Mastering AI with Pinecone OpenAI Tutorial

Mastering AI with Pinecone OpenAI Tutorial

Table of Contents:

1. Introduction
2. Loading the Data Set
3. Uploading Vector Embeddings to Pinecone
4. Querying Vector Embeddings
5. Conclusion

Introduction

Loading the Data Set

Uploading Vector Embeddings to Pinecone

Querying Vector Embeddings

Conclusion

In this article, we will explore how to load a data set and upload vector embeddings to Pinecone. We will also learn how to query those vector embeddings for responses.

Introduction

Welcome to this tutorial on loading and querying vector embeddings with Pinecone. In this article, we will guide You through the process of loading a data set, uploading vector embeddings to Pinecone, and querying these embeddings for Relevant responses. Let's get started!

Loading the Data Set

To begin, we need to load our data set into our code editor. Make sure you have the necessary libraries installed, such as Pinecone, OpenAI, and pandas. If you don't have them installed, you can use pip to install them.

Once the libraries are imported, we can proceed to load the data set. We will use the load_data_set function and specify the data set we want to load. In this example, we will be working with a data set called "track". We will also set the split parameter to "trend" and limit the data set to the first 1000 rows. We can use the pandas library to Visualize the loaded data set and ensure everything is working correctly.

Uploading Vector Embeddings to Pinecone

After loading the data set, we need to upload the vector embeddings to Pinecone. First, we need to initialize Pinecone by passing in the API key and environment variable. This allows us to set up the connection to Pinecone.

Next, we will use a text embedding model, such as text_embedding_add_002, to Create our embeddings. We will then load the batch of data from the data set using a loop and store the lines and IDs in variables. We can then use the OpenAI API to create the embeddings for each batch.

Once the embeddings are created, we can extract the necessary data and store it in variables. We will retrieve the embedding key for each Record and store it in the embed variable. We will also create metadata for our data, which will be stored as a dictionary with the key as "text" and the value as the corresponding row in the data set.

Finally, we can initialize the index in Pinecone and connect to it. We will set the dimension Based on the length of the embeddings and choose a metric, such as Cosine similarity, for comparison. Once the index is set up, we can upload the vectors, IDs, and metadata to Pinecone.

Querying Vector Embeddings

With the vector embeddings uploaded to Pinecone, we can now query them for relevant responses. We will define a query, such as "What can you say about Russia?", and use the OpenAI API to create embeddings for the query. We can then extract the necessary data from the response and store it in variables.

Conclusion

In this tutorial, we learned how to load a data set, upload vector embeddings to Pinecone, and query these embeddings for responses. Pinecone provides a powerful platform for working with high-dimensional vector data, allowing for efficient and accurate querying. Cheers to using Pinecone for managing and querying vector embeddings in your projects!