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chromadb-integration

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The AI-Based Pre-Screening Assistant Bot is an AI-powered technical interview assistant that evaluates candidates using adaptive questioning, real-time feedback, and automated report generation. Built with Streamlit and RAGPipeline, it dynamically adjusts question complexity, tracks candidate performance, and emails interview summaries

  • Updated Feb 24, 2025
  • Python

A production-ready AI agent pipeline using LangChain for LLM workflows, LangGraph for agent orchestration, and LangSmith for observability supporting document processing, weather queries, and general Q&A with built-in monitoring and debugging.

  • Updated Aug 5, 2025
  • Python

An AI-powered medical documentation system that generates comprehensive discharge summaries using Retrieval-Augmented Generation (RAG) architecture. This application leverages advanced language models, medical domain embeddings, and vector databases to assist healthcare professionals in creating accurate, structured clinical documentation.

  • Updated Nov 22, 2025
  • Python

KardiaFlow is an ultra-low-cost, specialized clinical RAG engine running on FastAPI and ChromaDB for <$0.0002 per query. Designed to fix the flaws of generic document AI—which often destroys lab tables and lacks reliable citations—it delivers a modern, full-stack medical intelligence workspace.

  • Updated Aug 16, 2026
  • HTML

Document Q&A agent template with per-user JWT auth and a pluggable vector store — FAISS or ChromaDB, one env var to switch. SQLite-backed audit trail included. Intermediate tier of the Document Intel Agent Template family — for real users, not yet a full enterprise deployment.

  • Updated Jul 17, 2026
  • Python

A zero-infra document Q&A agent template — ChromaDB + your choice of offline or hosted embeddings + OpenAI/Bedrock generation. Clone, pip install, and get a grounded RAG loop running in minutes. POC tier of the Document Intel Agent Template family.

  • Updated Jul 17, 2026
  • Python

RAG-PDF Assistant — A simple Retrieval-Augmented Generation (RAG) chatbot that answers questions using custom PDF documents. It uses HuggingFace embeddings for text representation, stores them in a Chroma vector database, and generates natural language answers with Google Gemini. In this example, the assistant is powered by a few school policy doc

  • Updated Aug 22, 2025
  • Python

Intelligent enterprise knowledge platform that ingests Box content, transcribes audio/video with Whisper, indexes documents using ChromaDB and local embeddings, and answers questions with Amazon Bedrock Claude.

  • Updated Jun 28, 2026
  • TypeScript

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