Elsai Utilities

The Elsai Utilities package provides helper classes for chunking and converting documents for use in retrieval-augmented generation (RAG) and vector database ingestion pipelines.

Prerequisites

• Python >= 3.9

Installation

To install the elsai-utilities package:

pip install --index-url https://elsai-core-package.optisolbusiness.com/root/elsai-utilities/ elsai-utilities==0.1.0

Components

1. The DocumentChunker class provides various ways to split text into structured chunks.

   from elsai_utilities.splitters import DocumentChunker
   
   chunker = DocumentChunker()
   
   contents = "# This is the first page.\n\n## This is the second page.\n\n### This is the third page."
   
   # Page-wise chunking
   chunks = chunker.chunk_page_wise(contents=contents, file_name="example.md")
   
   # Markdown header-wise chunking
   markdown_wise_chunks = chunker.chunk_markdown_header_wise(
       text=contents,
       file_name="example.md",
       headers_to_split_on=[("#", "Header 1"), ("##", "Header 2")],
       strip_headers=True
   )
   
   # Recursive character-wise chunking
   text = "This is a long piece of text that should be chunked recursively..."
   recursive_chunks = chunker.chunk_recursive(
       contents=text,
       file_name="example.md",
       chunk_size=50,
       chunk_overlap=10
   )
   

2. The DocumentConverter class converts LlamaIndex documents into LangChain-compatible Document objects.

   from elsai_utilities.converters import DocumentConverter
   
   converter = DocumentConverter()
   
   llama_index_document = {
       "text_resource": {
           "text": "This is a sample text extracted from LlamaIndex."
       }
   }
   
   langchain_document = converter.llama_index_to_langchain_document(
       llama_index_document=llama_index_document,
       file_name="example.md"
   )
   

3. The ConversationalIntelligence class provides comprehensive conversational analysis capabilities including follow-up question generation, action item detection, and topic/intent classification.

   from elsai_utilities.conversational_intelligence import ConversationalIntelligence
   
   # Initialize with your LLM instance (e.g., ChatOpenAI, Claude, etc.)
   ci = ConversationalIntelligence(llm=your_llm_instance)
   
   # Generate follow-up questions
   followup_questions = ci.generate_followup_questions(
       user_question="What is machine learning?",
       answer="Machine learning is a subset of AI that enables computers to learn from data.",
       context=["Previous discussion about AI", "User is a beginner"],
       num_questions=3
   )
   
   # Safe version with fallback questions
   safe_questions = ci.generate_followup_questions_safe(
       user_question="What is machine learning?",
       answer="Machine learning is a subset of AI...",
       num_questions=3,
       fallback_questions=["Can you tell me more?", "What are some examples?"]
   )
   
   # Detect action items from conversation
   messages = [
       "John, can you prepare the quarterly report by Friday?",
       "Sure, I'll have it ready. Should I include the budget analysis?",
       "Yes, and make sure to highlight the key metrics."
   ]
   
   action_items = ci.detect_action_items(
       messages=messages,
       include_context=True,
       extract_priority=True,
       extract_assignee=True,
       extract_due_date=True,
       min_confidence=0.7
   )
   
   # Detect topics and intents
   topic_intent_result = ci.detect_topics_and_intents(
       messages=messages,
       detect_topics=True,
       detect_intents=True,
       min_confidence=0.6,
       max_topics=5,
       max_intents=3
   )
   
   # Detect only topics
   topics = ci.detect_topics_only(
       messages=messages,
       min_confidence=0.6,
       max_topics=5
   )
   
   # Detect only intents
   intents = ci.detect_intents_only(
       messages=messages,
       min_confidence=0.6,
       max_intents=3
   )
   
   # Comprehensive conversation analysis
   analysis = ci.analyze_conversation(
       messages=messages,
       include_followup=True,
       include_actions=True,
       include_topics=True,
       include_intents=True
   )
   
   # Get conversation summary
   summary = ci.get_conversation_summary(messages=messages)
   

   Follow-up Question Generation - Generates contextually relevant follow-up questions - Supports conversation context and history - Includes safe fallback mechanisms - Validates question format and content

   Action Item Detection - Extracts actionable tasks from conversations - Identifies assignees, due dates, and priorities - Provides confidence scoring - Supports context extraction

   Topic and Intent Detection - Identifies main conversation topics - Classifies user intents and purposes - Supports confidence thresholds - Provides keyword extraction and categorization

   Comprehensive Analysis - Combines all intelligence features - Provides conversation summaries - Offers flexible configuration options - Returns structured, validated results

Return Types

The ConversationalIntelligence component returns structured objects:

• ActionItem Contains task, assignee, due_date, priority, context, and source_message.

• DetectedTopic Contains name, confidence, keywords, category, and context.

• DetectedIntent Contains intent_type, confidence, entities, intent_classification, context, and source_message.

• TopicIntentResponse Contains lists of detected topics and intents.