Development

Rakefile

@@ -189,3 +189,105 @@ Rake::RDocTask.new do |rdoc|
   rdoc.rdoc_files.include "lib/workflows/topic_model_trainer_workflow.rb"
   rdoc.rdoc_files.include "lib/workflows/topic_model_trainer_workflowtest.rb"
 end
+
+desc "Build all images"
+task "build-all" do
+  ALL_IMAGES.each do |image|
+    Rake::Task["build/#{image}"].invoke
+  end
+end
+
+desc "Tag all images"
+task "tag-all" do
+  ALL_IMAGES.each do |image|
+    Rake::Task["tag/#{image}"].invoke
+  end
+end
+
+desc "Push all images"
+task "push-all" do
+  ALL_IMAGES.each do |image|
+    Rake::Task["push/#{image}"].invoke
+  end
+end
+
+Rake::RDocTask.new do |rdoc|
+  rdoc.title    = "flowbots v0.1"
+  rdoc.rdoc_dir = "#{APP_ROOT}/doc"
+  rdoc.options += [
+    "-w",
+    "2",
+    "-H",
+    "-A",
+    "-f",
+    "darkfish", # This bit
+    "-m",
+    "README.md",
+    "--visibility",
+    "nodoc",
+    "--markup",
+    "markdown"
+  ]
+  rdoc.rdoc_files.include "README.md"
+  rdoc.rdoc_files.include "LICENSE"
+  rdoc.rdoc_files.include "exe/flowbots"
+
+  rdoc.rdoc_files.include "lib/api.rb"
+  rdoc.rdoc_files.include "lib/cli.rb"
+  rdoc.rdoc_files.include "lib/flowbots.rb"
+  rdoc.rdoc_files.include "lib/helper.rb"
+  rdoc.rdoc_files.include "lib/logging.rb"
+  rdoc.rdoc_files.include "lib/tasks.rb"
+  rdoc.rdoc_files.include "lib/ui.rb"
+  rdoc.rdoc_files.include "lib/workflows.rb"
+
+  rdoc.rdoc_files.include "lib/integrations/flowise.rb"
+
+  rdoc.rdoc_files.include "lib/processors/GrammarProcessor.rb"
+  rdoc.rdoc_files.include "lib/processors/NLPProcessor.rb"
+  rdoc.rdoc_files.include "lib/processors/TextProcessor.rb"
+  rdoc.rdoc_files.include "lib/processors/TextSegmentProcessor.rb"
+  rdoc.rdoc_files.include "lib/processors/TextTaggerProcessor.rb"
+  rdoc.rdoc_files.include "lib/processors/TextTokenizeProcessor.rb"
+  rdoc.rdoc_files.include "lib/processors/TopicModelProcessor.rb"
+
+  rdoc.rdoc_files.include "lib/tasks/accumulate_filtered_segments_task.rb"
+  rdoc.rdoc_files.include "lib/tasks/display_results_task.rb"
+  rdoc.rdoc_files.include "lib/tasks/file_loader_task.rb"
+  rdoc.rdoc_files.include "lib/tasks/filter_segments_task.rb"
+  rdoc.rdoc_files.include "lib/tasks/llm_analysis_task.rb"
+  rdoc.rdoc_files.include "lib/tasks/load_text_files_task.rb"
+  rdoc.rdoc_files.include "lib/tasks/nlp_analysis_task.rb"
+  rdoc.rdoc_files.include "lib/tasks/preprocess_text_file_task.rb"
+  rdoc.rdoc_files.include "lib/tasks/text_segment_task.rb"
+  rdoc.rdoc_files.include "lib/tasks/text_tagger_task.rb"
+  rdoc.rdoc_files.include "lib/tasks/text_tokenize_task.rb"
+  rdoc.rdoc_files.include "lib/tasks/tokenize_segments_task.rb"
+  rdoc.rdoc_files.include "lib/tasks/topic_modeling_task.rb"
+  rdoc.rdoc_files.include "lib/tasks/train_topic_model_task.rb"
+
+  rdoc.rdoc_files.include "lib/components/ExceptionAgent.rb"
+  rdoc.rdoc_files.include "lib/components/ExceptionHandler.rb"
+  rdoc.rdoc_files.include "lib/components/FileLoader.rb"
+  rdoc.rdoc_files.include "lib/components/OhmModels.rb"
+  rdoc.rdoc_files.include "lib/components/WorkflowAgent.rb"
+  rdoc.rdoc_files.include "lib/components/WorkflowOrchestrator.rb"
+  rdoc.rdoc_files.include "lib/components/word_salad.rb"
+
+  rdoc.rdoc_files.include "lib/grammars/markdown_yaml.rb"
+
+  rdoc.rdoc_files.include "lib/utils/command.rb"
+  rdoc.rdoc_files.include "lib/utils/transcribe.rb"
+  rdoc.rdoc_files.include "lib/utils/tts.rb"
+  rdoc.rdoc_files.include "lib/utils/writefile.rb"
+
+  rdoc.rdoc_files.include "lib/workflows/text_processing_workflow.rb"
+  rdoc.rdoc_files.include "lib/workflows/topic_model_trainer_workflow.rb"
+  rdoc.rdoc_files.include "lib/workflows/topic_model_trainer_workflowtest.rb"
+end
+
+Gokdok::Dokker.new do |gd|
+  gd.remote_path = "" # Put into the root directory
+  gd.repo_url = "[email protected]:b08x/flowbots.git"
+  gd.doc_home = "#{APP_ROOT}/doc"
+end

compressed_prompt_test.rb

@@ -1,20 +1,20 @@
 
 **Evaluation Test:**
 
-Now, to evaluate the effectiveness of the condensed prompt, I will employ various criteria: 
+Now, to evaluate the effectiveness of the condensed prompt, I will employ various criteria:
 
-- Clarity: Does the optimized version provide a clear and concise explanation of the benefits of using a visual representation? Does it eliminate ambiguity and ensure that the message is easily understandable? 
+- Clarity: Does the optimized version provide a clear and concise explanation of the benefits of using a visual representation? Does it eliminate ambiguity and ensure that the message is easily understandable?
 
-Evaluation: The optimized version excels in clarity. By elaborating on the advantages of the visual representation, it provides a more explicit explanation. The use of phrases like "elucidating the inherent sequential nature" and "providing insights into the flow of data" offers a precise understanding of how visuals aid in comprehension and communication. The message is straightforward and free from ambiguity, making it easily comprehensible to the target audience. 
+Evaluation: The optimized version excels in clarity. By elaborating on the advantages of the visual representation, it provides a more explicit explanation. The use of phrases like "elucidating the inherent sequential nature" and "providing insights into the flow of data" offers a precise understanding of how visuals aid in comprehension and communication. The message is straightforward and free from ambiguity, making it easily comprehensible to the target audience.
 
-- Completeness: Does the optimized version retain all the crucial information from the original prompt? Does it provide a comprehensive understanding of the topic without omitting relevant details? 
+- Completeness: Does the optimized version retain all the crucial information from the original prompt? Does it provide a comprehensive understanding of the topic without omitting relevant details?
 
-Evaluation: The optimized version effectively maintains the completeness of information. It expands on the ideas presented in the original prompt, offering a more detailed yet concise explanation. The addition of phrases like "distinct process stages" and "shared understanding of the system's architecture" ensures that the full scope of the visual representation's utility is conveyed. No relevant details have been omitted, and the optimized version successfully captures the core message of the original prompt. 
+Evaluation: The optimized version effectively maintains the completeness of information. It expands on the ideas presented in the original prompt, offering a more detailed yet concise explanation. The addition of phrases like "distinct process stages" and "shared understanding of the system's architecture" ensures that the full scope of the visual representation's utility is conveyed. No relevant details have been omitted, and the optimized version successfully captures the core message of the original prompt.
 
-- Eliciting Desired Responses: Will the optimized version be more effective in eliciting the desired response from the AI assistant or model? Does it provide a clear directive, enabling the AI to generate a more accurate and contextually appropriate response? 
+- Eliciting Desired Responses: Will the optimized version be more effective in eliciting the desired response from the AI assistant or model? Does it provide a clear directive, enabling the AI to generate a more accurate and contextually appropriate response?
 
-Evaluation: The optimized version is designed to elicit a more focused and accurate response from the AI assistant. By providing additional context and clarity, the AI has a better understanding of the specific benefits attributed to the visual representation. The use of phrases like "graphical depiction," "shared understanding," and "communication of complex ideas" offers a clear framework for the AI to generate a response that aligns with the prompt's intent. The optimized version reduces potential ambiguity and enhances the likelihood of receiving a contextually relevant and high-quality response from the AI. 
+Evaluation: The optimized version is designed to elicit a more focused and accurate response from the AI assistant. By providing additional context and clarity, the AI has a better understanding of the specific benefits attributed to the visual representation. The use of phrases like "graphical depiction," "shared understanding," and "communication of complex ideas" offers a clear framework for the AI to generate a response that aligns with the prompt's intent. The optimized version reduces potential ambiguity and enhances the likelihood of receiving a contextually relevant and high-quality response from the AI.
 
-Overall Conclusion: 
+Overall Conclusion:
 
 Based on the evaluation test, the optimized version of the prompt demonstrates superior effectiveness compared to the original. It achieves a higher standard of clarity by providing explicit and detailed explanations while maintaining the completeness of the information conveyed. The optimized version is also tailored to elicit more accurate and contextually appropriate responses from AI assistants or models, ensuring a more productive and efficient interaction. This comprehensive test underscores the value of careful prompt design and analysis, highlighting the potential for enhanced AI performance and output quality.