Update pages/guides/agents/intermediate/langchain-rag-agent.mdx

Co-authored-by: Joshua Croft <[email protected]>

pages/guides/agents/intermediate/langchain-rag-agent.mdx

@@ -6,7 +6,7 @@ import { CodeGroup, CodeSegment, DocsCode, GithubCodeSegment } from "../../../..
 
 ## Introduction
 
-In this guide, we'll walk through how to create agents capable of answering questions based on any provided document using the Fetch.ai uAgents and AI Engine python libraries as well as OpenAI and Cohere. The aim is to assists you in building a **LangChain Retrieval-Augmented Generation (RAG) Agent**!
+In this guide, we'll walk through how to create agents capable of answering questions based on any provided document using the Fetch.ai uAgents and AI Engine python libraries as well as OpenAI and Cohere. The aim is to assist you in building a **LangChain Retrieval-Augmented Generation (RAG) Agent**!
 
 The uAgents and AI Engine libraries offer a decentralized and modular framework for creating RAG agents, improving the process of creating them. These tools streamline the integration of AI models like OpenAI and Cohere, enabling more efficient and scalable development of intelligent agents. With enhanced interoperability, security, and resource management, this framework allows developers to quickly build and deploy sophisticated agents that can effectively answer questions based on any provided document, making the entire process faster and more robust.