I Made A Personal Search Engine with OpenAI and Pinecone

Summary

In this video, the creator shares their journey of building a personal search engine tailored for their YouTube content using OpenAI's embeddings API and Pinecone for hosting. The process involved extracting transcripts from their videos via YouTube's API, converting them into vectors for semantic search, and building a simple front-end web app with Next.js and Tailwind CSS. The search engine allows users to find relevant video segments quickly by using natural language, making it a valuable tool for revisiting past content. The creator encourages viewers to explore the GitHub repository for their own projects.

Takeaways

• 😀 A personal search engine is designed for searching personal content online rather than the entire internet.
• 🤔 The project was inspired by OpenAI's embeddings API, which made the search engine feasible and quick to build.
• 📜 YouTube's transcript API was utilized to convert video content into text transcripts easily.
• 📝 Transcripts are broken down sentence by sentence, enhancing the search functionality and result relevance.
• 🔍 Semantic search allows for better matching of similar concepts, even if the words used are different.
• 📊 Embeddings represent text as vectors, enabling mathematical operations and similarity measurement.
• 💾 The original text data was small (2 MB), but the embeddings file grew significantly to 972 MB.
• ☁️ Pinecone was chosen as the cloud service for hosting vectors due to its ease of use and functionality.
• 🌐 The front-end web app was developed using Next.js and Tailwind CSS, although the styling was kept simple.
• 🚀 The app takes a search term, retrieves its vector, and fetches the top five results from Pinecone, linking to the exact timestamp in videos.

Q & A

What is a personal search engine as described in the video?

A personal search engine is a tool created by the speaker to search through their own online content, such as YouTube videos, instead of searching the entire internet.

Why did the creator decide to build a personal search engine?

The creator was inspired to build the search engine after discovering OpenAI's embeddings API and thought it would be a cool project to undertake.

What are the three main steps involved in building the search engine?

The three main steps are: getting the data, hosting the data, and writing the web application to search through the data.

How did the creator obtain the data for the search engine?

The creator used YouTube's transcript API to convert their video content into text, resulting in time-stamped transcripts broken down by sentence.

What is the significance of breaking the text down by sentences?

Breaking the text down by sentences allows for more accurate and relevant search results, making it easier to find specific information.

What is semantic search and how does it differ from traditional search?

Semantic search understands the meaning behind words, allowing it to match similar concepts (like 'Python' and 'anaconda') even if they are not spelled the same, unlike traditional search which matches exact text.

How large was the file containing the embeddings after processing?

The file containing the embeddings was approximately 972 megabytes, significantly larger than the original 2 megabyte text file.

What mathematical concept is used to measure the similarity between search terms?

Cosine similarity is used to measure how similar two vectors are, indicating whether they have similar meanings based on their directional alignment.

Which service did the creator choose for hosting the data, and why?

The creator chose Pinecone for hosting the data because it simplifies the process of managing large amounts of data and allows for efficient vector similarity searches.

What technology stack was used to build the front-end web application?

The front-end web application was built using Next.js for the framework and Tailwind CSS for styling.

What features does the search engine offer in terms of search results?

The search engine provides the top five most relevant search results and links directly to the relevant timestamps in the original YouTube videos.

What does the creator plan to do in the future regarding the search engine?

The creator plans to improve the app by incorporating more data sources, enhancing the user interface, and optimizing performance.

How can viewers access the search engine project?

Viewers can access the project through a GitHub repository link provided by the creator, where they can use their own OpenAI and Pinecone API keys to try it out.