Building Knowledge Graphs from Web Data using CAMEL-AI and Firecrawl

Exploring the application of CAMEL-AI and Firecrawl, the post delves into constructing knowledge graphs from web-extracted data, using a case study of analyzing Yusuf Dikec's performance in the 2024 Paris Olympics. The process begins with setting up CAMEL and Firecrawl, including configuring API keys for secure interaction with external services. Firecrawl is highlighted for its efficiency in web scraping, while CAMEL's RAG model enhances information retrieval from URLs, demonstrated through a function that aggregates content and extracts relevant data for queries. The post also introduces AgentOps for monitoring CAMEL agents and describes building knowledge graphs using Neo4j for advanced analysis. Furthermore, it illustrates a multi-agent role-playing scenario where CAMEL agents interact to perform tasks such as web information retrieval and knowledge graph construction. The integration of tools like Qdrant for vector storage, DuckDuckGo Search for URL gathering, and OpenAI's language models is also discussed, showcasing the comprehensive capabilities of these technologies in research and data analysis.