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rawwerks
Offline, last seen 2 months ago
Joined September 25, 2024
how can i convert an existing neo4j knowledge graph into a llamaindex property graph that is hosted on the same neo4j instance?
16 comments
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do you have explicit instructions for hosting a fastpi app made with create-llama on either render.com or fly.io?
11 comments
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any best practices for adding/inserting data into a Property Graph?

if i run this twice (with different documents), it does seem that the 2nd set of documents is inserted into my neo4j knowledge graph, and the resulting query-engine can still answer questions about information from first set of documents. so at least with the underlying neo4j KG, it seems like the default is to insert the new entities, but not delete what is there.

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index = PropertyGraphIndex.from_documents(
    documents[:NUMBER_OF_ARTICLES],
    kg_extractors=[kg_extractor],
    llm=llm,
    embed_model=embed_model,
    property_graph_store=graph_store,
    show_progress=True,
)


that said, i'm still a bit traumatized from the normal vector database document persist/load caching stuff that is required, and i just want to make sure i know how to add docs to an existing property graph without any risk of deleting what is already there.

and related - how do i properly compute the new relationships, if i'm inserting stuff into an existing property graph?

this would be a super helpful example to have, i'm sure a lot of people aren't going to want to re-build the whole property graph every time they think of a new doc to add....
6 comments
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i think i need some help making sure i understand how to persist/load using the pipeline workflow.

is this a correct flow?
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# Generate an ingestion pipeline
pipeline = IngestionPipeline(
    transformations=[
        OpenAIEmbedding(),
    ],
    vector_store=vector_store,
)

#restore the pipeline
pipeline.load("pipeline_storage")

# Ingest directly into a vector db
pipeline.run(documents)

# save the pipeline
pipeline.persist("pipeline_storage")


my assumption is that if there is no diff, then pipeline.run won't actually re-embed the docs. if there is, it will run and persist for next time.
14 comments
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- how can I convert an existing neo4j knowledge graph into a llamaindex “property graph”?
3 comments
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is portkey the only way to collect user feedback with llamaindex? https://github.com/run-llama/llama_index/blob/8151b02fee851c7d9d9912390902c6e784b15233/docs/docs/examples/llm/portkey.ipynb#L37

i'm surprised there isn't anything for arize/traceloop, nor anything in the create-llama that allows for user feedback. just want to make sure i'm not missing something...
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@kapa.ai what would be a good schema for creating a knowledge graph that contains information about how to use a python package? For example, what schema would i use to create a property graph that would contain knowledge about how to use llamaindex?
17 comments
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rawwerks
·

Ingestion

anyone have an ingestion pipeline example for reading a directory with unstructuredreader()? my use case: i want to embed/store all PDFs in a folder, and be able to add new files without having to worry about re-embedding the old ones.

i tried to follow this example: https://docs.llamaindex.ai/en/stable/module_guides/loading/ingestion_pipeline/root.html , but when i build the index this way (vs plain old index = VectorStoreIndex.from_documents(documents)) - it's clearly not working.
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rawwerks
·

Search

@Logan M @jerryjliu0 - https://llamahub.ai is bugging out when you try to search:
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