Create Word Embeddings using Openai embeddings api store and query using Vector Database
- Perform a semantic search on certain data;
- Build a vector database that can store and process semantic search queries.
- Analyze the data using OpenAI and turn it into vectors that you can store in a database.
- Save the information and the vectors produced by OpenAI together.
The project involved using the Postman API to access OpenAI's text-embedding-ada-002 and create word embeddings for a text document. These embeddings were then stored in a vector database using a single store that included Azure Cloud. SQL was used to create a table and store the embeddings along with the text. Finally, semantic search was utilized to retrieve the most relevant text.
Storing vectors and querying by sending vectors is a common technique used in various applications where similarity search or recommendation systems are required. Some notable applications include:
Semantic Search: You can store vectors representing documents, articles, or textual data and query for similar content. This is useful in search engines, content recommendation, and information retrieval systems.
Product Recommendations: E-commerce platforms use vector representations of products and customer preferences to provide personalized recommendations.
Image Retrieval: In image search engines, you can store vectors representing images and query for visually similar images.
Music Recommendation: In music streaming services, vectors representing songs and user preferences can be used to recommend music.
Anomaly Detection: In cybersecurity and network monitoring, vectors can be used to detect unusual patterns or behavior.
Natural Language Processing: Vectors representing words or phrases are used in applications like sentiment analysis, chatbots, and language translation.
Content Filtering: Social media platforms and news websites use vectors to filter and recommend content to users.
Collaborative Filtering: Vectors representing user profiles and item profiles are used for collaborative filtering in recommendation systems.
Fraud Detection: Vectors can be used to detect fraudulent transactions or activities by identifying unusual patterns.
Customer Segmentation: In marketing and CRM, vectors can help segment customers based on their behavior and preferences.
Document Clustering: Vectors can be used to group similar documents or articles into clusters.
Healthcare: In medical applications, vectors can represent patient records for finding similar medical cases or for predictive modeling.