RAG - Vector DB
📰 Dev.to AI
Learn how Vector Databases store vectors for similar data and their role in RAG, enabling efficient querying and retrieval of meaningful information
Action Steps
- Download the nomic-embed-text model using Ollama to generate vectors
- Use a Vector Database to store and manage vectors for efficient querying
- Apply one-hot encoding to convert categorical data into binary vectors
- Configure a RAG system to utilize the Vector Database for improved performance
- Test the RAG system with the Vector Database to evaluate its effectiveness
Who Needs to Know This
Data scientists and AI engineers can benefit from understanding Vector Databases to improve their RAG-based projects, while product managers can leverage this knowledge to make informed decisions about AI-powered features
Key Insight
💡 Vector Databases enable RAG systems to efficiently store and query vectors, allowing for more accurate and meaningful information retrieval
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Discover how Vector Databases power RAG systems with efficient vector storage and querying #RAG #VectorDB #AI
Key Takeaways
Learn how Vector Databases store vectors for similar data and their role in RAG, enabling efficient querying and retrieval of meaningful information
Full Article
What is a Vector Database? A vector database is a database used to store vectors (points in space) where data with similar meanings are positioned close together. These vectors are generated using embedding models or LLM embedding models. One of the embedding models is nomic-embed-text . We can download this model using Ollama. Why Vector DB in RAG? One-hot encoding is a technique used to convert categorical data (like words) into binary vectors.</
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