The document discusses the integration of knowledge graphs with large language models (LLMs) to enhance information retrieval and improve the handling of specific queries through structured data. It emphasizes issues related to LLMs, such as knowledge cutoffs and hallucinations, and highlights techniques like retrieval-augmented generation and vector similarity search. Additionally, it presents applications of knowledge graphs in real-time analytics and multi-hop question answering, bridging structured and unstructured data for better insights.

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1
Knowledge-graph-based chatbot
By Tomaž Bratanič

2. About me
Tomaž Bratanič - Graph & LLM enthusiast Author of:
● https://bratanic-tomaz.medium.com/
● https://twitter.com/tb_tomaz
● https://github.com/tomasonjo
● bratanic.tomaz@gmail.com
Special discount code for 40% off: ctwnodes40
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3. © 2022 Neo4j, Inc. All rights reserved.
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LLMs are great, but:
• Knowledge
cutoff date
• Hallucinations
• Lack of
information
source
• Lack of
domain-specific
or private
information

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● Generating training
data is hard and
expensive
● Doesn’t solve
knowledge cutoff
issue, only pushed
it to a later date
● No data sources
● Hard to debug, is
the information
coming from
foundation training
set or from fine-
tuning set or is it
hallucinated?
Supervised fine-tuning an LLM
From Google documentations

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5
● Providing relevant
context in prompt
● Avoid relying on
facts provided by
LLMs
● Increased
transparency due to
provided sources
● The answer is as
good as the
provided context
Retrieval-augmented generation
(in-context passing)

6. Retrieval-augmented generation (in-context passing) flow

7. Chat with your PDF application

8. Vector similarity search
Given a question, find the most relevant documents based on a similarity metric (such as
Cosine Similarity) between vector of the question and vectors of contents.
Moving from keyword search to similarity (semantic) search.
Q: what is text
embedding?
abstractId similarity
456 0.923445
22 0.892114
… ...
Top K by similarity

9. Vector index in Neo4j - Added in 5.11
Define vector index Query vector index

10. Chat with your PDF - GenAI Stack
https://github.com/docker/genai-stack
PDF Input
User question
Generated answer

11. Chat with your PDF - GenAI Stack
https://github.com/docker/genai-stack
Neo4j LLM integrations with
LangChain and LlamaIndex can get
you started in less than 5 minutes

13. 13
Knowledge graphs
Go beyond unstructured text in your LLM applications

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● Consists of nodes
and relationships
● Nodes are used to
represent entities,
concepts, and more
● Relationships
describe the
context and how it
is connected to
other nodes
Knowledge graph

15. Retrieval-augmented generation flow for structured
information using generated Cypher statements
https://towardsdatascience.com/langchain-has-added-cypher-search-cb9d821120d5

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● Pass the schema
information to LLM
along with the
question to
generate a
database query
● Can be enhanced
by providing
specific query
examples in the
prompt
Using LLMs to generate queries

17. Few-shot example for Cypher generation
Also useful for
translating domain-
specific lingo to
Cypher

18. Answer generating chain
Giving an LLM presentation
with a pop quiz at the end.

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● Using vector or full
text index to map
values or entities
from the user
question to
database property
values
● Using vector
similarity search to
find most relevant
few-shot cypher
examples allows
you to scale to
more examples
Introducing dynamic prompting
https://github.com/tomasonjo/streamlit-neo4j-hackathon

20. Multi-hop question answering
Take for example the following question:
Did any of the former OpenAI employees start
their own company?
Could be broken into two sub questions:
● Who are the former employees of OpenAI?
● Did any of them start their own company?
Typical recommendation is to use vector similarity
search in combination with agents

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Potential problems
● Repeated information
in the top N
documents
● Hard to define ideal
N number of
retrieved documents
● Potentially a lot of
steps in the question
generation process
Multi-hop question answering

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● Move the bulk work
from query time to
ingestion time
● Don’t worry about
repeated
information
● Don’t worry about
determining the
ideal K number of
retrieved
documents
Multi-hop question answering - a
better way
https://blog.langchain.dev/constructing-knowledge-graphs-from-text-using-openai-functions/

23. Real-time analytics with
knowledge graph
Take for example the following question:
How many new customers we had last week?
Which services depend indirectly on the
authorization service?
Vector similarity search doesn’t natively
handle aggregations, transformation,
graph analytics, etc…
However, a graph database like Neo4j
offers a vast possibility of traditional and
graph analytics through the use of the
graph-based structured query language
Cypher and Graph Data Science plugin

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● Which products will
be affected if
particular system
goes down?
● Who owns a
particular system?
● Are there any active
systems that are
not part of a
product (could be
deprecated)?
● Are there any
existing tasks
related to particular
bug in authorization
service?
Real-time analytics - Microservices
(dev-ops) graph
https://blog.langchain.dev/using-a-knowledge-graph-to-implement-a-devops-rag-application/

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● Future of LLM
applications is the
combination of
structured and
unstructured data
● Explicit links
between structured
and unstructured
information give the
ability to perform
complex “metadata”
filtering
Combining structured and
unstructured information
Unstructured data

26. Questions
Tomaž Bratanič - Graph & LLM enthusiast Author of:
Book raffle:
https://forms.gle/HKWAaSnmzawYtVqr6
● https://bratanic-tomaz.medium.com/
● https://twitter.com/tb_tomaz
● https://github.com/tomasonjo
● bratanic.tomaz@gmail.com
Special discount code for 40% off: ctwnodes40