RAGFlow
github.com
Deep document understanding RAG engine with support for rich layouts including tables, charts, and scanned PDFs. Goes beyond naive chunking by preserving document structure during indexing, dramatically improving retrieval accuracy for complex enterprise documents.
More in Memory & Knowledge Management
Unified framework for building enterprise RAG pipelines with models specifically trained for document parsing. Includes a model catalog, vector store, and prompt history management. Optimized for CPU inference on commodity hardware.
Universal data connector API for syncing documents from Google Drive, Notion, Dropbox, OneDrive, and 30+ sources into your RAG pipeline. Handles OAuth, incremental sync, and document parsing automatically. One API key to access all user data sources.
Python client for building AI applications on Redis with vector similarity search. Enables semantic caching, semantic routing, and RAG pipelines on top of Redis. Leverages Redis speed for real-time vector search at low latency.
The AI-native open-source embedding database. ChromaDB makes it effortless to store, query, and filter embedding vectors locally or in the cloud — a go-to choice for prototyping RAG applications and production deployments that don't need managed infrastructure.
The leading data framework for connecting private data sources to LLMs. Provides sophisticated indexing, chunking, retrieval, and query engine abstractions — from simple Q&A over documents to complex multi-step reasoning across heterogeneous data sources.
Vector database and data lake for AI, storing embeddings alongside original data (images, text, audio). Supports streaming data from cloud storage and has native integrations with LangChain and LlamaIndex. Open-source with a managed cloud offering.