Pinned Repositories
gorilla
Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls)
gpt-rag
Sharing the learning along the way we been gathering to enable Azure OpenAI at enterprise scale in a secure manner. GPT-RAG core is a Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
gpt-rag-agentic
Implements the Agentic Orchestrator component of GPT-RAG, utilizing AutoGen's group chat capabilities to enable collaboration among specialized agents for handling complex tasks and generating coherent, precise responses.
gpt-rag-frontend
Provides a scalable and efficient web interface for GPT-RAG, employing the Backend for Front-End pattern to deliver seamless user interactions and integrate with backend services for an enhanced user experience.
gpt-rag-ingestion
Facilitates efficient data ingestion by optimizing data chunking and indexing, ensuring seamless integration with Azure Cognitive Search for effective retrieval in the Retrieval-Augmented Generation (RAG) process.
gpt-rag-orchestrator
Coordinates the retrieval and generation workflow within the RAG architecture, offering both Functional (using Semantic Kernel functions) and Agentic (leveraging AutoGen agents) modes to deliver accurate and contextually relevant user responses.
mlops-project-template
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
mlops-templates
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
mlops-v2
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
technical-docs-frontend
Frontend documentation for conversational assistant.
0Upjh80d's Repositories
0Upjh80d/gorilla
Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls)
0Upjh80d/gpt-rag
Sharing the learning along the way we been gathering to enable Azure OpenAI at enterprise scale in a secure manner. GPT-RAG core is a Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
0Upjh80d/gpt-rag-agentic
Implements the Agentic Orchestrator component of GPT-RAG, utilizing AutoGen's group chat capabilities to enable collaboration among specialized agents for handling complex tasks and generating coherent, precise responses.
0Upjh80d/gpt-rag-frontend
Provides a scalable and efficient web interface for GPT-RAG, employing the Backend for Front-End pattern to deliver seamless user interactions and integrate with backend services for an enhanced user experience.
0Upjh80d/gpt-rag-ingestion
Facilitates efficient data ingestion by optimizing data chunking and indexing, ensuring seamless integration with Azure Cognitive Search for effective retrieval in the Retrieval-Augmented Generation (RAG) process.
0Upjh80d/gpt-rag-orchestrator
Coordinates the retrieval and generation workflow within the RAG architecture, offering both Functional (using Semantic Kernel functions) and Agentic (leveraging AutoGen agents) modes to deliver accurate and contextually relevant user responses.
0Upjh80d/mlops-project-template
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
0Upjh80d/mlops-templates
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
0Upjh80d/mlops-v2
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
0Upjh80d/technical-docs-frontend
Frontend documentation for conversational assistant.