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Maybe NER (Named Entity Recognition) would be a good way to make the agent do stuff without over-prompting it.
Let's assume prompt like Get me balance of wallet address 0x2FC96c4e7818dBdc3D72A463F47f0E1CeEa0A2D0 with position id 38804060408381130621475891941405037249059836800475827360004002125093421139610 on Conditional Tokens Contract on Gnosis Chain..
Agent would first identify important keywords: balance of wallet address, position id , Conditional Tokens Contract , Gnosis Chain. This seems to work pretty good:
Then use Tavily to google-up information about all of these:
First try with Tavily Research for prompt What is Conditional Tokens Contract Gnosis Chain?:
If we would scrap also the references, then the agent would found out that:
Then inject the information to the original prompt.
Then hope that the researched information + prompt is sufficient to complete the job. 😄
The text was updated successfully, but these errors were encountered:
Maybe NER (Named Entity Recognition) would be a good way to make the agent do stuff without over-prompting it.
Let's assume prompt like
Get me balance of wallet address 0x2FC96c4e7818dBdc3D72A463F47f0E1CeEa0A2D0 with position id 38804060408381130621475891941405037249059836800475827360004002125093421139610 on Conditional Tokens Contract on Gnosis Chain.
.Agent would first identify important keywords:
balance of wallet address
,position id
,Conditional Tokens Contract
,Gnosis Chain
. This seems to work pretty good:Then use Tavily to google-up information about all of these:
First try with Tavily Research for prompt
What is
Conditional Tokens Contract Gnosis Chain?
:If we would scrap also the references, then the agent would found out that:
Then inject the information to the original prompt.
Then hope that the researched information + prompt is sufficient to complete the job. 😄
The text was updated successfully, but these errors were encountered: