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chore: update langchain links in docs (#10050)

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update langchain links

Co-authored-by: Mendon Kissling <59585235+mendonk@users.noreply.github.com>
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April I. Murphy
2025-10-02 07:50:44 -07:00
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docs/docs/Components/bundles-amazon.mdx
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@ -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.

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docs/docs/Components/bundles-anthropic.mdx
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@ -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.

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docs/docs/Components/bundles-azure.mdx
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@ -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.

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docs/docs/Components/bundles-elastic.mdx
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@ -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).
<details>
<summary>About vector store instances</summary>
@ -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).
<details>
<summary>About vector store instances</summary>

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docs/docs/Components/bundles-groq.mdx
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@ -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.

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docs/docs/Components/bundles-huggingface.mdx
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@ -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.

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docs/docs/Components/bundles-ibm.mdx
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@ -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

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docs/docs/Components/bundles-langchain.mdx
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@ -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

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docs/docs/Components/bundles-mongodb.mdx
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@ -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).
<details>
<summary>About vector store instances</summary>

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docs/docs/Components/bundles-pgvector.mdx
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@ -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).
<details>
<summary>About vector store instances</summary>

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docs/docs/Develop/data-types.mdx
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@ -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
<details>
<summary>LanguageModel is an instance of LangChain ChatModel</summary>
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.

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docs/docs/_partial-vector-store-instance.mdx
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@ -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.