> For the complete documentation index, see [llms.txt](https://percolation-labs.gitbook.io/percolation-labs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://percolation-labs.gitbook.io/percolation-labs/getting-started/quick-start.md).

# Quick start

To start trying Percolate, clone the [repo](https://github.com/Percolation-Labs/percolate) and from the root

```bash
git clone https://github.com/Percolation-Labs/percolate.git
docker compose up -d
```

You now have a postgres instance on port `5438` hat you can log into with `postgres:postgres`

{% hint style="info" %}
We manage environment variables that are needed for interacting with LLMs/APIs in different ways but using the percolate cli can be a generally useful way to bootstrap your environment. 
{% endhint %}

You can install percolate-db with pip but lets use the codebase for now\...

```bash
cd clients/python/percolate
#if you have API keys like OPEN_AI_KEY these are synced into your local instance
python percolate/cli.py add env --sync
```

Another thing you can do is index Percolate files so you can ask questions about Percolate. This will use your Open AI key to generate embeddings. 

```bash
python percolate/cli.py index 
```

Now you can ask questions from the cli

```bash
python percolate/cli.py ask 'are there SQL functions in Percolate for interacting with models like Claude?'
```

***

Percolate is a database - it wraps Postgres and adds extensions for vector and graph data. It also pushes agentic AI down into the data tier. Using your favourite Postgres client,

```sql
select * from percolate('What is the capital of ireland?')
--try different models
--select * from percolate('how can percolate help me with creating agentic systems',
--  'deepseek-chat')
--see what Models are in Percolate by default
--select * from p8."LangaugeModelApi"
```

This trivial example tests that we are connected to a langauge model(s) without using tools or data

If we want to use an Agent we can try the built in ones as an example 

{% hint style="info" %}
To understand creating agents see [Add agents](/percolation-labs/configure/add-agents.md) or [Percolating Python-first](/percolation-labs/recipes/percolating-python-first.md)
{% endhint %}

```bash
 select * from percolate_with_agent('give a brief summary of percolate', 
                                    'p8.PercolateAgent')
```

If you have created an agent using the example with the sample pets store tools

```sql
select * from percolate_with_agent('list some pets that are sold', 'MyFirstAgent')
```

 


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://percolation-labs.gitbook.io/percolation-labs/getting-started/quick-start.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.