No-code multi-agent framework to build LLM Agents, workflows and applications with your data
A pure Python-implemented, lightweight, server-optional, multi-end compatible, vector database deployable locally or remotely.
Experiment on QnA tabular data using LLMs and SQL
Use OpenAI, Redis, and streamlit to recommend hotels using Large Language Models
Your own GPT-powered Personal Assistant to whom you can ORDER or INSTRUCT to do some task or search for something using your VOICE commands.
Summarizes texts, videos and audios recursively. Allows custom prompts.
This project integrates LangChain v0.2.6, HuggingFace Serverless Inference API, and Meta-Llama-3-8B-Instruct. It provides a chat-like web interface to interact with a language model and maintain conversation history using the Runnable interface, the upgraded version of LLMChain. LLMChain has been deprecated since 0.1.17.
a basic proof-of-concept implementation of https://python.langchain.com/docs/use_cases/question_answering/
A web-based chat application for querying process execution data using natural language.
Successfully developed an LLM application that provides AI-powered, structured insights based on user queries. The app features a dynamic response generator with progress indicators, interactive upvote/downvote options, and a clean, engaging user interface built using Streamlit. Ideal for personalized meal, fitness, and health-related advice.
Successfully designed and developed a customer support chatbot that leverages LangChain and Pinecone for efficient retrieval-augmented generation (RAG), enabling intelligent and context-aware responses to user queries.
A payload compression toolkit that makes it easy to create ideal data structures for LLMs; from training data to chain payloads.
Successfully developed an LLM application that intelligently analyzes job descriptions and compares them against uploaded resumes to provide actionable insights.
Retrieval Augmented Generation (RAG) using LangChain Framework, FAISS vector store and FastEmbed text embedding model.
Knowledge graph powered openai based basketball chatbot
Successfully developed a Multi-Domain AI Personal Assistant using LangChain, OpenAI, and Streamlit. The application seamlessly integrates multiple specialized capabilities, including document-based question answering (QA), Python code execution, debugging, explanation and optimization, web search, latest news retrieval, and currency conversion.
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