#weaviate #rag #vectordb #langchain
GITHUB: github.com/ronidas39/LLMtutor...
TELEGRAM: t.me/ttyoutubediscussion
Welcome back to our channel, Total Technology Zone! This is Ronnie, and today we’re diving into tutorial 78. In this session, I'll be guiding you through the process of creating and using a Weaviate Vector Database for a Retrieval-Augmented Generation (RAG) setup. Weaviate is a powerful vector database that's gaining popularity due to its versatility and multiple implementation modes like on-premise, cloud, and managed services from providers like AWS and Azure.
*What You'll Learn:*
1. *Setting Up Weaviate in the Cloud:*
- We'll start by setting up Weaviate on the cloud. This step ensures we align with the current industry trend where about 90% of companies leverage cloud services.
2. *Establishing a Connection with Python and Weaviate Client:*
- We’ll walk through setting up a Python client to interact with our Weaviate database. This includes using the Weaviate client module and configuring the necessary API keys and URLs.
3. *Loading Data into the Vector Database:*
- Learn how to load a text file into the Weaviate database, split it into chunks, and prepare it for vector storage. This part covers using LangChain for text splitting and embeddings.
4. *Creating a RAG Setup:*
- Understand how to implement RAG using Weaviate. We’ll set up a retriever, create prompts, and integrate everything using LangChain to build a seamless question-answering system.
5. *Validating the Setup:*
- We’ll validate the entire setup by querying the database and retrieving relevant information, demonstrating the effectiveness of our RAG implementation.
*Key Components Used:*
- *Weaviate Vector Database:* Learn how to set up and interact with Weaviate, a versatile vector database.
- *LangChain:* Utilize LangChain for document loading, text splitting, and embeddings.
- *Python:* Implement the setup using Python and relevant libraries to create a functional RAG system.
- *OpenAI:* Integrate OpenAI’s GPT-4 for embedding and querying to enhance the capabilities of our RAG system.
*Why This Tutorial?*
Weaviate offers a range of functionalities, and many tutorials out there cover advanced aspects. However, this tutorial is designed to provide a comprehensive, step-by-step guide for beginners and intermediate users who want to get up to speed with Weaviate and RAG setups quickly. By the end of this video, you will have a clear understanding of how to create a RAG system with Weaviate, ensuring you can apply these concepts in real-world scenarios effectively.
*Additional Resources:*
- *GitHub Repository:* Check out the code and additional resources on my GitHub repository [here](github.com/your-repo-link).
- *Weaviate Documentation:* For more in-depth details on Weaviate, visit their official documentation [here](weaviate.io/docs).
- *LangChain Documentation:* Explore the LangChain documentation for further learning [here](langchain.readthedocs.io).
*Support and Feedback:*
If you found this video helpful, please give it a thumbs up and share it with your friends and colleagues who might benefit from it. Your support helps us reach a larger audience and continue providing quality content. Don't forget to subscribe to our channel and hit the bell icon to stay updated with our latest tutorials.
*Upcoming Tutorials:*
Stay tuned for more exciting and use case-oriented tutorials. We have covered various vector databases such as Chroma DB, Faiss, MongoDB, Neo4j, SQLite, and Elasticsearch. If you have any other databases or topics you would like us to cover, please let us know in the comments.
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*Time Stamps:*
- *00:00* - Introduction
- *02:00* - Setting Up Weaviate in the Cloud
- *05:30* - Connecting with Python and Weaviate Client
- *10:00* - Loading Data into Weaviate
- *15:00* - Creating RAG Setup
- *20:00* - Validating the Setup
- *25:00* - Conclusion and Next Steps
*Related Videos:*
- *Tutorial 76:* [Introduction to LangChain and Document Loaders]( / link-to-tutorial-76 )
- *Tutorial 77:* [Setting Up OpenAI GPT-4 with Python]( / link-to-tutorial-77 )
Be sure to check the video description for all relevant links and resources. Thanks again for your support, and we hope you enjoy the tutorial!
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Негізгі бет Rag with Weaviate using Langchain| Tutorial:78
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