Automate Stock Earnings Analysis with RAG and AI
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Streamline your stock earnings report analysis with this powerful RAG workflow. Leverage AI to automatically extract insights, identify trends, and generate concise summaries from financial documents, saving you valuable time.
About This Workflow
This n8n workflow empowers you to automate the analysis of stock earnings reports using Retrieval Augmented Generation (RAG). It ingests PDF earnings reports, splits them into manageable chunks, generates embeddings using Google Gemini, and stores them in a Pinecone vector database for efficient retrieval. When triggered, an AI agent analyzes specific queries about Google's last three quarters of earnings, drawing upon the vector store to provide detailed insights, identify trends, and spot outliers. The AI is further enhanced by access to both Google Gemini and OpenAI chat models, ensuring comprehensive and accurate analysis. This workflow is designed to significantly reduce manual effort in financial data analysis, providing actionable intelligence directly to you.
Key Features
- Automated Data Ingestion: Easily load and process PDF earnings reports.
- Intelligent Embeddings: Utilize Google Gemini for high-quality text embeddings.
- Vectorized Data Storage: Store and retrieve financial data efficiently with Pinecone.
- AI-Powered Analysis: Leverage advanced AI agents to extract key insights and trends.
- Customizable Queries: Ask specific questions about financial performance and receive tailored reports.
How To Use
- Configure Data Loader: Set up the
Default Data Loadernode to point to your PDF earnings reports. - Set up Embeddings: Connect your
Embeddings Google Gemininode and ensure your Google Gemini API credentials are set. - Configure Vector Store (Insert): Set up the
Pinecone Vector Storenode to insert data into your Pinecone index, ensuring your Pinecone API key and index name (company-earnings) are correctly configured. - Split Text: Configure the
Recursive Character Text Splitterto prepare documents for embedding. - Set up AI Agent: Define your specific analysis query in the
AI Agentnode and configure the system message to guide its behavior. - Configure Tools: Ensure the
Vector Store Toolis set up to query your Pinecone index. You can also configure theOpenAI Chat ModelandGoogle Gemini Chat Modelfor the AI agent to use. - Trigger Workflow: Use the
When clicking ‘Test workflow’node to manually trigger the analysis or integrate it into a larger automated process.
Apps Used
Workflow JSON
{
"id": "3b8c9245-aa21-4ebe-b1b0-551102b0e4cd",
"name": "Automate Stock Earnings Analysis with RAG and AI",
"nodes": 14,
"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.
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ID: 3b8c9245-aa21...
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