- Chat with any SQL database no-prompt-engineering with Source codes
- Chat with Rest API with Spotify Rest API as example
- Chat with Pdf or documents ingestion
- Chat with Mongo DB using prompt engineering with Source code link
- In enterprise environment, we could have lots of internal data sources, such as Wiki, Confluences, PDF, Databases, Images, etc.
- GenAI applications can work securedly with company Databases and documents to provide smart Automation flows
- This repository contains various examples, models and setup with custom AI applications.
- AI interacting with SQL, MongoDB, Cassandra NoSQL
- Vectorstore for semantic/hybrid search with Astra DB, Chroma DB, Faiss, Postgres Vector, MongoDb Vectorstore
- AI Models and Embeddings, including Paid and open-source
- Data Ingestion, including text splitter, chunks, pdf loader, website scraper with BeautifulSoup
- Langchain framework in Python, to interact with all available Foundation models
- HuggingFace, Ollama, Groq API for using models
- Expose AI backend as REST API, with LangServe, Python FastAPI. This would enables traditional Web Applications in NodeJS and Java to interact with AI Agent Backend
- Cloud Infrastructure for private AI apps, including AWS Sagemaker, AWS Bedrocks, Nvidia NIM, etc
- Agents, Tools, Prompt templates to build complex AI agent with LangGraph, Langflow