Train Guard | Paris .

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December 09, 2025 · Paris

Train Guard

This talk details preprocessing train accident reports to build a graph database, visualizing relationships between causes and consequences for analysis.

Overview
Tech stack
  • Neo4j
    Neo4j is the world's leading native graph database, purpose-built for high-performance management and traversal of connected data.
    Neo4j is the leading native graph database, leveraging the Property Graph Model (nodes, relationships, properties) for data storage and retrieval . It is an ACID-compliant, high-performance platform designed for managing highly connected data at scale (billions of nodes) . Queries execute using the declarative Cypher query language, which simplifies complex traversals that would cripple relational systems with numerous JOINs . This architecture delivers orders of magnitude performance improvements (often minutes to milliseconds) for relationship-based queries . Major use cases include fraud detection, recommendation engines, and knowledge graphs for AI, trusted by 84 of the Fortune 100 .
  • LLM Graph Builder
    A no-code application that uses LLMs to transform unstructured PDFs, web pages, and transcripts into structured Neo4j knowledge graphs.
    Neo4j LLM Graph Builder streamlines the creation of knowledge graphs by automating entity and relationship extraction from raw text. Built on the LangChain framework (specifically the llm-graph-transformer module), it supports models like GPT-4o, Gemini 1.5 Pro, and Claude 3.5 Sonnet to process diverse inputs including YouTube transcripts and S3 buckets. The tool maps data into a dual-layer structure: a lexical graph for document chunks and an entity graph for semantic connections. This architecture enables GraphRAG (Graph Retrieval-Augmented Generation), allowing users to query their data through a conversational interface that provides explainable, multi-hop insights directly from a Neo4j Aura database.
  • GPT-4
    GPT-4 is OpenAI’s large multimodal model: it processes both text and image inputs, delivering human-level performance on complex professional and academic benchmarks.
    This is OpenAI’s latest milestone in scaling deep learning: a large multimodal model accepting both text and image inputs. It demonstrates a significant capability leap over its predecessor, scoring in the top 10% on a simulated bar exam (GPT-3.5 scored in the bottom 10%). The model handles nuanced instructions and long-form content, supporting context windows up to 32,768 tokens (32K model). This capacity allows processing up to 25,000 words in a single, complex prompt. GPT-4 is engineered for enhanced reliability, steerability, and advanced reasoning across diverse tasks.
  • LangChain
    The open-source framework for building and deploying reliable, data-aware Large Language Model (LLM) applications.
    LangChain is the essential framework for engineering LLM-powered applications: it simplifies connecting models (like GPT-4 or Claude) to external data, computation, and APIs. The platform provides a modular set of components—Chains, Agents, Tools, and Memory—allowing developers to quickly build complex workflows like Retrieval-Augmented Generation (RAG) pipelines and sophisticated conversational agents. Its Python and JavaScript libraries, combined with LangChain Expression Language (LCEL), offer a standardized interface for rapid prototyping and moving applications to production with confidence.
  • OpenAI API
    OpenAI API: Your direct gateway to cutting-edge AI models (GPT-4o, DALL-E 3, Whisper), enabling scalable, multimodal intelligence integration into any application.
    The OpenAI API provides authenticated, programmatic access to a powerful suite of generative AI models. Developers leverage REST endpoints and official libraries (Python, Node.js) to integrate capabilities like advanced text generation (GPT-4o), image creation (DALL-E 3), and speech-to-text transcription (Whisper). This platform is engineered for scale, supporting millions of daily requests for tasks from complex reasoning to real-time customer support agents, ensuring your application gets reliable, state-of-the-art intelligence.
  • MCP
    MCP is the open-source standard for securely connecting AI agents (like LLMs) to external tools, data, and enterprise workflows.
    The Model Context Protocol (MCP) functions as a standardized integration layer: think of it as a USB-C port for AI applications. Developed and open-sourced by Anthropic, this protocol allows large language models (LLMs) to access real-time context and execute actions via external tools like GitHub, Jira, or proprietary databases . It uses a simple JSON-RPC interface to define tools, schemas, and endpoints, which enables AI agents to perform complex, state-changing tasks—such as creating a GitHub issue or running a test script—rather than just generating text . MCP is essential for building agentic AI systems that can autonomously pursue goals and operate within defined safety and permission boundaries .

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