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Qdrant Essentials: Day 3 - Using Sparse Vectors for Keyword-Based Text Retrieval in Qdrant
To interact with Qdrant, we'll install the Qdrant Python client and Qdrant's lightweight embedding library called FastEmbed.
Chapter 1: Basic Prompt Structure
Setup
Run the following setup cell to load your API key and establish the get_completion helper function.
GPT-5 New Params and Tools
We’re introducing new developer controls in the GPT-5 series that give you greater control over model responses—from shaping output length and style to enforcing strict formatting. Below is a quick overview of the latest features:
| # | Feature | Overview | Values / Usage |
|---|---|---|---|
| 1. | Verbosity Parameter | Lets you hint the model to be more or less expansive in its replies. Keep prompts stable and use the parameter in |
Codex Prompting Guide
Codex models advance the frontier of intelligence and efficiency and our recommended agentic coding model. Follow this guide closely to ensure you’re getting the best performance possible from this model. This guide is for anyone using the model directly via the API for maximum customizability; we also have the Codex SDK for simpler integrations.
In the API, the Codex-tuned model is gpt-5.2-codex (see the [model page](ht
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