This project explores how to build a fully local, self-hosted AI assistant capable of answering questions based on custom knowledge — without relying on paid APIs or external services.
The goal is simple: create a private, low-cost alternative to cloud-based AI tools while maintaining useful conversational capabilities.
Most AI tools today depend on recurring subscriptions and remote servers. This project aims to:
Run entirely on local hardware
Avoid ongoing costs
Maintain control over data and privacy
Provide useful, context-aware answers using custom documents
The system combines a local language model with a retrieval mechanism that feeds it relevant context.
Instead of relying only on what the model “knows,” it uses Retrieval-Augmented Generation (RAG). This means:
A knowledge base is created from documents
Relevant sections are retrieved when a question is asked
The model uses that context to generate an answer
Ubuntu – a base operating system
Ollama – runs local language models
Docker – containerized deployment
OpenWebUI – browser-based chat interface
Llama 3 – primary language model
The language model is run locally using Ollama, allowing inference without external APIs.
OpenWebUI provides a simple chat interface accessible through a browser, making the system easy to interact with.
A sample dataset (e.g., The Divine Comedy) is ingested and indexed. This allows the system to answer questions grounded in specific texts rather than generic responses.
When a user submits a query:
The system searches the indexed documents
Retrieves the most relevant passages
Sends them to the model as context
This significantly improves accuracy and relevance.
For testing, the local instance can be exposed using a tunneling service (e.g., ngrok), allowing remote access without full deployment.
The system successfully:
Runs entirely on local infrastructure
Answers questions based on custom documents
Provides a conversational interface similar to cloud AI tools
Eliminates recurring subscription costs
Performance depends on hardware, but even modest setups can produce usable results.
Slower than cloud-based models
Limited by local compute resources
Requires initial setup and configuration
Model quality may not match top-tier proprietary systems
The main takeaway is that useful AI systems don’t have to be expensive or centralized.
With the right tools, it’s possible to build a private, controlled, and cost-effective AI assistant that performs well for targeted use cases.
This project demonstrates it is possible to deploy our own conversational systems (LLMs), grounded in our own data, without depending on external AI providers.