AI-Powered RAG Workflow For Stock Earnings Report Analysis
This n8n workflow creates a financial analysis tool that generates reports on a company's quarterly earnings using the capabilities of OpenAI GPT-4o-mini, Google's Gemini AI and Pinecone's vector search. By analyzing PDFs of any company's earnings reports from their Investor Relations page, this workflow can answer complex financial questions and automatically compile findings into a structured Google Doc. How it works: Data loading and indexing Fetches links to PDF earnings document from a Google Sheet containing a list of file links. Downloads the PDFs from Google Drive. Parses the PDFs, splits the text into chunks, and generates embeddings using the Embeddings Google AI node (text-embedding-004 model). Stores the embeddings and corresponding text chunks in a Pinecone vector database for semantic search. Report generation with AI agent Utilizes an AI Agent node with a specifically crafted system prompt. The agent orchestrates the entire process. The agent uses a Vector Store Tool to access and retrieve information from the Pinecone database. Report delivery Saves the generated report as a Google Doc in a specified Google Drive location.
Tags
Related workflows
See all AI Automation→AI: Summarize podcast episode and enhance using Wikipedia
The workflow automates the process of creating a summarized and enriched podcast digest, which is then sent via email. Note that to use this template, you need to be on n8n version 1.19.4 or later.
Scrape and summarize webpages with AI
This workflow integrates both web scraping and NLP functionalities. It uses HTML parsing to extract links, HTTP requests to fetch essay content, and AI-based summarization using GPT-4o. It's an excellent example of an end-to-end automated task that is not only efficient but also provides real value by summarizing valuable content. Note that to use this template, you need to be on n8n version 1.50.0 or later.
Ask questions about a PDF using AI
The workflow first populates a Pinecone index with vectors from a Bitcoin whitepaper. Then, it waits for a manual chat message. When received, the chat message is turned into a vector and compared to the vectors in Pinecone. The most similar vectors are retrieved and passed to OpenAI for generating a chat response. Note that to use this template, you need to be on n8n version 1.19.4 or later.