Intelligent Q&A and Citation Generation Based on File Content
This workflow achieves efficient information retrieval and intelligent Q&A by automatically downloading specified files from Google Drive and splitting their content into manageable text blocks. Users can ask questions through a chat interface, and the system quickly searches for relevant content using a vector database and OpenAI models, generating accurate answers along with citations. This process significantly enhances the efficiency of document information acquisition and the credibility of answers, making it suitable for various scenarios such as academic research, enterprise knowledge management, and customer support.
Workflow Name
Intelligent Q&A and Citation Generation Based on File Content
Key Features and Highlights
This workflow supports automatic downloading of specified files from Google Drive (defaulting to the Bitcoin whitepaper), splitting the file content into manageable text chunks, and storing these chunks as vectors in the Pinecone vector database. Users input query questions via a chat interface, and the system intelligently retrieves relevant content chunks. It then leverages the OpenAI GPT-4o-mini model for semantic understanding and answer generation, accompanied by corresponding citation information to ensure answer accuracy and traceability.
Core Problems Addressed
- Difficulty in quickly retrieving and performing intelligent Q&A on traditional file contents
- Lack of intuitive access to answer sources and citations, affecting information credibility
- Low efficiency in manual querying and organizing document information
Application Scenarios
- Rapid information extraction and Q&A on papers, reports, and other documents in academic research
- Intelligent retrieval and decision support within enterprise knowledge bases
- Quick response to user inquiries in customer service or technical support scenarios based on document content
- Development of intelligent chatbots providing expert answers by integrating specified documents
Main Workflow Steps
- Set File URL: Configure the target document link via the “Set file URL in Google Drive” node.
- Download File: Automatically download the specified file from Google Drive.
- Load and Split Document: Use the default data loader and recursive character text splitter to divide the file content into multiple text chunks.
- Generate Text Vectors: Convert text chunks into vectors by calling the OpenAI Embeddings API.
- Store Vectors: Insert vector data into the Pinecone vector database for efficient retrieval.
- Receive User Query: Accept user input questions through the chat trigger node.
- Retrieve Relevant Text Chunks: Load the most relevant text chunks from Pinecone based on the query.
- Prepare Context: Organize the retrieved text chunks into contextual information.
- Generate Answer: Call the OpenAI chat model to generate answers by combining the context.
- Attach Citation Information: Generate a citation list based on the indexes of the used text chunks and append it to the answer.
Involved Systems and Services
- Google Drive: File storage and download
- Pinecone: Vector database responsible for storing text vectors and similarity search
- OpenAI: Provides text vector generation (Embeddings) and language model (ChatGPT) services
- n8n: Workflow orchestration and node-triggered execution platform
Target Users and Value
- Data analysts and researchers: Quickly query key information in large files to improve research efficiency.
- Enterprise knowledge management teams: Build intelligent knowledge bases to enhance employee self-service capabilities.
- Developers and technical personnel: Create intelligent Q&A bots with contextual citation functionality.
- Educators: Assist in Q&A and content comprehension of teaching materials.
By automating the structured storage of file content and intelligent Q&A, this workflow significantly enhances information retrieval efficiency and answer credibility, making it a powerful tool for intelligent document processing across multiple industries.