Skip to content

Fixing Grammar & Typos #347

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Feb 24, 2025
Merged

Fixing Grammar & Typos #347

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 7 additions & 7 deletions modules/genai-ecosystem/pages/customer-graph-agent.adoc
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
= GraphRAG Agent for \Customer & Retail Analytics
= GraphRAG Agent for Customer & Retail Analytics
include::_graphacademy_llm.adoc[]
:slug: customer-graph-agent
:author: Zach Blumenfeld
Expand All @@ -11,10 +11,10 @@ include::_graphacademy_llm.adoc[]

This is an *end-to-end worked example for building a GraphRAG agent to accelerate customer and retail analytics*. It covers the entire process from:

1. *Quickly constructing a graph from mixed unstructured and structured data sources*.
2. *Resolving and linking entities* in the graph along the way.
3. *Creating diverse graph retrieval tools*, including query templates, vector search, dynamic text2Cypher, and graph community detection to answer a boarder range of questions.
4. *Building an agent* with Semantic Kernel for conducting analytics and responding to complex user questions.
1. *Quickly constructing a graph from mixed unstructured and structured data sources*
2. *Resolving and linking entities* in the graph along the way
3. *Creating diverse graph retrieval tools*, including query templates, vector search, dynamic text2Cypher, and graph community detection to answer a boarder range of questions
4. *Building an agent* with Semantic Kernel for conducting analytics and responding to complex user questions

All of this using a central *source-of-truth graph schema* to govern the process and AI-interactions, ensuring higher data & retrieval quality.

Expand All @@ -27,7 +27,7 @@ This workflow can be adapted for *analytics, reporting, and Q&A* across various

Follow the instructions below to try it yourself! 🚀

image::ai-customer-graph-query-sample.png[align=center]
image::ai-customer-graph-query-sample.png[align="center"]


== Prerequisites
Expand Down Expand Up @@ -84,7 +84,7 @@ This script perform entity extraction on the `credit-notes.pdf` file and write e

Once complete, you can check the database to see the generated graph. Go to the https://console.neo4j.io/[Aura Console^] and navigate to the Query tab.

image::ai-customer-graph-unstruct-ingest-1-goto-query.png[align=left]
image::ai-customer-graph-unstruct-ingest-1-goto-query.png[align="left"]

Select the "Connect instance" button

Expand Down