GenAI workshop

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asciidoc/courses/genai-workshop/course.adoc

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+= Gen-AI - Hands-on Workshop
+:status: active
+:duration: 2 hours
+:caption: GenAI Beyond Chat with RAG, Knowledge Graphs and Python
+:usecase: blank-sandbox
+:key-points: A comma, separated, list of learnings
+:repository: neo4j-graphacademy/genai-workshop
+
+== Course Description
+
+In this GenAI and Neo4j workshop, you will learn how Neo4j can support your GenAI projects. 
+
+You will: 
+
+* Use Vector indexes and embeddings in Neo4j to perform similarity and keyword search
+* Use Python and Langchain to integrate with Neo4j and OpenAI
+* Learn about Large Language Models (LLMs), hallucination and integrating knowledge graphs
+* Explore Retrieval Augmented Generation (RAG) and its role in grounding LLM-generated content
+
+After completing this workshop, you will be able to explain the terms LLM, RAG, grounding, and knowledge graphs. You will also have the knowledge and skills to create simple LLM-based applications using Neo4j and Python.
+
+=== Prerequisites
+
+Before taking this course, you should have:
+
+* A basic understanding of Graph Databases and Neo4j
+* Knowledge of Python and capable of reading simple programs
+
+While not essential, we completing the GraphAcademy link:/courses/neo4j-fundamentals/[Neo4j Fundamentals^] course.
+
+=== Duration
+
+{duration}
+
+== What you need
+
+To complete the practical tasks within this workshop, you will need: 
+
+* Access to gitpod.io (you will need a github, gitpod, or bitbucket account) or a local Python environment
+* An OpenAI billing account and API key

asciidoc/courses/genai-workshop/modules/1-knowledge-graphs-vectors/lessons/1-getting-started/lesson.adoc

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+= Getting Started
+:order: 1
+:type: lesson
+:lab: {repository-link}
+:disable-cache: true
+
+We have created a link:https://github.com/neo4j-graphacademy/genai-workshop[repository^] for this workshop.
+It contains the starter code and resources you need. 
+
+A blank Neo4j Sandbox instance has also been created for you to use during this course.
+
+You can open a Neo4j Browser window throughout this course by clicking the link:#[Toggle Sandbox,role=classroom-sandbox-toggle] button in the bottom right-hand corner of the screen.
+
+== Get the code
+
+You can use Gitpod as an online IDE and workspace for this workshop.
+It will automatically clone the workshop repository and set up your environment.
+
+lab::Open `Gitpod workspace`[]
+
+[NOTE]
+You will need to login with a Github, Gitlab, or Bitbucket account.
+
+Alternatively, you can clone the repository and set up the environment yourself.
+
+[%collapsible]
+.Develop on your local machine
+====
+You will need link:https://python.org[Python] installed and the ability to install packages using `pip`.
+
+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:https://github.com/neo4j-graphacademy/genai-workshop[github.com/neo4j-graphacademy/genai-workshop] repository:
+
+[source,bash]
+----
+git clone https://github.com/neo4j-graphacademy/genai-workshop
+----
+
+Install the required packages using `pip`:
+
+[source,bash]
+----
+cd genai-workshop
+pip install -r requirements.txt
+----
+====
+
+== Setup the environment
+
+Create a copy of the `.env.example` file and name it `.env`. 
+Fill in the required values.
+
+[source]
+.Create a .env file
+----
+include::{repository-raw}/main/.env.example[]
+----
+
+Add your Open AI API key (`OPENAI_API_KEY`), which you can get from link:https://platform.openai.com[platformn.openai.com].
+
+Update the Neo4j sandbox connection details:
+
+NEO4J_URI:: [copy]#bolt://{sandbox_ip}:{sandbox_boltPort}#
+NEO4J_USERNAME:: [copy]#{sandbox_username}#
+NEO4J_PASSWORD:: [copy]#{sandbox_password}#
+
+== Test your setup
+
+You can test your setup by running `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.
+
+== Continue
+
+When you are ready, you can move on to the next task.
+
+read::Success - let's get started![]
+
+[.summary]
+== Summary
+
+You have setup your environment and are ready to start the workshop.

asciidoc/courses/genai-workshop/modules/1-knowledge-graphs-vectors/lessons/10-expand-graph/lesson.adoc

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+= Expand the Graph
+:order: 10
+:type: challenge
+:optional: true
+:sandbox: true
+
+In this *optional* challenge, you can extend the graph with additional data.
+
+== All Courses
+
+Currently, the graph contains data from a single course, `llm-fundamentals`, you can download the link:https://data.neo4j.com/llm-vectors-unstructured/courses.zip[lesson files for all the courses^].
+
+. Download the content for all the courses - link:https://data.neo4j.com/llm-vectors-unstructured/courses.zip[data.neo4j.com/llm-vectors-unstructured/courses.zip^]
+. Update the graph with the new data
+. Explore the graph and find the connections between the courses
+
+== Additional metadata 
+
+While the course content is unstructured, it contains metadata you can extract and include in the graph.
+
+Examples include:
+
+* The course title is the first level 1 heading in the file - `= Course Title`
+* Level 2 headings denote section titles - `== Section Title`
+* The lessons include parameters in the format `:parameter: value` at the top of the file, such as:
+** `:type:` - the type of lesson (e.g. `lesson`, `challenge`, `quiz`)
+** `:order:` - the order of the lesson in the module
+** `:optional:` - whether the lesson is optional
+
+Explore the course content and see what other data you can extract and include in the graph.
+
+When you are ready to move on, click Continue.
+
+== Continue
+
+When you are ready, you can move on to the next task.
+
+read::Move on[]
+
+[.summary]
+== Summary
+
+In this optional challenge, you extended the graph with additional data.
+

asciidoc/courses/genai-workshop/modules/1-knowledge-graphs-vectors/lessons/11-next-steps/lesson.adoc

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+= Next steps
+:order: 11
+:type: lesson
+
+Congratulations on completing this workshop!
+
+You have:
+
+* Used vector indexes to search for similar data
+* Created embeddings and vector indexes
+* Built a graph of unstructured data using Python and Langchain
+
+You can learn more about Neo4j at link:graphacademy.neo4j.com[Neo4j GraphAcademy].
+
+read::Finished[]
+
+[.summary]
+== Summary
+
+Congratulations on completing this workshop!

asciidoc/courses/genai-workshop/modules/1-knowledge-graphs-vectors/lessons/2-vectors/lesson.adoc

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+= Semantic Search, Vectors, and Embeddings
+:order: 2
+:type: lesson
+
+Machine learning and natural language processing (NLP) often use vectors and embeddings to represent and understand data.
+
+== Semantic Search
+
+Semantic search aims to understand search phrases' intent and contextual meaning, rather than focusing on individual keywords.
+
+Traditional keyword search often depends on exact-match keywords or proximity-based algorithms that find similar words.
+
+For example, if you input "apple" in a traditional search, you might predominantly get results about the fruit.
+
+However, in a semantic search, the engine tries to gauge the context: Are you searching about the fruit, the tech company, or something else?
+
+image:images/Apple-tech-or-fruit.png[An apple in the middle with a tech icons on the left and a food on the right,width=700,align=center]
+
+
+== What are Vectors
+
+Vectors are simply a list of numbers. 
+For example, the vector `[1, 2, 3]`` is a list of three numbers and could represent a point in three-dimensional space.
+
+image:images/3d-vector.svg[A diagram showing a 3d representation of the x,y,z coordinates 1,1,1 and 1,2,3]
+
+You can use vectors to represent many different types of data, including text, images, and audio.
+
+Using vectors with a dimensionality of hundreds and thousands in machine learning and natural language processing (NLP) is common.
+
+== What are Embeddings?
+
+When referring to vectors in the context of machine learning and NLP, the term "embedding" is typically used. 
+An embedding is a vector that represents the data in a useful way for a specific task.
+
+Each dimension in a vector can represent a particular semantic aspect of the word or phrase. 
+When multiple dimensions are combined, they can convey the overall meaning of the word or phrase.
+
+For example, the word "apple" might be represented by an embedding with the following dimensions:
+
+* fruit
+* technology
+* color
+* taste
+* shape
+
+You can create embeddings in various ways, but one of the most common methods is to use a **large language model**.
+
+For example, the embedding for the word "apple" is `0.0077788467, -0.02306925, -0.007360777, -0.027743412, -0.0045747845, 0.01289164, -0.021863015, -0.008587573, 0.01892967, -0.029854324, -0.0027962727, 0.020108491, -0.004530236, 0.009129008,` ... and so on.
+
+== How are vectors used in semantic search?
+
+You can use the _distance_ or _angle_ between vectors to gauge the semantic similarity between words or phrases.
+
+image::images/vector-distance.svg[A 3 dimensional chart illustrating the distance between vectors. The vectors are for the words "apple" and "fruit",width=700,align=center]
+
+Words with similar meanings or contexts will have vectors that are close together, while unrelated words will be farther apart.
+
+This principle is employed in semantic search to find contextually relevant results for a user's query.
+
+== Continue
+
+When you are ready, you can move on to the next task.
+
+read::Move on[]
+
+[.summary]
+== Summary
+
+You learned about semantic search, vectors, and embeddings.
+
+Next, you will use a Neo4j vector index to find similar data.