madhur71/Multi_Agent_Workflow

1 2Artificial Intelligence is widely used in healthcare, finance, education, and automation.3 4Natural Language Processing enables computers to understand and generate human language.5 6Large Language Models are trained on massive datasets to perform reasoning and text generation tasks.7 8Semantic search improves information retrieval by understanding the meaning of queries.9 10Vector databases store embeddings for efficient similarity search operations.11 12FAISS is a library developed by Facebook AI for fast vector similarity search.13 14Sentence Transformers convert text into dense vector embeddings for semantic comparison.15 16Question Answering systems extract precise answers from contextual information.17 18Text summarization reduces long documents into concise and meaningful summaries.19 20Sentiment analysis identifies emotional tone such as positive, negative, or neutral.21 22Generative AI models can create text, images, audio, and code from prompts.23 24Gradio helps developers build interactive machine learning web applications quickly.25 26PyTorch is a popular deep learning framework used for AI model training and inference.27 28Transformer models use self-attention mechanisms to process sequential data efficiently.29 30Retrieval pipelines improve chatbot accuracy by grounding responses in documents.31 32Autonomous AI agents can collaborate to complete complex reasoning tasks.33 34Multi-agent orchestration enables specialized AI agents to share responsibilities.35 36Context-aware systems use retrieved information to generate more accurate outputs.37 38Knowledge retrieval systems combine embeddings with vector search technologies.39 40AI assistants can perform summarization, retrieval, reasoning, and generation together.41 42