The post has been translated automatically. Original language: Russian English
Retrieval-Augmented Generation (RAG) is an AI framework that combines large language models (LLMs) with external knowledge bases to enhance the accuracy, relevance, and reliability of generated text.
LangChain is a framework designed to facilitate the development of applications that leverage large language models (LLMs) through RAG. It provides a structured approach to building RAG applications, which includes:
- Indexing: This involves loading data, splitting it into manageable chunks, and storing it in a vector database for efficient retrieval;
- Retrieval and Generation: LangChain employs a retrieval mechanism to fetch relevant information based on user queries;
- Integration with External Data: By connecting to various data sources, LangChain enables LLMs to access real-time information beyond their training data;
Gemini is a family of multimodal large language models (LLMs) developed by Google DeepMind, 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, LangChain as a framework for setting up the RAG system integrated with the Gemini Pro / Flash LLM. Source code and full-guideline can be found in the notebook below.
Kaggle Notebook: https://www.kaggle.com/code/armanzhalgasbayev/rag-llm-langchain-gemini-ai
Retrieval-Augmented Generation (RAG) is an AI framework that combines large language models (LLMs) with external knowledge bases to enhance the accuracy, relevance, and reliability of generated text.
LangChain is a framework designed to facilitate the development of applications that leverage large language models (LLMs) through RAG. It provides a structured approach to building RAG applications, which includes:
- Indexing: This involves loading data, splitting it into manageable chunks, and storing it in a vector database for efficient retrieval;
- Retrieval and Generation: LangChain employs a retrieval mechanism to fetch relevant information based on user queries;
- Integration with External Data: By connecting to various data sources, LangChain enables LLMs to access real-time information beyond their training data;
Gemini is a family of multimodal large language models (LLMs) developed by Google DeepMind, 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, LangChain as a framework for setting up the RAG system integrated with the Gemini Pro / Flash LLM. Source code and full-guideline can be found in the notebook below.
Kaggle Notebook: https://www.kaggle.com/code/armanzhalgasbayev/rag-llm-langchain-gemini-ai
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