RAG Workflow For Stock Earnings Report Analysis
This workflow utilizes RAG technology to automatically process and analyze the quarterly financial reports of publicly listed companies in PDF format, generating structured financial analysis reports. It accurately extracts key information through semantic retrieval and large language models, intelligently generating detailed reports that include content such as revenue, costs, and profits, which are then automatically saved to Google Docs. This process significantly enhances the efficiency and accuracy of financial data insights, helping investment analysts, financial advisors, and others quickly obtain in-depth analysis results.
Workflow Name
RAG Workflow For Stock Earnings Report Analysis
Key Features and Highlights
This workflow leverages Retrieval-Augmented Generation (RAG) technology to automate the processing and analysis of quarterly earnings report PDFs from publicly listed companies, producing structured financial analysis reports. By combining semantic vector search with large language models, it accurately extracts critical information and intelligently generates detailed reports covering revenue, costs, profits, key metric trends, and management commentary. The reports are automatically saved to Google Docs, enabling efficient and professional financial data insights.
Core Problems Addressed
- Manual compilation and analysis of large volumes of earnings reports is time-consuming and error-prone
- Key information in earnings reports is scattered, making it difficult to quickly obtain comprehensive and in-depth financial insights
- Lack of tools for automatically generating structured, easy-to-read financial analysis reports
This workflow achieves full automation of the earnings report analysis process through automatic downloading, text splitting, vector indexing, semantic retrieval, and intelligent report generation, significantly improving efficiency and accuracy.
Application Scenarios
- Investment analysts quickly obtaining financial performance and trends of listed companies
- Institutional and individual investors conducting regular financial data monitoring and decision support
- Financial advisors and research institutions automating the preparation of financial analysis reports
- Corporate finance teams organizing and managing internal financial data
Main Process Steps
- File List Retrieval: Read the list of target companies’ earnings report PDF URLs from Google Sheets.
- File Download: Automatically download the corresponding quarterly earnings report PDFs from Google Drive.
- Data Loading and Splitting: Load PDF content and split it into manageable text chunks using a recursive character splitter.
- Text Vectorization and Storage: Generate text embeddings using the Google Gemini model and insert them into the Pinecone vector database for fast semantic retrieval.
- Information Retrieval and Analysis: An AI agent retrieves relevant financial data from the vector database based on user queries and performs in-depth analysis with the language model.
- Report Generation: Automatically generate a Markdown-formatted financial report covering revenue, expenses, profits, key metrics, management commentary, trend analysis, and anomalies.
- Report Saving: Automatically update and save the generated financial analysis report to a designated Google Docs document.
Systems and Services Involved
- Google Sheets: Managing the list of earnings report files
- Google Drive: Storing earnings report PDF files
- Pinecone Vector Store: Efficient storage and retrieval of text vectors
- Google Gemini (PaLM) API: Generating text embeddings and powering the language model
- OpenAI Chat Model: Assisting natural language understanding and generation
- Google Docs: Saving and presenting the final financial analysis reports
- n8n Automation Platform: Seamlessly connecting and automating workflows across services
Target Users and Value
- Investment Analysts and Fund Managers: Quickly access high-quality, structured earnings insights to support investment decisions.
- Financial Advisors and Researchers: Enhance financial analysis efficiency and reduce tedious data preparation time.
- Corporate Finance Teams: Automate monitoring of competitors’ or industry financial dynamics.
- Data Science and AI Enthusiasts: Explore intelligent analysis applications combining vector databases with large language models.
This workflow enables users to effortlessly automate earnings data collection, intelligent analysis, and report generation, significantly enhancing the depth and efficiency of financial analysis.