Real-time AI Genome Processing - Powered by Groq
TLDRIn this medical demo, Peter introduces the challenges of using Large Language Models (LLMs) in medicine due to their tendency to hallucinate or create false information. To address this, he presents Retrieval-Augmented Generation (RAG), a technique that enhances LLMs' output by incorporating referenced sources. A vector database containing 3000 medical genomics abstracts from pharmGKB is used to demonstrate how RAG can provide more detailed and accurate responses, including working PubMed links for verification. Peter also showcases real-time genome annotation using an LLM with RAG, highlighting its potential in medical applications when reliability and accuracy are paramount.
Takeaways
- 🚑 **Reliability and Accuracy in Medicine**: In medical contexts, the importance of accurate and reliable information is paramount, which is a challenge for LLMs due to their tendency to hallucinate or fabricate information.
- 📚 **Slow Adoption of LLMs in Medicine**: The medical field has been slow to adopt generative LLMs because of concerns over the veracity of the information they provide.
- 🔍 **Referenced Information Requirement**: Medical professionals need to have access to referenced or sourced information to verify the data presented by LLMs.
- 📈 **RAG - Retrieval Augmented Generation**: RAG is a technique that enhances the accuracy and reliability of LLMs by using additional information to reduce hallucinations and provide referenced responses.
- 🔗 **Database of Text Sources**: RAG works by indexing a database of text sources to gather relevant information for the LLM to base its responses on.
- 🧬 **Medical Genomics Abstracts**: The demos use a vector database containing 3000 medical genomics abstracts sourced from pharmGKB, which provides clinical guidelines for gene-drug interactions.
- 🔬 **LLM Integration with Genomics Database**: The LLM, Mixtral, running on Groq hardware, is connected to a genomics database to provide detailed and referenced responses to medical queries.
- 📊 **Comparison of Outputs**: The demo shows a clear difference in the level of detail and the presence of working PubMed links in the LLM's output with and without RAG.
- 🧵 **Genome Annotation in Real Time**: Using RAG and an LLM, the genome can be annotated in real time, making DNA data more accessible and understandable.
- 🔬 **VCF Processing**: The LLM is used to process VCF files, which detail a person's gene variants, and to provide summaries and working PubMed links for each variant.
- ⚙️ **Groq Hardware for Real-time Processing**: Groq hardware is utilized to enable real-time processing and annotation of genomic data.
- 🌟 **Potential of LLMs in Medicine**: The demos demonstrate the potential for LLMs to be useful in medical settings when techniques like RAG are employed to improve reliability and accuracy.
Q & A
What is the primary concern with using LLMs in a medical context?
-The primary concern is that LLMs can hallucinate or make up information, which is a significant issue for medical applications where reliability and accuracy are paramount.
What does the term 'RAG' stand for, and how does it improve the LLMs' output?
-RAG stands for Retrieval Augmented Generation. It enhances the output accuracy and reliability of LLMs by augmenting the input with additional information surrounding a query, reducing hallucinations and allowing the LLM to provide referenced responses.
How does the RAG technique work in practice?
-When a user query comes in, the database of text sources is indexed, and relevant sources are gathered. The top sources are then fed into the LLM, which answers the user query based on these sources.
What type of database is used in the demos, and what does it contain?
-A vector database is used, containing 3000 medical genomics abstracts sourced from PharmGKB, which includes clinical guidelines for gene-drug interactions.
How does the LLM with RAG differ from one without RAG in terms of output detail?
-The LLM with RAG provides more content and detail, including gene variants and genes, and also includes working PubMed links for verification, whereas the LLM without RAG offers less detail and may miss some gene variants.
What is the significance of having working PubMed links in the output?
-Working PubMed links allow medical professionals to verify the information provided by the LLM, ensuring that they can check the source and be confident in the accuracy of the data.
What is the role of the LLM named Mixtral in the first demo?
-Mixtral is the LLM used in the first demo, running on Groq hardware, and is connected to the genomics database to provide answers to the query about gene variants associated with adjusted warfarin doses.
How does the LLM with RAG assist in annotating the genome?
-The LLM with RAG, in conjunction with the VCF (variant calling format) file, searches for matches in the gene-drug database and summarizes the abstracts where the information is found, providing real-time annotations of someone's DNA.
What is the VCF file, and how is it used in the genome annotation process?
-The VCF file is a sequence genome that shows the variants of each gene a person has. It is used by uploading it to the system, which then processes the VCF iteratively, searching for matches in the gene-drug database for annotation.
What is the significance of using LLMs in a medical setting with techniques like RAG?
-Using LLMs with RAG in a medical setting can improve the reliability and accuracy of the LLMs' output, making them a valuable tool for medical professionals by providing referenced and verifiable information.
What hardware is used to run the LLM Llama-2 70 billion in the second demo?
-The LLM Llama-2 70 billion is run on Groq hardware, which is used to annotate the genome in real time with the help of RAG.
Outlines
🧬 Introduction to Medical Demos with Groq-Powered LLMs
Peter introduces the video, explaining the importance of reliability and accuracy in medical contexts and the challenges of using LLMs due to their tendency to hallucinate or make up information. He emphasizes the need for referenced or sourced information in medicine and introduces RAG (Retrieval Augmented Generation) as a technique to enhance the output accuracy and reliability of LLMs. RAG works by indexing a database of text sources and feeding the most relevant ones into the LLM to provide referenced responses. The demo will use a vector database containing 3000 medical genomics abstracts from pharmGKB, a database with clinical guidelines on gene-drug interactions.
🔬 RAG Demo: Gene Variants and Warfarin Dosage
Peter demonstrates the first use case of RAG, showing how the genomics database is connected to the LLM, Mixtral, running on Groq hardware. He inputs a query regarding gene variants associated with adjusted warfarin doses and presents two outputs: one without RAG and one with RAG. The RAG output provides more detailed content, includes missing gene variants, and features working PubMed links for verification, illustrating the enhanced utility of an LLM in a medical context with RAG.
🧬 Real-Time Genome Annotation with LLM and RAG
In the second demo, Peter discusses the process of annotating the genome using an LLM, which is typically slow and difficult. He shows how Llama-2, a 70 billion parameter LLM running on Groq hardware with RAG, can annotate the genome in real time. The VCF (variant calling format) file, which lists a person's gene variants, is uploaded and processed to find matches in the gene-drug database. The LLM summarizes the relevant abstracts and provides working PubMed links. The demonstration successfully turns someone's DNA into readable annotations in real time, showcasing the potential of LLMs in medical settings when augmented with RAG for improved reliability and accuracy.
📘 Conclusion: The Role of LLMs in Medicine
Peter concludes the presentation by reiterating the potential for LLMs in medical settings, provided that their reliability and accuracy are enhanced through techniques like RAG. He thanks the audience for their attention and highlights the successful integration of Groq hardware and RAG in making LLMs a viable tool for medical professionals.
Mindmap
Keywords
💡Groq
💡LLMs (Large Language Models)
💡RAG (Retrieval-Augmented Generation)
💡Medical Genomics Abstracts
💡pharmGKB
💡Vector Database
💡Gene Variants
💡VCF (Variant Calling Format)
💡Annotation
💡Real-time Processing
💡Pubmed Links
Highlights
Peter demonstrates medical demos using Groq powered LLMs.
Reliability and accuracy are crucial in medical context, yet LLMs often hallucinate information.
The slow uptake of generative LLMs in medicine due to their inaccuracy.
Medical professionals require referenced or sourced information from LLMs.
Introduction of RAG (Retrieval Augmented Generation) to enhance LLM output accuracy.
RAG reduces hallucinations and allows LLMs to provide referenced responses.
Process of RAG involves indexing a database of text sources and feeding top sources into LLM.
Use of a vector database containing 3000 medical genomics abstracts from pharmGKB.
Demonstration of RAG with a query on gene variants associated with adjusted warfarin doses.
Comparison of LLM outputs with and without RAG, showing RAG provides more detailed responses.
Output with RAG includes working PubMed links for verification by medical professionals.
Customizing LLMs with RAG for practical use in a medical context.
Using Llama-2 70 billion on Groq hardware with RAG to annotate the genome in real time.
VCF (variant calling format) described as a sequence genome showing gene variants.
Real-time processing of VCF files to search for matches in the gene drug database.
LLM used to summarize gene drug interactions and provide working PubMed links.
Groq enables real-time DNA readability through LLM annotations.
LLMs have a place in medical settings when enhanced with techniques like RAG for reliability.