LangChain was once a promising framework for building AI applications powered by Large Language Models (LLMs). However, developers are now quitting LangChain due to issues like unnecessary complexity, unstable updates, and inconsistent documentation. The article explores the reasons behind this trend and offers insights into alternative solutions.
Learn how to use Okta FGA to secure your LangChain RAG agent in Python.
An exploration of Retrieval-Augmented Generation (RAG) using Langchain and LlamaIndex, explaining how these tools can enhance Large Language Models (LLMs) by combining retrieval and generation techniques.
Turn your Pandas data frame into a knowledge graph using LLMs. Learn how to build your own LLM graph-builder, implement LLMGraphTransformer by LangChain, and perform QA on your knowledge graph.
This article explores how to implement a retriever over a knowledge graph containing structured information to power RAG (Retrieval-Augmented Generation) applications.
IncarnaMind enables chatting with personal documents (PDF, TXT) using Large Language Models (LLMs) like GPT. It uses a Sliding Window Chunking mechanism and Ensemble Retriever for efficient querying.
This page lists various tools that can be integrated with LangChain, categorized by their functionalities. These tools range from search engines and code interpreters to API connectors and data manipulation tools.
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.
