i want to use llmsherpa with S3 objects V2. The flow sh...

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@kapa.ai i want to use llmsherpa with S3 objects V2. The flow should be reading the documents from S3 buckets and then parsing it using LLMsherpa. help me with the code for it

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kkapa.ai

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

kkapa.ai

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To read documents from S3 buckets and parse them using LLMsherpa, you can use the S3Reader class from LlamaIndex. This class is designed to read any S3 file or directory. If a key is not set, it parses the entire bucket (filtered by prefix).

Here is a basic example of how you can use it:

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from llama_index.readers.s3 import S3Reader

# Initialize the S3Reader
reader = S3Reader(
    bucket="your_bucket_name",  # replace with your bucket name
    aws_access_id="your_access_id",  # replace with your AWS access id
    aws_access_secret="your_access_secret",  # replace with your AWS access secret
    # add other parameters as needed
)

# Load the documents from S3
documents = reader.load_s3_files_as_docs()

This will load the files from your specified S3 bucket as a list of Document objects. You can then use these documents with LLMsherpa.

Please note that you need to replace "your_bucket_name", "your_access_id", and "your_access_secret" with your actual AWS S3 bucket name, access id, and access secret.

For more details, you can refer to the LlamaIndex S3Reader documentation ().

As for the LLMsherpa part, I'm afraid I don't have enough information to provide a confident answer yet. I recommend checking the LLMsherpa documentation for more details on how to use it with the Document objects.
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bbhrdwj

@kapa.ai i want to change chunk size in LayoutPDFReader

kkapa.ai

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

kkapa.ai

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The LayoutPDFReader class from LlamaIndex is used to read PDF files and it doesn't directly accept a chunk size parameter. However, you can control the chunk size when you are processing the documents after reading them.

Here is an example of how you can set the chunk size when processing documents:

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from llama_index.core import Settings
from llama_index.core.node_parser import SentenceSplitter

# Set the chunk size
Settings.chunk_size = 1024  # replace with your desired chunk size

# Initialize the node parser with the chunk size
node_parser = SentenceSplitter(chunk_size=Settings.chunk_size)

# Get nodes from documents
nodes = node_parser.get_nodes_from_documents(documents)

In this example, documents is a list of Document objects that you have read using LayoutPDFReader. You need to replace 1024 with your desired chunk size.

Please note that this is a general way to set the chunk size in LlamaIndex and it may not apply to all use cases. For more specific information, you should refer to the LlamaIndex documentation ().
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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bbhrdwj

@kapa.ai LLMSherpa has chunks in which text is repeated. what to do here?

kkapa.ai

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

kkapa.ai

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In LlamaIndex, if you're noticing repeated text in chunks, it might be due to the way text is split into chunks. LlamaIndex uses a TokenTextSplitter to split text into chunks. If a chunk is too large to fit within the context window, it is split using a TokenTextSplitter, allowing some text overlap between chunks. This is done to ensure that no information is lost during the splitting process.

If you're using response modes like refine, compact, or tree_summarize, the text chunks are processed in a way that might result in some overlap. For example, in refine mode, the answer and the next chunk (as well as the original question) are used in another query with the refine_template prompt, which might result in some repetition.

If the repetition is causing issues, you might want to consider using a different response mode. For example, simple_summarize mode truncates all text chunks to fit into a single LLM prompt, which might reduce repetition but could also lose some detail due to truncation.

Here's a brief overview of the response modes:

refine: Creates and refines an answer by sequentially going through each retrieved text chunk. This makes a separate LLM call per Node/retrieved chunk.
compact: Similar to refine but compacts (concatenates) the chunks beforehand, resulting in fewer LLM calls.
tree_summarize: Queries the LLM using the summary_template prompt as many times as needed so that all concatenated chunks have been queried.
simple_summarize: Truncates all text chunks to fit into a single LLM prompt. Good for quick summarization purposes, but may lose detail due to truncation.

kkapa.ai

For more details, you can refer to the LlamaIndex documentation on response modes ().
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