Hi everyone, I was wondering where I

ddigital_dream64

Hi everyone, I was wondering where I would add the parameter which would allow me to fetch more than 2 nodes or sources when querying my index. Thank you!

12 comments

ddigital_dream64

@kapa.ai How do i retrieve more than 2 context results when querying my data?

kkapa.ai

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

kkapa.ai

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To retrieve more than two context results when querying your data with LlamaIndex, you can adjust the similarity_top_k parameter in the as_query_engine method. This parameter determines the number of top similar results to return.

Here is an example of how to use it:

Plain Text

query_engine = index.as_query_engine(
    include_text=True,
    response_mode="tree_summarize",
    embedding_mode="hybrid",
    similarity_top_k=5,  # Adjust this value to retrieve more results
    explore_global_knowledge=True,
)

response = query_engine.query("Your query here")

In this example, similarity_top_k is set to 5, meaning the top 5 similar results will be returned. You can adjust this value according to your needs.

For more information, you can refer to the LlamaIndex documentation ().
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ddigital_dream64

@kapa.ai In that example, what does explore_global_knowledge do?

kkapa.ai

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

kkapa.ai

----------------------
The explore_global_knowledge parameter in the as_query_engine method is used to determine whether to include more global (cross-node) context when querying the data.

Here's the code snippet for reference:

Plain Text

query_engine = index.as_query_engine(
    include_text=True,
    response_mode="tree_summarize",
    embedding_mode="hybrid",
    similarity_top_k=5,
    explore_global_knowledge=True,  # This parameter
)

response = query_engine.query("Your query here")

When explore_global_knowledge is set to True, the query engine will explore and include more global context in the results. This can be useful when you want to retrieve information that is not limited to a specific node but spans across multiple nodes in the knowledge graph.

For more details, you can refer to the LlamaIndex documentation ().
----------------------
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ddigital_dream64

@kapa.ai How do I get metadata about which line or column the data was pulled from when the index was created from a CSV?

kkapa.ai

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

kkapa.ai

----------------------
Based on the provided extracts, there isn't specific information on how to get metadata about which line or column the data was pulled from when the index was created from a CSV using LlamaIndex.

However, LlamaIndex does support storing metadata in nodes and filtering based on that metadata during the retrieval step as shown in the Postgres Vector Store example ().

When creating your index, you might consider including the line or column information as part of the metadata for each node. This would allow you to retrieve this information during querying.

For more detailed instructions, you might want to refer to the LlamaIndex documentation () or the specific examples provided in the LlamaIndex GitHub repository.
----------------------
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ddigital_dream64

@kapa.ai How do I include a row and column number for each data point when indexing a CSV file?

kkapa.ai

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

kkapa.ai

----------------------
Based on the provided extracts, there isn't specific information on how to include a row and column number for each data point when indexing a CSV file using LlamaIndex.

However, when you're reading the CSV file and preparing your data for indexing, you could potentially add the row and column numbers as additional metadata. Here's a general idea of how you might do this using pandas:

Plain Text

import pandas as pd

# Read the CSV file
df = pd.read_csv('your_file.csv')

# Add row and column information
df['row'] = df.index
df = df.reset_index().melt(id_vars='index', var_name='column').rename(columns={'index':'row'})

# Now each row in the DataFrame has 'row' and 'column' fields

In this example, we first add a 'row' column to the DataFrame that corresponds to the index (i.e., the row number in the original CSV). Then we use the melt function to reshape the DataFrame so that each row corresponds to a single data point, with 'row' and 'column' fields indicating its original position in the CSV.

Please note that this is a general Python/pandas solution and the exact method to include this information when creating your LlamaIndex might differ. For more detailed instructions, you might want to refer to the LlamaIndex documentation () or the specific examples provided in the LlamaIndex GitHub repository.
----------------------
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