10. Getting Started With OpenAI Embeddings using Python | Generative AI | NLP
Link To Playlist - https://youtube.com/playlist?list=PLIiU1TcV3o51F712zlc4lT3vL3wryQlzY&si=mq4xwMnHopSXdGPB
Retrieval Augmented Generation (#RAG) represents a significant advancement in natural language processing (#NLP) that seamlessly integrates the processes of retrieval and generation. Traditionally, NLP models were either focused on generating text from scratch (such as in language generation tasks) or retrieving relevant information (such as in question-answering tasks), but RAG combines these functionalities into a single unified framework.
In #RAG, a retrieval mechanism is employed to gather relevant information from a large corpus of text based on a given query or context. This #retrieval step is crucial for providing the model with the necessary background knowledge or context to generate coherent and informative responses.
#Cosine similarity plays a pivotal role in RAG by enabling the model to measure the similarity between the query or context and the documents in the corpus. Cosine similarity is a metric used to determine how similar two #vectors are in a multi-dimensional space, such as the vector space representation of text embeddings. By computing the cosine similarity between the query or context embedding and the embeddings of the documents in the corpus, RAG can identify the most relevant documents for the given input.
Once the relevant documents are retrieved, the generation component of RAG utilizes this information to produce high-quality responses or outputs. By incorporating the retrieved information into the generation process, RAG ensures that the generated text is contextually relevant and coherent.
Overall, RAG leverages the power of both retrieval and generation mechanisms, seamlessly integrating them to enhance the capabilities of #nlponline models. This approach enables more robust and context-aware text generation, making it particularly effective for tasks such as open-domain question answering, summarization, and dialogue generation.
#RAG #OpenAI #CosineSimilarity #ArtificialIntelligence #TechInnovation #NLP #MachineLearning #generativeai #generativeaitools #machinelearning #machinelearningwithpython #machinelearningcommunity #naturallanguageprocessing #artificialintelligence #pythonprogramming #codingisfun #pythonprogramming #pythontutorial #aitutorial #aitutorialforbeginners
Link To Playlist – https://youtube.com/playlist?list=PLIiU1TcV3o51F712zlc4lT3vL3wryQlzY&si=mq4xwMnHopSXdGPB
Retrieval Augmented Generation (#RAG) represents a significant advancement in natural language processing (#NLP) that seamlessly integrates the processes of retrieval and generation. Traditionally, NLP models were either focused on generating text from scratch (such as in language generation tasks) or retrieving relevant information (such as in question-answering tasks), but RAG combines these functionalities into a single unified framework.
In #RAG, a retrieval mechanism is employed to gather relevant information from a large corpus of text based on a given query or context. This #retrieval step is crucial for providing the model with the necessary background knowledge or context to generate coherent and informative responses.
#Cosine similarity plays a pivotal role in RAG by enabling the model to measure the similarity between the query or context and the documents in the corpus. Cosine similarity is a metric used to determine how similar two #vectors are in a multi-dimensional space, such as the vector space representation of text embeddings. By computing the cosine similarity between the query or context embedding and the embeddings of the documents in the corpus, RAG can identify the most relevant documents for the given input.
Once the relevant documents are retrieved, the generation component of RAG utilizes this information to produce high-quality responses or outputs. By incorporating the retrieved information into the generation process, RAG ensures that the generated text is contextually relevant and coherent.
Overall, RAG leverages the power of both retrieval and generation mechanisms, seamlessly integrating them to enhance the capabilities of #nlponline models. This approach enables more robust and context-aware text generation, making it particularly effective for tasks such as open-domain question answering, summarization, and dialogue generation.
#RAG #OpenAI #CosineSimilarity #ArtificialIntelligence #TechInnovation #NLP #MachineLearning #generativeai #generativeaitools #machinelearning #machinelearningwithpython #machinelearningcommunity #naturallanguageprocessing #artificialintelligence #pythonprogramming #codingisfun #pythonprogramming #pythontutorial #aitutorial #aitutorialforbeginners
