g = index.get_networkx_graph() doesn't load the graph thats loaded in the index

Please stand by @Cukon as I search the knowledge base. I normally take about 20 seconds or so. :wave:

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The function get_networkx_graph() is a method of the KnowledgeGraphIndex class in LlamaIndex. It is used to get a networkx representation of the graph structure. The function takes an optional parameter limit which is the number of starting nodes to be included in the graph. By default, this value is set to 100.

Here is the function definition:

(Source: LlamaIndex API Reference ())

If g = index.get_networkx_graph() is not loading the graph that's loaded in the index, it could be due to several reasons:

1. The networkx library is not installed. This function requires networkx to be installed. You can install it using pip: %pip install networkx.

1. The index object might not be an instance of KnowledgeGraphIndex or it might not have been properly initialized.

1. The graph in the index might not have any nodes or it might have less nodes than the limit specified.

1. There might be an issue with the graph data in the index. The graph data should be in a format that can be converted to a networkx graph.

If you're still having issues, please provide more details about the error message you're getting or the behavior you're observing.
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@kapa.ai how many nodes does it need?

Let me check the knowledge base for you. One moment! :books:

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The get_networkx_graph() function in LlamaIndex doesn't require a specific number of nodes to create a graph. It will create a graph with whatever nodes are available in the index. However, by default, it only includes the first 100 nodes from the index in the graph due to the limit parameter set to 100.

If you want to include more or fewer nodes, you can adjust the limit parameter when calling the function. For example, if you want to include 200 nodes, you can call the function like this:



If the number of nodes in the index is less than the limit, the function will include all the nodes. If the index has no nodes, the function will return an empty graph.

Please note that the actual number of nodes in the graph can be less than the limit if the index doesn't have enough nodes. You can check the number of nodes in the graph using the number_of_nodes() function from networkx:



This will return the number of nodes in the graph g.
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@kapa.ai i keep getting this stuff that's like Alice is mother of bob and a philz coffee shop that's not in my dataset why is that there?

Let me check the knowledge base for you. One moment! :books:

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It seems like you're encountering unexpected results when querying your dataset with LlamaIndex. This could be due to a few reasons:

1. Pre-existing Knowledge: LlamaIndex uses Language Models (LLMs) which are pre-trained on a large corpus of text from the internet. These models have a lot of pre-existing knowledge about the world. If your query is ambiguous or doesn't have a clear answer in your dataset, the model might fall back on its pre-existing knowledge to generate a response. For example, it might generate common facts like "Alice is the mother of Bob" even if this information is not in your dataset.

1. Indexing Issue: There might be an issue with how your data is indexed. Make sure that your data is properly indexed and that the index is being used correctly in your queries.

1. Query Issue: The way you're formulating your queries might be leading to these unexpected results. Make sure your queries are specific and clear.

If you're still having issues, it would be helpful to have more details about your dataset, how you're using LlamaIndex, and the exact queries you're making. This will help in diagnosing the issue and providing a more specific solution.

For more information on how to use LlamaIndex, you can refer to the LlamaIndex Documentation ().
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@kapa.ai How can i get rid of this pre existing knowledge?

Let me check the knowledge base for you. One moment! :books:

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The pre-existing knowledge in LlamaIndex comes from the Language Models (LLMs) that it uses, which are pre-trained on a large corpus of text from the internet. This pre-existing knowledge is a fundamental part of how these models work and it can't be completely removed.

However, you can minimize the influence of this pre-existing knowledge by using a few strategies:

1. Use a smaller model: Larger models have more pre-existing knowledge. Using a smaller model might reduce the amount of pre-existing knowledge, but it could also reduce the model's ability to understand and generate text.

1. Fine-tuning: If you have a specific dataset, you can fine-tune the model on your dataset. This will make the model more familiar with your data and it might rely less on its pre-existing knowledge when generating responses. However, fine-tuning is a complex process that requires a lot of computational resources.

1. Prompt Engineering: You can design your prompts to be more specific and clear, which can guide the model to generate responses based on your data rather than its pre-existing knowledge.

1. Use a different LLM: LlamaIndex supports multiple LLMs. You might find that some LLMs are more suitable for your needs than others.

Remember that these strategies can only reduce the influence of the pre-existing knowledge, not completely remove it. The pre-existing knowledge is a fundamental part of how these models work.

For more information on how to use LlamaIndex, you can refer to the LlamaIndex Documentation ().
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@kapa.ai why are the file paths being posting?

Let me check the knowledge base for you. One moment! :books:

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The file paths you're seeing are likely part of the metadata associated with the data in your LlamaIndex. When data is indexed, additional information such as the file path, file name, and other details are often stored alongside the actual data. This metadata can be useful for tracking the source of the data, debugging issues, and other purposes.

In the context of the LlamaIndex examples you provided, the file paths are included in the text data as a way to reference the original source of the data. This is particularly useful when working with large datasets or when the same data might be stored in multiple files.

If you don't want to include file paths or other metadata in your data, you would need to modify how your data is indexed. However, keep in mind that removing this information could make it harder to trace back where specific pieces of data came from, which could be problematic in some cases.
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@kapa.ai how does max_triplets_per_chunk effect the data

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The max_triplets_per_chunk parameter in LlamaIndex's KnowledgeGraphIndex.from_documents() function determines the maximum number of triplets that can be included in a single chunk of data.

A triplet in this context is a set of three elements (subject, predicate, object) that represents a fact or a relationship in the knowledge graph. For example, in the statement "Alice is the mother of Bob", "Alice" is the subject, "is the mother of" is the predicate, and "Bob" is the object. This statement would be one triplet.

When LlamaIndex processes a large amount of data, it breaks the data into smaller chunks to make it more manageable. The max_triplets_per_chunk parameter controls the size of these chunks in terms of the number of triplets.

If you set max_triplets_per_chunk to a higher value, each chunk will contain more triplets, which means fewer chunks will be created. This could make the indexing process faster, but it could also consume more memory and make the individual chunks harder to work with.

On the other hand, if you set max_triplets_per_chunk to a lower value, each chunk will contain fewer triplets, which means more chunks will be created. This could make the indexing process slower, but it could also consume less memory and make the individual chunks easier to work with.

Here's an example of how you might use this parameter:



In this example, each chunk will contain a maximum of 2 triplets.

For more information on how to use LlamaIndex, you can refer to the LlamaIndex Documentation ().
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