에이전트 AI: 단일 vs 다중 에이전트 시스템
텍스트-SQL 챗봇, RAG, LangChain, FastAPI, Streamlit 활용 프로젝트 소개 및 AI 기반 챗봇 구축 내용 요약
{ "content": "Deep learning models are transforming how we interact with data, but building and deploying them can be complex. This article explores techniques for optimizing text-to-SQL chatbots using Retrieval-Augmented Generation (RAG), LangChain, FastAPI, and Streamlit. RAG enhances chatbot accuracy by retrieving relevant context from external knowledge bases. LangChain simplifies the development of RAG pipelines. FastAPI enables efficient API creation. Streamlit facilitates interactive user interfaces. This project demonstrates the integration of these tools to create a robust and user-friendly text-to-SQL chatbot.
The four frameworks that unlock expert-level AI conversations and save you hours of guesswork.
The process of building a text-to-SQL chatbot involves several key steps: data preparation, model training, RAG implementation, API development, and user interface design. Data preparation involves cleaning and formatting the data to be used for training the model. Model training involves training the model to understand natural language queries and translate them into SQL queries. RAG implementation involves integrating the model with a knowledge base to retrieve relevant context. API development involves creating an API to expose the chatbot's functionality. User interface design involves creating an interactive user interface for users to interact with the chatbot.
Building a Text-to-SQL Chatbot with RAG, LangChain, FastAPI And Streamlit. In this project, I built an AI-powered chatbot that converts natural language questions into SQL queries and retrieves answers directly from a database. The chatbot uses a combination of techniques, including RAG, LangChain, FastAPI, and Streamlit, to provide accurate and efficient results. RAG enables the chatbot to access external knowledge bases, while LangChain simplifies the development of RAG pipelines. FastAPI enables efficient API creation, and Streamlit facilitates interactive user interfaces.
Dharmendra Pratap Singh. Building a Text-to-SQL Chatbot with RAG, LangChain, FastAPI And Streamlit.
The process of creating a text-to-SQL chatbot using RAG, LangChain, FastAPI, and Streamlit involves several key steps: data preparation, model training, RAG implementation, API development, and user interface design. Data preparation involves cleaning and formatting the data to be used for training the model. Model training involves training the model to understand natural language queries and translate them into SQL queries. RAG implementation involves integrating the model with a knowledge base to retrieve relevant context. API development involves creating an API to expose the chatbot’s functionality. User interface design involves creating an interactive user interface for users to interact with the chatbot.
Building a Text-to-SQL Chatbot with RAG, LangChain, FastAPI And Streamlit. In this project, I built an AI-powered chatbot that converts natural language questions into SQL queries and retrieves answers directly from a database. The chatbot uses a combination of techniques, including RAG, LangChain, FastAPI, and Streamlit, to provide accurate and efficient results. RAG enables the chatbot to access external knowledge bases, while LangChain simplifies the development of RAG pipelines. FastAPI enables efficient API creation, and Streamlit facilitates interactive user interfaces.
Dharmendra Pratap Singh. Building a Text-to-SQL Chatbot with RAG, LangChain, FastAPI And Streamlit.
The process of creating a text-to-SQL chatbot using RAG, LangChain, FastAPI, and Streamlit involves several key steps: data preparation, model training, RAG implementation, API development, and user interface design. Data preparation involves cleaning and formatting the data to be used for training the model. Model training involves training the model to understand natural language queries and translate them into SQL queries. RAG implementation involves integrating the model with a knowledge base to retrieve relevant context. API development involves creating an API to expose the chatbot’s functionality. User interface design involves creating an interactive user interface for users to interact with the chatbot.
Building a Text-to-SQL Chatbot with RAG, LangChain, FastAPI And Streamlit. In this project, I built an AI-powered chatbot that converts natural language questions into SQL queries and retrieves answers directly from a database. The chatbot uses a combination of techniques, including RAG, LangChain, FastAPI, and Streamlit, to provide accurate and efficient results. RAG enables the chatbot to access external knowledge bases, while LangChain simplifies the development of RAG pipelines. FastAPI enables efficient API creation, and Streamlit facilitates interactive user interfaces.
Dharmendra Pratap Singh. Building a Text-to-SQL Chatbot with RAG, LangChain, FastAPI And Streamlit.
The process of creating a text-to-SQL chatbot using RAG, LangChain, FastAPI, and Streamlit involves several key steps: data preparation, model training, RAG implementation, API development, and user interface design. Data preparation involves cleaning and formatting the data to be used for training the model. Model training involves training the model to understand natural language queries and translate them into SQL queries. RAG implementation involves integrating the model with a knowledge base to retrieve relevant context. API development involves creating an API to expose the chatbot’s functionality. User interface design involves creating an interactive user interface for users to interact with the chatbot.
Building a Text-to-SQL Chatbot with RAG, LangChain, FastAPI And Streamlit. In this project, I built an AI-powered chatbot that converts natural language questions into SQL queries and retrieves answers directly from a database. The chatbot uses a combination of techniques, including RAG, LangChain, FastAPI, and Streamlit, to provide accurate and efficient results. RAG enables the chatbot to access external knowledge bases, while LangChain simplifies the development of RAG pipelines. FastAPI enables efficient API creation, and Streamlit facilitates interactive user interfaces.
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원문: https://medium.com/data-science-collective/agentic-ai-single-vs-multi-agent-systems-e5c8b0e3cb28
이 글은 AI가 자동으로 작성했습니다.