Member-only story
Deploying a Langchain Large Language Model (LLM) with Streamlit & Pinecone
Deploying a full-stack Large Language model application using Streamlit, Pinecone (vector DB) & Langchain
LangChain is a library that helps developers build applications powered by large language models (LLMs). It does this by providing a framework for connecting LLMs to other sources of data, such as the internet or your personal files. This allows developers to chain together multiple commands to create more complex applications.
Pinecone is one of the most popular vector database frameworks. A vector database is a type of database that is specifically designed to store and retrieve vector data efficiently. Vector data, in this context, refers to data that is represented as a set of numerical values, or “vectors,” which can be used to describe the characteristics of an object or a phenomenon. Examples of vector data include images, audio signals, genetic sequences, and text documents.
Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science.
Put them all together, and you get a full-stack LLM app! Which is quite amazing and isn’t really going to take that long at all! In this tutorial, we are going to be continuing my pet LLM project, building an LLM that answers questions on Langchain. In part 1, we extracted the data, and in part 2, we used langchain to build a powerful question-answering chain. In this part, we are going to be adding our vectors to Pinecone and deploying our chain to Streamlit so that we can have a question-answering UI that works just like ChatGPT. Before we begin, you should probably create an account on both Streamlit and Pinecone!
Part 1: Adding our vectors to Pinecone
After you have signed up to Pinecone, you will need to navigate to https://app.pinecone.io/ and grab both the API keys and the Environment name. Now let’s get to the beefy parts. Let’s create a script that will read our files, create vectors and upload to Pinecone, we will call this script pinecone_vec.pyAs always, we will simply start with the imports. We will need OpenAI’s embeddings (or feel free…
