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The nodes in my VectorStoreIndex have sequential identifiers. How do I add new nodes to th

@kapa.ai The nodes in my VectorStoreIndex have sequential identifiers. How do I add new nodes to the index with identifiers starting from the identifier of the last node in the index?
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Please stand by @bin4ry_d3struct0r as I search the knowledge base. I normally take about 20 seconds or so. :wave:
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To add new nodes to your VectorStoreIndex, you can use the insert method. However, the insert method does not automatically handle the assignment of sequential identifiers. You would need to manage the identifiers yourself before adding the nodes to the index.

Here is a basic example of how you can add documents to your index:

Plain Text
from llama_index.core import VectorStoreIndex

index = VectorStoreIndex([])
for doc in documents:
    index.insert(doc)

(Source: LlamaIndex Documentation ())

If you want to assign sequential identifiers to your nodes, you can do so before inserting them into the index. Here's an example of how you might do this:

Plain Text
from llama_index.core.schema import TextNode

# Assume last_id is the identifier of the last node in your index
last_id = get_last_id_from_your_index()

nodes = []
for i, text_chunk in enumerate(new_text_chunks):
    node_id = last_id + i + 1
    node = TextNode(text=text_chunk, id_=str(node_id))
    nodes.append(node)

index = VectorStoreIndex(nodes)


Please note that the above code is a simplified example and you might need to adjust it according to your specific use case. The get_last_id_from_your_index() function is a placeholder for the function or method you would use to retrieve the last identifier from your index.
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@kapa.ai Write the code for the get_last_id_from_your_index() function above.
Let me check the knowledge base for you. One moment! :books:
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I'm sorry, but the knowledge sources provided do not contain information on how to implement a get_last_id_from_your_index() function. This function's implementation would depend on the specifics of your VectorStoreIndex instance and how you're storing and managing your nodes.

You might need to iterate over the nodes in your index to find the one with the highest identifier, or if your index supports it, you might be able to query the index directly for this information. However, without more information about your specific setup, I can't provide a more detailed answer.

I recommend referring to the LlamaIndex documentation () or the source code of the VectorStoreIndex class for more information on how you might implement this function.
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@kapa.ai How do I retrieve nodes from a VectoreStoreIndex?
Let me check the knowledge base for you. One moment! :books:
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To retrieve nodes from a VectorStoreIndex, you can use the as_retriever method to create a retriever and then use the retrieve method of the retriever to retrieve nodes based on a query. The as_retriever method can take a vector_store_query_mode parameter to specify the query mode.

Here are some examples:

  1. Default query mode:
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retriever = index.as_retriever(vector_store_query_mode="default")
results = retriever.retrieve("Who directed inception?")


  1. Semantic hybrid query mode:
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retriever = index.as_retriever(vector_store_query_mode="semantic_hybrid")
results = retriever.retrieve("Who wrote Harry Potter?")


  1. MMR query mode:
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retriever = index.as_retriever(
    vector_store_query_mode="mmr",
    similarity_top_k=3,
    vector_store_kwargs={"mmr_threshold": 1.0},
)
nodes = retriever.retrieve("What did the author do during his time in Y Combinator?")


In all these examples, results or nodes will contain the retrieved nodes.

Please note that the specific query modes and their parameters might depend on the specific vector store you're using.

Sources:
  • LlamaIndex GitHub ()
  • LlamaIndex Documentation ()
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@kapa.ai How do I retrieve nodes' metadata from a VectoreStoreIndex?
Let me check the knowledge base for you. One moment! :books:
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To retrieve nodes' metadata from a VectorStoreIndex, you first need to retrieve the nodes themselves. Once you have the nodes, you can access their metadata through the metadata attribute. Here's an example:

Plain Text
from llama_index.core import VectorStoreIndex

# Assume you have an index and a query
index = VectorStoreIndex(nodes)
query = "Your query here"

# Create a retriever and retrieve nodes
retriever = index.as_retriever()
nodes = retriever.retrieve(query)

# Access metadata of each node
for node in nodes:
    print(node.metadata)


In this example, nodes is a list of nodes retrieved based on the query. Each node is an instance of a class (like TextNode or EntityNode) that has a metadata attribute. This attribute is a dictionary that contains the metadata of the node.

Please note that the specific contents of the metadata will depend on how you've set up your nodes and your index.

Sources:
  • LlamaIndex GitHub ()
  • LlamaIndex GitHub ()
  • LlamaIndex GitHub ()
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@kapa.ai Is there a way to directly retrieve the nodes stored in a VectoreStoreIndex without asking a query?
Let me check the knowledge base for you. One moment! :books:
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The VectorStoreIndex in LlamaIndex is designed to retrieve nodes based on a query. The main purpose of this index is to enable efficient retrieval of relevant nodes for a given query.

However, if you want to access all nodes in the index, you might need to iterate over the index or use specific methods provided by the underlying vector store. The exact method would depend on the specific vector store you're using.

Unfortunately, the provided knowledge sources do not contain specific information or code examples on how to retrieve all nodes from a VectorStoreIndex without a query.

For more details, you may want to refer to the LlamaIndex documentation () or the source code of the VectorStoreIndex class.
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