From adf7c5f41eb268c96289838c6b8769c1ebaf7e54 Mon Sep 17 00:00:00 2001 From: Ryan Halliday Date: Mon, 12 Feb 2024 15:08:03 +1300 Subject: [PATCH] docs: small formatting fixes (#427) --- docs/examples/action_items.md | 2 +- docs/examples/entity_resolution.md | 3 ++- docs/examples/search.md | 2 +- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/examples/action_items.md b/docs/examples/action_items.md index 2b15c207e..4989e6419 100644 --- a/docs/examples/action_items.md +++ b/docs/examples/action_items.md @@ -4,7 +4,7 @@ In this guide, we'll walk through how to extract action items from meeting trans !!! tips "Motivation" -Significant amount of time is dedicated to meetings, where action items are generated as the actionable outcomes of these discussions. Automating the extraction of action items can save time and guarantee that no critical tasks are overlooked. + Significant amount of time is dedicated to meetings, where action items are generated as the actionable outcomes of these discussions. Automating the extraction of action items can save time and guarantee that no critical tasks are overlooked. ## Defining the Structures diff --git a/docs/examples/entity_resolution.md b/docs/examples/entity_resolution.md index 92ec03a2e..b8393d8e3 100644 --- a/docs/examples/entity_resolution.md +++ b/docs/examples/entity_resolution.md @@ -3,7 +3,8 @@ In this guide, we demonstrate how to extract and resolve entities from a sample legal contract. Then, we visualize these entities and their dependencies as an entity graph. This approach can be invaluable for legal tech applications, aiding in the understanding of complex documents. !!! tips "Motivation" -Legal contracts are full of intricate details and interconnected clauses. Automatically extracting and visualizing these elements can make it easier to understand the document's overall structure and terms. + + Legal contracts are full of intricate details and interconnected clauses. Automatically extracting and visualizing these elements can make it easier to understand the document's overall structure and terms. ## Defining the Data Structures diff --git a/docs/examples/search.md b/docs/examples/search.md index 45b6e18db..998489143 100644 --- a/docs/examples/search.md +++ b/docs/examples/search.md @@ -10,7 +10,7 @@ In this example, we will demonstrate how to leverage the `MultiTask` and `enum.E The `Search` class is a Pydantic model that defines the structure of the search query. It has three fields: `title`, `query`, and `type`. The `title` field is the title of the request, the `query` field is the query to search for relevant content, and the `type` field is the type of search. The `execute` method is used to execute the search query. -```python hl_line +```python import instructor from openai import OpenAI from typing import Iterable