This repository contains various AI-powered applications built using LangChain, Ollama, and Streamlit. These AI agents can process text documents, analyze images, verify identification documents, and perform search-based retrieval for question-answering tasks.
- 📚 Legal Document Analysis: A RAG-based chatbot for answering questions from legal documents.
- 🌟 AI-Powered Image Analysis: Identify landmarks, analyze nutrition charts, and generate image descriptions.
- 👤 KYC Verification: Verifies identification details from uploaded documents.
- 🔍 Search Agent: Uses Wikipedia and DuckDuckGo to retrieve relevant information.
🛠️ To use these AI agents, clone this repository and install the required dependencies:
Each AI agent runs as a Streamlit app. Start any module using:
streamlit run script_name.py- Function: Processes legal documents and provides answers using a Retrieval-Augmented Generation (RAG) model.
- Libraries Used: LangChain, HuggingFace Embeddings, ChromaDB, FAISS.
- Run Command:
streamlit run rag.py
- Function: Analyzes nutrition charts from images and provides dietary recommendations.
- Libraries Used: LangChain, Ollama, Streamlit.
- Run Command:
streamlit run nutrition.py
- Function: Identifies landmarks from uploaded images.
- Libraries Used: LangChain, Ollama, Streamlit.
- Run Command:
streamlit run landmark.py
- Function: Verifies identification documents with text and image inputs.
- Libraries Used: LangChain, Ollama, Streamlit.
- Run Command:
streamlit run kyc.py
- Function: Fetches information from Wikipedia and DuckDuckGo based on user queries.
- Libraries Used: LangChain, Wikipedia API, DuckDuckGo Search.
- Run Command:
streamlit run agent.py
🚀 Contributions are welcome! Feel free to submit pull requests or open issues.
📚 This project is licensed under the MIT License.
🐝 Created by Vaishnavi Desai