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In recent months, there has been increasing talk of local AI, privacy, offline models, and chatbots that “know our documents”. Many tools promise to “use LLM locally,” but when you try to create a true personalized AI, capable of responding based on your own knowledge base, limitations and confusion immediately emerge.
This article was born from that experience:
using tools like LM Studio and discovering that, although they work well as local chats, they are not really designed for RAG and personalized knowledge bases.
Here we will clarify:
- what does it really mean to create a personalized AI locally
- what is RAG (Retrieval-Augmented Generation) without marketing
- why many tools are not suitable
- whyAnythingLLM today is almost unique
- what real open-source alternatives exist (with pros and cons)
- how to find your way around without having to redo a thousand tests
1. What "Local AI" Does NOT Mean
First, let's clear up a very common misconception.
👉 Running an LLM locally ≠ having a custom AI
Tools like:
- LM Studio
- Ollama (alone)
- GPT4All
allow you to:
- download a model
- chat offline
- do prompt engineering
But They do NOT natively allow you to:
- load a structured knowledge base
- index documents
- query documents semantically
- get answers based only on that context
They are local chats, not contextual AI.
And this is where RAG comes in.
2. What RAG really is (without buzzwords)
Retrieval-Augmented Generation (RAG) is an architecture, not a tool.
In practice:
- Your documents are ingested
PDF, TXT, Markdown, Word, code, etc. - Documents are broken down (chunking)
They are not fed “integers” to the model. - Each chunk is transformed into an embedding
numeric vectors that represent the meaning - The embeddings are saved in a vector database
Chroma, FAISS, LanceDB, etc. - When you ask a question
- the question is transformed into an embedding
- the system retrieves the semantically most similar chunks
- ONLY those chunks are passed to the LLM model
- The model responds using that context
👉 If even one of these steps is missing, it is not RAG.
3. Requirements for a True Local Custom AI
To be able to say "I'm building a local custom AI," all of the following conditions must be true:
- ✅ LLM model executed locally
- ✅ Locally generated embeddings
- ✅ Local vector database
- ✅ Uploaded and indexed documents
- ✅ Automatic context retrieval
- ✅ No cloud API
- ✅ No calls to external services
Many tools satisfy only the first point.
4. AnythingLLM: Why It's Different
What is AnythingLLM
AnythingLLM is an open-source project that combines in a single application:
- Local model management
- Document management
- Complete RAG pipeline
- Multiple knowledge bases
- Graphical interface
- Offline operation
And it's precisely this integration that makes it different.
What it allows you to do in practice
With AnythingLLM you can:
- create one or more workspaces
- upload documents (PDF, TXT, MD, CSV, etc.)
- build a knowledge base
- query the model only on those documents
- get contextualized answers
- keep everything local
Without:
- write code
- configure LangChain
- manually manage vector DB
- connect external services
Why (the famous) LM Studio is not A direct alternative
LM Studio:
- It's excellent for local inference
- It has a great UI
- It's stable and performant
But:
- It wasn't designed as a RAG system
- It doesn't manage knowledge bases
- It doesn't manage automatic retrieval
- It's not designed to "talk to your data"
👉 This is why a direct comparison is misleading: you need tools from different categories.
5. Are there alternatives to AnythingLLM?
Honest answer:
👉 Yes, but with compromises.
👉 No, if you're looking for the same "all-inclusive" experience.
6. True open-source alternatives (real RAG)
6.1 Inquisitive
Type: open-source, self-hosted
Level: medium
- Web UI
- Document uploading
- Indexing
- RAG chat
- Local template support
✅ Real RAG
❌ Smaller project
❌ Less polished than AnythingLLM
6.2 Langchain-Chatchat
Type: open-source
Level: technical
- RAG pipeline complete
- support for local LLMs
- vector database
- multi-document
✅ very powerful
❌ requires setup (Docker / CLI)
❌ not a “desktop app”
6.3 Kotaemon
Type: open-source
Level: medium
- web UI
- document management
- semantic retrieval
- Q&A on knowledge base
✅ RAG working
❌ progCommunity tool
❌ less documentation
6.4 Ollama + Flowise / LangChain (composable stack)
Here we're not talking about a single tool, but a stack.
- Ollama → LLM + local embeddings
- Chroma / FAISS → vector DB
- Flowise / LangChain → RAG pipeline
- Custom or web UI
✅ totally offline
✅ maximum flexibility
❌ high complexity
❌ maintenance at your expense
7. Why AnythingLLM is almost unique today
The key point is this:
AnythingLLM isn't just a tool, it's a product.
The others are frameworks or projects.
8. Final comparative table
| Tool | Real RAG | Offline | UI ready | Complexity |
|---|---|---|---|---|
| AnythingLLM | ✅ | ✅ | ✅ | Low |
| Inquisitive | ✅ | ✅ | ✅ | Average |
| Kotaemon | ✅ | ✅ | ✅ | Average |
| Langchain-Chatchat | ✅ | ✅ | ❌ | High |
| Ollama + stack | ✅ | ✅ | ❌ | Very High |
| LM Studio | ❌ | ✅ | ✅ | Low |
9. Step-by-step getting started with AnythingLLM
This section describes a practical path to get started with AnythingLLM as a local custom AI with knowledge base, without external APIs and without the cloud.
Step 1 – Installation
- Download AnythingLLM for your operating system (Windows, macOS, or Linux)
- Install the desktop application
- Verify that your system can run local models (sufficient RAM)
Step 2 – Local Model Configuration
- Configure a local LLM backend (e.g., via LocalAI or GGUF models)
- Select the model to use for chat
- Verify that inference works offline
Step 3 – Create a Workspace
- Create a new workspace in AnythingLLM
- Each workspace represents an AI with a separate knowledge base
Step 4 – Upload Documents
- Upload PDFs, text files, Markdown, CSV, or other documents
- Documents are automatically managed
- AnythingLLM handles chunking and embeddings
Step 5 – Indexing and RAG
- Documents are transformed into embeddings
- Embeddings are saved in a local vector database
- The RAG pipeline is ready to use
Step 6 – Query the Knowledge Base
- Ask questions directly in the chat
- The model responds using only documents uploaded
- No data leaves your machine
At this point, you have a truly personalized AI, offline, based exclusively on your knowledge.
10. Conclusion
Creating a locally personalized AI with its own knowledge base is not difficult, but it is easy to choose the wrong tool.
The fundamental distinction is this:
- Local Chat → LM Studio, GPT4All
- Contextual AI (RAG) → AnythingLLM, Inquisitive, RAG stack
If the goal is:
- Privacy
- Control
- Responses based on your documents
- No external API
👉 AnythingLLM is the benchmark today,
👉 Alternatives exist, but they require more expertise or compromises.
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