You will get Pproduction RAG pipeline with vector DB and API endpoint
Project details
I will build a working Retrieval-Augmented Generation (RAG) pipeline for your app: document ingestion + chunking + embeddings + vector search + an API endpoint you can integrate with your backend. You get a clean, reliable “ask your documents” system with clear setup steps and handoff documentation.
Deliverables:
Ingestion pipeline (chunking + metadata)
Embeddings + vector store integration
Query endpoint (FastAPI) returning retrieved passages + citations
Basic prompt/orchestration wiring for grounded answers
Setup instructions + runbook
Deliverables:
Ingestion pipeline (chunking + metadata)
Embeddings + vector store integration
Query endpoint (FastAPI) returning retrieved passages + citations
Basic prompt/orchestration wiring for grounded answers
Setup instructions + runbook
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
Deeplearning4j, Keras, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$750
|
Standard
$1,250
|
Advanced
$2,250
|
|---|---|---|---|
| Delivery Time | 4 days | 7 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | - |
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | - | ||
Taxonomy | - | - | - |
About Marcelo
AI Apps (RAG, Agents, APIs) | PhD Physicist
Oak Park, United States - 9:54 am local time
Fast turnaround, clean code, and reliability (logs, retries, cost controls) so it works outside a demo.
What I deliver fast (with clean handoff docs):
• RAG systems (chunking/embeddings/vector DB, evals, grounding)
• LLM integrations into products (OpenAI/Azure OpenAI, prompt/orchestration, tool use)
• Microservices + integrations (FastAPI/REST, webhooks, auth, data transforms)
• Production hardening (logging/correlation IDs, retries/idempotency, monitoring, cost controls)
If you need an MVP shipped or an existing AI feature stabilized and made cheaper, I can start immediately.
Background: PhD Physics; deep experience in scientific computing and complex systems when needed.
Steps for completing your project
After purchasing the project, send requirements so Marcelo can start the project.
Delivery time starts when Marcelo receives requirements from you.
Marcelo works on your project following the steps below.
Revisions may occur after the delivery date.
Confirm requirements & success criteria
Data ingestion + chunking + metadata pipeline