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AzureDataRetrievalAugmentedGenerationSamples/Python/CosmosDB-NoSQL_VectorSearch/CosmosDB-NoSQL-Vector_AzureOpenAI_Tutorial.ipynb at main · microsoft/AzureDataRetrievalAugmentedGenerationSamples

Samples to demonstrate pathways for Retrieval Augmented Generation (RAG) for Azure Data - microsoft/AzureDataRetrievalAugmentedGenerationSamples

#RAG #CosmosDB #Sample-Code

·github.com·Jun 12, 2024

AzureDataRetrievalAugmentedGenerationSamples/Python/CosmosDB-NoSQL_VectorSearch/CosmosDB-NoSQL-Vector_AzureOpenAI_Tutorial.ipynb at main · microsoft/AzureDataRetrievalAugmentedGenerationSamples

The architecture of today's LLM applications

Here’s everything you need to know to build your first LLM app and problem spaces you can start exploring today.

·github.blog·Jun 5, 2024

The architecture of today's LLM applications

Announcing LangChain RAG Template Powered by Redis - Redis

Explore the new LangChain RAG Template with Redis integration. Streamline AI development with efficient, adaptive APIs.

#RAG

·redis.io·Jun 3, 2024

Announcing LangChain RAG Template Powered by Redis - Redis

Build your own AI copilot with vCore-based Azure Cosmos DB for MongoDB and Azure OpenAI - Training

Build your own AI copilot with vCore-based Azure Cosmos DB for MongoDB and Azure OpenAI.

#RAG

·learn.microsoft.com·Jun 3, 2024

Build your own AI copilot with vCore-based Azure Cosmos DB for MongoDB and Azure OpenAI - Training

Building an AI Agent With Memory Using MongoDB, Fireworks AI, and LangChain | MongoDB

Creating your own AI agent equipped with a sophisticated memory system. This guide provides a detailed walkthrough on leveraging the capabilities of Fireworks AI, MongoDB, and LangChain to construct an AI agent that not only responds intelligently but also remembers past interactions.

#RAG

·mongodb.com·Jun 1, 2024

Building an AI Agent With Memory Using MongoDB, Fireworks AI, and LangChain | MongoDB