LLM ➕ OCR = 🔥 Intelligent Document Processing (IDP) with Amazon Textract, AWS Bedrock, & LangChain

In this video we are going to explore , how we can enhance an Intelligent Document Processing (IDP) workflow with Bedrock foundation models & Textract.
Prerequisite:
===========
Complete Amazon Bedrock In 2.5 Hours | Learn Generative AI on AWS with Python!
• Complete Amazon Bedroc...
Quickly Build high-accuracy Gen-AI applications using Amazon Kendra & LLM
• Quickly Build high-acc...
Code:
======
github.com/SatadruMukherjee/D...
Check this playlist for more Data Engineering related videos:
• Demystifying Data Engi...
Apache Kafka form scratch
• Apache Kafka for Pytho...
Messaging Made Easy: AWS SQS Playlist
• Messaging Made Easy: A...
Snowflake Complete Course from scratch with End-to-End Project with in-depth explanation--
doc.clickup.com/37466271/d/h/...
Explore our vlog channel:
/ @funwithourfam
Your Queries -
=============
Amazon Textract
Amazon Bedrock
LangChain
Building a Conversational Document Bot on Amazon Bedrock and Amazon Textract
Intelligent Document Processing with AWS AI Services and Amazon Bedrock
Amazon Textract Resources
Intelligent Document Processing - Machine Learning
Intelligent Document Processing
IDP
🙏🙏🙏🙏🙏🙏🙏🙏
YOU JUST NEED TO DO
3 THINGS to support my channel
LIKE
SHARE
&
SUBSCRIBE
TO MY KZbin CHANNEL

[@ccc_ccc789]

thanks very much!

[@KnowledgeAmplifier1]

You're welcome @ccc_ccc789! Happy Learning :-)

[@sridhartondapi]

can you perform the same steps without using the RAG implementation? reading the pdf(source data) and invoke the model with prompt?

[@KnowledgeAmplifier1]

Yes @sridhartondapi, it is possible to perform the steps without using the Retrieval-Augmented Generation (RAG) implementation. However, this approach may decrease performance. Directly reading the PDF and invoking the model with the entire content as a prompt can lead to: Increased Processing Time: Parsing large amounts of text without pre-filtering relevant information can significantly slow down the response time. Higher Resource Consumption: Handling large prompts requires more computational resources, which can affect the overall efficiency. Reduced Accuracy: Without the targeted retrieval step, the model may struggle to identify and focus on the most relevant information, potentially leading to less accurate results. Using RAG helps mitigate these issues by efficiently retrieving pertinent information before generating responses.