A collection of advanced vector database implementations using ChromaDB for Retrieval-Augmented Generation (RAG) systems and intelligent question-answering applications.
An intelligent document Q&A system that combines semantic search with LLM-powered answer generation using RAG architecture.
Key Features:
- Web-based document embedding using LangChain's WebBaseLoader
- Intelligent text chunking with RecursiveCharacterTextSplitter
- Semantic search using HuggingFace embeddings (all-MiniLM-L6-v2)
- Vector storage and similarity search with ChromaDB
- LLM integration for contextual answer generation
- Complete RAG pipeline implementation
Use Cases: Technical documentation Q&A, knowledge base search, customer support automation
A vector database system built on ChromaDB for scientific question answering using the OpenBookQA dataset.
Key Features:
- OpenBookQA dataset integration for science questions
- Efficient vector embedding generation
- ChromaDB implementation for semantic search
- Question-answering with retrieved context
- Performance evaluation and analysis
Use Cases: Educational Q&A systems, scientific knowledge retrieval, exam preparation tools
- Vector Database: ChromaDB
- LLM Framework: LangChain
- Embeddings: HuggingFace Transformers
- Language Models: GPT-2, OpenAI-compatible models
- Retrieval-Augmented Generation (RAG): Combining vector search with LLM generation
- Semantic Search: Dense vector representations for intelligent document retrieval
- Vector Databases: Efficient similarity search with ChromaDB
- LLM Integration: Connecting language models with retrieval systems
- Document Processing: Text chunking and embedding strategies
Vector-Data-Base/
├── Document_Question_Answering_System_with_LangChain_and_ChromaDB.ipynb
├── Vector_Database_Implementation_with_ChromaDB_and_OpenBookQA.ipynb
└── README.md