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Скачать или смотреть Building Your Own Agentic RAG Systems with Llama-Index | Tool Calling #2

  • Code With Prince
  • 2024-05-22
  • 2028
Building Your Own Agentic RAG Systems with Llama-Index | Tool Calling #2
codewithprinceprogrammingchannelpythonpython devsfuncodingLLamaIndexAgentic RAGAgentsLLM AgentsRAGretrieval augmented generationAgentic Retrieval augemented generationLLamaIndex Agentic RAGAgents In RAG systemsrouter query engineagentic RAG full coursetutorialAgentic RAG tutorialcode with princefunction callingtool calling in agentic RAGllama index function callingAgentic tool calling llmsagentic RAG function calling
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Описание к видео Building Your Own Agentic RAG Systems with Llama-Index | Tool Calling #2

🔍 Unlocking the Power of Function Calling in Agentic RAG Systems 🔍

In our last video, we delved into the basics of Agentic RAG and successfully built a straightforward RAG application using the Router Query Engine. We explored foundational concepts and practical steps to get our initial application up and running. Now, it's time to take things a step further!

Github Repo: https://github.com/Princekrampah/agen...
Medium Article:   / agentic-rag-with-llama-index-function-tool...  

In this follow-up video, we'll elevate our understanding by incorporating a powerful feature: function calling, also known as tool calling. This enhancement will significantly expand the capabilities of our RAG agents, enabling them to interact dynamically with external systems.

📌 Topics Covered in This Video:

What Is A Tool?
Why Tool Calling?
Setup And Initializations
Sample Functions For Tools
Creating Tools From Python Functions
Testing Out The Tool Calling
Vector Search With Metadata
Adding Metadata Filtering Capability
Auto-Retrieval Tool
Implementing Vector Query Search

A tool is essentially a Python function that we pass to the LLMs, allowing them to interact with the external world. You can write a tool that interfaces with your personal API, and the LLM can call this tool, passing in the necessary arguments based on the user's query. This allows for dynamic and powerful interactions between the LLM and external systems.

Join me as we embark on this next phase of our journey and explore how to integrate function calling into our RAG applications. Don't forget to like, comment, and subscribe for more exciting content!

📢 Let's dive in and enhance our RAG systems together!

#AgenticRAG #FunctionCalling #ToolCalling #PythonProgramming #LLM #MachineLearning #AI #ArtificialIntelligence #APIDevelopment #RouterQueryEngine #MetadataFiltering #AutoRetrieval #VectorSearch #TechTutorials #BackendDevelopment #PythonFunctions


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Hope you enjoy today's video. Please show your love and support by just liking and subscribing to the channel so we can grow a strong and powerful community. Activate the 🔔 beside the subscribe button to get the notification!📩 If you have any questions or requests feel free to leave them in the comments below.

Thank you for watching and see you in the next video!!

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