docs: product recommendation example (#922)

Author: badmonster0

docs/docs/examples/examples/product_recommendation.md

@@ -10,14 +10,16 @@ sidebar_custom_props:
 tags: [knowledge-graph]
 ---
 
-import { GitHubButton, YouTubeButton } from '../../../src/components/GitHubButton';
+import { GitHubButton, YouTubeButton, DocumentationButton } from '../../../src/components/GitHubButton';
 
 <GitHubButton url="https://github.com/cocoindex-io/cocoindex/tree/main/examples/product_recommendation"/>
 
 ## Overview
 
-In this blog, we will build a real-time product recommendation engine with LLM and graph database. In particular, we will use LLM to understand the category (taxonomy) of a product. In addition, we will use LLM to enumerate the complementary products - users are likely to buy together with the current product (pencil and notebook). We will use Graph to explore the relationships between products that can be further used for product recommendations or labeling.
-
+We will build a real-time product recommendation engine with LLM and graph database. In particular, we will:
+- Use LLM to understand the category (taxonomy) of a product. 
+- Use LLM to enumerate the complementary products - users are likely to buy together with the current product (pencil and notebook).
+- Use Graph to explore the relationships between products that can be further used for product recommendations or labeling.
 
 
 Product taxonomy is a way to organize product catalogs in a logical and hierarchical structure; a great detailed explanation can be found [here](https://help.shopify.com/en/manual/products/details/product-category). In practice, it is a complicated problem: a product can be part of multiple categories, and a category can have multiple parents.
@@ -26,15 +28,17 @@ 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 Neo4j](https://cocoindex.io/docs/ops/storages#Neo4j), a graph database.
-*   [Configure your OpenAI API key](https://cocoindex.io/docs/ai/llm#openai). Alternatively, you can switch to Ollama, which runs LLM models locally - [guide](https://cocoindex.io/docs/ai/llm#ollama).
+*   -  [Configure your OpenAI API key](https://cocoindex.io/docs/ai/llm#openai). Create a `.env` file from `.env.example`, and fill `OPENAI_API_KEY`.
 
-## Documentation
-You can read the official CocoIndex Documentation for Property Graph Targets [here](https://cocoindex.io/docs/ops/storages#property-graph-targets).
+Alternatively, we have native support for Gemini, Ollama, LiteLLM. You can choose your favorite LLM provider and work completely on-premises.
 
+<DocumentationButton href="https://cocoindex.io/docs/ai/llm" text="LLM" margin="0 0 16px 0" />
 
-## Data flow to build knowledge graph
 
-### Overview
+## Documentation
+<DocumentationButton href="https://cocoindex.io/docs/ops/targets#property-graph-targets" text="Property Graph Targets" margin="0 0 16px 0" />
+
+## Flow Overview
 
 The core flow is about [~100 lines of python code](https://github.com/cocoindex-io/cocoindex/blob/1d42ab31692c73743425f7712c9af395ef98c80e/examples/product_taxonomy_knowledge_graph/main.py#L75-L177)
 
@@ -48,7 +52,7 @@ We are going to declare a data flow
 4.  export data to neo4j
 
 
-### Add documents as source
+## Add source
 
 ```python
 @cocoindex.flow_def(name="StoreProduct")
@@ -64,7 +68,7 @@ Here `flow_builder.add_source` creates a [KTable](https://cocoindex.io/docs/core
 
 
 
-### Add data collectors
+## Add data collectors
 
 Add collectors at the root scope to collect the product, taxonomy and complementary taxonomy.
 
@@ -74,11 +78,11 @@ product_taxonomy = data_scope.add_collector()
 product_complementary_taxonomy = data_scope.add_collector()
 ```
 
-### Process each product
+## Process each product
 
 We will parse the JSON file for each product, and transform the data to the format that we need for downstream processing. 
 
-#### Data Mapping
+### Data mapping
 
 ```python
 @cocoindex.op.function(behavior_version=2)
@@ -98,8 +102,7 @@ Here we define a function for data mapping, e.g.,
 - clean up the `price` field
 - generate a markdown string for the product detail based on all the fields (for LLM to extract taxonomy and complementary taxonomy, we find that markdown works best as context for LLM).
 
-
-#### Flow
+### Process product JSON in the flow
 
 Within the flow, we plug in the data mapping transformation to process each product JSON.
 
@@ -111,15 +114,25 @@ with data_scope["products"].row() as product:
     product_node.collect(id=data["id"], url=data["url"], title=data["title"], price=data["price"])
 ```
 
+It performs the following transformations:
+
 1. The first `transform()` parses the JSON file.
-2. The second `transform()` performs the defined data mapping.
+
+    <DocumentationButton href="https://cocoindex.io/docs/ops/functions#parsejson" text="ParseJson" margin="0 0 16px 0" />
+    ![ParseJson](/img/examples/product_recommendation/parse_json.png)
+
+2.  The second `transform()` performs the defined data mapping.
+    ![Extract product info and data mapping](/img/examples/product_recommendation/extract_product.png)
+
 3. We collect the fields we need for the product node in Neo4j.
 
 
 
-### Extract taxonomy and complementary taxonomy using LLM
+## Extract taxonomy and complementary taxonomy 
 
-#### Product Taxonomy Definition
+![Product Taxonomy Info](/img/examples/product_recommendation/taxonomy.png)
+
+### Product Taxonomy Definition
 
 Since we are using LLM to extract product taxonomy, we need to provide a detailed instruction at the class-level docstring.
 
@@ -140,7 +153,7 @@ class ProductTaxonomy:
     name: str
 ```
 
-#### Define Product Taxonomy Info
+### 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.
 
@@ -162,7 +175,8 @@ class ProductTaxonomyInfo:
 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. 
 
 
-#### LLM Extraction
+
+### LLM Extraction
 
 Finally, we will use `cocoindex.functions.ExtractByLlm` to extract the taxonomy and complementary taxonomy from the product detail.
 
@@ -173,11 +187,15 @@ taxonomy = data["detail"].transform(cocoindex.functions.ExtractByLlm(
                 output_type=ProductTaxonomyInfo))
 ```
 
+<DocumentationButton href="https://cocoindex.io/docs/ops/functions#extractbyllm" text="ExtractByLlm" margin="0 0 16px 0" />
 
 
 For example, LLM takes the description of the *gel pen*, and extracts taxonomy to be *gel pen*.
 Meanwhile, it suggests that when people buy *gel pen*, they may also be interested in *notebook* etc as complimentary taxonomy.
 
+![Extract taxonomy and complementary taxonomy](/img/examples/product_recommendation/extract_taxonomy.png)
+
+### Collect taxonomy and complementary taxonomy
 
 And then we will collect the taxonomy and complementary taxonomy to the collector.
 ```python
@@ -188,15 +206,16 @@ with taxonomy['complementary_taxonomies'].row() as t:
 ```
 
 
-### Build knowledge graph
+## Build knowledge graph
 
-#### Basic concepts
+### Basic concepts
 All nodes for Neo4j need two things:
 1. Label: The type of the node. E.g., `Product`, `Taxonomy`.
 2. Primary key field: The field that uniquely identifies the node. E.g., `id` for `Product` nodes.
 
 CocoIndex uses the primary key field to match the nodes and deduplicate them. If you have multiple nodes with the same primary key, CocoIndex keeps only one of them.
 
+![Deduplication](/img/examples/product_recommendation/dedupe.png)
 
 There are two ways to map nodes:
 1. When you have a collector just for the node, you can directly export it to Neo4j. For example `Product`. We've collected each product explicitly.
@@ -211,7 +230,7 @@ product_taxonomy.collect(id=cocoindex.GeneratedField.UUID, product_id=data["id"]
 Collects a relationship, and taxonomy node is created from the relationship.
 
 
-#### Configure Neo4j connection:
+### Configure Neo4j connection
 
 ```python
 conn_spec = cocoindex.add_auth_entry(
@@ -223,7 +242,7 @@ conn_spec = cocoindex.add_auth_entry(
 ))
 ```
 
-#### Export `Product` nodes to Neo4j
+### Export `Product` nodes to Neo4j
 
 ```python
 product_node.export(
@@ -235,13 +254,15 @@ product_node.export(
     primary_key_fields=["id"],
 )
 ```
+![Export Product](/img/examples/product_recommendation/export_product.png)
+
 
 This exports Neo4j nodes with label `Product` from the `product_node` collector.
 - It declares Neo4j node label `Product`. It specifies `id` as the primary key field.
 - It carries all the fields from `product_node` collector to Neo4j nodes with label `Product`.
 
 
-#### Export `Taxonomy` nodes to Neo4j
+### Export `Taxonomy` nodes to Neo4j
 
 We don't have explicit collector for `Taxonomy` nodes. 
 They are part of the `product_taxonomy` and `product_complementary_taxonomy` collectors and fields are collected during the taxonomy extraction.
@@ -258,6 +279,7 @@ flow_builder.declare(
 )
 ```
 
+
 Next, export the `product_taxonomy` as relationship to Neo4j.
 
 ```python
@@ -287,38 +309,38 @@ product_taxonomy.export(
 )
 ```
 
+![Export Taxonomy](/img/examples/product_recommendation/export_taxonomy.png)
+
 
 
 Similarly, we can export the `product_complementary_taxonomy` as relationship to Neo4j.
 ```python
-    product_complementary_taxonomy.export(
-        "product_complementary_taxonomy",
-        cocoindex.storages.Neo4j(
-            connection=conn_spec,
-            mapping=cocoindex.storages.Relationships(
-                rel_type="PRODUCT_COMPLEMENTARY_TAXONOMY",
-                source=cocoindex.storages.NodeFromFields(
-                    label="Product",
-                    fields=[
-                        cocoindex.storages.TargetFieldMapping(
-                            source="product_id", target="id"),
-                    ]
-                ),
-                target=cocoindex.storages.NodeFromFields(
-                    label="Taxonomy",
-                    fields=[
-                        cocoindex.storages.TargetFieldMapping(
-                            source="taxonomy", target="value"),
-                    ]
-                ),
+product_complementary_taxonomy.export(
+    "product_complementary_taxonomy",
+    cocoindex.storages.Neo4j(
+        connection=conn_spec,
+        mapping=cocoindex.storages.Relationships(
+            rel_type="PRODUCT_COMPLEMENTARY_TAXONOMY",
+            source=cocoindex.storages.NodeFromFields(
+                label="Product",
+                fields=[
+                    cocoindex.storages.TargetFieldMapping(
+                        source="product_id", target="id"),
+                ]
+            ),
+            target=cocoindex.storages.NodeFromFields(
+                label="Taxonomy",
+                fields=[
+                    cocoindex.storages.TargetFieldMapping(
+                        source="taxonomy", target="value"),
+                ]
             ),
         ),
-        primary_key_fields=["id"],
-    )
+    ),
+    primary_key_fields=["id"],
+)
 ```
-
-
-
+![Export Complementary Taxonomy](/img/examples/product_recommendation/export_all.png)
 
 The `cocoindex.storages.Relationships` declares how to map relationships in Neo4j.
 
@@ -330,9 +352,7 @@ Note that different relationships may share the same source and target nodes.
 `NodeFromFields` takes the fields from the `entity_relationship` collector and creates `Taxonomy` nodes. 
 
 
-## Query and test your index
-🎉 Now you are all set!
-
+## Run the flow
 1.  Install the dependencies:
 
     ```
@@ -350,28 +370,29 @@ Note that different relationships may share the same source and target nodes.
     documents: 9 added, 0 removed, 0 updated
     ```
 
-3.  (Optional) I used CocoInsight to troubleshoot the index generation and understand the data lineage of the pipeline. 
-It is in free beta now, you can give it a try. Run following command to start CocoInsight:
-
-    ```
-    cocoindex server -ci main.py
-    ```
-
-    And then open the url https://cocoindex.io/cocoinsight.  It just connects to your local CocoIndex server, with Zero pipeline data retention.
-
-
-
-
-### Browse the knowledge graph
+## Browse the knowledge graph
 After the knowledge graph is built, you can explore the knowledge graph you built in Neo4j Browser.
 
 For the dev environment, you can connect to Neo4j browser using credentials:
 - username: `Neo4j`
 - password: `cocoindex`
+
 which is pre-configured in our docker compose [config.yaml](https://raw.githubusercontent.com/cocoindex-io/cocoindex/refs/heads/main/dev/Neo4j.yaml).
 
 You can open it at [http://localhost:7474](http://localhost:7474), and run the following Cypher query to get all relationships:
 
 ```cypher
 MATCH p=()-->() RETURN p
-```
+```
+
+![Neo4j Browser](/img/examples/product_recommendation/neo4j.png)
+
+## CocoInsight
+I used CocoInsight to troubleshoot the index generation and understand the data lineage of the pipeline. It is in free beta now, you can give it a try. Run following command to start CocoInsight:
+
+```
+cocoindex server -ci main.py
+```
+
+And then open the url `https://cocoindex.io/cocoinsight`.  It just connects to your local CocoIndex server, with Zero pipeline data retention.
+