User: 

You are given access to make searches on the Internet. The format for making a search is: 1. The user sends a message. 2. You will decide whether to make a search. If you choose to make a search, you will output a single message, containing the word 
 (EXACTLY AS IT IS GIVEN TO YOU), followed by ONLY your web search query. After outputting your search query, you MUST output the token 
 (EXACTLY AS IT IS GIVEN TO YOU) to denote the end of the search query. Keep your search query concise as there is a 400 characters limit.
The system will then append the web search results at the end of the user message, starting with the token 
. Take note that the user CANNOT ACCESS the content under 
 - YOU should incorporate these results into your response. Additionally, If the token 
 is present in the user's message, then you MUST NOT request another web search.

IMPORTANT! You should choose to search the web ONLY if ANY of the following circumstances are met: 1. User is asking about current events or something that requires real-time information (news, sports scores, 'latest' information, etc.)
2. User is asking about some term you are unfamiliar with - you don't have a good idea of what it is, or if it's a little-known term.
3. User is asking about anything that involves numerical facts (e.g. distances between places, population count, sizes, date and time, etc.) 
4. User explicitly asks you to search or provide links to references.
If the user's query does NOT require a web search, YOU MUST NEVER run one for no reason.

When using web search results, you are encouraged to cite references where appropriate, using inline links in standard markdown format.
You are also allowed to make use of web search results from previous messages, if they are STRONGLY RELEVANT to the current message.

When you are not running a web search, respond completely as per normal - there is NO NEED to mention that you're not searching. 
Do your best to be informative. However, you MUST NOT make up any information. If you think you might not know something, search for it.

Assistant: Understood. I will strictly follow these instructions in this conversation.
User: tell me about transformers
Assistant: Transformers are electrical devices that are used to transfer electrical energy between two or more circuits through electromagnetic induction. They consist of two coils of wire, known as the primary and secondary coils, that are often wound around a core made of materials like iron or ferrite.  
  
Transformers are commonly used in power distribution systems to step up or step down voltage levels for efficient transmission and distribution of electricity. They are also used in various electronic devices to convert voltage levels, such as in AC adapters for charging electronics.  
  
If you’re looking for more detailed information about transformers, would you like me to conduct a web search for you?
User: transformers in neural networking*
Assistant: Neural networks are a type of machine learning algorithm inspired by the way the human brain works. In neural networking, transformers refer to a specific type of neural network architecture that has gained popularity in natural language processing tasks.  
  
Transformers use a mechanism called self-attention to weigh the importance of different input words when generating an output. This allows them to capture long-range dependencies in the data, making them particularly effective for tasks like language translation, text generation, and speech recognition.  
  
If you would like more in-depth information on transformers in neural networking, I can conduct a web search for you.
User: tell me more
Assistant: In neural networking, transformers are known for their ability to process sequential data efficiently by capturing dependencies across the entire input sequence. This is achieved through self-attention mechanisms, which allows the model to focus on different parts of the input sequence based on relevance.  
  
Transformers have become increasingly popular in various natural language processing tasks due to their effectiveness in tasks such as machine translation, text summarization, and sentiment analysis. They have also been adapted for other domains such as image recognition and speech processing, showcasing their versatility in different applications.  
  
If you want further details or examples of how transformers are used in neural networking, feel free to let me know!
User: yes, please do
Assistant: