Automated Stock Earnings Report Analysis with RAG
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Streamline your stock market research with our RAG-powered workflow. Automatically analyze financial earnings reports to extract key insights, identify trends, and make informed investment decisions.
About This Workflow
Unlock the power of AI for your financial analysis needs. This n8n workflow leverages Retrieval Augmented Generation (RAG) to automatically process and analyze stock earnings reports. By integrating with vector databases like Pinecone and utilizing advanced language models (Google Gemini and OpenAI), it can ingest PDF earnings reports, embed them for semantic search, and then use an AI agent to answer specific questions about the data. The agent is pre-configured with a detailed system message, acting as a financial analyst to identify trends, outliers, and provide formatted reports, ultimately saving you valuable time and effort in your investment research.
Key Features
- Automated Data Ingestion: Effortlessly load and process financial documents like PDF earnings reports.
- Intelligent Data Retrieval: Semantic search powered by vector embeddings to quickly find relevant financial information.
- AI-Powered Analysis: An intelligent agent that synthesizes information, identifies trends, and spots outliers.
- Customizable Reporting: Generate detailed analysis in formats like markdown, with the ability to extend to Google Docs.
- Flexible Model Integration: Supports multiple advanced AI models for embedding and reasoning.
How To Use
- Configure Trigger: Set up the 'When clicking ‘Test workflow’' node to manually initiate the workflow.
- Load Data: Use the 'Default Data Loader' to point to your PDF earnings reports and 'Recursive Character Text Splitter' to segment them.
- Embed and Store: Connect the 'Embeddings Google Gemini' node to create vector embeddings of your text data and then the 'Pinecone Vector Store' node to insert these into your Pinecone index.
- Define AI Agent: Configure the 'AI Agent' node with your specific query (e.g., "Analyze the last 3 quarters of Google earnings") and a detailed 'systemMessage' to guide its analysis.
- Integrate Tools: Connect the 'Vector Store Tool' for the AI agent to query your financial data and the 'OpenAI Chat Model' or 'Google Gemini Chat Model' as the reasoning engine.
- Execute: Run the workflow and review the AI agent's output, which will contain the analysis based on your input.
Apps Used
Workflow JSON
{
"id": "8baca341-3b50-4577-b1c4-a5bd861480e1",
"name": "Automated Stock Earnings Report Analysis with RAG",
"nodes": 10,
"category": "Marketing",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 8baca341-3b50...
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