diff --git a/pgml-sdks/rust/pgml/javascript/examples/README.md b/pgml-sdks/rust/pgml/javascript/examples/README.md
index 440058e4f..77e13b638 100644
--- a/pgml-sdks/rust/pgml/javascript/examples/README.md
+++ b/pgml-sdks/rust/pgml/javascript/examples/README.md
@@ -11,3 +11,6 @@ In this example, we will use `hknlp/instructor-base` model to build text embeddi
### [Extractive Question Answering](./extractive_question_answering.js)
In this example, we will show how to use `vector_recall` result as a `context` to a HuggingFace question answering model. We will use `Builtins.transform()` to run the model on the database.
+
+### [Summarizing Question Answering](./summarizing_question_answering.js)
+This is an example to find documents relevant to a question from the collection of documents and then summarize those documents.
diff --git a/pgml-sdks/rust/pgml/javascript/examples/summarizing_question_answering.js b/pgml-sdks/rust/pgml/javascript/examples/summarizing_question_answering.js
new file mode 100644
index 000000000..a5e5fe19b
--- /dev/null
+++ b/pgml-sdks/rust/pgml/javascript/examples/summarizing_question_answering.js
@@ -0,0 +1,63 @@
+const pgml = require("pgml");
+require("dotenv").config();
+
+pgml.js_init_logger();
+
+const main = async () => {
+ // Initialize the collection
+ const collection = pgml.newCollection("my_javascript_sqa_collection");
+
+ // Add a pipeline
+ const model = pgml.newModel();
+ const splitter = pgml.newSplitter();
+ const pipeline = pgml.newPipeline(
+ "my_javascript_sqa_pipeline",
+ model,
+ splitter,
+ );
+ await collection.add_pipeline(pipeline);
+
+ // Upsert documents, these documents are automatically split into chunks and embedded by our pipeline
+ const documents = [
+ {
+ id: "Document One",
+ text: "PostgresML is the best tool for machine learning applications!",
+ },
+ {
+ id: "Document Two",
+ text: "PostgresML is open source and available to everyone!",
+ },
+ ];
+ await collection.upsert_documents(documents);
+
+ const query = "What is the best tool for machine learning?";
+
+ // Perform vector search
+ const queryResults = await collection
+ .query()
+ .vector_recall(query, pipeline)
+ .limit(1)
+ .fetch_all();
+
+ // Construct context from results
+ const context = queryResults
+ .map((result) => {
+ return result[1];
+ })
+ .join("\n");
+
+ // Query for summarization
+ const builtins = pgml.newBuiltins();
+ const answer = await builtins.transform(
+ { task: "summarization", model: "sshleifer/distilbart-cnn-12-6" },
+ [context],
+ );
+
+ // Archive the collection
+ await collection.archive();
+ return answer;
+};
+
+main().then((results) => {
+ console.log("Question summary: \n", results);
+});
diff --git a/pgml-sdks/rust/pgml/python/examples/README.md b/pgml-sdks/rust/pgml/python/examples/README.md
index e2e22eb6e..81416c038 100644
--- a/pgml-sdks/rust/pgml/python/examples/README.md
+++ b/pgml-sdks/rust/pgml/python/examples/README.md
@@ -15,3 +15,6 @@ In this example, we will show how to use `vector_recall` result as a `context` t
### [Table Question Answering](./table_question_answering.py)
In this example, we will use [Open Table-and-Text Question Answering (OTT-QA)
](https://github.com/wenhuchen/OTT-QA) dataset to run queries on tables. We will use `deepset/all-mpnet-base-v2-table` model that is trained for embedding tabular data for retrieval tasks.
+
+### [Summarizing Question Answering](./summarizing_question_answering.py)
+This is an example to find documents relevant to a question from the collection of documents and then summarize those documents.
diff --git a/pgml-sdks/rust/pgml/python/examples/extractive_question_answering.py b/pgml-sdks/rust/pgml/python/examples/extractive_question_answering.py
index 21cfc90b5..21b5f2e67 100644
--- a/pgml-sdks/rust/pgml/python/examples/extractive_question_answering.py
+++ b/pgml-sdks/rust/pgml/python/examples/extractive_question_answering.py
@@ -56,8 +56,7 @@ async def main():
"question-answering", [json.dumps({"question": query, "context": context})]
)
end = time()
- console.print("Results for query '%s'" % query, style="bold")
- console.print(answer)
+ console.print("Answer '%s'" % answer, style="bold")
console.print("Query time = %0.3f" % (end - start))
# Archive collection
diff --git a/pgml-sdks/rust/pgml/python/examples/summarizing_question_answering.py b/pgml-sdks/rust/pgml/python/examples/summarizing_question_answering.py
new file mode 100644
index 000000000..4c291aac0
--- /dev/null
+++ b/pgml-sdks/rust/pgml/python/examples/summarizing_question_answering.py
@@ -0,0 +1,71 @@
+from pgml import Collection, Model, Splitter, Pipeline, Builtins, py_init_logger
+import json
+from datasets import load_dataset
+from time import time
+from dotenv import load_dotenv
+from rich.console import Console
+import asyncio
+
+
+py_init_logger()
+
+
+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("Querying for context ...")
+ start = time()
+ results = (
+ await collection.query().vector_recall(query, pipeline).limit(5).fetch_all()
+ )
+ end = time()
+ console.print("\n Results for '%s' " % (query), style="bold")
+ console.print(results)
+ console.print("Query time = %0.3f" % (end - start))
+
+ # Construct context from results
+ context = " ".join(results[0][1].strip().split())
+ context = context.replace('"', '\\"').replace("'", "''")
+
+ # Query for summary
+ builtins = Builtins()
+ console.print("Querying for summary ...")
+ start = time()
+ summary = await builtins.transform(
+ {"task": "summarization", "model": "sshleifer/distilbart-cnn-12-6"},
+ [context],
+ )
+ end = time()
+ console.print("Summary '%s'" % summary, style="bold")
+ console.print("Query time = %0.3f" % (end - start))
+
+ # Archive collection
+ await collection.archive()
+
+
+if __name__ == "__main__":
+ asyncio.run(main())
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