
genai-workshop
Generative AI Deep Dive Workshops
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The Neo4j GenAI Workshop repository contains notebooks for a workshop focusing on building a Neo4j Graph, text embedding, and providing demos for content generation. The workshop includes data staging, loading, and exploration using Cypher queries. It also covers improvements in LLM response quality, GPT-4 usage, and vector search speed. The repository has undergone multiple updates to enhance course quality, simplify content, and provide better explainers and examples.
README:
Please see genai-workshop.ipynb
which serves as the self-contained workshop.
The other companion notebooks contain code for staging data, building the Neo4j Graph, and providing easy access to demos:
-
data-prep.ipynb
stages the workshop data, sampling and formatting data sourced from the H&M Personalized Fashion Recommendations Dataset. -
data-load.ipynb
loads the staged data into Neo4j, performs text embedding, and creates a vector index. -
genai-workshop-w-outputs.ipynb
is a reference notebook for following along and checking outputs. It is simplygenai-workshop.ipynb
with cell outputs intact. -
genai-example-app-only.ipynb
is a copy ofgenai-workshop.ipynb
that contains only the final section: the demo LLM GraphRAG app for content generation. It assumes you have already rungenai-workshop.ipynb
and exists only for instructor demo purposes.
-
Transition from using Neo4j Sandbox to AuraDS
-
Split out data loading
- Split out data load into a separate notebook
- Live workshops now begin with the dataset pre-loaded to cut down on time and spend more of the course walking through GraphRAG. the data-load.ipynb is kept for reference and replication.
- Remove
neo4j_tools
Python package. the functions/utilities are now included in data-load.ipynb
-
Updated Workshop Slides
-
Added more query exploration & improved explainer queries
- Add Browser-based graph exploration in beginning of workshop
- Include database tips & more Cypher queries in multiple steps
- Update explainer markdown and code cells for graph patterns and GDS for clarity
- Various other minor adjustments to markdown and code to improve course quality
-
Added
genai-workshop-with-outputs.ipynb
and GitHub Actions Workflow-
genai-workshp.ipynb
is now maintained with cleared outputs for better workshop experience and easier PR review - A GitHub actions workflow automatically tests data loading, workshop, and example-only notebooks and auto-commits
genai-workshop-with-outputs.ipynb
file for each PR.
-
-
improve LLM response quality and cleaned up code for LLM chains and vector stores
- parameterizing customer id so don't need to recreate chains & stores for each customer
- updated prompts to better account for seasonality and use all retrieved data
- update to use gpt-4o
-
Improve text embedding speed and reduce code by transitioning to native
genai.vector
Cypher functions -
Updated slides
-
Various other minor adjustments to markdown and code to improve course quality
-
(fix) Add
langchain_community
to the libraries that are pip installed in the notebooks -
Simplify and Shorten Course
- Shortened GDS section to just three cells to run
- Condensed Vector Search Section
- Condensed Loading to Single Notebook Cell
- Switched Recommendation Retriever to a Simple KG Query
- Adding
neo4j_tools
Package to hold convenience functions for loading data and reduce code footprint in main workshop notebook - Updated to GPT-4 throughout
- General Notebook Cleaning - Removed duplicate load statements, updating to newest llm packages, etc.
-
Provide Better Explainers & Examples
- Add A Chain for Printing Final Prompt to LLM with retrieval data to better explain process.
- Added Differentiated Names for Customer Examples in Demo App.
-
Added Additional Resources
- Added workshop slides
- Added "demo only" notebook
- Initial 5-part course with
- Building the knowledge graph
- Vector search & text embedding
- Graph patterns to improve semantic search
- knowledge graph inference & ML
- Building the LLM chain and demo app for generating content
Contributions are welcome! To contribute please:
- Make a PR with a descriptive name
- If you are updating
genai-workshop.ipynb
please ensure to clear all outputs before committing. - Do not alter the
genai-workshop-w-outputs.ipynb
file. This file is autogenerated upon creating/updating PRs.
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