Stock Q&A Workflow
This workflow creates an AI-based stock Q&A system that automatically downloads and processes PDF files from Google Drive. Using vector storage and semantic retrieval technology, users can submit questions in real-time, and the system generates accurate answers by combining relevant documents, significantly enhancing the efficiency and accuracy of information retrieval. It is suitable for financial analysts, investment advisors, and internal corporate teams, helping them quickly access and utilize professional knowledge to improve work efficiency.
Workflow Name
Stock Q&A Workflow
Key Features and Highlights
This workflow implements an AI-driven question-and-answer system specialized in the stock domain. It automatically downloads relevant PDF files from Google Drive, performs content chunking and vectorization for storage, and receives user queries via Webhook. Leveraging vector retrieval and OpenAI language models, it generates precise answers and delivers real-time responses to user inquiries.
Core Problems Addressed
It solves the inefficiency and complexity of querying stock-related document information through manual search. By combining a vector database with AI models, it enables rapid semantic search and intelligent Q&A over large volumes of unstructured financial documents, significantly enhancing the speed and accuracy of information retrieval.
Application Scenarios
- Financial analysts quickly accessing stock-related knowledge
- Investment advisors providing real-time Q&A support to clients
- Internal corporate finance or investment teams conducting intelligent document retrieval
- Any business scenario requiring Q&A based on professional documents
Main Process Steps
- File Acquisition and Processing: Triggered manually or via scheduled tasks, downloads specified PDF documents (e.g., Crowdstrike reports) from Google Drive.
- Text Chunking and Vectorization: Uses a recursive character splitter to divide documents into appropriately sized text chunks and calls OpenAI Embeddings API to generate text vectors.
- Vector Storage: Inserts the generated text vectors into the Qdrant vector database to build an index.
- Q&A Request Reception: Receives external user questions through a Webhook node.
- Semantic Retrieval and Answer Generation: Queries Qdrant for the most relevant vectorized texts and combines them with OpenAI language models to execute a retrieval-augmented Q&A chain.
- Response Delivery: Returns the generated answers to the requester via Webhook response.
Involved Systems or Services
- Google Drive (file storage and download)
- Qdrant (vector database for efficient semantic search)
- OpenAI (text vector generation and language model inference)
- Supabase (used for vector storage indexing, as detailed)
- Webhook (handling external HTTP request reception and response)
- Built-in n8n nodes (text splitting, manual triggers, and other basic functions)
Target Users and Value
- Financial industry professionals, analysts, and advisors, facilitating rapid comprehension and utilization of extensive professional reports and materials
- Data scientists and AI developers, serving as a reference template for building intelligent Q&A systems
- Corporate knowledge management teams, improving document retrieval efficiency and intelligence
- Any business users needing to transform unstructured documents into intelligent Q&A interfaces
By seamlessly integrating multiple advanced technologies, this workflow automates the processing and intelligent Q&A of stock domain documents, enabling users to quickly find required answers within complex information, thereby greatly enhancing work efficiency and decision support capabilities.