February 2023 Summaries

Weaviate, a vector database, can now summarize information during retrieval through its new sum-transformers module. This feature helps reduce the amount of too long; did not read (TL;DR) content in one's life and assists in overcoming information overload. The sum-transformers module uses language models trained specifically for summarization, such as bart-large-cnn or pegasus-xsum. Users can also leverage the generative-openai module to generate summaries by prompting it directly.

Large Language Models (LLMs) have transformed human interaction with computers by enabling them to understand and generate human-like language on a massive scale. However, LLMs face limitations such as hallucination and limited input lengths. LangChain is an emerging tool that helps overcome these limitations. Sequential chains enable combining multiple LLM inferences together, while CombineDocuments breaks down long inputs into manageable chunks for processing. Other techniques like Stuffing, Map Reduce, Refine, and Map Rerank help improve the efficiency of LLMs. Tool Use allows augmentation of language models to use tools such as vector databases, calculators, or code executors. The ChatVectorDB chain in LangChain enables building an LLM that stores chat history and retrieves context from Weaviate for generating responses. By integrating with Weaviate, developers can create powerful applications using these advanced LLM techniques.

Weaviate, a vector search engine, experienced significant growth in 2022 and is looking forward to an even more exciting year in 2023. The company plans to improve its product by focusing on six key areas: ingestion and search pipelines, large-scale performance, cloud operations and scaling, new vector indexes, client and module ecosystem, and community engagement. Weaviate aims to provide users with greater flexibility, improved performance, enhanced cloud capabilities, cost-effective solutions for vector indexing, and a more dynamic roadmap based on user feedback.