cohere, docs: update imports and installs to langchain_cohere (#19918)

cohere: update imports and installs to langchain_cohere

---------

Co-authored-by: Harry M <127103098+harry-cohere@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>

libs/partners/cohere/README.md

@@ -1 +1,93 @@
-# langchain-cohere
+# Cohere
+
+>[Cohere](https://cohere.ai/about) is a Canadian startup that provides natural language processing models
+> that help companies improve human-machine interactions.
+
+## Installation and Setup
+- Install the Python SDK :
+```bash
+pip install langchain-cohere
+```
+
+Get a [Cohere api key](https://dashboard.cohere.ai/) and set it as an environment variable (`COHERE_API_KEY`)
+
+## Cohere langchain integrations
+
+| API              | description                      | Endpoint docs                                          | Import                                                               | Example usage                                                 |
+| ---------------- | -------------------------------- | ------------------------------------------------------ | -------------------------------------------------------------------- | ------------------------------------------------------------- |
+| 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_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_cohere import ChatCohere
+from langchain_core.messages import HumanMessage
+chat = ChatCohere()
+messages = [HumanMessage(content="knock knock")]
+print(chat(messages))
+```
+
+### LLM
+
+
+```python
+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_cohere import ChatCohere
+from langchain.retrievers import CohereRagRetriever
+from langchain_core.documents import Document
+
+rag = CohereRagRetriever(llm=ChatCohere())
+print(rag.get_relevant_documents("What is cohere ai?"))
+```
+
+### Text Embedding
+
+```python
+from langchain_cohere import CohereEmbeddings
+
+embeddings = CohereEmbeddings(model="embed-english-light-v3.0")
+print(embeddings.embed_documents(["This is a test document."]))
+```

libs/partners/cohere/docs/cohere_agent.ipynb

@@ -99,8 +99,7 @@
     "from langchain.agents import AgentExecutor\n",
     "from langchain.retrievers import WikipediaRetriever\n",
     "from langchain.tools.retriever import create_retriever_tool\n",
-    "from langchain_cohere import create_cohere_tools_agent\n",
-    "from langchain_cohere.chat_models import ChatCohere\n",
+    "from langchain_cohere import create_cohere_tools_agent, ChatCohere\n",
     "from langchain_core.prompts import ChatPromptTemplate"
    ]
   },

libs/partners/cohere/langchain_cohere/embeddings.py

@@ -42,7 +42,7 @@ class CohereEmbeddings(BaseModel, Embeddings):
     """Maximum number of retries to make when generating."""
     request_timeout: Optional[float] = None
     """Timeout in seconds for the Cohere API request."""
-    user_agent: str = "langchain"
+    user_agent: str = "langchain:partner"
     """Identifier for the application making the request."""
 
     base_url: Optional[str] = None