mirror of
https://github.com/Crosstalk-Solutions/project-nomad.git
synced 2026-03-28 03:29:25 +01:00
feat(AI Assistant): performance improvements and smarter RAG context usage
This commit is contained in:
Jake Turner
Jake Turner
parent
460756f581
commit
96e5027055
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@ -5,7 +5,7 @@ import { modelNameSchema } from '#validators/download'
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import { chatSchema, getAvailableModelsSchema } from '#validators/ollama'
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import { inject } from '@adonisjs/core'
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import type { HttpContext } from '@adonisjs/core/http'
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import { DEFAULT_QUERY_REWRITE_MODEL, SYSTEM_PROMPTS } from '../../constants/ollama.js'
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import { DEFAULT_QUERY_REWRITE_MODEL, RAG_CONTEXT_LIMITS, SYSTEM_PROMPTS } from '../../constants/ollama.js'
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import logger from '@adonisjs/core/services/logger'
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import type { Message } from 'ollama'
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@ -66,9 +66,28 @@ export default class OllamaController {
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logger.debug(`[RAG] Retrieved ${relevantDocs.length} relevant documents for query: "${rewrittenQuery}"`)
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// If relevant context is found, inject as a system message
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// If relevant context is found, inject as a system message with adaptive limits
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if (relevantDocs.length > 0) {
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const contextText = relevantDocs
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// Determine context budget based on model size
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const { maxResults, maxTokens } = this.getContextLimitsForModel(reqData.model)
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let trimmedDocs = relevantDocs.slice(0, maxResults)
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// Apply token cap if set (estimate ~4 chars per token)
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// Always include the first (most relevant) result — the cap only gates subsequent results
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if (maxTokens > 0) {
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const charCap = maxTokens * 4
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let totalChars = 0
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trimmedDocs = trimmedDocs.filter((doc, idx) => {
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totalChars += doc.text.length
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return idx === 0 || totalChars <= charCap
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})
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}
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logger.debug(
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`[RAG] Injecting ${trimmedDocs.length}/${relevantDocs.length} results (model: ${reqData.model}, maxResults: ${maxResults}, maxTokens: ${maxTokens || 'unlimited'})`
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)
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const contextText = trimmedDocs
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.map((doc, idx) => `[Context ${idx + 1}] (Relevance: ${(doc.score * 100).toFixed(1)}%)\n${doc.text}`)
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.join('\n\n')
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@ -174,6 +193,25 @@ export default class OllamaController {
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return await this.ollamaService.getModels()
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}
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/**
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* Determines RAG context limits based on model size extracted from the model name.
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* Parses size indicators like "1b", "3b", "8b", "70b" from model names/tags.
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*/
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private getContextLimitsForModel(modelName: string): { maxResults: number; maxTokens: number } {
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// Extract parameter count from model name (e.g., "llama3.2:3b", "qwen2.5:1.5b", "gemma:7b")
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const sizeMatch = modelName.match(/(\d+\.?\d*)[bB]/)
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const paramBillions = sizeMatch ? parseFloat(sizeMatch[1]) : 8 // default to 8B if unknown
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for (const tier of RAG_CONTEXT_LIMITS) {
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if (paramBillions <= tier.maxParams) {
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return { maxResults: tier.maxResults, maxTokens: tier.maxTokens }
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}
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}
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// Fallback: no limits
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return { maxResults: 5, maxTokens: 0 }
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}
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private async rewriteQueryWithContext(
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messages: Message[]
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): Promise<string | null> {
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@ -199,8 +237,8 @@ export default class OllamaController {
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})
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.join('\n')
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const availableModels = await this.ollamaService.getAvailableModels({ query: null, limit: 500 })
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const rewriteModelAvailable = availableModels?.models.some(model => model.name === DEFAULT_QUERY_REWRITE_MODEL)
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const installedModels = await this.ollamaService.getModels(true)
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const rewriteModelAvailable = installedModels?.some(model => model.name === DEFAULT_QUERY_REWRITE_MODEL)
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if (!rewriteModelAvailable) {
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logger.warn(`[RAG] Query rewrite model "${DEFAULT_QUERY_REWRITE_MODEL}" not available. Skipping query rewriting.`)
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const lastUserMessage = [...messages].reverse().find(msg => msg.role === 'user')
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@ -16,11 +16,13 @@ import { join, resolve, sep } from 'node:path'
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import KVStore from '#models/kv_store'
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import { ZIMExtractionService } from './zim_extraction_service.js'
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import { ZIM_BATCH_SIZE } from '../../constants/zim_extraction.js'
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import { ProcessAndEmbedFileResponse, ProcessZIMFileResponse, RAGResult, RerankedRAGResult } from '../../types/rag.js'
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@inject()
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export class RagService {
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private qdrant: QdrantClient | null = null
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private qdrantInitPromise: Promise<void> | null = null
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private embeddingModelVerified = false
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public static UPLOADS_STORAGE_PATH = 'storage/kb_uploads'
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public static CONTENT_COLLECTION_NAME = 'nomad_knowledge_base'
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public static EMBEDDING_MODEL = 'nomic-embed-text:v1.5'
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@ -33,6 +35,7 @@ export class RagService {
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// Nomic Embed Text v1.5 uses task-specific prefixes for optimal performance
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public static SEARCH_DOCUMENT_PREFIX = 'search_document: '
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public static SEARCH_QUERY_PREFIX = 'search_query: '
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public static EMBEDDING_BATCH_SIZE = 8 // Conservative batch size for low-end hardware
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constructor(
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private dockerService: DockerService,
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@ -75,6 +78,16 @@ export class RagService {
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},
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})
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}
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// Create payload indexes for faster filtering (idempotent — Qdrant ignores duplicates)
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await this.qdrant!.createPayloadIndex(collectionName, {
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field_name: 'source',
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field_schema: 'keyword',
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})
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await this.qdrant!.createPayloadIndex(collectionName, {
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field_name: 'content_type',
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field_schema: 'keyword',
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})
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} catch (error) {
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logger.error('Error ensuring Qdrant collection:', error)
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throw error
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@ -148,14 +161,57 @@ export class RagService {
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/**
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* Preprocesses a query to improve retrieval by expanding it with context.
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* This helps match documents even when using different terminology.
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* TODO: We could probably move this to a separate QueryPreprocessor class if it grows more complex, but for now it's manageable here.
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*/
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private static QUERY_EXPANSION_DICTIONARY: Record<string, string> = {
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'bob': 'bug out bag',
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'bov': 'bug out vehicle',
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'bol': 'bug out location',
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'edc': 'every day carry',
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'mre': 'meal ready to eat',
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'shtf': 'shit hits the fan',
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'teotwawki': 'the end of the world as we know it',
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'opsec': 'operational security',
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'ifak': 'individual first aid kit',
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'ghb': 'get home bag',
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'ghi': 'get home in',
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'wrol': 'without rule of law',
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'emp': 'electromagnetic pulse',
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'ham': 'ham amateur radio',
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'nbr': 'nuclear biological radiological',
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'cbrn': 'chemical biological radiological nuclear',
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'sar': 'search and rescue',
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'comms': 'communications radio',
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'fifo': 'first in first out',
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'mylar': 'mylar bag food storage',
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'paracord': 'paracord 550 cord',
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'ferro': 'ferro rod fire starter',
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'bivvy': 'bivvy bivy emergency shelter',
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'bdu': 'battle dress uniform',
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'gmrs': 'general mobile radio service',
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'frs': 'family radio service',
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'nbc': 'nuclear biological chemical',
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}
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private preprocessQuery(query: string): string {
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// Future: this is a placeholder for more advanced query expansion techniques.
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// For now, we simply trim whitespace. Improvements could include:
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// - Synonym expansion using a thesaurus
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// - Adding related terms based on domain knowledge
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// - Using a language model to rephrase or elaborate the query
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const expanded = query.trim()
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let expanded = query.trim()
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// Expand known domain abbreviations/acronyms
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const words = expanded.toLowerCase().split(/\s+/)
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const expansions: string[] = []
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for (const word of words) {
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const cleaned = word.replace(/[^\w]/g, '')
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if (RagService.QUERY_EXPANSION_DICTIONARY[cleaned]) {
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expansions.push(RagService.QUERY_EXPANSION_DICTIONARY[cleaned])
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}
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}
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if (expansions.length > 0) {
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expanded = `${expanded} ${expansions.join(' ')}`
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logger.debug(`[RAG] Query expanded with domain terms: "${expanded}"`)
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}
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logger.debug(`[RAG] Original query: "${query}"`)
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logger.debug(`[RAG] Preprocessed query: "${expanded}"`)
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return expanded
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@ -187,22 +243,26 @@ export class RagService {
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RagService.EMBEDDING_DIMENSION
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)
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const allModels = await this.ollamaService.getModels(true)
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const embeddingModel = allModels.find((model) => model.name === RagService.EMBEDDING_MODEL)
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if (!this.embeddingModelVerified) {
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const allModels = await this.ollamaService.getModels(true)
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const embeddingModel = allModels.find((model) => model.name === RagService.EMBEDDING_MODEL)
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if (!embeddingModel) {
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try {
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const downloadResult = await this.ollamaService.downloadModel(RagService.EMBEDDING_MODEL)
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if (!downloadResult.success) {
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throw new Error(downloadResult.message || 'Unknown error during model download')
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if (!embeddingModel) {
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try {
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const downloadResult = await this.ollamaService.downloadModel(RagService.EMBEDDING_MODEL)
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if (!downloadResult.success) {
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throw new Error(downloadResult.message || 'Unknown error during model download')
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}
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} catch (modelError) {
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logger.error(
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`[RAG] Embedding model ${RagService.EMBEDDING_MODEL} not found locally and failed to download:`,
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modelError
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)
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this.embeddingModelVerified = false
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return null
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}
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} catch (modelError) {
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logger.error(
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`[RAG] Embedding model ${RagService.EMBEDDING_MODEL} not found locally and failed to download:`,
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modelError
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)
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return null
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}
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this.embeddingModelVerified = true
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}
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// TokenChunker uses character-based tokenization (1 char = 1 token)
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@ -227,7 +287,8 @@ export class RagService {
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const ollamaClient = await this.ollamaService.getClient()
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const embeddings: number[][] = []
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// Prepare all chunk texts with prefix and truncation
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const prefixedChunks: string[] = []
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for (let i = 0; i < chunks.length; i++) {
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let chunkText = chunks[i]
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@ -237,7 +298,6 @@ export class RagService {
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const estimatedTokens = this.estimateTokenCount(withPrefix)
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if (estimatedTokens > RagService.MAX_SAFE_TOKENS) {
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// This should be rare - log for debugging if it's occurring frequently
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const prefixTokens = this.estimateTokenCount(prefixText)
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const maxTokensForText = RagService.MAX_SAFE_TOKENS - prefixTokens
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logger.warn(
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@ -246,17 +306,30 @@ export class RagService {
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chunkText = this.truncateToTokenLimit(chunkText, maxTokensForText)
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}
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logger.debug(`[RAG] Generating embedding for chunk ${i + 1}/${chunks.length}`)
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prefixedChunks.push(RagService.SEARCH_DOCUMENT_PREFIX + chunkText)
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}
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const response = await ollamaClient.embeddings({
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// Batch embed chunks for performance
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const embeddings: number[][] = []
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const batchSize = RagService.EMBEDDING_BATCH_SIZE
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const totalBatches = Math.ceil(prefixedChunks.length / batchSize)
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for (let batchIdx = 0; batchIdx < totalBatches; batchIdx++) {
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const batchStart = batchIdx * batchSize
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const batch = prefixedChunks.slice(batchStart, batchStart + batchSize)
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logger.debug(`[RAG] Embedding batch ${batchIdx + 1}/${totalBatches} (${batch.length} chunks)`)
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const response = await ollamaClient.embed({
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model: RagService.EMBEDDING_MODEL,
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prompt: RagService.SEARCH_DOCUMENT_PREFIX + chunkText,
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input: batch,
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})
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embeddings.push(response.embedding)
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embeddings.push(...response.embeddings)
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if (onProgress) {
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await onProgress(((i + 1) / chunks.length) * 100)
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const progress = ((batchStart + batch.length) / prefixedChunks.length) * 100
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await onProgress(progress)
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}
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}
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@ -395,14 +468,7 @@ export class RagService {
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deleteAfterEmbedding: boolean,
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batchOffset?: number,
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onProgress?: (percent: number) => Promise<void>
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): Promise<{
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success: boolean
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message: string
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chunks?: number
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hasMoreBatches?: boolean
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articlesProcessed?: number
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totalArticles?: number
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}> {
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): Promise<ProcessZIMFileResponse> {
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const zimExtractionService = new ZIMExtractionService()
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// Process in batches to avoid lock timeout
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@ -540,14 +606,7 @@ export class RagService {
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deleteAfterEmbedding: boolean = false,
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batchOffset?: number,
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onProgress?: (percent: number) => Promise<void>
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): Promise<{
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success: boolean
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message: string
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chunks?: number
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hasMoreBatches?: boolean
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articlesProcessed?: number
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totalArticles?: number
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}> {
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): Promise<ProcessAndEmbedFileResponse> {
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try {
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const fileType = determineFileType(filepath)
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logger.debug(`[RAG] Processing file: ${filepath} (detected type: ${fileType})`)
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@ -631,14 +690,18 @@ export class RagService {
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return []
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}
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const allModels = await this.ollamaService.getModels(true)
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const embeddingModel = allModels.find((model) => model.name === RagService.EMBEDDING_MODEL)
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if (!this.embeddingModelVerified) {
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const allModels = await this.ollamaService.getModels(true)
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const embeddingModel = allModels.find((model) => model.name === RagService.EMBEDDING_MODEL)
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if (!embeddingModel) {
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logger.warn(
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`[RAG] ${RagService.EMBEDDING_MODEL} not found. Cannot perform similarity search.`
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)
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return []
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if (!embeddingModel) {
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logger.warn(
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`[RAG] ${RagService.EMBEDDING_MODEL} not found. Cannot perform similarity search.`
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)
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this.embeddingModelVerified = false
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return []
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}
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this.embeddingModelVerified = true
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}
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// Preprocess query for better matching
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@ -666,9 +729,9 @@ export class RagService {
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return []
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}
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const response = await ollamaClient.embeddings({
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const response = await ollamaClient.embed({
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model: RagService.EMBEDDING_MODEL,
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prompt: prefixedQuery,
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input: [prefixedQuery],
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})
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// Perform semantic search with a higher limit to enable reranking
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@ -678,7 +741,7 @@ export class RagService {
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)
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const searchResults = await this.qdrant!.search(RagService.CONTENT_COLLECTION_NAME, {
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vector: response.embedding,
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vector: response.embeddings[0],
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limit: searchLimit,
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score_threshold: scoreThreshold,
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with_payload: true,
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@ -687,7 +750,7 @@ export class RagService {
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logger.debug(`[RAG] Found ${searchResults.length} results above threshold ${scoreThreshold}`)
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// Map results with metadata for reranking
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const resultsWithMetadata = searchResults.map((result) => ({
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const resultsWithMetadata: RAGResult[] = searchResults.map((result) => ({
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text: (result.payload?.text as string) || '',
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score: result.score,
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keywords: (result.payload?.keywords as string) || '',
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@ -700,6 +763,7 @@ export class RagService {
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hierarchy: result.payload?.hierarchy as string | undefined,
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document_id: result.payload?.document_id as string | undefined,
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content_type: result.payload?.content_type as string | undefined,
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source: result.payload?.source as string | undefined,
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}))
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const rerankedResults = this.rerankResults(resultsWithMetadata, keywords, query)
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@ -711,8 +775,11 @@ export class RagService {
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)
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})
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// Apply source diversity penalty to avoid all results from the same document
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const diverseResults = this.applySourceDiversity(rerankedResults)
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// Return top N results with enhanced metadata
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return rerankedResults.slice(0, limit).map((result) => ({
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return diverseResults.slice(0, limit).map((result) => ({
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text: result.text,
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score: result.finalScore,
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metadata: {
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@ -748,34 +815,10 @@ export class RagService {
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* outweigh the overhead.
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*/
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private rerankResults(
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results: Array<{
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text: string
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score: number
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keywords: string
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chunk_index: number
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created_at: number
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article_title?: string
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section_title?: string
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full_title?: string
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hierarchy?: string
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document_id?: string
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content_type?: string
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}>,
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results: Array<RAGResult>,
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queryKeywords: string[],
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originalQuery: string
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): Array<{
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text: string
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score: number
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finalScore: number
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chunk_index: number
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created_at: number
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article_title?: string
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section_title?: string
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full_title?: string
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hierarchy?: string
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document_id?: string
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content_type?: string
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}> {
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): Array<RerankedRAGResult> {
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return results
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.map((result) => {
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let finalScore = result.score
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@ -851,6 +894,37 @@ export class RagService {
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.sort((a, b) => b.finalScore - a.finalScore)
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}
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/**
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* Applies a diversity penalty so results from the same source are down-weighted.
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* Uses greedy selection: for each result, apply 0.85^n penalty where n is the
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* number of results already selected from the same source.
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*/
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private applySourceDiversity(
|
||||
results: Array<RerankedRAGResult>
|
||||
) {
|
||||
const sourceCounts = new Map<string, number>()
|
||||
const DIVERSITY_PENALTY = 0.85
|
||||
|
||||
return results
|
||||
.map((result) => {
|
||||
const sourceKey = result.document_id || result.source || 'unknown'
|
||||
const count = sourceCounts.get(sourceKey) || 0
|
||||
const penalty = Math.pow(DIVERSITY_PENALTY, count)
|
||||
const diverseScore = result.finalScore * penalty
|
||||
|
||||
sourceCounts.set(sourceKey, count + 1)
|
||||
|
||||
if (count > 0) {
|
||||
logger.debug(
|
||||
`[RAG] Source diversity penalty for "${sourceKey}": ${result.finalScore.toFixed(4)} → ${diverseScore.toFixed(4)} (seen ${count}x)`
|
||||
)
|
||||
}
|
||||
|
||||
return { ...result, finalScore: diverseScore }
|
||||
})
|
||||
.sort((a, b) => b.finalScore - a.finalScore)
|
||||
}
|
||||
|
||||
/**
|
||||
* Retrieve all unique source files that have been stored in the knowledge base.
|
||||
* @returns Array of unique full source paths
|
||||
|
|
@ -866,12 +940,12 @@ export class RagService {
|
|||
let offset: string | number | null | Record<string, unknown> = null
|
||||
const batchSize = 100
|
||||
|
||||
// Scroll through all points in the collection
|
||||
// Scroll through all points in the collection (only fetch source field)
|
||||
do {
|
||||
const scrollResult = await this.qdrant!.scroll(RagService.CONTENT_COLLECTION_NAME, {
|
||||
limit: batchSize,
|
||||
offset: offset,
|
||||
with_payload: true,
|
||||
with_payload: ['source'],
|
||||
with_vector: false,
|
||||
})
|
||||
|
||||
|
|
|
|||
|
|
@ -64,6 +64,16 @@ export const FALLBACK_RECOMMENDED_OLLAMA_MODELS: NomadOllamaModel[] = [
|
|||
|
||||
export const DEFAULT_QUERY_REWRITE_MODEL = 'qwen2.5:3b' // default to qwen2.5 for query rewriting with good balance of text task performance and resource usage
|
||||
|
||||
/**
|
||||
* Adaptive RAG context limits based on model size.
|
||||
* Smaller models get overwhelmed with too much context, so we cap it.
|
||||
*/
|
||||
export const RAG_CONTEXT_LIMITS: { maxParams: number; maxResults: number; maxTokens: number }[] = [
|
||||
{ maxParams: 3, maxResults: 2, maxTokens: 1000 }, // 1-3B models
|
||||
{ maxParams: 8, maxResults: 4, maxTokens: 2500 }, // 4-8B models
|
||||
{ maxParams: Infinity, maxResults: 5, maxTokens: 0 }, // 13B+ (no cap)
|
||||
]
|
||||
|
||||
export const SYSTEM_PROMPTS = {
|
||||
default: `
|
||||
Format all responses using markdown for better readability. Vanilla markdown or GitHub-flavored markdown is preferred.
|
||||
|
|
@ -113,7 +123,7 @@ Ensure that your suggestions are comma-seperated with no conjunctions like "and"
|
|||
Do not use line breaks, new lines, or extra spacing to separate the suggestions.
|
||||
Format: suggestion1, suggestion2, suggestion3
|
||||
`,
|
||||
title_generation: `You are a title generator. Given the start of a conversation, generate a concise, descriptive title under 60 characters. Return ONLY the title text with no quotes, punctuation wrapping, or extra formatting.`,
|
||||
title_generation: `You are a title generator. Given the start of a conversation, generate a concise, descriptive title under 50 characters. Return ONLY the title text with no quotes, punctuation wrapping, or extra formatting.`,
|
||||
query_rewrite: `
|
||||
You are a query rewriting assistant. Your task is to reformulate the user's latest question to include relevant context from the conversation history.
|
||||
|
||||
|
|
|
|||
|
|
@ -4,13 +4,16 @@
|
|||
|
||||
### Features
|
||||
- **AI Assistant**: Added improved user guidance for troubleshooting GPU pass-through issues
|
||||
- **AI Assistant**: The last used model is now automatically selected when a new chat is started
|
||||
- **Settings**: Nomad now automatically performs nightly checks for available app updates, and users can select and apply updates from the Apps page in Settings
|
||||
|
||||
### Bug Fixes
|
||||
- **Settings**: Fixed an issue where the AI Assistant settings page would be shown in navigation even if the AI Assistant was not installed, thus causing 404 errors when clicked
|
||||
- **Security**: Path traversal and SSRF mitigations
|
||||
- **AI Assistant**: Fixed an issue that was causing intermittent failures saving chat session titles
|
||||
|
||||
### Improvements
|
||||
- **AI Assistant**: Extensive performance improvements and improved RAG intelligence/context usage
|
||||
|
||||
## Version 1.28.0 - March 5, 2026
|
||||
|
||||
|
|
|
|||
|
|
@ -5,3 +5,32 @@ export type EmbedJobWithProgress = {
|
|||
progress: number
|
||||
status: string
|
||||
}
|
||||
|
||||
export type ProcessAndEmbedFileResponse = {
|
||||
success: boolean
|
||||
message: string
|
||||
chunks?: number
|
||||
hasMoreBatches?: boolean
|
||||
articlesProcessed?: number
|
||||
totalArticles?: number
|
||||
}
|
||||
export type ProcessZIMFileResponse = ProcessAndEmbedFileResponse
|
||||
|
||||
export type RAGResult = {
|
||||
text: string
|
||||
score: number
|
||||
keywords: string
|
||||
chunk_index: number
|
||||
created_at: number
|
||||
article_title?: string
|
||||
section_title?: string
|
||||
full_title?: string
|
||||
hierarchy?: string
|
||||
document_id?: string
|
||||
content_type?: string
|
||||
source?: string
|
||||
}
|
||||
|
||||
export type RerankedRAGResult = Omit<RAGResult, 'keywords'> & {
|
||||
finalScore: number
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user