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graphRAG #617

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fiquick committed Mar 18, 2025
commit c3c3e0fd625c9fa5cdab24b54b319c1754fad7aa
53 changes: 53 additions & 0 deletions modules/ROOT/pages/graphrag.adoc
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. Prompt Engineering: Structured prompt templates ensure consistency and better responses from the LLM.
. Knowledge Graphs & Entity Linking: Using triples (subject-predicate-object) helps structure data effectively for retrieval and reasoning.
. Emil’s Demo: Showcased how Neo4j can automatically create a graph from articles, demonstrating knowledge extraction and structuring.

== Introduction

* Aura is simplifying Graph RAG – Making it easier to use.
* A wrapper for the code → The user interacts through the UI instead of coding everything to do with GraphRAG Agents themselves.
* “You know Cypher → Now you can create tools → Now you can create questions.”

Graph RAG answers complex questions but is hard to implement today.
Reduces the need for deep Cypher and technical knowledge.
Many want to build agents, but it’s not trivial.
In Aura:
A low-code/no-code experience.
Grounded in a knowledge graph (ensuring reliable answers).

== Neo4j Agent Overview

An Agent consist of

* Choice – The agent makes decisions.
* Tools – Functions the agent can use.
* Flow Router – Determines how tasks are handled.
* Components – Different parts that make up the agent system.

== Goal of the Agent

* Answer complex questions and take actions.
* Grounded in knowledge: Answers are based on a knowledge graph.

== How the UI Works

* Users can specify functions.
* The agent has tools/functions it can use (e.g., Cypher queries).
* The LLM determines access to these tools:
* The Agent selects 1 or 2 tools to answer the question.
* Chooses the best Gemini models for the approach.

Example: Movie Agent

Movie Graph DB in Aura – Create a tool like "Find Movies by Actor."

Input: Actor name → Queries: ACTOR ACTS IN / ACTOR IN MOVIE.

Example: "Which movies does Keanu Reeves act in?" → The agent selects the relevant tools and queries the data.

== Publishing the agent:
Developers send questions to endpoints.

== Industry-specific use cases:

* Security
* Supply Chain Graph
* Customer 360
* How can I answer tough questions?