Artificial intelligence systems gain efficiency through automated decision making. By integrating Agentic Strategies in your pipeline LlamaIndex Retrieval-Augmented Generation (RAG), you can maximize the potential of AI to create sophisticated and dynamic solutions. This article explores various Agentic Strategies, in particular the Routing, the Query transformations And the Data agents, to improve your AI systems.
Les Routers direct queries to the appropriate modules or databases depending on the nature of the request. By using the Large Language Models (LLM) for decision making, the Routers can dynamically assess the context of the request and direct it to the most suitable engine. This improves the accuracy of responses and the efficiency of your pipeline.
➡️ Example: For a user query related to financial data, a Router Well configured can identify the context of the query and direct it to a financial data engine, ensuring accurate and relevant information.
Les Query transformations consist in modifying the user's query to better match the context of the database or the type of information requested. This process may include reformulating, expanding, or breaking down complex queries into simpler sub-queries.
➡️ Example: If a user asks, “What are the environmental impacts of deforestation in the Amazon? ”, the module of Query transformation can break this demand down into sub-queries such as “What are the main causes of deforestation in the Amazon?” and “How does deforestation affect local wildlife?” ”.
The Sub Question Query Engine is an advanced strategy that breaks down complex queries into manageable sub-questions. This approach uses the power of LLM to generate a thought sequence and plan queries effectively.
➡️ Example: For a query like “Explain the economic, social, and environmental impacts of climate change,” the Sub Question Query Engine can create sub-questions that target each aspect individually, resulting in a complete and detailed answer.
Les Data agents represent the heyday of Agentic Strategies, offering a complete agent loop capable of Chain-of-Thought And of Query planning. These agents integrate with query engines RAG existing, improving decision-making processes with sophisticated AI capabilities.
➡️ Example: One Data Agent can handle a request such as “Develop a business strategy for a tech startup in the AI industry.” The agent would use the Chain-of-Thought Reasoning to plan the query, gather relevant data from multiple sources, and provide a strategic plan that covers market analysis, competitive landscape, and growth opportunities.
Create a OpenAI agent involves defining its decision-making process, integrating it with your query engine tools, and configuring function calls for specific tasks. This guide will take you through the essentials to build a OpenAI agent personalized adapted to your needs.
This approach combines the strengths of the language models ofOpenAI with the query engines in your pipeline RAG. Using the function call capabilities ofOpenAI, you can create agents that can handle complex queries with precision and depth.
One Retrieval-Augmented Agent Improves theOpenAI agent standard by incorporating external data recovery capabilities. This ensures that your agent has access to the most up-to-date and relevant information when responding to queries.
This experimental cookbook offers advanced techniques for integrating OpenAI agents With the Query Engines. It includes examples of Query planning, increased context, and decision-making processes to help you create highly sophisticated agents.
One Query planning effective is crucial for managing complex queries. This guide explores strategies for breaking down complex questions, planning the thought sequence, and using the Query Engines for complete answers.
Augmenting context means improving the agent's understanding of the request by providing additional information or context. This improves the relevance and accuracy of the responses generated by the agent.
The integration of Agentic Strategies to your pipeline LlamaIndex RAG can significantly improve its capabilities, allowing for more advanced decision-making and optimized request management. Whether you implement simple strategies to Routing And of Query transformations or if you are deploying Data agents sophisticated, these strategies will empower your AI systems to deliver better results.
Grégoire
CTO - Data Scientist
gregoire.mariot@strat37.com
.png){kind=logo}
