[Issue]: Neo4j graphrag import notebook is outdated

[DanielGBabel]

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Describe the issue

graphrag_import_neo4j_cypher.ipynb

This tutorial is indicating the "description_embeddings" and "title" columns and a few other things that have changed since v0.4.0

I need to know how can I get the description_embeddings again in the .parquet files since the new workflow removes them from there and now are represented directly into a vectorstore.

What would be the most appropiate way to import this to neo4j now ?

Steps to reproduce

1. Install graphrag v0.4.0 or higher +
2. index any inputs, and follow the instructions in the neo4j import notebook

GraphRAG Config Used

### This config file contains required core defaults that must be set, along with a handful of common optional settings.
### For a full list of available settings, see https://microsoft.github.io/graphrag/config/yaml/

### LLM settings ###
## There are a number of settings to tune the threading and token limits for LLM calls - check the docs.

encoding_model: cl100k_base # this needs to be matched to your model!

llm:
  api_key: ${GRAPHRAG_API_KEY} # set this in the generated .env file
  type: openai_chat # or azure_openai_chat
  model: gpt-4o-mini
  model_supports_json: true # recommended if this is available for your model.
  # audience: "https://cognitiveservices.azure.com/.default"
  # api_base: https://<instance>.openai.azure.com
  # api_version: 2024-02-15-preview
  # organization: <organization_id>
  # deployment_name: <azure_model_deployment_name>

parallelization:
  stagger: 0.3
  # num_threads: 50

async_mode: threaded # or asyncio

embeddings:
  async_mode: threaded # or asyncio
  vector_store: 
    type: lancedb
    db_uri: 'output/lancedb'
    container_name: default
    overwrite: true
  llm:
    api_key: ${GRAPHRAG_API_KEY}
    type: openai_embedding # or azure_openai_embedding
    model: text-embedding-3-large
    # api_base: https://<instance>.openai.azure.com
    # api_version: 2024-02-15-preview
    # audience: "https://cognitiveservices.azure.com/.default"
    # organization: <organization_id>
    # deployment_name: <azure_model_deployment_name>

### Input settings ###

input:
  type: file # or blob
  file_type: text # or csv
  base_dir: "input"
  file_encoding: utf-8
  file_pattern: ".*\\.(txt|md)$"

chunks:
  size: 1200
  overlap: 100
  group_by_columns: [id]

### Storage settings ###
## If blob storage is specified in the following four sections,
## connection_string and container_name must be provided

cache:
  type: file # or blob
  base_dir: "cache"

reporting:
  type: file # or console, blob
  base_dir: "logs"

storage:
  type: file # or blob
  base_dir: "output"

## only turn this on if running `graphrag index` with custom settings
## we normally use `graphrag update` with the defaults
update_index_storage:
  # type: file # or blob
  # base_dir: "update_output"

### Workflow settings ###

skip_workflows: []

entity_extraction:
  ## llm: override the global llm settings for this task
  ## parallelization: override the global parallelization settings for this task
  ## async_mode: override the global async_mode settings for this task
  prompt: "prompts/entity_extraction.txt"
  entity_types: [organization,person,geo,event,concept,component,specification, business entity, attribute, value, field, system, process, role]
  max_gleanings: 3

summarize_descriptions:
  prompt: "prompts/summarize_descriptions.txt"
  max_length: 500

claim_extraction:
  enabled: true
  prompt: "prompts/claim_extraction.txt"
  description: "Any claims or facts that could be relevant to information discovery."
  max_gleanings: 2

community_reports:
  prompt: "prompts/community_report.txt"
  max_length: 2000
  max_input_length: 8000

cluster_graph:
  max_cluster_size: 10

embed_graph:
  enabled: false # if true, will generate node2vec embeddings for nodes

umap:
  enabled: false # if true, will generate UMAP embeddings for nodes

snapshots:
  graphml: false
  embeddings: false
  transient: false

### Query settings ###
## The prompt locations are required here, but each search method has a number of optional knobs that can be tuned.
## See the config docs: https://microsoft.github.io/graphrag/config/yaml/#query

local_search:
  prompt: "prompts/local_search_system_prompt.txt"

global_search:
  map_prompt: "prompts/global_search_map_system_prompt.txt"
  reduce_prompt: "prompts/global_search_reduce_system_prompt.txt"
  knowledge_prompt: "prompts/global_search_knowledge_system_prompt.txt"

drift_search:
  prompt: "prompts/drift_search_system_prompt.txt"

Logs and screenshots

No response

Additional Information

• GraphRAG Version: v0.4.0 +
• Operating System: Linux
• Python Version: 3.12
• Related Issues: [Bug]: (#1296) KeyError: "Column(s) ['description_embedding'] do not exist" #1345
• Related Commits: 17658c5