Plan-and-Execute Agents Plan-and-Execute agents first plan what to do, then execute subtasks to accomplish the goal. This idea is primarily inspired by BabyAGI, followed by the "Plan-and-Solve" paper. Planning is almost always performed by an LLM (Large Language Model). Execution is typica...
Large language models inherently lack memory, as they process each input independently without retaining context from previous interactions. In practical applications, memory is simulated by appending the entire conversation history to each new prompt, allowing the model to generate contextually rel...
Understanding Retrieval-Augmented Generation (RAG) RAG is a methodology introduced to address knowledge-intensive NLP tasks. It merges two distinct forms of memory: parametric memory, encoded within pre-trained models like large language models (LLMs), and non-parametric memory, which consists of ex...
LangChain is a comprehensive framework designed to streamline the development of advanced language model-powered applications. It provides modular components, standardized interfaces, and integration tools that enable developers to build end-to-end systems leveraging large language models (LLMs) and...
This guide introduces core runnable operation utilities in LangChain that simplify building flexible, input-compatible LLM applicasion chains. RunnablePassthrough The RunnablePassthrough component is part of the langchain_core.runnables module. It is used too pass input values unmodified between cha...
When executing LangChain workflows that interface with OpenAI models, an error is raised if the OPENAI_API_KEY environment variable is not defined. In-Code Environment Variible Assignment A temporary key can be assigned within the script to bypass the initialization error. This is suitable for testi...