In this example, we’ll imagine that our chatbot needs to answer questions about the content of a website. To do that, we’ll need a way to store and access that information when the chatbot generates its response.
The longer holiday weekend edition.
- Opportunities and Risks of LLMs for Scalable Deliberation with Polis — Polis is a platform that leverages machine intelligence to scale up deliberative processes. In this paper, we explore the opportunities and risks associated with applying Large Language Models (LLMs) towards challenges with facilitating, moderating and summarizing the results of Polis engagements.
- How I Use PandasAI to Complete 10 Most Frequent Tasks in Data Science —
A Quick Introduction and Development Guide For Pandas AI
- Introduction to Haystack — Haystack is an open-source framework for building search systems that work intelligently over large document collections. Learn more about Haystack and how it works.
- Master Semantic Search at Scale: Index Millions of Documents with Lightning-Fast Inference Times using FAISS and Sentence Transformers — Dive into an end-to-end demo of a high-performance semantic search engine leveraging GPU acceleration, efficient indexing techniques, and robust sentence encoders on datasets up to 1M documents, achieving 50 ms inference times
- Natural Language to SQL using an Open Source LLM
- Leveraging LangChain, Pinecone, and LLMs for Document Question Answering: An Integrated Approach — Document Question Answering (DQA) is a crucial task in Natural Language Processing(NLP), aiming to develop automated systems capable of understanding and extracting relevant information from textual documents to answer user queries. With recent advancements in Large Language Models (LLMs) like ChatGPT and innovative tools and technologies such as LangChain and Pinecone, a new integrated approach to DQA has emerged.
- LlamaIndex: the ultimate LLM framework for indexing and retrieval — LlamaIndex, previously known as the GPT Index, is a remarkable data framework aimed at helping you build applications with LLMs by providing essential tools that facilitate data ingestion, structuring, retrieval, and integration with various application frameworks.
What I’m reading today.
- Semantic Search with Few Lines of Code — Use the sentence transformers library to implement a semantic search engine in minutes
- Choosing the Right Embedding Model: A Guide for LLM Applications — Optimizing LLM Applications with Vector Embeddings, affordable alternatives to OpenAI’s API and how we move from LlamaIndex to Langchain
- Making a Production LLM Prompt for Text-to-SQL Translation
What I’m reading today.
- How Unstructured and LlamaIndex can help bring the power of LLM’s to your own data
- All You Need to Know to Build Your First LLM App — A Step-by-Step Tutorial to Document Loaders, Embeddings, Vector Stores and Prompt Templates
- Answering Questions about any kind of Documents using Langchain (Not GPT3/GPT4) — Unlocking the Power of Langchain: A Comprehensive Python Guide to Answer Questions about Your Documents from Local Files, URLs, YouTube Videos, and Websites
- Build A Capable Machine For LLM and AI — Build A Dual GPUs PC for Machine Learning and AI with Minimum cost
- LlamaIndex: How to use Index correctly.
- Building a Question-Answer Bot With Langchain, Vicuna, and Sentence Transformers — A Q/A bot with open source
Code 0n Github: https://github.com/FlowiseAI/Flowise