From 9a570181e720acdcfa128e89c4e54a59a4cfe0fd Mon Sep 17 00:00:00 2001 From: isaac hershenson Date: Tue, 17 Sep 2024 12:39:54 -0700 Subject: [PATCH] fix links --- README.md | 2 +- docs/docs/how-tos/branching.ipynb | 2 +- docs/docs/how-tos/streaming-content.ipynb | 2 +- libs/langgraph/README.md | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index c8c49f688..a375771f0 100644 --- a/README.md +++ b/README.md @@ -224,7 +224,7 @@ final_state["messages"][-1].content * [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples. * [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet. -* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more. +* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more. * [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more. * [Cloud (beta)](https://langchain-ai.github.io/langgraph/cloud/): With one click, deploy LangGraph applications to LangGraph Cloud. diff --git a/docs/docs/how-tos/branching.ipynb b/docs/docs/how-tos/branching.ipynb index 422f47ef6..45984b85d 100644 --- a/docs/docs/how-tos/branching.ipynb +++ b/docs/docs/how-tos/branching.ipynb @@ -642,7 +642,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/docs/docs/how-tos/streaming-content.ipynb b/docs/docs/how-tos/streaming-content.ipynb index cb7422eda..0dae42642 100644 --- a/docs/docs/how-tos/streaming-content.ipynb +++ b/docs/docs/how-tos/streaming-content.ipynb @@ -7,7 +7,7 @@ "source": [ "# How to stream arbitrary nested content\n", "\n", - "The most common use case for streaming from inside a node is to stream LLM tokens, but you may have other long-running streaming functions you wish to render for the user. While individual nodes in LangGraph cannot return generators (since they are executed to completion for each [superstep](https://langchain-ai.github.io/langgraph/concepts/#core-design)), we can still stream arbitrary custom functions from within a node using a similar tact and calling `astream_events` on the graph.\n", + "The most common use case for streaming from inside a node is to stream LLM tokens, but you may have other long-running streaming functions you wish to render for the user. While individual nodes in LangGraph cannot return generators (since they are executed to completion for each [superstep](https://langchain-ai.github.io/langgraph/concepts/low_level)), we can still stream arbitrary custom functions from within a node using a similar tact and calling `astream_events` on the graph.\n", "\n", "We do so using a [RunnableGenerator](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableGenerator.html#langchain-core-runnables-base-runnablegenerator) (which your function will automatically behave as if wrapped as a [RunnableLambda](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableLambda.html#langchain_core.runnables.base.RunnableLambda)).\n", "\n", diff --git a/libs/langgraph/README.md b/libs/langgraph/README.md index c8c49f688..cd8d42b7d 100644 --- a/libs/langgraph/README.md +++ b/libs/langgraph/README.md @@ -224,7 +224,7 @@ final_state["messages"][-1].content * [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples. * [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet. -* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more. +* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more. * [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more. * [Cloud (beta)](https://langchain-ai.github.io/langgraph/cloud/): With one click, deploy LangGraph applications to LangGraph Cloud.