🚀 AutoLLM: Unlock the Power of 100+ Language Models! Step-by-Step Tutorial

Key Takeaways

this is huge imagine you have one application which can be integrated with 100 plus large language model 20 plus Vector databases it has a unified API and much more that's exactly what we're going to see today let's get [Music] started hi everyone I'm really excited to show you about Auto llm it is a unified tool which comprises of lot of features I'm going to to take you through step by step on how to set this up on your computer as a beginner's guide I'm going to follow up with this many different videos into more advanced way of setting up API grade your application and much more so stay tuned first to set this up you need to create a virtual environment that's cond create auto llm python equal 3.11 and click enter after the virtual environment is created activate by using cond activate Auto llm and click enter now pip install auto llm and click enter this will install the auto llm package now create a file called app.py if you see the folder structure it has a file called app.py it has a folder called data and also I created a file called text.txt that's where you have the data if you open the text.txt file you should be able to see a data which is about Auto llm and also about me we are going to run this autol llm and query this document first from autol llm Import Auto query engine next from llama index import simple directory reader the auto query engine is used to query the document which is in the data folder the simple directory reader is used to read the content within that folder now we are creating a documents variable and then simple directory reader and mentioning the folder name which is data and Dot load data this should automatically load the document within that folder next we are going to declare a variable called query engine that is auto query engine Dot from parameters documents equals documents this is how we declare the query engine so here we are passing the documents inside the query engine this will automatically create the embeddings so you don't need to manually worry about creating embeddings next response equals query _ engine. query and then you're going to enter the query explain me about Auto llm next we going to print the response that's it only four lines of code first you import auto query engine and simple directory reader Auto query engine automatically creates the embeddings required for the documents provided next you're querying the query engine and asking a question explain me about o llm and print the response I'm going to run this now go to your terminal and enter the open AI API key like this export and then click enter now type Python app.py and click enter this should automatically query the embeddings and also the large language model now you can see it's passing the documents into noes generating embeddings and you have the answer here so you can clearly see this autol LM has the cost calculator integrated with it totally it costed me 02494 and then you got the answer here Auto llm is a package that provides a simplified unified way to work with large language models that's it as simple as that so if you have any questions do mention that in the comment below I'm going to follow up with various videos such as how to create a fast API using AO llm and how to create a gradio app using Auto llm so stay tuned I hope you like this video do like share and subscribe and thanks for watching