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254 changes: 254 additions & 0 deletions docs/en/getting-started/quickstart.md
Original file line number Diff line number Diff line change
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---
title: "Toolbox Quickstart"
type: docs
weight: 1
description: This guide would help you set up a basic agentic application using toolbox.

---

## Step 1: Set up a database
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Should their be a "before you begin" step? Are we assuming the user has any tools installed or running a specific environment?

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Right now, we're not assuming anything about the user environment, except that they have python installed.
We're going over the whole process from scratch.


[Install postgres and configure a
database](https://neon.tech/postgresql/postgresql-getting-started) for your
system.

This process creates a database `postgres` with superuser `postgres`.
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I would like to see if we can capture this set up ourselves, without requiring the user to go to a new page. I think using docker might also be useful in streamlining?

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In that case, can we assume basic familiarity with docker and have docker installation as a prerequisite?
Do you think the friction of docker installation is worth ignoring?


## Step 2: Import data into the database

1. Connect to the database using command line.

```bash
psql -U postgres -d postgres
```

Here, the first postgres denotes the user and the second one denotes the
database name.

1. Create a table using the following command.

```sql
CREATE TABLE hotels(
id INTEGER NOT NULL PRIMARY KEY,
name VARCHAR NOT NULL,
location VARCHAR NOT NULL,
price_tier VARCHAR NOT NULL,
checkin_date DATE NOT NULL,
checkout_date DATE NOT NULL,
booked BIT NOT NULL
);
```
<!-- 1. Download the data csv file (TODO: Add csv filepath) -->

1. Insert data into the table.

```sql
INSERT INTO hotels(id, name, location, price_tier, checkin_date, checkout_date, booked) VALUES
(1, 'Hilton Basel', 'Basel', 'Luxury', '2024-04-22', '2024-04-20', B'0'),
(2, 'Marriott Zurich', 'Zurich', 'Upscale', '2024-04-14', '2024-04-21', B'0'),
(3, 'Hyatt Regency Basel', 'Basel', 'Upper Upscale', '2024-04-02', '2024-04-20', B'0'),
(4, 'Radisson Blu Lucerne', 'Lucerne', 'Midscale', '2024-04-24', '2024-04-05', B'0'),
(5, 'Best Western Bern', 'Bern', 'Upper Midscale', '2024-04-23', '2024-04-01', B'0'),
(6, 'InterContinental Geneva', 'Geneva', 'Luxury', '2024-04-23', '2024-04-28', B'0'),
(7, 'Sheraton Zurich', 'Zurich', 'Upper Upscale', '2024-04-27', '2024-04-02', B'0'),
(8, 'Holiday Inn Basel', 'Basel', 'Upper Midscale', '2024-04-24', '2024-04-09', B'0'),
(9, 'Courtyard Zurich', 'Zurich', 'Upscale', '2024-04-03', '2024-04-13', B'0'),
(10, 'Comfort Inn Bern', 'Bern', 'Midscale', '2024-04-04', '2024-04-16', B'0');
```

## Step 3: Create a tools config file

Create a `tools.yaml` file with the following content, updating the `password` field:

```yaml
sources:
my-pg-source:
kind: postgres
host: 127.0.0.1
port: 5432
database: postgres
user: postgres
password: {password}
tools:
search-hotels:
kind: postgres-sql
source: my-pg-source
description: Search for hotels based on location and name.
Returns a list of hotel dictionaries matching the search criteria.
parameters:
- name: location
type: string
description: The location of the hotel.
- name: name
type: string
description: The name of the hotel.
statement: SELECT * FROM hotels WHERE location ILIKE '%' || $1 || '%' AND
name ILIKE '%' || $2 || '%';
book-hotel:
kind: postgres-sql
source: my-pg-source
description: Book a hotel by its ID. Returns a message indicating
whether the hotel was successfully booked or not.
parameters:
- name: hotel_id
type: string
description: The ID of the hotel to book.
statement: UPDATE hotels SET booked = B'1' WHERE id = $1;
update-hotel:
kind: postgres-sql
source: my-pg-source
description: Update a hotel's check-in and check-out dates by its ID. Returns a
message indicating whether the hotel was successfully updated or not.
parameters:
- name: hotel_id
type: string
description: The ID of the hotel to update.
- name: checkin_date
type: string
description: The new check-in date of the hotel.
- name: checkout_date
type: string
description: The new check-out date of the hotel.
statement: UPDATE hotels SET checkin_date = CAST($2 as date),
checkout_date = CAST($3 as date) WHERE id = $1;
cancel-hotel:
kind: postgres-sql
source: my-pg-source
description: Cancel a hotel by its ID.
parameters:
- name: hotel_id
type: string
description: The ID of the hotel to cancel.
statement: UPDATE hotels SET booked = B'0' WHERE id = $1;
```

The config file defines four tools:
`search-hotels`, `book-hotel`, `update-hotel` and `cancel-hotel`.

Each tool specifies its description,
[kind](https://github.com/googleapis/genai-toolbox/tree/main/docs/sources#kinds-of-sources),
[source](https://github.com/googleapis/genai-toolbox/blob/main/docs/sources/README.md), required parameters
and the corresponding SQL statements to execute upon tool invocation.

## Step 4: Start a toolbox server locally

1. Download the latest toolbox binary.

```bash
curl -O https://storage.googleapis.com/genai-toolbox/v0.0.5/linux/amd64/toolbox
```

> **_NOTE:_** Use the [correct binary](https://github.com/googleapis/genai-toolbox/releases) corresponding to your OS and CPU architecture.

1. Provide binary execution permissions.

```bash
chmod +x toolbox
```

1. Run the Toolbox server.

```bash
./toolbox --tools_file "tools.yaml"
```

## Step 5: Start building using Toolbox

1. Install the `toolbox_langchain_sdk` package.

```bash
pip install toolbox_langchain_sdk
```

> **_NOTE:_**
> Right now, the toolbox_langchain_sdk package is not available on pip. To
> use the sdk, download the source code from [git](https://github.com/googleapis/genai-toolbox/tree/main/sdks/langchain) and install it locally
> using the command:
>
> ```bash
> pip install .
> ```

1. Create a python script to connect to the toolbox SDK.

```python
import asyncio
from toolbox_langchain_sdk import ToolboxClient

async def main():
client = ToolboxClient("http://127.0.0.1:5000")
# All our code will be added here
pass

asyncio.run(main())
```

1. Try out the `search-hotels` tool.

```python
search_tool = await client.load_tool('search-hotels')
response = await search_tool.ainvoke({"location": "Zurich", "name": ""})
print(response)
```

1. Load all tools.

```python
tools = await client.load_toolset()
```

1. Install required modules.

```bash
pip install langgraph langchain-google-vertexai
```

1. Create a LangGraph [ReAct
agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent).

```python
from langgraph.prebuilt import create_react_agent
from langchain_google_vertexai import ChatVertexAI
from langgraph.checkpoint.memory import MemorySaver

model = ChatVertexAI(model_name="gemini-pro", project="my-project") # Change the GCP project here
agent = create_react_agent(model, tools, checkpointer=MemorySaver())
```

1. Define the initial prompt and user queries.

```python
prompt = """
You're a helpful hotel assistant. You handle hotel searching, booking and
cancellations. When the user searches for a hotel, mention it's name, id,
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Suggested change
cancellations. When the user searches for a hotel, mention it's name, id,
cancellations. When the user searches for a hotel, mention its name, id,

location and price tier. Always mention hotel ids while performing any
searches. This is very important for any operations. For any bookings or
cancellations, please provide the appropriate confirmation.
Don't ask for confirmations from the user.
"""

queries = [
"Find hotels in Basel with Basel in it's name.",
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Suggested change
"Find hotels in Basel with Basel in it's name.",
"Find hotels in Basel with Basel in its name.",

"Can you book the Hilton Basel for me?",
"Oh wait, this is too expensive. Please cancel it and book the Hyatt Regency instead.",
"My check in dates would be from April 10, 2024 to April 19, 2024.",
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"My check in dates would be from April 10, 2024 to April 19, 2024.",
"My check-in dates would be from April 10, 2024 to April 19, 2024.",

]
```

1. Run the queries and observe the output!

```python
config = {"configurable": {"thread_id": "thread-1"}}

for query in queries:
inputs = {"messages": [("user", prompt + query)]}
response = await agent.ainvoke(inputs, stream_mode="values", config=config)
print(response["messages"][-1].content)
```

To verify the agent's actions, you can examine the hotels table. You should
observe that the `booked` column for the `Hyatt Regency Basel` has changed
from `0` to `1`, indicating that the hotel has been successfully booked.
Additionally, the `checkin_date` and `checkout_date` have been updated to
`2024-04-10` and `2024-04-19` from `2024-04-02` and `2024-04-20`
respectively.
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Would it be better to combine this into a single file users can copy and hack on locally?

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This structure could help users understand and customize the steps involved.
However, we could upload the code and YAML files into the github repo and add a link for users to access them.

We could add a note at the beginning, like:

The code and configuration files used in this quickstart are available [here](link_to_files).

What do you think?

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