Pondhouse Data Blog

Add PostgreSQL-backed memory to a self-made Python AI agent: store every interaction, summarize long histories with GPT-4o and feed context into the prompt.

Extend a framework-free Python AI agent with an AgentState enum, a multi-step reasoning loop and clarifying questions to the user. Full code included.

Build an AI agent in plain Python with GPT-4o function calling: a PostgreSQL tool, a Wikipedia tool, a tool-call loop and PostgreSQL memory.

Late chunking embeds the whole document first, then pools token embeddings into chunks. Python code with jina-embeddings-v3 and a similarity comparison.

A hands-on DSPy tutorial: the 8-step workflow, metrics and optimizers, and a summarization example where optimization lifted the score from 42% to 68%.

langchain-autotools turns a Python SDK client into LangChain agent tools. Covers setup, CrudControls for picking functions and an Azure Blob Storage example.

Firecrawl turns websites into LLM-ready markdown. Python examples for scraping, crawling, Pydantic-based extraction, LangChain and self-hosting with Docker.

Check a new Azure OpenAI model against your current one before upgrading, using the Prompt Flow evaluation SDK, a GPT-4o judge and Azure AI Studio.

Store embeddings in BigQuery, create them with ML.GENERATE_EMBEDDING, query them with VECTOR_SEARCH and run a full RAG pipeline with Gemini in one SQL query.

Python examples for sending images to GPT-4o via the OpenAI API to classify, describe and analyze them, plus detail modes and per-image costs.