[DOCS]: Fix typos and consistency issues in docs (#1360)

Author: prrao87

docs/docs/ai/llm.mdx

@@ -6,7 +6,7 @@ description: LLMs integrated with CocoIndex for various built-in functions
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-CocoIndex provides builtin functions integrating with various LLM APIs, for various inference tasks:
+CocoIndex provides built-in functions integrating with various LLM APIs, for various inference tasks:
 *   [Text Generation](#text-generation): use LLM to generate text.
 *   [Text Embedding](#text-embedding): embed text into a vector space.
 
@@ -36,7 +36,7 @@ We support the following types of LLM APIs:
 
 Generation is used as a building block for certain CocoIndex functions that process data using LLM generation.
 
-We have one builtin functions using LLM generation for now:
+We have one built-in function using LLM generation for now:
 
 *  [`ExtractByLlm`](/docs/ops/functions#extractbyllm): it extracts information from input text.
 
@@ -56,7 +56,7 @@ It has the following fields:
 
 Embedding means converting text into a vector space, usually for similarity matching.
 
-We provide a builtin function [`EmbedText`](/docs/ops/functions#embedtext) that converts a given text into a vector space.
+We provide a built-in function [`EmbedText`](/docs/ops/functions#embedtext) that converts a given text into a vector space.
 The spec takes the following fields:
 
 *   `api_type` (type: `cocoindex.LlmApiType`, required)
@@ -171,9 +171,9 @@ cocoindex.functions.EmbedText(
 
 Google exposes Gemini through Google AI Studio APIs.
 Based on [Gemini API recommendation](https://cloud.google.com/ai/gemini?hl=en), this is recommended for experimenting and prototyping purposes.
-You may use [Vertex AI](#vertex-ai) for production usages.
+You may use [Vertex AI](#vertex-ai) for production use.
 
-To use the Gemini by Google AI Studio API, you need to set the environment variable `GEMINI_API_KEY`.
+To use Gemini via the Google AI Studio API, you need to set the environment variable `GEMINI_API_KEY`.
 You can generate the API key from [Google AI Studio](https://aistudio.google.com/apikey).
 
 You can find the full list of models supported by Gemini [here](https://ai.google.dev/gemini-api/docs/models).

docs/docs/core/cli-commands.md

@@ -13,7 +13,7 @@ Modes of operation:
 
 **Usage:**
 
-```sh
+```bash
 cocoindex drop [OPTIONS] [APP_TARGET] [FLOW_NAME]...
 ```
 
@@ -45,7 +45,7 @@ flow.
 
 **Usage:**
 
-```sh
+```bash
 cocoindex evaluate [OPTIONS] APP_FLOW_SPECIFIER
 ```
 
@@ -72,7 +72,7 @@ the backend.
 
 **Usage:**
 
-```sh
+```bash
 cocoindex ls [OPTIONS] [APP_TARGET]
 ```
 
@@ -95,7 +95,7 @@ It will allow tools like CocoInsight to access the server.
 
 **Usage:**
 
-```sh
+```bash
 cocoindex server [OPTIONS] APP_TARGET
 ```
 
@@ -128,7 +128,7 @@ storage and target (to export to).
 
 **Usage:**
 
-```sh
+```bash
 cocoindex setup [OPTIONS] APP_TARGET
 ```
 
@@ -160,7 +160,7 @@ flow.
 
 **Usage:**
 
-```sh
+```bash
 cocoindex show [OPTIONS] APP_FLOW_SPECIFIER
 ```
 
@@ -184,7 +184,7 @@ or `module:FlowName`. If `:FlowName` is omitted, updates all flows.
 
 **Usage:**
 
-```sh
+```bash
 cocoindex update [OPTIONS] APP_FLOW_SPECIFIER
 ```
 

docs/docs/core/settings.mdx

@@ -28,7 +28,7 @@ See [List of Environment Variables](#list-of-environment-variables) for specific
 
 :::tip
 
-You can consider place a `.env` file in your directory.
+You can consider placing a `.env` file in your directory.
 The [CLI](cli#environment-variables) will load environment variables from the `.env` file (see [CLI](cli#environment-variables) for more details).
 From your own main module, you can also load environment variables with a package like [`python-dotenv`](https://github.com/theskumar/python-dotenv).
 
@@ -69,7 +69,7 @@ cocoindex.init(
 For example, you can call it in the main function of your application.
 Once the `cocoindex.init()` is called with a `cocoindex.Settings` dataclass object as argument, the `@cocoindex.settings` function and environment variables will be ignored.
 
-This is more flexible, as you can more easily construct `cocoindex.Settings` based on other stuffs you loaded earlier.
+This is more flexible, as you can more easily construct `cocoindex.Settings` based on other objects or instances you loaded earlier.
 But be careful that if you call `cocoindex.init()` only under the path of main (e.g. within `if __name__ == "__main__":` guard), it won't be executed when you're using CocoIndex CLI, as it won't execute your main logic.
 
 :::info
@@ -123,7 +123,7 @@ If not set, all flows are in a default unnamed namespace.
 
     :::tip
 
-    Please be careful that all values in `url` needs to be url-encoded if they contain special characters.
+    Please be careful that all values in `url` need to be URL-encoded if they contain special characters.
     For this reason, prefer to use the separated `user` and `password` fields for username and password.
 
     :::
@@ -140,7 +140,7 @@ If not set, all flows are in a default unnamed namespace.
 
 If you use the Postgres database hosted by [Supabase](https://supabase.com/), please click **Connect** on your project dashboard and find the following URL:
 
-* If you're on a IPv6 network, use the URL under **Direct connection**. You can visit [IPv6 test](https://test-ipv6.com/) to see if you have IPv6 Internet connection.
+* If you're on an IPv6 network, use the URL under **Direct connection**. You can visit [IPv6 test](https://test-ipv6.com/) to see if you have IPv6 Internet connection.
 * Otherwise, use the URL under **Session pooler**.
   Note that Supabase has a pool size limit of 15 by default, while CocoIndex's default `max_connections` value is 25.
   You can adjust either value to make sure Supabase's pool size limit is greater than CocoIndex's `max_connections` value.
@@ -156,7 +156,7 @@ If you use the Postgres database hosted by [Supabase](https://supabase.com/), pl
 * `source_max_inflight_rows` (type: `int | None`, default: `1024`): The maximum number of concurrent inflight rows for all source operations.
 * `source_max_inflight_bytes` (type: `int | None`, default: `None`): The maximum number of concurrent inflight bytes for all source operations.
 
-See also [flow definition docs](/docs/core/flow_def#control-processing-concurrency) about why it's necessary to control processing concurrency, and how to configure it on per-source basis.
+See also [flow definition docs](/docs/core/flow_def#control-processing-concurrency) about why it's necessary to control processing concurrency, and how to configure it on a per-source basis.
 If both global and per-source limits are specified, both need to be satisfied to admit additional source rows.
 
 ## List of Environment Variables

docs/docs/examples/examples/custom_source_hackernews.md

@@ -35,7 +35,7 @@ In many scenarios, pipelines don't just read from clean tables. They depend on:
 - Legacy systems
 - Non-standard data models that don’t fit traditional connectors
 
-CocoIndex’s Custom Source API makes these integrations *declarative*, incremental, and safe by default. 
+CocoIndex’s Custom Source API makes these integrations *declarative*, incremental, and safe by default.
 
 ## Overview
 
@@ -50,7 +50,7 @@ The pipeline consists of three major parts:
 2. Build an index with CocoIndex Flow
     - Collect thread content
     - Collect all comments recursively
-    - Export to a Postgres table 
+    - Export to a Postgres table
 3. Add a lightweight query handler
     - Uses PostgreSQL full-text search
     - Returns ranked matches for a keyword query
@@ -68,7 +68,7 @@ Every custom source defines two lightweight data types:
 - Key Type → uniquely identifies an item
 - Value Type → the full content for that item
 
-In hacker news, each news is a thread, and each thread can have multiple comments. 
+In hacker news, each news is a thread, and each thread can have multiple comments.
 ![HackerNews Thread and Comments](/img/examples/custom_source_hackernews/hackernews.png)
 
 For HackerNews, let’s define keys like this:
@@ -189,7 +189,7 @@ async def list(
     # Use HackerNews search API
     search_url = "https://hn.algolia.com/api/v1/search_by_date"
     params: dict[str, Any] = {"hitsPerPage": self._spec.max_results}
-    
+
     if self._spec.tag:
         params["tags"] = self._spec.tag
     async with self._session.get(search_url, params=params) as response:
@@ -390,9 +390,9 @@ Your app can now query it as a real-time search index.
 
 ## Querying & Searching the HackerNews Index
 
-With the index flow complete, the next step is to add a query handler. 
+With the index flow complete, the next step is to add a query handler.
 This allows you to search and explore your indexed HackerNews data directly in CocoInsight.
-You can implement the query logic using any preferred library or framework. 
+You can implement the query logic using any preferred library or framework.
 
 <DocumentationButton url="https://cocoindex.io/docs/query#query-handler" text="Query Handler" margin="0 0 16px 0" />
 
@@ -433,9 +433,9 @@ def search_text(query: str) -> cocoindex.QueryOutput:
             return cocoindex.QueryOutput(results=results)
 ```
 
-This example shows how to create a query handler that lets you search HackerNews threads and comments stored in CocoIndex. 
+This example shows how to create a query handler that lets you search HackerNews threads and comments stored in CocoIndex.
 - The handler looks up the correct database table, then uses PostgreSQL’s full-text search functions (`to_tsvector` and `plainto_tsquery`) to find entries that match your search terms.
-- Matching results are sorted by their relevance (`ts_rank`) and by creation time, then converted to dictionaries. 
+- Matching results are sorted by their relevance (`ts_rank`) and by creation time, then converted to dictionaries.
 - Finally, these results are returned in a `cocoindex.QueryOutput` object—making it easy to perform fast, ranked searches across your indexed HackerNews content.
 
 ##  Running Your HackerNews Custom Source
@@ -480,7 +480,7 @@ cocoindex update -L main
 
 ## 3. Troubleshoot & Inspect with CocoInsight
 
-CocoInsight lets you **visualize and debug your flow**, see the lineage of your data, and understand what’s happening under the hood. 
+CocoInsight lets you **visualize and debug your flow**, see the lineage of your data, and understand what’s happening under the hood.
 
 Start the server:
 
@@ -491,9 +491,9 @@ cocoindex server -ci main
 Then open the UI in your browser: [`https://cocoindex.io/cocoinsight`](https://cocoindex.io/cocoinsight)
 
 > CocoInsight has zero pipeline data retention — it’s safe for debugging and inspecting your flows locally.
-> 
+>
 
-Note that this requires QueryHandler setup in previous step. 
+Note that this requires QueryHandler setup in previous step.
 
 
 ## What You Can Build Next
@@ -547,8 +547,8 @@ Custom Sources extend this model to *any* API — internal, external, legacy, or
 This unlocks a simple but powerful pattern:
 
 > If you can fetch it, CocoIndex can index it, diff it, and sync it.
-> 
+>
 
 ## ⭐ Try It, Fork It, Star It
 
-If you found this useful, a **star on [GitHub](https://github.com/cocoindex-io/cocoindex)** means a lot — it helps others discover CocoIndex and supports further development.
+If you found this useful, a **star on [GitHub](https://github.com/cocoindex-io/cocoindex)** means a lot — it helps others discover CocoIndex and supports further development.

docs/docs/examples/examples/hackernews_trending_topics.md

@@ -21,7 +21,7 @@ import { DocumentationButton, GitHubButton } from '../../../src/components/GitHu
 
 In the age of information overload, understanding what's trending—and why—is crucial for developers, researchers, and data engineers. HackerNews is one of the most influential tech communities, but manually tracking emerging topics across thousands of threads and comments is practically impossible.
 
-What if you could automatically index HackerNews content, extract topics using AI, and query trending discussions in real-time? That's exactly what CocoIndex enables through its [**Custom Sources**](https://cocoindex.io/blogs/custom-source) framework 
+What if you could automatically index HackerNews content, extract topics using AI, and query trending discussions in real-time? That's exactly what CocoIndex enables through its [**Custom Sources**](https://cocoindex.io/blogs/custom-source) framework
 combined with [LLM-powered extraction](https://cocoindex.io/docs/ai/llm).
 
 In this post, we'll explore the **HackerNews Trending Topics** example, a production-ready pipeline that demonstrates some of the most powerful concepts in CocoIndex: incremental data syncing, LLM-powered information extraction, and queryable indexes.
@@ -120,7 +120,7 @@ class _HackerNewsThreadKey(NamedTuple):
     thread_id: str
 ```
 
-CocoIndex recommends to use stable keys. HN thread IDs are perfect keys.
+CocoIndex recommends using stable keys. HN thread IDs are perfect keys.
 
 #### Value Type
 
@@ -319,7 +319,7 @@ The genius of Custom Sources is the **separation of discovery from fetching**.
 Sync 1:
   list()      → 200 thread IDs + timestamps (fast)
   get_value() → 200 full threads (expensive)
-  
+
 Sync 2 (30s later):
   list()      → 200 thread IDs + timestamps (fast)
   [CocoIndex detects only 15 changed]
@@ -418,7 +418,7 @@ def hackernews_trending_topics_flow(
 
 ```
 
-This block sets up a CocoIndex flow that fetches HackerNews stories and prepares them for indexing. It registers a flow called **HackerNewsTrendingTopics**, then adds a `HackerNewsSource` that retrieves up to 200 stories and refreshes every 30 seconds, storing the result in `data_scope["threads"]` for downstream steps. 
+This block sets up a CocoIndex flow that fetches HackerNews stories and prepares them for indexing. It registers a flow called **HackerNewsTrendingTopics**, then adds a `HackerNewsSource` that retrieves up to 200 stories and refreshes every 30 seconds, storing the result in `data_scope["threads"]` for downstream steps.
 
 <DocumentationButton url="https://cocoindex.io/docs/core/flow_def" text="Flow Definition Docs" margin="0 0 16px 0" />
 
@@ -502,7 +502,7 @@ with data_scope["threads"].row() as thread:
         )
 ```
 
-This block processes each HackerNews thread as it flows through the pipeline. Inside `data_scope["threads"].row()`, each `thread` represents a single story record. 
+This block processes each HackerNews thread as it flows through the pipeline. Inside `data_scope["threads"].row()`, each `thread` represents a single story record.
 
 - We use an LLM (`gpt-5-mini`) to extract semantic **topics** from the thread's text by applying `ExtractByLlm`, which returns a list of `Topic` objects.
 - We use `message_index` to collect relevant metadata for this thread.
@@ -630,7 +630,7 @@ def search_by_topic(topic: str) -> cocoindex.QueryOutput:
 
 ```
 
-This block adds a query interface to the flow so users can search HackerNews content by topic. 
+This block adds a query interface to the flow so users can search HackerNews content by topic.
 
 The `@hackernews_trending_topics_flow.query_handler()` decorator registers `search_by_topic()` as a query endpoint for the flow.
 
@@ -668,7 +668,7 @@ def get_threads_for_topic(topic: str) -> list[dict[str, Any]]:
             ]
 ```
 
-This function finds all HackerNews threads related to a given topic and ranks them. It looks up the topic table in the database, calculates a **score** for each thread (higher for main threads, lower for comments), and finds the most recent mention. 
+This function finds all HackerNews threads related to a given topic and ranks them. It looks up the topic table in the database, calculates a **score** for each thread (higher for main threads, lower for comments), and finds the most recent mention.
 
 It then returns a list of threads with their URL, score, and latest mention time, so you can see which threads are most relevant or trending for that topic.
 
@@ -681,7 +681,7 @@ By weighting 5:1, the system prioritizes original discussions over tangential me
 
 ### get_trending_topics(limit) → Top 20 topics by score
 
-This function finds the **most popular topics** on HackerNews. 
+This function finds the **most popular topics** on HackerNews.
 
 ```python
 @hackernews_trending_topics_flow.query_handler()
@@ -755,7 +755,7 @@ cocoindex server -ci main
 Then open the UI in your browser: [**`https://cocoindex.io/cocoinsight`**](https://cocoindex.io/cocoinsight)
 
 > CocoInsight has zero pipeline data retention — it’s safe for debugging and inspecting your flows locally.
-> 
+>
 
 Note that this requires QueryHandler setup in previous step.
 
@@ -823,23 +823,23 @@ By integrating with an AI agent framework, the indexed topics and threads become
 ## Why Use CocoIndex for This?
 
 1. **Incremental Sync**
-    
+
     Traditional pipelines fetch everything repeatedly. CocoIndex fetches only new or updated threads, dramatically reducing API calls and latency.
-    
+
 2. **Declarative & Modular**
-    
+
     Flows, collectors, and query handlers are modular. You can add new sources (Reddit, Twitter, internal chat logs) or new transformations (summaries, sentiment analysis, embeddings) without rewriting the entire pipeline.
-    
+
 3. **LLM Integration is Seamless**
-    
+
     CocoIndex treats LLMs as first-class citizens for structured extraction. You don’t need complex glue code — the framework handles transformation, type enforcement, and storage.
-    
+
 4. **Queryable Structured Index**
-    
+
     Topics and messages are stored in Postgres, ready for SQL queries or API-based search. You can serve both analytics dashboards and AI agents from the same structured store.
-    
+
 5. **Supports Continuous Workflows**
-    
+
     CocoIndex pipelines can run live, with real-time updates every few seconds. Combine this with AI agents, and you have a **self-updating knowledge system** that reasons over the latest information.
 
 
@@ -849,4 +849,4 @@ CocoIndex is designed for systems that need to continuously monitor, detect, and
 
 ## Support Us ❤️
 
-Enjoying CocoIndex? Give us a [⭐️ on GitHub](https://github.com/cocoindex-io/cocoindex) and share it with your peers. Every star helps more developers discover the project and strengthens the community.
+Enjoying CocoIndex? Give us a [⭐️ on GitHub](https://github.com/cocoindex-io/cocoindex) and share it with your peers. Every star helps more developers discover the project and strengthens the community.