@Bastian Brand was asking about an implementation of RAG without libraries, so I ventured to implement that. In this repository, I was able to do MOST of that but in order to easily log runs and switch between different LLMs I used LiteLLM and Langfuse logging. Anyone else implemented this?

how best can i create an LLM agent that can search through content in a vector database AND also anwser queries sent to a SQL database at the same time?

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Just finished watching the video "Introduction to LangChain" in 3rd course. Planning to take up the short course at https://learn.deeplearning.ai/courses/langchain/lesson/1/introduction Their free courses offer a video + play area to practice!

1. Language Models (LLMs): These models are trained on text data and are capable of understanding and generating human language. They can perform tasks such as translation, summarization, question answering, and more. Examples include GPT-3 and BERT. 2. Computer Vision Models: These models are trained on image data and can understand and interpret visual information. They can perform tasks such as object detection, image classification, and more. 3. Generative Models: These models can generate new data that is similar to the data they were trained on. There are several types of generative models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). They can generate realistic images, text, and other types of data. 4. Multimodal Models: These models can understand and generate multiple types of data, such as text and images. They can perform tasks that require understanding of both visual and textual information.