Langchain AI technology page Top Builders
Explore the top contributors showcasing the highest number of Langchain AI technology page app submissions within our community.
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you are able to combine them with other sources of computation or knowledge. This library is aimed at assisting in the development of those types of applications.
|Type||Large Language Model framework|
LangChain - Resources
Resources to get stared with LangChain
LangChain - Use cases
Use cases for LangChain
- Question Answering over specific documents Documentation for Question Answering over specific documents
- Chatbots Documentation for Chatbots build with LangChain
- Agents Documentation for Agents systems build with LangChain
LangChain - Example Projects
Implementations of LangChain
- Question Answering on Financial Data PDFs Chat with financial documents (quarterly investor update PDFs) in this case
- Code Understanding - Chat with GitHub repo Chat with any number of codebases with context
- Question Answering over Notion Database Ask questions to your Notion database in natural language
- Chat-LangChain Implementation of a locally hosted chatbot specifically focused on question answering over the LangChain documentation
- GPT+WolframAlpha Demonstrates a conversational agent implemented with OpenAI GPT3 and LangChain
Langchain AI technology page Hackathon projects
Discover innovative solutions crafted with Langchain AI technology page, developed by our community members during our engaging hackathons.
Parses pdf with pypdf Index Construction with LlamaIndex's GPTSimpleVectorIndex the text-embedding-ada-002 model is used to create embeddings see vector store index page to learn more indexes and files are stored on s3 Query the index uses the latest ChatGPT model gpt-3.5-turbo local mode for app (no s3) global variable use_s3 to toggle between local and s3 mode deploy app to streamlit cloud have input box for openai key uses pyarrow local FS to store files update code for new langchain update Custom prompts and tweak settings create a settings page for tweaking model parameters and provide custom prompts example Add ability to query on multiple files Compose indices of multiple lectures and query on all of them loop through all existing index, create the ones that haven't been created, and compose them together
Unleashing Creativity with Yi 34B and Langchain
100s of new arxiv papers are uploaded everyday How do I get new ideas to apply them for my field ? How do i get a quick gist of the paper and related papers? Loom video of Demo: https://www.loom.com/share/a9d151a59a7c4fe68a8cc94b6a118d9d?sid=90a5d924-d995-4716-8a3b-c4da861e2e1d This helps improve creativity of researchers by applying the ideas of a specific ARXIV paper to a completely new field. This helps create a gist of the large arxiv paper with just the URL There by improving their productivity It also helps create a summary of all the related papers by doing a vector search based on the title
Bio Explorer helps researchers find novel platforms for cancer treatment . The OpenAI GPT Chatbot represents a groundbreaking advancement in the field of artificial intelligence, particularly tailored to address complex questions in the realm of biology. This innovative tool is specifically engineered to delve into the vast and intricate world of cancer research, aiming to uncover novel and potentially life-saving cures for this challenging disease. At the core of its functionality lies the integration of the scientific method with advanced automation technologies, enabling the chatbot to process, analyze, and synthesize vast amounts of biological data at unprecedented speeds.
Eco Mentor is a groundbreaking AI-powered platform focused on environmental education and sustainability. Aimed at fostering a deeper understanding of ecological concerns and promoting sustainable practices, the platform caters to individuals eager to make environmentally conscious choices. The core problem addressed is the gap in accessible, personalized environmental education and community involvement in sustainability initiatives. Eco Mentor offers a solution by integrating AI to deliver customized learning experiences, connecting users with local eco-friendly projects, and providing interactive challenges and tools for eco-conscious living. Unique features include a real-time impact visualization of users' eco-actions, a forum for sharing experiences, and AI assistance for eco-friendly shopping. The platform targets environmentally conscious individuals, educators, and students, making sustainability an engaging, collaborative journey.
Exploring the Realm of Gamified Mathematical GPTs
In the ever-evolving landscape of education and technology, the intersection of artificial intelligence and mathematics has given rise to a fascinating phenomenon - Gamified Mathematical Generative Pre-trained Transformers (GPTs). These innovative tools not only bridge the gap between traditional teaching methods and modern technological advancements but also transform the way individuals engage with and perceive the realm of mathematics. At the heart of this exploration is the concept of gamification, a pedagogical approach that leverages game elements and design principles to make learning more engaging, interactive, and enjoyable. When applied to mathematical education through the integration of GPTs, it opens up a portal to a world where learning becomes an immersive and exciting adventure. Imagine a virtual tutor, a Math Magician, if you will, powered by advanced language models like GPT, capable of dynamically generating mathematical problems, providing step-by-step solutions, and adapting its teaching style based on the learner's unique pace and preferences. This interactive and personalized approach not only caters to individual learning needs but also instills a sense of curiosity and enthusiasm for mathematics. One key aspect of Gamified Mathematical GPTs is their ability to present mathematical concepts in a contextualized and relatable manner. Through the creation of scenarios, storylines, and interactive challenges, learners are transported into a mathematical universe where abstract concepts find real-world applications. This not only enhances comprehension but also nurtures critical thinking skills as learners grapple with problem-solving in a gamified context.
For every concept/topic typed in the CustomGPT application, there will be a story, a game or a scenario given as a response as default. The objective of the MyFunTutor is to demonstrate and articulate a concept with real-time examples and scenarios where the learners apply the concepts as a First Pointer Shooter perspective and thus ensure that the concepts are perceived rather than learnt or memorized. Moreover, the current systems and applications will need explicit commands from the asker/user to mention the requirements. But MyFunTutor will be answering the concepts in three different contexts, like a simple version, intermediate version and an advanced version.