x

docs/docs/integrations/chat/anthropic.ipynb

@@ -55,12 +55,7 @@
    "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass(\"Enter your Anthropic API key: \")"
-   ]
+   "source": ["import getpass\nimport os\n\nif \"ANTHROPIC_API_KEY\" not in os.environ:\n    os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass(\"Enter your Anthropic API key: \")"]
   },
   {
    "cell_type": "markdown",
@@ -76,10 +71,7 @@
    "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
+   "source": ["# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n# os.environ[\"LANGSMITH_TRACING\"] = \"true\""]
   },
   {
    "cell_type": "markdown",
@@ -97,9 +89,7 @@
    "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%pip install -qU langchain-anthropic"
-   ]
+   "source": ["%pip install -qU langchain-anthropic"]
   },
   {
    "cell_type": "markdown",
@@ -117,18 +107,7 @@
    "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from langchain_anthropic import ChatAnthropic\n",
-    "\n",
-    "llm = ChatAnthropic(\n",
-    "    model=\"claude-3-5-sonnet-20240620\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=1024,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
+   "source": ["from langchain_anthropic import ChatAnthropic\n\nllm = ChatAnthropic(\n    model=\"claude-3-5-sonnet-20240620\",\n    temperature=0,\n    max_tokens=1024,\n    timeout=None,\n    max_retries=2,\n    # other params...\n)"]
   },
   {
    "cell_type": "markdown",
@@ -157,17 +136,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
+   "source": ["messages = [\n    (\n        \"system\",\n        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n    ),\n    (\"human\", \"I love programming.\"),\n]\nai_msg = llm.invoke(messages)\nai_msg"]
   },
   {
    "cell_type": "code",
@@ -183,9 +152,7 @@
      ]
     }
    ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
+   "source": ["print(ai_msg.content)"]
   },
   {
    "cell_type": "markdown",
@@ -214,28 +181,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+   "source": ["from langchain_core.prompts import ChatPromptTemplate\n\nprompt = ChatPromptTemplate.from_messages(\n    [\n        (\n            \"system\",\n            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n        ),\n        (\"human\", \"{input}\"),\n    ]\n)\n\nchain = prompt | llm\nchain.invoke(\n    {\n        \"input_language\": \"English\",\n        \"output_language\": \"German\",\n        \"input\": \"I love programming.\",\n    }\n)"]
   },
   {
    "cell_type": "markdown",
@@ -273,20 +219,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
-    "\n",
-    "\n",
-    "class GetWeather(BaseModel):\n",
-    "    \"\"\"Get the current weather in a given location\"\"\"\n",
-    "\n",
-    "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
-    "\n",
-    "\n",
-    "llm_with_tools = llm.bind_tools([GetWeather])\n",
-    "ai_msg = llm_with_tools.invoke(\"Which city is hotter today: LA or NY?\")\n",
-    "ai_msg.content"
-   ]
+   "source": ["from langchain_core.pydantic_v1 import BaseModel, Field\n\n\nclass GetWeather(BaseModel):\n    \"\"\"Get the current weather in a given location\"\"\"\n\n    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n\n\nllm_with_tools = llm.bind_tools([GetWeather])\nai_msg = llm_with_tools.invoke(\"Which city is hotter today: LA or NY?\")\nai_msg.content"]
   },
   {
    "cell_type": "code",
@@ -310,9 +243,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "ai_msg.tool_calls"
-   ]
+   "source": ["ai_msg.tool_calls"]
   },
   {
    "cell_type": "markdown",

docs/docs/integrations/chat/azure_chat_openai.ipynb

@@ -58,7 +58,10 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"AZURE_OPENAI_API_KEY\"] = getpass.getpass(\"Enter your AzureOpenAI API key: \")\n",
+    "if \"AZURE_OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"AZURE_OPENAI_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your AzureOpenAI API key: \"\n",
+    "    )\n",
     "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = \"https://YOUR-ENDPOINT.openai.azure.com/\""
    ]
   },

docs/docs/integrations/chat/databricks.ipynb

@@ -90,7 +90,10 @@
     "import os\n",
     "\n",
     "os.environ[\"DATABRICKS_HOST\"] = \"https://your-workspace.cloud.databricks.com\"\n",
-    "os.environ[\"DATABRICKS_TOKEN\"] = getpass.getpass(\"Enter your Databricks access token: \")"
+    "if \"DATABRICKS_TOKEN\" not in os.environ:\n",
+    "    os.environ[\"DATABRICKS_TOKEN\"] = getpass.getpass(\n",
+    "        \"Enter your Databricks access token: \"\n",
+    "    )"
    ]
   },
   {

docs/docs/integrations/chat/fireworks.ipynb

@@ -48,12 +48,7 @@
    "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Enter your Fireworks API key: \")"
-   ]
+   "source": ["import getpass\nimport os\n\nif \"FIREWORKS_API_KEY\" not in os.environ:\n    os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Enter your Fireworks API key: \")"]
   },
   {
    "cell_type": "markdown",
@@ -69,10 +64,7 @@
    "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
+   "source": ["# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n# os.environ[\"LANGSMITH_TRACING\"] = \"true\""]
   },
   {
    "cell_type": "markdown",
@@ -90,9 +82,7 @@
    "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%pip install -qU langchain-fireworks"
-   ]
+   "source": ["%pip install -qU langchain-fireworks"]
   },
   {
    "cell_type": "markdown",
@@ -112,18 +102,7 @@
    "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from langchain_fireworks import ChatFireworks\n",
-    "\n",
-    "llm = ChatFireworks(\n",
-    "    model=\"accounts/fireworks/models/llama-v3-70b-instruct\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
+   "source": ["from langchain_fireworks import ChatFireworks\n\nllm = ChatFireworks(\n    model=\"accounts/fireworks/models/llama-v3-70b-instruct\",\n    temperature=0,\n    max_tokens=None,\n    timeout=None,\n    max_retries=2,\n    # other params...\n)"]
   },
   {
    "cell_type": "markdown",
@@ -152,17 +131,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
+   "source": ["messages = [\n    (\n        \"system\",\n        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n    ),\n    (\"human\", \"I love programming.\"),\n]\nai_msg = llm.invoke(messages)\nai_msg"]
   },
   {
    "cell_type": "code",
@@ -178,9 +147,7 @@
      ]
     }
    ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
+   "source": ["print(ai_msg.content)"]
   },
   {
    "cell_type": "markdown",
@@ -209,28 +176,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+   "source": ["from langchain_core.prompts import ChatPromptTemplate\n\nprompt = ChatPromptTemplate.from_messages(\n    [\n        (\n            \"system\",\n            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n        ),\n        (\"human\", \"{input}\"),\n    ]\n)\n\nchain = prompt | llm\nchain.invoke(\n    {\n        \"input_language\": \"English\",\n        \"output_language\": \"German\",\n        \"input\": \"I love programming.\",\n    }\n)"]
   },
   {
    "cell_type": "markdown",

docs/docs/integrations/chat/friendli.ipynb

@@ -40,12 +40,7 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"FRIENDLI_TOKEN\"] = getpass.getpass(\"Friendi Personal Access Token: \")"
-   ]
+   "source": ["import getpass\nimport os\n\nif \"FRIENDLI_TOKEN\" not in os.environ:\n    os.environ[\"FRIENDLI_TOKEN\"] = getpass.getpass(\"Friendi Personal Access Token: \")"]
   },
   {
    "cell_type": "markdown",
@@ -59,11 +54,7 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from langchain_community.chat_models.friendli import ChatFriendli\n",
-    "\n",
-    "chat = ChatFriendli(model=\"llama-2-13b-chat\", max_tokens=100, temperature=0)"
-   ]
+   "source": ["from langchain_community.chat_models.friendli import ChatFriendli\n\nchat = ChatFriendli(model=\"llama-2-13b-chat\", max_tokens=100, temperature=0)"]
   },
   {
    "cell_type": "markdown",
@@ -97,16 +88,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "from langchain_core.messages.human import HumanMessage\n",
-    "from langchain_core.messages.system import SystemMessage\n",
-    "\n",
-    "system_message = SystemMessage(content=\"Answer questions as short as you can.\")\n",
-    "human_message = HumanMessage(content=\"Tell me a joke.\")\n",
-    "messages = [system_message, human_message]\n",
-    "\n",
-    "chat.invoke(messages)"
-   ]
+   "source": ["from langchain_core.messages.human import HumanMessage\nfrom langchain_core.messages.system import SystemMessage\n\nsystem_message = SystemMessage(content=\"Answer questions as short as you can.\")\nhuman_message = HumanMessage(content=\"Tell me a joke.\")\nmessages = [system_message, human_message]\n\nchat.invoke(messages)"]
   },
   {
    "cell_type": "code",
@@ -125,9 +107,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "chat.batch([messages, messages])"
-   ]
+   "source": ["chat.batch([messages, messages])"]
   },
   {
    "cell_type": "code",
@@ -145,9 +125,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "chat.generate([messages, messages])"
-   ]
+   "source": ["chat.generate([messages, messages])"]
   },
   {
    "cell_type": "code",
@@ -166,10 +144,7 @@
      ]
     }
    ],
-   "source": [
-    "for chunk in chat.stream(messages):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
+   "source": ["for chunk in chat.stream(messages):\n    print(chunk.content, end=\"\", flush=True)"]
   },
   {
    "cell_type": "markdown",
@@ -194,9 +169,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "await chat.ainvoke(messages)"
-   ]
+   "source": ["await chat.ainvoke(messages)"]
   },
   {
    "cell_type": "code",
@@ -215,9 +188,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "await chat.abatch([messages, messages])"
-   ]
+   "source": ["await chat.abatch([messages, messages])"]
   },
   {
    "cell_type": "code",
@@ -235,9 +206,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "await chat.agenerate([messages, messages])"
-   ]
+   "source": ["await chat.agenerate([messages, messages])"]
   },
   {
    "cell_type": "code",
@@ -256,10 +225,7 @@
      ]
     }
    ],
-   "source": [
-    "async for chunk in chat.astream(messages):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
+   "source": ["async for chunk in chat.astream(messages):\n    print(chunk.content, end=\"\", flush=True)"]
   }
  ],
  "metadata": {

docs/docs/integrations/chat/google_generative_ai.ipynb

@@ -56,12 +56,7 @@
    "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"GOOGLE_API_KEY\"] = getpass.getpass(\"Enter your Google AI API key: \")"
-   ]
+   "source": ["import getpass\nimport os\n\nif \"GOOGLE_API_KEY\" not in os.environ:\n    os.environ[\"GOOGLE_API_KEY\"] = getpass.getpass(\"Enter your Google AI API key: \")"]
   },
   {
    "cell_type": "markdown",
@@ -77,10 +72,7 @@
    "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
+   "source": ["# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n# os.environ[\"LANGSMITH_TRACING\"] = \"true\""]
   },
   {
    "cell_type": "markdown",
@@ -98,9 +90,7 @@
    "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%pip install -qU langchain-google-genai"
-   ]
+   "source": ["%pip install -qU langchain-google-genai"]
   },
   {
    "cell_type": "markdown",
@@ -118,18 +108,7 @@
    "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
-    "\n",
-    "llm = ChatGoogleGenerativeAI(\n",
-    "    model=\"gemini-1.5-pro\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
+   "source": ["from langchain_google_genai import ChatGoogleGenerativeAI\n\nllm = ChatGoogleGenerativeAI(\n    model=\"gemini-1.5-pro\",\n    temperature=0,\n    max_tokens=None,\n    timeout=None,\n    max_retries=2,\n    # other params...\n)"]
   },
   {
    "cell_type": "markdown",
@@ -158,17 +137,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
+   "source": ["messages = [\n    (\n        \"system\",\n        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n    ),\n    (\"human\", \"I love programming.\"),\n]\nai_msg = llm.invoke(messages)\nai_msg"]
   },
   {
    "cell_type": "code",
@@ -185,9 +154,7 @@
      ]
     }
    ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
+   "source": ["print(ai_msg.content)"]
   },
   {
    "cell_type": "markdown",
@@ -216,28 +183,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+   "source": ["from langchain_core.prompts import ChatPromptTemplate\n\nprompt = ChatPromptTemplate.from_messages(\n    [\n        (\n            \"system\",\n            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n        ),\n        (\"human\", \"{input}\"),\n    ]\n)\n\nchain = prompt | llm\nchain.invoke(\n    {\n        \"input_language\": \"English\",\n        \"output_language\": \"German\",\n        \"input\": \"I love programming.\",\n    }\n)"]
   },
   {
    "cell_type": "markdown",
@@ -255,20 +201,7 @@
    "id": "238b2f96-e573-4fac-bbf2-7e52ad926833",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from langchain_google_genai import (\n",
-    "    ChatGoogleGenerativeAI,\n",
-    "    HarmBlockThreshold,\n",
-    "    HarmCategory,\n",
-    ")\n",
-    "\n",
-    "llm = ChatGoogleGenerativeAI(\n",
-    "    model=\"gemini-1.5-pro\",\n",
-    "    safety_settings={\n",
-    "        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,\n",
-    "    },\n",
-    ")"
-   ]
+   "source": ["from langchain_google_genai import (\n    ChatGoogleGenerativeAI,\n    HarmBlockThreshold,\n    HarmCategory,\n)\n\nllm = ChatGoogleGenerativeAI(\n    model=\"gemini-1.5-pro\",\n    safety_settings={\n        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,\n    },\n)"]
   },
   {
    "cell_type": "markdown",

docs/docs/integrations/chat/groq.ipynb

@@ -46,12 +46,7 @@
    "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"GROQ_API_KEY\"] = getpass.getpass(\"Enter your Groq API key: \")"
-   ]
+   "source": ["import getpass\nimport os\n\nif \"GROQ_API_KEY\" not in os.environ:\n    os.environ[\"GROQ_API_KEY\"] = getpass.getpass(\"Enter your Groq API key: \")"]
   },
   {
    "cell_type": "markdown",
@@ -67,10 +62,7 @@
    "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
+   "source": ["# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n# os.environ[\"LANGSMITH_TRACING\"] = \"true\""]
   },
   {
    "cell_type": "markdown",
@@ -99,9 +91,7 @@
      ]
     }
    ],
-   "source": [
-    "%pip install -qU langchain-groq"
-   ]
+   "source": ["%pip install -qU langchain-groq"]
   },
   {
    "cell_type": "markdown",
@@ -119,18 +109,7 @@
    "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from langchain_groq import ChatGroq\n",
-    "\n",
-    "llm = ChatGroq(\n",
-    "    model=\"mixtral-8x7b-32768\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
+   "source": ["from langchain_groq import ChatGroq\n\nllm = ChatGroq(\n    model=\"mixtral-8x7b-32768\",\n    temperature=0,\n    max_tokens=None,\n    timeout=None,\n    max_retries=2,\n    # other params...\n)"]
   },
   {
    "cell_type": "markdown",
@@ -159,17 +138,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
+   "source": ["messages = [\n    (\n        \"system\",\n        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n    ),\n    (\"human\", \"I love programming.\"),\n]\nai_msg = llm.invoke(messages)\nai_msg"]
   },
   {
    "cell_type": "code",
@@ -187,9 +156,7 @@
      ]
     }
    ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
+   "source": ["print(ai_msg.content)"]
   },
   {
    "cell_type": "markdown",
@@ -218,28 +185,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+   "source": ["from langchain_core.prompts import ChatPromptTemplate\n\nprompt = ChatPromptTemplate.from_messages(\n    [\n        (\n            \"system\",\n            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n        ),\n        (\"human\", \"{input}\"),\n    ]\n)\n\nchain = prompt | llm\nchain.invoke(\n    {\n        \"input_language\": \"English\",\n        \"output_language\": \"German\",\n        \"input\": \"I love programming.\",\n    }\n)"]
   },
   {
    "cell_type": "markdown",

docs/docs/integrations/chat/huggingface.ipynb

@@ -36,13 +36,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"HUGGINGFACEHUB_API_TOKEN\"):\n",
-    "    os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = getpass.getpass(\"Enter your token: \")"
-   ]
+   "source": ["import getpass\nimport os\n\nif not os.getenv(\"HUGGINGFACEHUB_API_TOKEN\"):\n    os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = getpass.getpass(\"Enter your token: \")"]
   },
   {
    "cell_type": "markdown",
@@ -73,14 +67,7 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = getpass.getpass(\n",
-    "    \"Enter your Hugging Face API key: \"\n",
-    ")"
-   ]
+   "source": ["import getpass\nimport os\n\nif \"HUGGINGFACEHUB_API_TOKEN\" not in os.environ:\n    os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = getpass.getpass(\n        \"Enter your Hugging Face API key: \"\n    )"]
   },
   {
    "cell_type": "code",
@@ -98,9 +85,7 @@
      ]
     }
    ],
-   "source": [
-    "%pip install --upgrade --quiet  langchain-huggingface text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2 bitsandbytes accelerate"
-   ]
+   "source": ["%pip install --upgrade --quiet  langchain-huggingface text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2 bitsandbytes accelerate"]
   },
   {
    "cell_type": "markdown",
@@ -134,19 +119,7 @@
      ]
     }
    ],
-   "source": [
-    "from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint\n",
-    "\n",
-    "llm = HuggingFaceEndpoint(\n",
-    "    repo_id=\"HuggingFaceH4/zephyr-7b-beta\",\n",
-    "    task=\"text-generation\",\n",
-    "    max_new_tokens=512,\n",
-    "    do_sample=False,\n",
-    "    repetition_penalty=1.03,\n",
-    ")\n",
-    "\n",
-    "chat_model = ChatHuggingFace(llm=llm)"
-   ]
+   "source": ["from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint\n\nllm = HuggingFaceEndpoint(\n    repo_id=\"HuggingFaceH4/zephyr-7b-beta\",\n    task=\"text-generation\",\n    max_new_tokens=512,\n    do_sample=False,\n    repetition_penalty=1.03,\n)\n\nchat_model = ChatHuggingFace(llm=llm)"]
   },
   {
    "cell_type": "markdown",
@@ -343,21 +316,7 @@
      "output_type": "display_data"
     }
    ],
-   "source": [
-    "from langchain_huggingface import ChatHuggingFace, HuggingFacePipeline\n",
-    "\n",
-    "llm = HuggingFacePipeline.from_model_id(\n",
-    "    model_id=\"HuggingFaceH4/zephyr-7b-beta\",\n",
-    "    task=\"text-generation\",\n",
-    "    pipeline_kwargs=dict(\n",
-    "        max_new_tokens=512,\n",
-    "        do_sample=False,\n",
-    "        repetition_penalty=1.03,\n",
-    "    ),\n",
-    ")\n",
-    "\n",
-    "chat_model = ChatHuggingFace(llm=llm)"
-   ]
+   "source": ["from langchain_huggingface import ChatHuggingFace, HuggingFacePipeline\n\nllm = HuggingFacePipeline.from_model_id(\n    model_id=\"HuggingFaceH4/zephyr-7b-beta\",\n    task=\"text-generation\",\n    pipeline_kwargs=dict(\n        max_new_tokens=512,\n        do_sample=False,\n        repetition_penalty=1.03,\n    ),\n)\n\nchat_model = ChatHuggingFace(llm=llm)"]
   },
   {
    "cell_type": "markdown",
@@ -373,16 +332,7 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from transformers import BitsAndBytesConfig\n",
-    "\n",
-    "quantization_config = BitsAndBytesConfig(\n",
-    "    load_in_4bit=True,\n",
-    "    bnb_4bit_quant_type=\"nf4\",\n",
-    "    bnb_4bit_compute_dtype=\"float16\",\n",
-    "    bnb_4bit_use_double_quant=True,\n",
-    ")"
-   ]
+   "source": ["from transformers import BitsAndBytesConfig\n\nquantization_config = BitsAndBytesConfig(\n    load_in_4bit=True,\n    bnb_4bit_quant_type=\"nf4\",\n    bnb_4bit_compute_dtype=\"float16\",\n    bnb_4bit_use_double_quant=True,\n)"]
   },
   {
    "cell_type": "markdown",
@@ -396,20 +346,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "llm = HuggingFacePipeline.from_model_id(\n",
-    "    model_id=\"HuggingFaceH4/zephyr-7b-beta\",\n",
-    "    task=\"text-generation\",\n",
-    "    pipeline_kwargs=dict(\n",
-    "        max_new_tokens=512,\n",
-    "        do_sample=False,\n",
-    "        repetition_penalty=1.03,\n",
-    "    ),\n",
-    "    model_kwargs={\"quantization_config\": quantization_config},\n",
-    ")\n",
-    "\n",
-    "chat_model = ChatHuggingFace(llm=llm)"
-   ]
+   "source": ["llm = HuggingFacePipeline.from_model_id(\n    model_id=\"HuggingFaceH4/zephyr-7b-beta\",\n    task=\"text-generation\",\n    pipeline_kwargs=dict(\n        max_new_tokens=512,\n        do_sample=False,\n        repetition_penalty=1.03,\n    ),\n    model_kwargs={\"quantization_config\": quantization_config},\n)\n\nchat_model = ChatHuggingFace(llm=llm)"]
   },
   {
    "cell_type": "markdown",
@@ -423,21 +360,7 @@
    "execution_count": 11,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from langchain_core.messages import (\n",
-    "    HumanMessage,\n",
-    "    SystemMessage,\n",
-    ")\n",
-    "\n",
-    "messages = [\n",
-    "    SystemMessage(content=\"You're a helpful assistant\"),\n",
-    "    HumanMessage(\n",
-    "        content=\"What happens when an unstoppable force meets an immovable object?\"\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "ai_msg = chat_model.invoke(messages)"
-   ]
+   "source": ["from langchain_core.messages import (\n    HumanMessage,\n    SystemMessage,\n)\n\nmessages = [\n    SystemMessage(content=\"You're a helpful assistant\"),\n    HumanMessage(\n        content=\"What happens when an unstoppable force meets an immovable object?\"\n    ),\n]\n\nai_msg = chat_model.invoke(messages)"]
   },
   {
    "cell_type": "code",
@@ -454,9 +377,7 @@
      ]
     }
    ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
+   "source": ["print(ai_msg.content)"]
   },
   {
    "cell_type": "markdown",

docs/docs/integrations/chat/mistralai.ipynb

@@ -48,12 +48,7 @@
    "id": "2461605e",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"MISTRAL_API_KEY\"] = getpass.getpass(\"Enter your Mistral API key: \")"
-   ]
+   "source": ["import getpass\nimport os\n\nif \"MISTRAL_API_KEY\" not in os.environ:\n    os.environ[\"MISTRAL_API_KEY\"] = getpass.getpass(\"Enter your Mistral API key: \")"]
   },
   {
    "cell_type": "markdown",
@@ -69,10 +64,7 @@
    "id": "007209d5",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
+   "source": ["# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n# os.environ[\"LANGSMITH_TRACING\"] = \"true\""]
   },
   {
    "cell_type": "markdown",
@@ -90,9 +82,7 @@
    "id": "1ab11a65",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%pip install -qU langchain_mistralai"
-   ]
+   "source": ["%pip install -qU langchain_mistralai"]
   },
   {
    "cell_type": "markdown",
@@ -110,16 +100,7 @@
    "id": "e6c38580",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from langchain_mistralai import ChatMistralAI\n",
-    "\n",
-    "llm = ChatMistralAI(\n",
-    "    model=\"mistral-large-latest\",\n",
-    "    temperature=0,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
+   "source": ["from langchain_mistralai import ChatMistralAI\n\nllm = ChatMistralAI(\n    model=\"mistral-large-latest\",\n    temperature=0,\n    max_retries=2,\n    # other params...\n)"]
   },
   {
    "cell_type": "markdown",
@@ -146,17 +127,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
+   "source": ["messages = [\n    (\n        \"system\",\n        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n    ),\n    (\"human\", \"I love programming.\"),\n]\nai_msg = llm.invoke(messages)\nai_msg"]
   },
   {
    "cell_type": "code",
@@ -172,9 +143,7 @@
      ]
     }
    ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
+   "source": ["print(ai_msg.content)"]
   },
   {
    "cell_type": "markdown",
@@ -203,28 +172,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+   "source": ["from langchain_core.prompts import ChatPromptTemplate\n\nprompt = ChatPromptTemplate.from_messages(\n    [\n        (\n            \"system\",\n            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n        ),\n        (\"human\", \"{input}\"),\n    ]\n)\n\nchain = prompt | llm\nchain.invoke(\n    {\n        \"input_language\": \"English\",\n        \"output_language\": \"German\",\n        \"input\": \"I love programming.\",\n    }\n)"]
   },
   {
    "cell_type": "markdown",

docs/docs/integrations/chat/yi.ipynb

@@ -37,12 +37,7 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"YI_API_KEY\"] = getpass.getpass(\"Enter your Yi API key: \")"
-   ]
+   "source": ["import getpass\nimport os\n\nif \"YI_API_KEY\" not in os.environ:\n    os.environ[\"YI_API_KEY\"] = getpass.getpass(\"Enter your Yi API key: \")"]
   },
   {
    "cell_type": "markdown",
@@ -56,10 +51,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
+   "source": ["# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n# os.environ[\"LANGSMITH_TRACING\"] = \"true\""]
   },
   {
    "cell_type": "markdown",
@@ -75,9 +67,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community"
-   ]
+   "source": ["%pip install -qU langchain_community"]
   },
   {
    "cell_type": "markdown",
@@ -95,17 +85,7 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from langchain_community.chat_models.yi import ChatYi\n",
-    "\n",
-    "llm = ChatYi(\n",
-    "    model=\"yi-large\",\n",
-    "    temperature=0,\n",
-    "    timeout=60,\n",
-    "    yi_api_base=\"https://api.01.ai/v1/chat/completions\",\n",
-    "    # other params...\n",
-    ")"
-   ]
+   "source": ["from langchain_community.chat_models.yi import ChatYi\n\nllm = ChatYi(\n    model=\"yi-large\",\n    temperature=0,\n    timeout=60,\n    yi_api_base=\"https://api.01.ai/v1/chat/completions\",\n    # other params...\n)"]
   },
   {
    "cell_type": "markdown",
@@ -130,19 +110,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "from langchain_core.messages import HumanMessage, SystemMessage\n",
-    "\n",
-    "messages = [\n",
-    "    SystemMessage(content=\"You are an AI assistant specializing in technology trends.\"),\n",
-    "    HumanMessage(\n",
-    "        content=\"What are the potential applications of large language models in healthcare?\"\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
+   "source": ["from langchain_core.messages import HumanMessage, SystemMessage\n\nmessages = [\n    SystemMessage(content=\"You are an AI assistant specializing in technology trends.\"),\n    HumanMessage(\n        content=\"What are the potential applications of large language models in healthcare?\"\n    ),\n]\n\nai_msg = llm.invoke(messages)\nai_msg"]
   },
   {
    "cell_type": "markdown",
@@ -169,28 +137,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+   "source": ["from langchain_core.prompts import ChatPromptTemplate\n\nprompt = ChatPromptTemplate.from_messages(\n    [\n        (\n            \"system\",\n            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n        ),\n        (\"human\", \"{input}\"),\n    ]\n)\n\nchain = prompt | llm\nchain.invoke(\n    {\n        \"input_language\": \"English\",\n        \"output_language\": \"German\",\n        \"input\": \"I love programming.\",\n    }\n)"]
   },
   {
    "cell_type": "markdown",