docs: Add a readme for the pgai docs.

docs/README.md

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+
+<p align="center">
+    <img height="200" src="/docs/images/pgai_logo.png" alt="pgai"/>
+</p>
+
+<div align=center>
+
+<h3>pgai documentation</h3>
+
+[![Discord](https://img.shields.io/badge/Join_us_on_Discord-black?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/KRdHVXAmkp)
+[![Try Timescale for free](https://img.shields.io/badge/Try_Timescale_for_free-black?style=for-the-badge&logo=timescale&logoColor=white)](https://tsdb.co/gh-pgai-signup)
+</div>
+
+pgai is a PostgreSQL extension that simplifies data storage and retrieval for [Retrieval Augmented Generation](https://en.wikipedia.org/wiki/Prompt_engineering#Retrieval-augmented_generation) (RAG), and other AI applications.
+In particular, it automates the creation and sync of embeddings for your data stored in PostgreSQL, simplifies
+[semantic search](https://en.wikipedia.org/wiki/Semantic_search), and allows you to call LLM models from SQL.
+
+The pgai documentation helps you setup, use and develop the projects that make up pgai.  
+
+
+## Vectorizer
+
+Vectorizer automates the embedding process within your database management by treating embeddings as a declarative,
+DDL-like feature — like an index.
+
+- **Get started**:
+  * [Vectorizer quickstart](/docs/vectorizer-quick-start.md): setup your developer environment, create and run a vectorizer.
+  * [Vectorizer quickstart for OpenAI](/docs/vectorizer-quick-start-openai.md): setup your developer environment, create and run a vectorizer using OpenAI.
+  * [Vectorizer quickstart for Voyage](/docs/vectorizer-quick-start-voyage.md): setup your developer environment, create and run a vectorizer using Voyage. 
+- **Use**:
+  * [Automate AI embedding with pgai Vectorizer](/docs/vectorizer.md): a comprehensive overview of Vectorizer features,
+    demonstrating how it streamlines the process of working with vector embeddings in your database.
+  * [Run vectorizers using pgai vectorizer worker](/docs/vectorizer-worker.md): run vectorizers on a self-hosted TimescaleDB instance.
+- **Develop**:
+  * [Add a Vectorizer embedding integration](/docs/vectorizer-add-a-embedding-integration.md):
+- **Reference**:
+  * [pgai Vectorizer API reference](/docs/vectorizer-api-reference.md): API reference for Vectorizer functions 
+
+## pgai
+
+Simplifies data storage and retrieval for AI apps. 
+
+- **Get started**:
+  * [Install pgai with Docker](/docs/install_docker.md): run pgai in a container environment.
+  * [Setup pgai with Anthropic](/docs/anthropic.md): configure pgai to connect to your Anthropic account.
+  * [Setup pgai with Cohere](/docs/cohere.md): configure pgai to connect to your Cohere account.
+  * [Setup pgai with Ollama](/docs/ollama.md): configure pgai to connect to your Ollama account.
+  * [Setup pgai with OpenAI](/docs/openai.md): configure pgai to connect to your OpenAI account.
+  * [Setup pgai with Voyage AI](/docs/voyageai.md): configure pgai to connect to your Voyage AI account.
+- **Use**:
+  * [Delayed embed](/docs/delayed_embed.md): run pgai using pgai or TimescaleDB background actions.
+  * [Load dataset from Hugging Face](/docs/load_dataset_from_huggingface.md): load datasets from Hugging Face's datasets library directly into your PostgreSQL database.
+  * [Moderate comments using OpenAI](/docs/moderate.md): use triggers or actions to moderate comments using OpenAI.
+  * [Secure pgai with user privilages](/docs/privileges.md): grant the necessary permissions for a specific user or role to use pgai functionality.
+- **Develop**:
+  * [Install pgai from source](/docs/install_from_source.md): create an environment to develop pgai. 
+
+
+
+
+
+
+
+
+

docs/moderate.md

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-# Moderate
+# Moderate comments using OpenAI
 
 Let's say you want to moderate comments using OpenAI. You can do it in two ways:
 

docs/privileges.md

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+# Secure pgai with user privilages
 
 The ai.grant_ai_usage function is an important security and access control tool 
 in the pgai extension. Its primary purpose is to grant the necessary permissions 

docs/adding-embedding-integration.md → docs/vectorizer-add-a-embedding-integration.md

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-# Adding a Vectorizer embedding integration
+# Add a Vectorizer embedding integration
 
 We welcome contributions to add new vectorizer embedding integrations.