Use Embeddings for Semantic Search — Beginner’s Guide using TradingView

Use Embeddings for Semantic Search — Beginner's Guide using TradingView. Get practical lessons and hands-on examples at AIComputerClasses in Indore to master artificial intelligence (AI) skills quickly. Ideal for beginners and working professionals seeking fast skill gains. Includes references to tools like ChatGPT, Power BI, Excel, Figma, or Python where appropriate. This article from AIComputerClasses Indore breaks down use embeddings for semantic search — beginner's guide using TradingView into actionable steps.

Use Embeddings for Semantic Search — Beginner’s Guide using TradingView

In the world of Artificial Intelligence, semantic search is transforming how machines understand human language. Unlike traditional keyword searches that rely on exact matches, semantic search focuses on meaning — it connects similar ideas even if the exact words differ.

This is made possible by a powerful concept known as embeddings — numerical representations of text that allow algorithms to understand context and relationships.

At AI Computer Classes – Indore, we help students and professionals master these next-generation AI concepts through hands-on exercises using tools like ChatGPT, Python, and even TradingView for data visualization and analytics.

💡 What Are Embeddings?

In simple terms, embeddings are vectors (lists of numbers) that represent the meaning of data — whether it’s text, images, or code.

When you convert sentences or words into embeddings, similar ideas end up closer together in a virtual 3D space.

ExampleConceptVector Meaning“Apple fruit”FoodCloser to “Banana”“Apple Inc.”TechCloser to “Microsoft”

These relationships make it possible for AI systems to “understand” human intent — not just match words.

🔍 Why Semantic Search Matters

Traditional search engines only look for keyword matches, so searching “best smartphone deals” may not show results for “top mobile offers.”

Semantic search fixes that problem by:

• Understanding synonyms and context
• Ranking results based on meaning, not just words
• Enhancing chatbots, document retrieval, and AI assistants

💬 Real-World Example:

ChatGPT uses embeddings to remember context and give coherent replies — that’s semantic search in action!

⚙️ How Embeddings Work (Step-by-Step)

1. Input Text: “AI courses in Indore”
2. Tokenization: Break text into words or tokens
3. Embedding Model: Convert tokens into numerical vectors
4. Similarity Search: Compare vector distances (using cosine similarity)
5. Result: Sentences with similar meanings appear closer

This process allows systems to find related content — even if phrased differently.

🧩 Using TradingView for Visualization

While TradingView is primarily a financial charting tool, it can also be a great platform for visualizing embeddings and similarity relationships.

At AI Computer Classes – Indore, we show how to creatively use TradingView to:

• Plot cosine similarity scores over time
• Display semantic clusters as trend lines
• Combine embeddings with Power BI dashboards for hybrid analytics

💬 Example: Visualize how “AI,” “machine learning,” and “deep learning” evolve in popularity using embedding scores plotted like stock data in TradingView.

🧠 Build Your First Semantic Search in Python

Here’s a simple code snippet to demonstrate how embeddings can power a semantic search system:

from openai import OpenAI
import numpy as np

client = OpenAI()

# Sample data
sentences = ["AI courses in Indore", "Learn Python with Power BI", "Stock market trading tips"]

# Convert each sentence into embeddings
embeddings = [client.embeddings.create(input=s, model="text-embedding-3-small").data[0].embedding for s in sentences]

# Compare semantic similarity (cosine)
def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

print("Similarity between AI and Python course:", cosine_similarity(embeddings[0], embeddings[1]))

🧩 This small script helps learners understand how text meaning can be computed mathematically — the foundation of many AI-powered apps.

📊 Real-World Applications of Embeddings

1. Search Engines: Improve accuracy by understanding intent
2. Chatbots: Maintain context between messages
3. Recommendation Systems: Suggest similar movies, products, or articles
4. TradingView AI: Analyze patterns in market sentiment
5. Power BI Integration: Embed semantic understanding in dashboards

💡 Learn from Experts at AI Computer Classes – Indore!

Gain hands-on training in AI tools, Python coding, and visualization techniques.

👉 Join our latest batch now at AI Computer Classes

📍 Located in Old Palasia, Indore

🔧 Using ChatGPT to Generate Embeddings

ChatGPT and OpenAI’s APIs can directly generate embeddings using their text-embedding models.

Here’s how you can experiment:

• Use text-embedding-3-small for lightweight searches
• Use text-embedding-3-large for higher accuracy
• Store embeddings in a database or CSV file
• Run similarity searches in Excel or Power BI

💬 Example Prompt for ChatGPT:

“Generate embeddings for the following five phrases and compare their similarity scores.”

📈 Combining Embeddings with Power BI or Excel

To make the learning interactive:

• Export embedding vectors to Excel
• Use Power BI to visualize clustering (using scatter plots)
• Apply DAX formulas to calculate similarity indexes

This blends AI + data analytics, giving you both coding and visualization expertise.

🧮 Practice Activity: Semantic Search Demo

At AI Computer Classes – Indore, students complete this fun hands-on project:

1. Generate embeddings for 10 text entries (e.g., product descriptions).
2. Use Python to compute similarities.
3. Visualize results in TradingView or Power BI.
4. Discuss insights and improvement strategies.

This approach makes complex AI math tangible through visual feedback.

🔐 MetaMask + Trading Data Integration

Here’s an advanced twist — combining AI embeddings with MetaMask or blockchain data:

• Analyze transaction notes or DeFi comments semantically
• Detect patterns in on-chain communication
• Visualize AI-derived clusters in TradingView charts

It’s a powerful blend of AI, blockchain, and analytics, ideal for futuristic career roles.

🌍 Career Scope: Why Learn Embeddings Now

By mastering embeddings and semantic search, you can pursue roles in:

• 🤖 AI Engineering
• 🧠 NLP (Natural Language Processing)
• 📈 Data Science & Analytics
• 💬 Chatbot Development
• 💼 Research & Product Innovation

As industries shift toward AI-driven automation, embedding knowledge becomes a core professional asset.

💡 Learn from Experts at AI Computer Classes – Indore!

Boost your career with real-world AI projects using Python, Power BI, and TradingView.

👉 Join our next AI batch now at AI Computer Classes

📍 Old Palasia, Indore

🌟 Conclusion

Embeddings are the backbone of modern AI understanding — enabling systems to comprehend meaning, not just words.

By mastering them, you unlock the ability to build smarter searches, personalized recommendations, and intelligent analytics dashboards.

At AI Computer Classes – Indore, we bring these concepts to life with hands-on AI labs, data tools, and visual learning experiences that make even advanced topics simple and practical.

Whether you’re an aspiring data scientist, Python programmer, or AI enthusiast — this is your chance to step into the world of semantic intelligence. 🚀

📞 Contact AI Computer Classes – Indore

✉ Email: hello@aicomputerclasses.com

📱 Phone: +91 91113 33255

📍 Address: 208, Captain CS Naidu Building, near Greater Kailash Road, opposite School of Excellence For Eye, Opposite Grotto Arcade, Old Palasia, Indore, Madhya Pradesh 452018

🌐 Website: www.aicomputerclasses.com