diff --git a/pgml-sdks/pgml/python/examples/rag_question_answering.py b/pgml-sdks/pgml/python/examples/rag_question_answering.py
new file mode 100644
index 000000000..94db6846c
--- /dev/null
+++ b/pgml-sdks/pgml/python/examples/rag_question_answering.py
@@ -0,0 +1,92 @@
+from pgml import Collection, Model, Splitter, Pipeline, Builtins, OpenSourceAI
+import json
+from datasets import load_dataset
+from time import time
+from dotenv import load_dotenv
+from rich.console import Console
+import asyncio
+
+
+async def main():
+ load_dotenv()
+ console = Console()
+
+ # Initialize collection
+ collection = Collection("squad_collection")
+
+ # Create a pipeline using the default model and splitter
+ model = Model()
+ splitter = Splitter()
+ pipeline = Pipeline("squadv1", model, splitter)
+ await collection.add_pipeline(pipeline)
+
+ # Prep documents for upserting
+ data = load_dataset("squad", split="train")
+ data = data.to_pandas()
+ data = data.drop_duplicates(subset=["context"])
+ documents = [
+ {"id": r["id"], "text": r["context"], "title": r["title"]}
+ for r in data.to_dict(orient="records")
+ ]
+
+ # Upsert documents
+ await collection.upsert_documents(documents[:200])
+
+ # Query for context
+ query = "Who won more than 20 grammy awards?"
+
+ console.print("Question: %s"%query)
+ console.print("Querying for context ...")
+
+ start = time()
+ results = (
+ await collection.query().vector_recall(query, pipeline).limit(5).fetch_all()
+ )
+ end = time()
+
+ #console.print("Query time = %0.3f" % (end - start))
+
+ # Construct context from results
+ context = " ".join(results[0][1].strip().split())
+ context = context.replace('"', '\\"').replace("'", "''")
+ console.print("Context is ready...")
+
+ # Query for answer
+ system_prompt = """Use the following pieces of context to answer the question at the end.
+ If you don't know the answer, just say that you don't know, don't try to make up an answer.
+ Use three sentences maximum and keep the answer as concise as possible.
+ Always say "thanks for asking!" at the end of the answer."""
+ user_prompt_template = """
+ ####
+ Documents
+ ####
+ {context}
+ ###
+ User: {question}
+ ###
+ """
+
+ user_prompt = user_prompt_template.format(context=context, question=query)
+ messages = [
+ {"role": "system", "content": system_prompt},
+ {"role": "user", "content": user_prompt},
+ ]
+
+ # Using OpenSource LLMs for Chat Completion
+ client = OpenSourceAI()
+ chat_completion_model = "HuggingFaceH4/zephyr-7b-beta"
+ console.print("Generating response using %s LLM..."%chat_completion_model)
+ response = client.chat_completions_create(
+ model=chat_completion_model,
+ messages=messages,
+ temperature=0.3,
+ max_tokens=256,
+ )
+ output = response["choices"][0]["message"]["content"]
+ console.print("Answer: %s"%output)
+ # Archive collection
+ await collection.archive()
+
+
+if __name__ == "__main__":
+ asyncio.run(main())
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