Category: Langchain
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RAG Application with Cohere Command-R and Rerank – Part 1
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Introduction The Retrieval-Augmented Generation approach combines LLMs with a retrieval system to improve response quality. However, inaccurate retrieval can lead to sub-optimal responses. Cohere’s re-ranker model enhances this process by evaluating and ordering search results based on contextual relevance, improving accuracy and saving time for specific information seekers. This article provides a guide on implementing…
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Advanced RAG Technique : Langchain ReAct and Cohere
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in API, blogathon, framework, Generative AI, Guide, Intermediate, langchaian, Langchain, Large Language Models, LLM, LLMs, Models, Python, query, strategy, vectorIntroduction This article explores Adaptive Question-Answering (QA) frameworks, specifically the Adaptive RAG strategy. It discusses how this framework dynamically selects the most suitable method for large language models (LLMs) based on query complexity. It highlights the learning objectives, features, and implementation of Adaptive RAG, its efficiency, and its integration with Langchain and Cohere LLM. The…
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RAG and Streamlit Chatbot: Chat with Documents Using LLM
Introduction This article aims to create an AI-powered RAG and Streamlit chatbot that can answer users questions based on custom documents. Users can upload documents, and the chatbot can answer questions by referring to those documents. The interface will be generated using Streamlit, and the chatbot will use open-source Large Language Model (LLM) models, making…