Creating Retrieval Augmented Generation (RAG) with Langchain, Gradio, and ChromaDB on Hugging Face 🤖💻
In this video, I'll guide you through building a Retrieval Augmented Generation (RAG) model using Langchain, Anysacle, Gradio, and ChromaDB and then deploying it on Hugging Face.
RAG is a powerful technique that combines the capabilities of pre-trained large language models (LLMs) with external data sources to generate more accurate and informative text. By accessing relevant information from knowledge bases or databases, RAG models can produce text that is not only fluent but also grounded in real-world knowledge.
In this video, I have covered the following key steps:
1. Indexing data sources into a vector database using ChromaDB and Langchain abstractions. We'll explore how to choose the right embedding model and chunking strategies to optimize retrieval. 🗂️
2. Setting up a Gradio interface to accept user queries and dynamically assemble the prompt, incorporating the user input and the relevant context retrieved from the vector database. 🤖🔍
3. Integrating the RAG model into the Gradio interface and deploying the entire application to Hugging Face. 🚀
4. Demonstrating the capabilities of the RAG model by running sample queries and showcasing the improved accuracy, contextual relevance, and reduced bias compared to standalone language models. 📊
By the end of this video, you'll have a practical understanding of building and deploying a RAG-powered application that can leverage external knowledge to provide more informative and trustworthy responses to user queries. This skill is essential for building advanced natural language processing applications in various domains like finance, healthcare, and customer support. 💡
Links:
Hugging Face App URL: huggingface.co/spaces/mayankc... 🤖
GitHub Repository: github.com/mayankchugh-learni... 💻
Medium Blog: / mayankchugh.jobathk
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