example: add the example hn_trending_topics

README.md

@@ -201,6 +201,7 @@ It defines an index flow like this:
 | [Custom Source HackerNews](examples/custom_source_hn) | Index HackerNews threads and comments, using *CocoIndex Custom Source* |
 | [Custom Output Files](examples/custom_output_files) | Convert markdown files to HTML files and save them to a local directory, using *CocoIndex Custom Targets* |
 | [Patient intake form extraction](examples/patient_intake_extraction) | Use LLM to extract structured data from patient intake forms with different formats |
+| [HackerNews Trending Topics](examples/hn_trending_topics) | Extract trending topics from HackerNews threads and comments, using *CocoIndex Custom Source* and LLM |
 
 More coming and stay tuned 👀!
 

examples/hn_trending_topics/.env.example

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+# Postgres database address for cocoindex
+COCOINDEX_DATABASE_URL=postgres://cocoindex:cocoindex@localhost/cocoindex
+
+OPENAI_API_KEY=

examples/hn_trending_topics/.gitignore

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+.env

examples/hn_trending_topics/README.md

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+# HackerNews Trending Topics Example
+
+[![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
+
+In this example, we use [CocoIndex Custom Source](https://cocoindex.io/docs/custom_ops/custom_targets) to define a source to get HackerNews recent content by calling [HackerNews API](https://hn.algolia.com/api).
+We build an index for HackerNews threads and their comments, and use LLM to extract trending topics from the text.
+
+The pipeline uses `ExtractByLlm` to identify topics like product names, technologies, models, and company names mentioned in threads and comments, storing them in canonical form (avoiding acronyms unless very popular).
+
+We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
+
+## Features
+
+- **Custom Source Integration**: Fetches HackerNews threads and comments via API
+- **LLM Topic Extraction**: Automatically extracts topics using `ExtractByLlm` function
+- **Canonical Topic Forms**: Topics are stored in canonical form (e.g., "Large Language Model" instead of "LLM")
+- **Multiple Query Handlers**:
+  - `search_by_topic`: Search content by specific topic
+  - `get_trending_topics`: Get trending topics ranked by mention count
+
+## Steps
+
+### Indexing Flow
+
+1. We define a custom source connector `HackerNews` to get HackerNews recent threads by calling HackerNews API.
+2. For each thread and comment, we extract topics using LLM (`ExtractByLlm`).
+3. We build two indexes:
+   - `hn_messages`: Full text of threads and comments
+   - `hn_topics`: Extracted topics with references to their source content, keyed by (topic, message_id)
+
+## Prerequisite
+
+[Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one.
+
+## Run
+
+Install dependencies:
+
+```bash
+pip install -e .
+```
+
+Update the target:
+
+```bash
+cocoindex update --setup main
+```
+
+Each time when you run the `update` command, cocoindex will only re-process threads that have changed, and keep the target in sync with the recent 500 threads from HackerNews.
+
+You can also run `update` command in live mode, which will keep the target in sync with the source continuously:
+
+```bash
+cocoindex update --setup -L main.py
+```
+
+## Query Examples
+
+After running the pipeline, you can query the extracted topics:
+
+```bash
+# Get trending topics
+cocoindex query main.py get_trending_topics --limit 20
+
+# Search content by specific topic
+cocoindex query main.py search_by_topic --topic "Claude"
+
+# Search by text content
+cocoindex query main.py search_text --query "artificial intelligence"
+```
+
+## CocoInsight
+
+I used CocoInsight (Free beta now) to troubleshoot the index generation and understand the data lineage of the pipeline.
+It just connects to your local CocoIndex server, with Zero pipeline data retention. Run following command to start CocoInsight:
+
+```
+cocoindex server -ci -L main
+```
+
+Then open the CocoInsight UI at [https://cocoindex.io/cocoinsight](https://cocoindex.io/cocoinsight).