Run an LLM Locally to Interact with Your Documents: Complete Ollama & Open WebUI Guide

Interacting with internal documents using cloud-hosted AI APIs creates unavoidable privacy and compliance liabilities. When analyzing proprietary source code, patient health records, corporate financial ledgers, legal contracts, or personal journals, uploading raw text to third-party endpoints risks data leakage, training ingestion, and unpredictable API billing.

The solution is a self-contained, 100% private local Retrieval-Augmented Generation (RAG) stack. By pairing Ollama (for local model orchestration and embeddings) with Open WebUI (for an enterprise-grade ChatGPT-like workspace), you can index, chunk, embed, and query large document collections entirely on your local machine with zero external network calls.

In this step-by-step engineering tutorial, we configure an offline document retrieval pipeline: analyzing dual-model embedding-versus-generation mechanics, tuning token chunking and overlap thresholds across hardware tiers, binding knowledge collections to custom model profiles, and engineering grounded system prompts that eliminate hallucinations.

---

The Dual-Model Local RAG Architecture

A common misconception when deploying document Q&A systems is assuming a single large language model handles both document indexing and conversation. In practice, a production RAG pipeline requires two distinct models working in tandem:

1. The Embedding Model (nomic-embed-text): Converts parsed document paragraphs into high-dimensional numerical vectors (embeddings). When you ask a question, the embedding model converts your query into a vector and performs fast cosine similarity math across your vector database to extract the most relevant text chunks.
2. The Generative Chat Model (llama3, gemma3, or qwen2.5): Receives your prompt along with the retrieved document chunks injected into its context window, synthesizing a coherent, factual response strictly grounded in the source text.

flowchart TD
    subgraph Ingestion Pipeline
        Docs[Raw Documents\nPDF, Markdown, Text] --> Chunker[Text Splitter\nChunk Size + Overlap]
        Chunker --> Embedder["Embedding Model\n(nomic-embed-text)"]
        Embedder --> VectorDB[(Local Vector Store\nOpen WebUI DB)]
    end

    subgraph Query & Generation Pipeline
        UserQuery[User Question] --> QueryEmbed["Embed Query\n(nomic-embed-text)"]
        QueryEmbed --> Similarity[Cosine Similarity Search]
        VectorDB --> Similarity
        Similarity --> RetrievedChunks[Top-K Document Chunks]
        RetrievedChunks --> PromptAssembly[System Prompt + Context + Query]
        UserQuery --> PromptAssembly
        PromptAssembly --> Generator["Local Chat LLM\n(Ollama GPU Inference)"]
        Generator --> FinalAnswer[Synthesized Grounded Answer]
    end

---

1. Hardware Sizing & Model Selection by Tier

Before pulling weights, match your machine's physical RAM and GPU VRAM with appropriate model scales:

---

2. Step-by-Step Installation & Core Setup

Step 1: Install Ollama & Pull Models

Install Ollama from the official installer or package manager. Once running, open your terminal and pull both the embedding model and your selected generation model:

# 1. Pull the high-performance embedding model
ollama pull nomic-embed-text

# 2. Pull your chosen conversational model (e.g., Gemma 3 4B or Llama 3)
ollama pull gemma3:4b

# 3. Verify local models are registered and available
ollama list

Ollama automatically detects GPU acceleration (CUDA on Windows/Linux or Metal on macOS) and exposes an OpenAI-compatible REST server locally on http://localhost:11434.

Step 2: Install and Launch Open WebUI

Open WebUI is an extensible, self-hosted web interface that includes native document parsing, vector indexing, and memory persistence out of the box.

Install Open WebUI via Python's package manager:

# Recommended: Create a clean virtual environment
python -m venv openwebui_env
source openwebui_env/bin/activate  # On Windows: .\openwebui_env\Scripts\activate

# Install Open WebUI
pip install open-webui

# Launch the local service
open-webui serve

Once initialized, navigate to http://localhost:8080/ in your web browser. Create an initial admin account (all account credentials and tokens remain stored in your local SQLite/Chroma database).

---

3. Configuring Document Chunking & Embedding Engines

To prepare Open WebUI for processing local files, configure its ingestion pipeline through the administrative settings:

1. Click your profile avatar in the bottom-left corner and navigate to Admin Panel → Settings → Documents.
2. Embedding Model Engine: Select Ollama from the dropdown menu (leave the API Key field empty).
3. Embedding Model: Enter nomic-embed-text.
4. Retrieval Mode: Under the Retrieval section, enable Full Context Mode or configure top-k retrieval (typically k=4 to k=6).

Recommended Chunk Size & Overlap Parameters

Documents cannot be ingested as massive single blobs; they must be parsed into smaller, overlapping segments before vectorization.

• Chunk Size: The maximum number of tokens in a single segment. Smaller chunks yield faster, more pinpoint retrieval; larger chunks preserve surrounding paragraph nuance.
• Chunk Overlap: The number of shared tokens between consecutive chunks. Overlap prevents critical context from being split across chunk boundaries.

[!IMPORTANT] Re-indexing Rule: Once documents are uploaded and indexed, modifying your chunk size or overlap settings does not retroactively update existing files. You must delete the collection and re-upload documents if you adjust chunking parameters.

---

4. Ingesting Documents & Creating Custom Model Profiles

Step 1: Create a Knowledge Collection

1. In Open WebUI, navigate to Workspace → Knowledge.
2. Click Add Knowledge Base, provide a descriptive name (e.g., Financial-Reports-2026 or Engineering-Docs), and click Create.
3. Drag and drop your target files (PDF, DOCX, TXT, Markdown, CSV).
4. Monitor the processing progress. The background worker parses the text, calculates chunk splits, and calls nomic-embed-text via Ollama to store embeddings in the local vector index.

Step 2: Bind Knowledge to a Custom Model Profile

Rather than manually tagging documents with # in every single chat turn, bind your Knowledge Collection directly to a custom model persona:

1. Navigate to Workspace → Models and click Create a Model.
2. Model Name: Give your persona a descriptive name (e.g., DocAnalyst-Gemma).
3. Base Model: Select your downloaded model (e.g., gemma3:4b).
4. Knowledge: Attach the Knowledge Collection you created in Step 1.
5. System Prompt: Define strict grounding boundaries to prevent extrapolation.

Grounded System Prompt Template

You are an expert, meticulous document intelligence assistant analyzing proprietary internal documents.

Rules:
1. Ground all answers strictly in the provided document context.
2. If the answer cannot be determined directly from the retrieved context, explicitly state: "The provided documents do not contain sufficient information to answer this question." Do not speculate or extrapolate.
3. When providing facts, data points, or quotes, cite the specific document title and section where the information originated.
4. Maintain a direct, analytical tone without conversational filler.

By enforcing these constraints in the custom profile, the model reliably resists hallucinations and provides trustworthy citations for enterprise auditing.

---

5. Querying & Operational Best Practices

Open a new conversation, select your custom model from the top dropdown, and begin querying your documentation:

User: What were the key infrastructure bottlenecks identified in the Q3 retrospective?

Assistant: Based on the "Q3-Infrastructure-Retrospective.md" document (Section 4.2), three primary bottlenecks were identified:
1. High Redis cache evictions during peak batch indexing runs.
2. PCIe bus saturation when offloading unquantized 70B weights.
3. Insufficient KV cache allocation during multi-turn agent loops.

Operational Tips

• 5-Minute Streaming Timeout: By default, web browsers close idle server-sent event (SSE) streams after 5 minutes. If querying a massive collection on a slower CPU causes generation to exceed 5 minutes, the interface may pause visibly. Simply refresh the page and click Continue Response to stream the completed generation from disk.
• Monitoring Extraction Errors: If a specific PDF fails to process during upload, inspect your terminal running open-webui serve. Complex scanned PDFs without OCR layers require pre-processing with a local tool like pdfplumber or pandoc before ingestion.

---

Summary & Next Steps

With Ollama managing local GPU inference and Open WebUI orchestrating vector embeddings via nomic-embed-text, you now possess a sovereign, air-gapped document intelligence platform. No corporate data leaves your machine, no cloud API tokens are billed, and queries execute with minimal latency.

Related Local LLM Architecture Guides

• Self-Hosting Your First LLM: Hardware & Deployment Playbook – Detailed VRAM calculations and cloud GPU cost analysis.
• Run LLMs Locally: 6 Practical Methods – Comparing Ollama, vLLM, LM Studio, Jan, llama.cpp, and llamafile.
• Local RAG Tutorial with Ollama & LangChain – Programmatic Python RAG pipeline construction.
• GPU Cloud Pricing Tool – Sizing hardware for large-scale multi-user RAG clusters.