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Fiverr Pro
English
$
USD
I will build a production rag pipeline with vector database and evaluation
Mudassar Ali
Level 1
About this gig
Most RAG demos work on a handful of PDFs and fall apart on real data. I build the version that survives production.
What I build:
Document ingestion and chunking tuned to your content, not defaults
Vector database setup: Pinecone, Qdrant, Weaviate, pgvector or Chroma
Hybrid search (semantic plus keyword) with reranking for accuracy
Source citations on every answer so users can verify
Evaluation suite that measures retrieval accuracy before and after
Chat or query API your app can call, with auth and rate limits
Incremental re-indexing so your data stays current
Token and cost controls, caching, monitoring and logging
Stack: Python, FastAPI, LangChain, LlamaIndex, OpenAI, Claude, PostgreSQL, Redis, Docker, AWS and GCP.
Why me: 6+ years building production backends, 36 completed orders on Fiverr with a 4.9 rating and 100% on-time delivery. I ship systems that run after handover, with documentation and tests, not notebooks.
Have documents, a database or a knowledge base you want queryable? Message me a short description and I will tell you honestly whether RAG is the right approach and what it will cost.
Get to know Mudassar Ali
Mudassar Ali
Full Stack Engineer
Level 1
- FromPakistan
- Member sinceDec 2020
- Avg. response time1 hour
- Last delivery1 week
Languages
Urdu, Punjabi, English
Hello, I am a Full Stack Developer with 6+ years of experience in web development. I specialize in Backend focused engineering using PHP Laravel, Node/NestJs and Next.js with additional hands on experience with .net, python and vue js. I also have exposure developing cloud native apps. I have hands-on experience in developing custom web apps, CRM's, SAAS, Multi-Tenant, projects and custom automations. I can work in different provided roles as i have experience working as solo developer, as team member and team lead.
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FAQ
Which package fits my project?
Tell me roughly how much content you have and where it lives. Starter suits a single source under 500 pages. Production suits a full corpus where answer accuracy matters. Platform suits multiple systems that keep changing.
Which vector database do you use?
Whichever fits your case: Pinecone, Qdrant, Weaviate, Chroma or pgvector. If you already run PostgreSQL, pgvector often avoids a new service and cost entirely. I recommend based on scale and budget, not preference.
How accurate will the answers be?
I will not promise a number before seeing your data. What I do promise is measurement: Standard and Premium include an evaluation set so you see retrieval accuracy as a figure, and every answer carries citations so wrong output is traceable.
Who pays for the OpenAI or Claude API usage?
You do, on your own accounts, so you keep control and full visibility of spend. Embedding and query costs are the two drivers. I estimate your realistic monthly cost before we start and build in caching and token limits to keep it down.
Is my data kept private?
Everything runs on your own infrastructure and provider accounts. I use API endpoints that do not train on your data, and I will sign an NDA. If your documents cannot leave your servers at all, I can build fully self-hosted with open models.
What do you need from me to start?
A sample of your documents or database access, 10 to 20 example questions users will ask, and your provider API key. The example questions matter most: they become the evaluation set I tune retrieval against.
