Polish document for quickstart

docs/docs/getting_started/quickstart.md

@@ -11,22 +11,18 @@ This guide will help you get up and running with CocoIndex in just a few minutes
 *   loads the data into a vector store (PG Vector)
 
 
-## Step 1: Set up the CocoIndex environment
+## Prerequisite: Install CocoIndex environment
 
-1.  Open the terminal and create a new directory for your project:
+We'll need to install a bunch of dependencies for this project.
 
-    ```bash
-    mkdir cocoindex-quickstart
-    cd cocoindex-quickstart
-    ```
-
- 2. Install CocoIndex:
+1.  Install CocoIndex:
 
     ```bash
     pip install cocoindex
     ```
-
-3.  You need to setup a Postgres database with pgvector extension installed; or bring up a Postgres database using docker compose:
+
+2.  You can skip this step if you already have a Postgres database with pgvector extension installed.
+    If not, the easiest way is to bring up a Postgres database using docker compose:
 
     - Make sure Docker Compose is installed: [docs](https://docs.docker.com/compose/install/)
     - Start a Postgres SQL database for cocoindex using our docker compose config:
@@ -35,11 +31,23 @@ This guide will help you get up and running with CocoIndex in just a few minutes
     docker compose -f <(curl -L https://raw.githubusercontent.com/cocoindex-io/cocoindex/refs/heads/main/dev/postgres.yaml) up -d
     ```
 
+## Step 1: Prepare directory for your project
+
+1.  Open the terminal and create a new directory for your project:
+
+    ```bash
+    mkdir cocoindex-quickstart
+    cd cocoindex-quickstart
+    ```
+
+2.  Prepare input files for the index. Put them in a directory, e.g. `markdown_files`.
+    If you don't have any files at hand, you may download the example [markdown_files.zip](markdown_files.zip) and unzip it in the current directory.
+
 ## Step 2: Create the Python file `quickstart.py`
 
 Create a new file `quickstart.py` and import the `cocoindex` library:
 
-```python
+```python title="quickstart.py"
 import cocoindex
 ```
 
@@ -53,11 +61,12 @@ Then we'll put the following pieces into the file:
 
 Starting from the indexing flow:
 
-```python
+```python title="quickstart.py"
 @cocoindex.flow_def(name="TextEmbedding")
 def text_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
     # Add a data source to read files from a directory
-    data_scope["documents"] = flow_builder.add_source(cocoindex.sources.LocalFile(path="markdown_files"))
+    data_scope["documents"] = flow_builder.add_source(
+        cocoindex.sources.LocalFile(path="markdown_files"))
 
     # Add a collector for data to be exported to the vector index
     doc_embeddings = data_scope.add_collector()
@@ -109,7 +118,7 @@ Notes:
 
 Starting from the query handler:
 
-```python
+```python title="quickstart.py"
 query_handler = cocoindex.query.SimpleSemanticsQueryHandler(
     name="SemanticsSearch",
     flow=text_embedding_flow,
@@ -127,7 +136,7 @@ This handler queries the vector index `"doc_embeddings"`, and uses the same embe
 
 The main function is used to interact with users and run queries using the query handler above.
 
-```python
+```python title="quickstart.py"
 @cocoindex.main_fn()
 def _main():
     # Run queries to demonstrate the query capabilities.
@@ -178,9 +187,6 @@ Now we have tables needed by this CocoIndex flow.
 
 ### Step 3.2: Build the index
 
-Before building the index, make sure input markdown files are in the `markdown_files` directory.
-You may download the example [markdown_files.zip](markdown_files.zip) and unzip it in the current directory.
-
 Now we're ready to build the index:
 
 ```bash