0x773d06ba159991bf32ac940dfbb413bcdbe8304e19abc129f444dcadc612d6ce
Transaction
Balance changes
Address | Token(s) swapped | Balance | Price | Value change | |
---|---|---|---|---|---|
| VIRTUAL | +$771.65 | |||
Virtual Protocol | +1584 | | +$771.65 | ||
| +8.4402431e+26 | ||||
| VIRTUAL | +$12.67 | |||
Virtual Protocol | +26 | | +$12.67 | ||
| Eth | +$0.23 | |||
Ether | +0.00007341494 | | +$0.23 | ||
| Eth | +$0.01 | |||
Ether | +0.0000032186572 | | +$0.01 | ||
| Eth | +$0.00 | |||
Ether | +0.00000071131396 | | +$0.00 | ||
| Eth, VIRTUAL | -$784.56 | |||
Ether | -0.000077344911 | | -$0.24 | ||
Virtual Protocol | -1610 | | -$784.32 | ||
| +1.5597569e+26 |
Invocation flow
Full trace
- 0CALL3001380 gas [RECV] TransparentUpgradeableProxy.launch (_name=RAG Multi Agent, _ticker=RAGMA, cores=[4 elements], desc=RAGMA is an advanced multi-agent RAG system, revolutionizing Retrieval-Augmented Generation by embedding autonomous, goal-driven agents within a corrective framework inspired by CRAG (Corrective Retrieval Augmented Generation). It leverages LLMs enhanced with confidence-based retrieval evaluators, optimizing relevance and minimizing hallucinations. By deploying decompose-then-recompose algorithms, RAGMA distills high-value insights, integrating dynamic web searches with static corpora. Designed for seamless integration, its plug-and-play architecture empowers crypto-focused generative AI applications with precision and adaptability. Reference : https://arxiv.org/pdf/2401.15884, img=https://s3.ap-southeast-1.amazonaws.com/virtualprotocolcdn/name_65b7e08c7d.jpeg, urls=[4 elements], purchaseAmount=1610000000000000000000) ( 0xe92989d3e68159ade37df2b81b3a579503eef623, 0xf528395d61b66e55e55d90fd477b4e07b784185c, 1359)
- 1
- 2DELEGATECALL2996124 gas Bonding.launch (_name=RAG Multi Agent, _ticker=RAGMA, cores=[4 elements], desc=RAGMA is an advanced multi-agent RAG system, revolutionizing Retrieval-Augmented Generation by embedding autonomous, goal-driven agents within a corrective framework inspired by CRAG (Corrective Retrieval Augmented Generation). It leverages LLMs enhanced with confidence-based retrieval evaluators, optimizing relevance and minimizing hallucinations. By deploying decompose-then-recompose algorithms, RAGMA distills high-value insights, integrating dynamic web searches with static corpora. Designed for seamless integration, its plug-and-play architecture empowers crypto-focused generative AI applications with precision and adaptability. Reference : https://arxiv.org/pdf/2401.15884, img=https://s3.ap-southeast-1.amazonaws.com/virtualprotocolcdn/name_65b7e08c7d.jpeg, urls=[4 elements], purchaseAmount=1610000000000000000000) ( 0xe92989d3e68159ade37df2b81b3a579503eef623, 0xf528395d61b66e55e55d90fd477b4e07b784185c, 1359)
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