Ingest, retrieve, and cite

RAG knowledge-base stack

A stack for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.

Built for Teams building support, research, internal documentation, or compliance assistants.

Outcomes

  • Ingest documents
  • Chunk and index content
  • Retrieve context
  • Cite sources in answers

Workflow map

How the stack fits together

01

Ingest

Collect documents, pages, or notes and preserve source metadata.

02

Index

Chunk content and store embeddings in a retrievable format.

03

Retrieve

Fetch only the relevant context for each user question.

04

Answer

Generate grounded responses with citations and confidence checks.

Recommended stack

Start with these skills

Ranked by workflow relevance, quality score, GitHub adoption, and maintenance freshness.

#1QdrantExcellent · 100

Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

33K starsApache-2.0rag-knowledge
Compare
$ npx skills add qdrant/qdrant
#2RAG TechniquesExcellent · 100

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.

28K starsUnknownrag-knowledge
Compare
$ npx skills add NirDiamant/RAG_Techniques
#3WeKnoraExcellent · 100

Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.

16K starsUnknownrag-knowledge
Compare
$ npx skills add Tencent/WeKnora
#4WeaviateExcellent · 100

Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.

16K starsBSD-3-Clauserag-knowledge
Compare
$ npx skills add weaviate/weaviate
#5TxtaiExcellent · 100

💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows

13K starsApache-2.0rag-knowledge
Compare
$ npx skills add neuml/txtai
#6LancedbExcellent · 100

Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.

11K starsApache-2.0rag-knowledge
Compare
$ npx skills add lancedb/lancedb
#7Anything LlmExcellent · 100

Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience

63K starsMITrag-knowledge
Compare
$ npx skills add Mintplex-Labs/anything-llm
#8PageIndexExcellent · 100

📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG

34K starsMITrag-knowledge
Compare
$ npx skills add VectifyAI/PageIndex

Ideal for

  • - Internal docs assistants
  • - Research archives
  • - Support knowledge bases
  • - Policy lookup

Avoid when

  • - The corpus changes every few seconds
  • - You cannot expose source documents to the agent runtime