A development has combined AutoGen with Semantic Kernel to integrate Google’s Gemini Flash model into a multi-agent setup. It starts by creating a GeminiWrapper class that holds a GenerativeModel instance for the chosen model. A SemanticKernelGeminiPlugin then defines AI functions. Specialist agents cover roles like code review, text analysis and creative strategy. The result is an AI assistant that delivers structured insights across varied tasks.
The setup requires installation of core libraries: pyautogen, semantic-kernel, google-generativeai and python-dotenv. The script imports modules such as os, asyncio and typing. It loads autogen for agent management, genai for API access and classes from semantic-kernel to register decorated functions. This foundation provides both an orchestration layer and semantic function support for advanced AI operations.
A placeholder for GEMINI_API_KEY stays in the code. The genai client uses this key to authenticate requests. A config_list defines model parameters, endpoint type and base URL. Agents refer to this list when they invoke the Flash model. That setup ensures all interactions with Gemini use consistent settings and proper authentication.
A GeminiWrapper class centralizes calls to the Flash model. It holds a GenerativeModel instance and exposes a generate_response method. This method sends a prompt along with a temperature setting into the generate_content API. The response caps at 2048 tokens and returns generated text or an error report. That scalable abstraction lets agents use a simple interface for content creation.
A SemanticKernelGeminiPlugin class brings together the kernel and the wrapper. Decorator annotations mark functions like analyze_text, generate_summary, code_analysis and creative_solution. Each function crafts a prompt structure and forwards it to the wrapper for processing. Registration of these methods in the kernel lets agents invoke them as semantic operations with typed inputs and outputs.
The AdvancedGeminiAgent class ties agent orchestration and semantic functions into one workflow. It initializes the plugin, the wrapper and a suite of specialist agents with runtime logging. Roles include system assistant, code reviewer, creative analyst, data specialist and a user proxy. Methods handle interactions between kernel functions, agent messages and direct Gemini calls. That design produces an end-to-end pipeline for query analysis.
A main function starts the agent, logs status updates and runs example queries. Each query moves through semantic analysis, agent collaboration and direct Gemini Flash calls. The code collects results from each stage and prints them. Demo prompts showcase how the system handles tasks like summarization, code review and creative ideation with clear output at each step.
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Tencent’s Hunyuan team released an open-source large language model called Hunyuan-A13B. The architecture uses a sparse Mixture-of-Experts layout with billions of parameters distributed across expert modules. Early benchmarks show gains in tasks like code completion and reasoning. The team published training configurations and inference scripts. That release may foster community-driven research in efficient MoE model design.