You will get Enterprise RAG Data Cleaning & Semantic Chunking (Python)
Project details
The Problem:
Building a RAG (Retrieval-Augmented Generation) system? If you feed messy, unstructured data (PDFs, Word docs with weird formatting) into your Vector Database, your LLM will hallucinate. "Garbage in, garbage out."
The Solution:
I engineered an Enterprise-Grade Text Cleaning & Semantic Chunking Pipeline in Python. It acts as a strict filter before your data hits the Vector DB.
Key Pipeline Features:
Advanced Regex Sanitization: Automatically strips out OCR artifacts, invisible characters, and normalizes whitespaces.
Semantic Chunking: Intelligently splits long documents into meaningful chunks based on token size, not just arbitrary characters.
Context Overlap: Preserves overlapping text between chunks so the LLM never loses the semantic context of a sentence.
Metadata Ready: Outputs clean JSON/Dict lists ready to be injected with metadata (source, page number).
Business Impact:
Clean data dramatically increases your RAG retrieval accuracy, stops LLM hallucinations, and saves you from debugging messy prompts.
Building a RAG (Retrieval-Augmented Generation) system? If you feed messy, unstructured data (PDFs, Word docs with weird formatting) into your Vector Database, your LLM will hallucinate. "Garbage in, garbage out."
The Solution:
I engineered an Enterprise-Grade Text Cleaning & Semantic Chunking Pipeline in Python. It acts as a strict filter before your data hits the Vector DB.
Key Pipeline Features:
Advanced Regex Sanitization: Automatically strips out OCR artifacts, invisible characters, and normalizes whitespaces.
Semantic Chunking: Intelligently splits long documents into meaningful chunks based on token size, not just arbitrary characters.
Context Overlap: Preserves overlapping text between chunks so the LLM never loses the semantic context of a sentence.
Metadata Ready: Outputs clean JSON/Dict lists ready to be injected with metadata (source, page number).
Business Impact:
Clean data dramatically increases your RAG retrieval accuracy, stops LLM hallucinations, and saves you from debugging messy prompts.
Machine Learning Tools
ChatGPT, PythonWhat's included
| Service Tiers |
Starter
$40
|
Standard
$100
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 1 day | 2 days | 3 days |
Number of Revisions | 1 | 2 | 2 |
Model Validation/Testing | - | - | - |
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Frequently asked questions
About Yanzu
Generative AI Engineer | RAG Systems & Custom LLM Agents
Xuchang, China - 2:57 pm local time
Senior AI/ML Engineer | RAG, Custom LLMs & Python Backend Expert
Overview:
I build AI systems that don't just compute—they understand.
I am a Senior Machine Learning Engineer with over 7 years of Python backend experience. I specialize in taking state-of-the-art ML, Deep Learning, and Generative AI concepts and turning them into seamless, powerful software solutions.
Recently, I've been heavily focused on solving the "hallucination" and "context" problems in modern AI.
What I bring to the table:
RAG Architecture: I have hands-on experience building enterprise-grade Knowledge Base systems using RAG, allowing LLMs to interact flawlessly with your proprietary data.
Advanced AI Integration: I seamlessly weave complex neural networks and AI logic into streamlined backend workflows, ensuring zero compromise on system performance.
From Concept to Production: My 7 years of engineering background means I know how to deploy models securely and efficiently. I don't leave you with a fragile Jupyter Notebook; I deliver production-tier code.
I deeply value collaborative, transparent communication within a team. Whether you need to optimize a bloated data pipeline or build an AI sidekick with persistent memory, I have the technical depth to make it happen.
If you are looking for an engineer who blends deep technical rigor with creative AI problem-solving, let's talk.
Steps for completing your project
After purchasing the project, send requirements so Yanzu can start the project.
Delivery time starts when Yanzu receives requirements from you.
Yanzu works on your project following the steps below.
Revisions may occur after the delivery date.
Data Analysis & Regex Customization
I will analyze your raw documents and customize the text cleaning pipeline to remove OCR artifacts and messy formatting.
Semantic Chunking Setup
I will implement the chunking logic with the perfect size and overlap tailored for your specific LLM use case.