RAG

Build retrieval-augmented generation pipelines — ingest documents, split them, embed them, and retrieve them to ground LLM responses.

📄️ RAG Pipeline

RAGPipeline is the full orchestrator. The RAG facade wraps it for the happy path.

📄️ Document Loaders

Loaders ingest content and return a List[Document]. All loaders share the same interface.

📄️ Text Splitters

Text splitters break documents into chunks for embedding and retrieval. SynapseKit provides eight splitters — all extend BaseSplitter and share the same split(text) → list[str] interface.

📄️ Retriever

The Retriever finds the most relevant chunks for a query using vector similarity and optional BM25 reranking.

📄️ Vector Store Backends

22 backends available. All implement the VectorStore ABC and share the same interface.

📄️ Output Parsers

Parsers extract structured data from raw LLM text output.

📄️ Prompt Templates

Prompt templates help you build reusable, parameterised prompts and message lists.

📄️ PromptHub

PromptHub is a local filesystem prompt registry that lets you version, push, pull, and share prompt templates across your team — without any external service.