docs: integrations reference update 9 (#25511)

Added missed provider pages. Added missed references and descriptions.

docs/docs/integrations/platforms/google.mdx

@@ -269,6 +269,18 @@ See a [usage example](/docs/integrations/document_loaders/google_cloud_storage_f
 from langchain_google_community import GCSFileLoader
 ```
 
+### Cloud Vision loader
+
+Install the python package:
+
+```bash
+pip install langchain-google-community[vision]
+```
+
+```python
+from langchain_google_community.vision import CloudVisionLoader
+```
+
 ### El Carro for Oracle Workloads
 
 > Google [El Carro Oracle Operator](https://github.com/GoogleCloudPlatform/elcarro-oracle-operator)

docs/docs/integrations/providers/clarifai.mdx

@@ -1,6 +1,11 @@
 # Clarifai
 
 >[Clarifai](https://clarifai.com) is one of first deep learning platforms having been founded in 2013. Clarifai provides an AI platform with the full AI lifecycle for data exploration, data labeling, model training, evaluation and inference around images, video, text and audio data. In the LangChain ecosystem, as far as we're aware, Clarifai is the only provider that supports LLMs, embeddings and a vector store in one production scale platform, making it an excellent choice to operationalize your LangChain implementations.
+>
+> `Clarifai` provides 1,000s of AI models for many different use cases. You can [explore them here](https://clarifai.com/explore) to find the one most suited for your use case. These models include those created by other providers such as OpenAI, Anthropic, Cohere, AI21, etc. as well as state of the art from open source such as Falcon, InstructorXL, etc. so that you build the best in AI into your products. You'll find these organized by the creator's user_id and into projects we call applications denoted by their app_id. Those IDs will be needed in additional to the model_id and optionally the version_id, so make note of all these IDs once you found the best model for your use case!
+>
+>Also note that given there are many models for images, video, text and audio understanding, you can build some interested AI agents that utilize the variety of AI models as experts to understand those data types.
+
 
 ## Installation and Setup
 - Install the Python SDK:
@@ -10,13 +15,7 @@ pip install clarifai
 [Sign-up](https://clarifai.com/signup) for a Clarifai account, then get a personal access token to access the Clarifai API from your [security settings](https://clarifai.com/settings/security) and set it as an environment variable (`CLARIFAI_PAT`).
 
 
-## Models
-
-Clarifai provides 1,000s of AI models for many different use cases. You can [explore them here](https://clarifai.com/explore) to find the one most suited for your use case. These models include those created by other providers such as OpenAI, Anthropic, Cohere, AI21, etc. as well as state of the art from open source such as Falcon, InstructorXL, etc. so that you build the best in AI into your products. You'll find these organized by the creator's user_id and into projects we call applications denoted by their app_id. Those IDs will be needed in additional to the model_id and optionally the version_id, so make note of all these IDs once you found the best model for your use case!
-
-Also note that given there are many models for images, video, text and audio understanding, you can build some interested AI agents that utilize the variety of AI models as experts to understand those data types.
-
-### LLMs
+## LLMs
 
 To find the selection of LLMs in the Clarifai platform you can select the text to text model type [here](https://clarifai.com/explore/models?filterData=%5B%7B%22field%22%3A%22model_type_id%22%2C%22value%22%3A%5B%22text-to-text%22%5D%7D%5D&page=1&perPage=24).
 
@@ -28,16 +27,18 @@ llm = Clarifai(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_
 For more details, the docs on the Clarifai LLM wrapper provide a [detailed walkthrough](/docs/integrations/llms/clarifai).
 
 
-### Text Embedding Models
+## Embedding Models
 
-To find the selection of text embeddings models in the Clarifai platform you can select the text to embedding model type [here](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22model_type_id%22%2C%22value%22%3A%5B%22text-embedder%22%5D%7D%5D).
+To find the selection of embeddings models in the Clarifai platform you can select the text to embedding model type [here](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22model_type_id%22%2C%22value%22%3A%5B%22text-embedder%22%5D%7D%5D).
 
 There is a Clarifai Embedding model in LangChain, which you can access with:
 ```python
 from langchain_community.embeddings import ClarifaiEmbeddings
 embeddings = ClarifaiEmbeddings(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID)
 ```
-For more details, the docs on the Clarifai Embeddings wrapper provide a [detailed walkthrough](/docs/integrations/text_embedding/clarifai).
+
+See a [usage example](/docs/integrations/document_loaders/couchbase).
+
 
 ## Vectorstore
 

docs/docs/integrations/providers/clickup.mdx

@@ -0,0 +1,20 @@
+# ClickUp
+
+>[ClickUp](https://clickup.com/) is an all-in-one productivity platform that provides small and large teams across industries with flexible and customizable work management solutions, tools, and functions.
+>
+>It is a cloud-based project management solution for businesses of all sizes featuring communication and collaboration tools to help achieve organizational goals.
+
+## Installation and Setup
+
+1. Create a [ClickUp App](https://help.clickup.com/hc/en-us/articles/6303422883095-Create-your-own-app-with-the-ClickUp-API)
+2. Follow [these steps](https://clickup.com/api/developer-portal/authentication/) to get your client_id and client_secret.
+
+## Toolkits
+
+```python
+from langchain_community.agent_toolkits.clickup.toolkit import ClickupToolkit
+from langchain_community.utilities.clickup import ClickupAPIWrapper
+```
+
+See a [usage example](/docs/integrations/tools/clickup).
+

docs/docs/integrations/providers/cloudflare.mdx

@@ -8,6 +8,13 @@
 > learning models, on the `Cloudflare` network, from your code via REST API.
 
 
+## LLMs
+
+See [installation instructions and usage example](/docs/integrations/llms/cloudflare_workersai).
+
+```python
+from langchain_community.llms.cloudflare_workersai import CloudflareWorkersAI
+```
 
 ## Embedding models
 

docs/docs/integrations/providers/clova.mdx

@@ -0,0 +1,14 @@
+# Clova
+
+>[CLOVA Studio](https://api.ncloud-docs.com/docs/ai-naver-clovastudio-summary) is a service 
+> of [Naver Cloud Platform](https://www.ncloud.com/) that uses `HyperCLOVA` language models, 
+> a hyperscale AI technology, to output phrases generated through AI technology based on user input.
+
+
+## Embedding models
+
+See [installation instructions and usage example](/docs/integrations/text_embedding/clova).
+
+```python
+from langchain_community.embeddings import ClovaEmbeddings
+```

docs/docs/integrations/providers/cogniswitch.mdx

@@ -0,0 +1,53 @@
+# CogniSwitch
+
+>[CogniSwitch](https://www.cogniswitch.ai/aboutus) is an API based data platform that 
+> enhances enterprise data by extracting entities, concepts and their relationships 
+> thereby converting this data into a multidimensional format and storing it in 
+> a database that can accommodate these enhancements. In our case the data is stored 
+> in a knowledge graph. This enhanced data is now ready for consumption by LLMs and 
+> other GenAI applications ensuring the data is consumable and context can be maintained. 
+> Thereby eliminating hallucinations and delivering accuracy.
+
+## Toolkit
+
+See [installation instructions and usage example](/docs/integrations/tools/cogniswitch).
+
+```python
+from langchain_community.agent_toolkits import CogniswitchToolkit
+```
+
+## Tools
+
+### CogniswitchKnowledgeRequest
+
+>Tool that uses the CogniSwitch service to answer questions.
+
+```python
+from langchain_community.tools.cogniswitch.tool import CogniswitchKnowledgeRequest
+```
+
+### CogniswitchKnowledgeSourceFile
+
+>Tool that uses the CogniSwitch services to store data from file.
+
+```python
+from langchain_community.tools.cogniswitch.tool import CogniswitchKnowledgeSourceFile
+```
+
+### CogniswitchKnowledgeSourceURL
+
+>Tool that uses the CogniSwitch services to store data from a URL.
+
+```python
+from langchain_community.tools.cogniswitch.tool import CogniswitchKnowledgeSourceURL
+```
+
+### CogniswitchKnowledgeStatus
+
+>Tool that uses the CogniSwitch services to get the status of the document or url uploaded.
+
+```python
+from langchain_community.tools.cogniswitch.tool import CogniswitchKnowledgeStatus
+```
+
+