RAG

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

📄️ RAG Pipeline — SynapseKit Python LLM Framework

Build RAG pipelines in Python with SynapseKit. Async-native, hybrid BM25+vector search, 53 loaders, 11 vector stores. LangChain RAG alternative.

📄️ Document Loaders — SynapseKit RAG Python

53 document loaders for RAG in Python: PDF, Word, CSV, YouTube, S3, Notion, HubSpot, and more. Drop-in LangChain document loader alternative.

📄️ 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 — Hybrid Search | SynapseKit RAG Python

Hybrid BM25 + vector search retriever in Python. MMR diversity, metadata filtering, and async retrieval for production RAG pipelines with SynapseKit.

📄️ Vector Stores — SynapseKit Python RAG Framework

11 vector store backends for Python RAG: Chroma, Pinecone, Weaviate, Qdrant, Redis, FAISS, and more. Unified async interface, MMR support.

📄️ 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.

📄️ Knowledge Graph Retrieval

Multi-hop retrieval over an entity graph built from your documents. Entities and relationships are extracted using an LLM, stored in a graph backend, and then traversed at query time to surface non-obvious connections.