AgenticBook

Redis Vector Library

github.com

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.

PythonRedisCaching

More in Memory & Knowledge Management

CarbonRetrieval & RAG Layers

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.

APIConnectorsSync
Deep LakeRetrieval & RAG Layers

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.

PythonOpen SourceMultimodal
LLMWareRetrieval & RAG Layers

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.

PythonEnterpriseCPU
ChromaDBVector Databases

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.

LocalVectorOpen-Source
LlamaIndexRAG & Knowledge Retrieval

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.

RAGPythonTypeScript
LlamaParseRetrieval & RAG Layers

Proprietary document parsing API from LlamaIndex specialized in extracting structured data from complex PDFs, including tables, charts, and multi-column layouts. Produces clean markdown output optimized for RAG ingestion. The parsing backbone of many production RAG systems.

APIPDFTables