（LangGraph教程）6. Deployment部署——Lesson 3：连接已部署的服务（未索引）

https://academy.langchain.com/courses/intro-to-langgraph

https://github.com/shangxiang0907/langchain-academy

文章目录

[Connecting to a LangGraph Platform Deployment 连接到 LangGraph 平台部署](#Connecting to a LangGraph Platform Deployment 连接到 LangGraph 平台部署)
- [Deployment Creation 部署创建](#Deployment Creation 部署创建)
- [Using the API 使用 API](#Using the API 使用 API)
- SDK
- [Remote Graph 远程图](#Remote Graph 远程图)
- [Runs Runs（运行）](#Runs Runs（运行）)
- - [Background Runs 后台运行](#Background Runs 后台运行)
  - [Streaming Runs 流式运行](#Streaming Runs 流式运行)
- [Threads Threads（线程）](#Threads Threads（线程）)
- - [Check thread state 检查线程状态](#Check thread state 检查线程状态)
  - [Copy threads 复制线程](#Copy threads 复制线程)
  - [Human in the loop 人工介入循环](#Human in the loop 人工介入循环)
- [Across-thread memory 跨线程内存](#Across-thread memory 跨线程内存)
- - [Search items 搜索条目](#Search items 搜索条目)
  - [Add items 添加条目](#Add items 添加条目)
  - [Delete items 删除条目](#Delete items 删除条目)

Connecting to a LangGraph Platform Deployment 连接到 LangGraph 平台部署

Deployment Creation 部署创建

We just created a deployment for the task_maistro app from module 5.

我们刚刚为模块 5 中的 task_maistro 应用创建了一个部署。

We used the the LangGraph CLI to build a Docker image for the LangGraph Server with our task_maistro graph.
- 我们使用 LangGraph CLI 为 LangGraph Server 构建了包含 task_maistro 图的 Docker 镜像。
We used the provided docker-compose.yml file to create three separate containers based on the services defined:
- 我们使用提供的 docker-compose.yml 文件，基于所定义的服务创建了三个独立容器：
  - langgraph-redis: Creates a new container using the official Redis image.
    - langgraph-redis：使用官方 Redis 镜像创建一个新容器。
  - langgraph-postgres: Creates a new container using the official Postgres image.
    - langgraph-postgres：使用官方 Postgres 镜像创建一个新容器。
  - langgraph-api: Creates a new container using our pre-built task_maistro Docker image.
    - langgraph-api：使用我们预先构建的 task_maistro Docker 镜像创建一个新容器。
  $cd module-6/deployment$ docker compose up

Once running, we can access the deployment through:

运行后，我们可通过以下方式访问该部署：

API: http://localhost:8123
- API：http://localhost:8123
Docs: http://localhost:8123/docs
- 文档：http://localhost:8123/docs
LangGraph Studio: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:8123
- LangGraph Studio：https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:8123

Using the API 使用 API

LangGraph Server exposes many API endpoints for interacting with the deployed agent.

LangGraph Server 暴露了多个 API 端点，用于与已部署的智能体交互。

We can group these endpoints into a few common agent needs:

我们可以将这些端点按常见智能体需求分组：

Runs: Atomic agent executions
- Runs（运行）：原子化智能体执行
Threads: Multi-turn interactions or human in the loop
- Threads（线程）：多轮交互或人工介入
Store: Long-term memory
- Store（存储）：长期记忆

We can test requests directly in the API docs.

我们可直接在 API 文档中测试请求。

SDK

The LangGraph SDKs (Python and JS) provide a developer-friendly interface to interact with the LangGraph Server API presented above.

LangGraph SDK（Python 和 JS 版本）为上述 LangGraph Server API 提供了开发者友好的接口。

python 复制代码

%%capture --no-stderr
%pip install -U langgraph_sdk

python 复制代码

from langgraph_sdk import get_client

# Connect via SDK
url_for_cli_deployment = "http://localhost:8123"
client = get_client(url=url_for_cli_deployment)

Remote Graph 远程图

If you are working in the LangGraph library, Remote Graph is also a useful way to connect directly to the graph.

若你在 LangGraph 库中工作，远程图也是一种直接连接图的实用方式。

python 复制代码

%%capture --no-stderr
%pip install -U langchain_openai langgraph langchain_core

python 复制代码

from langgraph.pregel.remote import RemoteGraph
from langchain_core.messages import convert_to_messages
from langchain_core.messages import HumanMessage, SystemMessage

# Connect via remote graph
url = "http://localhost:8123"
graph_name = "task_maistro" 
remote_graph = RemoteGraph(graph_name, url=url)

Runs Runs（运行）

A "run" represents a single execution of your graph.

一次"运行"代表你的图的单次执行。

Each time a client makes a request:

每次客户端发起请求时：

The HTTP worker generates a unique run ID

HTTP 工作者生成唯一运行 ID

This run and its results are stored in PostgreSQL

该运行及其结果被存储在 PostgreSQL 中

You can query these runs to:

你可以查询这些运行以：
Check their status
- 检查其状态
Get their results
- 获取其结果
Track execution history
- 追踪执行历史

You can see a full set of How To guides for various types of runs here.

各种类型运行的完整操作指南请参见此处。

Let's looks at a few of the interesting things we can do with runs.

让我们来看几个关于运行的有趣功能。

Background Runs 后台运行

The LangGraph server supports two types of runs:

LangGraph 服务器支持两种运行类型：

Fire and forget - Launch a run in the background, but don't wait for it to finish
- 即发即弃（Fire and forget） ------ 在后台启动一次运行，但不等待其完成
Waiting on a reply (blocking or polling) - Launch a run and wait/stream its output
- 等待回复（阻塞式或轮询式） ------ 启动一次运行并等待/流式传输其输出

Background runs and polling are quite useful when working with long-running agents.

后台运行和轮询在处理长时间运行的智能体时非常有用。

Let's see how this works:

让我们查看其工作原理：

python 复制代码

# Create a thread
thread = await client.threads.create()
thread

复制代码

{'thread_id': '7f71c0dd-768b-4e53-8349-42bdd10e7caf',
 'created_at': '2024-11-14T19:36:08.459457+00:00',
 'updated_at': '2024-11-14T19:36:08.459457+00:00',
 'metadata': {},
 'status': 'idle',
 'config': {},
 'values': None}

python 复制代码

# Check any existing runs on a thread
thread = await client.threads.create()
runs = await client.runs.list(thread["thread_id"])
print(runs)

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[]

python 复制代码

# Ensure we've created some ToDos and saved them to my user_id
user_input = "Add a ToDo to finish booking travel to Hong Kong by end of next week. Also, add a ToDo to call parents back about Thanksgiving plans."
config = {"configurable": {"user_id": "Test"}}
graph_name = "task_maistro" 
run = await client.runs.create(thread["thread_id"], graph_name, input={"messages": [HumanMessage(content=user_input)]}, config=config)

python 复制代码

# Kick off a new thread and a new run
thread = await client.threads.create()
user_input = "Give me a summary of all ToDos."
config = {"configurable": {"user_id": "Test"}}
graph_name = "task_maistro" 
run = await client.runs.create(thread["thread_id"], graph_name, input={"messages": [HumanMessage(content=user_input)]}, config=config)

python 复制代码

# Check the run status
print(await client.runs.get(thread["thread_id"], run["run_id"]))

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{'run_id': '1efa2c00-63e4-6f4a-9c5b-ca3f5f9bff07', 'thread_id': '641c195a-9e31-4250-a729-6b742c089df8', 'assistant_id': 'ea4ebafa-a81d-5063-a5fa-67c755d98a21', 'created_at': '2024-11-14T19:38:29.394777+00:00', 'updated_at': '2024-11-14T19:38:29.394777+00:00', 'metadata': {}, 'status': 'pending', 'kwargs': {'input': {'messages': [{'id': None, 'name': None, 'type': 'human', 'content': 'Give me a summary of all ToDos.', 'example': False, 'additional_kwargs': {}, 'response_metadata': {}}]}, 'config': {'metadata': {'created_by': 'system'}, 'configurable': {'run_id': '1efa2c00-63e4-6f4a-9c5b-ca3f5f9bff07', 'user_id': 'Test', 'graph_id': 'task_maistro', 'thread_id': '641c195a-9e31-4250-a729-6b742c089df8', 'assistant_id': 'ea4ebafa-a81d-5063-a5fa-67c755d98a21'}}, 'webhook': None, 'subgraphs': False, 'temporary': False, 'stream_mode': ['values'], 'feedback_keys': None, 'interrupt_after': None, 'interrupt_before': None}, 'multitask_strategy': 'reject'}

We can see that it has 'status': 'pending' because it is still running.

我们可以看到其 'status': 'pending'（状态为'待处理'），因为它仍在运行中。

What if we want to wait until the run completes, making it a blocking run?

如果我们希望等待运行完成，使其变为阻塞式运行，该怎么办？

We can use client.runs.join to wait until the run completes.

我们可以使用 client.runs.join 等待运行完成。

This ensures that no new runs are started until the current run completes on the thread.

这确保在当前线程上的运行完成前，不会启动新的运行。

python 复制代码

# Wait until the run completes
await client.runs.join(thread["thread_id"], run["run_id"])
print(await client.runs.get(thread["thread_id"], run["run_id"]))

复制代码

{'run_id': '1efa2c00-63e4-6f4a-9c5b-ca3f5f9bff07', 'thread_id': '641c195a-9e31-4250-a729-6b742c089df8', 'assistant_id': 'ea4ebafa-a81d-5063-a5fa-67c755d98a21', 'created_at': '2024-11-14T19:38:29.394777+00:00', 'updated_at': '2024-11-14T19:38:29.394777+00:00', 'metadata': {}, 'status': 'success', 'kwargs': {'input': {'messages': [{'id': None, 'name': None, 'type': 'human', 'content': 'Give me a summary of all ToDos.', 'example': False, 'additional_kwargs': {}, 'response_metadata': {}}]}, 'config': {'metadata': {'created_by': 'system'}, 'configurable': {'run_id': '1efa2c00-63e4-6f4a-9c5b-ca3f5f9bff07', 'user_id': 'Test', 'graph_id': 'task_maistro', 'thread_id': '641c195a-9e31-4250-a729-6b742c089df8', 'assistant_id': 'ea4ebafa-a81d-5063-a5fa-67c755d98a21'}}, 'webhook': None, 'subgraphs': False, 'temporary': False, 'stream_mode': ['values'], 'feedback_keys': None, 'interrupt_after': None, 'interrupt_before': None}, 'multitask_strategy': 'reject'}

Now the run has 'status': 'success' because it has completed.

现在运行状态为 'status': 'success'（成功），因为它已完成。

Streaming Runs 流式运行

Each time a client makes a streaming request:

每次客户端发起流式请求时：

The HTTP worker generates a unique run ID

HTTP 工作者生成唯一运行 ID

The Queue worker begins work on the run

队列工作者开始处理该运行

During execution, the Queue worker publishes update to Redis

执行期间，队列工作者向 Redis 发布更新

The HTTP worker subscribes to updates from Redis for ths run, and returns them to the client

HTTP 工作者订阅该运行在 Redis 中的更新，并将其返回给客户端

This enabled streaming!

这实现了流式传输！

We've covered streaming in previous modules, but let's pick one method -- streaming tokens -- to highlight.

我们在之前的模块中已涵盖流式传输，但让我们选取其中一种方法------流式传输 token------加以说明。

Streaming tokens back to the client is especially useful when working with production agents that may take a while to complete.

将 token 流式传输回客户端，在处理可能耗时较长的生产环境智能体时尤为有用。

We stream tokens using stream_mode="messages-tuple".

我们使用 stream_mode="messages-tuple" 流式传输 token。

python 复制代码

user_input = "What ToDo should I focus on first."
async for chunk in client.runs.stream(thread["thread_id"], 
                                      graph_name, 
                                      input={"messages": [HumanMessage(content=user_input)]},
                                      config=config,
                                      stream_mode="messages-tuple"):

    if chunk.event == "messages":
        print("".join(data_item['content'] for data_item in chunk.data if 'content' in data_item), end="", flush=True)

复制代码

You might want to focus on "Call parents back about Thanksgiving plans" first. It has a shorter estimated time to complete (15 minutes) and doesn't have a specific deadline, so it could be a quick task to check off your list. Once that's done, you can dedicate more time to "Finish booking travel to Hong Kong," which is more time-consuming and has a deadline.

Threads Threads（线程）

Whereas a run is only a single execution of the graph, a thread supports multi-turn interactions.

与仅表示图单次执行的运行不同，线程支持多轮交互。

When the client makes a graph execution execution with a thread_id, the server will save all checkpoints (steps) in the run to the thread in the Postgres database.

当客户端使用 thread_id 发起图执行请求时，服务器会将运行中的所有检查点（步骤）保存至 PostgreSQL 数据库中的该线程。

The server allows us

a variety of ways to work with threads.

服务器允许我们以多种方式操作线程。

Check thread state 检查线程状态

For example, we can easily access the state checkpoints saved to any specific thread.

例如，我们可以轻松访问保存到任意特定线程的状态检查点。

python 复制代码

thread_state = await client.threads.get_state(thread['thread_id'])
for m in convert_to_messages(thread_state['values']['messages']):
    m.pretty_print()

复制代码

================================[1m Human Message [0m=================================

Give me a summary of all ToDos.
==================================[1m Ai Message [0m==================================

Here's a summary of your current ToDo list:

1. **Task:** Finish booking travel to Hong Kong
   - **Status:** Not started
   - **Deadline:** November 22, 2024
   - **Solutions:** 
     - Check flight prices on Skyscanner
     - Book hotel through Booking.com
     - Arrange airport transfer
   - **Estimated Time to Complete:** 120 minutes

2. **Task:** Call parents back about Thanksgiving plans
   - **Status:** Not started
   - **Deadline:** None
   - **Solutions:** 
     - Check calendar for availability
     - Discuss travel arrangements
     - Confirm dinner plans
   - **Estimated Time to Complete:** 15 minutes

Let me know if there's anything else you'd like to do with your ToDo list!
================================[1m Human Message [0m=================================

What ToDo should I focus on first.
==================================[1m Ai Message [0m==================================

You might want to focus on "Call parents back about Thanksgiving plans" first. It has a shorter estimated time to complete (15 minutes) and doesn't have a specific deadline, so it could be a quick task to check off your list. Once that's done, you can dedicate more time to "Finish booking travel to Hong Kong," which is more time-consuming and has a deadline.

Copy threads 复制线程

We can also copy (i.e. "fork") an existing thread.

我们还可以复制（即"分叉"）一个现有线程。

This will keep the existing thread's history, but allow us to create independent runs that do not affect the original thread.

这将保留现有线程的历史记录，同时允许我们创建独立运行，且不会影响原始线程。

python 复制代码

# Copy the thread
copied_thread = await client.threads.copy(thread['thread_id'])

python 复制代码

# Check the state of the copied thread
copied_thread_state = await client.threads.get_state(copied_thread['thread_id'])
for m in convert_to_messages(copied_thread_state['values']['messages']):
    m.pretty_print()

复制代码

================================[1m Human Message [0m=================================

Give me a summary of all ToDos.
==================================[1m Ai Message [0m==================================

Here's a summary of your current ToDo list:

1. **Task:** Finish booking travel to Hong Kong
   - **Status:** Not started
   - **Deadline:** November 22, 2024
   - **Solutions:** 
     - Check flight prices on Skyscanner
     - Book hotel through Booking.com
     - Arrange airport transfer
   - **Estimated Time to Complete:** 120 minutes

2. **Task:** Call parents back about Thanksgiving plans
   - **Status:** Not started
   - **Deadline:** None
   - **Solutions:** 
     - Check calendar for availability
     - Discuss travel arrangements
     - Confirm dinner plans
   - **Estimated Time to Complete:** 15 minutes

Let me know if there's anything else you'd like to do with your ToDo list!
================================[1m Human Message [0m=================================

What ToDo should I focus on first.
==================================[1m Ai Message [0m==================================

You might want to focus on "Call parents back about Thanksgiving plans" first. It has a shorter estimated time to complete (15 minutes) and doesn't have a specific deadline, so it could be a quick task to check off your list. Once that's done, you can dedicate more time to "Finish booking travel to Hong Kong," which is more time-consuming and has a deadline.

Human in the loop 人工介入循环

We covered Human in the loop in Module 3, and the server supports all Human in the loop features that we discussed.

我们在第 3 模块中已介绍过人工介入循环，服务器支持我们讨论过的全部人工介入循环功能。

As an example, we can search, edit, and continue graph execution from any prior checkpoint.

例如，我们可以从任意先前检查点搜索、编辑并继续图执行。

python 复制代码

# Get the history of the thread
states = await client.threads.get_history(thread['thread_id'])

# Pick a state update to fork
to_fork = states[-2]
to_fork['values']

复制代码

{'messages': [{'content': 'Give me a summary of all ToDos.',
   'additional_kwargs': {'example': False,
    'additional_kwargs': {},
    'response_metadata': {}},
   'response_metadata': {},
   'type': 'human',
   'name': None,
   'id': '3680da45-e3a5-4a47-b5b1-4fd4d3e8baf9',
   'example': False}]}

python 复制代码

to_fork['values']['messages'][0]['id']

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'3680da45-e3a5-4a47-b5b1-4fd4d3e8baf9'

python 复制代码

to_fork['next']

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['task_mAIstro']

python 复制代码

to_fork['checkpoint_id']

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'1efa2c00-6609-67ff-8000-491b1dcf8129'

Let's edit the state.

让我们编辑状态。

Remember how our reducer on messages works:

还记得我们作用于 messages 的规约器（reducer）是如何工作的吗？

It will append, unless we supply a message ID.
- 除非提供消息 ID，否则它将追加消息。
We supply the message ID to overwrite the message, rather than appending to state!
- 我们提供消息 ID 是为了覆盖该消息，而非向状态追加！

python 复制代码

forked_input = {"messages": HumanMessage(content="Give me a summary of all ToDos that need to be done in the next week.",
                                         id=to_fork['values']['messages'][0]['id'])}

# Update the state, creating a new checkpoint in the thread
forked_config = await client.threads.update_state(
    thread["thread_id"],
    forked_input,
    checkpoint_id=to_fork['checkpoint_id']
)

python 复制代码

# Run the graph from the new checkpoint in the thread
async for chunk in client.runs.stream(thread["thread_id"], 
                                      graph_name, 
                                      input=None,
                                      config=config,
                                      checkpoint_id=forked_config['checkpoint_id'],
                                      stream_mode="messages-tuple"):

    if chunk.event == "messages":
        print("".join(data_item['content'] for data_item in chunk.data if 'content' in data_item), end="", flush=True)

复制代码

Here's a summary of your ToDos that need to be done in the next week:

1. **Finish booking travel to Hong Kong**
   - **Status:** Not started
   - **Deadline:** November 22, 2024
   - **Solutions:** 
     - Check flight prices on Skyscanner
     - Book hotel through Booking.com
     - Arrange airport transfer
   - **Estimated Time to Complete:** 120 minutes

It looks like this task is due soon, so you might want to prioritize it. Let me know if there's anything else you need help with!

Across-thread memory 跨线程内存

In module 5, we covered how the LangGraph memory store can be used to save information across threads.

在第 5 模块中，我们介绍了如何使用 LangGraph 内存 store 在多个线程间保存信息。

Our deployed graph, task_maistro, uses the store to save information -- such as ToDos -- namespaced to the user_id.

我们部署的图 task_maistro 使用 store 保存信息（例如待办事项 ToDos），这些信息按 user_id 命名空间隔离。

Our deployment includes a Postgres database, which stores these long-term (across-thread) memories.

我们的部署包含一个 PostgreSQL 数据库，用于存储这些长期（跨线程）内存。

There are several methods available for interacting with the store in our deployment using the LangGraph SDK.

在我们的部署中，可通过 LangGraph SDK 使用多种方法与 store 交互。

Search items 搜索条目

The task_maistro graph uses the store to save ToDos namespaced by default to (todo, todo_category, user_id).

task_maistro 图使用 store 保存待办事项（ToDos），默认按 (todo, todo_category, user_id) 命名空间隔离。

The todo_category is by default set to general (as you can see in deployment/configuration.py).

todo_category 默认设为 general（如 deployment/configuration.py 中所示）。

We can simply supply this tuple to search for all ToDos.

我们只需提供该元组即可搜索所有待办事项（ToDos）。

python 复制代码

items = await client.store.search_items(
    ("todo", "general", "Test"),
    limit=5,
    offset=0
)
items['items']

复制代码

[{'value': {'task': 'Finish booking travel to Hong Kong',
   'status': 'not started',
   'deadline': '2024-11-22T23:59:59',
   'solutions': ['Check flight prices on Skyscanner',
    'Book hotel through Booking.com',
    'Arrange airport transfer'],
   'time_to_complete': 120},
  'key': '18524803-c182-49de-9b10-08ccb0a06843',
  'namespace': ['todo', 'general', 'Test'],
  'created_at': '2024-11-14T19:37:41.664827+00:00',
  'updated_at': '2024-11-14T19:37:41.664827+00:00'},
 {'value': {'task': 'Call parents back about Thanksgiving plans',
   'status': 'not started',
   'deadline': None,
   'solutions': ['Check calendar for availability',
    'Discuss travel arrangements',
    'Confirm dinner plans'],
   'time_to_complete': 15},
  'key': '375d9596-edf8-4de2-985b-bacdc623d6ef',
  'namespace': ['todo', 'general', 'Test'],
  'created_at': '2024-11-14T19:37:41.664827+00:00',
  'updated_at': '2024-11-14T19:37:41.664827+00:00'}]

Add items 添加条目

In our graph, we call put to add items to the store.

在我们的图中，我们调用 put 将条目添加至 store。

We can use put with the SDK if we want to directly add items to the store outside our graph.

如果我们希望在图外部直接向 store 添加条目，可使用 SDK 的 put 方法。

python 复制代码

from uuid import uuid4
await client.store.put_item(
    ("testing", "Test"),
    key=str(uuid4()),
    value={"todo": "Test SDK put_item"},
)

python 复制代码

items = await client.store.search_items(
    ("testing", "Test"),
    limit=5,
    offset=0
)
items['items']

复制代码

[{'value': {'todo': 'Test SDK put_item'},
  'key': '3de441ba-8c79-4beb-8f52-00e4dcba68d4',
  'namespace': ['testing', 'Test'],
  'created_at': '2024-11-14T19:56:30.452808+00:00',
  'updated_at': '2024-11-14T19:56:30.452808+00:00'},
 {'value': {'todo': 'Test SDK put_item'},
  'key': '09b9a869-4406-47c5-a635-4716bd79a8b3',
  'namespace': ['testing', 'Test'],
  'created_at': '2024-11-14T19:53:24.812558+00:00',
  'updated_at': '2024-11-14T19:53:24.812558+00:00'}]

Delete items 删除条目

We can use the SDK to delete items from the store by key.

我们可使用 SDK 通过键从 store 删除条目。

python 复制代码

[item['key'] for item in items['items']]

复制代码

['3de441ba-8c79-4beb-8f52-00e4dcba68d4',
 '09b9a869-4406-47c5-a635-4716bd79a8b3']

python 复制代码

await client.store.delete_item(
       ("testing", "Test"),
        key='3de441ba-8c79-4beb-8f52-00e4dcba68d4',
    )

python 复制代码

items = await client.store.search_items(
    ("testing", "Test"),
    limit=5,
    offset=0
)
items['items']

复制代码

[{'value': {'todo': 'Test SDK put_item'},
  'key': '09b9a869-4406-47c5-a635-4716bd79a8b3',
  'namespace': ['testing', 'Test'],
  'created_at': '2024-11-14T19:53:24.812558+00:00',
  'updated_at': '2024-11-14T19:53:24.812558+00:00'}]