docs: update cohere chat integration

docs/docs/integrations/chat/cohere.ipynb

@@ -15,39 +15,107 @@
    "id": "bf733a38-db84-4363-89e2-de6735c37230",
    "metadata": {},
    "source": [
-    "# ChatCohere\n",
+    "# Cohere\n",
     "\n",
-    "This notebook covers how to get started with Cohere chat models."
+    "This notebook covers how to get started with [Cohere chat models](https://cohere.com/chat).\n",
+    "\n",
+    "Head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.cohere.ChatCohere.html) for detailed documentation of all attributes and methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3607d67e-e56c-4102-bbba-df2edc0e109e",
+   "metadata": {},
+   "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",
+    "\n",
+    "```bash\n",
+    "pip install -U langchain-community 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": 54,
+   "execution_count": 6,
+   "id": "2108b517-1e8d-473d-92fa-4f930e8072a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"COHERE_API_KEY\"] = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf690fbb",
+   "metadata": {},
+   "source": [
+    "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7f11de02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c26754b-b3c9-4d93-8f36-43049bd943bf",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "\n",
+    "ChatCohere supports all [ChatModel](/docs/modules/model_io/chat/) functionality:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
    "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage\n",
-    "from langchain_community.chat_models import ChatCohere"
+    "from langchain_community.chat_models import ChatCohere\n",
+    "from langchain_core.messages import HumanMessage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 2,
    "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "chat = ChatCohere()"
+    "chat = ChatCohere(model=\"command\", max_tokens=256, temperature=0.75)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 3,
    "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
    "metadata": {
     "tags": []
@@ -59,41 +127,43 @@
        "AIMessage(content=\"Who's there?\")"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "messages = [HumanMessage(content=\"knock knock\")]\n",
-    "chat(messages)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
-   "metadata": {},
-   "source": [
-    "## `ChatCohere` also supports async and streaming functionality:"
+    "chat.invoke(messages)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
+   "execution_count": 4,
+   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"Who's there?\")"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "from langchain.callbacks.manager import CallbackManager\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
+    "await chat.ainvoke(messages)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
+   "execution_count": 5,
+   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
    "metadata": {
     "tags": []
    },
@@ -104,55 +174,76 @@
      "text": [
       "Who's there?"
      ]
-    },
+    }
+   ],
+   "source": [
+    "for chunk in chat.stream(messages):\n",
+    "    print(chunk.content, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "064288e4-f184-4496-9427-bcf148fa055e",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "text/plain": [
-       "LLMResult(generations=[[ChatGenerationChunk(text=\"Who's there?\", message=AIMessageChunk(content=\"Who's there?\"))]], llm_output={}, run=[RunInfo(run_id=UUID('1e9eaefc-9c99-4fa9-8297-ef9975d4751e'))])"
+       "[AIMessage(content=\"Who's there?\")]"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "await chat.agenerate([messages])"
+    "chat.batch([messages])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1c56460",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/expression_language)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
-   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 7,
+   "id": "0851b103",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
+    "chain = prompt | chat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae950c0f-1691-47f1-b609-273033cae707",
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Who's there?"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "AIMessageChunk(content=\"Who's there?\")"
+       "AIMessage(content=\"Why did the bear go to the chiropractor?\\n\\nBecause she was feeling a bit grizzly!\\n\\nHope you found that joke about bears to be a little bit amusing! If you'd like to hear another one, just let me know. In the meantime, if you have any other questions or need assistance with a different topic, feel free to let me know. \\n\\nJust remember, even if you have a sore back like the bear, it's always best to consult a licensed professional for injuries or pain you may be experiencing. \\n\\nWould you like me to tell you another joke?\")"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "chat = ChatCohere(\n",
-    "    streaming=True,\n",
-    "    verbose=True,\n",
-    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
-    ")\n",
-    "chat(messages)"
+    "chain.invoke({\"topic\": \"bears\"})"
    ]
   }
  ],
@@ -172,7 +263,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,