pathwaycom/llm-app
Ready-to-run cloud templates for building real-time RAG, AI pipelines, and enterprise search applications that synchronize with various live data sources.
Awesome Infra for AI › Vector Databases & Retrieval Infrastructure
NeumTry/NeumAI
Neum AI serves as a comprehensive data platform designed to facilitate Retrieval Augmented Generation (RAG) for Large Language Models. Its primary function is to enable developers to leverage their data by extracting information from various sources (such as document storage and NoSQL databases), processing this content into vector embeddings, and then ingesting these embeddings into vector databases for efficient similarity search. The platform offers a distributed architecture capable of handling billions of data points, ensuring high throughput and parallelization for embedding generation and ingestion. Key features include built-in data connectors for common data sources, embedding services, and vector stores, allowing for real-time synchronization to keep data current. It also provides customizable data pre-processing capabilities, including loading, chunking, and selection, crucial for preparing data for RAG. Neum AI supports cohesive data management by automatically augmenting and tracking metadata, which enhances the richness of the retrieval experience. The platform aims to streamline the integration of services like data connectors, embedding models, and vector databases, thereby accelerating the development and scaling of RAG applications. It supports deployment both as a cloud service and for local development via a Python package, with options for self-hosting. The project explicitly lists a variety of supported source, embedding, and sink connectors, including popular vector databases like Weaviate, Qdrant, Pinecone, and LanceDB.
https://github.com/NeumTry/NeumAI
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