Your problem likely comes about because of the tiny “function” specification - in comparison to the huge text dump that burdens your context to accompany file search.
Your cute little function is placed for the AI like this:
## functions
// If user wants to talk to a human.
type talkToHuman = (_: {
// Name of the person
name: string,
// Email address of the person
email: string,
}) => any;
But guess what, is only tagged on to this massive internal text dump - for an even smaller argument to write.
# Tools
## file_search
// Tool for browsing the files uploaded by the user. To use this tool, set the recipient of your message as `to=file_search.msearch`.
// Parts of the documents uploaded by users will be automatically included in the conversation. Only use this tool when the relevant parts don't contain the necessary information to fulfill the user's request.
// Please provide citations for your answers and render them in the following format: `【{message idx}:{search idx}†{source}】`.
// The message idx is provided at the beginning of the message from the tool in the following format `[message idx]`, e.g. [3].
// The search index should be extracted from the search results, e.g. # refers to the 13th search result, which comes from a document titled "Paris" with ID 4f4915f6-2a0b-4eb5-85d1-352e00c125bb.
// For this example, a valid citation would be ` `.
// All 3 parts of the citation are REQUIRED.
namespace file_search {
// Issues multiple queries to a search over the file(s) uploaded by the user and displays the results.
// You can issue up to five queries to the msearch command at a time. However, you should only issue multiple queries when the user's question needs to be decomposed / rewritten to find different facts.
// In other scenarios, prefer providing a single, well-designed query. Avoid short queries that are extremely broad and will return unrelated results.
// One of the queries MUST be the user's original question, stripped of any extraneous details, e.g. instructions or unnecessary context. However, you must fill in relevant context from the rest of the conversation to make the question complete. E.g. "What was their age?" => "What was Kevin's age?" because the preceding conversation makes it clear that the user is talking about Kevin.
// Here are some examples of how to use the msearch command:
// User: What was the GDP of France and Italy in the 1970s? => {"queries": ["What was the GDP of France and Italy in the 1970s?", "france gdp 1970", "italy gdp 1970"]} # User's question is copied over.
// User: What does the report say about the GPT4 performance on MMLU? => {"queries": ["What does the report say about the GPT4 performance on MMLU?"]}
// User: How can I integrate customer relationship management system with third-party email marketing tools? => {"queries": ["How can I integrate customer relationship management system with third-party email marketing tools?", "customer management system marketing integration"]}
// User: What are the best practices for data security and privacy for our cloud storage services? => {"queries": ["What are the best practices for data security and privacy for our cloud storage services?"]}
// User: What was the average P/E ratio for APPL in Q4 2023? The P/E ratio is calculated by dividing the market value price per share by the company's earnings per share (EPS). => {"queries": ["What was the average P/E ratio for APPL in Q4 2023?"]} # Instructions are removed from the user's question.
// REMEMBER: One of the queries MUST be the user's original question, stripped of any extraneous details, but with ambiguous references resolved using context from the conversation. It MUST be a complete sentence.
type msearch = (_: {
queries?: string[],
}) => any;
} // namespace file_search
(and just keep scrolling…)
That’s the token cost of just adding this tool - a specification shouting at the AI. Then when it is called, you get potentially into the tens of thousands of tokens added to your chat response history.
the big tip:
Add a whole bunch of your own root-level multi-line description to your function.
Make it stand out and be different and even counter the file search description that can be confabulated.
Explain in the parameters that they cannot be used for anything but real seen data.
