AI-Powered RAG Workflow for Stock Earnings Analysis
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Automate the complex task of analyzing stock earnings reports with this powerful Retrieval Augmented Generation (RAG) workflow. Ingest PDF financial documents, leverage AI agents for deep analysis, and generate comprehensive financial reports with unparalleled efficiency and accuracy.
About This Workflow
This n8n workflow revolutionizes financial analysis by automating the processing and interpretation of stock earnings reports. It starts by ingesting PDF documents, splitting them into manageable chunks, and transforming them into semantic embeddings using Google Gemini. These embeddings are then stored in a Pinecone vector database. An advanced AI Agent, powered by OpenAI, acts as a skilled financial analyst, querying this vector store to retrieve precise information. The agent then synthesizes insights, identifies trends, and generates detailed, markdown-formatted financial reports, including the ability to save findings directly to Google Docs. Say goodbye to manual data crunching and hello to instant, data-driven financial intelligence.
Key Features
- Intelligent RAG Architecture: Combines powerful LLMs (OpenAI) with a vector database (Pinecone) for highly accurate, context-aware financial analysis.
- Automated PDF Ingestion & Processing: Seamlessly loads and pre-processes PDF earnings reports, splitting text for optimal retrieval.
- Advanced AI Financial Analyst: An n8n AI Agent, configured as a specialized financial analyst, understands complex queries and synthesizes reports.
- Multi-Model Embedding & Generation: Utilizes Google Gemini for robust embeddings and OpenAI for sophisticated report generation.
- Customizable Reporting: Prompts can be tailored to focus on specific metrics, trends, and output formats, including integration with Google Docs.
How To Use
- Set Up Credentials: Configure your n8n credentials for Pinecone, Google Gemini (PaLM), and OpenAI APIs.
- Ingest Data: Use the
Default Data Loaderto specify the PDF earnings reports you wish to analyze. Connect this to theRecursive Character Text SplitterandEmbeddings Google Gemini. - Populate Vector Store: Ensure the first
Pinecone Vector Storenode (in 'insert' mode) is configured to yourcompany-earningsindex to store the processed embeddings. - Configure AI Agent: Customize the
AI Agent's prompt and system message to define the scope of analysis (e.g., specific companies, timeframes, report structure, and Google Docs output). The example is set up for Google's earnings. - Run Workflow: Trigger the workflow to first ingest data, then activate the AI Agent to perform its analysis and generate your financial report.
Apps Used
Workflow JSON
{
"id": "cff34780-b787-4bba-8cb8-ccee99f7086b",
"name": "AI-Powered RAG Workflow for Stock Earnings Analysis",
"nodes": 20,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: cff34780-b787...
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