unique-swot 0.2.3

Unique Swot package is a tool that for conducting a swot analysis based on internal and external documents

SWOT Analysis Tool

A sophisticated AI-powered tool for generating comprehensive SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis reports based on internal documents, earnings calls, and external web resources.

Overview

The SWOT Analysis Tool is an agentic tool built on the Unique Toolkit framework that automatically analyzes multiple data sources to produce structured, well-cited SWOT analysis reports. It leverages large language models with structured output to extract insights and generate professional-grade analysis documents.

Architecture

Project Structure

unique_swot/
├── service.py                 # Main SwotAnalysisTool
├── services/
│   ├── citations.py          # Citation management
│   ├── executor.py           # Execution orchestration
│   ├── notifier.py           # Progress notifications
│   ├── collection/           # Source collection
│   ├── generation/           # LLM-based generation
│   ├── memory/              # State management
│   └── report/              # Report rendering
├── tests/                   # Comprehensive test suite
└── docs/                    # Architecture documentation

How It Works

1. Plan Reception & Validation

The tool receives a SWOT plan specifying which components to analyze (Strengths, Weaknesses, Opportunities, Threats) and the analysis objective. Each component can be set to:

• GENERATE - Create new analysis from sources
• MODIFY - Update existing analysis with new information
• NOT_REQUESTED - Skip this component

2. Multi-Source Data Collection

The tool collects relevant information from multiple sources:

• Knowledge Base: Internal documents filtered by metadata
• Earnings Calls: Financial earnings call transcripts fetched from Quartr API, automatically converted to DOCX, and ingested into the knowledge base
• Web Sources: External web research and articles

All collected content is registered in a central registry with unique identifiers for citation tracking. The tool intelligently caches earnings call content to avoid redundant ingestion.

3. Two-Phase Generation Process

For each requested SWOT component:

Phase 1: Extraction

• Sources are split into manageable batches based on token limits
• Each batch is processed by the language model with structured output enforcement
• The LLM extracts specific SWOT items (e.g., individual strengths) with reasoning
• Extracted items include citation placeholders linking back to source chunks
• Results from all batches are aggregated into a structured model

Phase 2: Summarization

• The aggregated extraction results are synthesized
• The LLM generates a coherent narrative summary
• The summary maintains all citation references
• Output is formatted with proper structure and citations

4. Citation Management

The tool implements a streamlined citation system:

• Inline Citations: [chunk_X] → Converted to document-level references like [1: p5]
• Citation Footer: DOCX reports include a comprehensive citations section listing all referenced documents
• Reference Tracking: Automatic deduplication of document references

This ensures every claim in the report is traceable back to its source material with minimal clutter.

5. Report Delivery

The final report can be delivered in two formats:

DOCX Mode (Document):

• Markdown converted to professional Word document
• Full citations with document titles and page numbers
• Uploaded as downloadable attachment

Chat Mode (Markdown):

• Rich markdown formatting displayed in chat
• Inline superscript citations
• Clickable references to source documents

6. Memory & Caching

• Extraction results are cached in memory for potential modifications
• Content chunk registry persisted for citation lookup
• Supports iterative refinement of analysis

Key Features

🎯 Comprehensive Analysis

• Analyzes all four SWOT dimensions
• Processes multiple sources simultaneously
• Maintains context across large document sets

📊 Intelligent Batch Processing

• Automatically splits sources based on token limits
• Processes batches in parallel where possible (planned but not supported yet)
• Handles sources of any size

🔗 Advanced Citation System

• Every point backed by source references
• Document-level inline citations with page numbers
• Citation footer in DOCX reports
• Traceable to specific pages and documents

📈 Real-Time Progress Tracking

• Visual progress bar with emoji indicators
• Step-by-step progress updates with contextual messages
• Percentage completion calculation
• Support for success/failure states

💾 State Management

• Caches extraction results for modifications
• Persistent storage in Knowledge Base
• Quick lookups via Short-Term Memory

🎨 Multiple Output Formats

• Professional DOCX reports
• Rich markdown for chat
• Customizable templates

🛡️ Robust Error Handling

• Graceful degradation on failures
• Automatic retry logic
• Detailed error logging

Workflow Example

User Request → SWOT Plan
    ↓
Source Collection (KB + Earnings + Web)
    ↓
For Each Component (Strengths, Weaknesses, Opportunities, Threats):
    ├── Split Sources into Batches
    ├── Extract SWOT Items from Each Batch (LLM)
    ├── Aggregate Extraction Results
    ├── Generate Summary (LLM)
    └── Save to Memory
    ↓
Format Citations
    ↓
Render Report (DOCX or Markdown)
    ↓
Deliver to User

Core Services

Service	Responsibility
SwotAnalysisTool	Main orchestrator, validates plans, manages workflow
SourceCollectionManager	Collects data from KB, earnings calls, and web
SWOTExecutionManager	Executes analysis for each SWOT component
BatchProcessor	Manages batch processing with token limits
CitationManager	Tracks and formats citations
MemoryService	Persists state and caches results
ReportDeliveryService	Renders and delivers final reports
ProgressNotifier	Provides real-time progress updates

Configuration

The tool is configured via SwotAnalysisToolConfig which includes:

• Report generation settings (batch size, token limits)
• Language model configuration
• Prompt templates for extraction and summarization
• Report rendering preferences (DOCX vs Chat)
• Cache scope for state management

Technology Stack

• Framework: Unique Toolkit (Python)
• Validation: Pydantic models with strict typing
• LLM Integration: Structured output with schema enforcement
• Storage: Knowledge Base for persistence, Short-Term Memory for caching
• Templating: Jinja2 for report templates
• Document Processing: DOCX generation, Markdown rendering
• Progress Tracking: Real-time message execution updates

Use Cases

• Strategic Planning: Generate comprehensive SWOT analyses for business strategy
• Market Analysis: Analyze market position based on multiple data sources
• Competitive Intelligence: Assess strengths and weaknesses vs competitors
• Investment Research: Evaluate opportunities and threats for investments
• Due Diligence: Comprehensive analysis for M&A or partnerships

Output Quality

The tool produces high-quality analysis by:

• Using structured output to enforce consistency
• Extracting before summarizing for thorough coverage
• Maintaining citation integrity throughout the pipeline
• Validating all outputs against Pydantic schemas
• Applying domain-specific prompt engineering