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@Logan M Hey Logan. What would be the

Hey Logan. What would be the optimal way for ingesting multiple documents into nodes that are stored in S3 bucket? Currently i am using S3.list_objects and reading the files then converting them into text. Also, i am manually creating nodes for every page of every pdf. Can you help me understand whether this approach is good for multiple hundred pdfs lets say?
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I think there is a S3 reader that can reader, that can help you with reading the files and preparing final list of docs to be ingested.
https://llamahub.ai/l/readers/llama-index-readers-s3?from=readers
@kapa.ai i am creating document objects from the text i get after retrieving the text from S3 bucket using list_objects_v2. the document objects are stored in a list. The problem is that i get a list of dictionaries and not document object. Help me out
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Based on the information provided in the knowledge sources, it seems like you are trying to load files from an S3 bucket and convert them into Document objects using LlamaIndex.

The S3Reader class in LlamaIndex provides a method load_s3_files_as_docs which loads files from S3 and returns a list of Document objects. Here is the source code for this method:

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def load_s3_files_as_docs(self, temp_dir=None) -> List[Document]:
    """Load file(s) from S3."""
    loader = self._get_simple_directory_reader()
    return loader.load_data()

(Source: LlamaIndex S3Reader Documentation ())

If you are getting a list of dictionaries instead of Document objects, it's possible that the data loader you are using is not correctly converting the data into Document objects.

You can manually convert your data into Document objects using the Document struct exposed by LlamaIndex. Here is an example of how to do this:

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from llama_index.core import Document

text_list = [text1, text2, ...]  # replace with your list of texts
documents = [Document(text=t) for t in text_list]

(Source: LlamaIndex Document Creation Documentation ())

Please replace text1, text2, ... with your actual texts. This will create a list of Document objects from your list of texts.
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@kapa.ai how to import S3 reader
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To import the S3Reader class from LlamaIndex, you can use the following Python import statement:

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from llama_index.readers.s3 import S3Reader


Before you can use S3Reader, make sure you have installed the necessary packages. You can install them using pip:

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!pip install llama-index s3fs boto3


(Source: LlamaIndex S3Reader Documentation ())
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@kapa.ai i am using the S3 loader but the file that i get is encrypted. How to access the decrypted file
Let me check the knowledge base for you. One moment! :books:
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Based on the knowledge sources provided, there is no direct information about handling encrypted files with the S3Reader in LlamaIndex.

However, if your files are encrypted on S3, you would typically need to decrypt them after downloading. The decryption process would depend on the encryption method that was used.

If you're using AWS's server-side encryption with S3 managed keys (SSE-S3), then the files are automatically decrypted when they are downloaded, assuming the necessary permissions are in place.

If you're using a client-side encryption or a different method, you would need to implement the decryption process in your code after downloading the file but before processing it with LlamaIndex.

For more specific help, you might need to refer to the documentation of the encryption/decryption library you're using, or the AWS S3 documentation if you're using server-side encryption.

Please note that handling encrypted data securely is a complex topic and should be done with care. If you're not sure, it might be a good idea to seek advice from a security expert.
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@kapa.ai can i convert langchain document to llamaindex document
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Yes, you can convert a LangChain document to a LlamaIndex Document. The Document class in LlamaIndex provides a class method from_langchain_format that takes a LangChain document as input and returns a LlamaIndex Document. Here is how you can use it:

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from llama_index.core.schema import Document

# assuming `lc_doc` is your LangChain document
llama_doc = Document.from_langchain_format(lc_doc)


This method converts the LangChain document format to the LlamaIndex Document format.

(Source: LlamaIndex Document.from_langchain_format Documentation ())
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