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Updating Customer Experience Intro for Clarity #340

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Sep 25, 2024
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Updating Customer Experience Intro for Clarity #340

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8 changes: 4 additions & 4 deletions modules/genai-ecosystem/pages/ai-for-customer-experiences.adoc
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Expand Up @@ -11,14 +11,14 @@ include::_graphacademy_llm.adoc[]

//image::https://dist.neo4j.com/wp-content/uploads/20240618104511/build-kg-genai-e1718732751482.png[width=800, align=center,link="https://llm-graph-builder.neo4jlabs.com/",window="_blank"]

Use GenAI + GraphRAG to improve customer experiences throughout multiple touch-points in the journey:
Use Graph-powered RAG (GraphRAG) to improve customer experiences throughout multiple touch-points in their journey:

* *Discovery:* Generate personalized marketing and email content
* *Search:* Offer tailored results based on semantic similarity
* *Recommendations:* Provide targeted product suggestions
* *Support:* Deliver compliant AI scripts for customer assistance

This short guide walks through setting up a full-stack GraphRAG application demonstrating all the above using Neo4j, LangChain (with LangServ), and OpenAI. The app focuses on a retail example using the https://github.com/neo4j-product-examples/graphrag-customer-experience#:~:text=H%26M%20Personalized%20Fashion%20Recommendations%20Dataset[H&M Personalized Fashion Recommendations Dataset^], a sample of real customer purchase data that includes rich information around products including names, types, descriptions, department sections, etc. All code can be found in the https://github.com/neo4j-product-examples/graphrag-customer-experience[GitHub repository^].
This short guide walks through setting up a full-stack GraphRAG application demonstrating all the above using Neo4j, LangChain (with LangServe), and OpenAI. The app focuses on a retail example using the https://github.com/neo4j-product-examples/graphrag-customer-experience#:~:text=H%26M%20Personalized%20Fashion%20Recommendations%20Dataset[H&M Personalized Fashion Recommendations Dataset^], a sample of real customer purchase data that includes rich information around products including names, types, descriptions, department sections, etc. All code can be found in the https://github.com/neo4j-product-examples/graphrag-customer-experience[GitHub repository^].

image::ai-cust-exp-architecture.png[align=center]

Expand All @@ -36,8 +36,8 @@ Clone the repository
git clone https://github.com/neo4j-product-examples/graphrag-customer-experience.git
----

create a `.env` file with the below. Fill in your OpenAI key. You can use our pre-loaded retail demo database to start.
The git repository has directions for creating the database from source data if you are interested.
create a `.env` file with the below. Fill in your OpenAI key. You can use our pre-loaded demo database to start, just copy the Neo4j uri, password, username, and database credentials below.
Alternatively, the https://github.com/neo4j-product-examples/graphrag-customer-experience[GitHub repository^] has directions for creating your own database from source data if you are interested.

[source, bash]
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