Programming Tutorials

  Building applications on top of Large Language Models (LLMs) inevitably leads to a single, frustrating bottleneck: the "chatty" API response. You ask Llama 3 or Mistral for a user profile object, and instead of raw data, you get:   "Sure! Here is the JSON you requested..."   followed by a markdown code block. For a hobby project, you might hack together a Regular Expression to strip out the text. In production, this is a fatal flaw. Application logic relies on deterministic data structures, not conversational nuances. If your parser fails because the LLM decided to add a trailing comma or a polite introduction, your user experience breaks. This guide details exactly how to bypass the conversational layer in Ollama to force rigorous, parseable JSON output using Python and Pydantic. The Root Cause: Why LLMs Fail at JSON To fix the problem, we must understand why it occurs. LLMs are not databases; they are probabilistic engines designed to predict the next token in a...

  Few things break a backend developer’s flow like a persistent   422 Unprocessable Entity   error. You define your Pydantic models, set up your FastAPI routes, and send a request via Postman or   curl . Instead of a successful   200 OK , you get a   422 . The error implies the server understands the content type (usually  application/json ), but the syntax of the data is incorrect. In the context of FastAPI, this almost exclusively means  Pydantic validation failed . This guide moves beyond generic advice. We will dissect the root causes of validation errors, implement global debugging handlers to expose hidden schema mismatches, and solve the common "Body vs. Query" parameter confusion. The Root Cause: How FastAPI validates Data FastAPI relies heavily on Pydantic (v2 in modern implementations) to parse and validate incoming data. When you define a route, FastAPI reads the type hints and creates a validation schema. If the incoming payload does n...