AI Agent Accuracy: Web Scrape & Milvus Vector DB for Smarter AI
Boost AI agent accuracy by up to 90% and eliminate hours of manual data preparation by automating knowledge base updates from web sources.
Keeping AI agents updated with specific, real-time information is challenging and time-consuming, leading to outdated or inaccurate responses. This workflow automates web scraping, extracts relevant text, and loads it into a Milvus vector database, enabling AI agents to access a continuously updated knowledge base for precise, data-driven responses.
Documentation
Build a Smart AI Agent with Milvus and Dynamic Web Data
This n8n workflow demonstrates how to build an intelligent AI agent capable of answering questions based on dynamically scraped web content. By integrating web scraping with a Milvus vector database, you can create a continuously updated, domain-specific knowledge base that enhances your AI agent's accuracy and relevance.
Key Features
- Automated web scraping of articles from any specified URL.
- Intelligent text extraction and chunking for optimal AI processing and context window management.
- Dynamic Milvus vector database population with fresh content, ensuring your agent always has access to current information.
- Seamless AI agent integration for conversational knowledge retrieval and enhanced decision-making.
How It Works
This workflow operates in two main phases: data ingestion and AI agent interaction. First, it scrapes a list of articles from a specified URL (e.g., Paul Graham's essays), extracts clean text, and then generates embeddings using OpenAI. These vectorized text chunks are then inserted into a Milvus vector database. In the second phase, an AI agent, powered by an OpenAI chat model, is configured to use this Milvus database as a retrieval tool, enabling it to answer questions based on the ingested content.