This is a production-grade backend service for an enterprise AI platform, VoiceSageX, providing Retrieval-Augmented Generation (RAG), agent-based conversational AI, multi-agent orchestration, long-term memory, and model inference. Built with FastAPI, it exposes numerous RESTful endpoints under `/Voicecomm/VoiceSageX/` for knowledge base management (document parsing, chunk editing, vector indexing), agent chat (single and multi-agent with LangGraph), MCP tool integration, and model invocation. The architecture follows a modular, pipeline-oriented design: the RAG subsystem implements a clear stage-based flow (extraction, cleaning, splitting, vectorization, retrieval, reranking) with strategy and factory patterns, while the agent subsystem uses a class hierarchy (BaseAgent β HistoryAgent β MemoryAgent) and supports streaming. The system relies on PostgreSQL with pgvector for vector storage, external sandbox for code execution, and multiple model providers (OpenAI, Azure, SiliconFlow, Ollama). Configuration is environment-aware (dev/qa/prod) and loaded from JSON files. The codebase demonstrates solid engineering practices with async patterns, Pydantic models, and consistent error handling, though there is some duplication across similar API endpoints and potential dependency drift risks from langchain and langgraph versions.
The OpenAI Cookbook is a sprawling collection of example code, notebooks, and guides for accomplishing tasks with the OpenAI API. It is not a single application but a repository of patterns. Its dominant character is data research and example code: it contains hundreds of Jupyter notebooks and Python scripts demonstrating embeddings, fine-tuning, RAG, agents, evals, MCP integrations, and vector database usage. The repo also includes full-stack demo applications (React/Next.js/Flask) for real-time translation, deployment management, and supply-chain copilots, making it a hybrid. There is no unified data lineage; instead, each example subsection defines its own data flow from API calls to local CSV/JSONL inputs. The quality is uneven: some eval harnesses and agent demos are well-structured, while many notebooks are scratchy and rely on inaccessible private data. The repository is maintained (last commit June 2026, CI workflows active) but shows medium technical drift, with some examples pinned to older library versions and many notebooks requiring manual environment setup.
This repository implements a full-stack AI-powered food recommendation application called 'δ»ε€©εδ»δΉ' (What to eat today). The system combines a Next.js frontend with a Python Flask backend that uses a GraphRAG (Retrieval-Augmented Generation) architecture leveraging Neo4j for graph-based recipe relationships and Milvus for vector similarity search. Users interact via a chat interface to receive personalized recipe recommendations with step-by-step cooking instructions. The application includes a comprehensive recipe knowledge base in markdown format, Docker-based deployment orchestration, and a modular RAG pipeline with intelligent query routing and session management.
This repository is a tutorial and research exploration of Graph RAG (Retrieval-Augmented Generation) combining vector databases (Weaviate) and knowledge graphs (RDF/SPARQL). It contains Jupyter notebooks that demonstrate building knowledge graphs from PubMed articles, World Bank documents, and Netflix movies, comparing vector search and structured query performance for retrieval-augmented generation. A Streamlit app provides an interactive demo for medical article search with MeSH term refinement and LLM summarization. The code is primarily educational, with reusable query modules for Weaviate, RDF, and Databricks, but lacks dependency pinning and has hardcoded credentials, limiting reproducibility. The repository is hybrid, with both application (Streamlit app) and data research (notebooks, datasets) elements, but the dominant character is data research as the core value lies in the experimental notebooks and data pipelines.