video1.0<iframe src="https://www.loom.com/embed/636def729b114136b07ef28a5d58c20d" frameborder="0" width="1728" height="1296" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12961728Loomhttps://www.loom.com12961728https://cdn.loom.com/sessions/thumbnails/636def729b114136b07ef28a5d58c20d-00001.gif180.36Welcome to FilingFinder, an innovative application that extracts and interprets key financial data from Meta's 10-K filings using advanced natural language processing techniques. This project is built on Chainlit and designed for financial analysts seeking rapid access to specific financial disclosures. HF space: https://huggingface.co/spaces/rajkstats/FilingFinder app.py - Environment Setup: Initializes the environment by loading necessary configurations and environment variables using dotenv. - Document Loader: Uses PyMuPDFLoader to load the PDF document from a specified URL, which is then processed for data extraction. - Text Splitter: Implements RecursiveCharacterTextSplitter to handle text splitting based on token length, ensuring efficient processing of large documents without losing contextual relevance. - Vector Store: Establishes a Qdrant vector store to maintain embeddings of the document text, facilitating quick retrieval of information based on query similarity. - LLM Integration: Utilizes ChatOpenAI as the language model for generating responses based on the retrieved information, providing a conversational interface. - Asynchronous Handling: Employs asynchronous functions to enhance performance, especially in handling I/O operations like document loading and data querying.