Embedefy

Traffic volume compared to previous month

• #1

  Enhance language models with Retrieval-Augmented Generation (RAG) to incorporate real-time and proprietary data into responses, improving the accuracy and context-awareness of AI chatbots and other conversational applications.

• #2

  Integrate embeddings into a wide range of AI applications, such as semantic search, clustering, recommendations, anomaly detection, and classification, to leverage the power of vector representations and their ability to capture semantic relationships.

• #3

  Simplify the process of obtaining embeddings by using the Embedefy API or PostgreSQL extension, allowing developers to focus on building their applications rather than managing the complexities of generating and maintaining embeddings.

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