Question Answering with Huggingface Agents & Weaviate: LLM + QA pipeline with Opensource Models

Once the LLM that can follow the given task can be programmed with prompts, and tools then developing applications will require connecting with external services and vector stores. Weaviate and many other vector stores help to store the vector embeddings of text/image/audio/video data, which can be searched with similarity search. The data and the code is located at This video discusses how QA pipeline can be implemented using the HF agents. The errors provided by the HF Agents are still under development, while the LLMs used as agents themselves are built of following simple tasks. Video provides the necessary background and the process required for building a production ready QA pipeline. It also provides the intro to prompt modification of the agents. Hope you like this video, and subscribe to the channel. Further uploads related to Big Data, Large Language models and Artificial Intelligence will be shared to your Youtube Dashboard Directly. The supporting playlists are Practical Projects Playlist Huggingface Playlist Python Data Engineering Playlist Python Ecosystem of Libraries ChatGPT and AI Playlist AWS and Python AWS Wrangler PS: Got a question or have a feedback on my content. Get in touch By leaving a Comment in the video @twitter Handle is @KQrios