Insert and Retrieve Documents

Workflow Name

Insert and Retrieve Documents

Key Features and Highlights

This workflow automatically scrapes the latest article list from Paul Graham’s website, extracts article links, and limits content retrieval to the first three articles. After cleaning and extracting plain text from the articles, the text is chunked and converted into vector embeddings using OpenAI’s text embedding model. These vectors are batch-inserted into the Milvus vector database. Users can submit queries via a chat interface; the system performs semantic search on Milvus to retrieve relevant text chunks and leverages the GPT-4 model to generate intelligent answers based on context, accompanied by source citations to ensure accuracy and traceability.

Core Problems Addressed

• Automating large-scale text data crawling, parsing, and structured storage
• Transforming unstructured text into efficient vector representations for fast semantic retrieval
• Combining powerful language models to enable precise question answering based on document content
• Providing source citations to enhance the credibility and transparency of answers

Application Scenarios

• Knowledge base construction and management: Automatically collect and structurally store professional articles for easy subsequent querying and analysis
• Intelligent customer service and Q&A systems: Deliver expert answers and decision support based on specific document collections
• Content aggregation and research assistance: Quickly retrieve and cite relevant article content to improve research efficiency
• Enterprise internal document management and intelligent retrieval

Main Workflow Steps

1. Manually trigger the workflow execution
2. Fetch the article list page from Paul Graham’s website via HTTP request
3. Extract article links using an HTML parsing node and split them into individual records
4. Limit content retrieval to the first three articles
5. Send HTTP requests to obtain the full text of each article
6. Parse HTML to extract plain text content, excluding images and navigation elements
7. Chunk the article text using a text splitter
8. Generate vector embeddings using OpenAI’s text embedding model
9. Insert the vector data into the Milvus vector database to support subsequent retrieval
10. Receive user queries through a chat trigger node
11. Perform semantic search in Milvus based on the query vector to obtain relevant text chunks
12. Call the GPT-4 model to answer questions using the retrieved context and generate comprehensive responses with citations

Involved Systems or Services

• HTTP request nodes: Web content fetching
• HTML content parsing nodes: Link and text extraction
• OpenAI API: Text embedding (text-embedding-ada-002), chat language model (gpt-4o-mini)
• Milvus vector database: Vector storage and retrieval
• n8n workflow automation platform and its built-in nodes
• LangChain components: Text splitting, vector storage interface, information extraction

Target Users and Value Proposition

• Content aggregation platform operators who need to regularly collect and manage large volumes of article data
• AI developers and data scientists building semantic search-based intelligent Q&A systems
• Enterprise knowledge management teams aiming to improve internal document utilization and retrieval efficiency
• Researchers and scholars seeking quick access to and citation of professional articles
• Any users requiring transformation of unstructured text into structured knowledge and natural language interaction for information retrieval

This workflow integrates the entire pipeline of crawling, processing, storing, retrieving, and intelligent Q&A, significantly simplifying text knowledge management processes and enhancing content utilization value.