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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:
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1. *Quickly constructing a graph from mixed unstructured and structured data sources*.
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2. *Resolving and linking entities* in the graph along the way.
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3. *Creating diverse graph retrieval tools*, including query templates, vector search, dynamic text2Cypher, and graph community detection to answer a boarder range of questions.
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4. *Building an agent* with Semantic Kernel for conducting analytics and responding to complex user questions.
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1. *Quickly constructing a graph from mixed unstructured and structured data sources*
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2. *Resolving and linking entities* in the graph along the way
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3. *Creating diverse graph retrieval tools*, including query templates, vector search, dynamic text2Cypher, and graph community detection to answer a boarder range of questions
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4. *Building an agent* with Semantic Kernel for conducting analytics and responding to complex user questions
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All of this using a central *source-of-truth graph schema* to govern the process and AI-interactions, ensuring higher data & retrieval quality.
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@@ -27,7 +27,7 @@ This workflow can be adapted for *analytics, reporting, and Q&A* across various
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Follow the instructions below to try it yourself! 🚀
@@ -84,7 +84,7 @@ This script perform entity extraction on the `credit-notes.pdf` file and write e
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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.
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