使用自然语言生成头条、小红书、百家号等文章
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Updated
Jun 27, 2024 - Python
使用自然语言生成头条、小红书、百家号等文章
In this we implements a Retrieval-Augmented Generation (RAG) based conversational AI agent designed for intelligent knowledge extraction from PDF documents. Leveraging LangChain and Google’s Gemini LLM
An adaptive Retrieval-Augmented Generation (RAG) system that dynamically routes queries between vector search and web search for accurate, grounded answers.
Examples of top-used LangChain document loaders including CSVLoader, DirectoryLoader, PyPDFLoader, TextLoader, and WebBaseLoader. These loaders standardize raw data into LangChain Document objects for further processing, splitting, embeddings, and RAG workflows.
Created a custom chatbot using Langchain. Here's a summary of what I did: Data Extraction: I gathered data about technical courses from the Brainlox website using Langchain’s URL loaders. Embedding Creation & Storage: I converted this data into embeddings and stored it in a vector store for efficient searching. API Development: I built a Flask
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