Talk to your text files in a Pinecone Vector Databases with GPT-4: A Step-by-Step Tutorial (LangChain 🦜🔗, Pinecone, OpenAI embeddings)
The notebook that I use in this article can be found on my GitHub.
In the past, I have already written some articles on how to use Large Language Models (LLMs), LangChain and Vector Databases like Chroma DB in Python. In this article, this journey continues. I will show how you can store PDF files in a Pinecone vector database using Python and create a GPT-4 powered chatbot that can answer questions about the document.
In this article, I will:
- Explain what vector databases are
- Explain what Pinecone is
- Show how to use embeddings
- How to create a Pinecone index in Python
- Create a LangChain Q&A chain to talk to your data
Vector Databases
Let us start by discussing what Vector Databases are and why they are so good at handling complex data.
A vector (or embedding) is an array of numbers. That on its own is exciting, but what is even more exciting is that these arrays can represent more complex data like text, images, audio or even video. In the case of text, these representations are…
