You will get a robust RAG pipeline with LangChain and Langgraph
Project details
I design and build robust Retrieval-Augmented Generation (RAG) pipelines that deliver accurate, context-aware answers from your own data sources.No hallucinations. No brittle scripts. Just production-grade architectures — clean, modular, and fully documented.
What You Get
End-to-End RAG Architecture: Retriever, chunker, embedder, generator, evaluator
Framework Options: LangChain, LlamaIndex, or custom lightweight implementation
LLM Flexibility: OpenAI, Anthropic, or open models (Llama 3, Mistral, Falcon)
Vector Database Integration: FAISS, Chroma, Pinecone, or Qdrant
Optimized Prompting: Context-aware, dynamically constructed queries
Deployment Ready: Streamlit, FastAPI, or Hugging Face Spaces
Clear Code + Docs: Production-quality
Why Work With Me
Engineering-first approach — built for performance, not just demos
Deep understanding of embeddings, retrieval, and context optimization
End-to-end testing for retrieval accuracy and latency
Tech Stack: Python · LangChain · LlamaIndex · Hugging Face · FAISS · Chroma · OpenAI API · Streamlit · FastAPI
Let’s discuss your data sources and desired deployment stack — I’ll architect the RAG system that fits your exact environment.
What You Get
End-to-End RAG Architecture: Retriever, chunker, embedder, generator, evaluator
Framework Options: LangChain, LlamaIndex, or custom lightweight implementation
LLM Flexibility: OpenAI, Anthropic, or open models (Llama 3, Mistral, Falcon)
Vector Database Integration: FAISS, Chroma, Pinecone, or Qdrant
Optimized Prompting: Context-aware, dynamically constructed queries
Deployment Ready: Streamlit, FastAPI, or Hugging Face Spaces
Clear Code + Docs: Production-quality
Why Work With Me
Engineering-first approach — built for performance, not just demos
Deep understanding of embeddings, retrieval, and context optimization
End-to-end testing for retrieval accuracy and latency
Tech Stack: Python · LangChain · LlamaIndex · Hugging Face · FAISS · Chroma · OpenAI API · Streamlit · FastAPI
Let’s discuss your data sources and desired deployment stack — I’ll architect the RAG system that fits your exact environment.
AI Algorithms
AdaBoost, Convolutional Neural Network, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Multilayer Perceptron, Recurrent Neural Network, Transformer Model, Variational Autoencoder, YOLOAI Applications
AI Chatbot, Anomaly Detection, Conversational AI, Facial Recognition, Image Recognition, Natural Language Generation, Object Detection, Object Localization, Sentiment Analysis, Text Recognition, Time Series Analysis, Time Series ForecastingAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging Face, PyTorch, Streamlit, TensorFlowAI Models
BERT, ChatGPT, GPT-3, GPT-4, GPT-Neo, LLaMA, Naive Bayes ClassifierWhat's included
| Service Tiers |
Starter
$90
|
Standard
$250
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 days |
Number of Revisions | 1 | 1 | 1 |
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 Salman
Data Science And Machine Learning
Rajshahi, Bangladesh - 2:48 pm local time
I specialize in developing and deploying machine learning models to solve complex business problems. With expertise in supervised and unsupervised learning, deep learning, and data preprocessing, I build custom solutions using tools like Python, TensorFlow, and scikit-learn. I have experience in creating models for classification, prediction, and natural language processing (NLP). My approach focuses on delivering measurable results by turning data into actionable insights. Let's work together to harness the power of AI for your project!
Steps for completing your project
After purchasing the project, send requirements so Salman can start the project.
Delivery time starts when Salman receives requirements from you.
Salman works on your project following the steps below.
Revisions may occur after the delivery date.
requirement
give requirement