AutoRAG Setup & Configuration

AutoRAG (Automatic Retrieval-Augmented Generation) provides intelligent document processing and contextual AI assistance for legal professionals. This guide covers the complete setup and configuration process.

Overview

AutoRAG enhances the ZServed platform with:

Intelligent Document Processing - Automatic analysis and indexing of legal documents
Contextual AI Responses - AI assistance based on case-specific documents and legal knowledge
Advanced Search - Semantic search across documents using vector embeddings
Legal Knowledge Base - Pre-trained legal concepts and precedents
Real-time Analysis - Live document analysis and insights

Prerequisites

System Requirements

ZServed platform already deployed
Cloudflare Vectorize enabled
OpenAI API access (GPT-4 recommended)
At least 1GB storage for embeddings

Service Dependencies

Cloudflare Workers AI - For embedding generation
Vectorize - For vector storage and similarity search
D1 Database - For metadata and configuration storage
R2 Storage - For processed document storage

Installation Process

1. Enable AutoRAG Schema

Apply the AutoRAG database schema:

# Apply AutoRAG schema
wrangler d1 execute zserved-db --file=migrations/ai-analytics-schema.sql --remote

# For tenant-specific deployment
wrangler d1 execute "zserved-db-{tenant}" --file=migrations/ai-analytics-schema.sql --remote

2. Configure Vectorize Indices

Create dedicated Vectorize indices for AutoRAG:

# Create document embeddings index
wrangler vectorize create autorag-documents \
  --dimensions=1536 \
  --metric=cosine

# Create knowledge base index
wrangler vectorize create autorag-knowledge \
  --dimensions=1536 \
  --metric=cosine

# For tenant-specific indices
wrangler vectorize create autorag-documents-{tenant} \
  --dimensions=1536 \
  --metric=cosine

3. Update Wrangler Configuration

Add AutoRAG bindings to your wrangler.jsonc:

{
  "name": "zserved",
  "main": "src/index.ts",
  "compatibility_date": "2024-01-01",
  "node_compat": true,
  "bindings": [
    {
      "name": "AUTORAG_DOCUMENTS",
      "type": "vectorize",
      "index_name": "autorag-documents"
    },
    {
      "name": "AUTORAG_KNOWLEDGE",
      "type": "vectorize",
      "index_name": "autorag-knowledge"
    }
  ]
}

4. Environment Configuration

Add AutoRAG-specific environment variables:

# AutoRAG Configuration
AUTORAG_ENABLED="true"
AUTORAG_MODEL="gpt-4-turbo-preview"
AUTORAG_EMBEDDING_MODEL="text-embedding-3-large"
AUTORAG_MAX_TOKENS="4000"
AUTORAG_TEMPERATURE="0.1"

# Knowledge Base Configuration
AUTORAG_KB_ENABLED="true"
AUTORAG_KB_UPDATE_INTERVAL="24h"
AUTORAG_KB_SOURCES="legal-precedents,statutes,regulations"

Configuration Options

Core AutoRAG Settings

Configure AutoRAG behavior in your platform:

export const AutoRAGConfig = {
  // Document Processing
  documentProcessing: {
    enabled: true,
    autoIndex: true,
    chunkSize: 1000,
    chunkOverlap: 200,
    supportedTypes: [
      'application/pdf',
      'text/plain',
      'application/msword',
      'application/vnd.openxmlformats-officedocument.wordprocessingml.document'
    ]
  },

  // AI Model Configuration
  models: {
    embedding: 'text-embedding-3-large',
    generation: 'gpt-4-turbo-preview',
    analysis: 'gpt-4'
  },

  // Search Configuration
  search: {
    maxResults: 20,
    minSimilarity: 0.7,
    boostFactors: {
      recentDocuments: 1.2,
      caseSpecific: 1.5,
      legalPrecedents: 1.3
    }
  },

  // Knowledge Base
  knowledgeBase: {
    enabled: true,
    autoUpdate: true,
    updateInterval: '24h',
    sources: [
      'legal-precedents',
      'federal-statutes',
      'state-regulations',
      'case-law'
    ]
  }
};

Vector Embedding Configuration

Configure embedding generation and storage:

export const EmbeddingConfig = {
  // Embedding Model Settings
  model: 'text-embedding-3-large',
  dimensions: 1536,

  // Processing Settings
  batchSize: 100,
  maxRetries: 3,
  timeout: 30000,

  // Storage Configuration
  indexing: {
    autoIndex: true,
    updateExisting: false,
    versioning: true
  },

  // Performance Optimization
  caching: {
    enabled: true,
    ttl: 3600, // 1 hour
    maxSize: 10000 // max cached embeddings
  }
};

Knowledge Base Setup

1. Legal Knowledge Sources

Initialize the legal knowledge base:

# Run knowledge base seeding script
npm run seed-knowledge-base

# Or manually seed specific areas
npm run seed-knowledge -- --area="family-law"
npm run seed-knowledge -- --area="criminal-defense"
npm run seed-knowledge -- --area="contract-law"

2. Custom Knowledge Sources

Add firm-specific legal knowledge:

export const CustomKnowledge = {
  // Firm-specific templates
  templates: [
    {
      type: 'motion',
      category: 'family-law',
      title: 'Motion for Temporary Custody',
      content: '...',
      jurisdiction: 'michigan'
    }
  ],

  // Local regulations
  localLaw: [
    {
      jurisdiction: 'kent-county',
      type: 'family-court-rule',
      content: '...'
    }
  ],

  // Precedents and case law
  precedents: [
    {
      case: 'Example v. Example',
      citation: '123 Mich App 456 (2023)',
      summary: '...',
      relevantLaw: ['custody', 'best-interest']
    }
  ]
};

3. Automated Knowledge Updates

Configure automatic knowledge base updates:

export class KnowledgeUpdater {
  async updateKnowledgeBase() {
    // Check for new legal updates
    const updates = await this.fetchLegalUpdates();

    // Process and embed new content
    for (const update of updates) {
      const embedding = await this.generateEmbedding(update.content);
      await this.storeKnowledge(update, embedding);
    }

    // Clean outdated entries
    await this.cleanOutdatedKnowledge();
  }

  private async fetchLegalUpdates() {
    // Integration with legal databases
    // Westlaw, LexisNexis, or other legal data sources
  }
}

Document Processing Pipeline

1. Automatic Document Indexing

Configure automatic processing of uploaded documents:

export class DocumentProcessor {
  async processDocument(file: File, metadata: DocumentMetadata) {
    // Extract text content
    const content = await this.extractText(file);

    // Chunk document for processing
    const chunks = this.chunkDocument(content);

    // Generate embeddings
    const embeddings = await Promise.all(
      chunks.map(chunk => this.generateEmbedding(chunk))
    );

    // Store in vector database
    await this.storeDocumentVectors(file.id, chunks, embeddings);

    // Extract legal concepts
    const concepts = await this.extractLegalConcepts(content);

    // Store metadata
    await this.storeDocumentMetadata(file.id, {
      ...metadata,
      concepts,
      processed: true,
      processingDate: new Date()
    });
  }

  private chunkDocument(content: string): string[] {
    // Intelligent chunking preserving legal structure
    // Respect paragraph boundaries, legal citations, etc.
  }
}

2. Real-time Analysis

Enable real-time document analysis:

export class RealTimeAnalyzer {
  async analyzeDocument(documentId: string) {
    // Retrieve document and related vectors
    const document = await this.getDocument(documentId);
    const vectors = await this.getDocumentVectors(documentId);

    // Find similar documents
    const similar = await this.findSimilarDocuments(vectors);

    // Identify key legal issues
    const issues = await this.identifyLegalIssues(document.content);

    // Generate summary and recommendations
    const analysis = await this.generateAnalysis({
      document,
      similarDocuments: similar,
      legalIssues: issues
    });

    return analysis;
  }
}

Integration with ZServed Features

1. Gavel AI Integration

Enhance Gavel AI with AutoRAG capabilities:

export class GavelAutoRAG {
  async enhancedChat(message: string, context: ChatContext) {
    // Retrieve relevant documents
    const relevantDocs = await this.searchRelevantDocuments(
      message,
      context.caseId
    );

    // Get knowledge base context
    const knowledgeContext = await this.getKnowledgeContext(message);

    // Generate enhanced response
    const response = await this.generateResponse({
      userMessage: message,
      documentContext: relevantDocs,
      knowledgeContext,
      conversationHistory: context.history
    });

    return {
      response,
      sources: [...relevantDocs, ...knowledgeContext],
      confidence: this.calculateConfidence(response)
    };
  }
}

2. Client Portal Integration

Add AutoRAG insights to client portal:

export class ClientPortalAutoRAG {
  async getCaseInsights(caseId: string) {
    // Analyze case documents
    const documents = await this.getCaseDocuments(caseId);
    const analysis = await this.analyzeDocuments(documents);

    // Generate client-friendly summary
    const summary = await this.generateClientSummary(analysis);

    // Identify next steps
    const nextSteps = await this.suggestNextSteps(analysis);

    return {
      summary,
      nextSteps,
      keyDocuments: analysis.importantDocuments,
      timeline: analysis.timeline
    };
  }
}

Performance Optimization

1. Caching Strategy

Implement intelligent caching for AutoRAG:

export class AutoRAGCache {
  private embeddingCache = new Map();
  private responseCache = new Map();

  async getCachedEmbedding(text: string) {
    const hash = this.hashText(text);
    if (this.embeddingCache.has(hash)) {
      return this.embeddingCache.get(hash);
    }

    const embedding = await this.generateEmbedding(text);
    this.embeddingCache.set(hash, embedding);
    return embedding;
  }

  async getCachedResponse(query: string, context: string) {
    const cacheKey = this.generateCacheKey(query, context);
    if (this.responseCache.has(cacheKey)) {
      return this.responseCache.get(cacheKey);
    }

    const response = await this.generateResponse(query, context);
    this.responseCache.set(cacheKey, response);
    return response;
  }
}

2. Batch Processing

Optimize with batch processing:

export class BatchProcessor {
  async processBatch(items: ProcessingItem[]) {
    // Group by processing type
    const groups = this.groupByType(items);

    // Process embeddings in batches
    if (groups.embeddings?.length > 0) {
      await this.processBatchEmbeddings(groups.embeddings);
    }

    // Process analysis in batches
    if (groups.analysis?.length > 0) {
      await this.processBatchAnalysis(groups.analysis);
    }
  }

  private async processBatchEmbeddings(items: EmbeddingItem[]) {
    const batchSize = 100;
    for (let i = 0; i < items.length; i += batchSize) {
      const batch = items.slice(i, i + batchSize);
      await this.processEmbeddingBatch(batch);
    }
  }
}

Monitoring & Analytics

1. AutoRAG Metrics

Track AutoRAG performance:

export class AutoRAGMetrics {
  async trackMetrics() {
    const metrics = {
      // Performance metrics
      avgResponseTime: await this.getAverageResponseTime(),
      embeddingGenerationRate: await this.getEmbeddingRate(),
      searchAccuracy: await this.getSearchAccuracy(),

      // Usage metrics
      dailyQueries: await this.getDailyQueryCount(),
      documentProcessingVolume: await this.getProcessingVolume(),
      knowledgeBaseSize: await this.getKnowledgeBaseSize(),

      // Quality metrics
      userSatisfactionScore: await this.getUserSatisfaction(),
      responseRelevanceScore: await this.getRelevanceScore()
    };

    await this.logMetrics(metrics);
    return metrics;
  }
}

2. Usage Analytics

Monitor AutoRAG usage patterns:

export class UsageAnalytics {
  async generateUsageReport(timeframe: string) {
    return {
      queryVolume: await this.getQueryVolume(timeframe),
      popularDocumentTypes: await this.getPopularDocTypes(timeframe),
      commonQueries: await this.getCommonQueries(timeframe),
      performanceMetrics: await this.getPerformanceMetrics(timeframe),
      userEngagement: await this.getUserEngagement(timeframe)
    };
  }
}

Security & Compliance

1. Data Privacy

Ensure AutoRAG complies with legal data requirements:

export class AutoRAGSecurity {
  async processDocument(document: Document) {
    // Check for privileged information
    const privilegedContent = await this.detectPrivileged(document);
    if (privilegedContent.length > 0) {
      throw new Error('Document contains attorney-client privileged information');
    }

    // Check for PII
    const piiDetected = await this.detectPII(document);
    if (piiDetected.length > 0) {
      document = await this.redactPII(document, piiDetected);
    }

    // Encrypt before storage
    const encrypted = await this.encryptDocument(document);
    return encrypted;
  }
}

2. Access Controls

Implement strict access controls:

export class AutoRAGAccess {
  async checkAccess(userId: string, documentId: string, operation: string) {
    // Verify user permissions
    const userPerms = await this.getUserPermissions(userId);
    const docPerms = await this.getDocumentPermissions(documentId);

    // Check tenant isolation
    if (userPerms.tenantId !== docPerms.tenantId) {
      throw new Error('Cross-tenant access denied');
    }

    // Verify operation permissions
    if (!this.hasPermission(userPerms, operation)) {
      throw new Error('Insufficient permissions');
    }

    return true;
  }
}

Troubleshooting

Common Issues

Embedding Generation Fails:

# Check OpenAI API status
curl -H "Authorization: Bearer $OPENAI_API_KEY" \
  https://api.openai.com/v1/models

Vector Search Returns No Results:

# Check Vectorize index
wrangler vectorize get autorag-documents

# Verify embeddings exist
wrangler vectorize query autorag-documents \
  --vector="[0.1, 0.2, ...]" \
  --top-k=5

Document Processing Stalls:

# Check processing queue
wrangler d1 execute zserved-db \
  --command "SELECT * FROM autorag_processing_queue WHERE status = 'pending';"

Performance Issues

Slow Response Times:
- Enable caching for frequent queries
- Batch embedding generation
- Optimize vector index configuration
High Resource Usage:
- Implement request queuing
- Use smaller embedding models for non-critical operations
- Cache frequently accessed embeddings

Support

For AutoRAG-specific issues:

Check the AutoRAG troubleshooting guide
Review performance optimization tips
Contact support with AutoRAG logs and configuration details

Next Steps

After AutoRAG setup:

Test document processing - Upload test documents and verify processing
Configure knowledge base - Add firm-specific legal knowledge
Train users - Provide training on AutoRAG features
Monitor performance - Set up dashboards and alerts
Optimize settings - Fine-tune based on usage patterns

For advanced configuration, see the AutoRAG Advanced Guide.