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# Knowledge graph based chatbot

Are token limits and hallucinations breaking your standard RAG? Discover how integrating Neo4j knowledge graphs enables deterministic, multi-hop chatbots that flawlessly execute complex logic.

- **Speakers:** Tomaz Bratanic
- **Event:** World Congress 2023
- **Published:** October 6, 2023
- **Duration:** 24:44
- **URL:** https://www.wearedevelopers.com/videos/754-knowledge-graph-based-chatbot

## Summary

Large Language Models (LLMs) have revolutionized conversational AI, but they still suffer from critical limitations, including knowledge cutoffs, hallucinations, and a complete inability to access private, company-specific data. The standard mitigation is Retrieval-Augmented Generation (RAG), which traditionally relies on processing unstructured data—such as PDFs—into vector embeddings. While frameworks like LangChain, LlamaIndex, and Haystack make it easy to power vector similarity searches, purely unstructured approaches often struggle with duplicate context, missing source citations, and the inherent limits of an LLM's input window size.

Integrating structured knowledge graphs, like those built natively in Neo4j, offers a more deterministic and efficient alternative to standard vector-only storage. Instead of blindly retrieving chunks of text, an initial LLM translates the user's natural language into a Cypher query by referencing the graph database's active schema. This executes a lookup fetching precise entities—nodes representing concepts and relationships denoting connections—and passes only relevant facts to the primary LLM. This structured data retrieval uniquely enables multi-hop question answering, cleanly addressing interconnected logic (e.g., 'Which former OpenAI employees started their own company?') without requiring iterative searches or bloating the token window.

Ultimately, modern conversational AI architectures can merge both structured and unstructured data retrieval. By linking chunked article text directly to specific organizational or employee nodes in a graph database, developers can execute composite querying natively. A chatbot can summarize unstructured news while flawlessly mapping organizational hierarchies, returning responses that are both transparent and highly accurate. The open-source tooling around LLMs paired with Neo4j allows engineering teams to deploy contextual chatbots across use cases spanning supply chain analytics, HR skill discovery, and microservice ecosystem mapping.

**Keywords:** knowledge graph chatbots, retrieval-augmented generation, llm hallucinations, langchain framework, vector embeddings, vector similarity search, neo4j graph databases, cypher query generation, multi-hop question answering, llamaindex integration, haystack framework, structured data retrieval, named entity extraction, schema-aware llm querying, conversational ai architecture, unstructured data ingestion

## Chapters

1. **Common limitations of natural language processing models** (00:04) — Native model challenges include strict knowledge cutoffs, factual hallucinations, and a lack of domain-specific private data.
1. **Extending model capabilities through external framework integrations** (02:37) — Using plugins and development frameworks allows text models to interact dynamically with real-time and private service information.
1. **Solving context problems with retrieval augmented generation** (03:45) — Injecting relevant external text into dynamic prompts prevents hallucinations and enables accurate system source citation.
1. **Using vector embeddings for unstructured text search** (05:03) — Converting raw text chunks into dense numeric arrays enables semantic similarity comparisons for retrieving document context.
1. **Representing structured domain data within knowledge graphs** (07:48) — Mapping complex application domains into interconnected nodes and lines provides an architecture built for explicit logical querying.
1. **Generating graph database queries with language models** (09:27) — Translating user intent directly into Cypher syntax retrieves exact contextual database records instead of noisy textual passages.
1. **Handling multi-hop question answering with connected entities** (12:23) — Compiled graph database queries easily resolve complex logic patterns requiring traversal across multiple distinct conceptual relationships.
1. **Extracting and querying domain specific knowledge graphs** (13:55) — Structuring raw documents during initial data ingestion creates scalable property graphs supporting complex hardware or system architectures.
1. **Demonstrating a knowledge graph powered chatbot interface** (18:22) — A customized client engine executes backend Cypher lookups to map relationships and summarize internal engineering news.
1. **Approaches for data extraction and fuzzy entity matching** (22:53) — Handling unstructured text feeds effectively involves named extraction techniques and relying on natural language understanding to correct misspellings.

## Related Moments

- [Introduction to generative AI and knowledge graphs](https://www.wearedevelopers.com/videos/1154-large-language-models-knowledge-graphs) (from "Large Language Models ❤️ Knowledge Graphs")
- [Unlocking generative AI capabilities using knowledge graphs](https://www.wearedevelopers.com/videos/100318-context-graphs-for-explainable-decision-aware-ai-agents) (from "Context Graphs for Explainable, Decision-Aware AI Agents")
- [Enhancing language models with graph retrieval augmented generation](https://www.wearedevelopers.com/videos/1311-graphs-and-rags-everywhere-but-what-are-they-andreas-kollegger-neo4j) (from "Graphs and RAGs Everywhere... But What Are They? - Andreas Kollegger - Neo4j")
- [Enhancing language models with retrieval-augmented generation](https://www.wearedevelopers.com/videos/844-enter-the-brave-new-world-of-genai-with-vector-search) (from "Enter the Brave New World of GenAI with Vector Search")
- [Understanding basic retrieval-augmented generation architectures in chatbots](https://www.wearedevelopers.com/videos/1130-chatbots-are-going-to-destroy-infrastructures-and-your-cloud-bills) (from "Chatbots are going to destroy infrastructures and your cloud bills")
- [Enhancing conversational intent through modern large language models](https://www.wearedevelopers.com/videos/1641-hello-jarvis-building-voice-interfaces-for-your-llms) (from "Hello JARVIS - Building Voice Interfaces for Your LLMS")

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