Scaling a simple RAG pipeline from simple notes to full books. This post elaborates on how to utilize larger files with your RAG pipeline by adding an extra step to the process — chunking.
This article details building a Retrieval-Augmented Generation (RAG) system to assist with research paper tasks, specifically question answering over a PDF document. It covers document loading, splitting, embedding with Sentence Transformers, using ChromaDB as a vector database, and implementing a query interface with LangChain.
This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
LangChain's ElasticsearchRetriever enables full flexibility in defining retrieval strategies, allowing users to experiment with different approaches.
This article discusses how to overcome limitations of retrieval-augmented generation (RAG) models by creating an AI assistant using advanced SQL vector queries. The author uses tools such as MyScaleDB, OpenAI, LangChain, Hugging Face and the HackerNews API to develop an application that enhances the accuracy and efficiency of data retrieval process.
LLM LangChain Vector DB overview
