In this tutorial, we will build a RAG system with a self-querying retriever in the LangChain framework. This will enable us to filter the retrieved movies using metadata, thus providing more meaningful movie recommendations.
This article guides you through the process of building a simple agent in LangChain using Tools and Toolkits. It explains the basics of Agents, their components, and how to build a Mathematics Agent that can perform simple mathematical operations.
The article discusses the use of large language models (LLMs) as reasoning engines for powering agent workflows, focusing specifically on ReAct agents. It explains how these agents combine reasoning and action capabilities and provides examples of how they function. Challenges faced while implementing such agents are also mentioned, along with ways to overcome them. Additionally, the integration of open-source models within LangChain is highlighted.
The article argues that instead of developing numerous tools for LLM, giving it direct access to a terminal is more efficient and future-proof. It references Rich Sutton's "The Bitter Lesson" and discusses how the terminal's existing command-line tools can be utilized by LLM for various tasks, highlighting the importance of general methods over specialized tools.
SciPhi-AI/R2R is a framework for rapid development and deployment of production-ready RAG pipelines. The framework enables the deployment, customization, extension, autoscaling, and optimization of RAG pipeline systems, making it easier for the OSS community to use them. It includes several code examples and client applications that demonstrate application deployment and interaction. The core abstractions come in the form of ingestion, embedding, RAG, and eval pipelines.
The authors map the landscape of frameworks for abstracting interactions with and between large language models, and suggest two systems of organization for reasoning about the various approaches to, and philosophies of, LLM abstraction.
open-source LLM tools offer transparency, flexibility, cost-effectiveness, and heightened data security.
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
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