An agentic rag flow for AI agents is demonstrated, focusing on retrieval-augmented generation. The approach includes using Crew AI tools to build an AI agent capable of fetching information from both a document archive and the web when necessary. The process involves document uploads, question answering via external APIs, and establishing a connection with the web for information retrieval. Additionally, the video promotes a Discord community for collaboration and resources within the AI space, along with practical coding demonstrations for building AI workflows.
Demonstrating an AI agent for retrieval-augmented generation, including web connections.
Utilizing external APIs to retrieve information when not found in the database.
Installing Crew AI and Lang Chain libraries for building AI workflows.
Using PDF Search tool to manage document retrieval.
Running the rag tool for document retrieval based on user queries.
Incorporating external APIs not only broadens the informational base for AI models but also highlights the need for a robust architecture that can handle multiple data sources efficiently. This method ensures that agents are not limited to confined datasets, which is essential for environments requiring real-time information retrieval. Additionally, the emphasis on building community networks within AI spaces can foster collaboration and innovation, promoting a more integrated approach to solving complex AI challenges.
This technique allows an AI model to access external data sources when generating answers.
Crew AI simplifies the process of constructing agents for complex AI workflows.
Taviz provides API access for seamless information retrieval from the internet.
It supports multi-agent development and can be integrated with multiple data sources.
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OpenAI's models are integrated into various applications for natural language processing.
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