Build AI Investment Banker: Streamlit & Annual Report Guide
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In a thrilling demonstration, Nicholas Renotte unveils the creation of an AI-powered investment banker using Streamlit and an annual report. The process kicks off with the installation of crucial dependencies like Langchain and OpenAI, alongside securing an OpenAI API key for seamless operation. By crafting a Python file and importing essential libraries, setting the API key, and establishing the OpenAI Service as an LLM, Renotte sets the stage for an adrenaline-pumping coding adventure. Through Streamlit, users can effortlessly pass prompts to the LLM and receive lightning-fast responses, promising an exhilarating tech journey for enthusiasts.
To elevate the experience further, Renotte delves into the integration of personal documents by introducing additional dependencies such as Pi PDFLoader and Chroma. By loading documents into Chroma for advanced querying and similarity searches, users can unlock a new level of interaction with their own data. The introduction of the Vector Store Agent from Langchain adds a layer of sophistication to the process, promising a seamless transition towards a more personalized AI experience. With the incorporation of classes like Create Vector Store Agent and Vector Store Toolkit, users can empower their LLM to access and interpret document data with unparalleled precision.
As the AI investment banker springs into action, users can expect a riveting journey through financial insights extracted from the annual report. From net profit figures to sustainability initiatives, the AI companion delivers a punchy performance, akin to a high-octane race down a tech-savvy track. By summarizing the bank's financial performance with precision and flair, the AI investment banker showcases its prowess in distilling complex data into digestible insights. Despite the potential delay in processing longer documents, the promise of crafting a personalized investment banker with just 45 lines of code injects a dose of adrenaline into the tech realm, setting the stage for an electrifying coding escapade.
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Request for tutorials on using local LLMs and fine-tuning weights on open-source models
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