sagarcloud17

Chat-with-Your-Own-PDF-Files

This project provides a web-based interface for uploading PDF documents and interacting with them via a chatbot using LangChain, OpenAI, and Streamlit. It allows users to ask questions about the contents of their uploaded PDFs, and the chatbot will retrieve and respond with relevant information.

Financial-Agent---Agentic-AI

This repository contains the code for an Agentic AI application made using Phidata Framework that provides answers to financial-related questions. This application leverages the power of DuckDuckGo web search and Yfinance to gather relevant information, and utilizes the GPT-4o language model to generate human-like responses.

GenAI-Restaurant-name-and-menu-items-generator

Generation of Restaurant name and Menu items using Generative AI tools

LangGraph-with-Single-and-Multi-nodes-

This code showcases the LangGraph framework, which enables the development of conversational AI models that can answer general questions and retrieve information from external sources. The framework is built on top of the LangChain library and utilizes Google's Gemma2-9b Large Language Model (LLM)

python-for-data-engineering

This repo contains all the code used in the Python for Data Engineering Course

Python_Projects

RAG-with-Llama-3-and-Qdrant-vectordb

Implementation of Retrieval-Augmented Generation (RAG) using Llama 3 LLM and Qdrant vector database. This repository integrates Llama 3 with Qdrant vector database for efficient text retrieval and generation, enabling more accurate and informative responses.

RAG-with-Llama-3.1-calling-via-Groq-API--and-Qdrant-vectordb-

This repository provides a implementation of Retrieval Augmented Generation (RAG) using the LLaMA-3.1 8b language model, Qdrant vector database, and Langchain framework for question answering on PDFs.

RAG-with-Neo4j-Graph-Database-and-Google-s-Gemma2-9B-LLM-model

This repository showcases an innovative implementation of Retrieval-Augmented Generation (RAG) using the Neo4J graph database and Google's Gemma 9B Large Language Model (LLM). This approach leverages the strengths of both technologies to generate high-quality outputs

Uber_ETL_Project

Modern Data Engineering Project

sagarcloud17/RAG-with-Llama-3.1-calling-via-Groq-API--and-Qdrant-vectordb-

This repository provides a implementation of Retrieval Augmented Generation (RAG) using the LLaMA-3.1 8b language model, Qdrant vector database, and Langchain framework for question answering on PDFs.

sagarcloud17/RAG-with-Neo4j-Graph-Database-and-Google-s-Gemma2-9B-LLM-model

This repository showcases an innovative implementation of Retrieval-Augmented Generation (RAG) using the Neo4J graph database and Google's Gemma 9B Large Language Model (LLM). This approach leverages the strengths of both technologies to generate high-quality outputs

sagarcloud17/Chat-with-Your-Own-PDF-Files

This project provides a web-based interface for uploading PDF documents and interacting with them via a chatbot using LangChain, OpenAI, and Streamlit. It allows users to ask questions about the contents of their uploaded PDFs, and the chatbot will retrieve and respond with relevant information.

sagarcloud17/Financial-Agent---Agentic-AI

This repository contains the code for an Agentic AI application made using Phidata Framework that provides answers to financial-related questions. This application leverages the power of DuckDuckGo web search and Yfinance to gather relevant information, and utilizes the GPT-4o language model to generate human-like responses.

sagarcloud17/GenAI-Restaurant-name-and-menu-items-generator

Generation of Restaurant name and Menu items using Generative AI tools

sagarcloud17/LangGraph-with-Single-and-Multi-nodes-

This code showcases the LangGraph framework, which enables the development of conversational AI models that can answer general questions and retrieve information from external sources. The framework is built on top of the LangChain library and utilizes Google's Gemma2-9b Large Language Model (LLM)

sagarcloud17/python-for-data-engineering

This repo contains all the code used in the Python for Data Engineering Course

sagarcloud17/Python_Projects

sagarcloud17/RAG-with-Llama-3-and-Qdrant-vectordb

Implementation of Retrieval-Augmented Generation (RAG) using Llama 3 LLM and Qdrant vector database. This repository integrates Llama 3 with Qdrant vector database for efficient text retrieval and generation, enabling more accurate and informative responses.

sagarcloud17/Uber_ETL_Project

Modern Data Engineering Project