{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/19b0f86d7e074025b12ca675c2257f25\" frameborder=\"0\" width=\"1684\" height=\"1263\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1263,"width":1684,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1263,"thumbnail_width":1684,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/19b0f86d7e074025b12ca675c2257f25-1d07a404cf31f723.gif","duration":276.405,"title":"Leveraging LlamaFarm for Efficient FDA Document Analysis","description":"In this video, I walk you through how we utilize LlamaFarm as our core tool to analyze FDA documents by recursively identifying unanswered questions. We ingest the documents into a vector database and employ specialized agents to extract and validate questions, providing confidence scores for each. The process is efficient and allows us to manage hundreds or thousands of documents, ensuring we focus on high-confidence answers. I encourage you to explore this setup and consider how you might adapt it for your own projects. If you have any questions, feel free to reach out!"}