Genai workshop (#187)

* workshop 1 structure

* updates

* course updates

* coming soon

* updates

* moved note

* fix cypher

* fix msg

* add link to gitpod

* separate imports

* Bring genai-workshop up to date (#185)

* s/new/news

* Introduction to vector indexes and unstructured data (#180)

* 1.18.3

---------

Co-authored-by: Adam Cowley <adam@adamcowley.co.uk>

* workshop 2 structure

* workshop 2 lessons

* updates

* fixes after walkthrough

* add summary

---------

Co-authored-by: Adam Cowley <adam@adamcowley.co.uk>

Author: martinohanlon

.vscode/settings.json

@@ -20,4 +20,5 @@
       "scheme": "file"
     }
   ],
+  "asciidoc.antora.enableAntoraSupport": false,
 }

asciidoc/courses/genai-workshop/course.adoc

@@ -2,7 +2,7 @@
 :status: active
 :duration: 2 hours
 :caption: GenAI Beyond Chat with RAG, Knowledge Graphs and Python
-:usecase: blank-sandbox
+:usecase: recommendations
 :key-points: A comma, separated, list of learnings
 :repository: neo4j-graphacademy/genai-workshop
 
@@ -13,7 +13,7 @@ In this GenAI and Neo4j workshop, you will learn how Neo4j can support your GenA
 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
+* 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
 
@@ -38,3 +38,9 @@ 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
+
+[.includes]
+== This workshop includes
+
+* [lessons]#7 lessons#
+* [challenges]#11 short hands-on challenges#

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

@@ -13,7 +13,7 @@ You can open a Neo4j Browser window throughout this course by clicking the link:
 
 == Get the code
 
-You can use Gitpod as an online IDE and workspace for this workshop.
+You can use link:https://gitpod.io[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`[]

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

@@ -8,9 +8,9 @@ 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
+* Built a graph of unstructured data using Python and LangChain
 
-You can learn more about Neo4j at link:graphacademy.neo4j.com[Neo4j GraphAcademy].
+You can learn more about Neo4j on link:https://graphacademy.neo4j.com[ GraphAcademy^].
 
 read::Finished[]
 

asciidoc/courses/genai-workshop/modules/1-knowledge-graphs-vectors/lessons/3-search-vector/lesson.adoc

@@ -3,12 +3,13 @@
 :type: challenge
 :sandbox: true
 
-The Neo4j sandbox contains a sample of 1000 movies.
-Running the following Cypher query will return the titles and plots for the movies in the database:
+The Neo4j sandbox contains a sample of 1000 embeddings for movie plots.
+Running the following Cypher query will return the titles and plots for the movies that have embeddings:
 
 [source, cypher]
 ----
 MATCH (m:Movie)
+WHERE m.plotEmbedding IS NOT NULL
 RETURN m.title, m.plot
 ----
 

asciidoc/courses/genai-workshop/modules/1-knowledge-graphs-vectors/lessons/3-search-vector/reset.cypher

@@ -1,22 +1,8 @@
 LOAD CSV WITH HEADERS
-FROM 'https://data.neo4j.com/llm-vectors-unstructured/movies-plot-embedding.csv' AS row
-MERGE (m:Movie {movieId: toInteger(row.movieId)})
-SET
-m.tmdbId = toInteger(row.tmdbId),
-m.imdbId = toInteger(row.imdbId),
-m.released = row.released,
-m.title = row.title,
-m.year = toInteger(row.year),
-m.plot = row.plot,
-m.budget = toInteger(row.budget),
-m.imdbRating = toFloat(row.imdbRating),
-m.poster = row.poster,
-m.runtime = toInteger(row.runtime),
-m.imdbVotes = toInteger(row.imdbVotes),
-m.revenue = toInteger(row.revenue),
-m.url = row.url
-WITH m, row
-CALL db.create.setNodeVectorProperty(m, 'plotEmbedding', apoc.convert.fromJsonList(row.plotEmbedding));
+FROM 'https://data.neo4j.com/rec-embed/movie-plot-embeddings-1k.csv'
+AS row
+MATCH (m:Movie {movieId: row.movieId})
+CALL db.create.setNodeVectorProperty(m, 'plotEmbedding', apoc.convert.fromJsonList(row.embedding));
 
 CREATE VECTOR INDEX moviePlots IF NOT EXISTS
 FOR (m:Movie)

asciidoc/courses/genai-workshop/modules/1-knowledge-graphs-vectors/lessons/4-embeddings/lesson.adoc

@@ -40,7 +40,7 @@ WITH genai.vector.encode(
     { token: "sk-..." }) AS myMoviePlot
 CALL db.index.vector.queryNodes('moviePlots', 6, myMoviePlot)
 YIELD node, score
-RETURN node.text, score
+RETURN node.title, node.plot, score
 ----
 
 Experiment with different movie plots and observe the results.

asciidoc/courses/genai-workshop/modules/1-knowledge-graphs-vectors/lessons/5-create-vector-index/lesson.adoc

@@ -9,7 +9,7 @@ You previously used a vector index to find similar text; you can also use a vect
 
 == Movie posters
 
-GraphAcademy has loaded a dataset of movie posters into the sandbox. 
+GraphAcademy has loaded a sample of 1000 movie poster embeddings into the sandbox. 
 Each movie has a URL to a poster image:
 
 [source, cypher]
@@ -98,12 +98,13 @@ image:images/babe-similar-posters.jpg[3 movie posters, Babe, Lassie, Before the
 
 Pick a different movie and write a similar Cypher query to find similar posters.
 
-You can view all the movie titles using this Cypher:
+You can view the movies that have a poster embedding using this Cypher:
 
 [source, cypher]
 ----
 MATCH (m:Movie)
-RETURN m.title
+WHERE m.posterEmbedding IS NOT NULL
+RETURN m.title, m.poster
 ----
 
 == Continue

asciidoc/courses/genai-workshop/modules/1-knowledge-graphs-vectors/lessons/5-create-vector-index/reset.cypher

@@ -1,19 +1,4 @@
-LOAD CSV WITH HEADERS
-FROM 'https://data.neo4j.com/llm-vectors-unstructured/movies-poster-embedding.csv' AS row
-MERGE (m:Movie {movieId: toInteger(row.movieId)})
-SET
-m.tmdbId = toInteger(row.tmdbId),
-m.imdbId = toInteger(row.imdbId),
-m.released = row.released,
-m.title = row.title,
-m.year = toInteger(row.year),
-m.plot = row.plot,
-m.budget = toInteger(row.budget),
-m.imdbRating = toFloat(row.imdbRating),
-m.poster = row.poster,
-m.runtime = toInteger(row.runtime),
-m.imdbVotes = toInteger(row.imdbVotes),
-m.revenue = toInteger(row.revenue),
-m.url = row.url
-WITH m, row
-CALL db.create.setNodeVectorProperty(m, 'posterEmbedding', apoc.convert.fromJsonList(row.posterEmbedding));
+LOAD CSV WITH HEADERS FROM "https://data.neo4j.com/rec-embed/movie-poster-embeddings-1k.csv" AS row
+match (m:Movie {movieId:row.movieId})
+WITH row,m
+CALL db.create.setNodeVectorProperty(m, 'posterEmbedding', apoc.convert.fromJsonList(row.posterEmbedding))

asciidoc/courses/genai-workshop/modules/1-knowledge-graphs-vectors/lessons/6-unstructured-data/lesson.adoc

@@ -40,4 +40,4 @@ read::Move on[]
 
 You learned about how you can store unstructured data in a graph.
 
-In the next task, you will use Python and Langchain to load, chunk, embed, and store unstructured data in Neo4j.
+In the next task, you will use Python and LangChain to load, chunk, embed, and store unstructured data in Neo4j.