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 nodes and edges \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/v0.2/docs/concepts/#langchain-expression-language-lcel).\n", + "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/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/docs/docs/how-tos/react-agent-structured-output.ipynb b/docs/docs/how-tos/react-agent-structured-output.ipynb index b816dc49c..79b09d751 100644 --- a/docs/docs/how-tos/react-agent-structured-output.ipynb +++ b/docs/docs/how-tos/react-agent-structured-output.ipynb @@ -100,7 +100,7 @@ "\n", "Now we can define how we want to structure our output, define our graph state, and also our tools and the models we are going to use.\n", "\n", - "To use structured output, we will use the `with_structured_output` method from LangChain, which you can read more about [here](https://python.langchain.com/v0.2/docs/how_to/structured_output/).\n", + "To use structured output, we will use the `with_structured_output` method from LangChain, which you can read more about [here](https://python.langchain.com/docs/how_to/structured_output/).\n", "\n", "We are going to use a single tool in this example for finding the weather, and will return a structured weather response to the user." ] diff --git a/docs/docs/how-tos/state-context-key.ipynb b/docs/docs/how-tos/state-context-key.ipynb index a6e983b31..9cca2dab1 100644 --- a/docs/docs/how-tos/state-context-key.ipynb +++ b/docs/docs/how-tos/state-context-key.ipynb @@ -87,7 +87,7 @@ "\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/v0.2/docs/how_to/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/docs/how_to/custom_tools) on how to do that.\n" ] }, { @@ -280,7 +280,7 @@ "## 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/v0.2/docs/concepts/#langchain-expression-language-lcel).\n", + "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/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", @@ -426,7 +426,7 @@ "## Use it!\n", "\n", "We can now use it!\n", - "This now exposes the [same interface](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel) as all other LangChain runnables." + "This now exposes the [same interface](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel) as all other LangChain runnables." ] }, { diff --git a/docs/docs/how-tos/state-model.ipynb b/docs/docs/how-tos/state-model.ipynb index fe02507df..addaaae08 100644 --- a/docs/docs/how-tos/state-model.ipynb +++ b/docs/docs/how-tos/state-model.ipynb @@ -94,7 +94,7 @@ "\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/v0.2/docs/how_to/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/docs/how_to/custom_tools) on how to do that.\n" ] }, { @@ -243,7 +243,7 @@ "## 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/v0.2/docs/concepts/#langchain-expression-language-lcel).\n", + "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/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", @@ -385,7 +385,7 @@ "## Use it!\n", "\n", "We can now use it!\n", - "This now exposes the [same interface](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel) as all other LangChain runnables." + "This now exposes the [same interface](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel) as all other LangChain runnables." ] }, { diff --git a/docs/docs/how-tos/streaming-tokens.ipynb b/docs/docs/how-tos/streaming-tokens.ipynb index e358e4f6c..170373cd1 100644 --- a/docs/docs/how-tos/streaming-tokens.ipynb +++ b/docs/docs/how-tos/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",
"
Compatibility
\n", diff --git a/docs/docs/tutorials/extraction/retries.ipynb b/docs/docs/tutorials/extraction/retries.ipynb index 2603b790b..7cc088469 100644 --- a/docs/docs/tutorials/extraction/retries.ipynb +++ b/docs/docs/tutorials/extraction/retries.ipynb @@ -435,7 +435,7 @@ "from langchain_core.prompts import ChatPromptTemplate\n", "\n", "# Or you can use ChatGroq, ChatOpenAI, ChatGoogleGemini, ChatCohere, etc.\n", - "# See https://python.langchain.com/v0.2/docs/integrations/chat/ for more info on tool calling\n", + "# See https://python.langchain.com/docs/integrations/chat/ for more info on tool calling\n", "llm = ChatAnthropic(model=\"claude-3-haiku-20240307\")\n", "bound_llm = bind_validator_with_retries(llm, tools=tools)\n", "prompt = ChatPromptTemplate.from_messages(\n", diff --git a/docs/docs/tutorials/introduction.ipynb b/docs/docs/tutorials/introduction.ipynb index 63063a9d5..579f9dbaf 100644 --- a/docs/docs/tutorials/introduction.ipynb +++ b/docs/docs/tutorials/introduction.ipynb @@ -404,7 +404,7 @@ "\n", "Before we start, make sure you have the necessary packages installed and API keys set up:\n", "\n", - "First, install the requirements to use the [Tavily Search Engine](https://python.langchain.com/v0.2/docs/integrations/tools/tavily_search/), and set your [TAVILY_API_KEY](https://tavily.com/)." + "First, install the requirements to use the [Tavily Search Engine](https://python.langchain.com/docs/integrations/tools/tavily_search/), and set your [TAVILY_API_KEY](https://tavily.com/)." ] }, { diff --git a/docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb b/docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb index 688d2ff68..9c51ccda5 100644 --- a/docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb +++ b/docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb @@ -454,7 +454,7 @@ "\n", "We'll first define the tools for the agent to use in our demo. We'll give it the class search engine + calculator combo.\n", "\n", - "If you don't want to sign up for tavily, you can replace it with the free [DuckDuckGo](https://python.langchain.com/v0.2/docs/integrations/tools/ddg/)." + "If you don't want to sign up for tavily, you can replace it with the free [DuckDuckGo](https://python.langchain.com/docs/integrations/tools/ddg/)." ] }, { diff --git a/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb b/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb index ae626ba97..bb383477e 100644 --- a/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb +++ b/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb @@ -102,7 +102,7 @@ "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/v0.2/docs/how_to/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/docs/how_to/custom_tools) on how to do that." ] }, { diff --git a/docs/docs/tutorials/rag/langgraph_agentic_rag.ipynb b/docs/docs/tutorials/rag/langgraph_agentic_rag.ipynb index 5802ee040..060c230ef 100644 --- a/docs/docs/tutorials/rag/langgraph_agentic_rag.ipynb +++ b/docs/docs/tutorials/rag/langgraph_agentic_rag.ipynb @@ -7,7 +7,7 @@ "source": [ "# Agentic RAG\n", "\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", + "[Retrieval Agents](https://python.langchain.com/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", diff --git a/docs/docs/tutorials/rag/langgraph_crag.ipynb b/docs/docs/tutorials/rag/langgraph_crag.ipynb index caa2e3f78..74b5190c9 100644 --- a/docs/docs/tutorials/rag/langgraph_crag.ipynb +++ b/docs/docs/tutorials/rag/langgraph_crag.ipynb @@ -27,7 +27,7 @@ "\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/v0.2/docs/integrations/tools/tavily_search/) for web search.\n", + "* We'll use [Tavily Search](https://python.langchain.com/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/docs/docs/tutorials/rag/langgraph_crag_local.ipynb b/docs/docs/tutorials/rag/langgraph_crag_local.ipynb index 906e5a8d2..e93ad7337 100644 --- a/docs/docs/tutorials/rag/langgraph_crag_local.ipynb +++ b/docs/docs/tutorials/rag/langgraph_crag_local.ipynb @@ -26,7 +26,7 @@ "\n", "* If *any* documents are irrelevant, we'll supplement retrieval with web search. \n", "* We'll skip the knowledge refinement, but this can be added back as a node if desired. \n", - "* We'll use [Tavily Search](https://python.langchain.com/v0.2/docs/integrations/tools/tavily_search/) for web search.\n", + "* We'll use [Tavily Search](https://python.langchain.com/docs/integrations/tools/tavily_search/) for web search.\n", "\n", "" ] @@ -43,7 +43,7 @@ "* Download [Ollama app](https://ollama.ai/).\n", "* Pull your model of choice, e.g.: `ollama pull llama3`\n", "\n", - "We'll use [Tavily](https://python.langchain.com/v0.2/docs/integrations/tools/tavily_search/) for web search.\n", + "We'll use [Tavily](https://python.langchain.com/docs/integrations/tools/tavily_search/) for web search.\n", "\n", "We'll use a vectorstore with [Nomic local embeddings](https://blog.nomic.ai/posts/nomic-embed-text-v1) or, optionally, OpenAI embeddings.\n", "\n", diff --git a/docs/docs/tutorials/rewoo/rewoo.ipynb b/docs/docs/tutorials/rewoo/rewoo.ipynb index 8c68a3460..88dffde26 100644 --- a/docs/docs/tutorials/rewoo/rewoo.ipynb +++ b/docs/docs/tutorials/rewoo/rewoo.ipynb @@ -41,7 +41,7 @@ "\n", "## Setup\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/v0.2/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/docs/integrations/tools/ddg/)).\n", "\n", "Let's install the required packages and set our API keys" ] diff --git a/docs/docs/tutorials/usaco/usaco.ipynb b/docs/docs/tutorials/usaco/usaco.ipynb index 490e29240..49b3a6e72 100644 --- a/docs/docs/tutorials/usaco/usaco.ipynb +++ b/docs/docs/tutorials/usaco/usaco.ipynb @@ -336,7 +336,7 @@ "source": [ "#### Node 1: Solver\n", "\n", - "Create a `solver` node that prompts an LLM \"agent\" to use a [writePython tool](https://python.langchain.com/v0.2/docs/integrations/chat/anthropic/#integration-details) to generate the submitted code." + "Create a `solver` node that prompts an LLM \"agent\" to use a [writePython tool](https://python.langchain.com/docs/integrations/chat/anthropic/#integration-details) to generate the submitted code." ] }, {