GenAI Convergence: RAGs, Graphs, and LLMs

Tickets are free but mandatory, and can ONLY be issued from the below link: https://lu.ma/69unkg9g
IMP: RSVP here on meetup.com does NOT constitute a ticket​============================================

Join us for an exciting session on the intersection of Generative AI, Retrieval-Augmented Generation (RAGs), Graph Databases, and Large Language Models (LLMs) at Microsoft Reactor, Bengaluru.

You'll learn how Neo4j and GraphRAG are forming the backbone of cutting-edge conversational agents, discover how knowledge graphs can be built from text documents using LLMs, and explore the integration of LangChain with Neo4j's innovative LLM Builder to enhance AI-driven solutions.

Speakers:

1. Ravi Ranjan, Manager Data Science at Publicis Sapient
   Topic: Neo4j and GraphRAG: Building the Backbone of Conversational AI
   Talk Description: In today's rapidly advancing field of conversational AI, integrating knowledge graphs has become essential for enhancing the intelligence and reliability of language models. In this session, we will explore how Neo4j, a leading graph database platform, can be leveraged to construct knowledge graphs that power Graph Retrieval-Augmented Generation (GraphRAG) systems. By transforming unstructured data into interconnected knowledge graphs, GraphRAG addresses common challenges like hallucinations and contextual misunderstandings in AI models, enabling them to provide more accurate and context-rich responses. We will gain practical insights into building and querying knowledge graphs with Neo4j and learn how GraphRAG significantly improves the performance of conversational AI applications.
   Speaker Bio: Hello, I'm Ravi Ranjan. With a foundation in Computer Science, I've spent over ten years as a Data Scientist and Machine Learning Engineer, specializing in AI and ML at the core of enterprise data solutions. My experience spans a variety of industries, including the Automobile, Banking, Retail, and Insurance sectors, delivering AI solutions globally. One of my standout accomplishments is designing a hyper-personalized recommendation system that boosted test drive bookings by an extraordinary 900%. Currently, as a Manager of Data Science, I am focused on developing a scalable GenAI platform for generative content review across various formats of brand assets.
2. Abhishek Mishra, DevRel Engineer at TuneAI
   Topic: Building Knowledge Graphs from Text Documents Using LLMs and Graph Database
   Talk Description: In this talk, we'll explore the intersection of Large Language Models (LLMs) and graph databases - in this case Neo4j, focusing on how to construct rich, interconnected knowledge graphs from unstructured text documents. We'll learn the practical techniques for leveraging the semantic understanding capabilities of LLMs to extract entities and relationships, and demonstrate how to store and query this knowledge graph.
   Speaker Bio: Abhishek Mishra is DevRel Engineer at Tune AI and a Python Software Foundation Fellow. He is community-first person and is an organizer of PyCon India, GDG Chennai, MumPy and many other community initiatives.
3. Alison Cossette, Developer Advocate at Neo4j
   Topic: Practical GraphRAG
   Talk Description: We all know that LLMs hallucinate and RAG can help by providing current, relevant information to the model for generative tasks. But can we do better than just vector retrievals? A knowledge graph can represent data (and reality) at high fidelity and can make this
   rich context available based on the user's questions. But how to turn your text data into graphs data structures?
   Here is where the language skills of LLM can help to extract entities and relationships from text, which you then can correlate with sources,
   cluster into communities and navigate while answering the questions.
   In this talk we will both dive into Microsoft Research's GraphRAG approach as well as run the indexing and search live with Neo4j and LangChain.
   Speaker Bio: Alison Cossette is a Data Science Strategist, Educator, and Developer Advocate at Neo4j, specializing in Graph Data Science. With a strong background in AI and ethical AI practices, Alison bridges complex data concepts with real-world applications. She did Master’s studies at Northwestern University and has conducted research with Stanford's Human-Computer Interaction Crowd Research Collective. Passionate about responsible AI, Alison engages with professionals, policymakers, and the public to promote ethical AI development. She also hosts a podcast, driving innovation and education in data science and AI, making her a respected figure in the field.

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Tickets are free but mandatory, and can ONLY be issued from the below link: https://lu.ma/69unkg9g
IMP: RSVP here on meetup.com does NOT constitute a ticket​