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
philippgille/chromem-go
Chromem-go is an embeddable vector database implemented in Go, providing a Chroma-like API without external dependencies. Its primary purpose is to allow Go applications to incorporate retrieval augmented generation (RAG) and other embedding-based features directly within the application, similar to how SQLite is used for relational data. This eliminates the need to run a separate server-side database. The project focuses on simplicity and performance for common use cases, rather than supporting millions of documents or extensive features, making it ideal for scenarios where a lightweight, in-process solution is preferred. It handles document storage alongside their embeddings and supports various embedding creators, including hosted (OpenAI, Azure OpenAI, GCP Vertex AI, Cohere, Mistral, Jina, mixedbread.ai) and local (Ollama, LocalAI) options, or a custom embedding function. Essential functionalities like collection creation, adding documents, and nearest-neighbor search for querying similar content are provided. The database supports multithreaded processing for adding and querying, leveraging Go's native concurrency features, and includes experimental WebAssembly binding. It's particularly useful for integrating precise, up-to-date knowledge into LLM applications to combat knowledge cut-off and hallucinations without fine-tuning.
https://github.com/philippgille/chromem-go
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