Class: Ragdoll::Core::Client

Inherits:
Object
  • Object
show all
Defined in:
lib/ragdoll/core/client.rb

Instance Method Summary collapse

Constructor Details

#initialize ⇒ Client

Returns a new instance of Client.



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# File 'lib/ragdoll/core/client.rb', line 8

def initialize
  # Setup configuration services
  @config_service = Ragdoll::ConfigurationService.new
  @model_resolver = Ragdoll::ModelResolver.new(@config_service)

  # Setup logging
  setup_logging

  # Setup database connection
  Database.setup(@config_service.config.database)

  @embedding_service = Ragdoll::EmbeddingService.new(
    client: nil,
    config_service: @config_service,
    model_resolver: @model_resolver
  )
  @search_engine = Ragdoll::SearchEngine.new(@embedding_service, config_service: @config_service)
end

Instance Method Details

#add_directory(path:, recursive: false) ⇒ Object 



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# File 'lib/ragdoll/core/client.rb', line 245

def add_directory(path:, recursive: false)
  results = []
  pattern = recursive ? File.join(path, "**", "*") : File.join(path, "*")

  Dir.glob(pattern).each do |file_path|
    next unless File.file?(file_path)

    begin
      doc_id = add_document(path: file_path)
      results << { file: file_path, document_id: doc_id, status: "success" }
    rescue StandardError => e
      results << { file: file_path, error: e.message, status: "error" }
    end
  end

  results
end

#add_document(path:, force: false) ⇒ Object

Document management



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# File 'lib/ragdoll/core/client.rb', line 187

def add_document(path:, force: false)
  # Parse the document
  parsed = Ragdoll::DocumentProcessor.parse(path)

  # Extract title from metadata or use filename
  title = parsed[:metadata][:title] ||
          File.basename(path, File.extname(path))

  # Add document to database
  doc_id = Ragdoll::DocumentManagement.add_document(path, parsed[:content], {
                                             title: title,
                                             document_type: parsed[:document_type],
                                             **parsed[:metadata]
                                           }, force: force)

  # Queue background jobs for processing if content is available
  embeddings_queued = false
  if parsed[:content].present?
    Ragdoll::GenerateEmbeddingsJob.perform_later(doc_id)
    Ragdoll::GenerateSummaryJob.perform_later(doc_id)
    Ragdoll::ExtractKeywordsJob.perform_later(doc_id)
    embeddings_queued = true
  end

  # Return success information
  {
    success: true,
    document_id: doc_id,
    title: title,
    document_type: parsed[:document_type],
    content_length: parsed[:content]&.length || 0,
    embeddings_queued: embeddings_queued,
    message: "Document '#{title}' added successfully with ID #{doc_id}"
  }
rescue StandardError => e # StandardError => e
  {
    success: false,
    error: e.message,
    message: "Failed to add document: #{e.message}"
  }
end

#add_text(content:, title:, **options) ⇒ Object 



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# File 'lib/ragdoll/core/client.rb', line 229

def add_text(content:, title:, **options)
  # Add document to database
  doc_id = Ragdoll::DocumentManagement.add_document(title, content, {
                                             title: title,
                                             document_type: "text",
                                             **options
                                           })

  # Queue background job for embeddings
  Ragdoll::GenerateEmbeddingsJob.perform_later(doc_id,
                                               chunk_size: options[:chunk_size],
                                               chunk_overlap: options[:chunk_overlap])

  doc_id
end

#delete_document(id:) ⇒ Object 



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# File 'lib/ragdoll/core/client.rb', line 307

def delete_document(id:)
  Ragdoll::DocumentManagement.delete_document(id)
end

#document_status(id:) ⇒ Object 



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# File 'lib/ragdoll/core/client.rb', line 271

def document_status(id:)
  document = Ragdoll::Document.find(id)
  embeddings_count = document.all_embeddings.count

  {
    id: document.id,
    title: document.title,
    status: document.status,
    embeddings_count: embeddings_count,
    embeddings_ready: embeddings_count.positive?,
    content_preview: document.content&.first(200) || "No content",
    message: case document.status
             when "processed"
               "Document processed successfully with #{embeddings_count} embeddings"
             when "processing"
               "Document is being processed"
             when "pending"
               "Document is pending processing"
             when "error"
               "Document processing failed"
             else
               "Document status: #{document.status}"
             end
  }
rescue ActiveRecord::RecordNotFound
  {
    success: false,
    error: "Document not found",
    message: "Document with ID #{id} does not exist"
  }
end

#enhance_prompt(prompt:, context_limit: 5, **options) ⇒ Object

Primary method for RAG applications Returns context-enhanced content for AI prompts



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# File 'lib/ragdoll/core/client.rb', line 29

def enhance_prompt(prompt:, context_limit: 5, **options)
  context_data = get_context(query: prompt, limit: context_limit, **options)

  if context_data[:context_chunks].any?
    enhanced_prompt = build_enhanced_prompt(prompt, context_data[:combined_context])
    {
      enhanced_prompt: enhanced_prompt,
      original_prompt: prompt,
      context_sources: context_data[:context_chunks].map { |chunk| chunk[:source] },
      context_count: context_data[:total_chunks]
    }
  else
    {
      enhanced_prompt: prompt,
      original_prompt: prompt,
      context_sources: [],
      context_count: 0
    }
  end
end

#get_context(query:, limit: 10, **options) ⇒ Object

Get relevant context without prompt enhancement



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# File 'lib/ragdoll/core/client.rb', line 51

def get_context(query:, limit: 10, **options)
  search_response = search_similar_content(query: query, limit: limit, **options)
  
  # Handle both old format (array) and new format (hash with results/statistics)
  if search_response.is_a?(Hash) && search_response.key?(:results)
    results = search_response[:results]
  else
    # Fallback for old format
    results = search_response || []
  end

  context_chunks = results.map do |result|
    {
      content: result[:content],
      source: result[:document_location],
      similarity: result[:similarity],
      chunk_index: result[:chunk_index]
    }
  end

  combined_context = context_chunks.map { |chunk| chunk[:content] }.join("\n\n")

  {
    context_chunks: context_chunks,
    combined_context: combined_context,
    total_chunks: context_chunks.length
  }
end

#get_document(id:) ⇒ Object 



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# File 'lib/ragdoll/core/client.rb', line 263

def get_document(id:)
  document_hash = Ragdoll::DocumentManagement.get_document(id)
  return nil unless document_hash

  # DocumentManagement.get_document already returns a hash with all needed info
  document_hash
end

#healthy? ⇒ Boolean

Health check

Returns:

  • (Boolean) 


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# File 'lib/ragdoll/core/client.rb', line 328

def healthy?
  Database.connected? && stats[:total_documents] >= 0
rescue StandardError
  false
end

#hybrid_search(query:, **options) ⇒ Object

Hybrid search combining semantic and full-text search



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# File 'lib/ragdoll/core/client.rb', line 120

def hybrid_search(query:, **options)
  start_time = Time.current
  
  # Extract tracking options
  session_id = options[:session_id]
  user_id = options[:user_id]
  track_search = options.fetch(:track_search, true)
  
  # Generate embedding for the query
  query_embedding = @embedding_service.generate_embedding(query)

  # Perform hybrid search
  results = Ragdoll::Document.hybrid_search(query, query_embedding: query_embedding, **options)
  
  execution_time = ((Time.current - start_time) * 1000).round
  
  # Record search if tracking enabled
  if track_search && query && !query.empty?
    begin
      # Format results for search recording - hybrid search returns different format
      search_results = results.map do |result|
        {
          embedding_id: result[:embedding_id] || result[:id],
          similarity: result[:similarity] || result[:score] || 0.0
        }
      end
      
      # Extract filters from options
      filters = options.slice(:document_type, :status).compact
      search_options = options.slice(:limit, :semantic_weight, :text_weight).compact
      
      Ragdoll::Search.record_search(
        query: query,
        query_embedding: query_embedding,
        results: search_results,
        search_type: "hybrid",
        filters: filters,
        options: search_options,
        execution_time_ms: execution_time,
        session_id: session_id,
        user_id: user_id
      )
    rescue => e
      # Log error but don't fail the search
      puts "Warning: Hybrid search tracking failed: #{e.message}" if ENV["RAGDOLL_DEBUG"]
    end
  end

  {
    query: query,
    search_type: "hybrid",
    results: results,
    total_results: results.length,
    semantic_weight: options[:semantic_weight] || 0.7,
    text_weight: options[:text_weight] || 0.3
  }
rescue StandardError => e
  {
    query: query,
    search_type: "hybrid",
    results: [],
    total_results: 0,
    error: "Hybrid search failed: #{e.message}"
  }
end

#list_documents(**options) ⇒ Object 



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# File 'lib/ragdoll/core/client.rb', line 311

def list_documents(**options)
  Ragdoll::DocumentManagement.list_documents(options)
end

#search(query:, **options) ⇒ Object

Semantic search++ should incorporate hybrid search



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# File 'lib/ragdoll/core/client.rb', line 86

def search(query:, **options)
  # Pass through tracking options to the search engine
  search_response = search_similar_content(query: query, **options)
  
  # Handle both old format (array) and new format (hash with results/statistics)
  if search_response.is_a?(Hash) && search_response.key?(:results)
    results = search_response[:results]
    statistics = search_response[:statistics]
    execution_time_ms = search_response[:execution_time_ms]
    
    {
      query: query,
      results: results,
      total_results: results.length,
      statistics: statistics,
      execution_time_ms: execution_time_ms
    }
  else
    # Fallback for old format
    results = search_response || []
    {
      query: query,
      results: results,
      total_results: results.length
    }
  end
end

#search_analytics(days: 30) ⇒ Object 



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# File 'lib/ragdoll/core/client.rb', line 320

def search_analytics(days: 30)
  # This could be implemented with additional database queries
  Ragdoll::Embedding.where("returned_at > ?", days.days.ago)
                   .group("DATE(returned_at)")
                   .count
end

#search_similar_content(query:, **options) ⇒ Object

Search similar content (core functionality)



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# File 'lib/ragdoll/core/client.rb', line 115

def search_similar_content(query:, **options)
  @search_engine.search_similar_content(query, **options)
end

#stats ⇒ Object

Analytics and stats



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# File 'lib/ragdoll/core/client.rb', line 316

def stats
  Ragdoll::DocumentManagement.get_document_stats
end

#update_document(id:, **updates) ⇒ Object 



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# File 'lib/ragdoll/core/client.rb', line 303

def update_document(id:, **updates)
  Ragdoll::DocumentManagement.update_document(id, **updates)
end