A sophisticated LangGraph-based agent that automates financial options analysis with real-time data from Polygon.io, smart caching, persistent memory, and professional-grade analysis. Built for traders, analysts, and developers who need intelligent options data processing
Natural language search over HuggingFace model cards - built with RAG, LoRA fine-tuning, and LLM evaluation to compare approaches.
Agentic RAG System – A multi-agent Retrieval-Augmented Generation (RAG) system built with CrewAI for business intelligence and document analysis. It integrates ChromaDB for document storage and retrieval, real-time web search, and specialized agents for code execution and visualization, enabling automated trend analysis and insights generation
AI-powered support copilot for ticket classification and query resolution. RAG, Chroma DB, Streamlit. Atlan AI Engineer Internship.
Middleware engine for transforming conventional solutions into Agentic AI frameworks using CrewAI and FastAPI
Semantic Retrieval Engine for Contrasting Ideas and Opposing Viewpoints.
A concierge agent made using Pipecat and Langgraph with COT reasoning.
Local RAG chatbot that answers questions about pizza restaurant reviews using LangChain, Ollama, and Chroma vector database - fully offline with no API dependencies
Adaptive RAG is an advanced retrieval-augmented generation system that intelligently combines dynamic query analysis with self-corrective mechanisms to choose the most effective strategy for answering user queries.
An AI-powered YouTube Content Synthesizer using RAG (Retrieval-Augmented Generation). Built with Streamlit, LangChain, and Gemini 2.0 to transform video transcripts into structured notes, important topics, and an interactive chatbot.
CRAG -A pipeline that uses tunable thresholds to validate document relevance, refines content at sentence level, and generates citation‑aware answers exclusively from verified sources avoiding hallucinations.
⚖️RAG Legal System: Pipeline avanzado de consulta documental sobre leyes españolas (BOE). Implementa LangChain, Groq (Llama 3), ChromaDB y un sistema de Reranking con Flashrank. Incluye framework de evaluación "LLM-as-a-Judge".
Powerful book recommender using Large Language Models (LLMs) and semantic vector search. It analyzes book descriptions to recommend contextually and emotionally similar titles. Includes zero-shot classification and an interactive Gradio interface for seamless user experience.
Multimodal AI assistant with RAG, web search & email generation – Built for Esprit
A fully local RAG pipeline that answers natural language questions about movie reviews. Uses Ollama for embeddings + local LLM, Chroma for the vector store, and LangChain to retrieve, summarize, and generate answers.
Advanced RAG System for Intelligent Document Querying. Built with Python, FastAPI, and React, leveraging Gemini Pro and ChromaDB for high-precision context retrieval from PDFs and codebases.
Relevant for: LLM Intern, AI Engineer Intern, RAG Systems, AI Assistants, LangChain Applications.
AI-powered book recommendation system using semantic search, sentiment analysis, and interactive web interface. Find books through natural language queries and emotional tone filtering.
Multi-agent RAG research assistant with 3-agent pipeline (Seed/Sourcing/Research), intelligent model routing (o1, o1-mini, sonar-pro), Chroma DB vector store, and Vectara factual consistency scoring.
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