Project Overview

Designed and implemented a modular multi-agent system using CrewAI for orchestrating data processing tasks. This advanced system specializes in automated data analysis and insight generation, featuring real-time processing capabilities and natural language reporting.

Built modular agents for data profiling, statistical analysis, visualization, and Markdown report generation using CrewAI.

Utilized FAISS and Sentence-Transformers to retrieve relevant statistical knowledge for guiding analysis decisions.

Integrated OpenAI GPT-4 APIs to summarize insights, interpret findings, and enhance report clarity.

Ensured secure, user-isolated workflows, so each user has access to insights derived only from their own data.

Automated visual output generation using Matplotlib and Seaborn with smart chart selection logic.

System Architecture

The system architecture demonstrates the seamless integration of multiple AI agents working collaboratively to process, analyze, and generate insights from complex datasets.

Key Features

Multi-Agent System

Data Exploration Agent: Identifies patterns, outliers, and data quality issues.

Statistical Analysis Agent: Applies statistical methods and validates assumptions.

Visualization Agent: Generates diverse and insightful visualizations.

Insight Generation Agent: Produces actionable insights in Markdown format.

RAG Integration

Retrieves relevant statistical methods and best practices from the knowledge base.

Ensures analysis follows established guidelines.

Visualization Generation

Creates bar charts, pie charts, scatter plots, heatmaps, and more.

Automatically selects the most relevant plots based on data characteristics.

Insight Report

Generates a professional Markdown report summarizing findings, visualizations, and recommendations.

Project Overview

Developed a full-stack AI assistant using FastAPI for the backend and Streamlit for the frontend interface. This advanced conversational chatbot specializes in document-based Q&A with context-aware responses, featuring secure document processing and user session isolation.

Integrated Pinecone as a vector store and used LangChain for chunk retrieval and context-based RAG querying.

Created secure document ingestion and chunking pipeline for PDF, DOCX, and TXT formats with embedding support.

Implemented OpenAI's embedding and reranking APIs to enhance response relevance.

Designed user authentication and session isolation to ensure user-specific data access and privacy.

Added observability through Langfuse to monitor chatbot interactions and trace performance.

System Architecture

The system architecture demonstrates the seamless integration of FastAPI backend, Streamlit frontend, Pinecone vector database, and LangChain for efficient document processing and conversational AI capabilities.

Key Features

Document Upload and Preprocessing

Users can upload documents in various formats, including PDF, DOC, DOCX, and TXT.

During the upload process, parallel processing techniques such as text cleaning and chunking are applied to prepare the document for storage in the vector database.

Vector Database Integration

The project uses Pinecone as the vector database to store document embeddings.

Uploaded documents are split into smaller chunks, embedded using a custom embedding class, and stored in the vector database for efficient retrieval.

Custom UltraSafe API Integration

Custom classes were created to interact with the UltraSafeAI API for embedding generation and reranking.

The UltraSafeAIEmbeddings class handles the generation of embeddings for document chunks and queries.

Reranking for Enhanced Retrieval

During the retrieval process, the system applies reranking to ensure the most relevant document chunks are prioritized.

This is achieved by leveraging the UltraSafeAI reranking API, which sorts the retrieved chunks based on their relevance scores.

Interactive Q&A

Users can ask questions about their uploaded documents, and the chatbot provides context-aware answers.

The chatbot strictly adheres to the provided document context, ensuring accurate and reliable responses.

User Authentication and Session Management

Secure user authentication is implemented using hashed passwords and session cookies.

Each user's documents and chat sessions are isolated, ensuring privacy and security.

Langfuse Observability

The project integrates Langfuse for observability, providing detailed logs and tracing for chatbot interactions and performance monitoring.

Project Overview

This advanced AI agent specializes in automated data analysis and insight generation. Built with cutting-edge machine learning algorithms, it processes complex datasets to extract meaningful patterns and generate actionable business intelligence.

The system features real-time data processing capabilities, interactive visualizations, and natural language reporting. It can handle various data formats including CSV, JSON, and database connections, making it versatile for different business needs.

Key features include predictive analytics, anomaly detection, automated report generation, and integration with popular business intelligence tools. The agent continuously learns from new data inputs to improve its analysis accuracy over time.