Langchain & RAG Engineering

CORE AI ENGINEERING FOCUS:
Retrieval-Augmented Generation (RAG)
AI Chatbot Engineering
Enterprise Knowledge Systems
AI Search & Retrieval
Conversational AI
Vector Databases
LLM Orchestration
AI Workflow Automation
Context-Aware AI Systems
Production AI Infrastructure

I design and build intelligent AI systems using:
LangChain
RAG Architecture
Vector Databases
Conversational AI
Enterprise Knowledge Systems
LLM Orchestration
AI Search & Retrieval
Generative AI Platforms

DOCUMENT PROCESSING PIPELINES
DOCUMENT LOADING

Handling multiple enterprise data sources using LangChain loaders.

PDF ingestion
DOCX processing
CSV/Excel integration
Web content loading
API data ingestion
Audio/video transcript loading



DOCUMENT SPLITTING
Advanced chunking strategies for efficient retrieval.

Recursive text splitting
Semantic chunking
Token-aware chunking
Context preservation
Sliding window strategies
Metadata-aware segmentation

VECTOR DATABASE ENGINEERING
EMBEDDINGS & VECTOR STORES
Building semantic search systems using embeddings.

OpenAI Embeddings
Similarity Search
Vector Indexing
Semantic Retrieval
Context Matching
Dense Vector Search



VECTOR DATABASES
Integration with modern vector storage systems.

ChromaDB
Pinecone
FAISS
Weaviate
Redis Vector Search
Elasticsearch Vector Search

AI RETRIEVAL SYSTEMS
ADVANCED RETRIEVAL TECHNIQUES

Improving relevance and contextual accuracy.

Similarity retrieval
MMR retrieval
Metadata filtering
Hybrid search
Conversational retrieval
Query transformation

AI USE CASES
AI Chat Systems

AI Knowledge Bases
Document Summarization
AI Customer Support
Intelligent Search Engines
AI Research Assistants
AI Workflow Automation
Enterprise AI Portals
AI-powered SaaS Platforms