This article provides an overview of LangChain, a tool that facilitates the integration of AI, particularly LLMs, into your code or project. It explains the concept of 'chains' in LangChain, which are sequences of operations that involve processing inputs, interacting with an AI, and handling outputs. The article also mentions the need for API keys for the AI you plan to use.
Mariya Mansurova explores using CrewAI's multi-agent framework to create a solution for writing documentation based on tables and answering related questions.
This article introduces Langchain, a platform for productionizing large language model (LLM) applications, and discusses the first principles of building LLM agents. The author explains the difference between simple LLM usage and techniques such as 'chain of thought' and 'tree of thoughts'. The article also provides examples of how to use Langchain's built-in tools and custom tools for planning, memory, and tools in LLM agents.
LangChain's ElasticsearchRetriever enables full flexibility in defining retrieval strategies, allowing users to experiment with different approaches.
This article discusses how to overcome limitations of retrieval-augmented generation (RAG) models by creating an AI assistant using advanced SQL vector queries. The author uses tools such as MyScaleDB, OpenAI, LangChain, Hugging Face and the HackerNews API to develop an application that enhances the accuracy and efficiency of data retrieval process.
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 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.
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.
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.
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.
