Knowledge graph based chatbot

How do you build a chatbot that never hallucinates? Go beyond vector search by grounding your LLM in a knowledge graph for precise, controllable, and verifiable answers.

#1about 2 minutes

Understanding the limitations of large language models

Standard LLMs can hallucinate, lack up-to-date information, and cannot cite sources, making them unreliable for domain-specific tasks.

#2about 2 minutes

Introducing retrieval-augmented generation to improve accuracy

Retrieval-augmented generation (RAG) solves LLM limitations by providing relevant context from a knowledge base to ground the model's answers in facts.

#3about 2 minutes

Implementing RAG with vector similarity search on documents

Unstructured documents like PDFs are chunked, converted into vector embeddings, and stored in a vector database for similarity search against user queries.

#4about 5 minutes

Using knowledge graphs for precise, structured data retrieval

Knowledge graphs represent information as nodes and relationships, allowing an LLM to generate precise Cypher queries instead of relying on semantic search.

#5about 2 minutes

Answering complex multi-hop questions with graph queries

Knowledge graphs excel at answering multi-hop questions that require connecting multiple pieces of information, which is difficult for unstructured RAG systems.

#6about 2 minutes

Exploring real-world use cases for knowledge graphs

Knowledge graphs can power chatbots for various domains, including supply chain management, HR and skills mapping, and microservice architecture analysis.

#7about 1 minute

Building a hybrid chatbot with structured and unstructured data

A hybrid approach combines the benefits of both systems by connecting unstructured text chunks as nodes within a structured knowledge graph.

#8about 4 minutes

Demonstrating a chatbot powered by a knowledge graph

The live demo showcases a chatbot that answers questions by generating Cypher queries for structured data and summarizing connected unstructured articles.

#9about 2 minutes

Q&A on data pipelines and error handling

The Q&A covers methods for converting unstructured data into a knowledge graph using named entity extraction and how LLMs handle spelling mistakes in queries.