QueryAI is a cutting-edge AI application that allows users to interactively query and gain insights from multiple data sources, including PDFs and websites. By leveraging advanced embeddings and retrieval-augmented generation (RAG), QueryAI provides accurate, context-aware responses to user queries in real-time.
- Multi-Source Knowledge Integration: Combine PDFs and web content into a unified knowledge base for seamless querying.
- Real-Time Conversational AI: Ask questions and receive contextually relevant answers using Google Gemini LLM.
- Vector Database Powered: Efficient storage and retrieval of information using FAISS vector store with HuggingFace embeddings.
- Chunking & Retrieval: Automatically splits large documents into manageable chunks for better retrieval and context management.
- Backend & AI: Python, LangChain, FAISS, HuggingFace Embeddings, GoogleGenerativeAI
- Document Processing: PyPDF2 for PDFs, WebBaseLoader for URLs
- Web App: Streamlit for interactive user interface
- Environment Management: dotenv for environment variables
- Source Input: Users provide one or more PDFs or URLs via the Streamlit sidebar.
- Processing & Chunking: PDFs and web content are processed into text chunks for embeddings.
- Vector Store Creation: Chunks are converted into embeddings and stored in a FAISS vector store for efficient retrieval.
- Query & Response: Users ask questions in natural language. The RAG pipeline retrieves relevant context and generates an AI response.
- Multi-Source Knowledge Retrieval: Process and query multiple documents and websites simultaneously for enriched responses.
- Real-Time Interaction: Instantaneous conversational experience with context-aware responses, enabling rapid decision-making.
- Persistent Vector Store: FAISS vector store allows scalable and efficient retrieval across large datasets.
- Clone the repository:
git clone https://github.com/jayeshgit65/QueryAI-AI-Powered-Multi-Source-Knowledge-Assistant
cd QueryAI-AI-Powered-Multi-Source-Knowledge-Assistant- Install dependencies:
pip install -r requirements.txt-
Create a
.envfile and configure your API keys (if any). -
Run the Streamlit app:
streamlit run app.py- Add PDF or URL sources in the sidebar, process them, and start querying!
- Enables data-driven insights from heterogeneous sources in one platform.
- Improves productivity by providing accurate, contextual answers in real-time.
- Suitable for researchers, analysts, and enterprises to query internal and external knowledge sources efficiently.
This project is licensed under the MIT License.