Update README files

README.md

@@ -4,6 +4,22 @@
 
 This repo showcases and compares various search algorithms and models using [Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search](https://github.com/amazon-science/esci-data).
 
+## Project Structure
+
+```
+├── .github
+│   ├── dependabot.yml
+│   └── workflows
+│       ├── {project_A}-deploy.yml
+│       └── {project_A}-test.yml
+├── Makefile                 # Common commands
+├── pyproject.toml           # Common Python configurations
+├── README.md
+└── src
+    ├── {project_A}
+    └── {project_B}
+```
+
 ## Installation
 
 Copy `.envrc.example` and fill in the necessary environment variables. Afterwards, proceed with installing the dependencies.
@@ -16,58 +32,10 @@ $ poetry env use python
 $ poetry install
 ```
 
-The following libraries are necessary for Japanese text processing.
-
-```shell
-# For macOS
-$ brew install mecab mecab-ipadic
-$ poetry run python -m unidic download
-```
-
-## Dataset
-
-Clone https://github.com/amazon-science/esci-data and copy `esci-data/shopping_queries_dataset/*` into `amazon-product/search/data/raw/`. Then, run the following command to preprocess the dataset.
-
-```shell
-$ poetry run inv data.merge-and-split
-```
-
-## Index Products
-
-This project involves indexing products into search engines. If you'd like to test it on your own machine, you can start by launching Elasticsearch or Vespa locally. Then, execute the document indexing pipeline against the created index.
-
-```shell
-$ docker compose --profile elasticsearch up
-$ poetry run inv es.create-index --index-name=products_jp
-$ poetry run inv indexing.feed \
-  --index-name=products_jp \
-  --locale=jp \
-  --dest=es \
-  --dest-host=http://localhost:9200 \
-  --nrows=10
-```
-
-## Demo
-
-The command below launches the [Streamlit](https://streamlit.io/) demo app.
-
-```shell
-# Launch Elasticsearch beforehand
-$ docker compose --profile elasticsearch up
-
-$ poetry run inv demo.es
-```
-
-![](https://user-images.githubusercontent.com/883148/203654537-8b495c9c-f8af-4c3f-90f9-60edacf647b9.png)
-
 ## Development
 
 Run the following tasks after adding any modifications.
 
 ```shell
-$ poetry run black .
-$ poetry run ruff . --fix
-$ poetry run mypy src
-$ poetry run pytest tests/unit -vv
-$ poetry run pytest tests/integration -vv
+$ make lint
 ```

src/amazon-product-search/README.md

@@ -0,0 +1,42 @@
+# Core - Amazon Product Search
+
+## Installation
+
+```shell
+$ pyenv install 3.11.8
+$ pyenv local 3.11.8
+$ pip install poetry
+$ poetry env use python
+$ poetry install
+```
+
+The following libraries are necessary for Japanese text processing.
+
+```shell
+# For macOS
+$ brew install mecab mecab-ipadic
+$ poetry run python -m unidic download
+```
+
+## Dataset
+
+Clone https://github.com/amazon-science/esci-data and copy `esci-data/shopping_queries_dataset/*` into `amazon-product/search/data/raw/`. Then, run the following command to preprocess the dataset.
+
+```shell
+$ poetry run inv data.merge-and-split
+```
+
+## Index Products
+
+This project involves indexing products into search engines. If you'd like to test it on your own machine, you can start by launching Elasticsearch or Vespa locally. Then, execute the document indexing pipeline against the created index.
+
+```shell
+$ docker compose --profile elasticsearch up
+$ poetry run inv es.create-index --index-name=products_jp
+$ poetry run inv indexing.feed \
+  --index-name=products_jp \
+  --locale=jp \
+  --dest=es \
+  --dest-host=http://localhost:9200 \
+  --nrows=10
+```

src/demo/README.md

@@ -0,0 +1,24 @@
+# Demo - Amazon Product Search
+
+## Installation
+
+```shell
+$ pyenv install 3.11.8
+$ pyenv local 3.11.8
+$ pip install poetry
+$ poetry env use python
+$ poetry install
+```
+
+## Demo
+
+The command below launches the [Streamlit](https://streamlit.io/) demo app.
+
+```shell
+$ make run_eda
+$ make run_tokenization
+$ make run_es
+$ make run_vespa
+```
+
+![](https://user-images.githubusercontent.com/883148/203654537-8b495c9c-f8af-4c3f-90f9-60edacf647b9.png)