Projects - Ali Baygi

RAG Multi-Bot Platform

An advanced Retrieval-Augmented Generation (RAG) platform featuring multiple specialized AI bots for intelligent question answering and document processing. Built with LangChain and modern LLM technologies, this system implements sophisticated retrieval strategies for accurate and context-aware responses.

Key Features:

✓ Multi-bot architecture with specialized agents for different domains
✓ Advanced RAG implementation with vector databases and semantic search
✓ Document ingestion and preprocessing pipeline
✓ Conversational memory and context management

Python LangChain RAG Vector DB LLMs

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Multi-Agent

RAG System

RAG with LangChain, Docling & PostgreSQL

Production-ready RAG system integrating LangChain with Docling for advanced document processing and PostgreSQL for robust data management. This notebook-based project demonstrates end-to-end implementation of document ingestion, embedding generation, and intelligent retrieval.

Key Features:

✓ Integration with Docling for sophisticated document parsing
✓ PostgreSQL backend with pgvector for scalable vector storage
✓ LangChain orchestration for retrieval workflows
✓ Jupyter notebooks with comprehensive examples

Jupyter Notebook LangChain PostgreSQL Docling RAG

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Document

Intelligence

Real-time ML Pattern Recognition

High-performance machine learning system for real-time pattern detection and anomaly recognition using Apache Kafka. This project showcases streaming ML capabilities with low-latency inference and continuous model updates for production environments.

Key Features:

✓ Real-time data streaming with Apache Kafka
✓ Online ML models for pattern detection and classification
✓ Low-latency inference pipeline for production workloads
✓ Monitoring and alerting for anomaly detection

Python Apache Kafka Real-time ML Streaming Pattern Recognition

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⚡

Real-time

ML Processing

Deep Investment Analysis

AI-powered investment analysis platform leveraging deep learning and advanced analytics for comprehensive financial research. This system combines market data analysis, sentiment analysis, and predictive modeling to provide intelligent investment insights.

Key Features:

✓ Deep learning models for financial prediction and analysis
✓ Multi-source data integration (market, news, fundamentals)
✓ NLP-based sentiment analysis from financial news
✓ Risk assessment and portfolio optimization

Python Deep Learning Finance NLP Analytics

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Financial

Intelligence

Deep Stock Analysis Platform

A sophisticated deep-agent-powered stock research platform that combines AI agents with comprehensive market analysis. This forked and enhanced project provides intelligent stock screening, technical analysis, and automated research capabilities for informed investment decisions.

Key Features:

✓ AI agent-based stock research and analysis automation
✓ Technical indicators and chart pattern recognition
✓ Fundamental analysis with financial metrics evaluation
✓ Automated report generation and insights

Python AI Agents Stock Analysis Automation Research

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Stock

Research AI

Featured Projects

RAG Multi-Bot Platform

Key Features:

RAG with LangChain, Docling & PostgreSQL

Key Features:

Real-time ML Pattern Recognition

Key Features:

Deep Investment Analysis

Key Features:

Deep Stock Analysis Platform

Key Features:

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