LangChain OpenAI Python | Tutorial | RAG Custom Data Vector Embedding Semantic Search Chroma DB - P7
Dive into the cutting-edge world of AI with "LangChain OpenAI Python | Examples | RAG Custom Data Vector Embedding Semantic Search Chroma DB - P7," the latest installment in our in-depth tutorial series. This video takes you on a comprehensive journey through developing a Retrieval-Augmented Generation (RAG) Bot, leveraging the synergy of LangChain, Python, OpenAI, and Google Colab. Designed for developers, data scientists, and anyone passionate about AI technology, this tutorial provides a hands-on approach to understanding and implementing semantic search capabilities with custom data.
👨💻 *Tutorial Highlights:*
1. **Text Data Processing**: Discover how to break down a large text file into manageable chunks using the LangChain Recursive Text Splitter, preparing the data for further AI analysis.
2. **Vector Embeddings Creation**: Learn the intricacies of generating vector embeddings from text data using OpenAI Embeddings, a foundational step for enabling semantic understanding and searches.
3. **Chroma DB Integration**: Step-by-step guide on creating a Chroma DB database to house vector embeddings, facilitating efficient and accurate similarity searches.
4. **Advanced Retrieval with Chroma DB**: Implement a sophisticated retriever using Chroma DB in conjunction with LangChain's LLMChainExtractor & ContextualCompressionRetriever for enhanced semantic search capabilities.
5. **Semantic Searches Execution**: Experience the power of your RAG Bot as it performs semantic searches, delivering precise and contextually relevant results.
6. **Python OOP Approach**: Dive deep into the Python code, structured around Object-Oriented Programming principles including Classes, Constructors, and Methods, to enhance your AI development skills.
*Essential Imports for Your Project:*
`from langchain.vectorstores import Chroma`
`from langchain.document_loaders import TextLoader`
`from langchain.text_splitter import RecursiveCharacterTextSplitter`
`from langchain.retrievers import ContextualCompressionRetriever`
`from langchain.retrievers.document_compressors import LLMChainExtractor`
`from langchain_openai.chat_models import ChatOpenAI`
`from langchain_openai import OpenAIEmbeddings`
This video not only boosts your technical skills but also equips you with practical methodologies for applying semantic search technologies in AI applications, using custom data for a wide array of uses.
*Special Note for JavaScript/TypeScript Developers:*
"This series is also great for Javascript/Typescript developers like me. In case you're looking to transition into Python-based AI development, this series can serve as practice exercises for both Python and OpenAI API. This can be Python for Javascript & Typescript Developers."
Stay tuned for more content, as we continue to explore the vast possibilities of AI, aiming to provide detailed explanations and step-by-step guides on the latest in AI development.
#LangChain #OpenAI #Python #GoogleColab #SemanticSearch #VectorEmbeddings #ChromaDB #RAGBot #AIDevelopment #MachineLearning #TechTutorial #DataScience #AIProgramming #DocumentProcessing #InformationRetrieval #PythonForDevelopers #CodingTutorial #ArtificialIntelligence
Remember to like, share, and comment on the video with your thoughts and questions. Your engagement helps us tailor our content to meet your learning needs and interests. Let's push the boundaries of AI together!
More to come... So stay tuned!
Watch video LangChain OpenAI Python | Tutorial | RAG Custom Data Vector Embedding Semantic Search Chroma DB - P7 online without registration, duration hours minute second in high quality. This video was added by user HTMLFiveDev 17 February 2024, don't forget to share it with your friends and acquaintances, it has been viewed on our site 73 once and liked it 1 people.