You will get a LangChain RAG pipeline to chat with your documents using AI
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Project details
Want to chat with your documents using AI? I build LangChain RAG pipelines that answer questions from your own data — accurately, with source citations.
Most chatbots hallucinate. RAG fixes that: instead of asking the LLM to remember, the system retrieves the exact relevant sections from your documents and feeds them as context. The result is factual, grounded answers that cite their sources.
What I deliver:
• Document ingestion (PDF, DOCX, TXT, URLs, CSV)
• Vector database setup (ChromaDB for Starter/Standard, Pinecone for Advanced)
• LangChain retrieval chain with OpenAI GPT-4 or Claude
• FastAPI /chat endpoint with streaming support
• Chat history and conversation memory (Standard+)
Advanced tier uses LangGraph for multi-step reasoning and hybrid search (semantic + BM25 keyword) for best-in-class accuracy.
Stack: Python · LangChain · ChromaDB · Pinecone · OpenAI · FastAPI · Docker
Most chatbots hallucinate. RAG fixes that: instead of asking the LLM to remember, the system retrieves the exact relevant sections from your documents and feeds them as context. The result is factual, grounded answers that cite their sources.
What I deliver:
• Document ingestion (PDF, DOCX, TXT, URLs, CSV)
• Vector database setup (ChromaDB for Starter/Standard, Pinecone for Advanced)
• LangChain retrieval chain with OpenAI GPT-4 or Claude
• FastAPI /chat endpoint with streaming support
• Chat history and conversation memory (Standard+)
Advanced tier uses LangGraph for multi-step reasoning and hybrid search (semantic + BM25 keyword) for best-in-class accuracy.
Stack: Python · LangChain · ChromaDB · Pinecone · OpenAI · FastAPI · Docker
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging FaceAI Models
BERT, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$499
|
Standard
$849
|
Advanced
$1,249
|
|---|---|---|---|
| Delivery Time | 5 days | 8 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 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $200
Additional Revision
+$50Frequently asked questions
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PV
Pankaj V.
May 20, 2026
Backend-Focused Full Stack Engineer – FastAPI Productionization
Bilal delivered a complex backend
system covering payments, authentication,
caching, and international support.
Strong technical knowledge across
FastAPI, Redis, PostgreSQL, and
Razorpay integration. Responsive to
feedback and thorough in documentation.
Would work with him again.
system covering payments, authentication,
caching, and international support.
Strong technical knowledge across
FastAPI, Redis, PostgreSQL, and
Razorpay integration. Responsive to
feedback and thorough in documentation.
Would work with him again.
BG
Bill G.
Dec 7, 2025
Paid n8n Developer Interview - Build a Live Demo
Bilal showed excellent capability in Python, FastAPI/Django fundamentals, and translating logic into an n8n workflow for our live demo. Very good communication, responsive throughout, and delivered the milestone on time. Strong developer with a thoughtful approach. Would gladly collaborate again.
LW
Luna W.
Sep 3, 2025
Python Developer Needed for Web Scraping (HOUZZ + Google + LinkedIn)
About Bilal
AI Automation Engineer | Python, AI Agents, FastAPI, RAG, n8n
100%
Job Success
Lahore, Pakistan - 7:07 pm local time
I’m an AI Automation Engineer with 5+ years of experience building AI agents, workflow automation systems, and production-grade Python applications.
I help startups and businesses automate operations using Python, n8n, FastAPI, RAG pipelines, OpenAI, LangChain, Django, and LLM-powered agent systems.
𝗥𝗲𝗰𝗲𝗻𝘁 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲:
⤷ AI-powered workflow automation systems
⤷ Custom GPT and LLM integrations
⤷ Backend APIs and SaaS platforms
⤷ Web scraping and data extraction pipelines
⤷ Time-series analytics and AWS-based systems
⤷ Internal AI tools and business process automation
𝐖𝐡𝐚𝐭 𝐈 𝐜𝐚𝐧 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐰𝐢𝐭𝐡:
⤷ AI Automation & AI Agents
⤷ OpenAI / GPT Integrations
⤷ LangChain & LLM Applications
⤷ Python Backend Development (FastAPI, Django, Flask)
⤷ REST APIs & SaaS Architectures
⤷ Web Scraping & Data Pipelines
⤷ PostgreSQL, Redis, Celery, Docker, AWS
⤷ Deployment, Scaling & Performance Optimization
Python & AI Automation Stack
Python · FastAPI · LangChain · LangGraph · OpenAI · Anthropic Claude · n8n · PostgreSQL · Redis · ChromaDB · Docker · AWS · Supabase · Celery · RabbitMQ
💬 Why Clients Hire Me for AI Automation
⤷ Top-rated 5-star developer
⤷ Smooth communication and fast turnaround
⤷ Focused on long-term collaboration and clean code
Whether you need an AI MVP, AI agent, Python backend, automation platform, or custom AI workflow, I can help turn your idea into a reliable production-ready product.
📈 Let's build your next AI automation solution with performance, stability, and future scale in mind.
📩 Message me to discuss your project.
Also searched as: FastAPI developer, LangChain engineer, LangGraph developer, AI agent developer, n8n developer, RAG developer, agentic AI engineer, Python automation engineer, OpenAI integration developer, AI workflow automation engineer, backend API developer, LLM application developer, Python backend engineer, multi-agent system developer, n8n automation specialist, LangChain RAG developer, FastAPI backend developer, AI agent builder.
Steps for completing your project
After purchasing the project, send requirements so Bilal can start the project.
Delivery time starts when Bilal receives requirements from you.
Bilal works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Data Review
Review your documents, confirm LLM and embedding model choice, define chunking strategy and metadata fields.
Ingestion Pipeline
Build document loaders, text splitter, embedding pipeline, and ChromaDB/Pinecone vector store setup.