docs: integrations reference updates 10 (#25556)

Added missed provider pages. Added descriptions, links.

docs/docs/integrations/platforms/google.mdx

@@ -625,6 +625,7 @@ from langchain.retrievers import GoogleVertexAISearchRetriever
 > from Google Cloud allows enterprises to search, store, govern, and manage documents and their AI-extracted
 > data and metadata in a single platform.
 
+Note: `GoogleDocumentAIWarehouseRetriever` is deprecated, use `DocumentAIWarehouseRetriever` (see below).
 ```python
 from langchain.retrievers import GoogleDocumentAIWarehouseRetriever
 docai_wh_retriever = GoogleDocumentAIWarehouseRetriever(
@@ -636,6 +637,10 @@ documents = docai_wh_retriever.invoke(
 )
 ```
 
+```python
+from langchain_google_community.documentai_warehouse import DocumentAIWarehouseRetriever
+```
+
 ## Tools
 
 ### Text-to-Speech

docs/docs/integrations/platforms/microsoft.mdx

@@ -466,6 +466,22 @@ See a [usage example](/docs/integrations/tools/playwright).
 from langchain_community.agent_toolkits import PlayWrightBrowserToolkit
 ```
 
+#### PlayWright Browser individual tools
+
+You can use individual tools from the PlayWright Browser Toolkit.
+
+```python
+from langchain_community.tools.playwright import ClickTool
+from langchain_community.tools.playwright import CurrentWebPageTool
+from langchain_community.tools.playwright import ExtractHyperlinksTool
+from langchain_community.tools.playwright import ExtractTextTool
+from langchain_community.tools.playwright import GetElementsTool
+from langchain_community.tools.playwright import NavigateTool
+from langchain_community.tools.playwright import NavigateBackTool
+```
+
+
+```python
 ## Graphs
 
 ### Azure Cosmos DB for Apache Gremlin

docs/docs/integrations/providers/connery.mdx

@@ -0,0 +1,28 @@
+# Connery
+
+>[Connery SDK](https://github.com/connery-io/connery-sdk) is an NPM package that 
+> includes both an SDK and a CLI, designed for the development of plugins and actions.
+>
+>The CLI automates many things in the development process. The SDK 
+> offers a JavaScript API for defining plugins and actions and packaging them 
+> into a plugin server with a standardized REST API generated from the metadata. 
+> The plugin server handles authorization, input validation, and logging. 
+> So you can focus on the logic of your actions.
+> 
+> See the use cases and examples in the [Connery SDK documentation](https://sdk.connery.io/docs/use-cases/)
+
+## Toolkit
+
+See [usage example](/docs/integrations/tools/connery).
+
+```python
+from langchain_community.agent_toolkits.connery import ConneryToolkit
+```
+
+## Tools
+
+### ConneryAction
+
+```python
+from langchain_community.tools.connery import ConneryService
+```

docs/docs/integrations/providers/dashvector.mdx

@@ -6,12 +6,27 @@ This document demonstrates to leverage DashVector within the LangChain ecosystem
 It is broken into two parts: installation and setup, and then references to specific DashVector wrappers.
 
 ## Installation and Setup
+
+
 Install the Python SDK:
+
 ```bash
 pip install dashvector
 ```
 
-## VectorStore
+You must have an API key. Here are the [installation instructions](https://help.aliyun.com/document_detail/2510223.html).
+
+
+## Embedding models
+
+```python
+from langchain_community.embeddings import DashScopeEmbeddings
+```
+
+See the [use example](/docs/integrations/vectorstores/dashvector).
+
+
+## Vector Store
 
 A DashVector Collection is wrapped as a familiar VectorStore for native usage within LangChain, 
 which allows it to be readily used for various scenarios, such as semantic search or example selection.

docs/docs/integrations/providers/dataforseo.mdx

@@ -19,7 +19,7 @@ os.environ["DATAFORSEO_PASSWORD"] = "your_password"
 
 ## Utility
 
-The DataForSEO utility wraps the API. To import this utility, use:
+The `DataForSEO` utility wraps the API. To import this utility, use:
 
 ```python
 from langchain_community.utilities.dataforseo_api_search import DataForSeoAPIWrapper
@@ -36,6 +36,13 @@ from langchain.agents import load_tools
 tools = load_tools(["dataforseo-api-search"])
 ```
 
+This will load the following tools:
+
+```python
+from langchain_community.tools import DataForSeoAPISearchRun
+from langchain_community.tools import DataForSeoAPISearchResults
+```
+
 ## Example usage
 
 ```python

docs/docs/integrations/providers/dingo.mdx

@@ -1,10 +1,21 @@
 # DingoDB
 
-This page covers how to use the DingoDB ecosystem within LangChain.
+>[DingoDB](https://github.com/dingodb) is a distributed multi-modal vector 
-It is broken into two parts: installation and setup, and then references to specific DingoDB wrappers.
+> database. It combines the features of a data lake and a vector database, 
+> allowing for the storage of any type of data (key-value, PDF, audio, 
+> video, etc.) regardless of its size. Utilizing DingoDB, you can construct 
+> your own Vector Ocean (the next-generation data architecture following data 
+> warehouse and data lake). This enables 
+> the analysis of both structured and unstructured data through 
+> a singular SQL with exceptionally low latency in real time.
 
 ## Installation and Setup
-- Install the Python SDK with `pip install dingodb`
+
+Install the Python SDK
+
+```bash
+pip install dingodb
+```
 
 ## VectorStore
 
@@ -12,6 +23,7 @@ There exists a wrapper around DingoDB indexes, allowing you to use it as a vecto
 whether for semantic search or example selection.
 
 To import this vectorstore:
+
 ```python
 from langchain_community.vectorstores import Dingo
 ```

docs/docs/integrations/providers/docarray.mdx

@@ -20,7 +20,7 @@ LangChain provides an access to the `In-memory` and `HNSW` vector stores from th
 See a [usage example](/docs/integrations/vectorstores/docarray_hnsw).
 
 ```python
-from langchain_community.vectorstores DocArrayHnswSearch
+from langchain_community.vectorstores import DocArrayHnswSearch
 ```
 See a [usage example](/docs/integrations/vectorstores/docarray_in_memory).
 
@@ -28,3 +28,10 @@ See a [usage example](/docs/integrations/vectorstores/docarray_in_memory).
 from langchain_community.vectorstores DocArrayInMemorySearch
 ```
 
+## Retriever
+
+See a [usage example](/docs/integrations/retrievers/docarray_retriever).
+
+```python
+from langchain_community.retrievers import DocArrayRetriever
+```

docs/docs/integrations/providers/pandas.mdx

@@ -0,0 +1,29 @@
+# Pandas
+
+>[pandas](https://pandas.pydata.org) is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,
+built on top of the `Python` programming language.
+
+## Installation and Setup
+
+Install the `pandas` package using `pip`:
+
+```bash
+pip install pandas
+```
+
+
+## Document loader
+
+See a [usage example](/docs/integrations/document_loaders/pandas_dataframe).
+
+```python
+from langchain_community.document_loaders import DataFrameLoader
+```
+
+## Toolkit
+
+See a [usage example](/docs/integrations/tools/pandas).
+
+```python
+from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
+```