{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/636def729b114136b07ef28a5d58c20d\" frameborder=\"0\" width=\"1728\" height=\"1296\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1296,"width":1728,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1296,"thumbnail_width":1728,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/636def729b114136b07ef28a5d58c20d-00001.gif","duration":180.36,"title":"FilingFinder - a langchain RAG on Meta - Financials","description":"Welcome 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.\n\nHF space: https://huggingface.co/spaces/rajkstats/FilingFinder\n\napp.py\n- Environment Setup: Initializes the environment by loading necessary configurations and environment variables using dotenv.\n- Document Loader: Uses PyMuPDFLoader to load the PDF document from a specified URL, which is then processed for data extraction.\n- Text Splitter: Implements RecursiveCharacterTextSplitter to handle text splitting based on token length, ensuring efficient processing of large documents without losing contextual relevance.\n- Vector Store: Establishes a Qdrant vector store to maintain embeddings of the document text, facilitating quick retrieval of information based on query similarity.\n- LLM Integration: Utilizes ChatOpenAI as the language model for generating responses based on the retrieved information, providing a conversational interface.\n- Asynchronous Handling: Employs asynchronous functions to enhance performance, especially in handling I/O operations like document loading and data querying."}