTokens,
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apiKey,
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stream,
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}
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const response = await executeProviderRequest(selectedProvider, providerRequest)
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// Handle different response types
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if (response instanceof ReadableStream) {
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if (stream) {
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return response // Return the stream directly for streaming requests
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}
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throw new Error('Unexpected streaming response when non-streaming was requested')
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}
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if ('stream' in response && 'execution' in response) {
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// Handle StreamingExecution for providers like Anthropic
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if (stream) {
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return response.stream // Return the stream from StreamingExecution
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}
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throw new Error('Unexpected streaming execution response when non-streaming was requested')
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}
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// At this point, we have a ProviderResponse
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const content = response.content || 'Sorry, I could not generate a response.'
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// Clean up any object serialization artifacts
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const cleanedContent = content
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.replace(/\[object Object\],?/g, '') // Remove [object Object] artifacts
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.replace(/\s+/g, ' ') // Normalize whitespace
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.trim()
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return cleanedContent
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} catch (error) {
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logger.error('Failed to generate LLM response:', error)
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throw new Error(
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`Failed to generate response using ${selectedProvider}: ${error instanceof Error ? error.message : 'Unknown error'}`
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)
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}
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}
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/**
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* POST /api/docs/ask
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* Ask questions about Sim Studio documentation using RAG (no chat functionality)
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*/
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export async function POST(req: NextRequest) {
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const requestId = crypto.randomUUID()
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try {
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const body = await req.json()
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const { query, topK, provider, model, stream } = DocsQuerySchema.parse(body)
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const config = getCopilotConfig()
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const ragConfig = getCopilotModel('rag')
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logger.info(`[${requestId}] Docs RAG query: "${query}"`, {
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provider: provider || ragConfig.provider,
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model: model || ragConfig.model,
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topK,
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})
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// Step 1: Generate embedding for the query
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logger.info(`[${requestId}] Generating query embedding...`)
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const queryEmbedding = await generateSearchEmbedding(query)
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if (queryEmbedding.length === 0) {
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return NextResponse.json({ error: 'Failed to generate query embedding' }, { status: 500 })
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}
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// Step 2: Search for relevant docs chunks
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logger.info(`[${requestId}] Searching docs for top ${topK} chunks...`)
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const chunks = await searchDocs(queryEmbedding, topK)
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if (chunks.length === 0) {
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return NextResponse.json({
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success: true,
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response:
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"I couldn't find any relevant documentation for your question. Please try rephrasing your query or check if you're asking about a feature that exists in Sim Studio.",
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sources: [],
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metadata: {
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requestId,
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chunksFound: 0,
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query,
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provider: provider || ragConfig.provider,
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model: model || ragConfig.model,
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},
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})
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}
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// Step 3: Generate response using LLM
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logger.info(`[${requestId}] Generating LLM response with ${chunks.length} chunks...`)
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const response = await generateResponse(query, chunks, provider, model, stream, [])
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// Step 4: Format sources for response
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const sources = chunks.map((chunk) => ({
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title: chunk.headerText,
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document: chunk.sourceDocument,
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link: chunk.sourceLink,
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similarity: Math.round(chunk.similarity * 100) / 100,
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}))
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// Handle streaming response
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if (response instanceof ReadableStream) {
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logger.info(`[${requestId}] Returning streaming response`)
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const encoder = new TextEncoder()
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const decoder = new TextDecoder()
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return new Response(
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new ReadableStream({
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async start(controller) {
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const reader = response.getReader()
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// Send initial metadata
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const metadata = {
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type: 'metadata',
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sources,
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metadata: {
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requestId,
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chunksFound: chunks.length,
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query,
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topSimilarity: sources[0]?.similarity,
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provider: provider || ragConfig.provider,
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model: model || ragConfig.model,
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},
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}
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controller.enqueue(encoder.encode(`data: ${JSON.stringify(metadata)}\n\n`))
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try {
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while (true) {
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const { done, value } = await reader.read()
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if (done) break
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const chunkText = decoder.decode(value)
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const cleanedChunk = chunkText.replace(/\[object Object\],?/g, '')
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const contentChunk = {
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type: 'content',
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content: cleanedChunk,
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}
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controller.enqueue(encoder.encode(`data: ${JSON.stringify(contentChunk)}\n\n`))
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}
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controller.enqueue(encoder.encode(`data: {"type":"done"}\n\n`))
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} catch (error) {
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logger.error(`[${requestId}] Streaming error:`, error)
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const errorChunk = {
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type: 'error',
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error: 'Streaming failed',
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}
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controller.enqueue(encoder.encode(`data: ${JSON.stringify(errorChunk)}\n\n`))
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} finally {
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controller.close()
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}
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},
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}),
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{
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headers: {
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'Content-Type': 'text/event-stream',
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'Cache-Control': 'no-cache',
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Connection: 'keep-alive',
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},
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}
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)
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}
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logger.info(`[${requestId}] RAG response generated successfully`)
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return NextResponse.json({
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success: true,
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response,
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sources,
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metadata: {
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requestId,
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chunksFound: chunks.length,
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query,
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topSimilarity: sources[0]?.similarity,
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provider: provider || ragConfig.provider,
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model: model || ragConfig.model,
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},
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})
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} catch (error) {
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if (error instanceof z.ZodError) {
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return NextResponse.json(
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{ error: 'Invalid request data', details: error.errors },
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{ status: 400 }
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)
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}
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logger.error(`[${requestId}] RAG error:`, error)
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return NextResponse.json({ error: 'Internal server error' }, { status: 500 })
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}
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}
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