Note
\n", "\n",
- " In this how-to, we will create our agent from scratch to be transparent (but verbose). You can accomplish similar functionality using the create_react_agent(model, tools=tool, interrupt_before=[\"agent\" | \"tools\"], interrupt_after=[\"agent\" | \"tools\"], checkpointer=checkpointer) (API doc) constructor. This may be more appropriate if you are used to LangChain’s AgentExecutor class.\n",
+ " In this how-to, we will create our agent from scratch to be transparent (but verbose). You can accomplish similar functionality using the create_react_agent(model, tools=tool, interrupt_before=[\"agent\" | \"tools\"], interrupt_after=[\"agent\" | \"tools\"], checkpointer=checkpointer) (API doc) constructor. This may be more appropriate if you are used to LangChain’s AgentExecutor class.\n",
"
Note
\n", "\n",
- " In this how-to, we will create our agent from scratch to be transparent (but verbose). You can accomplish similar functionality using either `interrupt_before` or `interrupt_after` in the create_react_agent(model, tools=tool, interrupt_before=[\"tools\" | \"agent\"], interrupt_after=[\"tools\" | \"agent\"]) (API doc) constructor. This may be more appropriate if you are used to LangChain’s AgentExecutor class.\n",
+ " In this how-to, we will create our agent from scratch to be transparent (but verbose). You can accomplish similar functionality using either `interrupt_before` or `interrupt_after` in the create_react_agent(model, tools=tool, interrupt_before=[\"tools\" | \"agent\"], interrupt_after=[\"tools\" | \"agent\"]) (API doc) constructor. This may be more appropriate if you are used to LangChain’s AgentExecutor class.\n",
"
Note
\n", "\n",
- " In this how-to, we will create our agent from scratch to be transparent (but verbose). You can accomplish similar functionality using the create_react_agent(model, tools=tool, checkpointer=checkpointer) (API doc) constructor. This may be more appropriate if you are used to LangChain’s AgentExecutor class.\n",
+ " In this how-to, we will create our agent from scratch to be transparent (but verbose). You can accomplish similar functionality using the create_react_agent(model, tools=tool, checkpointer=checkpointer) (API doc) constructor. This may be more appropriate if you are used to LangChain’s AgentExecutor class.\n",
"
Note
\n", @@ -255,7 +255,7 @@ "## Define the graph \n", "\n", "We now need to define a few different nodes in our graph.\n", - "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/docs/expression_language/).\n", + "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel).\n", "There are two main nodes we need for this:\n", "\n", "1. The agent: responsible for deciding what (if any) actions to take.\n", diff --git a/examples/plan-and-execute/plan-and-execute.ipynb b/examples/plan-and-execute/plan-and-execute.ipynb index 1612fd730..5f044c5eb 100644 --- a/examples/plan-and-execute/plan-and-execute.ipynb +++ b/examples/plan-and-execute/plan-and-execute.ipynb @@ -105,7 +105,7 @@ "source": [ "## Define Tools\n", "\n", - "We will first define the tools we want to use. For this simple example, we will use a built-in search tool via Tavily. However, it is really easy to create your own tools - see documentation [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools) on how to do that." + "We will first define the tools we want to use. For this simple example, we will use a built-in search tool via Tavily. However, it is really easy to create your own tools - see documentation [here](https://python.langchain.com/v0.2/docs/how_to/custom_tools) on how to do that." ] }, { diff --git a/examples/rag/langgraph_agentic_rag.ipynb b/examples/rag/langgraph_agentic_rag.ipynb index 0eefd0744..01049aa82 100644 --- a/examples/rag/langgraph_agentic_rag.ipynb +++ b/examples/rag/langgraph_agentic_rag.ipynb @@ -7,11 +7,11 @@ "source": [ "# LangGraph Retrieval Agent\n", "\n", - "[Retrieval Agents](https://python.langchain.com/docs/use_cases/question_answering/conversational_retrieval_agents) are useful when we want to make decisions about whether to retrieve from an index.\n", + "[Retrieval Agents](https://python.langchain.com/v0.2/docs/tutorials/qa_chat_history/#agents) are useful when we want to make decisions about whether to retrieve from an index.\n", "\n", "To implement a retrieval agent, we simple need to give an LLM access to a retriever tool.\n", "\n", - "We can incorporate this into [LangGraph](https://python.langchain.com/docs/langgraph)." + "We can incorporate this into [LangGraph](https://langchain-ai.github.io/langgraph/)." ] }, { diff --git a/examples/rag/langgraph_crag.ipynb b/examples/rag/langgraph_crag.ipynb index 70940d173..fbe536e6d 100644 --- a/examples/rag/langgraph_crag.ipynb +++ b/examples/rag/langgraph_crag.ipynb @@ -23,11 +23,11 @@ "* If all documents fall below the relevance threshold or if the grader is unsure, then the framework seeks an additional datasource\n", "* It will use web search to supplement retrieval\n", " \n", - "We will implement some of these ideas from scratch using [LangGraph](https://python.langchain.com/docs/langgraph):\n", + "We will implement some of these ideas from scratch using [LangGraph](https://langchain-ai.github.io/langgraph/):\n", "\n", "* Let's skip the knowledge refinement phase as a first pass. This can be added back as a node, if desired. \n", "* If *any* documents are irrelevant, let's opt to supplement retrieval with web search. \n", - "* We'll use [Tavily Search](https://python.langchain.com/docs/integrations/tools/tavily_search) for web search.\n", + "* We'll use [Tavily Search](https://python.langchain.com/v0.2/docs/integrations/tools/tavily_search/) for web search.\n", "* Let's use query re-writing to optimize the query for web search.\n", "\n", "" diff --git a/examples/rag/langgraph_crag_local.ipynb b/examples/rag/langgraph_crag_local.ipynb index 70ed86f7d..a898f93f9 100644 --- a/examples/rag/langgraph_crag_local.ipynb +++ b/examples/rag/langgraph_crag_local.ipynb @@ -23,11 +23,11 @@ "* If all documents fall below the relevance threshold or if the grader is unsure, then the framework seeks an additional datasource\n", "* It will use web search to supplement retrieval\n", " \n", - "We will implement some of these ideas from scratch using [LangGraph](https://python.langchain.com/docs/langgraph):\n", + "We will implement some of these ideas from scratch using [LangGraph](https://langchain-ai.github.io/langgraph/):\n", "\n", "* Let's skip the knowledge refinement phase as a first pass. This can be added back as a node, if desired. \n", "* If *any* documents are irrelevant, let's opt to supplement retrieval with web search. \n", - "* We'll use [Tavily Search](https://python.langchain.com/docs/integrations/tools/tavily_search) for web search.\n", + "* We'll use [Tavily Search](https://python.langchain.com/v0.2/docs/integrations/tools/tavily_search/) for web search.\n", "* Let's use query re-writing to optimize the query for web search.\n", "\n", "\n", diff --git a/examples/rag/langgraph_self_rag.ipynb b/examples/rag/langgraph_self_rag.ipynb index d27eb3478..3f309a237 100644 --- a/examples/rag/langgraph_self_rag.ipynb +++ b/examples/rag/langgraph_self_rag.ipynb @@ -40,7 +40,7 @@ "* `y (generation)` is a useful response to `x (question)`.\n", "* Output: `{5, 4, 3, 2, 1}`\n", "\n", - "We will implement some of these ideas from scratch using [LangGraph](https://python.langchain.com/docs/langgraph).\n", + "We will implement some of these ideas from scratch using [LangGraph](https://langchain-ai.github.io/langgraph/).\n", "\n", "" ] diff --git a/examples/rag/langgraph_self_rag_local.ipynb b/examples/rag/langgraph_self_rag_local.ipynb index dce12a9cf..df160e9aa 100644 --- a/examples/rag/langgraph_self_rag_local.ipynb +++ b/examples/rag/langgraph_self_rag_local.ipynb @@ -40,7 +40,7 @@ "* `y (generation)` is a useful response to `x (question)`.\n", "* Output: `{5, 4, 3, 2, 1}`\n", "\n", - "We will implement some of these ideas from scratch using [LangGraph](https://python.langchain.com/docs/langgraph).\n", + "We will implement some of these ideas from scratch using [LangGraph](https://langchain-ai.github.io/langgraph/).\n", "\n", "" ] diff --git a/examples/respond-in-format.ipynb b/examples/respond-in-format.ipynb index 6d71adeed..a77a30791 100644 --- a/examples/respond-in-format.ipynb +++ b/examples/respond-in-format.ipynb @@ -131,7 +131,7 @@ "\n", "We will first define the tools we want to use.\n", "For this simple example, we will use create a placeholder search engine.\n", - "It is really easy to create your own tools - see documentation [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools) on how to do that.\n" + "It is really easy to create your own tools - see documentation [here](https://python.langchain.com/v0.2/docs/how_to/custom_tools) on how to do that.\n" ] }, { @@ -282,7 +282,7 @@ "## Define the nodes\n", "\n", "We now need to define a few different nodes in our graph.\n", - "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/docs/expression_language/).\n", + "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel).\n", "There are two main nodes we need for this:\n", "\n", "1. The agent: responsible for deciding what (if any) actions to take.\n", @@ -417,7 +417,7 @@ "## Use it!\n", "\n", "We can now use it!\n", - "This now exposes the [same interface](https://python.langchain.com/docs/expression_language/) as all other LangChain runnables." + "This now exposes the [same interface](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel) as all other LangChain runnables." ] }, { @@ -500,7 +500,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.1" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/examples/rewoo/rewoo.ipynb b/examples/rewoo/rewoo.ipynb index 818882380..ee9228fe1 100644 --- a/examples/rewoo/rewoo.ipynb +++ b/examples/rewoo/rewoo.ipynb @@ -36,7 +36,7 @@ "\n", "## 0. Prerequisites\n", "\n", - "For this example, we will provide the agent with a Tavily search engine tool. You can get an API key [here](https://app.tavily.com/sign-in) or replace with a free tool option (e.g., [duck duck go search](https://python.langchain.com/docs/integrations/tools/ddg)).\n", + "For this example, we will provide the agent with a Tavily search engine tool. You can get an API key [here](https://app.tavily.com/sign-in) or replace with a free tool option (e.g., [duck duck go search](https://python.langchain.com/v0.2/docs/integrations/tools/ddg/)).\n", "\n", "To see the full langsmith trace, you can s" ] diff --git a/examples/state-model.ipynb b/examples/state-model.ipynb index 31b818821..5a1bca9fe 100644 --- a/examples/state-model.ipynb +++ b/examples/state-model.ipynb @@ -92,7 +92,7 @@ "\n", "We will first define the tools we want to use.\n", "For this simple example, we will use create a placeholder search engine.\n", - "However, it is really easy to create your own tools - see documentation [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools) on how to do that.\n" + "However, it is really easy to create your own tools - see documentation [here](https://python.langchain.com/v0.2/docs/how_to/custom_tools) on how to do that.\n" ] }, { @@ -231,7 +231,7 @@ "## Define the nodes\n", "\n", "We now need to define a few different nodes in our graph.\n", - "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/docs/expression_language/).\n", + "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel).\n", "There are two main nodes we need for this:\n", "\n", "1. The agent: responsible for deciding what (if any) actions to take.\n", @@ -400,7 +400,7 @@ "## Use it!\n", "\n", "We can now use it!\n", - "This now exposes the [same interface](https://python.langchain.com/docs/expression_language/) as all other LangChain runnables." + "This now exposes the [same interface](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel) as all other LangChain runnables." ] }, { diff --git a/examples/streaming-tokens.ipynb b/examples/streaming-tokens.ipynb index d91594246..28e786648 100644 --- a/examples/streaming-tokens.ipynb +++ b/examples/streaming-tokens.ipynb @@ -14,7 +14,7 @@ "Note
\n", "\n",
- " In this how-to, we will create our agent from scratch to be transparent (but verbose). You can accomplish similar functionality using the create_react_agent(model, tools=tool) (API doc) constructor. This may be more appropriate if you are used to LangChain’s AgentExecutor class.\n",
+ " In this how-to, we will create our agent from scratch to be transparent (but verbose). You can accomplish similar functionality using the create_react_agent(model, tools=tool) (API doc) constructor. This may be more appropriate if you are used to LangChain’s AgentExecutor class.\n",
"
Note:
\n", "\n",
- " In this how-to, we will create our agent from scratch to be transparent (but verbose). You can accomplish similar functionality using the create_react_agent(model, tools=tool, checkpointer=checkpointer) (API doc) constructor. This may be more appropriate if you are used to LangChain’s AgentExecutor class.\n",
+ " In this how-to, we will create our agent from scratch to be transparent (but verbose). You can accomplish similar functionality using the create_react_agent(model, tools=tool, checkpointer=checkpointer) (API doc) constructor. This may be more appropriate if you are used to LangChain’s AgentExecutor class.\n",
"