In-Browser Semantic AI Search with PGlite and Transformers.js

In this tutorial, you will learn how to create a local semantic search engine using Pinecone and the Hugging Face Transformers library. This search engine is capable of searching through text data based on its meaning rather than just keywords. The main components of this search engine are: 1. A Pinecone index, which stores the embeddings of your documents. 2. A transformer model from the Hugging Face Transformers library, which generates these embeddings. 3. An inner product search function that retrieves the most relevant documents based on their similarity to a given query embedding. To create this search engine, you will need: 1. Node.js and npm installed on your machine. 2. A Pinecone account with an index created. 3. The Hugging Face Transformers library installed in your project. Here's a step-by-step guide to building the search engine: 1. Set up your project: - Create a new directory for your project and navigate into it. - Run `npm init` to create a package.json file, then install Pinecone and the Hugging Face Transformers library by running `npm install pinecone-client @huggingface/transformers`. 2. Create an environment variable for your Pinecone API key: - Add a new entry to your .env file with the key `PINECONE_API_KEY` and set its value to your actual Pinecone API key. 3. Implement the search engine: - In your main JavaScript file (e.g., index.js), import the required modules and initialize a new Pinecone client with your API key from the .env file. - Define a function to insert documents into the Pinecone index. This function should take an array of document objects, where each object has two properties: `id` (a unique identifier for the document) and `text` (the content of the document). The function should then use the transformer model to generate embeddings for each document, insert these embeddings into the Pinecone index along with their corresponding metadata, and return an array containing the IDs of all inserted documents. - Define a function to search through the documents in the Pinecone index based on a given query text. This function should use the transformer model to generate an embedding for the query text, then perform an inner product search using this query embedding and return the top 10 most relevant documents along with their similarity scores. - Finally, test your search engine by inserting some sample documents into the Pinecone index and performing a few searches. That's it! You now have a fully functional local semantic search engine that can efficiently search through text data based on its meaning. This search engine is perfect for applications like chatbots, question answering systems, or any other use case where you need to find relevant information quickly and accurately.