- standardization, governance, simplified troubleshooting, and reusability in ML application development.
- integrations with vector databases and LLM providers to support new applications -
provides tutorials on integrating
- integrations with vector databases and LLM providers to support new applications -
provides tutorials on integrating
LangChain has many advanced retrieval methods to help address these challenges. (1) Multi representation indexing: Create a document representation (like a summary) that is well-suited for retrieval (read about this using the Multi Vector Retriever in a blog post from last week). (2) Query transformation: in this post, we'll review a few approaches to transform humans questions in order to improve retrieval. (3) Query construction: convert human question into a particular query syntax or language, which will be covered in a future post
Learn how to summarize large documents using LangChain and OpenAI, addressing contextual limits and cost effectively. This tutorial covers text preprocessing, semantic chunking, K-means clustering, and document summarization.
A personal productivity assistant that utilizes Retrieval-Augmented Generation (RAG). Allows users to chat with their documents and apps using various AI models. A local and private alternative to OpenAI GPTs and ChatGPT.
Prompting GPT-4 for multi-visual interactive dashboard creation
Kresmo is an Arduino sketch that uses an OpenAI-compatible API to generate a random and brief pithy saying. The sketch uses the U8g2 library for displaying text on an OLED screen, and the WiFi library for connecting to the internet. The ESP32-C3-0.42 module combines all this hardware into one tiny board.
This is an open source effort to create a similar experience to OpenAI's GPTs and Assistants API. It builds upon LangChain, LangServe and LangSmith. OpenGPTs gives you more control
