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$
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I will build a private local rag document search system with no cloud or API
RenarZ
About this gig
Need to search private documents without sending data to the cloud or paying per-query API costs?
I provide a developer-ready local RAG document search system that runs on your machine. Index and search manuals, reports, contracts, and internal knowledge using local embeddings, FAISS search, and no external AI API.
Choose the package that fits your workflow:
- BASIC: Single-collection semantic search with a CLI workflow.
- STANDARD: Multi-collection search with cross-dataset queries and a CLI workflow.
- PREMIUM: Streamlit web UI with collection selection, index status, query routing, global ranking, debug context, and optional local answers through Ollama.
You receive the selected source-code package, documentation, quick-start instructions, and guidance for indexing and first use.
Need help getting started? Add the 30-minute setup guidance extra. It covers installation, indexing, and first-run setup only.
This gig does not include custom code, feature development, deployment, cloud hosting, data cleaning, model training, fine-tuning, app integrations, or long-term support.
For Premium, Ollama is optional and runs locally. Review requirements before ordering.
Get to know RenarZ
RenarZ
Python spreadsheet automation Data cleaning Fast, async, on deadline
- FromArgentina
- Member sinceJul 2026
Languages
Spanish, English
I help people stop wasting time on manual, repetitive work.
I specialize in Python, Pandas, and spreadsheet automation. I make your Google Sheets and Excel run themselves, clean messy datasets into ready-to-use files, and turn documents into clear study material — delivered working, not half-finished.
I'm fully remote and async: you tell me what you need and the deadline, I handle the rest and deliver on time. I always confirm scope before starting, so no surprises.
Honest quotes, clear communication. Message me before you order.
Other AI Development Services I Offer
FAQ
What is included with each package?
Each package includes the selected RAG Local AI source code, its package-specific README, quick-start instructions, and the stated features. Basic supports one collection via CLI; Standard adds multi-collection CLI search; Premium adds the Streamlit web UI and optional Ollama answers.
Is this a cloud service or does it use paid APIs?
No. The system runs locally using local embeddings and FAISS search. It does not require a cloud API or per-query API fees. Premium can optionally use Ollama running on your own machine for AI-generated answers.
Do I need technical experience?
Yes. This is a developer-ready source-code package. You should be comfortable with Python, virtual environments, installing dependencies, and running CLI commands. Premium also requires Streamlit; Ollama is optional for local AI answers.
Can you install or deploy it for me?
Full deployment is not included. You can add the 30-minute setup guidance extra for focused help with installation, indexing, and first-run setup. It does not include custom code, deployment, or infrastructure configuration.
Can I use my own documents?
Yes. Add your documents to the appropriate local collection folder, build the index, and search them locally. Basic is designed for one collection; Standard and Premium support multiple collections.
What happens if Ollama is not installed or unavailable?
Premium still works in retrieval mode. It returns the most relevant local document content instead of generating an LLM answer. Ollama is optional and only needed for AI-generated answers.
Does this include custom features, integrations, or fine-tuning?
No. This gig provides the defined RAG Local AI package and limited setup guidance only. Custom features, integrations, data preparation, fine-tuning, model training, hosting, and long-term support are outside this gig’s scope.
Can I resell or redistribute the source code?
The package is provided for your own development and internal use. Resale, redistribution, or white-label use is not included unless explicitly agreed in writing before purchase.
