examples: simplify postgres source example to be a single flow (#938)

Author: badmonster0

examples/postgres_source/.env.example

@@ -3,14 +3,13 @@
 [![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 
 This example demonstrates how to use Postgres tables as the source for CocoIndex.
-It reads structured data from existing PostgreSQL tables, performs calculations, generates embeddings, and stores them in a separate CocoIndex table.
+It reads structured product data from existing PostgreSQL tables, performs calculations, generates embeddings, and stores them in a separate CocoIndex table.
 
 We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
 
-This example contains two flows:
+This example contains one flow:
 
-1. `postgres_message_indexing_flow`: Read from a simpler table `source_messages` (single primary key), and generate embeddings for the `message` column. 
-2. `postgres_product_indexing_flow`: Read from a more complex table `source_products` (composite primary key), compute additional fields and generates embeddings.
+`postgres_product_indexing_flow`: Read from a table `source_products` (composite primary key), compute additional fields like total value and full description, then generate embeddings for semantic search.
 
 
 ## Prerequisites
@@ -25,7 +24,7 @@ Before running the example, you need to:
 
 2.  Follow the [CocoIndex PostgreSQL setup guide](https://cocoindex.io/docs/getting_started/quickstart) to install and configure PostgreSQL with pgvector extension.
 
-3.  Create source tables `source_messages` and `source_products` with sample data:
+3.  Create source table `source_products` with sample data:
 
     ```bash
     $ psql "postgres://cocoindex:cocoindex@localhost/cocoindex" -f ./prepare_source_data.sql

examples/postgres_source/README.md

@@ -2,58 +2,6 @@
 import os
 
 
-@cocoindex.flow_def(name="PostgresMessageIndexing")
-def postgres_message_indexing_flow(
-    flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope
-) -> None:
-    """
-    Define a flow that reads data from a PostgreSQL table, generates embeddings,
-    and stores them in another PostgreSQL table with pgvector.
-    """
-
-    data_scope["messages"] = flow_builder.add_source(
-        cocoindex.sources.Postgres(
-            table_name="source_messages",
-            # Optional. Use the default CocoIndex database if not specified.
-            database=cocoindex.add_transient_auth_entry(
-                cocoindex.sources.DatabaseConnectionSpec(
-                    url=os.getenv("SOURCE_DATABASE_URL"),
-                )
-            ),
-            # Optional.
-            ordinal_column="created_at",
-        )
-    )
-
-    indexed_messages = data_scope.add_collector()
-    with data_scope["messages"].row() as message_row:
-        # Use the indexing column for embedding generation
-        message_row["embedding"] = message_row["message"].transform(
-            cocoindex.functions.SentenceTransformerEmbed(
-                model="sentence-transformers/all-MiniLM-L6-v2"
-            )
-        )
-        # Collect the data - include key columns and content
-        indexed_messages.collect(
-            id=message_row["id"],
-            author=message_row["author"],
-            message=message_row["message"],
-            embedding=message_row["embedding"],
-        )
-
-    indexed_messages.export(
-        "output",
-        cocoindex.targets.Postgres(),
-        primary_key_fields=["id"],
-        vector_indexes=[
-            cocoindex.VectorIndexDef(
-                field_name="embedding",
-                metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY,
-            )
-        ],
-    )
-
-
 @cocoindex.op.function()
 def calculate_total_value(
     price: float,
@@ -76,7 +24,7 @@ def postgres_product_indexing_flow(
     flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope
 ) -> None:
     """
-    Define a flow that reads data from a PostgreSQL table, generates embeddings,
+    Define a flow that reads product data from a PostgreSQL table, generates embeddings,
     and stores them in another PostgreSQL table with pgvector.
     """
     data_scope["products"] = flow_builder.add_source(

examples/postgres_source/main.py

@@ -1,38 +1,7 @@
 -- Usage: run with psql from your shell, for example:
 -- $ psql "postgres://cocoindex:cocoindex@localhost/cocoindex" -f ./prepare_source_data.sql
 -- ========================================
--- Simple schema: source_messages (single primary key)
--- ========================================
-DROP TABLE IF EXISTS source_messages CASCADE;
-CREATE TABLE source_messages (
-    id uuid NOT NULL PRIMARY KEY DEFAULT gen_random_uuid(),
-    author text NOT NULL,
-    message text NOT NULL,
-    created_at timestamp DEFAULT CURRENT_TIMESTAMP
-);
-INSERT INTO source_messages (author, message)
-VALUES (
-        'Jane Smith',
-        'Hello world! This is a test message.'
-    ),
-    (
-        'John Doe',
-        'PostgreSQL source integration is working great!'
-    ),
-    (
-        'Jane Smith',
-        'CocoIndex makes database processing so much easier.'
-    ),
-    (
-        'John Doe',
-        'Embeddings and vector search are powerful tools.'
-    ),
-    (
-        'John Doe',
-        'Natural language processing meets database technology.'
-    ) ON CONFLICT DO NOTHING;
--- ========================================
--- Multiple schema: source_products (composite primary key)
+-- Product schema: source_products (composite primary key)
 -- ========================================
 DROP TABLE IF EXISTS source_products CASCADE;
 CREATE TABLE source_products (