Building RAG on Tables and Texts Using LANGCHAIN & UNSTRUCTURED | Financial Chatbots
Tired of your chatbot struggling with tabular data? This video shows you how to build a powerful RAG (Retrieve, Augment, Generate) system using Langchain and Unstructured to extract and process tabular data from financial documents. Learn how to create a chatbot that can effectively answer questions based on complex financial information. We'll cover everything from data extraction to model fine-tuning. Don't miss out on this game-changing tutorial!
Github Repo With Used PDF file: https://github.com/Princekrampah/unst...
TIMESTAMPS:
00:00-05:18 Introdcution And Project Overview
05:20- 08:25 Installationg Of Dependencies
08:25 -14:20 Load PDF Document In Unstructured
14:25-15:00 Working With Element Types In Unstructured
17:00-22:25 Working With Unstructured API Client
22:25-29:08 Extract Data From PDF With Unstructured API Client
29: 08-44:05 Preprocess Extracted Elements
44:04-55:22 Generating Summaries
55:43-58:50 Creating Embeddings
58:51-1:03:30 Information Retrieval From Multivector Retriever
1:03:30 -1:07:11 Building Chatbot
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#langchain #unstructured #financialchatbots #rag #ai #machinelearning #python #dataanalysis #chatbots #natural language processing #machinelearning #multivectorRetriever #langchain #llm #RetrievalAugmentedGeneration #programming
Thank you for watching and see you in the next video!!
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