Arno Angerer@arnoan
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Germany
4 years of experience
About me
Data Engineer | Python Aficionado | Life-Long Learner | Current goal: grokking Django/WebDev/LLMs I have prior experience developing LLM-based applications, also familiar with RAG and MRKL architectures. Tech-Stack for LLM projects: OpenAI/Azure OpenAI Services/Cohere as LLM provider, LangChain (LCEL), Chroma/Weaviate for VectorDB & retrieval, Streamlit for Prototype UI, Django (supplemented with HTMX, TailwindCSS & AlpineJS) to further flesh out a prototype (user management, persistent state, settings, integration with other relevant information..) once the initial idea is validated.
is under improvement
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Term Aware Guard
Rarely people read the whole data corpus of terms of service when they sign up for companies and we came with a solution: The primary objective of Term Aware Guard is to simplify the readability of Terms and Conditions, providing a summarized version and ensuring that users are well-informed about data privacy beforehand. It is a web app with built with Next.js using GCP for hosting and Firebase for storing the companies the user has signed up for. The web app uses Python environment REST API hosted on Digital Ocean for the backend. We use Google Vertex AI PaLM2 on text-bison002 model for inference, a large language model with impressive capabilities. Terms and conditions of companies change constantly, and fine tuning the model everytime that happens could be very costly and inefficient, for that we found a solution. We use Retrieval augmented generation(RAG) to enhance the model's summarization capabilities and to get the context of the latest updated version ToS of companies. The model is evaluated with the help of TruLens to measure its quality with feedback and metrics. We used Apify for gathering the data by web scrapping and Pinecone as a vector store where RAG gets context from.
