chore: import upstream snapshot with attribution

examples/rag_quickstart.py

@@ -0,0 +1,70 @@
+"""
+RAG Quick Start
+Easy to use way to get started with RAG using YOUR data
+
+For a complete application see this: https://github.com/neuml/rag
+
+TxtAI has many example notebooks covering everything the framework provides
+Examples: https://neuml.github.io/txtai/examples
+
+Install TxtAI
+  pip install txtai[pipeline-data]
+"""
+
+# pylint: disable=C0103
+import os
+
+from txtai import Embeddings, RAG
+from txtai.pipeline import Textractor
+
+# Step 1: Collect files from local directory
+#
+# Defaults to "data". Set to whereever your files are.
+path = "data"
+files = [os.path.join(path, f) for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
+
+# Step 2: Text Extraction / Chunking
+#
+# Using section based chunking here. More complex options available such as semantic chunking, iterative chunking etc.
+# Documentation: https://neuml.github.io/txtai/pipeline/data/textractor
+# Supports Chonkie chunking as well: https://docs.chonkie.ai/oss/chunkers/overview
+textractor = Textractor(backend="docling", sections=True)
+chunks = []
+for f in files:
+    for chunk in textractor(f):
+        chunks.append((f, chunk))
+
+# Step 3: Build an embeddings database
+#
+# The `path` parameter sets the vector embeddings model. Supports Hugging Face models, llama.cpp, Ollama, vLLM and more.
+# Documentation: https://neuml.github.io/txtai/embeddings/
+embeddings = Embeddings(content=True, path="Qwen/Qwen3-Embedding-0.6B", maxlength=2048)
+embeddings.index(chunks)
+
+# Step 4: Create RAG pipeline
+#
+# Combines an embeddings database and an LLM.
+# Supports Hugging Face models, llama.cpp, Ollama, vLLM and more
+# Documentation: https://neuml.github.io/txtai/pipeline/text/rag
+
+# User prompt template
+template = """
+  Answer the following question using the provided context.
+
+  Question:
+  {question}
+
+  Context:
+  {context}
+"""
+
+rag = RAG(
+    embeddings,
+    "Qwen/Qwen3-0.6B",
+    system="You are a friendly assistant",
+    template=template,
+    output="flatten",
+)
+
+question = "Summarize the main advancements made by BERT"
+print(rag(question, maxlength=2048, stripthink=True))