Improve AI engineer content (#7924)
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# OpenAI API |
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The OpenAI API provides access to powerful AI models like GPT, Codex, DALL-E, and Whisper, enabling developers to integrate capabilities such as text generation, code assistance, image creation, and speech recognition into their applications via a simple, scalable interface. |
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Learn more from the following resources: |
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- [@official@Open AI API](https://openai.com/api/) |
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# RAG & Implementation |
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Retrieval-Augmented Generation (RAG) combines information retrieval with language generation to produce more accurate, context-aware responses. It uses two components: a retriever, which searches a database to find relevant information, and a generator, which crafts a response based on the retrieved data. Implementing RAG involves using a retrieval model (e.g., embeddings and vector search) alongside a generative language model (like GPT). The process starts by converting a query into embeddings, retrieving relevant documents from a vector database, and feeding them to the language model, which then generates a coherent, informed response. This approach grounds outputs in real-world data, resulting in more reliable and detailed answers. |
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Learn more from the following resources: |
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- [@article@What is RAG?](https://aws.amazon.com/what-is/retrieval-augmented-generation/) |
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- [@video@What is Retrieval-Augmented Generation? IBM](https://www.youtube.com/watch?v=T-D1OfcDW1M) |
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# Vector Databases |
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Vector databases are systems specialized in storing, indexing, and retrieving high-dimensional vectors, often used as embeddings for data like text, images, or audio. Unlike traditional databases, they excel at managing unstructured data by enabling fast similarity searches, where vectors are compared to find the closest matches. This makes them essential for tasks like semantic search, recommendation systems, and content discovery. Using techniques like approximate nearest neighbor (ANN) search, vector databases handle large datasets efficiently, ensuring quick and accurate retrieval even at scale. |
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Learn more from the following resources: |
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- [@article@Vector Databases](https://developers.cloudflare.com/vectorize/reference/what-is-a-vector-database/) |
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- [@article@What are Vector Databases?](https://www.mongodb.com/resources/basics/databases/vector-databases) |
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# Video Understanding |
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Video understanding with multimodal AI involves analyzing and interpreting both visual and audio content to provide a more comprehensive understanding of videos. Common use cases include video summarization, where AI extracts key scenes and generates summaries; content moderation, where the system detects inappropriate visuals or audio; and video indexing for easier search and retrieval of specific moments within a video. Other applications include enhancing video-based recommendations, security surveillance, and interactive entertainment, where video and audio are processed together for real-time user interaction. |
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Learn more from the following resources: |
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- [@article@Video Understanding](https://dl.acm.org/doi/10.1145/3503161.3551600) |
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- [@opensource@Awesome LLM for Video Understanding](https://github.com/yunlong10/Awesome-LLMs-for-Video-Understanding) |
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# What are Embeddings |
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Embeddings are dense, numerical vector representations of data, such as words, sentences, images, or audio, that capture their semantic meaning and relationships. By converting data into fixed-length vectors, embeddings allow machine learning models to process and understand the data more effectively. For example, word embeddings represent similar words with similar vectors, enabling tasks like semantic search, recommendation systems, and clustering. Embeddings make it easier to compare, search, and analyze complex, unstructured data by mapping similar items close together in a high-dimensional space. |
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Visit the following resources to learn more: |
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- [@official@Introducing Text and Code Embeddings](https://openai.com/index/introducing-text-and-code-embeddings/) |
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- [@article@What are Embeddings](https://www.cloudflare.com/learning/ai/what-are-embeddings/) |
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