cohere, docs: update imports and installs to langchain_cohere

docs/docs/get_started/quickstart.mdx

@@ -149,7 +149,7 @@ llm = ChatAnthropic(anthropic_api_key="...")
 First we'll need to import the Cohere SDK package.
 
 ```shell
-pip install cohere
+pip install langchain-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:
@@ -161,15 +161,15 @@ export COHERE_API_KEY="..."
 We can then initialize the model:
 
 ```python
-from langchain_community.chat_models import ChatCohere
+from langchain_cohere 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
+from langchain_cohere import ChatCohere
 
 llm = ChatCohere(cohere_api_key="...")
 ```

docs/docs/integrations/chat/cohere.ipynb

@@ -29,18 +29,18 @@
    "source": [
     "## Setup\n",
     "\n",
-    "The integration lives in the `langchain-community` package. We also need to install the `cohere` package itself. We can install these with:\n",
+    "The integration lives in the `langchain-cohere` package. We can install these with:\n",
     "\n",
     "```bash\n",
-    "pip install -U langchain-community langchain-cohere\n",
+    "pip install -U langchain-cohere\n",
     "```\n",
     "\n",
     "We'll also need to get a [Cohere API key](https://cohere.com/) and set the `COHERE_API_KEY` environment variable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "id": "2108b517-1e8d-473d-92fa-4f930e8072a7",
    "metadata": {},
    "outputs": [],
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "id": "7f11de02",
    "metadata": {},
    "outputs": [],
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
    "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
    "metadata": {
     "tags": []
@@ -95,19 +95,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "chat = ChatCohere(model=\"command\", max_tokens=256, temperature=0.75)"
+    "chat = ChatCohere(model=\"command\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
    "metadata": {
     "tags": []
@@ -116,10 +116,10 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"4! That's one, two, three, four. Keep adding and we'll reach new heights!\", response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'token_count': {'prompt_tokens': 73, 'response_tokens': 21, 'total_tokens': 94, 'billed_tokens': 25}})"
+       "AIMessage(content='4 && 5 \\n6 || 7 \\n\\nWould you like to play a game of odds and evens?', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, id='run-3475e0c8-c89b-4937-9300-e07d652455e1-0')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
    "metadata": {
     "tags": []
@@ -140,10 +140,10 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='4! According to the rules of addition, 1 + 2 equals 3, and 3 + 3 equals 6.', response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'token_count': {'prompt_tokens': 73, 'response_tokens': 28, 'total_tokens': 101, 'billed_tokens': 32}})"
+       "AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-1635e63e-2994-4e7f-986e-152ddfc95777-0')"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
    "metadata": {
     "tags": []
@@ -164,7 +164,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4! It's a pleasure to be of service in this mathematical game."
+      "4 && 5"
      ]
     }
    ],
@@ -175,17 +175,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 18,
    "id": "064288e4-f184-4496-9427-bcf148fa055e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[AIMessage(content='4! According to the rules of addition, 1 + 2 equals 3, and 3 + 3 equals 6.', response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'token_count': {'prompt_tokens': 73, 'response_tokens': 28, 'total_tokens': 101, 'billed_tokens': 32}})]"
+       "[AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-8d6fade2-1b39-4e31-ab23-4be622dd0027-0')]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 19,
    "id": "0851b103",
    "metadata": {},
    "outputs": [],
@@ -219,17 +219,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "id": "ae950c0f-1691-47f1-b609-273033cae707",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='What do you call a bear with no teeth? A gummy bear!', response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'token_count': {'prompt_tokens': 72, 'response_tokens': 14, 'total_tokens': 86, 'billed_tokens': 20}})"
+       "AIMessage(content='What color socks do bears wear?\\n\\nThey don’t wear socks, they have bear feet. \\n\\nHope you laughed! If not, maybe this will help: laughter is the best medicine, and a good sense of humor is infectious!', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, id='run-ef7f9789-0d4d-43bf-a4f7-f2a0e27a5320-0')"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/docs/integrations/providers/cohere.mdx

@@ -6,7 +6,7 @@
 ## Installation and Setup
 - Install the Python SDK :
 ```bash
-pip install cohere
+pip install langchain-cohere
 ```
 
 Get a [Cohere api key](https://dashboard.cohere.ai/) and set it as an environment variable (`COHERE_API_KEY`)
@@ -15,18 +15,18 @@ Get a [Cohere api key](https://dashboard.cohere.ai/) and set it as an environmen
 
 |API|description|Endpoint docs|Import|Example usage|
 |---|---|---|---|---|
-|Chat|Build chat bots|[chat](https://docs.cohere.com/reference/chat)|`from langchain_community.chat_models import ChatCohere`|[cohere.ipynb](/docs/integrations/chat/cohere)|
-|LLM|Generate text|[generate](https://docs.cohere.com/reference/generate)|`from langchain_community.llms import Cohere`|[cohere.ipynb](/docs/integrations/llms/cohere)|
+|Chat|Build chat bots|[chat](https://docs.cohere.com/reference/chat)|`from langchain_cohere import ChatCohere`|[cohere.ipynb](/docs/integrations/chat/cohere)|
+|LLM|Generate text|[generate](https://docs.cohere.com/reference/generate)|`from langchain_cohere import Cohere`|[cohere.ipynb](/docs/integrations/llms/cohere)|
 |RAG Retriever|Connect to external data sources|[chat + rag](https://docs.cohere.com/reference/chat)|`from langchain.retrievers import CohereRagRetriever`|[cohere.ipynb](/docs/integrations/retrievers/cohere)|
-|Text Embedding|Embed strings to vectors|[embed](https://docs.cohere.com/reference/embed)|`from langchain_community.embeddings import CohereEmbeddings`|[cohere.ipynb](/docs/integrations/text_embedding/cohere)|
+|Text Embedding|Embed strings to vectors|[embed](https://docs.cohere.com/reference/embed)|`from langchain_cohere import CohereEmbeddings`|[cohere.ipynb](/docs/integrations/text_embedding/cohere)|
 |Rerank Retriever|Rank strings based on relevance|[rerank](https://docs.cohere.com/reference/rerank)|`from langchain.retrievers.document_compressors import CohereRerank`|[cohere.ipynb](/docs/integrations/retrievers/cohere-reranker)|
 
 ## Quick copy examples
 
 ### Chat
 
 ```python
-from langchain_community.chat_models import ChatCohere
+from langchain_cohere import ChatCohere
 from langchain_core.messages import HumanMessage
 chat = ChatCohere()
 messages = [HumanMessage(content="knock knock")]
@@ -37,17 +37,45 @@ print(chat(messages))
 
 
 ```python
-from langchain_community.llms import Cohere
+from langchain_cohere import Cohere
 
 llm = Cohere(model="command")
 print(llm.invoke("Come up with a pet name"))
 ```
 
+### ReAct Agent
+
+```python
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_cohere import ChatCohere, create_cohere_react_agent
+from langchain.prompts import ChatPromptTemplate
+from langchain.agents import AgentExecutor
+
+llm = ChatCohere()
+
+internet_search = TavilySearchResults(max_results=4)
+internet_search.name = "internet_search"
+internet_search.description = "Route a user query to the internet"
+
+prompt = ChatPromptTemplate.from_template("{input}")
+
+agent = create_cohere_react_agent(
+    llm,
+    [internet_search],
+    prompt
+)
+
+agent_executor = AgentExecutor(agent=agent, tools=[internet_search], verbose=True)```
+
+agent_executor.invoke({
+    "input": "In what year was the company that was founded as Sound of Music added to the S&P 500?",
+})
+```
 
 ### RAG Retriever
 
 ```python
-from langchain_community.chat_models import ChatCohere
+from langchain_cohere import ChatCohere
 from langchain.retrievers import CohereRagRetriever
 from langchain_core.documents import Document
 
@@ -58,7 +86,7 @@ print(rag.get_relevant_documents("What is cohere ai?"))
 ### Text Embedding
 
 ```python
-from langchain_community.embeddings import CohereEmbeddings
+from langchain_cohere import CohereEmbeddings
 
 embeddings = CohereEmbeddings(model="embed-english-light-v3.0")
 print(embeddings.embed_documents(["This is a test document."]))

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

@@ -55,7 +55,7 @@ embeddings_model = OpenAIEmbeddings()
 To start we'll need to install the Cohere SDK package:
 
 ```bash
-pip install cohere
+pip install langchain-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:
@@ -67,14 +67,14 @@ 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
+from langchain_cohere import CohereEmbeddings
 
 embeddings_model = CohereEmbeddings(cohere_api_key="...")
 ```
 
 Otherwise you can initialize without any params:
 ```python
-from langchain_community.embeddings import CohereEmbeddings
+from langchain_cohere import CohereEmbeddings
 
 embeddings_model = CohereEmbeddings()
 ```

docs/docs/modules/model_io/quick_start.mdx

@@ -99,7 +99,7 @@ chat_model = ChatAnthropic(anthropic_api_key="...")
 First we'll need to install their partner package:
 
 ```shell
-pip install cohere
+pip install langchain-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:
@@ -111,15 +111,15 @@ export COHERE_API_KEY="..."
 We can then initialize the model:
 
 ```python
-from langchain_community.chat_models import ChatCohere
+from langchain_cohere import ChatCohere
 
 chat_model = 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
+from langchain_cohere import ChatCohere
 
 chat_model = ChatCohere(cohere_api_key="...")
 ```