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[Docs] Replace Absolute External Links (#949)

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docs/docs/cloud/quick_start.md
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# Quick Start
This quick start guide will cover how to build a simple agent that can look up things on the internet. We will then deploy it to LangGraph Cloud, use the LangGraph Studio to visualize and test it out, and use the LangGraph SDK to interact with it.
## Set up requirements
@@ -9,10 +10,9 @@ This tutorial will use:
- Tavily for the search engine - sign up and get an API key [here](https://app.tavily.com/)
- LangSmith for hosting - sign up and get an API key [here](https://smith.langchain.com/)
## Set up local files
1. Create a new application with the following directory and files:
1. Create a new application with the following directory and files:
<my-app>/
|-- agent.py # code for your LangGraph agent
@@ -20,48 +20,49 @@ This tutorial will use:
|-- langgraph.json # configuration file for LangGraph
|-- .env # environment files with API keys
2. The `agent.py` file should contain Python code for defining your graph. The following code is a simple example, the important thing is that at some point in your file you compile your graph and assign the compiled graph to a variable (in this case the `graph` variable). This example code uses `create_react_agent`, a prebuilt agent, read more about it [here](..//concepts/agentic_concepts.md#react-agent).
2. The `agent.py` file should contain Python code for defining your graph. The following code is a simple example, the important thing is that at some point in your file you compile your graph and assign the compiled graph to a variable (in this case the `graph` variable). This example code uses `create_react_agent`, a prebuilt agent, read more about it [here](..//concepts/agentic_concepts.md#react-agent).
```python
from langchain_anthropic import ChatAnthropic
from langchain_community.tools.tavily_search import TavilySearchResults
from langgraph.prebuilt import create_react_agent
model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
tools = [TavilySearchResults(max_results=2)]
graph = create_react_agent(model, tools)
```
3. The `requirements.txt` file should contain any dependencies for your graph(s). In this case we only require four packages for our graph to run:
3. The `requirements.txt` file should contain any dependencies for your graph(s). In this case we only require four packages for our graph to run:
langgraph
langchain_anthropic
tavily-python
langchain_community
4. The [`langgraph.json`][langgraph.json] file is a configuration file that describes what graph(s) you are going to host. In this case we only have one graph to host: the compiled `graph` object from `agent.py`.
4. The [`langgraph.json`][langgraph.json] file is a configuration file that describes what graph(s) you are going to host. In this case we only have one graph to host: the compiled `graph` object from `agent.py`.
```json
{
"dependencies": ["."],
"graphs": {
"agent": "./agent.py:graph"
},
"env": ".env"
"dependencies": ["."],
"graphs": {
"agent": "./agent.py:graph"
},
"env": ".env"
}
```
Learn more about the LangGraph CLI configuration file [here](./reference/cli.md#configuration-file).
5. The `.env` file should have any environment variables needed to run your graph. This will only be used for local testing, so if you are not testing locally you can skip this step. NOTE: if you do add this, you should NOT check this into git. For this graph, we need two environment variables:
5. The `.env` file should have any environment variables needed to run your graph. This will only be used for local testing, so if you are not testing locally you can skip this step. NOTE: if you do add this, you should NOT check this into git. For this graph, we need two environment variables:
```shell
ANTHROPIC_API_KEY=...
TAVILY_API_KEY=...
```
Now that we have set everything up on our local file system, we are ready to host our graph.
Now that we have set everything up on our local file system, we are ready to host our graph.
## Test the graph build locally
@@ -124,13 +125,13 @@ Turn the `<my-app>` directory into a GitHub repo. You can use the GitHub CLI if
### Deploy from GitHub with LangGraph Cloud
Once you have created your github repository with a Python file containing your compiled graph as well as a `langgraph.json` file containing the configuration for hosting your graph, you can head over to LangSmith and click on the 🚀 icon on the left navbar to create a new deployment. Then click the `+ New Deployment` button.
Once you have created your github repository with a Python file containing your compiled graph as well as a `langgraph.json` file containing the configuration for hosting your graph, you can head over to LangSmith and click on the 🚀 icon on the left navbar to create a new deployment. Then click the `+ New Deployment` button.
![Langsmith Workflow](./img/cloud_deployment.png)
***If you have not deployed to LangGraph Cloud before:*** there will be a button that shows up saying Import from GitHub. You’ll need to follow that flow to connect LangGraph Cloud to GitHub.
**_If you have not deployed to LangGraph Cloud before:_** there will be a button that shows up saying Import from GitHub. You’ll need to follow that flow to connect LangGraph Cloud to GitHub.
***Once you have set up your GitHub connection:*** the new deployment page will look as follows:
**_Once you have set up your GitHub connection:_** the new deployment page will look as follows:
![Deployment before being filled out](./deployment/img/deployment_page.png)
@@ -140,7 +141,7 @@ To deploy your application, you should do the following:
2. Search for your repo to deploy in the search bar and select it
3. Choose any name
4. In the `LangGraph API config file` field, enter the path to your `langgraph.json` file (which in this case is just `langgraph.json`)
5. For Git Reference, you can select either the git branch for the code you want to deploy, or the exact commit SHA.
5. For Git Reference, you can select either the git branch for the code you want to deploy, or the exact commit SHA.
6. If your chain relies on environment variables, add those in. They will be propagated to the underlying server so your code can access them. In this case, we need `ANTHROPIC_API_KEY` and `TAVILY_API_KEY`.
Putting this all together, you should have something as follows for your deployment details:
@@ -157,7 +158,7 @@ After your deployment is complete, your deployments page should look as follows:
![Deployed page](./deployment/img/deployed_page.png)
You can see that by default, you get access to the `Trace Count` monitoring chart and `Recent Traces` run view. These are powered by LangSmith.
You can see that by default, you get access to the `Trace Count` monitoring chart and `Recent Traces` run view. These are powered by LangSmith.
You can click on `All Charts` to view all monitoring info for your server, or click on `See tracing project` to get more information on an individual trace.
@@ -181,7 +182,7 @@ On this page you can test out your graph by passing in starting states and click
## Use with the SDK
Once you have tested that your hosted graph works as expected using LangGraph Studio, you can start using your hosted graph all over your organization by using the LangGraph SDK. Let's see how we can access our hosted graph and execute our run from a python file.
Once you have tested that your hosted graph works as expected using LangGraph Studio, you can start using your hosted graph all over your organization by using the LangGraph SDK. Let's see how we can access our hosted graph and execute our run from a python file.
First, make sure you have the SDK installed by calling `pip install langgraph_sdk`.
@@ -224,31 +225,30 @@ async for chunk in client.runs.stream(
input=input,
stream_mode="updates",
):
if chunk.data and chunk.event != "metadata":
if chunk.data and chunk.event != "metadata":
print(chunk.data)
```
{'agent': {'messages': [{'content': "Hi Bagatur! It's nice to meet you. How can I assist you today?", 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_9cb5d38cf7'}, 'type': 'ai', 'name': None, 'id': 'run-c89118b7-1b1e-42b9-a85d-c43fe99881cd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
## What's Next
Congratulations! If you've worked your way through this tutorial you are well on your way to becoming a LangGraph Cloud expert. Here are some other resources to check out to help you out on the path to expertise:
### LangGraph Cloud How-tos
If you want to learn more about streaming from hosted graphs, check out the Streaming [how-to guides](https://langchain-ai.github.io/langgraph/cloud/how-tos/#streaming).
If you want to learn more about streaming from hosted graphs, check out the Streaming [how-to guides](how-tos/index.md#streaming).
To learn more about double-texting and all the ways you can handle it in your application, read up on these [how-to guides](https://langchain-ai.github.io/langgraph/cloud/how-tos/#double-texting).
To learn more about double-texting and all the ways you can handle it in your application, read up on these [how-to guides](how-tos/index.md#double-texting).
To learn about how to include different human-in-the-loop behavior in your graph, take a look at [these how-tos](https://langchain-ai.github.io/langgraph/cloud/how-tos/#human-in-the-loop).
To learn about how to include different human-in-the-loop behavior in your graph, take a look at [these how-tos](how-tos/index.md#human-in-the-loop).
### LangGraph Tutorials
Before hosting, you have to write a graph to host. Here are some tutorials to get you more comfortable with writing LangGraph graphs and give you inspiration for the types of graphs you want to host.
[This tutorial](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/) walks you through how to write a customer support bot using LangGraph.
[This tutorial](../tutorials/customer-support/customer-support.ipynb) walks you through how to write a customer support bot using LangGraph.
If you are interested in writing a SQL agent, check out [this tutorial](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/).
If you are interested in writing a SQL agent, check out [this tutorial](../tutorials/sql-agent.ipynb).
Check out the [LangGraph tutorials](https://langchain-ai.github.io/langgraph/tutorials/) page to read about more exciting use cases.
Check out the [LangGraph tutorials](../tutorials/index.md) page to read about more exciting use cases.