diff --git a/docs/docs/examples/examples/document_ai.md b/docs/docs/examples/examples/document_ai.md
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@@ -0,0 +1,156 @@
+---
+title: Bring your own parser as building block with Google Document AI
+description: Use Google Document AI to parse document, embed the resulting text, and store it in a vectorized database for semantic search.
+sidebar_class_name: hidden
+slug: /examples/document_ai
+canonicalUrl: '/examples/document_ai'
+sidebar_custom_props:
+ image: /img/examples/document_ai/cover.png
+ tags: [vector-index, custom-building-block]
+tags: [vector-index, custom-building-block]
+---
+import { GitHubButton, DocumentationButton, ExampleButton } from '../../../src/components/GitHubButton';
+
+
+
+
+
+
+CocoIndex is a flexible ETL framework with incremental processing. We don’t build parser ourselves, and users can bring in any open source or commercial parser that works best for their scenarios. In this example, we show how to use **Google Document AI to parse document**, embed the resulting text, and store it in a vectorized database for semantic search.
+
+## Set up
+- [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one.
+- Configure Project and Processor ID for Document AI API
+ - [Official Google document AI API](https://cloud.google.com/document-ai/docs/try-docai) with free live demo.
+ - Sign in to [Google Cloud Console](https://console.cloud.google.com/), create or open a project, and enable Document AI API.
+ - 
+ - 
+- update `.env` with `GOOGLE_CLOUD_PROJECT_ID` and `GOOGLE_CLOUD_PROCESSOR_ID`.
+
+
+## Create Your building block to convert PDFs to Markdown
+
+We define a `ToMarkdown` custom function spec, which leverages Google Document AI to parse PDF content:
+
+```python
+class ToMarkdown(cocoindex.op.FunctionSpec):
+ """Convert a PDF to markdown using Google Document AI."""
+```
+
+The corresponding executor class handles API initialization and parsing logic:
+
+```python
+@cocoindex.op.executor_class(cache=True, behavior_version=1)
+class DocumentAIExecutor:
+ """Executor for Google Document AI to parse PDF files."""
+
+ spec: ToMarkdown
+ _client: documentai.DocumentProcessorServiceClient
+ _processor_name: str
+
+ def prepare(self):
+ # Initialize the Document AI client
+ project_id = os.environ.get("GOOGLE_CLOUD_PROJECT_ID")
+ location = os.environ.get("GOOGLE_CLOUD_LOCATION", "us")
+ processor_id = os.environ.get("GOOGLE_CLOUD_PROCESSOR_ID")
+
+ opts = ClientOptions(api_endpoint=f"{location}-documentai.googleapis.com")
+ self._client = documentai.DocumentProcessorServiceClient(client_options=opts)
+ self._processor_name = self._client.processor_path(project_id, location, processor_id)
+
+ async def __call__(self, content: bytes) -> str:
+ """Parse PDF content and convert to markdown text."""
+ request = documentai.ProcessRequest(
+ name=self._processor_name,
+ raw_document=documentai.RawDocument(content=content, mime_type="application/pdf")
+ )
+ response = self._client.process_document(request=request)
+ return response.document.text
+```
+
+Make sure you configure the `cache` and `behavior_version` parameters for heavy operations like this.
+
+- `cache`: Whether the executor will enable cache for this function. When True, the executor will cache the result of the function for reuse during reprocessing. We recommend to set this to True for any function that is computationally intensive.
+
+- `behavior_version`: The version of the behavior of the function. When the version is changed, the function will be re-executed even if cache is enabled. It's required to be set if cache is True.
+
+
+
+
+
+
+## Define the flow
+
+```python
+@cocoindex.flow_def(name="DocumentAiPdfEmbedding")
+def pdf_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
+ # flow definition
+```
+
+### Add source & collector
+
+```python
+data_scope["documents"] = flow_builder.add_source(
+ cocoindex.sources.LocalFile(path="pdf_files", binary=True)
+)
+
+doc_embeddings = data_scope.add_collector()
+```
+
+
+
+
+
+### Process each document
+
+```python
+with data_scope["documents"].row() as doc:
+ doc["markdown"] = doc["content"].transform(ToMarkdown())
+ doc["chunks"] = doc["markdown"].transform(
+ cocoindex.functions.SplitRecursively(),
+ language="markdown",
+ chunk_size=2000,
+ chunk_overlap=500
+ )
+ with doc["chunks"].row() as chunk:
+ chunk["embedding"] = chunk["text"].call(text_to_embedding)
+ doc_embeddings.collect(
+ id=cocoindex.GeneratedField.UUID,
+ filename=doc["filename"],
+ location=chunk["location"],
+ text=chunk["text"],
+ embedding=chunk["embedding"]
+ )
+```
+
+1. Convert them to Markdown using Document AI.
+2. Split the Markdown into chunks.
+3. Embed each chunk.
+
+## Export to Postgres
+
+```python
+doc_embeddings.export(
+ "doc_embeddings",
+ cocoindex.storages.Postgres(),
+ primary_key_fields=["id"],
+ vector_indexes=[
+ cocoindex.VectorIndexDef(
+ field_name="embedding",
+ metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY
+ )
+ ]
+)
+```
+
+## End to End Example
+
+For a step-by-step walkthrough of each indexing stage and the query path, check out this example:
+
+
+
+## Other sources
+
+CocoIndex natively supports Google Drive, Amazon S3, Azure Blob Storage, and more with native incremental processing out of box - when new or updated files are detected, the pipeline will capture the changes and only process what's changed.
+
+
diff --git a/docs/docs/examples/examples/postgres_source.md b/docs/docs/examples/examples/postgres_source.md
index b3e4628ed..3db35ed80 100644
--- a/docs/docs/examples/examples/postgres_source.md
+++ b/docs/docs/examples/examples/postgres_source.md
@@ -292,8 +292,10 @@ This [example](https://cocoindex.io/docs/examples/image_search#fast-api-applicat
## Why One Framework for Structured + Unstructured?
-- One mental model: Treat files, APIs, and databases uniformly; AI steps are ordinary ops.
-- Incremental by default: Use an ordinal column to sync only changes; no fragile glue jobs.
-- Consistency: Embeddings are always derived from the exact transformed row state.
-- Operational simplicity: One deployment, one lineage view, fewer moving parts.
+- Unified workflow: All data— files, APIs, or databases—is processed through a single, consistent system, and AI operations are handled alongside standard data transformations.
+- True incremental processing with live updates: Out-of-box incremental support from the framework, process only what’s changed, avoiding redundant computation and ensuring faster updates to downstream indexes.
+
+- Reliable consistency: Embeddings and derived data always reflect the accurate, transformed state of each row, ensuring results are dependable and current in a single flow.
+
+- Streamlined operations: A single deployment manages everything, providing clear data lineage and reducing the complexity of the data stack.
\ No newline at end of file
diff --git a/docs/src/components/GitHubButton/index.tsx b/docs/src/components/GitHubButton/index.tsx
index d87892e8c..f59bea313 100644
--- a/docs/src/components/GitHubButton/index.tsx
+++ b/docs/src/components/GitHubButton/index.tsx
@@ -1,6 +1,6 @@
import type { ReactNode } from 'react';
import { FaGithub, FaYoutube } from 'react-icons/fa';
-import { MdMenuBook } from 'react-icons/md';
+import { MdMenuBook, MdDriveEta } from 'react-icons/md';
type ButtonProps = {
href: string;
@@ -73,4 +73,20 @@ function DocumentationButton({ url, text, margin }: DocumentationButtonProps): R
);
}
-export { GitHubButton, YouTubeButton, DocumentationButton };
+// ExampleButton as requested
+type ExampleButtonProps = {
+ href: string;
+ text: string;
+ margin?: string;
+};
+
+function ExampleButton({ href, text, margin }: ExampleButtonProps): ReactNode {
+ return (
+
+ );
+}
+
+export { GitHubButton, YouTubeButton, DocumentationButton, ExampleButton };
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