update implementation count in intro paragraph

Author: nicoalbanese

docs/guides/basic-RAG.md

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 # Basic RAG
-Retrieval-augmented generation (RAG) is an AI framework that synergizes the capabilities of LLMs and information retrieval systems. It's useful to answer questions or generate content leveraging external knowledge. There are two main steps in RAG: 1) retrieval: retrieve relevant information from a knowledge base with text embeddings stored in a vector store; 2) generation: insert the relevant information to the prompt for the LLM to generate information. In this guide, we will walk through a very basic example of RAG with four implementations:
+Retrieval-augmented generation (RAG) is an AI framework that synergizes the capabilities of LLMs and information retrieval systems. It's useful to answer questions or generate content leveraging external knowledge. There are two main steps in RAG: 1) retrieval: retrieve relevant information from a knowledge base with text embeddings stored in a vector store; 2) generation: insert the relevant information to the prompt for the LLM to generate information. In this guide, we will walk through a very basic example of RAG with five implementations:
 
 - RAG from scratch with Mistral
 - RAG with Mistral and LangChain