Pipeline Architecture

The TEXTRA-IA Research pipeline is composed of several interconnected components that work together to process, analyze, and synthesize scientific publications. This document details each component and their interactions.

Input Processing

The system accepts three types of input:

Audio Input
- Processed through audio-to-text conversion
- Converted text feeds into the main context processing pipeline
Text Input
- Direct text input from scientific publications
- Feeds directly into context processing
Image Input
- Undergoes specialized image processing
- Generates image descriptions using computer vision
- Descriptions are integrated with textual context

Context and Synthesis

The core processing pipeline consists of:

Context Processing
- Aggregates inputs from multiple sources
- Integrates text, transcribed audio, and image descriptions
- Produces PDF output for archival
- Feeds into the multi-agent system
Multi-Agent System
- Coordinates analysis across specialized agents
- Manages task distribution and aggregation
- Ensures coherent processing flow
Synthesis
- Generates final outputs based on multi-agent processing
- Creates comprehensive research summaries and analyses

Knowledge Base Creation

The knowledge base creation process involves:

Batch Processing
- Handles multiple PDF documents simultaneously
- Extracts structured information
- Stores in database format
Database Structure
- PDF metadata storage
  - ClÃ© (hashlib)
  - Title
  - References
  - Notes/clés
  - Date
  - Path
  - Vectorized (boolean)
- Figure handling
  - ID
  - Image description
  - Type
Vectorization
- Converts processed text into vector representations
- Utilizes LVM finetuned models
- Enables efficient similarity search and analysis

Concepts Graph Processing

The concept graph generation consists of two main phases:

Processing Phase
- Vector DB integration
- NER (Named Entity Recognition)
- Relation extraction
- Concept clustering
Graph Creation Phase
- Node creation from extracted concepts
- Edge definition between related concepts
- Edge weighting based on relationship strength
- Final graph generation

Research Synthesis System

The research synthesis component operates in two stages:

Retrieval Stage
- Vector search in the knowledge base
- Context ranking for relevance
- Extraction of relevant excerpts
- Topic analysis for categorization
Synthesis Stage
- Information consolidation
- Formatting for output
- Final synthesis generation

Timeline and Trend Analysis

A dedicated system for temporal analysis includes:

Data Sources
- Vector DB for concept information
- Temporal data for chronological analysis
- Publication metadata for contextual information
Analysis Components
- Temporal clustering of research topics
- Keyword evolution tracking
- Citation pattern analysis
Output Generation
- Growth trajectory visualization
- Trend scoring and analysis
- Timeline generation
- Trend visualization
- Emerging concepts identification

Integration Points

The system maintains several critical integration points:

Data Flow
- Seamless transfer between components
- Consistent data format maintenance
- Error handling and recovery
Vector Database
- Central repository for processed information
- Enables efficient retrieval and analysis
- Maintains relationships between concepts
Output Generation
- Multiple output formats (PDF, visualizations, summaries)
- Customizable based on user needs
- Integration with existing research workflows