Knowladge Base
Create the Knowledge Base for Your Assistant:
By uploading training data in JSON format, or
By manually adding key/value pairs
What is a vector database?
This is a special type of database that stores information in the form of lists of numbers (vectors). Each object—whether it’s a word, image, text, or something else—is converted into an array of numbers.
In our example, the image above shows 300 dimensions, which means each object is represented by 300 numbers:
[0.34, 2.35, 8.34, ...] – this is a vector of 300 numbers.
Semantic (meaning-based) similarity This represents the similarity in meaning between objects in the database.
Left side (similar objects):
Wolf, Dog, Cat – these are close to each other because they are all animals.
Chicken – stands separately but is still nearby, as it has a meaningful connection between animal and bird.
Kitten – is closest to Cat in meaning, so if a query is related to the word “kitten,” the database will search for all words and sentences semantically related to animals, names, or other meaning-bearing concepts and return the best response.
Right side (technology companies):
🍌 Banana
🍎 Apple – Apple the company
G – Google
Apple – similar to Apple Inc.
How does it work?
Embedding: The AI model converts words/images into numbers.
Placement in Space: Objects with similar meanings are placed close to each other.
Search: When you search for something, the system finds the nearest vectors.
What is RAG?
RAG (Retrieval-Augmented Generation) is a technique that combines search and AI generation. It is a way for AI to answer questions based on your specific knowledge base.
The 3 main steps of RAG:
1️⃣ Retrieval (Search)
The user asks a question
The system converts the question into a vector
It searches the vector database for the most relevant information
Finds the 3–5 most similar documents/fragments
2️⃣ Augmentation
The retrieved documents are added to the question as context
The AI receives both your question and the relevant information
3️⃣ Generation
The AI model analyzes the context
Generates an answer based on the specific documents
The answer is accurate and well-founded
Practical Example:
Traditional AI:
Question: “What is our company’s vacation policy?”
Answer: “I don’t know your specific company policy...”
With RAG:
Question: “What is our company’s vacation policy?”
Retrieval: The system finds relevant sections about vacation in the company’s HR documents
Augmentation: This information is provided to the AI
Generation: “According to your company policy, employees have 20 days of paid vacation per year, plus 10 additional days for medical leave…”
Use Cases: 📚 Documentation – Company internal knowledge base 🏥 Healthcare – Analysis of medical records ⚖️ Legal – Search through legal documents 🛍️ E-commerce – Intelligent product recommendations 📞 Customer Service – Automated support
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