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4 changes: 3 additions & 1 deletion modules/ROOT/content-nav.adoc
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////
Generic Start
////
* Neo4j Aura

* Introduction
** xref:index.adoc[About Aura]
** xref:new-console.adoc[New Neo4j Aura console]
** xref:microsoft-fabric.adoc[Workload for Microsoft Fabric]
** xref:visual-tour/index.adoc[Visual tour]

* xref:graph-rag.adoc[GraphRAG]
* xref:agents.adoc[Agents]

* xref:graph-analytics/index.adoc[]

* Quick start
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24 changes: 24 additions & 0 deletions modules/ROOT/pages/agents.adoc
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= Agents

Agents are running individual Cypher queries.

Agents are applications that use generative AI models to think and act towards goals.

In Aura, you have the query, tool with inputs and description - and you can do GraphRAG with each individual tool.

You break down user questions into individual tasks.

* Automates complex enterprise workflows.
* Uses reasoning and planning to determine the steps.
* Utilizes tools to access data and take actions such as querying databases or calling APIs.

Good at improving the quality of results and reasoning with each other.
Different types of agents that are doing different things in the workflows.

A system might look like this:

* Have an orchestration layer
* Have some genAI models hookedup
* Have some tools

These all collaborate so you have *better results than one LLM can give you together.*
68 changes: 68 additions & 0 deletions modules/ROOT/pages/graph-rag.adoc
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= GraphRAG

* Make your LLMs smarter using knowledge graphs.

== Talk with data

* You could achieve cost savings.
Using GraphRAG means you could save money on your system, getting less token cost for better query results.

* Explainability is so important.

Neo4j Aura provides a fully managed cloud service for building and running GraphRAG workloads and AI agents.

Aura eliminates the operational overhead of deploying and maintaining a Neo4j cluster, letting you focus on model integration and retrieval accuracy.

It's recommended you start out with a Free (it's good to try stuff out) or Professional instance (you can get a 2 week trial without adding card details).

GraphRAG enables you to relationships and bring together diverse information sources, into one knowledge base.
An example where GraphRAG is used integrated with LLMs and Cypher queries to retrieve and visualize relationships between data points *so you can talk with your data.*

== GraphRAG Python Package

Explore these resources to learn more and experiment with GraphRAG and related tools:

link:https://neo4j.com/developer/genai-ecosystem/graphrag-python[GraphRAG Python Package] A library that allows building GenAI applications.

link:https://www.youtube.com/watch?v=hDJlruy60AM[Walkthrough video] A guided demo of the workflow.

https://github.com/neo4j-product-examples/graphrag-python-examples/blob/main/end-to-end-lupus.ipynb[Github - Example notebook] Step-by-step instructions for building with GraphRAG in Python that demonstrates a meta-analysis of Lupus, a disease that is notoriously difficult to diagnose.

The GraphRAG Python package is a great way to start experimenting with knowledge graphs and AI.
You will see information from the PDFs populate your Aura instance.
Once your data is ingested, you can explore the indexed document chunks directly in Aura and explore the knowledge graph:

image::chunks_in_rag.png[Viewing chunks in Aura]

== Learning resources

link:graphrag.com[graphrag.com] Curated resources and background on GraphRAG.

link:https://neo4j.com/developer/genai-ecosystem/[Neo4j GenAI ecosystem] Overview of how Neo4j integrates with the wider generative AI landscape.

link:https://graphacademy.neo4j.com/courses/llm-chatbot-python/[Graph Academy] Build an LLM Chatbot with Python in this free, hands-on training course.

== Open source tools

https://llm-graph-builder.neo4jlabs.com[LLM Graph Builder demo] Hosted interface for experimenting without setup.

https://github.com/neo4j-labs/llm-graph-builder/issues[LLM Graph Builder (GitHub)] Contribute or track development.

== `.env` credentials

When you create an instance, you'll download the connection details from the console.

image::env.png[env example]

Create a `.env` file in your project with the following format:

[source,properties]
----
NEO4J_URI=neo4j+s://<dbid>.databases.neo4j.io
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD=<password>

AI_API_KEY=<your-api-key>
----