What is an API?
- API stands for Application Programming Interface.
- It acts as a bridge between different software applications, allowing them to communicate and exchange data.
- Think of it as a set of rules or protocols that define how applications can interact with each other.
- APIs enable developers to integrate data, services, and functionality from other applications without building everything from scratch.
How Do APIs Work?
- Computers follow communication protocols to interact with each other. These protocols define the rules for communication.
- For web-based APIs, the HTTP protocol (Hyper Text Transfer Protocol) is commonly used.
- The request-response cycle characterizes how APIs work:
- A client (e.g., your mobile phone or browser) sends a request to a server (a bigger computer).
- The server processes the request and sends back a response.
- The client receives the response and acts accordingly.
- The client provides information in the request, such as:
- URL: The web address where the request is made.
- Method: Whether data is requested (GET) or sent (POST).
API using Plumber in R
- Plumber is an R package that allows you to create web APIs by annotating your existing R functions.
Hosting Plumber APIs
- DigitalOcean: Use the DigitalOcean integration included in Plumber for cloud hosting.
- RStudio Connect: A commercial platform for publishing R content, including Plumber APIs.
- Other approaches include PM2, Docker, and OpenCPU. (References: https://www.rplumber.io/ , https://rstudio.github.io/cheatsheets/html/plumber.html)
Word Embedding
- Dense vectors representing words based on context and meaning.
- Learned from large text corpora using machine learning models.
- Used for semantic understanding and similarity measurement.
- Essential for tasks like data matching and natural language processing.
Cosine Similarity
1. What is Cosine Similarity?
- Cosine similarity is a mathematical metric used to measure the similarity between two vectors in a multi-dimensional space.
- It’s particularly valuable in high-dimensional spaces, such as when dealing with text data.
- The key idea is to calculate the cosine of the angle between the vectors, rather than comparing them directly based on individual values.
2. Vector Representation
- Imagine we have two vectors representing data points (e.g., documents, word embeddings).
- Each dimension of the vector corresponds to a feature (e.g., word frequency, importance).
- For text data, words are often converted into vectors using techniques like word embeddings.
3. Calculating Cosine Similarity *Given two vectors A and B, the cosine similarity is computed as follows:
- |A| and |B| denote the Euclidean norms (lengths) of the vectors.
- The resulting value ranges from -1 (completely opposite) to 1 (identical).
- Higher values indicate greater similarity.
4. Applications and Interpretation
i. Semantic Data Matching: In text analysis, cosine similarity helps compare documents, sentences, or word embeddings. If two vectors (e.g., document representations) have similar meanings or topics, their cosine similarity will be high.
ii. Information Retrieval: Search engines use cosine similarity to find relevant records to a query efficiently.
iii. Recommendation Systems: Cosine similarity aids in suggesting similar items (e.g., movies, products) based on user preferences.
iv. Document Clustering: Grouping similar documents together using cosine similarity (reference: https://medium.com/@techclaw/cosine-similarity-between-two-arrays-for-word-embeddings-c8c1c98811b).
In summary, cosine similarity provides a robust way to understand semantic relationships between data points, making it a powerful tool for various NLP tasks!