chore(docs): minor updates (#960)

Co-authored-by: Joshua Croft <joshuacroft@Joshuas-MacBook-Pro.local>
fetchai · Sep 25, 2024 · 0dbab1d · 0dbab1d
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diff --git a/pages/guides/agents/intermediate/langchain-rag-agent.mdx b/pages/guides/agents/intermediate/langchain-rag-agent.mdx
@@ -5,9 +5,9 @@ import PackageVersion from 'components/package-version'
 
 ## 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` 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 assists you in building a **LangChain Retrieval-Augmented Generation (RAG) Agent**!
 
-The process of creating RAG agents has been improved by offering a decentralized and modular framework through the uagents and ai_engine libraries. 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.
+The process of creating RAG agents has been improved by offering a decentralized and modular framework through the uAgents and ai_engine libraries. 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.
 
     <Callout type="info" emoji="ℹ️"> 
       Check out the [AI Engine package ↗️](https://pypi.org/project/uagents-ai-engine/) and [uAgents ↗️](https://pypi.org/project/uagents/) packages to download them and start integrating this tools within your Agents project!

diff --git a/pages/guides/fetch-network/ledger/governance.mdx b/pages/guides/fetch-network/ledger/governance.mdx
@@ -48,7 +48,7 @@ There are **three types** of governance proposal:
 
 In order for the proposal to be open for voting, it needs to come with a deposit that is greater than a parameter called _minDeposit_. The deposit need not be provided in its entirety by the submitter. If the initial proposer's deposit is not sufficient, the proposal enters the **deposit period** status. Then, any FET holder can increase the deposit by sending a _depositTx_ transaction to the network.
 
-Once the deposit reaches _minDeposit_, the proposal enters the **voting period**, which lasts 2 weeks. Any bonded FET holder can then cast a vote on this proposal, by choosing one of the following options for voting:
+Once the deposit reaches _minDeposit_, the proposal enters the **voting period**, which lasts 5 days. Any bonded FET holder can then cast a vote on this proposal, by choosing one of the following options for voting:
 
     * Yes.
     * No.