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Fiverr Pro
English
$
USD
I will build a secure rag system with vector database and source citations
Muhammad Bilal
Level 1
About this gig
Stop shipping chatbots that hallucinate. A RAG system grounds your AI's answers in your actual documents, databases, or knowledge base every answer comes with a citation to the source. This gig delivers a production RAG pipeline with vector database, retrieval optimization, and source-grounded outputs you can deploy and monitor.
What's included: document ingestion (PDF, DOCX, web, Markdown), chunking tuned to your content, embedding model selection, vector DB setup (Pinecone, ChromaDB, or pgvector), hybrid retrieval, source citations, and basic evals for retrieval quality.
Portfolio proof: I built a Secure RAG AI Agent using LangChain, FAISS, and ChromaDB with a secure document-embedding pipeline and source-grounded answers, deployed with FastAPI and Docker. Full source on my GitHub (MuhammadBilal0021/rag-document-assistant).
Not included: training custom embedding models, a full chat UI (see my AI Web App gig), migrating existing data warehouses.
To start: message me.
1. what documents/data the RAG should ground on,
2. what questions users will ask,
3. your expected query volume.
Get to know Muhammad Bilal
Muhammad Bilal
Data Scientist
Level 1
- FromPakistan
- Member sinceJul 2020
- Avg. response time2 hours
- Last delivery3 months
Languages
Urdu, English, German, Spanish
I'm a dedicated data scientist with expertise in data analysis, machine learning, and predictive modeling. I transform complex data into actionable insights and predictive models that drive business decisions. Alongside my data science work, I offer specialized book formatting services for Amazon KDP, IngramSpark, Lulu, and more. From precise formatting and captivating cover design to metadata optimization and publishing assistance, I ensure your book meets all requirements and stands out. Let's work together to bring your data projects and publishing dreams to life!
My Portfolio
FAQ
Which vector database should I use?
ChromaDB if you want open-source and self-hosted. Pinecone if you want fully managed. pgvector if you already use PostgreSQL. I'll recommend based on your scale and budget.
How do you prevent hallucinations?
Hybrid retrieval fetches the right context, prompting instructs the model to answer only from that context, and a faithfulness eval flags ungrounded answers.
Can it handle 50K+ documents?
Yes, on Premium tier, using metadata filtering and a scalable vector DB. Embedding costs are billed directly via your API provider.
Do you support non-English documents?
Yes, using multilingual embedding models covering 100+ languages.
