📚 Additional Use Cases for EmbeddingFramework¶

This document provides 15 different real-world use cases with input/output examples to help you leverage EmbeddingFramework effectively.

1️⃣ Semantic Search in Documents¶

results = vector_db.query("renewable energy", top_k=3)

Output:

[
  {"text": "Solar power is a renewable energy source.", "score": 0.95},
  {"text": "Wind energy is sustainable and clean.", "score": 0.93}
]

2️⃣ FAQ Bot¶

question = "What is AI?"
results = vector_db.query(question, top_k=1)

Output:

{"text": "AI stands for Artificial Intelligence.", "score": 0.99}

3️⃣ Duplicate Detection¶

emb1 = embedding_provider.embed_texts(["Hello world"])
emb2 = embedding_provider.embed_texts(["Hello world!"])

Output: Cosine similarity = 0.98

4️⃣ Recommendation System¶

user_profile = "Loves science fiction books"
results = vector_db.query(user_profile, top_k=5)

5️⃣ Summarization Preprocessing¶

chunks = split_text(long_article, chunk_size=500)

Output: List of smaller text chunks.

6️⃣ Multi-Language Search¶

query = "energía renovable"  # Spanish
results = vector_db.query(query, top_k=3)

7️⃣ Image Caption Search (with pre-generated captions)¶

results = vector_db.query("A cat sitting on a sofa", top_k=2)

8️⃣ Legal Document Search¶

results = vector_db.query("contract termination clause", top_k=3)

9️⃣ Academic Paper Finder¶

results = vector_db.query("quantum computing algorithms", top_k=3)

🔟 Customer Support Ticket Classification¶

ticket = "My internet is not working"
embedding = embedding_provider.embed_texts([ticket])

1️⃣1️⃣ Code Snippet Search¶

results = vector_db.query("binary search implementation in Python", top_k=2)

1️⃣2️⃣ Plagiarism Detection¶

Compare embeddings of two documents for similarity.

1️⃣3️⃣ Personalized Learning Path¶

Match student profile embeddings with course content embeddings.

1️⃣4️⃣ News Article Clustering¶

Cluster embeddings of news articles to group similar topics.

1️⃣5️⃣ Voice Command Matching¶

Convert speech to text, embed, and match with predefined commands.

These examples demonstrate the versatility of EmbeddingFramework across industries like search, recommendation, NLP, legal tech, edtech, and more.