New course - genai-graphrag-python (#420)

* new course structure

* llm-knowledge-graph-construction updates

* in progress updates

* in progress updates

* in progress updates

* in progress updates

* in progress updats

* updates after walkthrough

* set image alts

* minor caption change

* course summary

* QA review updates

* minor update

* lesson summary

* updates post review

* update branch

* make course active

Author: martinohanlon

asciidoc/courses/genai-graphrag-python/banner.png

@@ -1,8 +1,55 @@
-= Constructing Knowledge Graphs with Neo4j GraphRAG Python
-:categories: llms:99
+= Constructing Knowledge Graphs with Neo4j GraphRAG for Python
+:categories: llms:10, advanced:7, processing:5, generative-ai:4
+:status: active
+:duration: 2 hours
+:caption: Learn how to use Python and LLMs to convert unstructured data into knowledge graphs.
+:usecase: blank-sandbox
+:key-points: Create a knowledge graph using Neo4j GraphRAG for Python, Model a knowledge graph of structure and unstructured data, Query a knowledge graph using retrievers, Customize the knowledge graph build process
+:repository: neo4j-graphacademy/genai-graphrag-python
+:banner-style: light
 
-In this course, you will learn how to:
+== Course Description
 
-* Use the `neo4j_graphrag` Python package to build graph retrieval agumented generation (GraphRAG) applications.
-* Build pipelines to construct knowledge graphs from unstructured text.
-* Combine semantic search and relationships to improve the quality of LLM generated responses.
+In this hands-on course, you will learn how to create knowledge graphs using link:https://neo4j.com/docs/neo4j-graphrag-python/current/[Neo4j GraphRAG for Python^].
+
+You will:
+
+* Use the `neo4j_graphrag` Python package to build knowledge graphs from unstructured data.
+* Add structured data to the knowledge graph to improve LLM responses.
+* Create retrievers to search the knowledge graph.
+* Learn how you can customize the build process to suit your data and use case.
+
+Finally, you will use what you have learned to build a knowledge graph from your data.
+
+=== Prerequisites
+
+This is an advanced course and you should:
+
+* Understand graph and Neo4 fundamental concepts - link:/courses/neo4j-fundamentals[Neo4j and Graph Fundamentals^].
+* Have an understanding of how Generative AI, LLMs, and vector indexes are related to Neo4j - link:/courses/genai-fundamentals[Neo4j & GenerativeAI Fundamentals^].
+* Be able to read and write simple Cypher queries - link:/courses/cypher-fundamentals[Cypher Fundamentals^].
+* Understand how you can use an LLM to generate a knowledge graph - link:/courses/llm-knowledge-graph-construction[https://graphacademy.neo4j.com/courses/llm-knowledge-graph-construction/^].
+* Have experience with programming in Python.
+
+=== Duration
+
+{duration}
+
+=== What you will learn
+
+How to:
+
+* Use the Neo4j GraphRAG for Python package to create a knowledge graph from unstructured data.
+* Enhance a knowledge graph by adding structured data.
+* Create Retrievers to search a knowledge graph.
+* Customize the knowledge graph build process to suit your data and use case.
+* Model a knowledge graph of both structured and unstructured data.
+
+
+
+[.includes]
+== This course includes
+
+* [lessons]#16 lessons#
+* [challenges]#7 hands-on challenges#
+* [quizes]#8 simple quizzes to support your learning#

asciidoc/courses/genai-graphrag-python/course.adoc

@@ -0,0 +1,169 @@
+= Constructing Knowledge Graphs
+:type: lesson
+:order: 1
+
+In this lesson you will review the process of constructing knowledge graphs from unstructured text using an LLM.
+
+== The construction process
+
+Typically, you would follow these steps:
+
+. Gather the data
+. Chunk the data
+. _Vectorize_ the data
+. Pass the data to an LLM to extract nodes and relationships
+. Use the output to generate the graph
+
+=== Gather your data sources
+
+The first step is to gather your unstructured data.
+The data can be in the form of text documents, PDFs, publicly available data, or any other source of information.
+
+Depending on the format, you may need to reformat the data into a format (typically text) that the LLM can process.
+
+The data sources should contain the information you want to include in your knowledge graph.
+
+=== Chunk the data
+
+The next step is to break down the data into _right-sized_ parts.
+This process is known as _chunking_.
+
+The size of the chunks depends on the LLM you are using, the complexity of the data, and what you want to extract from the data.
+
+You may not need to chunk the data if the LLM can process the entire document at once and it fits your requirements.
+
+=== Vectorize the data
+
+Depending on your requirements for querying and searching the data, you may need to create *vector embeddings*.
+You can use any embedding model to create embeddings for each data chunk, but the same model must be used for all embeddings.
+
+Placing these vectors into a link:https://neo4j.com/docs/cypher-manual/current/indexes/semantic-indexes/vector-indexes/[Vector index^] allows you to perform semantic searches, similarity searches, and clustering on the data.
+
+[TIP]
+.Chunking, Vectors, and Similarity Search
+You can learn more about how to chunk documents, vectors, similarity search, and embeddings in the GraphAcademy course link:https://graphacademy.neo4j.com/courses/llm-vectors-unstructured/1-introduction/2-semantic-search/[Introduction to Vector Indexes and Unstructured Data^].
+
+=== Extract nodes and relationships
+
+The next step is to pass the unstructured text data to the LLM to extract the nodes and relationships.
+
+You should provide a suitable prompt that will instruct the LLM to:
+
+- Identify the entities in the text.
+- Extract the relationships between the entities.
+- Format the output so you can use it to generate the graph, for example, as JSON or another structured format.
+
+Optionally, you may also provide additional context or constraints for the extraction, such as the type of entities or relationships you are interested in extracting.
+
+
+=== Generate the graph
+
+Finally, you can use the output from the LLM to generate the graph by creating the nodes and relationships within Neo4j.
+
+The entity and relationship types would become labels and relationship types in the graph.
+The _names_ would be the node and relationship identifiers.
+
+== Example
+
+If you wanted to construct a knowledge graph based on the link:https://en.wikipedia.org/wiki/Neo4j[Neo4j Wikipedia page^], you would:
+
+. **Gather** the text from the page. +
++
+image::images/neo4j-wiki.png["A screenshot of the Neo4j wiki page"]
+. Split the text into **chunks**.
++
+    Neo4j is a graph database management system (GDBMS) developed 
+    by Neo4j Inc.
++
+{sp}
++
+    The data elements Neo4j stores are nodes, edges connecting them, 
+    and attributes of nodes and edges...
+
+. Generate **embeddings** and **vectors** for each chunk.
++
+    [0.21972137987, 0.12345678901, 0.98765432109, ...]
+
+. **Extract** the entities and relationships using an **LLM**.
++
+Send the text to the LLM with an appropriate prompt, for example:
++
+    Your task is to identify the entities and relations requested 
+    with the user prompt from a given text. You must generate the 
+    output in a JSON format containing a list with JSON objects.
+
+    Text:
+    {text}
++
+Parse the entities and relationships output by the LLM.
++
+[source, json]
+----
+{
+  "node_types": [
+    {
+      "label": "GraphDatabase",
+      "properties": [
+        {
+          "name": "Neo4j", "type": "STRING"
+        }
+      ]
+    },
+    {
+      "label": "Company",
+      "properties": [
+        {
+          "name": "Neo4j Inc", "type": "STRING"
+        }
+      ]
+    },
+    {
+      "label": "Programming Language",
+      "properties": [
+        {
+          "name": "Java", "type": "STRING"
+        }
+      ]
+    }
+  ],
+  "relationship_types": [
+    {
+      "label": "DEVELOPED_BY"
+    },
+    {
+      "label": "IMPLEMENTED_IN"
+    }
+  ],
+  "patterns": [
+    ["Neo4j", "DEVELOPED_BY", "Neo4j Inc"],
+    ["Neo4j", "IMPLEMENTED_IN", "Java"],
+  ]
+}
+----
+. **Generate** the graph.
++
+Use the data to construct the graph in Neo4j by creating nodes and relationships based on the entities and relationships extracted by the LLM.
++
+[source, cypher, role=noplay nocopy]
+.Generate the graph
+----
+MERGE (neo4jInc:Company {id: 'Neo4j Inc'})
+MERGE (neo4j:GraphDatabase {id: 'Neo4j'})
+MERGE (java:ProgrammingLanguage {id: 'Java'})
+MERGE (neo4j)-[:DEVELOPED_BY]->(neo4jInc)
+MERGE (neo4j)-[:IMPLEMENTED_IN]->(java)
+----
+
+
+
+[.quiz]
+== Check your understanding
+
+include::questions/1-steps.adoc[leveloffset=+1]
+
+[.summary]
+== Lesson Summary
+
+In this lesson, you learned about how to construct a knowledge graph.
+
+In the next lesson, you will setup your development environment to build knowledge graphs using Python and Neo4j.

asciidoc/courses/genai-graphrag-python/illustration.svg

@@ -0,0 +1,27 @@
+[.question]
+= 1. Knowledge graph construction steps
+
+Which of the following steps could be considered **optional**?
+
+* [ ] Gather your data sources
+* [x] Chunk the data
+* [x] _Vectorize_ the data
+* [ ] Pass the data to an LLM to extract nodes and relationships
+* [ ] Use the output to generate the graph
+
+[TIP,role=hint]
+.Hint
+====
+The essential parts of the process are obtaining the data to pass to the LLM and using the output to generate the graph.
+====
+
+[TIP,role=solution]
+.Solution
+====
+The optional steps are:
+
+* Chunk the data
+* _Vectorize_ the data
+
+It may not be necessary to chunk the data or vectorize it depending on the LLM you are using, the complexity of the data, and your requirements.
+====

asciidoc/courses/genai-graphrag-python/modules/1-introduction/lessons/1-knowledge-graph-construction/images/neo4j-wiki.png

@@ -1,11 +1,14 @@
 = Setup your development environment
-:order: 0
 :type: lesson
-:lab: {repository-link}
-:disable-cache: true
+:order: 2
 :branch: main
 
-In this module, you will use Python, LangChain, and OpenAI to create a knowledge graph from unstructured data.
+
+During this course, you will: 
+
+* Use the Neo4j link:https://neo4j.com/docs/neo4j-graphrag-python/current/[GraphRAG for Python ()`neo4j_graphrag`) package to create a knowledge graph from unstructured and structured data
+* Create vector and text to Cypher retrievers that use the knowledge graph to provide context to an LLM
+
 You must set up a development environment to run the code examples and exercises.
 
 include::../../../../../../shared/courses/codespace/get-started.adoc[]
@@ -17,18 +20,18 @@ You will need link:https://python.org[Python] installed and the ability to insta
 
 You may want to set up a virtual environment using link:https://docs.python.org/3/library/venv.html[`venv`^] or link:https://virtualenv.pypa.io/en/latest/[`virtualenv`^] to keep your dependencies separate from other projects.
 
-Clone the link:{repository-link}[github.com/neo4j-graphacademy/llm-knowledge-graph-construction] repository:
+Clone the link:{repository-link}[github.com/neo4j-graphacademy/genai-graphrag-python] repository:
 
 [source,bash]
 ----
-git clone https://github.com/neo4j-graphacademy/llm-knowledge-graph-construction
+git clone https://github.com/neo4j-graphacademy/genai-graphrag-python
 ----
 
-Install the required packages using `pip` and download the required data:
+Install the required packages using `pip`:
 
 [source,bash]
 ----
-cd llm-knowledge-graph
+cd genai-graphrag-python
 pip install -r requirements.txt
 ----
 
@@ -45,20 +48,34 @@ Fill in the required values.
 [source]
 .Create a .env file
 ----
-include::{repository-raw}/{branch}/.env.example[]
+# Create a copy of this file and name it .env
+OPENAI_API_KEY="sk-..."
+NEO4J_URI="{instance-scheme}://{instance-ip}:{instance-boltPort}"
+NEO4J_USERNAME="{instance-username}"
+NEO4J_PASSWORD="{instance-password}"
+NEO4J_DATABASE="{instance-database}"
 ----
+// include::{repository-raw}/{branch}/.env.example[]
 
 Add your Open AI API key (`OPENAI_API_KEY`), which you can get from link:https://platform.openai.com[platform.openai.com^].
 
-Update the Neo4j sandbox connection details:
+ifeval::[{course-completed}==true]
+
+.Course completed
+[IMPORTANT]
+====
+You have completed this course. 
+
+The Neo4j sandbox instance is no longer available, you can create a Neo4j cloud instance using link:https://console.neo4j.io[Neo4j AuraDB^]
+====
+
+endif::[]
+
 
-NEO4J_URI:: [copy]#bolt://{instance-ip}:{instance-boltPort}#
-NEO4J_USERNAME:: [copy]#{instance-username}#
-NEO4J_PASSWORD:: [copy]#{instance-password}#
 
 == Test your setup
 
-You can test your setup by running `llm-knowledge_graph/test_environment.py` - this will attempt to connect to the Neo4j sandbox and the OpenAI API.
+You can test your setup by running `genai-graphrag-python/test_environment.py` - this will attempt to connect to the Neo4j sandbox and the OpenAI API.
 
 You will see an `OK` message if you have set up your environment correctly. If any tests fail, check the contents of the `.env` file.
 
@@ -68,9 +85,13 @@ When you are ready, you can move on to the next task.
 
 read::Success - let's get started![]
 
+
+
+read::Continue[]
+
 [.summary]
-== Summary
+== Lesson Summary
 
-You have setup your environment and are ready to start this module.
+In this lesson, you setup your development environment to build a knowledge graph.
 
-In the next lesson, you will explore a strategy for storing unstructured data in a graph.
+In the next module, you will create a knowledge graph from unstructured and structured data using an LLM.

asciidoc/courses/genai-graphrag-python/modules/1-introduction/lessons/1-knowledge-graph-construction/lesson.adoc

@@ -0,0 +1,15 @@
+= Introduction
+:order: 1
+
+Welcome to Constructing Knowledge Graphs with Neo4j GraphRAG for Python.
+
+== Module Overview
+
+In this module, you will:
+
+* Review the process of creating knowledge graphs from unstructured text.
+* Setup a development environment to build your own knowledge graph.
+
+If you are ready, let's get going!
+
+link:./1-knowledge-graph-construction/[Ready? Let's go →, role=btn]