AI Earnings Report Analysis with Vector Databases
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This n8n workflow empowers an AI agent to analyze earnings reports using Pinecone and Supabase vector databases. It leverages chat triggers and HTTP requests to query and process financial data, providing detailed insights with citations.
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About This Workflow
Overview
This n8n workflow is designed to build an AI-powered assistant capable of analyzing financial earnings reports. It utilizes Langchain's capabilities to connect with powerful vector databases like Pinecone and Supabase, allowing for efficient storage and retrieval of document embeddings.
The workflow starts with a chat trigger to receive user queries. It then uses OpenRouter chat models and HTTP requests to interact with the Pinecone assistant, searching through a knowledge base of earnings reports. The results are processed by a tool calculator. For data ingestion, it uses form triggers to accept PDF uploads, processes them with default data loaders, generates embeddings with OpenAI, and stores them in both Pinecone and Supabase vector stores. Finally, it utilizes agent nodes with specific system messages to orchestrate the analysis, query the vector stores as tools, and generate responses that cite their sources.
Key Features
- Integrates with Pinecone and Supabase for scalable vector storage.
- Supports PDF uploads for data ingestion via form triggers.
- Leverages OpenAI for embedding generation.
- Utilizes OpenRouter for flexible LLM chat model selection.
- AI agent designed for detailed earnings report analysis and source citation.
How To Use
- Configure the
When chat message receivednode to trigger the AI assistant. - Set up your Pinecone and Supabase API credentials in n8n.
- Upload your earnings reports via the
On form submissionnode, ensuring the PDF is correctly handled by theDefault Data Loader. - Configure the embedding nodes (e.g.,
Embeddings OpenAI) and vector store nodes (Pinecone Vector Store,Supabase Vector Store) with your specific index/table names and credentials. - Customize the system messages in the
agentnodes to define the AI's persona and instructions for analysis and citation. - Use the
OpenRouter Chat Modelnodes for AI inference andtoolCalculatornodes for auxiliary processing.
Apps Used
Workflow JSON
{
"id": "80a147f3-41a5-4405-8317-8b7ee6b1b5dd",
"name": "AI Earnings Report Analysis with Vector Databases",
"nodes": 0,
"category": "AI Automation",
"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: 80a147f3-41a5...
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