From bcabbd78d7c66c3bee21724e69b756465ef376c4 Mon Sep 17 00:00:00 2001 From: "April I. Murphy" <36110273+aimurphy@users.noreply.github.com> Date: Thu, 2 Oct 2025 07:50:44 -0700 Subject: [PATCH] chore: update langchain links in docs (#10050) update langchain links Co-authored-by: Mendon Kissling <59585235+mendonk@users.noreply.github.com> --- docs/docs/Components/bundles-amazon.mdx | 2 +- docs/docs/Components/bundles-anthropic.mdx | 2 +- docs/docs/Components/bundles-azure.mdx | 2 +- docs/docs/Components/bundles-elastic.mdx | 4 ++-- docs/docs/Components/bundles-groq.mdx | 2 +- docs/docs/Components/bundles-huggingface.mdx | 2 +- docs/docs/Components/bundles-ibm.mdx | 2 +- docs/docs/Components/bundles-langchain.mdx | 12 ++++++------ docs/docs/Components/bundles-mongodb.mdx | 2 +- docs/docs/Components/bundles-pgvector.mdx | 2 +- docs/docs/Develop/data-types.mdx | 6 +++--- docs/docs/_partial-vector-store-instance.mdx | 2 +- 12 files changed, 20 insertions(+), 20 deletions(-) diff --git a/docs/docs/Components/bundles-amazon.mdx b/docs/docs/Components/bundles-amazon.mdx index 11f13feb14..80066a6dd8 100644 --- a/docs/docs/Components/bundles-amazon.mdx +++ b/docs/docs/Components/bundles-amazon.mdx @@ -15,7 +15,7 @@ This page describes the components that are available in the **Amazon** bundle. This component generates text using [Amazon Bedrock LLMs](https://docs.aws.amazon.com/bedrock). It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)). -Specifically, the **Language Model** output is an instance of [`ChatBedrock`](https://python.langchain.com/docs/integrations/chat/bedrock/) configured according to the component's parameters. +Specifically, the **Language Model** output is an instance of [`ChatBedrock`](https://docs.langchain.com/oss/python/integrations/chat/bedrock) configured according to the component's parameters. Use the **Language Model** output when you want to use an Amazon Bedrock model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component. diff --git a/docs/docs/Components/bundles-anthropic.mdx b/docs/docs/Components/bundles-anthropic.mdx index 0898aac8f6..0c3f3f1153 100644 --- a/docs/docs/Components/bundles-anthropic.mdx +++ b/docs/docs/Components/bundles-anthropic.mdx @@ -17,7 +17,7 @@ For more information about Anthropic features and functionality used by Anthropi The **Anthropic** component generates text using Anthropic Chat and Language models like Claude. It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)). -Specifically, the **Language Model** output is an instance of [`ChatAnthropic`](https://python.langchain.com/docs/integrations/chat/anthropic/) configured according to the component's parameters. +Specifically, the **Language Model** output is an instance of [`ChatAnthropic`](https://docs.langchain.com/oss/python/integrations/chat/anthropic) configured according to the component's parameters. Use the **Language Model** output when you want to use an Anthropic model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component. diff --git a/docs/docs/Components/bundles-azure.mdx b/docs/docs/Components/bundles-azure.mdx index f5748bad19..3d20c8d007 100644 --- a/docs/docs/Components/bundles-azure.mdx +++ b/docs/docs/Components/bundles-azure.mdx @@ -15,7 +15,7 @@ This page describes the components that are available in the **Azure** bundle. This component generates text using [Azure OpenAI LLMs](https://learn.microsoft.com/en-us/azure/ai-services/openai/). It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)). -Specifically, the **Language Model** output is an instance of [`AzureChatOpenAI`](https://python.langchain.com/docs/integrations/chat/azure_chat_openai/) configured according to the component's parameters. +Specifically, the **Language Model** output is an instance of [`AzureChatOpenAI`](https://docs.langchain.com/oss/python/integrations/chat/azure_chat_openai) configured according to the component's parameters. Use the **Language Model** output when you want to use an Azure OpenAI model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component. diff --git a/docs/docs/Components/bundles-elastic.mdx b/docs/docs/Components/bundles-elastic.mdx index 4a884f1c18..d863033152 100644 --- a/docs/docs/Components/bundles-elastic.mdx +++ b/docs/docs/Components/bundles-elastic.mdx @@ -15,7 +15,7 @@ This page describes the components that are available in the **Elastic** bundle. ## Elasticsearch -The **Elasticsearch** component reads and writes to an Elasticsearch instance using [`ElasticsearchStore`](https://python.langchain.com/docs/integrations/vectorstores/elasticsearch/). +The **Elasticsearch** component reads and writes to an Elasticsearch instance using [`ElasticsearchStore`](https://docs.langchain.com/oss/python/integrations/vectorstores/elasticsearch).
About vector store instances @@ -55,7 +55,7 @@ For information about accepted values and functionality, see the [Elasticsearch ## OpenSearch -The **OpenSearch** component reads and writes to OpenSearch instances using [`OpenSearchVectorSearch`](https://python.langchain.com/docs/integrations/vectorstores/opensearch/). +The **OpenSearch** component reads and writes to OpenSearch instances using [`OpenSearchVectorSearch`](https://docs.langchain.com/oss/python/integrations/vectorstores/opensearch).
About vector store instances diff --git a/docs/docs/Components/bundles-groq.mdx b/docs/docs/Components/bundles-groq.mdx index e5cf18a412..28a6fbdf24 100644 --- a/docs/docs/Components/bundles-groq.mdx +++ b/docs/docs/Components/bundles-groq.mdx @@ -16,7 +16,7 @@ For more information, see the [Groq documentation](https://groq.com/). This component generates text using Groq's language models. It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)). -Specifically, the **Language Model** output is an instance of [`ChatGroq`](https://python.langchain.com/docs/integrations/chat/groq/) configured according to the component's parameters. +Specifically, the **Language Model** output is an instance of [`ChatGroq`](https://docs.langchain.com/oss/python/integrations/chat/groq) configured according to the component's parameters. Use the **Language Model** output when you want to use a Groq model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component. diff --git a/docs/docs/Components/bundles-huggingface.mdx b/docs/docs/Components/bundles-huggingface.mdx index dd33a1118c..1a97f4637f 100644 --- a/docs/docs/Components/bundles-huggingface.mdx +++ b/docs/docs/Components/bundles-huggingface.mdx @@ -18,7 +18,7 @@ The **Hugging Face** component generates text using a specified model by sending Authentication is required. This component can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)). -Specifically, the **Language Model** output is an instance of [`HuggingFaceHub`](https://python.langchain.com/docs/integrations/providers/huggingface/) configured according to the component's parameters. +Specifically, the **Language Model** output is an instance of [`ChatHuggingFace`](https://docs.langchain.com/oss/python/integrations/chat/huggingface) configured according to the component's parameters. Use the **Language Model** output when you want to use a Hugging Face model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component. diff --git a/docs/docs/Components/bundles-ibm.mdx b/docs/docs/Components/bundles-ibm.mdx index fa5d12e2fe..28957d93f4 100644 --- a/docs/docs/Components/bundles-ibm.mdx +++ b/docs/docs/Components/bundles-ibm.mdx @@ -48,7 +48,7 @@ The **IBM watsonx.ai** component can output either a **Model Response** ([`Messa Use the **Language Model** output when you want to use an IBM watsonx.ai model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component. For more information, see [Language model components](/components-models). -The `LanguageModel` output from the **IBM watsonx.ai** component is an instance of [ChatWatsonx](https://python.langchain.com/docs/integrations/chat/ibm_watsonx/) configured according to the [component's parameters](#ibm-watsonxai-parameters). +The `LanguageModel` output from the **IBM watsonx.ai** component is an instance of `[ChatWatsonx](https://docs.langchain.com/oss/python/integrations/chat/ibm_watsonx)` configured according to the [component's parameters](#ibm-watsonxai-parameters). ## IBM watsonx.ai Embeddings diff --git a/docs/docs/Components/bundles-langchain.mdx b/docs/docs/Components/bundles-langchain.mdx index 79f159ccb8..588609abbf 100644 --- a/docs/docs/Components/bundles-langchain.mdx +++ b/docs/docs/Components/bundles-langchain.mdx @@ -30,7 +30,7 @@ For more information, see the [LangChain CSV agent documentation](https://python This component is based on the [**Agent** core component](/agents). This component creates an OpenAI Tools Agent. -For more information, see the [LangChain OpenAI agent documentation](https://api.python.langchain.com/en/latest/agents/langchain.agents.openai_functions_agent.base.create_openai_functions_agent.html). +For more information, see the [LangChain OpenAI agent documentation](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.openai_functions_agent.base.create_openai_functions_agent.html). ### OpenAI Tools Agent parameters @@ -52,7 +52,7 @@ For more information, see the [LangChain OpenAI agent documentation](https://api This component is based on the [**Agent** core component](/agents). This component creates an agent for interacting with OpenAPI services. -For more information, see the [LangChain OpenAPI toolkit documentation](https://python.langchain.com/docs/integrations/tools/openapi/). +For more information, see the [LangChain OpenAPI toolkit documentation](https://docs.langchain.com/oss/python/integrations/tools/openapi). ### OpenAPI Agent parameters @@ -67,7 +67,7 @@ For more information, see the [LangChain OpenAPI toolkit documentation](https:// ## Prompt Hub -This component fetches prompts from the [LangChain Hub](https://docs.smith.langchain.com/old/category/prompt-hub). +This component fetches prompts from the [LangChain Hub](https://docs.langchain.com/langsmith/manage-prompts#public-prompt-hub). Like the [**Prompt Template** core component](/components-prompts), additional fields are added to the component for each variable in the prompt. For example, the default prompt `efriis/my-first-prompt` adds fields for `profession` and `question`. @@ -85,7 +85,7 @@ For example, the default prompt `efriis/my-first-prompt` adds fields for `profes This component is based on the [**Agent** core component](/agents). This component creates an agent for interacting with SQL databases. -For more information, see the [LangChain SQL agent documentation](https://python.langchain.com/docs/tutorials/sql_qa/). +For more information, see the [LangChain SQL agent documentation](https://docs.langchain.com/oss/python/langchain/sql-agent). ### SQL Agent parameters @@ -123,7 +123,7 @@ The **LangChain** bundle includes the following text splitter components: This component is based on the [**Agent** core component](/agents). This component creates an agent for structured tool calling with various language models. -For more information, see the [LangChain tool calling documentation](https://python.langchain.com/docs/concepts/tool_calling/). +For more information, see the [LangChain tool calling documentation](https://docs.langchain.com/oss/python/langchain/agents#tools). ### Tool Calling Agent parameters @@ -145,7 +145,7 @@ This component is based on the [**Agent** core component](/agents). This component creates an XML Agent using LangChain. The agent uses XML formatting for tool instructions to the LLM. -For more information, see the [LangChain XML Agent documentation](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.xml.base.XMLAgent.html). +For more information, see the [LangChain XML Agent documentation](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.xml.base.create_xml_agent.html). ### XML Agent parameters diff --git a/docs/docs/Components/bundles-mongodb.mdx b/docs/docs/Components/bundles-mongodb.mdx index 09a90b0da3..a4ba735bdc 100644 --- a/docs/docs/Components/bundles-mongodb.mdx +++ b/docs/docs/Components/bundles-mongodb.mdx @@ -15,7 +15,7 @@ This page describes the components that are available in the **MongoDB** bundle. ## MongoDB Atlas -The **MongoDB Atlas** component reads and writes to MongoDB Atlas vector stores using an instance of [`MongoDBAtlasVectorSearch`](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas/). +The **MongoDB Atlas** component reads and writes to MongoDB Atlas vector stores using an instance of [`MongoDBAtlasVectorSearch`](https://docs.langchain.com/oss/python/integrations/vectorstores/mongodb_atlas).
About vector store instances diff --git a/docs/docs/Components/bundles-pgvector.mdx b/docs/docs/Components/bundles-pgvector.mdx index 0a16c4012b..a187f2ceae 100644 --- a/docs/docs/Components/bundles-pgvector.mdx +++ b/docs/docs/Components/bundles-pgvector.mdx @@ -15,7 +15,7 @@ This page describes the components that are available in the **pgvector** bundle ## pgvector vector store -The **PGVector** component reads and writes to PostgreSQL vector stores using an instance of [`PGVector`](https://python.langchain.com/docs/integrations/vectorstores/pgvector/). +The **PGVector** component reads and writes to PostgreSQL vector stores using an instance of [`PGVector`](https://docs.langchain.com/oss/python/integrations/vectorstores/pgvector).
About vector store instances diff --git a/docs/docs/Develop/data-types.mdx b/docs/docs/Develop/data-types.mdx index ee29d273f7..7ae03e998f 100644 --- a/docs/docs/Develop/data-types.mdx +++ b/docs/docs/Develop/data-types.mdx @@ -133,7 +133,7 @@ The `Embeddings` data type is used specifically by components that either produc For example, embedding model components output `Embeddings` data that you can connect to an **Embedding** input port on a vector store component. -For information about the underlying Python classes that produce `Embeddings`, see the [LangChain Embedding models documentation](https://python.langchain.com/docs/integrations/text_embedding/). +For information about the underlying Python classes that produce `Embeddings`, see the [LangChain Embedding models documentation](https://docs.langchain.com/oss/python/integrations/text_embedding). ## LanguageModel @@ -147,9 +147,9 @@ For more information about using these components in flows and toggling `Languag
LanguageModel is an instance of LangChain ChatModel -Because Langflow is built on LangChain, `LanguageModel` is actually an instance of a [LangChain chat model](https://python.langchain.com/docs/concepts/chat_models/) that uses the configuration parameters set in the originating component. +Because Langflow is built on LangChain, `LanguageModel` is actually an instance of a [LangChain chat model](https://docs.langchain.com/oss/python/integrations/chat) that uses the configuration parameters set in the originating component. -Often, components produce an instance of an [integrated chat model](https://python.langchain.com/docs/integrations/chat/) that is designed to support the specific model provider, such as [`ChatOpenAI`](https://python.langchain.com/docs/integrations/chat/openai/) or [`ChatAnthropic`](https://python.langchain.com/docs/integrations/chat/anthropic/). +Often, components produce an instance of an integrated chat model that is designed to support the specific model provider, such as [`ChatOpenAI`](https://docs.langchain.com/oss/python/integrations/chat/openai) or [`ChatAnthropic`](https://docs.langchain.com/oss/python/integrations/chat/anthropic). You can inspect the [component code](/concepts-components#component-code) to see the specific `Chat` instance it produces. diff --git a/docs/docs/_partial-vector-store-instance.mdx b/docs/docs/_partial-vector-store-instance.mdx index e3407dc53c..ef2bed6a54 100644 --- a/docs/docs/_partial-vector-store-instance.mdx +++ b/docs/docs/_partial-vector-store-instance.mdx @@ -1,4 +1,4 @@ -Because Langflow is based on LangChain, vector store components use an instance of [LangChain vector store](https://python.langchain.com/docs/integrations/vectorstores/) to drive the underlying read and write functions. +Because Langflow is based on LangChain, vector store components use an instance of [LangChain vector store](https://docs.langchain.com/oss/python/integrations/vectorstores) to drive the underlying read and write functions. These instances are provider-specific and configured according to the component's parameters, such as the connection string, index name, and schema. In component code, this is often instantiated as `vector_store`, but some vector store components use a different name, such as the provider name.