You will get I will build a custom AI Chatbot with Streamlit and RAG
Project details
Struggling to make sense of large document sets? I build production-ready AI applications that turn your static data into interactive knowledge.
I specialize in RAG (Retrieval-Augmented Generation) pipelines using Python and Streamlit. This isn't just a basic chatbot; it’s a high-concurrency platform capable of processing complex PDFs, CSVs, and text data with sub-second retrieval times.
What you get:
Custom RAG Pipeline: Secure document ingestion and vector storage.
Intuitive UI: Clean, branded Streamlit dashboards for your team.
Optimized Performance: Fast response times using Groq, OpenAI, or Anthropic.
Scalable Deployment: Ready to host on Render, AWS, or Heroku.
Check out my portfolio to see a live demo of a system currently handling 400+ concurrent users!
I specialize in RAG (Retrieval-Augmented Generation) pipelines using Python and Streamlit. This isn't just a basic chatbot; it’s a high-concurrency platform capable of processing complex PDFs, CSVs, and text data with sub-second retrieval times.
What you get:
Custom RAG Pipeline: Secure document ingestion and vector storage.
Intuitive UI: Clean, branded Streamlit dashboards for your team.
Optimized Performance: Fast response times using Groq, OpenAI, or Anthropic.
Scalable Deployment: Ready to host on Render, AWS, or Heroku.
Check out my portfolio to see a live demo of a system currently handling 400+ concurrent users!
AI Algorithms
Large Language Model, Multimodal Large Language Model, Regression Analysis, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment AnalysisAI Development Language
PythonAI Tools
Hugging Face, PyTorch, StreamlitAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$250
|
Standard
$600
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | - | - | - |
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | ||
Model Documentation | - | - | - |
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code |
About Caleb
Full-Stack AI Engineer | Custom RAG & Streamlit Applications
Ventura, United States - 12:37 pm local time
I build secure, blazing-fast internal AI tools that automate workflows and let businesses chat directly with their proprietary data.
Most recently, I engineered a multi-cloud AI application that successfully scaled to handle 400+ concurrent users, utilizing a high-performance stack:
• Frontend: Streamlit
• Backend: FastAPI (Python)
• AI Engine: Groq / LLaMA / OpenAI integrations
• Data & Caching: Google Cloud Firestore & Upstash Redis
What I can build for your business in 2-4 weeks:
Custom AI Knowledge Bases (RAG): Stop searching through folders. I can build a secure web interface where your employees can instantly "chat" with your company's PDFs, contracts, and training manuals to get exact answers.
Automated Content Generators: Custom AI pipelines that instantly generate client reports, marketing copy, or data summaries based on your specific business rules.
Data Visualization Dashboards: Turning complex datasets into interactive, visual graphs.
I don't just build prototypes; I build production-ready architecture designed to scale without crashing. Let's talk about automating your workflow.
Steps for completing your project
After purchasing the project, send requirements so Caleb can start the project.
Delivery time starts when Caleb receives requirements from you.
Caleb works on your project following the steps below.
Revisions may occur after the delivery date.
Architecture & Requirements
We’ll discuss your data sources and specific AI needs to map out the RAG pipeline
Development & Integration
I build the Python backend, connect the LLM, and design the Streamlit interface.