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https://github.com/gurpreetkaurjethra/medical-rag-using-bio-mistral-7b
This is a RAG implementation using Open Source stack. BioMistral 7B has been used to build this app along with PubMedBert as an embedding model, Qdrant as a self hosted Vector DB, and Langchain & Llama CPP as an orchestration frameworks.
https://github.com/gurpreetkaurjethra/medical-rag-using-bio-mistral-7b
ai bert-embeddings bootstrap5 deployment docker fastapi generative-ai langchain large-language-models llama mistral pubmed qdrant rag retrieval-augmented-generation sentence-transformers
Last synced: 2 months ago
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This is a RAG implementation using Open Source stack. BioMistral 7B has been used to build this app along with PubMedBert as an embedding model, Qdrant as a self hosted Vector DB, and Langchain & Llama CPP as an orchestration frameworks.
- Host: GitHub
- URL: https://github.com/gurpreetkaurjethra/medical-rag-using-bio-mistral-7b
- Owner: GURPREETKAURJETHRA
- License: mit
- Created: 2024-03-25T06:11:50.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-07-31T06:50:15.000Z (6 months ago)
- Last Synced: 2024-11-19T08:46:25.091Z (2 months ago)
- Topics: ai, bert-embeddings, bootstrap5, deployment, docker, fastapi, generative-ai, langchain, large-language-models, llama, mistral, pubmed, qdrant, rag, retrieval-augmented-generation, sentence-transformers
- Language: HTML
- Homepage: https://github.com/GURPREETKAURJETHRA/END-TO-END-GENERATIVE-AI-PROJECTS
- Size: 3.75 MB
- Stars: 9
- Watchers: 1
- Forks: 2
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Build a Medical RAG App using BioMistral, Qdrant, and Llama.cpp
This is a RAG implementation using Open Source stack. BioMistral 7B has been used to build this app along with PubMedBert as an embedding model, Qdrant as a self hosted Vector DB, and Langchain & Llama CPP as an orchestration frameworks.
## Description & Key Features:
- Build a cutting-edge Medical Retrieval Augmented Generation (RAG) Application using a suite of powerful technologies tailored for the medical domain.
- BioMistral 7B, a new large language model specifically designed for medical applications, offering unparalleled accuracy and insight into complex medical queries.
- Qdrant, a self-hosted vector database that we run inside a Docker container. This robust tool serves as the backbone for managing and retrieving high-dimensional data vectors, such as those generated by our medical language model.
- To enhance our model's understanding of medical texts, I utilize PubMed BERT embeddings, an embeddings model specifically crafted for the medical domain.
- This ensures our application can grasp the nuances of medical literature and queries, providing more precise and relevant answers.
- A crucial component of our setup is Llama.cpp, a library that enables the inference of large language models on CPU machines. This quantized model approach allows for efficient and cost-effective deployment without compromising on performance.
- For orchestrating our application components, I introduce LangChain, an orchestration framework that seamlessly integrates our tools and services, ensuring smooth operation and scalability.
- On the backend, I leverage FastAPI, a modern, fast (high-performance) web framework for building APIs with Python 3.7+. FastAPI provides the speed and ease of use needed to create a responsive and efficient backend for our medical RAG application.
- Finally, for the web UI, I employ Bootstrap 5.3, the latest version of the world’s most popular front-end open-source toolkit. This enables us to create a sleek, intuitive, and mobile-responsive user interface that makes our medical RAG application accessible and easy to use.
- We set up the environment to integrate these technologies into a cohesive and functional medical RAG application.
## Implementation Expert Guide:
[Demo ▶️](https://www.youtube.com/watch?v=A_m3tCqdts4)
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## ©️ License 🪪
Distributed under the MIT License. See `LICENSE` for more information.
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#### **If you like this LLM Project do drop ⭐ to this repo**
#### Follow me on [](https://www.linkedin.com/in/gurpreetkaurjethra/) [](https://github.com/GURPREETKAURJETHRA/)
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