docs: fix some typos (#2790)

This commit is contained in:
Eugene Yurtsev
2024-12-17 13:53:57 -05:00
committed by GitHub
3 changed files with 24 additions and 22 deletions
+19 -17
View File
@@ -2,27 +2,29 @@
!!! note "Prerequisites"
Before you start, make sure you have the following:
Before you begin, ensure you have the following:
- [A GitHub account](https://github.com/)
- [A LangSmith account](https://smith.langchain.com/)
In this quickstart, we'll deploy a **pre-built** LangGraph application into LangGraph cloud.
- [GitHub account](https://github.com/)
- [LangSmith account](https://smith.langchain.com/)
## Create a repository on GitHub
To deploy a LangGraph application to **LangGraph Cloud**, you need to have your application code in a GitHub repository. Either a public or private repository will work.
To deploy a LangGraph application to **LangGraph Cloud**, your application code must reside in a GitHub repository. Both public and private repositories are supported.
You can deploy any [LangGraph Application](../concepts/application_structure.md) to LangGraph Cloud.
For this guide, we'll use the pre-built Python [**ReAct Agent**](https://github.com/langchain-ai/react-agent) template.
??? note "Get Required API Keys for the ReAct Agent template"
This **ReAct Agent** application requires an API key from [Anthropic](https://console.anthropic.com/) and [Tavily](https://app.tavily.com/). You can get these API keys by signing up on their respective websites.
**Alternative**: If you'd prefer a scaffold application that doesn't require API keys, use the [**New LangGraph Project**](https://github.com/langchain-ai/new-langgraph-project) template instead of the **ReAct Agent** template.
We will use the pre-built python [**ReAct Agent**](https://github.com/langchain-ai/react-agent) template for this quickstart. This application requires two API keys (`ANTHROPIC_API_KEY` and `TAVILY_API_KEY`) to run.
1. Go to the [ReAct Agent](https://github.com/langchain-ai/react-agent) repository.
2. Fork the repository to your GitHub account by clicking the `Fork` button in the top right corner.
??? note "Get API Keys for the ReAct Agent template"
- **ANTHROPIC_API_KEY**: Get an API key from [Anthropic](https://console.anthropic.com/).
- **TAVILY_API_KEY**: Get an API key on the [Tavily website](https://app.tavily.com/).
## Deploy to LangGraph Cloud
??? note "1. Log in to [LangSmith](https://smith.langchain.com/)"
@@ -123,7 +125,7 @@ Once your application is deployed, you can test it in **LangGraph Studio**.
The API calls below are for the **ReAct Agent** template. If you're deploying a different application, you may need to adjust the API calls accordingly.
Before using, you need to get the URL of your LangGraph deployment. You can find this in the `Deployment` view. Click the URL to copy it to the clipboard.
Before using, you need to get the `URL` of your LangGraph deployment. You can find this in the `Deployment` view. Click the `URL` to copy it to the clipboard.
You also need to make sure you have set up your API key properly, so you can authenticate with LangGraph Cloud.
@@ -144,7 +146,7 @@ export LANGSMITH_API_KEY=...
```python
from langgraph_sdk import get_client
client = get_client(url="http://localhost:8123")
client = get_client(url="your-deployment-url", api_key="your-langsmith-api-key")
async for chunk in client.runs.stream(
None, # Threadless run
@@ -175,7 +177,7 @@ export LANGSMITH_API_KEY=...
```python
from langgraph_sdk import get_sync_client
client = get_sync_client(url="http://localhost:8123")
client = get_sync_client(url="your-deployment-url", api_key="your-langsmith-api-key")
for chunk in client.runs.stream(
None, # Threadless run
@@ -207,7 +209,7 @@ export LANGSMITH_API_KEY=...
const { Client } = await import("@langchain/langgraph-sdk");
// only set the apiUrl if you changed the default port when calling langgraph up
const client = new Client({ apiUrl: "http://localhost:8123"});
const client = new Client({ apiUrl: "your-deployment-url", apiKey: "your-langsmith-api-key" });
const streamResponse = client.runs.stream(
null, // Threadless run
@@ -233,7 +235,7 @@ export LANGSMITH_API_KEY=...
```bash
curl -s --request POST \
--url "http://localhost:8123/runs/stream" \
--url <DEPLOYMENT_URL> \
--header 'Content-Type: application/json' \
--data "{
\"assistant_id\": \"agent\",
+3 -3
View File
@@ -11,9 +11,9 @@ New to LangGraph or LLM app development? Read this material to get up and runnin
## Get Started 🚀 {#quick-start}
- [LangGraph Quickstart](introduction.ipynb): Build a chatbot that can use tools and keep track of conversation history. Add human-in-the-loop capabilities and explore how time-travel works.
- [LangGraph Server Quickstart](langgraph-platform/local-server.md): Launch a LangGraph server locally and interact with it using the REST API and LangGraph Studio Web UI.
- [LangGraph Cloud QuickStart](../cloud/quick_start.md): Deploy a LangGraph app using LangGraph Cloud.
- [LangGraph Template Quickstart](../concepts/template_applications.md): Quickly start building with LangGraph Platform using a template application.
- [LangGraph Server Quickstart](langgraph-platform/local-server.md): Launch a LangGraph server locally and interact with it using REST API and LangGraph Studio Web UI.
- [LangGraph Template Quickstart](../concepts/template_applications.md): Start building with LangGraph Platform using a template application.
- [Deploy with LangGraph Cloud Quickstart](../cloud/quick_start.md): Deploy a LangGraph app using LangGraph Cloud.
## Use cases 🛠️
@@ -1,4 +1,4 @@
# Quick Start: Launch Local LangGraph Server
# QuickStart: Launch Local LangGraph Server
This is a quick start guide to help you get a LangGraph app up and running locally.
@@ -226,7 +226,7 @@ Now that you have a LangGraph app running locally, take your journey further by
### 🌐 Deploy to LangGraph Cloud
- **[LangGraph Cloud QuickStart](../../cloud/quick_start.md)**: Deploy your LangGraph app using LangGraph Cloud.
- **[LangGraph Cloud Quickstart](../../cloud/quick_start.md)**: Deploy your LangGraph app using LangGraph Cloud.
### 📚 Learn More about LangGraph Platform