This article provides a complete, practical walkthrough of the Googledocs Manual Automate Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Automating Stock Earnings Report Analysis with n8n, Pinecone, and Google Gemini: A RAG-Powered Workflow
   
   Meta Description:
   Discover how to automate financial analysis with a no-code n8n workflow combining Retrieval-Augmented Generation (RAG), Pinecone vector database, and Google Gemini. This step-by-step process streamlines the generation of stock earnings reports using AI.
   
   Keywords:
   n8n workflow, RAG architecture, Pinecone vector store, Google Gemini, earnings report automation, financial analysis AI, Google Docs automation, Vertex AI, Google Cloud, no-code automation, stock analysis with AI, PDF earnings analysis, AI document processing, language models in finance
   
   Article:
   
   Automated Financial Analysis with AI: An n8n RAG Workflow for Earnings Report Insights
   
   In today’s data-driven economy, financial analysts and investors constantly seek quicker, more accurate insights from vast piles of earnings reports. Manually skimming through quarterly performance PDFs is not only time-consuming but also prone to human error. Enter: a powerful no-code Retrieval-Augmented Generation (RAG) workflow powered by n8n, with integrations into Pinecone, Google Gemini (Vertex AI), and Google Workspace tools. This workflow streamlines the entire process of data collection, processing, and financial report generation for stock earnings—specifically Google's (Alphabet Inc.) past three quarters.
   
   Let’s break down how this automated system operates and why it's a game-changer for financial analysis.
   
   How It Works
   
   The "RAG Workflow for Stock Earnings Report Analysis" in n8n is designed to tackle the challenge of extracting structured insights from unstructured PDF earnings reports. By leveraging Retrieval-Augmented Generation (RAG), financial intelligence is generated using both vector-based search and state-of-the-art language models.
   
   The workflow operates in two core phases:
   1. Data Collection and Vector Embedding
   2. AI-Driven Analysis and Report Generation
   
   Phase 1: Loading Financial Data into a Vector Store
   
   At the heart of this workflow is a Pinecone vector database, built specifically to store embeddings of financial text extracted from PDF earnings reports.
   
   Here’s how the data flows:
   
   - The workflow begins when "List Of Files To Load (Google Sheets)" pulls a list of file URLs from a Google Sheet containing links to quarterly earnings PDFs stored in Google Drive.
   - Each file is then downloaded using "Download File From Google Drive". 
   - The "Default Data Loader" processes the PDFs, preparing them for embedding.
   - A "Recursive Character Text Splitter" divides the document into smaller semantically coherent chunks.
   - Text chunks are converted into embeddings via "Embeddings Google Gemini", using Google’s latest Vertex AI model, and stored in Pinecone using the "Pinecone Vector Store" node.
   - These embeddings allow for semantic search during report generation, improving precision and relevance over keyword-based methods.
   
   Phase 2: AI-Powered Report Generation
   
   Once the data is vectorized and stored, it’s time for insight generation:
   
   - The core of the workflow is the "AI Agent", which receives a question such as “Give me a report on Google's last 3 quarter earnings. Format it in markdown. Focus on the differences and trends. Spot any outliers.”
   - The AI Agent has access to two key tools:
     - Vector Store Tool: Retrieves relevant text chunks from the last three quarters using the Pinecone index.
     - Google Docs Tool: Saves the final analysis in a structured and formatted Markdown document on Google Docs.
   - Language model capabilities are provided by both OpenAI and Google Gemini (Vertex AI), enabling comparative evaluations.
   - The resulting report includes detailed sections on revenues, expenses, profitability, and trend analysis, along with citations and management commentary.
   
   An example output includes Markdown-formatted summaries such as:
   - Revenue trends (e.g., Cloud revenue growing at 35%)
   - Expense insights (e.g., rise in TAC and depreciation)
   - Profitability fluctuations
   - Outliers like swings in other income or extraordinary gains/losses
   
   Why This Matters
   
   Traditional financial analysis involves hours of work reviewing and cross-referencing PDFs. This workflow dramatically reduces analysis time from hours to minutes with surprising accuracy—yet remains fully customizable.
   
   By using n8n, a flexible no-code workflow automation tool, financial analysts can now:
   - Automate document scraping and parsing
   - Leverage next-gen LLMs for financial summarization
   - Create reusable and scalable pipelines
   - Maintain structured archives in tools they already use, like Google Docs
   
   This approach democratizes access to AI, giving analysts smart tools without needing to write code.
   
   Third-Party APIs and Tools Used
   
   1. Google Sheets API (via OAuth2)
      - To fetch URLs of quarterly earnings reports from a centralized watchlist.
   
   2. Google Drive API
      - To download earnings report PDFs.
   
   3. Google Docs API
      - To create and update financial reports in Markdown format, shareable across teams.
   
   4. Pinecone API
      - As a vector database, enabling high-speed semantic search of earnings report content.
   
   5. Google Gemini (Vertex AI) API
      - For both text embeddings and large language model (LLM) chat capabilities.
   
   6. OpenAI API
      - As an alternative LLM for flexible and precise natural language understanding.
   
   Conclusion
   
   This n8n-powered workflow shows how low-code platforms can bring together cutting-edge AI models, document storage, and vector databases to fully automate a once-manual process. By combining Pinecone for semantic search, Google Gemini for language understanding, and Google Workspace for storage and formatting, this system gives financial professionals a powerful assistant that never sleeps.
   
   As AI tools grow more accessible, expect to see similar RAG workflows become the backbone of business intelligence—ushering in an era where data-driven insights are not just generated intelligently, but instantaneously.
   
   —
   
   Want to try it for yourself? Simply set up your Google Cloud project, get API credentials from Pinecone and Google, and import the workflow into n8n. Then sit back and watch your financial summaries write themselves.
   
   Keywords Recap:
   n8n, RAG, Google Gemini, Pinecone, financial automation, Google Docs, Google Drive, OpenAI, PDF analysis, semantic search, AI workflow, stock earnings, Alphabet Inc., no-code AI
   
   — 
   
   Written with assistance from an AI Financial Analyst.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.