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Intended Positioning Statement

Positioning: For organisations managing high complexity systems, LC is an intelligent onboarding and maintenance LMS tool that turns long technical documentation and chief engineer knowledge into digestable, graph-based learning maps reducing the amount of time and effort engineers need to keep up.

A Graph-Based RAG Framework for Technical Onboarding and Knowledge Retention

1. Research Overview

This project investigates the efficacy of Retrieval-Augmented Generation (RAG) combined with Knowledge Graph (KG) architectures to mitigate "Institutional Memory Loss" in high-complexity engineering environments. The core hypothesis is that transforming unstructured, multimodal technical data into a dynamic, navigable graph significantly reduces the cognitive load required for system mastery compared to traditional linear documentation.

2. Problem Statement

In large-scale technical organizations, critical system architecture is often "siloed" within the oral tradition of senior engineers or buried in dense, unindexed documentation. This creates a "Knowledge Bottleneck" that hinders onboarding efficiency. This research proposes an Autonomous Knowledge Synthesis pipeline to automate the extraction and visualization of these complex relationships.

3. Technical Architecture & Methodology

Phase 1: Semantic Indexing & Grounding

• Orchestration: LangChain for document preprocessing and recursive character splitting.
• Vector Engine: Pinecone for high-dimensional similarity searches.
• Inference: Claude-3-series wrapper for context-aware response generation.
• Constraint: Implementation of Source-Anchored Grounding to ensure 0% hallucination; every response is programmatically linked to a specific document coordinate (page/paragraph).

Phase 2: Multimodal Extraction (STT)

• Aural Processing: Utilizing OpenAI Whisper to process recorded technical seminars, sprint reviews, and architectural deep-dives.
• Entity-Relationship Extraction: Developing custom prompts to output Structured JSON triplets (Subject-Predicate-Object) from transcripts, capturing the "hidden" logic of system dependencies.

Phase 3: Knowledge Graph Visualization

• Graph Engine: PyVis and NetworkX for rendering interactive 2D/3D topologies.
• Dynamic Learning Paths: A specialized "Onboarding" module that generates individualized concept lists and prerequisite chains based on specific researcher roles or academic requirements.

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4. Key Features (Experimental)

• Deep-Dive Indexing: Hierarchical navigation allowing researchers to move from high-level system overviews to niche technical sub-components.
• Contextual Chatbot: A grounded interface for querying the dataset with full transparency of source materials.
• Progress Tracking: A "Learning Path" generator that monitors the participant’s coverage of the knowledge graph.

5. Proposed Evaluation Metrics

To validate the effectiveness of the AKS framework, the following metrics will be tracked:

1. Retrieval Accuracy: Precision and Recall of the RAG pipeline compared to standard keyword searches.
2. Cognitive Load Reduction: Qualitative and quantitative analysis of time-to-information-retrieval among research participants.
3. Graph Fidelity: The accuracy of the STT-to-JSON pipeline in identifying valid architectural relationships.

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6. Future Work: Visual Documentation

Future iterations of this research will explore the integration of Vision-capable LLMs to interpret technical diagrams, flowcharts, and manual schematics, further enriching the Knowledge Graph's multimodal capabilities.

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7. Setup & Installation (Local Research Environment)

# Clone the repository
git clone [https://github.com/your-username/aks-research.git](https://github.com/your-username/aks-research.git)

# Install dependencies
pip install langchain pinecone-client openai pyvis

# Configure environment variables
export PINECONE_API_KEY='your_key'
export OPENAI_API_KEY='your_key'