How to Create Vector Embeddings in Python

When building a retrieval-augmented generation (RAG) app, you need to prepare your data by creating vector embeddings in various ways such as locally, via API, via a framework or with Astra DB's Vectorize. Pre-trained embedding models like Sentence Transformers and all-MiniLM-L6-v2 can be used to generate vector embeddings. Local embedding models are useful for experimentation on laptops or hardware acceleration, while APIs provided by services like OpenAI, Google and Cohere offer an alternative option. Frameworks like LangChain and LlamaIndex provide standardized interfaces that abstract the complexities of embedding models and APIs. Astra Vectorize enables Astra DB to automatically generate vector embeddings as documents are inserted or queries are performed, simplifying code maintenance, improving performance and efficiency.

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