Cool new RAG framework (in JavaScript)
@langchain
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Dream: AI No-Code Tool for Building Web Apps with Natural Language
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Dream –an AI no-code tool to build fully functional web apps and components with natural language. built by Student Hacker, @calixo888
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RAG Stack Documentation: Organizing Key Strategies
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Deconstructing the RAG stack We repeatedly hear that navigating the idea maze around RAG is a challenge. We've overhauled our RAG docs and made a series of guides to organize / explain key RAG strategies. As an entry point, see our new RAG docs: https://
python.langchain.com/docs/use_cases
/question_answering/
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Tuna: No-Code LLM Fine-Tuning Dataset Generation Tool
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Tuna: a no-code tool for quickly generating LLM fine-tuning datasets from scratch built by Student Hacker, @itsandrewgao
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GPT-Crawler: Building Custom AI Chatbots for Websites
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GPT-crawler template Easily building chat assistants for websites (documentation, etc) is an extremely popular use-case. GPT-crawler from @builderio is rapidly growing in popularity for building custom GPTs for sites: https://
builder.io/blog/custom-gpt This template shows how to -
Switching LLM Providers: Prompting Strategies and LangSmith Hub
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Wondering how to switch LLM providers? It's not as simple as changing the endpoint Different LLMs often require different prompting strategies. One way to explore different prompting strategies? LangSmith Prompt Hub – a collection of different prompts for
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RAG Enhancement with Decision-Making Agents and Neo4j
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Enhancing RAG with Decision-Making Agents and Neo4j Vector and Graph Chain Tools Using LangChain Templates and LangServe A length title – but the blog is well worth the read LLMs + Graph DBs are always a fun read Sourav Joshi covers how to integrate Neo4j Vector and
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Multi-Index Retrieval and Routing Strategies for AI
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Multi-index retrieval templates Sometimes you want to or need to use information from multiple indexes in your application. There's two basic ways to do this: Route: given a new input, choose (or route to) the most relevant index and retrieve from it. This is most useful when
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Building Research Assistants with Swappable Retrievers
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Building a research assistant (long form YouTube tutorial) Starts by building a web research assistant, but covers how to swap in an arbitrary retriever. Uses an Arxiv retriever… what retriever would you want to swap in?
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OpenGPTs Improvements: Catching Up with OpenAI GPTs
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Good overview of OpenGPTs improvements from @Saboo_Shubham_ Blog: https://
unwindai.substack.com/p/opengpt-catc
hing-up-with-openai-gpts-70a
… The only downside for Shubham is that he's going to have to write a lot more of these… we're planning another big release next week. And then another one the week after… got some