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docs: re-enable strict mode (#4638)

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- Turn on strict mode to fail docs build if there are any internal
broken anchor links.
- Fix broken anchor links.
- Delete storm tutorial that has fake markdown inside it (we'll figure
out how to deal with it later)
This commit is contained in:
Eugene Yurtsev
2025-05-09 17:25:11 -04:00
committed by GitHub
parent af80d6b339
commit 200c09b662
19 changed files with 48 additions and 1822 deletions
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docs/docs/cloud/deployment/setup.md
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## Specify Dependencies
Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-api-config).
Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-configuration-file).
The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
docs/docs/cloud/deployment/setup_pyproject.md
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## Specify Dependencies
Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-api-config).
Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-configuration-file).
The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
docs/docs/cloud/deployment/standalone_container.md
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`<database_name_1>` and `database_name_2` are different databases within the same instance, but `<hostname_1>` is shared. **The same database cannot be used for separate deployments**.
1. `LANGSMITH_API_KEY`: (if using [Lite](../../concepts/langgraph_server.md#server-versions)) LangSmith API key. This will be used to authenticate ONCE at server start up.
1. `LANGGRAPH_CLOUD_LICENSE_KEY`: (if using [Enterprise](../../concepts/langgraph_data_plane.md#lite-vs-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
1. `LANGGRAPH_CLOUD_LICENSE_KEY`: (if using [Enterprise](../../concepts/langgraph_data_plane.md#licensing)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
1. `LANGSMITH_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGSMITH_ENDPOINT` to the hostname of the self-hosted LangSmith instance.
## Kubernetes (Helm)
docs/docs/cloud/how-tos/human_in_the_loop_time_travel.md
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## Setup
We are not going to show the full code for the graph we are hosting, but you can see it [here](../../how-tos/human_in_the_loop/time-travel.ipynb#build-the-agent) if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input.
The examples below are executed against a specific deployment on LangGraph Cloud. You will use
the SDK in a similar way, but you will expect to see different results based on the graph you have deployed.
### SDK initialization
docs/docs/cloud/how-tos/streaming.md
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### Supported stream modes
| Mode | Description | LangGraph Library Method |
|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------|
| [`values`](#stream-graph-state) | Stream the full graph state after each [super-step](../../concepts/low_level.md#graphs). | `.stream()` / `.astream()` with [`stream_mode="values"`](../../how-tos/streaming.md#stream-graph-state) |
| [`updates`](#stream-graph-state) | Streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g., multiple nodes are run), those updates are streamed separately. | `.stream()` / `.astream()` with [`stream_mode="updates"`](../../how-tos/streaming.md#stream-graph-state) |
| [`messages-tuple`](#messages) | Streams LLM tokens and metadata for the graph node where the LLM is invoked (useful for chat apps). | `.stream()` / `.astream()` with [`stream_mode="messages"`](../../how-tos/streaming.md#messages) |
| [`debug`](#debug) | Streams as much information as possible throughout the execution of the graph. | `.stream()` / `.astream()` with [`stream_mode="debug"`](../../how-tos/streaming.md#stream-graph-state) |
| [`custom`](#stream-custom-data) | Streams custom data from inside your graph | `.stream()` / `.astream()` with [`stream_mode="custom"`](../../how-tos/streaming.md#stream-custom-data) |
| [`events`](#stream-events) | Stream all events (including the state of the graph); mainly useful when migrating large LCEL apps. | `.astream_events()`
| Mode | Description | LangGraph Library Method |
|----------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|
| [`values`](#stream-graph-state) | Stream the full graph state after each [super-step](../../concepts/low_level.md#graphs). | `.stream()` / `.astream()` with [`stream_mode="values"`](../../how-tos/streaming.md#stream-graph-state) |
| [`updates`](#stream-graph-state) | Streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g., multiple nodes are run), those updates are streamed separately. | `.stream()` / `.astream()` with [`stream_mode="updates"`](../../how-tos/streaming.md#stream-graph-state) |
| [`messages-tuple`](#messages) | Streams LLM tokens and metadata for the graph node where the LLM is invoked (useful for chat apps). | `.stream()` / `.astream()` with [`stream_mode="messages"`](../../how-tos/streaming.md#messages) |
| [`debug`](#debug) | Streams as much information as possible throughout the execution of the graph. | `.stream()` / `.astream()` with [`stream_mode="debug"`](../../how-tos/streaming.md#stream-graph-state) |
| [`custom`](#stream-custom-data) | Streams custom data from inside your graph | `.stream()` / `.astream()` with [`stream_mode="custom"`](../../how-tos/streaming.md#stream-custom-data) |
| [`events`](#stream-events) | Stream all events (including the state of the graph); mainly useful when migrating large LCEL apps. | `.astream_events()` |
### Stream multiple modes
@@ -644,13 +644,7 @@ The streamed output from [`messages-tuple` mode](#supported-stream-modes) is a t
- `message_chunk`: the token or message segment from the LLM.
- `metadata`: a dictionary containing details about the graph node and LLM invocation.
> If your LLM is not available as a LangChain integration, you can stream its outputs using `custom` mode instead. See [use with any LLM](#use-with-any-llm) for details.
!!! warning "Manual config required for async in Python < 3.11"
When using Python < 3.11 with async code in your graph, you must explicitly pass `RunnableConfig` to `ainvoke()` to enable proper streaming. See [Async with Python < 3.11](#async) for details or upgrade to Python 3.11+.
??? example "Example graph"
```python
@@ -748,43 +742,7 @@ The streamed output from [`messages-tuple` mode](#supported-stream-modes) is a t
## Stream custom data
To send **custom user-defined data** from inside a LangGraph node or tool, follow these steps:
1. Use `get_stream_writer()` to access the stream writer and emit custom data.
2. Set `stream_mode="custom"` when calling `.stream()` or `.astream()` to get the custom data in the stream. You can combine multiple modes (e.g., `["updates", "custom"]`), but at least one must be `"custom"`.
!!! warning "No `get_stream_writer()` in async for Python < 3.11"
In async code running on Python < 3.11, `get_stream_writer()` will not work.
Instead, add a `writer` parameter to your node or tool and pass it manually.
See [Async with Python < 3.11](#async) for usage examples.
??? example "Example graph"
```python
from typing import TypedDict
from langgraph.config import get_stream_writer
from langgraph.graph import StateGraph, START
class State(TypedDict):
query: str
answer: str
def node(state: State):
writer = get_stream_writer() # (1)!
writer({"custom_key": "Generating custom data inside node"}) # (2)!
return {"answer": "some data"}
graph = (
StateGraph(State)
.add_node(node)
.add_edge(START, "node")
.compile()
)
```
1. Get the stream writer to send custom data.
2. Emit a custom key-value pair (e.g., progress update).
To send **custom user-defined data**:
=== "Python"
@@ -829,8 +787,6 @@ To send **custom user-defined data** from inside a LangGraph node or tool, follo
}"
```
See [this guide](../../how-tos/streaming.md#stream-custom-data) for more examples.
## Stream events
To stream all events, including the state of the graph: