chore: import upstream snapshot with attribution

examples/workflow_quickstart.py

@@ -0,0 +1,59 @@
+"""
+Workflow Quick Start
+Easy to use way to get started with deterministic workflows.
+
+TxtAI has many example notebooks covering everything the framework provides
+Examples: https://neuml.github.io/txtai/examples
+
+Install TxtAI
+  pip install txtai[pipeline-data]
+"""
+
+from txtai import LLM, Workflow
+from txtai.pipeline import Summary, Textractor, Translation
+from txtai.workflow import Task
+
+# Step 1: Define available pipelines
+textractor = Textractor(backend="docling", headers={"user-agent": "Mozilla/5.0"})
+summary = Summary()
+translate = Translation()
+
+# Step 2: Define workflow tasks
+workflow = Workflow([Task(textractor), Task(summary), Task(lambda inputs: [translate(x, "fr") for x in inputs])])
+
+# Step 3: Run the workflow
+print(list(workflow(["https://neuml.com"])))
+
+# Each component above is a single model that specializes in a task
+# LLMs can also be used to accomplish the same tasks
+
+
+# pylint: disable=E0102,C0116
+def summary(text):
+    return f"""
+Summarize the following text in 40 words or less.
+
+{text}
+"""
+
+
+def translate(text, language):
+    return f"""
+Translate the following text to {language}.
+
+{text}
+"""
+
+
+textractor = Textractor(backend="docling", headers={"user-agent": "Mozilla/5.0"})
+llm = LLM("Qwen/Qwen3-4B-Instruct-2507")
+
+workflow = Workflow(
+    [
+        Task(textractor),
+        Task(lambda inputs: llm([summary(x) for x in inputs], maxlength=25000, defaultrole="user")),
+        Task(lambda inputs: llm([translate(x, "fr") for x in inputs], maxlength=25000, defaultrole="user")),
+    ]
+)
+
+print(list(workflow(["https://neuml.com"])))