How to orchestrate Agents on Databricks using LangChain / LangGraph / OpenAI and more скачать в хорошем качестве

How to orchestrate Agents on Databricks using LangChain / LangGraph / OpenAI and more

Databricks provides a platform for building custom agents. Github repo: https://github.com/sarbaniAi/databric... LangChain and LangGraph are key frameworks for agent development. OpenAI can also be integrated with Databricks for agent development, along with all other AI orchestration frameworks. Integration with MLflow allows for easy model registration and management. Testing and iteration are crucial for agent performance. ReviewApps provide a user-friendly interface for business users. Flexibility in choosing frameworks is essential due to rapid changes in technology. Understanding the orchestration of existing models is key in LLM applications. Chapters 00:00 Introduction to Agent Orchestration Frameworks 01:52 Exploring Different Orchestration Frameworks 05:44 Demystifying Databricks Agent Framework from LangChain, LangGraph and other frameworks 09:44 Building Agents with Databricks and Langchain 14:35 Integrating MLflow with Databricks Agents 18:26 Understanding Agent Registration in MLflow 22:06 Creating and Using Tools in Agent Development 26:46 Implementing Hybrid Search in Agents 29:04 Integrating MLflow with Custom Agents 30:37 Testing and Iterating with MLflow 34:20 Error Handling and Tool Selection in LLMs 38:32 Balancing Guidance and Flexibility in System Prompts 39:29 Registering Agents and Managing Resources 44:17 Deploying Agents and Evaluation Frameworks 47:41 User Interaction and Feedback in ReviewApp 50:09 Exploring OpenAI Integration with Databricks 53:34 Understanding the Role of Agent Code in Orchestration