Vector Databases - Master the Core of AI in 1 Hour [Mini-course]
AI Summary
Summary: Crash Course on Vector Databases
Introduction to Vector Databases
- Vector Databases: Specialized databases for large language models.
- Purpose: To efficiently store and query data that large language models deal with.
- Key Components: Vectors, dimensionality, and similarity search.
Understanding Vectors
- Vector Definition: A mathematical quantity with magnitude and direction, represented as an ordered list of numbers.
- Importance in Databases: Vectors encode information into numerical values, allowing for efficient similarity queries.
Significance of Vector Databases
- Handling Unstructured Data: Over 80% of data is unstructured (e.g., images, audio, PDFs).
- Efficiency: Vector databases convert unstructured data into vectors, enabling faster and more reliable queries.
- Machine Learning Compatibility: They interface with machine learning models and large language models.
Use Cases for Vector Databases
- Image retrieval and similarity search.
- Recommendation systems.
- Natural language processing (NLP).
- Fraud detection.
- Bioinformatics.
Building Vector Databases with Chroma
- Setup: Install Chroma DB and set up a development environment.
- Workflow:
- Create a Chroma DB client.
- Create a collection.
- Add documents to the collection.
- Query the collection and retrieve results based on similarity.
Chroma Database Workflow Overview
- App Layer: Integrates with components to handle queries and generate responses.
- Chroma DB Layer: Stores and manages embeddings of documents.
- LLM Context Window: Processes embeddings to generate relevant answers.
- Query Embeddings: Transforms user queries into a format suitable for Chroma DB processing.
- Results: Final output from the large language model, returned to the user.
Advantages of Vector Databases
- Efficient representation of complex data.
- Rapid discovery and organization.
- Scalability and performance optimization.
- Improved user experience with real-time interactions.
Conclusion
- Vector databases transform complex, high-dimensional data into manageable, searchable, and scalable formats.
- They are essential for leveraging the power of Big Data and AI in modern data architecture and applications.