You will get RAG Pipeline Setup: FAISS + FastAPI Starter Build
Project details
I build production RAG pipelines, not tutorial-level demos. At Uamuzi Foundation I run a live semantic retrieval system in production using this exact stack (SentenceTransformers + FAISS), so what you're getting is battle-tested architecture, not something I'm trying for the first time on your project.
This starter build gets you from raw documents to a working, queryable API: chunking, embedding generation, FAISS vector indexing, and a FastAPI endpoint you can call directly or plug into whatever you're building next a chatbot, an internal search tool, a knowledge base, anything that needs to answer questions from your own data.
You'll get clean, documented code (not a black box), a short walkthrough of how it works, and a
This starter build gets you from raw documents to a working, queryable API: chunking, embedding generation, FAISS vector indexing, and a FastAPI endpoint you can call directly or plug into whatever you're building next a chatbot, an internal search tool, a knowledge base, anything that needs to answer questions from your own data.
You'll get clean, documented code (not a black box), a short walkthrough of how it works, and a
Machine Learning Tools
BERT, MLflow, NLTK, NumPy, Python, PyTorch, SQLWhat's included
| Service Tiers |
Starter
$150
|
Standard
$220
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 3 days | 4 days | 5 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 1 | 2 |
Number of Scenarios | 1 | 2 | 3 |
Model Validation/Testing | - | - | |
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
About Sandra
ML Engineer and Data Scientist | RAG Pipelines | FastAPI | PostgreSQL
Nairobi, Kenya - 10:55 pm local time
At KingdomPay I built fraud detection models on M-Pesa transaction
data. At Uamuzi Foundation I deployed a FAISS-based recommendation
system with SentenceTransformers. At Inua360 — my co-founded startup
— I own the full stack: FastAPI, PostgreSQL, Docker, LightGBM, and
React dashboard serving live SME data.
On Upwork I take on focused engagements:
- ML model development (classification, fraud detection, forecasting)
- RAG pipeline builds (FAISS, LangChain, FastAPI)
- ETL and data pipeline engineering
- PostgreSQL database design and optimization
- Python data analysis and dashboards
I deliver working code with documentation. No ghosting, no vague
updates, no excuses.
Steps for completing your project
After purchasing the project, send requirements so Sandra can start the project.
Delivery time starts when Sandra receives requirements from you.
Sandra works on your project following the steps below.
Revisions may occur after the delivery date.
2
I review your documents/data and confirm chunking strategy and embedding model choice.