MongoDB Atlas Vector Search integrates with LangChain to provide “Long term memory” to LLMs and as a store for chat conversations.
MongoDB Atlas Vector Search integrates with LlamaIndex to provide “Long term memory” to LLMs as well as provide a store for document chunks.
Vector Embeddings generated by OpenAI can be stored in MongoDB Atlas Vector Search to build high-performance Generative AI applications.
Hugging Face provides access to many open source models that can be easily used for generating vector embeddings and storing them in Atlas Vector Search.
Vector Embeddings generated by Cohere can be stored in MongoDB Atlas Vector Search to build high-performance Generative AI applications.
Nomic provides the ability to visualize and explore vector embedding data easily in the web browser, as well as generate vector embeddings via thegpt4all. It works easily with Atlas Vector Search.
Semantic Kernel is an SDK that simplifies building LLM application with programming languages like C# and python. Atlas Vector search integrates to provide “memory” for LLM applications.
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