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AzureDataRetrievalAugmentedGenerationSamples/Python/CosmosDB-NoSQL_VectorSearch/CosmosDB-NoSQL-Vector_AzureOpenAI_Tutorial.ipynb at main · microsoft/AzureDataRetrievalAugmentedGenerationSamples
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
SolutionsSolutions#RAG#CosmosDB#Sample-Code
·github.com·
AzureDataRetrievalAugmentedGenerationSamples/Python/CosmosDB-NoSQL_VectorSearch/CosmosDB-NoSQL-Vector_AzureOpenAI_Tutorial.ipynb at main · microsoft/AzureDataRetrievalAugmentedGenerationSamples
Building an AI Agent With Memory Using MongoDB, Fireworks AI, and LangChain | MongoDB
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.
SolutionsSolutions#RAG
·mongodb.com·
Building an AI Agent With Memory Using MongoDB, Fireworks AI, and LangChain | MongoDB
Build an LLM RAG Chatbot With LangChain – Real Python
Build an LLM RAG Chatbot With LangChain – Real Python
Large language models (LLMs) have taken the world by storm, demonstrating unprecedented capabilities in natural language tasks. In this step-by-step tutorial, you'll leverage LLMs to build your own retrieval-augmented generation (RAG) chatbot using synthetic data with LangChain and Neo4j.
TutorialTutorial#RAG
·realpython.com·
Build an LLM RAG Chatbot With LangChain – Real Python