examples: add query path for postgres source example

examples/postgres_source/main.py

@@ -1,5 +1,15 @@
-import cocoindex
+from typing import Any
 import os
+import datetime
+
+from dotenv import load_dotenv
+from psycopg_pool import ConnectionPool
+from pgvector.psycopg import register_vector  # type: ignore[import-untyped]
+from psycopg.rows import dict_row
+from numpy.typing import NDArray
+
+import numpy as np
+import cocoindex
 
 
 @cocoindex.op.function()
@@ -19,6 +29,21 @@ def make_full_description(
     return f"Category: {category}\nName: {name}\n\n{description}"
 
 
+@cocoindex.transform_flow()
+def text_to_embedding(
+    text: cocoindex.DataSlice[str],
+) -> cocoindex.DataSlice[NDArray[np.float32]]:
+    """
+    Embed the text using a SentenceTransformer model.
+    This is a shared logic between indexing and querying, so extract it as a function.
+    """
+    return text.transform(
+        cocoindex.functions.SentenceTransformerEmbed(
+            model="sentence-transformers/all-MiniLM-L6-v2"
+        )
+    )
+
+
 @cocoindex.flow_def(name="PostgresProductIndexing")
 def postgres_product_indexing_flow(
     flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope
@@ -32,13 +57,14 @@ def postgres_product_indexing_flow(
             table_name="source_products",
             # Optional. Use the default CocoIndex database if not specified.
             database=cocoindex.add_transient_auth_entry(
-                cocoindex.sources.DatabaseConnectionSpec(
-                    url=os.getenv("SOURCE_DATABASE_URL"),
+                cocoindex.DatabaseConnectionSpec(
+                    url=os.environ["SOURCE_DATABASE_URL"],
                 )
             ),
             # Optional.
             ordinal_column="modified_time",
-        )
+        ),
+        refresh_interval=datetime.timedelta(seconds=30),
     )
 
     indexed_product = data_scope.add_collector()
@@ -80,3 +106,59 @@ def postgres_product_indexing_flow(
             )
         ],
     )
+
+
+def search(pool: ConnectionPool, query: str, top_k: int = 5) -> list[dict[str, Any]]:
+    # Get the table name, for the export target in the text_embedding_flow above.
+    table_name = cocoindex.utils.get_target_default_name(
+        postgres_product_indexing_flow, "output"
+    )
+    # Evaluate the transform flow defined above with the input query, to get the embedding.
+    query_vector = text_to_embedding.eval(query)
+    # Run the query and get the results.
+    with pool.connection() as conn:
+        register_vector(conn)
+        with conn.cursor(row_factory=dict_row) as cur:
+            cur.execute(
+                f"""
+                SELECT
+                    product_category,
+                    product_name,
+                    description,
+                    amount,
+                    total_value,
+                    (embedding <=> %s) AS distance
+                FROM {table_name}
+                ORDER BY distance ASC
+                LIMIT %s
+            """,
+                (query_vector, top_k),
+            )
+            return cur.fetchall()
+
+
+def _main() -> None:
+    # Initialize the database connection pool.
+    pool = ConnectionPool(os.environ["COCOINDEX_DATABASE_URL"])
+    # Run queries in a loop to demonstrate the query capabilities.
+    while True:
+        query = input("Enter search query (or Enter to quit): ")
+        if query == "":
+            break
+        # Run the query function with the database connection pool and the query.
+        results = search(pool, query)
+        print("\nSearch results:")
+        for result in results:
+            score = 1.0 - result["distance"]
+            print(
+                f"[{score:.3f}] {result['product_category']} | {result['product_name']} | {result['amount']} | {result['total_value']}"
+            )
+            print(f"    {result['description']}")
+            print("---")
+        print()
+
+
+if __name__ == "__main__":
+    load_dotenv()
+    cocoindex.init()
+    _main()

examples/postgres_source/pyproject.toml

@@ -3,7 +3,13 @@ name = "postgres-source"
 version = "0.1.0"
 description = "Demonstrate how to use Postgres tables as the source for CocoIndex."
 requires-python = ">=3.11"
-dependencies = ["cocoindex[embeddings]>=0.2.1"]
+dependencies = [
+    "cocoindex[embeddings]>=0.2.1",
+    "python-dotenv>=1.0.1",
+    "pgvector>=0.4.1",
+    "psycopg[binary,pool]",
+    "numpy",
+]
 
 [tool.setuptools]
 packages = []