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Search-Augmented Generation (RAG) is an artificial intelligence platform that combines large language models (LLM) with external knowledge bases to improve the accuracy, relevance, and reliability of generated text.
LangChain is a framework designed to facilitate the development of applications using Large Language Models (LLM) using RAG. It provides a structured approach to building RAG applications that includes:
- Indexation: This includes loading data, splitting it into manageable chunks, and storing it in a vector database for efficient retrieval.;
- Search and Generation: LangChain uses a search engine to obtain relevant information based on user requests;
- Integration with external data: By connecting to various data sources, LangChain allows masters to access information in real time, in addition to training data.;
Gemini is a family of multimodal large Language Models (LLM) developed by Google DeepMind and designed to process and understand various types of data, including text, images, audio, and video.
Source: https://codelabs.developers.google.com/static/multimodal-rag-gemini
In our guide, we used ChromaDB as a vector database, and LangChain as a platform for configuring the RAG system integrated with Gemini Pro/Flash LLM. The source code and complete guide can be found in the notebook below.
Kaggle Notebook: https://www.kaggle.com/code/armanzhalgasbayev/rag-llm-langchain-gemini-ai
Search-Augmented Generation (RAG) is an artificial intelligence platform that combines large language models (LLM) with external knowledge bases to improve the accuracy, relevance, and reliability of generated text.
LangChain is a framework designed to facilitate the development of applications using Large Language Models (LLM) using RAG. It provides a structured approach to building RAG applications that includes:
- Indexation: This includes loading data, splitting it into manageable chunks, and storing it in a vector database for efficient retrieval.;
- Search and Generation: LangChain uses a search engine to obtain relevant information based on user requests;
- Integration with external data: By connecting to various data sources, LangChain allows masters to access information in real time, in addition to training data.;
Gemini is a family of multimodal large Language Models (LLM) developed by Google DeepMind and designed to process and understand various types of data, including text, images, audio, and video.
Source: https://codelabs.developers.google.com/static/multimodal-rag-gemini
In our guide, we used ChromaDB as a vector database, and LangChain as a platform for configuring the RAG system integrated with Gemini Pro/Flash LLM. The source code and complete guide can be found in the notebook below.
Kaggle Notebook: https://www.kaggle.com/code/armanzhalgasbayev/rag-llm-langchain-gemini-ai
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