Accelerate Clinical Trials with BioRAG
Secure document interaction using RAG with LangChain, Qdrant, and Azure Blob Storage via a Streamlit application
In the dynamic world of Natural Language Processing (NLP), efficiently managing and extracting information from large document sets is essential. LangChain, an open-source framework, streamlines the development of applications powered by language models. When paired with Qdrant, a high-performance vector database, and Azure Blob Storage for document storage, LangChain revolutionizes how you interact with your documents via a Streamlit application.
Why LangChain with Azure Blob Storage, Open AI and Qdrant?
- LangChain: Provides a robust framework for integrating language models into your applications. It simplifies the process of document loading, embedding creation, and chain building for question-answering tasks.
- Qdrant: Qdrant is a vector database that allows for efficient storage, retrieval, and management of high-dimensional vector embeddings. Once your data is stored, you can perform similarity searches, filtering, and clustering operations to leverage the power of vector search for applications like recommendation systems, semantic search, and anomaly detection. Qdrant is designed for storing and searching the vector embeddings which allows the system to understand and retrieve relevant information.
- Azure Blob Storage: A cloud-based solution for storing large amounts of unstructured sedata, such as documents, images, and videos. It offers massive scalability, robust security, and flexible storage tiers to manage your data efficiently. With Azure Blob Storage, you can securely store your documents and access them globally, ensuring that your data is always available when needed.
- OpenAI on Azure: By using OpenAI's models on Azure, you can enhance the natural language understanding capabilities of your application. OpenAI's powerful models, such as GPT, can be integrated directly into your pipeline for tasks like content generation, answering complex questions, and improving document comprehension. With Azure OpenAI Service, you can easily scale your application while benefiting from the platform's enterprise-level security, compliance, and reliability. This integration allows for efficient processing of natural language inputs alongside LangChain and Qdrant, ensuring fast and accurate document retrieval and analysis.
- Streamlit Output: Streamlit allows for the development of a web-based interface where users can input product reviews and receive immediate sentiment analysis. This makes the model accessible to non-technical users who can benefit from sentiment insights in real-time without needing to understand the underlying code. This means that businesses can enhance and automate their current systems with minimal disruption.
Combining Retrieval-Augmented Generation (RAG) with LangChain, Qdrant and Azure Blob Storage offers a streamlined and efficient way to manage document analysis and question-answering tasks. This integration leverages the power of language models and vector search to provide accurate and contextually relevant information retrieval. Learn more in our blog post.
In this template, we provide python code with templates to import libraries, create/index/store embeddings for documents, build the chat engine, and more. Learn how to work directly with OpenAI inside the Domino platform to query documents and answer questions about documents via a custom Streamlit web application. Get answers to questions quicker for use cases related to research documents, customer support knowledge bases, or corporate documentation.