# Help Need for Custom Chatbot **URL:** \ **Category:** Discussion & Solutions\ **Tags:** solved\ **Created:** [January 26, 2026, 5:02pm UTC](https://onehack.st/t/help-need-for-custom-chatbot/317412 "2026-01-26T17:02:40Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![Justin\_H](https://onehack.st/user_avatar/onehack.st/justin_h/32/164323_2.png) [@Justin\_H](https://onehack.st/u/Justin_H)\ **Post date:** [January 26, 2026, 5:02pm UTC](https://onehack.st/t/help-need-for-custom-chatbot/317412/1 "2026-01-26T17:02:40Z")
I want to build a **fully offline, private chatbot** that runs locally and answers **only from data I provide**. Requirements: - No cloud / no external APIs - Ingest my own data (PDFs, text, code, notes) - Strictly limited to my data (no external knowledge leakage) - Supports adding new data over time Any pointers or real-world experiences appreciated. ---
**Author:** ![KrishnDhola](https://onehack.st/user_avatar/onehack.st/krishndhola/32/160605_2.png) [@KrishnDhola](https://onehack.st/u/KrishnDhola)\ **Post date:** [January 27, 2026, 4:20am UTC](https://onehack.st/t/help-need-for-custom-chatbot/317412/2 "2026-01-27T04:20:58Z")
Check “Ollama” ---
**Author:** ![TheStrength](https://onehack.st/user_avatar/onehack.st/thestrength/32/46753_2.png) [@TheStrength](https://onehack.st/u/TheStrength)\ **Post date:** [February 8, 2026, 11:58am UTC](https://onehack.st/t/help-need-for-custom-chatbot/317412/3 "2026-02-08T11:58:00Z")
For a custom chatbot with RAG, your best starting points depend on how much you want to build yourself: **Ready-to-deploy stacks (fastest path):** - RAG Web UI — beginner-friendly, handles PDF/DOCX/Markdown out of the box - DataBridge — open-source, supports text, PDFs, images, and video, fully local - Offline RAG System (Docker + Llama 3) — containerized, no cloud APIs, good for sensitive data **Build-from-scratch guides (more control):** - [RAG с нуля: doc loading, chunking, Qdrant + LLaMA](https://habr.com/ru/companies/amvera/articles/927138/) — covers the full pipeline end-to-end - [ChromaDB + DeepSeek local assistant](https://habr.com/ru/companies/amvera/articles/897830/) — lightweight, fully local, high-quality walkthrough - [5 open-source tools RAG in one evening](https://habr.com/ru/articles/955798/) — minimalist stack, fast to get running **If privacy is critical:** DocuDeeper (100% offline, GDPR-compliant) or RAG-Guard (zero-trust, browser-based, nothing leaves your device). **Choosing a vector DB?** This [comparative review of Qdrant, Milvus, ChromaDB, LanceDB, pgvector, Weaviate](https://habr.com/ru/articles/961088/) is the best single resource for that decision. **Pro tip:** Start with ChromaDB as your vector store — it’s embedded (no separate server), has the simplest setup, and you can migrate to Qdrant or pgvector later when you need to scale. ---
**Author:** ![Justin\_H](https://onehack.st/user_avatar/onehack.st/justin_h/32/164323_2.png) [@Justin\_H](https://onehack.st/u/Justin_H)\ **Post date:** [February 10, 2026, 1:29am UTC](https://onehack.st/t/help-need-for-custom-chatbot/317412/4 "2026-02-10T01:29:48Z")
Thank You. ---
**Author:** ![Ze380y](https://onehack.st/user_avatar/onehack.st/ze380y/32/48760_2.png) [@Ze380y](https://onehack.st/u/Ze380y)\ **Post date:** [May 4, 2026, 11:08am UTC](https://onehack.st/t/help-need-for-custom-chatbot/317412/5 "2026-05-04T11:08:04Z")
You named the four constraints clean — **fully offline, your data only, no leakage, can keep adding more**. That’s the right shape. Most tutorials send you to a stack that quietly breaks two of them. > ⚡ **First curveball:** depending on how many docs you have, you might not need RAG at all. RAG = chop docs → vectors → retrieve top-k → stuff into prompt. The standard answer because it scales. But for ~500 personal docs that fit in 200K tokens, modern small models swallow the whole corpus in one read. **No vector DB. No chunking. No embedder.** Less plumbing to break. | Corpus shape | Pick | | --- | --- | | **Under 100 docs** , fits in 200K tokens | Long-context only — [Qwen 2.5 (1M ctx)](https://ollama.com/library/qwen2.5) + AnythingLLM “attach” mode | | **Hundreds → low thousands** | Hybrid — RAG retrieves top-5, model reads them in full | | **Thousands+, growing fast** | RAG **+ folder-watching tool** | * * * **Personal stack that ships:** [AnythingLLM Desktop](https://anythingllm.com/) + [Ollama](https://ollama.com/) (Qwen 2.5 7B + `nomic-embed-text` on a 16GB Mac). > _Bit me once when I dropped 30 new PDFs into the folder and nothing updated — turns out [**AnythingLLM watches single files, not folders.**](https://github.com/Mintplex-Labs/anything-llm/issues/3561) Issue’s been open since March 2025._ If “drop a file, it shows up in chat tomorrow” matters to you, use [**Khoj**](https://github.com/khoj-ai/khoj) instead — folder-watch is its design assumption. * * * ⚠ **“Use only the context” in your system prompt is not enough.** The model still leaks training knowledge when retrieval misses, and you can’t see it from outside. Real fix lives below 👇 > **🪄 Pick your stack in 30 seconds** > > _Tap a name to jump to its repo._ > > **If you don’t already use Obsidian:** > > - [**AnythingLLM**](https://anythingllm.com/) → workspace-style RAG, mixed PDFs/notes > - [**Khoj**](https://github.com/khoj-ai/khoj) → only one with native folder-watch (+ Obsidian/Emacs/web/WhatsApp clients) > - [**Open WebUI**](https://github.com/open-webui/open-webui) → self-hosted ChatGPT-clone, plays nicest with Ollama > - [**Open Notebook**](https://github.com/lfnovo/open-notebook) → NotebookLM-clone, Docker, podcast generation included > > **If you already live in Obsidian:** > > - [**Smart Connections**](https://github.com/brianpetro/obsidian-smart-connections) → local-first, on-device embeddings, no API key, indexes inside the vault > - [**Copilot for Obsidian**](https://github.com/logancyang/obsidian-copilot) → 100k+ users, free tier fully local > > > 💡 Copilot’s _Plus_ tier routes through their backend even with your own API key. Worth knowing before paying. > **🎯 Hardware floor — what runs at what speed** > > _Real tokens/sec from [LocalLLM.in’s April 2026 benchmarks](https://localllm.in/blog/best-local-llms-8gb-vram-2025)._ > > | You have | Model that fits | Speed | > | --- | --- | --- | > | 8GB RAM, no GPU | `Phi-4-mini 3.8B` (Q4\_K\_M) | 15–20 t/s — usable but slow | > | 8GB VRAM (RTX 4060) | `Qwen2.5-Coder 7B` / `Qwen3 8B` | 40–55 t/s — sweet spot | > | 16GB unified (Mac M2/M3) | `Llama 3.3 8B` / `Mistral Small 3` | 30–60 t/s | > | 16GB+ VRAM | `Qwen 3 14B` / `Phi-4 14B` | ~30 t/s — biggest quality jump per dollar | > > > 💡 If retrieval comes back with junk on PDFs, blame the chunker not the model. Default `chunk_size=512` mangles tables. Use [**Docling**](https://github.com/docling-project/docling) for PDF ingest — handles tables, equations, and section metadata. Skip PyPDF2. > **🧨 The leakage stack the tutorials skip** > > Three layers because the prompt alone is fragile. > > **① Grounding canary in the prompt** > Tell the model to use a unique made-up phrase (“paperclip”) whenever it can’t answer from your docs. > > - Phrase shows up → system worked ✅ > - Phrase missing + answer is wrong → you caught a leak 🪤 > > One `grep`. Free. > > **② Architecture-side fix** > Microsoft’s [**Highlight & Summarize paper**](https://arxiv.org/pdf/2508.02872) splits the model into two roles: > > - _Highlighter_ pulls relevant passages > - _Summarizer_ sees only those passages, **never your raw question** > > Prevents query injection by design. ~50 lines to bolt onto any stack. > > **③ Fine-tune for grounding** > [**ALoFTRAG**](https://arxiv.org/pdf/2501.11929) auto-generates training pairs from your own corpus and LoRA-fine-tunes the LLM to ground harder. **+8.3% citation accuracy** in their numbers. Runs locally on consumer GPUs. > > > 💡 Even just step ① puts you ahead of every tutorial on Google page 1. * * * You came in asking for pointers; you’re leaving with the cheat code most tutorials don’t ship — that the moving parts everyone talks about (vector DB, embedder, chunker) might not even be necessary for your corpus. **One install. No API keys. No email. Your data never leaves the box.**