Indexify A Data Framework For LLMs | Introduction To Indexify
Hello guys, welcome back to another exciting tutorial on data frameworks for Large Language Models (LLMs)! In this video, we'll explore how to build a data extraction pipeline using Indexify. We'll then use the extracted data to create a Retrieval-Augmented Generation (RAG) based application with OpenAI. To demonstrate the versatility of Indexify, we'll also build a Streamlit RAG application using Llama-index and Mistral, showcasing how Indexify can seamlessly integrate with your existing LLM and LLM orchestration stack like Llama-index and LangChain. Let's dive in!
Git repo: https://github.com/Princekrampah/inde...
Indexify docs: https://getindexify.ai/
Streamlit: https://streamlit.io/
OpenAI API key: https://openai.com/
Mistral API key: https://mistral.ai/
LlamaIndex and Mistral: https://docs.llamaindex.ai/en/stable/...
Medium article:
Project Overview:
Data Ingestion Pipeline:
Load articles from Wikipedia
Structured extraction and chunking of documents
Extract embeddings using Sentence Transformers
Store embeddings in Vector DB (LanceDB)
Perform Named Entity Recognition with an LLM
Build a RAG application on top of the extracted data
Interact with the visual UI of the extraction pipeline
What Is Indexify?
Indexify is an open-source data framework featuring a real-time extraction engine and ready-made extraction adapters. It provides reliable out-of-the-box extraction for all types of unstructured data, including documents, presentations, videos, and audio.
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📌 Tags:
#LLM #Indexify #DataExtraction #RAGApplication #OpenAI #Streamlit #LlamaIndex #LangChain #Mistral #AI #MachineLearning #DataPipeline #NLP #NaturalLanguageProcessing #TechTutorial #AIFramework #Embeddings #NamedEntityRecognition #VectorDB
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