docs: Add Cohere examples in documentation (langchain-ai#17794)

- Description: Add cohere examples to documentation 
- Issue:N/A
- Dependencies: N/A

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

docs/docs/get_started/quickstart.mdx

@@ -65,10 +65,10 @@ We will link to relevant docs.
 
 ## LLM Chain
 
-For this getting started guide, we will provide two options: using OpenAI (a popular model available via API) or using a local open source model.
+We'll show how to use models available via API, like OpenAI and Cohere, and local open source models, using integrations like Ollama.
 
 <Tabs>
-  <TabItem value="openai" label="OpenAI" default>
+  <TabItem value="openai" label="OpenAI (API)" default>
 
 First we'll need to import the LangChain x OpenAI integration package.
 
@@ -99,7 +99,7 @@ llm = ChatOpenAI(openai_api_key="...")
 ```
 
   </TabItem>
-  <TabItem value="local" label="Local">
+  <TabItem value="local" label="Local (using Ollama)">
 
 [Ollama](https://ollama.ai/) allows you to run open-source large language models, such as Llama 2, locally.
 
@@ -112,6 +112,37 @@ Then, make sure the Ollama server is running. After that, you can do:
 ```python
 from langchain_community.llms import Ollama
 llm = Ollama(model="llama2")
+```
+
+  </TabItem>
+  <TabItem value="cohere" label="Cohere (API)" default>
+
+First we'll need to import the Cohere SDK package.
+
+```shell
+pip install cohere
+```
+
+Accessing the API requires an API key, which you can get by creating an account and heading [here](https://dashboard.cohere.com/api-keys). Once we have a key we'll want to set it as an environment variable by running:
+
+```shell
+export COHERE_API_KEY="..."
+```
+
+We can then initialize the model:
+
+```python
+from langchain_community.chat_models import ChatCohere
+
+llm = ChatCohere()
+```
+
+If you'd prefer not to set an environment variable you can pass the key in directly via the `cohere_api_key` named parameter when initiating the Cohere LLM class:
+
+```python
+from langchain_community.chat_models import ChatCohere
+
+llm = ChatCohere(cohere_api_key="...")
 ```
 
   </TabItem>
@@ -200,10 +231,10 @@ docs = loader.load()
 
 Next, we need to index it into a vectorstore. This requires a few components, namely an [embedding model](/docs/modules/data_connection/text_embedding) and a [vectorstore](/docs/modules/data_connection/vectorstores).
 
-For embedding models, we once again provide examples for accessing via OpenAI or via local models.
+For embedding models, we once again provide examples for accessing via API or by running local models.
 
 <Tabs>
-  <TabItem value="openai" label="OpenAI" default>
+  <TabItem value="openai" label="OpenAI (API)" default>
 
 Make sure you have the `langchain_openai` package installed an the appropriate environment variables set (these are the same as needed for the LLM).
 
@@ -214,7 +245,7 @@ embeddings = OpenAIEmbeddings()
 ```
 
 </TabItem>
-<TabItem value="local" label="Local">
+<TabItem value="local" label="Local (using Ollama)">
 
 Make sure you have Ollama running (same set up as with the LLM).
 
@@ -224,6 +255,17 @@ from langchain_community.embeddings import OllamaEmbeddings
 embeddings = OllamaEmbeddings()
 ```
   </TabItem>
+<TabItem value="cohere" label="Cohere (API)" default>
+
+Make sure you have the `cohere` package installed an the appropriate environment variables set (these are the same as needed for the LLM).
+
+```python
+from langchain_community.embeddings import CohereEmbeddings
+
+embeddings = CohereEmbeddings()
+```
+
+</TabItem>
 </Tabs>
 
 Now, we can use this embedding model to ingest documents into a vectorstore.

docs/docs/modules/data_connection/text_embedding/index.mdx

@@ -17,6 +17,11 @@ The base Embeddings class in LangChain provides two methods: one for embedding d
 
 ### Setup
 
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+<Tabs>
+  <TabItem value="openai" label="OpenAI" default>
 To start we'll need to install the OpenAI partner package:
 
 ```bash
@@ -44,6 +49,39 @@ from langchain_openai import OpenAIEmbeddings
 embeddings_model = OpenAIEmbeddings()
 ```
 
+  </TabItem>
+  <TabItem value="cohere" label="Cohere">
+
+To start we'll need to install the Cohere SDK package:
+
+```bash
+pip install cohere
+```
+
+Accessing the API requires an API key, which you can get by creating an account and heading [here](https://dashboard.cohere.com/api-keys). Once we have a key we'll want to set it as an environment variable by running:
+
+```shell
+export COHERE_API_KEY="..."
+```
+
+If you'd prefer not to set an environment variable you can pass the key in directly via the `cohere_api_key` named parameter when initiating the Cohere LLM class:
+
+```python
+from langchain_community.embeddings import CohereEmbeddings
+
+embeddings_model = CohereEmbeddings(cohere_api_key="...")
+```
+
+Otherwise you can initialize without any params:
+```python
+from langchain_community.embeddings import CohereEmbeddings
+
+embeddings_model = CohereEmbeddings()
+```
+
+  </TabItem>
+</Tabs>
+
 ### `embed_documents`
 #### Embed list of texts
 

docs/docs/modules/model_io/quick_start.mdx

@@ -46,7 +46,7 @@ llm = ChatOpenAI(openai_api_key="...")
 ```
 
   </TabItem>
-  <TabItem value="local" label="Local">
+  <TabItem value="local" label="Local (using Ollama)">
 
 [Ollama](https://ollama.ai/) allows you to run open-source large language models, such as Llama 2, locally.
 
@@ -62,6 +62,37 @@ from langchain_community.chat_models import ChatOllama
 
 llm = Ollama(model="llama2")
 chat_model = ChatOllama()
+```
+
+  </TabItem>
+  <TabItem value="cohere" label="Cohere">
+
+First we'll need to install their partner package:
+
+```shell
+pip install cohere
+```
+
+Accessing the API requires an API key, which you can get by creating an account and heading [here](https://dashboard.cohere.com/api-keys). Once we have a key we'll want to set it as an environment variable by running:
+
+```shell
+export COHERE_API_KEY="..."
+```
+
+We can then initialize the model:
+
+```python
+from langchain_community.chat_models import ChatCohere
+
+llm = ChatCohere()
+```
+
+If you'd prefer not to set an environment variable you can pass the key in directly via the `cohere_api_key` named parameter when initiating the Cohere LLM class:
+
+```python
+from langchain_community.chat_models import ChatCohere
+
+llm = ChatCohere(cohere_api_key="...")
 ```
 
   </TabItem>