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5 items tagged with "rag"
Build a complete AI agent from data ingestion to deployment. Learn RAG, search, function calling, evaluation, and web deployment — all on YOUR GitHub project.
Asia Amodeo's intelligent email agent helps manage an inbox. It integrates with the Gmail API, indexes emails in Elasticsearch, and offers a Streamlit chat interface to find specific emails or see what's important for the day, addressing 'email fatigue' by making it easier to sift through communication.
An agent that tracks cybersecurity incidents reported to the SEC, built by Scott DeGeest. Helps supply chain professionals quickly find out if a company has disclosed a data breach or ransomware attack. Handles raw XML/PDF from the SEC, invalid XML, subsidiary relationships (e.g. Change Healthcare ↔ United Health Group), and token monitoring for cost and context limits.
Vancesca Dinh's agent helps users identify goals and understand the 'why' behind them, grounded in data from the Huberman Lab podcast and medical publications. Pipeline includes RSS feeds, Faster Whisper transcription, Qdrant embeddings, query rewriting for better search, Logfire, Pydantic, and guardrails to keep the agent on-task.
We build a course FAQ assistant from the bottom up. First we expose a plain Python search(query) function to the OpenAI Responses API. Then we turn the same idea into a reusable agent loop, compare toyaikit, OpenAI Agents SDK, and PydanticAI, and finally move the FAQ tools behind an MCP server that can be used from a notebook, PydanticAI, Cursor, and VS Code. Links The main resources: AI Bootcamp: From RAG to Agents Prerequisite workshop: Building a Coding Agent Data Engineering Zoomcamp FAQ source document Parsed FAQ JSON FAQ parsing notebook The system you will build The final setup looks like this: flowchart LR NOTEBOOK["Jupyter notebook"] OPENAI["OpenAI Responses API"] FRAMEWORKS["Agents SDK<br/>PydanticAI"] MCPCLIENT["MCP clients<br/>toyaikit, PydanticAI, Cursor"] MCPSERVER["FastMCP server<br/>SSE or stdio"] TOOLS["FAQ tools<br/>search, add_entry"] INDEX["minsearch index<br/>FAQ JSON"] NOTEBOOK -->|function calling| OPENAI NOTEBOOK --> FRAMEWORKS FRAMEWORKS -->|tool calls| TOOLS NOTEBOOK -->|MCP client| MCPCLIENT MCPCLIENT -->|MCP protocol| MCPSERVER MCPSERVER --> TOOLS TOOLS --> INDEX The FAQ data comes from the Data Engineering Zoomcamp FAQ. The first half of the workshop keeps the tools inside the notebook so you can see the agent loop directly. The second half moves the same tools into mcp_faq/, which makes them reusable by any MCP client.