Key Takeaways
- AnythingLLM is the best all-in-one RAG tool for business teams — no coding, multi-user, runs on Ollama locally
- LlamaIndex gives developers the most flexible pipeline control with Python SDK
- PrivateGPT is the simplest option for single users with a folder of PDFs
- Cognita adds enterprise features: RBAC, audit logs, Slack/Teams integrations
- Chroma is the best standalone vector database if you build your own RAG stack
- All five run fully offline — zero document data leaves your network
📍 In One Sentence
Local RAG tools let you query business documents with AI without sending data to external servers.
| Tool | No-Code UI | Multi-User | Local LLM | File Types | License |
|---|---|---|---|---|---|
| AnythingLLM | Yes | Yes (workspaces) | Ollama, LM Studio | PDF, DOCX, XLSX, CSV, URL, YouTube | MIT |
| LlamaIndex | No (Python SDK) | Custom | Ollama, llama.cpp | Any (with loaders) | MIT |
| PrivateGPT | Basic UI | No | Ollama, llama.cpp | PDF, DOCX, TXT | Apache 2.0 |
| Cognita | Yes | Yes (RBAC) | Ollama, Bedrock | PDF, DOCX, HTML, S3 | Apache 2.0 |
| Chroma | No (API) | Yes (server mode) | Any | Any (with client) | Apache 2.0 |
| Setup | RAM | VRAM | Use Case |
|---|---|---|---|
| 7B model + 10K docs | 16GB | 8GB | Single-user, small document set |
| 14B model + 50K docs | 32GB | 16GB | Team use, department knowledge base |
| 32B model + 200K docs | 64GB | 24GB | Enterprise, multi-department |
| Mac Mini M4 Pro 48GB | 48GB (shared) | N/A | Best value all-in-one team server |
Can RAG tools work with SharePoint documents?
AnythingLLM and Cognita both support SharePoint as a data source. LlamaIndex has a SharePoint reader loader in llama-hub. PrivateGPT and Chroma require manual document export before ingestion.
What embedding model should I use for business documents?
nomic-embed-text (via Ollama) gives the best accuracy-to-speed ratio for English business documents. For multilingual corpora (German, French, Chinese), use mxbai-embed-large or multilingual-e5-large.
How many documents can these tools handle?
AnythingLLM handles 100K+ documents with Chroma or Weaviate as backend. PrivateGPT works best under 10K documents with its default SQLite backend. LlamaIndex scales to millions of documents depending on the vector database chosen.
Do RAG tools work with Excel spreadsheets?
AnythingLLM ingests XLSX files and treats each sheet as a separate context block. LlamaIndex has an ExcelReader loader. PrivateGPT handles only PDF/DOCX/TXT natively — Excel requires conversion first.
What LLM should I use for business RAG?
Qwen3 14B Q4_K_M is the best local model for business RAG in 2026 — strong instruction following, 128K context, good at structured data extraction. For 8GB VRAM, use Qwen3 7B or Llama 3.3 8B instead.