Field level description for data classes in Product Recommendation example

docs/docs/examples/examples/product_recommendation.md

@@ -29,9 +29,9 @@ Product taxonomy is a way to organize product catalogs in a logical and hierarch
 
 ## Prerequisites
 
-* [Install PostgreSQL](https://cocoindex.io/docs/getting_started/installation#-install-postgres). CocoIndex uses PostgreSQL internally for incremental processing.
+- [Install PostgreSQL](https://cocoindex.io/docs/getting_started/installation#-install-postgres). CocoIndex uses PostgreSQL internally for incremental processing.
 - [Install Neo4j](https://cocoindex.io/docs/targets/neo4j), a graph database.
-- - [Configure your OpenAI API key](https://cocoindex.io/docs/ai/llm#openai). Create a `.env` file from `.env.example`, and fill `OPENAI_API_KEY`.
+- [Configure your OpenAI API key](https://cocoindex.io/docs/ai/llm#openai). Create a `.env` file from `.env.example`, and fill `OPENAI_API_KEY`.
 
 Alternatively, we have native support for Gemini, Ollama, LiteLLM. You can choose your favorite LLM provider and work completely on-premises.
 
@@ -134,44 +134,43 @@ It performs the following transformations:
 
 ### Product Taxonomy Definition
 
-Since we are using LLM to extract product taxonomy, we need to provide a detailed instruction at the class-level docstring.
+Since we are using LLM to extract product taxonomy, we need to provide a detailed instruction at the field-level description.
 
 ```python
-@dataclasses.dataclass
-class ProductTaxonomy:
-    """
-    Taxonomy for the product.
-
-    A taxonomy is a concise noun (or short noun phrase), based on its core functionality, without specific details such as branding, style, etc.
-
-    Always use the most common words in US English.
-
-    Use lowercase without punctuation, unless it's a proper noun or acronym.
-
-    A product may have multiple taxonomies. Avoid large categories like "office supplies" or "electronics". Use specific ones, like "pen" or "printer".
-    """
-    name: str
+class ProductTaxonomy(BaseModel):
+   """
+   Taxonomy for the product.
+   """
+
+   name: str = Field(
+      description="A taxonomy is a concise noun (or short noun phrase), based on its core functionality, "
+                  "without specific details such as branding, style, etc. Always use the most common words in US "
+                  "English. Use lowercase without punctuation, unless it's a proper noun or acronym. A product may "
+                  "have multiple taxonomies. Avoid large categories like 'office supplies' or 'electronics'. Use "
+                  "specific ones, like 'pen' or 'printer'."
+   )
 ```
 
 ### Define Product Taxonomy Info
 
-Basically we want to extract all possible taxonomies for a product, and think about what other products are likely to be bought together with the current product.
+Basically, we want to extract all possible taxonomies for a product and think about what other products are likely to be bought together with the current product.
 
 ```python
-@dataclasses.dataclass
-class ProductTaxonomyInfo:
-    """
-    Taxonomy information for the product.
-
-    Fields:
-    - taxonomies: Taxonomies for the current product.
-    - complementary_taxonomies: Think about when customers buy this product, what else they might need as complementary products. Put labels for these complentary products.
-    """
-    taxonomies: list[ProductTaxonomy]
-    complementary_taxonomies: list[ProductTaxonomy]
+class ProductTaxonomyInfo(BaseModel):
+   """
+   Taxonomy information for the product.
+   """
+
+   taxonomies: list[ProductTaxonomy] = Field(
+      description="Taxonomies for the current product."
+   )
+   complementary_taxonomies: list[ProductTaxonomy] = Field(
+      "Think about when customers buy this product, what else they might need as complementary products. Put labels "
+      "for these complementary products."
+   )
 ```
 
-For each product, we want some insight about its taxonomy and complementary taxonomy and we could use that as bridge to find related product using knowledge graph.
+For each product, we want some insight about its taxonomy and complementary taxonomy, and we could use that as a bridge to find a related product using the knowledge graph.
 
 ### LLM Extraction
 

examples/product_recommendation/main.py

@@ -2,7 +2,7 @@
 This example shows how to extract relationships from Markdown documents and build a knowledge graph.
 """
 
-import dataclasses
+from pydantic import BaseModel, Field
 import datetime
 import cocoindex
 from jinja2 import Template
@@ -21,7 +21,6 @@
 GraphDbDeclaration = cocoindex.targets.Neo4jDeclaration
 conn_spec = neo4j_conn_spec
 
-
 # Template for rendering product information as markdown to provide information to LLMs
 PRODUCT_TEMPLATE = """
 # {{ title }}
@@ -42,48 +41,45 @@
  """
 
 
-@dataclasses.dataclass
-class ProductInfo:
+class ProductInfo(BaseModel):
     id: str
     title: str
     price: float
     detail: str
 
 
-@dataclasses.dataclass
-class ProductTaxonomy:
+class ProductTaxonomy(BaseModel):
     """
     Taxonomy for the product.
-
-    A taxonomy is a concise noun (or short noun phrase), based on its core functionality, without specific details such as branding, style, etc.
-
-    Always use the most common words in US English.
-
-    Use lowercase without punctuation, unless it's a proper noun or acronym.
-
-    A product may have multiple taxonomies. Avoid large categories like "office supplies" or "electronics". Use specific ones, like "pen" or "printer".
     """
 
-    name: str
+    name: str = Field(
+        description="A taxonomy is a concise noun (or short noun phrase), based on its core functionality, "
+        "without specific details such as branding, style, etc. Always use the most common words in US "
+        "English. Use lowercase without punctuation, unless it's a proper noun or acronym. A product may "
+        "have multiple taxonomies. Avoid large categories like 'office supplies' or 'electronics'. Use "
+        "specific ones, like 'pen' or 'printer'."
+    )
 
 
-@dataclasses.dataclass
-class ProductTaxonomyInfo:
+class ProductTaxonomyInfo(BaseModel):
     """
     Taxonomy information for the product.
-
-    Fields:
-    - taxonomies: Taxonomies for the current product.
-    - complementary_taxonomies: Think about when customers buy this product, what else they might need as complementary products. Put labels for these complentary products.
     """
 
-    taxonomies: list[ProductTaxonomy]
-    complementary_taxonomies: list[ProductTaxonomy]
+    taxonomies: list[ProductTaxonomy] = Field(
+        description="Taxonomies for the current product."
+    )
+    complementary_taxonomies: list[ProductTaxonomy] = Field(
+        "Think about when customers buy this product, what else they might need as complementary products. Put labels "
+        "for these complementary products."
+    )
 
 
 @cocoindex.op.function(behavior_version=2)
 def extract_product_info(product: cocoindex.Json, filename: str) -> ProductInfo:
-    # Print  markdown for LLM to extract the taxonomy and complimentary taxonomy
+    """Print markdown for LLM to extract the taxonomy and complimentary taxonomy."""
+
     return ProductInfo(
         id=f"{filename.removesuffix('.json')}",
         title=product["title"],
@@ -97,7 +93,7 @@ def store_product_flow(
     flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope
 ) -> None:
     """
-    Define an example flow that extracts triples from files and build knowledge graph.
+    Define an example flow that extracts triples from files and build the knowledge graph.
     """
     data_scope["products"] = flow_builder.add_source(
         cocoindex.sources.LocalFile(path="products", included_patterns=["*.json"]),

examples/product_recommendation/pyproject.toml

@@ -3,7 +3,7 @@ name = "cocoindex-ecommerce-taxonomy"
 version = "0.1.0"
 description = "Simple example for CocoIndex: extract taxonomy from e-commerce products and build knowledge graph."
 requires-python = ">=3.11"
-dependencies = ["cocoindex>=0.3.9", "jinja2>=3.1.6"]
+dependencies = ["cocoindex>=0.3.9", "jinja2>=3.1.6", "pydantic>=2.11.9"]
 
 [tool.setuptools]
 packages = []