x

together[patch]: Update @root_validator for pydantic 2 compatibility (#25423 )
This PR updates usage of @root_validator to be compatible with pydantic 2.
2026-02-08 18:19:21 +00:00 · 2024-08-15 12:26:25 -04:00 · 2024-08-15 11:27:42 -04:00 · 2024-08-15 11:26:44 -04:00 · 2024-08-15 11:26:24 -04:00
16 changed files with 149 additions and 588 deletions
--- a/docs/docs/integrations/chat/anthropic.ipynb
+++ b/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",
--- a/docs/docs/integrations/chat/azure_chat_openai.ipynb
+++ b/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/\""
   ]
  },
--- a/docs/docs/integrations/chat/databricks.ipynb
+++ b/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",
+    "    )"
   ]
  },
  {
--- a/docs/docs/integrations/chat/fireworks.ipynb
+++ b/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",
--- a/docs/docs/integrations/chat/friendli.ipynb
+++ b/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": {
--- a/docs/docs/integrations/chat/google_generative_ai.ipynb
+++ b/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",
--- a/docs/docs/integrations/chat/groq.ipynb
+++ b/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",
--- a/docs/docs/integrations/chat/huggingface.ipynb
+++ b/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",
--- a/docs/docs/integrations/chat/mistralai.ipynb
+++ b/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",
--- a/docs/docs/integrations/chat/yi.ipynb
+++ b/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",
--- a/libs/partners/ai21/langchain_ai21/ai21_base.py
+++ b/libs/partners/ai21/langchain_ai21/ai21_base.py
@@ -28,7 +28,7 @@ class AI21Base(BaseModel):
    num_retries: Optional[int] = None
    """Maximum number of retries for API requests before giving up."""

-    @root_validator()
+    @root_validator(pre=True)
    def validate_environment(cls, values: Dict) -> Dict:
        api_key = convert_to_secret_str(
            values.get("api_key") or os.getenv("AI21_API_KEY") or ""
@@ -46,7 +46,13 @@ class AI21Base(BaseModel):
            os.getenv("AI21_TIMEOUT_SEC", _DEFAULT_TIMEOUT_SEC)
        )
        values["timeout_sec"] = timeout_sec
+        return values

+    @root_validator(pre=False, skip_on_failure=True)
+    def post_init(cls, values: Dict) -> Dict:
+        api_key = values["api_key"]
+        api_host = values["api_host"]
+        timeout_sec = values["timeout_sec"]
        if values.get("client") is None:
            values["client"] = AI21Client(
                api_key=api_key.get_secret_value(),
--- a/libs/partners/mistralai/langchain_mistralai/chat_models.py
+++ b/libs/partners/mistralai/langchain_mistralai/chat_models.py
@@ -73,7 +73,7 @@ from langchain_core.pydantic_v1 import (
 )
 from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
 from langchain_core.tools import BaseTool
-from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from langchain_core.utils import secret_from_env
 from langchain_core.utils.function_calling import convert_to_openai_tool
 from langchain_core.utils.pydantic import is_basemodel_subclass

@@ -360,7 +360,10 @@ class ChatMistralAI(BaseChatModel):

    client: httpx.Client = Field(default=None)  #: :meta private:
    async_client: httpx.AsyncClient = Field(default=None)  #: :meta private:
-    mistral_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
+    mistral_api_key: Optional[SecretStr] = Field(
+        alias="api_key",
+        default_factory=secret_from_env("MISTRAL_API_KEY", default=None),
+    )
    endpoint: str = "https://api.mistral.ai/v1"
    max_retries: int = 5
    timeout: int = 120
@@ -465,15 +468,9 @@ class ChatMistralAI(BaseChatModel):
        combined = {"token_usage": overall_token_usage, "model_name": self.model}
        return combined

-    @root_validator()
+    @root_validator(pre=False, skip_on_failure=True)
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate api key, python package exists, temperature, and top_p."""
-
-        values["mistral_api_key"] = convert_to_secret_str(
-            get_from_dict_or_env(
-                values, "mistral_api_key", "MISTRAL_API_KEY", default=""
-            )
-        )
        api_key_str = values["mistral_api_key"].get_secret_value()
        # todo: handle retries
        if not values.get("client"):
--- a/libs/partners/together/langchain_together/chat_models.py
+++ b/libs/partners/together/langchain_together/chat_models.py
@@ -1,6 +1,5 @@
 """Wrapper around Together AI's Chat Completions API."""

-import os
 from typing import (
    Any,
    Dict,
@@ -12,8 +11,8 @@ import openai
 from langchain_core.language_models.chat_models import LangSmithParams
 from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import (
-    convert_to_secret_str,
-    get_from_dict_or_env,
+    from_env,
+    secret_from_env,
 )
 from langchain_openai.chat_models.base import BaseChatOpenAI

@@ -311,13 +310,27 @@ class ChatTogether(BaseChatOpenAI):

    model_name: str = Field(default="meta-llama/Llama-3-8b-chat-hf", alias="model")
    """Model name to use."""
-    together_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
-    """Automatically inferred from env are `TOGETHER_API_KEY` if not provided."""
-    together_api_base: Optional[str] = Field(
-        default="https://api.together.ai/v1/", alias="base_url"
+    together_api_key: Optional[SecretStr] = Field(
+        alias="api_key",
+        default_factory=secret_from_env("TOGETHER_API_KEY", default=None),
+    )
+    """Together AI API key.
+    
+    Automatically read from env variable `TOGETHER_API_KEY` if not provided.
+    """
+    together_api_base: str = Field(
+        default_factory=from_env(
+            "TOGETHER_API_BASE", default="https://api.together.ai/v1/"
+        ),
+        alias="base_url",
    )

-    @root_validator()
+    class Config:
+        """Pydantic config."""
+
+        allow_population_by_field_name = True
+
+    @root_validator(pre=False, skip_on_failure=True)
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        if values["n"] < 1:
@@ -325,13 +338,6 @@ class ChatTogether(BaseChatOpenAI):
        if values["n"] > 1 and values["streaming"]:
            raise ValueError("n must be 1 when streaming.")

-        values["together_api_key"] = convert_to_secret_str(
-            get_from_dict_or_env(values, "together_api_key", "TOGETHER_API_KEY")
-        )
-        values["together_api_base"] = values["together_api_base"] or os.getenv(
-            "TOGETHER_API_BASE"
-        )
-
        client_params = {
            "api_key": (
                values["together_api_key"].get_secret_value()
--- a/libs/partners/together/langchain_together/embeddings.py
+++ b/libs/partners/together/langchain_together/embeddings.py
@@ -1,7 +1,6 @@
 """Wrapper around Together AI's Embeddings API."""

 import logging
-import os
 import warnings
 from typing import (
    Any,
@@ -25,9 +24,9 @@ from langchain_core.pydantic_v1 import (
    root_validator,
 )
 from langchain_core.utils import (
-    convert_to_secret_str,
-    get_from_dict_or_env,
+    from_env,
    get_pydantic_field_names,
+    secret_from_env,
 )

 logger = logging.getLogger(__name__)
@@ -115,10 +114,19 @@ class TogetherEmbeddings(BaseModel, Embeddings):

    Not yet supported.
    """
-    together_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
-    """API Key for Solar API."""
+    together_api_key: Optional[SecretStr] = Field(
+        alias="api_key",
+        default_factory=secret_from_env("TOGETHER_API_KEY", default=None),
+    )
+    """Together AI API key.
+    
+    Automatically read from env variable `TOGETHER_API_KEY` if not provided.
+    """
    together_api_base: str = Field(
-        default="https://api.together.ai/v1/", alias="base_url"
+        default_factory=from_env(
+            "TOGETHER_API_BASE", default="https://api.together.ai/v1/"
+        ),
+        alias="base_url",
    )
    """Endpoint URL to use."""
    embedding_ctx_length: int = 4096
@@ -198,18 +206,9 @@ class TogetherEmbeddings(BaseModel, Embeddings):
        values["model_kwargs"] = extra
        return values

-    @root_validator()
-    def validate_environment(cls, values: Dict) -> Dict:
-        """Validate that api key and python package exists in environment."""
-        together_api_key = get_from_dict_or_env(
-            values, "together_api_key", "TOGETHER_API_KEY"
-        )
-        values["together_api_key"] = (
-            convert_to_secret_str(together_api_key) if together_api_key else None
-        )
-        values["together_api_base"] = values["together_api_base"] or os.getenv(
-            "TOGETHER_API_BASE"
-        )
+    @root_validator(pre=False, skip_on_failure=True)
+    def post_init(cls, values: Dict) -> Dict:
+        """Logic that will post Pydantic initialization."""
        client_params = {
            "api_key": (
                values["together_api_key"].get_secret_value()
--- a/libs/partners/together/langchain_together/llms.py
+++ b/libs/partners/together/langchain_together/llms.py
@@ -11,8 +11,10 @@ from langchain_core.callbacks import (
    CallbackManagerForLLMRun,
 )
 from langchain_core.language_models.llms import LLM
-from langchain_core.pydantic_v1 import SecretStr, root_validator
-from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
+from langchain_core.utils import (
+    secret_from_env,
+)

 logger = logging.getLogger(__name__)

@@ -36,8 +38,14 @@ class Together(LLM):

    base_url: str = "https://api.together.ai/v1/completions"
    """Base completions API URL."""
-    together_api_key: SecretStr
-    """Together AI API key. Get it here: https://api.together.ai/settings/api-keys"""
+    together_api_key: SecretStr = Field(
+        alias="api_key",
+        default_factory=secret_from_env("TOGETHER_API_KEY"),
+    )
+    """Together AI API key.
+    
+    Automatically read from env variable `TOGETHER_API_KEY` if not provided.
+    """
    model: str
    """Model name. Available models listed here:
        Base Models: https://docs.together.ai/docs/inference-models#language-models
@@ -74,21 +82,11 @@ class Together(LLM):
        """Configuration for this pydantic object."""

        extra = "forbid"
+        allow_population_by_field_name = True

    @root_validator(pre=True)
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key exists in environment."""
-        values["together_api_key"] = convert_to_secret_str(
-            get_from_dict_or_env(values, "together_api_key", "TOGETHER_API_KEY")
-        )
-        return values
-
-    @root_validator()
-    def validate_max_tokens(cls, values: Dict) -> Dict:
-        """The v1 completions endpoint, has max_tokens as required parameter.
-
-        Set a default value and warn if the parameter is missing.
-        """
        if values.get("max_tokens") is None:
            warnings.warn(
                "The completions endpoint, has 'max_tokens' as required argument. "
--- a/libs/partners/together/tests/unit_tests/test_llms.py
+++ b/libs/partners/together/tests/unit_tests/test_llms.py
@@ -9,7 +9,7 @@ from langchain_together import Together
 def test_together_api_key_is_secret_string() -> None:
    """Test that the API key is stored as a SecretStr."""
    llm = Together(
-        together_api_key="secret-api-key",  # type: ignore[arg-type]
+        together_api_key="secret-api-key",  # type: ignore[call-arg]
        model="togethercomputer/RedPajama-INCITE-7B-Base",
        temperature=0.2,
        max_tokens=250,
@@ -38,7 +38,7 @@ def test_together_api_key_masked_when_passed_via_constructor(
 ) -> None:
    """Test that the API key is masked when passed via the constructor."""
    llm = Together(
-        together_api_key="secret-api-key",  # type: ignore[arg-type]
+        together_api_key="secret-api-key",  # type: ignore[call-arg]
        model="togethercomputer/RedPajama-INCITE-7B-Base",
        temperature=0.2,
        max_tokens=250,
@@ -52,7 +52,18 @@ def test_together_api_key_masked_when_passed_via_constructor(
 def test_together_uses_actual_secret_value_from_secretstr() -> None:
    """Test that the actual secret value is correctly retrieved."""
    llm = Together(
-        together_api_key="secret-api-key",  # type: ignore[arg-type]
+        together_api_key="secret-api-key",  # type: ignore[call-arg]
+        model="togethercomputer/RedPajama-INCITE-7B-Base",
+        temperature=0.2,
+        max_tokens=250,
+    )
+    assert cast(SecretStr, llm.together_api_key).get_secret_value() == "secret-api-key"
+
+
+def test_together_uses_actual_secret_value_from_secretstr_api_key() -> None:
+    """Test that the actual secret value is correctly retrieved."""
+    llm = Together(
+        api_key="secret-api-key",  # type: ignore[arg-type]
        model="togethercomputer/RedPajama-INCITE-7B-Base",
        temperature=0.2,
        max_tokens=250,
Author	SHA1	Message	Date
Eugene Yurtsev	f8c20e18e7	x	2024-08-15 12:26:25 -04:00
Eugene Yurtsev	831708beb7	together[patch]: Update @root_validator for pydantic 2 compatibility (#25423 ) This PR updates usage of @root_validator to be compatible with pydantic 2.	2024-08-15 11:27:42 -04:00
Eugene Yurtsev	a114255b82	ai21[patch]: Update @root_validators for pydantic2 migration (#25401 ) Update @root_validators for pydantic 2 migration.	2024-08-15 11:26:44 -04:00
Eugene Yurtsev	6f68c8d6ab	mistralai[patch]: Update root validator for compatibility with pydantic 2 (#25403 )	2024-08-15 11:26:24 -04:00