pinecone: release 0.2.0.dev1 (#26386)

TODO: update `aiohttp` >= 3.10.

cookbook/code-analysis-deeplake.ipynb

@@ -90,7 +90,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "# Please manually enter OpenAI Key"
    ]
   },

docs/api_reference/requirements.txt

@@ -8,4 +8,4 @@ myst-nb>=1.1.1
 pyyaml
 sphinx-design
 sphinx-copybutton
-beautifulso[up4
+beautifulsoup4

docs/docs/how_to/assign.ipynb

@@ -45,7 +45,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/binding.ipynb

@@ -49,7 +49,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/chat_model_caching.ipynb

@@ -50,7 +50,8 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
     "llm = ChatOpenAI()"
    ]

docs/docs/how_to/configure.ipynb

@@ -58,7 +58,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {
@@ -281,7 +282,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
+    "if \"ANTHROPIC_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/few_shot_examples_chat.ipynb

@@ -66,7 +66,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/functions.ipynb

@@ -54,7 +54,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/llm_caching.ipynb

@@ -25,7 +25,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "# Please manually enter OpenAI Key"
    ]
   },

docs/docs/how_to/merge_message_runs.ipynb

@@ -11,12 +11,30 @@
     "\n",
     "The `merge_message_runs` utility makes it easy to merge consecutive messages of the same type.\n",
     "\n",
+    "### Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "198ce37f-4466-45a2-8878-d75cd01a5d23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-core langchain-anthropic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5c3ca6e-e5b3-4151-8307-9101713a20ae",
+   "metadata": {},
+   "source": [
     "## Basic usage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "1a215bbb-c05c-40b0-a6fd-d94884d517df",
    "metadata": {},
    "outputs": [
@@ -24,11 +42,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "SystemMessage(content=\"you're a good assistant.\\nyou always respond with a joke.\")\n",
+      "SystemMessage(content=\"you're a good assistant.\\nyou always respond with a joke.\", additional_kwargs={}, response_metadata={})\n",
       "\n",
-      "HumanMessage(content=[{'type': 'text', 'text': \"i wonder why it's called langchain\"}, 'and who is harrison chasing anyways'])\n",
+      "HumanMessage(content=[{'type': 'text', 'text': \"i wonder why it's called langchain\"}, 'and who is harrison chasing anyways'], additional_kwargs={}, response_metadata={})\n",
       "\n",
-      "AIMessage(content='Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn\\'t have the same ring to it!\\nWhy, he\\'s probably chasing after the last cup of coffee in the office!')\n"
+      "AIMessage(content='Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn\\'t have the same ring to it!\\nWhy, he\\'s probably chasing after the last cup of coffee in the office!', additional_kwargs={}, response_metadata={})\n"
      ]
     }
    ],
@@ -63,38 +81,6 @@
     "Notice that if the contents of one of the messages to merge is a list of content blocks then the merged message will have a list of content blocks. And if both messages to merge have string contents then those are concatenated with a newline character."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "11f7e8d3",
-   "metadata": {},
-   "source": [
-    "The `merge_message_runs` utility also works with messages composed together using the overloaded `+` operation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b51855c5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "messages = (\n",
-    "    SystemMessage(\"you're a good assistant.\")\n",
-    "    + SystemMessage(\"you always respond with a joke.\")\n",
-    "    + HumanMessage([{\"type\": \"text\", \"text\": \"i wonder why it's called langchain\"}])\n",
-    "    + HumanMessage(\"and who is harrison chasing anyways\")\n",
-    "    + AIMessage(\n",
-    "        'Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn\\'t have the same ring to it!'\n",
-    "    )\n",
-    "    + AIMessage(\n",
-    "        \"Why, he's probably chasing after the last cup of coffee in the office!\"\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "merged = merge_message_runs(messages)\n",
-    "print(\"\\n\\n\".join([repr(x) for x in merged]))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "1b2eee74-71c8-4168-b968-bca580c25d18",
@@ -107,23 +93,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "id": "6d5a0283-11f8-435b-b27b-7b18f7693592",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=[], response_metadata={'id': 'msg_01D6R8Naum57q8qBau9vLBUX', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 84, 'output_tokens': 3}}, id='run-ac0c465b-b54f-4b8b-9295-e5951250d653-0', usage_metadata={'input_tokens': 84, 'output_tokens': 3, 'total_tokens': 87})"
+       "AIMessage(content=[], additional_kwargs={}, response_metadata={'id': 'msg_01KNGUMTuzBVfwNouLDpUMwf', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 84, 'output_tokens': 3}}, id='run-b908b198-9c24-450b-9749-9d4a8182937b-0', usage_metadata={'input_tokens': 84, 'output_tokens': 3, 'total_tokens': 87})"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# pip install -U langchain-anthropic\n",
+    "%pip install -qU langchain-anthropic\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "\n",
     "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)\n",
@@ -146,19 +139,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "id": "460817a6-c327-429d-958e-181a8c46059c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[SystemMessage(content=\"you're a good assistant.\\nyou always respond with a joke.\"),\n",
-       " HumanMessage(content=[{'type': 'text', 'text': \"i wonder why it's called langchain\"}, 'and who is harrison chasing anyways']),\n",
-       " AIMessage(content='Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn\\'t have the same ring to it!\\nWhy, he\\'s probably chasing after the last cup of coffee in the office!')]"
+       "[SystemMessage(content=\"you're a good assistant.\\nyou always respond with a joke.\", additional_kwargs={}, response_metadata={}),\n",
+       " HumanMessage(content=[{'type': 'text', 'text': \"i wonder why it's called langchain\"}, 'and who is harrison chasing anyways'], additional_kwargs={}, response_metadata={}),\n",
+       " AIMessage(content='Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn\\'t have the same ring to it!\\nWhy, he\\'s probably chasing after the last cup of coffee in the office!', additional_kwargs={}, response_metadata={})]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -167,6 +160,53 @@
     "merger.invoke(messages)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4178837d-b155-492d-9404-d567accc1fa0",
+   "metadata": {},
+   "source": [
+    "`merge_message_runs` can also be placed after a prompt:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "620530ab-ed05-4899-b984-bfa4cd738465",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='A convergent series is an infinite series whose partial sums approach a finite value as more terms are added. In other words, the sequence of partial sums has a limit.\\n\\nMore formally, an infinite series Σ an (where an are the terms of the series) is said to be convergent if the sequence of partial sums:\\n\\nS1 = a1\\nS2 = a1 + a2  \\nS3 = a1 + a2 + a3\\n...\\nSn = a1 + a2 + a3 + ... + an\\n...\\n\\nconverges to some finite number S as n goes to infinity. We write:\\n\\nlim n→∞ Sn = S\\n\\nThe finite number S is called the sum of the convergent infinite series.\\n\\nIf the sequence of partial sums does not approach any finite limit, the infinite series is said to be divergent.\\n\\nSome key properties:\\n- A series converges if and only if the sequence of its partial sums is a Cauchy sequence.\\n- Absolute/conditional convergence criteria help determine if a given series converges.\\n- Convergent series have many important applications in mathematics, physics, engineering etc.', additional_kwargs={}, response_metadata={'id': 'msg_01MfV6y2hep7ZNvDz24A36U4', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 29, 'output_tokens': 267}}, id='run-9d925f58-021e-4bd0-94fc-f8f5e91010a4-0', usage_metadata={'input_tokens': 29, 'output_tokens': 267, 'total_tokens': 296})"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\"system\", \"You're great a {skill}\"),\n",
+    "        (\"system\", \"You're also great at explaining things\"),\n",
+    "        (\"human\", \"{query}\"),\n",
+    "    ]\n",
+    ")\n",
+    "chain = prompt | merger | llm\n",
+    "chain.invoke({\"skill\": \"math\", \"query\": \"what's the definition of a convergent series\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51ba533a-43c7-4e5f-bd91-a4ec23ceeb34",
+   "metadata": {},
+   "source": [
+    "LangSmith Trace: https://smith.langchain.com/public/432150b6-9909-40a7-8ae7-944b7e657438/r/f4ad5fb2-4d38-42a6-b780-25f62617d53f"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4548d916-ce21-4dc6-8f19-eedb8003ace6",
@@ -194,7 +234,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/migrate_agent.ipynb

@@ -65,9 +65,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\""
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API key:\\n\")"
    ]
   },
   {
@@ -1325,7 +1327,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/output_parser_json.ipynb

@@ -47,7 +47,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/output_parser_xml.ipynb

@@ -41,7 +41,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
+    "if \"ANTHROPIC_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/output_parser_yaml.ipynb

@@ -39,7 +39,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/parallel.ipynb

@@ -60,7 +60,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/passthrough.ipynb

@@ -46,7 +46,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/sequence.ipynb

@@ -62,7 +62,8 @@
     "\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass()\n",
+    "if \"ANTHROPIC_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"ANTHROPIC_API_KEY\"] = getpass()\n",
     "\n",
     "model = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)"
    ]

docs/docs/how_to/structured_output.ipynb

@@ -257,17 +257,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 19,
    "id": "9194bcf2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Response(output=Joke(setup='Why was the cat sitting on the computer?', punchline='To keep an eye on the mouse!', rating=8))"
+       "FinalResponse(final_output=Joke(setup='Why was the cat sitting on the computer?', punchline='Because it wanted to keep an eye on the mouse!', rating=7))"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -293,28 +293,28 @@
     "    response: str = Field(description=\"A conversational response to the user's query\")\n",
     "\n",
     "\n",
-    "class Response(BaseModel):\n",
-    "    output: Union[Joke, ConversationalResponse]\n",
+    "class FinalResponse(BaseModel):\n",
+    "    final_output: Union[Joke, ConversationalResponse]\n",
     "\n",
     "\n",
-    "structured_llm = llm.with_structured_output(Response)\n",
+    "structured_llm = llm.with_structured_output(FinalResponse)\n",
     "\n",
     "structured_llm.invoke(\"Tell me a joke about cats\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 20,
    "id": "84d86132",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Response(output=ConversationalResponse(response=\"I'm just a digital assistant, so I don't have feelings, but I'm here and ready to help you. How can I assist you today?\"))"
+       "FinalResponse(final_output=ConversationalResponse(response=\"I'm just a bunch of code, so I don't have feelings, but I'm here and ready to help you! How can I assist you today?\"))"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -915,9 +915,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv-2",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv-2"
   },
   "language_info": {
    "codemirror_mode": {

docs/docs/how_to/tool_calling_parallel.ipynb

@@ -57,7 +57,8 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
    ]

docs/docs/how_to/tool_choice.ipynb

@@ -57,7 +57,8 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
    ]

docs/docs/how_to/tool_results_pass_to_model.ipynb

@@ -53,7 +53,8 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
    ]

docs/docs/how_to/tool_streaming.ipynb

@@ -58,7 +58,8 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "llm_with_tools = llm.bind_tools(tools)"

docs/docs/how_to/tools_few_shot.ipynb

@@ -46,7 +46,8 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "llm_with_tools = llm.bind_tools(tools)"

docs/docs/integrations/chat/ai21.ipynb

@@ -50,18 +50,19 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "62e0dbc3",
    "metadata": {
     "tags": []
    },
+   "outputs": [],
    "source": [
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"AI21_API_KEY\"] = getpass()"
-   ],
-   "outputs": [],
-   "execution_count": null
+    "if \"AI21_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"AI21_API_KEY\"] = getpass()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -73,14 +74,14 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "7c2e19d3-7c58-4470-9e1a-718b27a32056",
    "metadata": {},
+   "outputs": [],
    "source": [
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
@@ -115,15 +116,15 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "c40756fb-cbf8-4d44-a293-3989d707237e",
    "metadata": {},
+   "outputs": [],
    "source": [
     "from langchain_ai21 import ChatAI21\n",
     "\n",
     "llm = ChatAI21(model=\"jamba-instruct\", temperature=0)"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
@@ -135,8 +136,10 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "46b982dc-5d8a-46da-a711-81c03ccd6adc",
    "metadata": {},
+   "outputs": [],
    "source": [
     "messages = [\n",
     "    (\n",
@@ -147,9 +150,7 @@
     "]\n",
     "ai_msg = llm.invoke(messages)\n",
     "ai_msg"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
@@ -163,6 +164,7 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "39353473fce5dd2e",
    "metadata": {
     "collapsed": false,
@@ -170,6 +172,7 @@
      "outputs_hidden": false
     }
    },
+   "outputs": [],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
@@ -191,25 +194,26 @@
     "        \"input\": \"I love programming.\",\n",
     "    }\n",
     ")"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "# Tool Calls / Function Calling",
-   "id": "39c0ccd229927eab"
+   "id": "39c0ccd229927eab",
+   "metadata": {},
+   "source": "# Tool Calls / Function Calling"
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "This example shows how to use tool calling with AI21 models:",
-   "id": "2bf6b40be07fe2d4"
+   "id": "2bf6b40be07fe2d4",
+   "metadata": {},
+   "source": "This example shows how to use tool calling with AI21 models:"
   },
   {
-   "metadata": {},
    "cell_type": "code",
+   "execution_count": null,
+   "id": "a181a28df77120fb",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import os\n",
     "from getpass import getpass\n",
@@ -219,7 +223,8 @@
     "from langchain_core.tools import tool\n",
     "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
     "\n",
-    "os.environ[\"AI21_API_KEY\"] = getpass()\n",
+    "if \"AI21_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"AI21_API_KEY\"] = getpass()\n",
     "\n",
     "\n",
     "@tool\n",
@@ -276,10 +281,7 @@
     "            print(f\"Assistant: {llm_answer.content}\")\n",
     "    else:\n",
     "        print(f\"Assistant: {response.content}\")"
-   ],
-   "id": "a181a28df77120fb",
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",

docs/docs/integrations/chat/anyscale.ipynb

@@ -54,7 +54,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"ANYSCALE_API_KEY\"] = getpass()"
+    "if \"ANYSCALE_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"ANYSCALE_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/integrations/chat/everlyai.ipynb

@@ -47,7 +47,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"EVERLYAI_API_KEY\"] = getpass()"
+    "if \"EVERLYAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"EVERLYAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/integrations/chat/gigachat.ipynb

@@ -47,7 +47,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"GIGACHAT_CREDENTIALS\"] = getpass()"
+    "if \"GIGACHAT_CREDENTIALS\" not in os.environ:\n",
+    "    os.environ[\"GIGACHAT_CREDENTIALS\"] = getpass()"
    ]
   },
   {

docs/docs/integrations/document_transformers/ai21_semantic_text_splitter.ipynb

@@ -58,7 +58,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"AI21_API_KEY\"] = getpass()"
+    "if \"AI21_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"AI21_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/integrations/llm_caching.ipynb

@@ -24,7 +24,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/integrations/llms/ai21.ipynb

@@ -67,7 +67,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"AI21_API_KEY\"] = getpass()"
+    "if \"AI21_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"AI21_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/integrations/llms/anthropic.ipynb

@@ -67,7 +67,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
+    "if \"ANTHROPIC_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/integrations/llms/gigachat.ipynb

@@ -47,7 +47,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"GIGACHAT_CREDENTIALS\"] = getpass()"
+    "if \"GIGACHAT_CREDENTIALS\" not in os.environ:\n",
+    "    os.environ[\"GIGACHAT_CREDENTIALS\"] = getpass()"
    ]
   },
   {

docs/docs/integrations/retrievers/embedchain.ipynb

@@ -68,7 +68,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/integrations/retrievers/outline.ipynb

@@ -128,7 +128,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"OpenAI API Key:\")"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"OpenAI API Key:\")"
    ]
   },
   {

docs/docs/integrations/retrievers/self_query/astradb.ipynb

@@ -48,7 +48,8 @@
     "\n",
     "from langchain_openai.embeddings import OpenAIEmbeddings\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"OpenAI API Key:\")\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"OpenAI API Key:\")\n",
     "\n",
     "embeddings = OpenAIEmbeddings()"
    ]

docs/docs/integrations/text_embedding/gigachat.ipynb

@@ -44,7 +44,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"GIGACHAT_CREDENTIALS\"] = getpass()"
+    "if \"GIGACHAT_CREDENTIALS\" not in os.environ:\n",
+    "    os.environ[\"GIGACHAT_CREDENTIALS\"] = getpass()"
    ]
   },
   {

docs/docs/integrations/vectorstores/apache_doris.ipynb

@@ -245,7 +245,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/integrations/vectorstores/cassandra.ipynb

@@ -90,7 +90,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"OPENAI_API_KEY = \")"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"OPENAI_API_KEY = \")"
    ]
   },
   {

docs/docs/integrations/vectorstores/kdbai.ipynb

@@ -61,7 +61,8 @@
    "source": [
     "KDBAI_ENDPOINT = input(\"KDB.AI endpoint: \")\n",
     "KDBAI_API_KEY = getpass(\"KDB.AI API key: \")\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"OpenAI API Key: \")"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"OpenAI API Key: \")"
    ]
   },
   {

docs/docs/integrations/vectorstores/sklearn.ipynb

@@ -44,7 +44,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter your OpenAI key:\")"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter your OpenAI key:\")"
    ]
   },
   {

docs/docs/tutorials/query_analysis.ipynb

@@ -50,7 +50,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %pip install -qU langchain langchain-community langchain-openai youtube-transcript-api pytube langchain-chroma"
+    "%%capture --no-stderr\n",
+    "%pip install -qU langchain langchain-community langchain-openai youtube-transcript-api pytube langchain-chroma"
    ]
   },
   {
@@ -595,7 +596,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/versions/migrating_chains/constitutional_chain.ipynb

@@ -39,7 +39,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/versions/migrating_chains/conversation_chain.ipynb

@@ -38,7 +38,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/versions/migrating_chains/conversation_retrieval_chain.ipynb

@@ -46,7 +46,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/versions/migrating_chains/llm_math_chain.ipynb

@@ -37,7 +37,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/versions/migrating_chains/llm_router_chain.ipynb

@@ -100,7 +100,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/versions/migrating_chains/multi_prompt_chain.ipynb

@@ -40,7 +40,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/versions/migrating_chains/retrieval_qa.ipynb

@@ -44,7 +44,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

libs/community/Makefile

@@ -45,6 +45,7 @@ lint_tests: PYTHON_FILES=tests
 lint_tests: MYPY_CACHE=.mypy_cache_test
 
 lint lint_diff lint_package lint_tests:
+	./scripts/check_pydantic.sh .
 	./scripts/lint_imports.sh .
 	./scripts/check_pickle.sh .
 	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES)

libs/community/poetry.lock

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+    {file = "cffi-1.17.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:c03e868a0b3bc35839ba98e74211ed2b05d2119be4e8a0f224fba9384f1fe02e"},
+    {file = "cffi-1.17.1-cp39-cp39-win32.whl", hash = "sha256:e31ae45bc2e29f6b2abd0de1cc3b9d5205aa847cafaecb8af1476a609a2f6eb7"},
+    {file = "cffi-1.17.1-cp39-cp39-win_amd64.whl", hash = "sha256:d016c76bdd850f3c626af19b0542c9677ba156e4ee4fccfdd7848803533ef662"},
+    {file = "cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824"},
 ]
 
 [package.dependencies]
@@ -1745,7 +1776,7 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.3.0.dev1"
+version = "0.3.0.dev2"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1755,7 +1786,7 @@ develop = true
 [package.dependencies]
 aiohttp = "^3.8.3"
 async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""}
-langchain-core = "^0.3.0.dev2"
+langchain-core = "^0.3.0.dev5"
 langchain-text-splitters = "^0.3.0.dev1"
 langsmith = "^0.1.17"
 numpy = [
@@ -1774,7 +1805,7 @@ url = "../langchain"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.0.dev4"
+version = "0.3.0.dev5"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1783,7 +1814,7 @@ develop = true
 
 [package.dependencies]
 jsonpatch = "^1.33"
-langsmith = "^0.1.112"
+langsmith = "^0.1.117"
 packaging = ">=23.2,<25"
 pydantic = "^2.7.4"
 PyYAML = ">=5.3"
@@ -3551,7 +3582,9 @@ python-versions = ">=3.7"
 files = [
     {file = "SQLAlchemy-2.0.34-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:95d0b2cf8791ab5fb9e3aa3d9a79a0d5d51f55b6357eecf532a120ba3b5524db"},
     {file = "SQLAlchemy-2.0.34-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:243f92596f4fd4c8bd30ab8e8dd5965afe226363d75cab2468f2c707f64cd83b"},
+    {file = "SQLAlchemy-2.0.34-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9ea54f7300553af0a2a7235e9b85f4204e1fc21848f917a3213b0e0818de9a24"},
     {file = "SQLAlchemy-2.0.34-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:173f5f122d2e1bff8fbd9f7811b7942bead1f5e9f371cdf9e670b327e6703ebd"},
+    {file = "SQLAlchemy-2.0.34-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:196958cde924a00488e3e83ff917be3b73cd4ed8352bbc0f2989333176d1c54d"},
     {file = "SQLAlchemy-2.0.34-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:bd90c221ed4e60ac9d476db967f436cfcecbd4ef744537c0f2d5291439848768"},
     {file = "SQLAlchemy-2.0.34-cp310-cp310-win32.whl", hash = "sha256:3166dfff2d16fe9be3241ee60ece6fcb01cf8e74dd7c5e0b64f8e19fab44911b"},
     {file = "SQLAlchemy-2.0.34-cp310-cp310-win_amd64.whl", hash = "sha256:6831a78bbd3c40f909b3e5233f87341f12d0b34a58f14115c9e94b4cdaf726d3"},
@@ -3572,19 +3605,25 @@ files = [
     {file = "SQLAlchemy-2.0.34-cp312-cp312-win32.whl", hash = "sha256:fbb034f565ecbe6c530dff948239377ba859420d146d5f62f0271407ffb8c580"},
     {file = "SQLAlchemy-2.0.34-cp312-cp312-win_amd64.whl", hash = "sha256:707c8f44931a4facd4149b52b75b80544a8d824162602b8cd2fe788207307f9a"},
     {file = "SQLAlchemy-2.0.34-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:24af3dc43568f3780b7e1e57c49b41d98b2d940c1fd2e62d65d3928b6f95f021"},
+    {file = "SQLAlchemy-2.0.34-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e60ed6ef0a35c6b76b7640fe452d0e47acc832ccbb8475de549a5cc5f90c2c06"},
     {file = "SQLAlchemy-2.0.34-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:413c85cd0177c23e32dee6898c67a5f49296640041d98fddb2c40888fe4daa2e"},
+    {file = "SQLAlchemy-2.0.34-cp37-cp37m-musllinux_1_2_aarch64.whl", hash = "sha256:25691f4adfb9d5e796fd48bf1432272f95f4bbe5f89c475a788f31232ea6afba"},
     {file = "SQLAlchemy-2.0.34-cp37-cp37m-musllinux_1_2_x86_64.whl", hash = "sha256:526ce723265643dbc4c7efb54f56648cc30e7abe20f387d763364b3ce7506c82"},
     {file = "SQLAlchemy-2.0.34-cp37-cp37m-win32.whl", hash = "sha256:13be2cc683b76977a700948411a94c67ad8faf542fa7da2a4b167f2244781cf3"},
     {file = "SQLAlchemy-2.0.34-cp37-cp37m-win_amd64.whl", hash = "sha256:e54ef33ea80d464c3dcfe881eb00ad5921b60f8115ea1a30d781653edc2fd6a2"},
     {file = "SQLAlchemy-2.0.34-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:43f28005141165edd11fbbf1541c920bd29e167b8bbc1fb410d4fe2269c1667a"},
     {file = "SQLAlchemy-2.0.34-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b68094b165a9e930aedef90725a8fcfafe9ef95370cbb54abc0464062dbf808f"},
+    {file = "SQLAlchemy-2.0.34-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6a1e03db964e9d32f112bae36f0cc1dcd1988d096cfd75d6a588a3c3def9ab2b"},
     {file = "SQLAlchemy-2.0.34-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:203d46bddeaa7982f9c3cc693e5bc93db476ab5de9d4b4640d5c99ff219bee8c"},
+    {file = "SQLAlchemy-2.0.34-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:ae92bebca3b1e6bd203494e5ef919a60fb6dfe4d9a47ed2453211d3bd451b9f5"},
     {file = "SQLAlchemy-2.0.34-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:9661268415f450c95f72f0ac1217cc6f10256f860eed85c2ae32e75b60278ad8"},
     {file = "SQLAlchemy-2.0.34-cp38-cp38-win32.whl", hash = "sha256:895184dfef8708e15f7516bd930bda7e50ead069280d2ce09ba11781b630a434"},
     {file = "SQLAlchemy-2.0.34-cp38-cp38-win_amd64.whl", hash = "sha256:6e7cde3a2221aa89247944cafb1b26616380e30c63e37ed19ff0bba5e968688d"},
     {file = "SQLAlchemy-2.0.34-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:dbcdf987f3aceef9763b6d7b1fd3e4ee210ddd26cac421d78b3c206d07b2700b"},
     {file = "SQLAlchemy-2.0.34-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ce119fc4ce0d64124d37f66a6f2a584fddc3c5001755f8a49f1ca0a177ef9796"},
+    {file = "SQLAlchemy-2.0.34-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a17d8fac6df9835d8e2b4c5523666e7051d0897a93756518a1fe101c7f47f2f0"},
     {file = "SQLAlchemy-2.0.34-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9ebc11c54c6ecdd07bb4efbfa1554538982f5432dfb8456958b6d46b9f834bb7"},
+    {file = "SQLAlchemy-2.0.34-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:2e6965346fc1491a566e019a4a1d3dfc081ce7ac1a736536367ca305da6472a8"},
     {file = "SQLAlchemy-2.0.34-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:220574e78ad986aea8e81ac68821e47ea9202b7e44f251b7ed8c66d9ae3f4278"},
     {file = "SQLAlchemy-2.0.34-cp39-cp39-win32.whl", hash = "sha256:b75b00083e7fe6621ce13cfce9d4469c4774e55e8e9d38c305b37f13cf1e874c"},
     {file = "SQLAlchemy-2.0.34-cp39-cp39-win_amd64.whl", hash = "sha256:c29d03e0adf3cc1a8c3ec62d176824972ae29b67a66cbb18daff3062acc6faa8"},
@@ -4340,4 +4379,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "59c1fe138c1696cc75b88108ee68b03018202799bf7096273427a22d36c0912e"
+content-hash = "1a81994350c65c891f5f592a522975bc6688cfad016f2af5fe8ad93a76209066"

libs/community/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-community"
-version = "0.3.0.dev1"
+version = "0.3.0.dev2"
 description = "Community contributed LangChain integrations."
 authors = []
 license = "MIT"
@@ -33,8 +33,8 @@ ignore-words-list = "momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogy
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = { version = "^0.3.0.dev2", allow-prereleases = true }
-langchain = { version = "^0.3.0.dev1", allow-prereleases = true }
+langchain-core = { version = "^0.3.0.dev5", allow-prereleases = true }
+langchain = { version = "^0.3.0.dev2", allow-prereleases = true }
 SQLAlchemy = ">=1.4,<3"
 requests = "^2"
 PyYAML = ">=5.3"

libs/community/scripts/check_pydantic.sh

@@ -0,0 +1,55 @@
+#!/bin/bash
+#
+# This script searches for lines starting with "import pydantic" or "from pydantic"
+# in tracked files within a Git repository.
+#
+# Usage: ./scripts/check_pydantic.sh /path/to/repository
+
+# Check if a path argument is provided
+if [ $# -ne 1 ]; then
+  echo "Usage: $0 /path/to/repository"
+  exit 1
+fi
+
+repository_path="$1"
+
+# Check that we are not using features that cannot be captured via init.
+# pre-init is a custom decorator that we introduced to capture the same semantics
+# as @root_validator(pre=False, skip_on_failure=False) available in pydantic 1.
+count=$(git grep -E '(@root_validator)|(@validator)|(@field_validator)|(@pre_init)' -- "*.py" | wc -l)
+# PRs that increase the current count will not be accepted.
+# PRs that decrease update the code in the repository
+# and allow decreasing the count of are welcome!
+current_count=129
+
+if [ "$count" -gt "$current_count" ]; then
+  echo "The PR seems to be introducing new usage of @root_validator and/or @field_validator."
+  echo "git grep -E '(@root_validator)|(@validator)|(@field_validator)|(@pre_init)' | wc -l returned $count"
+  echo "whereas the expected count should be equal or less than $current_count"
+  echo "Please update the code to instead use @model_validator or __init__"
+  exit 1
+elif [ "$count" -lt "$current_count" ]; then
+    echo "Please update the $current_count variable in ./scripts/check_pydantic.sh to $count"
+    exit 1
+fi
+
+# We do not want to be using pydantic-settings. There's already a pattern to look
+# up env settings in the code base, and we want to be using the existing pattern
+# rather than relying on an external dependency.
+count=$(git grep -E '^import pydantic_settings|^from pydantic_settings' -- "*.py" | wc -l)
+
+# PRs that increase the current count will not be accepted.
+# PRs that decrease update the code in the repository
+# and allow decreasing the count of are welcome!
+current_count=8
+
+if [ "$count" -gt "$current_count" ]; then
+  echo "The PR seems to be introducing new usage pydantic_settings."
+  echo "git grep -E '^import pydantic_settings|^from pydantic_settings' | wc -l returned $count"
+  echo "whereas the expected count should be equal or less than $current_count"
+  echo "Please update the code to use Field(default_factory=from_env(..)) or Field(default_factory=secret_from_env(..))"
+  exit 1
+elif [ "$count" -lt "$current_count" ]; then
+    echo "Please update the $current_count variable in ./scripts/check_pydantic.sh to $count"
+    exit 1
+fi

libs/core/langchain_core/_api/internal.py

@@ -1,11 +1,5 @@
 import inspect
 
-_INTERNAL_PREFIXES = [
-    "langchain",  # For example langchain-core, langchain, langchain-anthropic
-    "langserve",
-    "langgraph",
-]
-
 
 def is_caller_internal(depth: int = 2) -> bool:
     """Return whether the caller at `depth` of this function is internal."""
@@ -24,11 +18,6 @@ def is_caller_internal(depth: int = 2) -> bool:
         if caller_module is None:
             return False
         caller_module_name = caller_module.__name__
-
-        for prefix in _INTERNAL_PREFIXES:
-            if caller_module_name.startswith(prefix):
-                return True
+        return caller_module_name.startswith("langchain")
     finally:
         del frame
-
-    return False

libs/core/langchain_core/messages/utils.py

@@ -272,7 +272,7 @@ def _create_message_from_message_type(
         message = RemoveMessage(**kwargs)
     else:
         raise ValueError(
-            f"Unexpected message type: {message_type}. Use one of 'human',"
+            f"Unexpected message type: '{message_type}'. Use one of 'human',"
             f" 'user', 'ai', 'assistant', 'function', 'tool', or 'system'."
         )
     return message

libs/core/langchain_core/prompts/base.py

@@ -29,7 +29,7 @@ from langchain_core.prompt_values import (
 )
 from langchain_core.runnables import RunnableConfig, RunnableSerializable
 from langchain_core.runnables.config import ensure_config
-from langchain_core.runnables.utils import create_model
+from langchain_core.utils.pydantic import create_model_v2
 
 if TYPE_CHECKING:
     from langchain_core.documents import Document
@@ -125,8 +125,9 @@ class BasePromptTemplate(
         optional_input_variables = {
             k: (self.input_types.get(k, str), None) for k in self.optional_variables
         }
-        return create_model(
-            "PromptInput", **{**required_input_variables, **optional_input_variables}
+        return create_model_v2(
+            "PromptInput",
+            field_definitions={**required_input_variables, **optional_input_variables},
         )
 
     def _validate_input(self, inner_input: Any) -> Dict:

libs/core/langchain_core/runnables/base.py

@@ -71,7 +71,6 @@ from langchain_core.runnables.utils import (
     accepts_config,
     accepts_run_manager,
     asyncio_accepts_context,
-    create_model,
     gather_with_concurrency,
     get_function_first_arg_dict_keys,
     get_function_nonlocals,
@@ -83,6 +82,7 @@ from langchain_core.runnables.utils import (
 )
 from langchain_core.utils.aiter import aclosing, atee, py_anext
 from langchain_core.utils.iter import safetee
+from langchain_core.utils.pydantic import create_model_v2
 
 if TYPE_CHECKING:
     from langchain_core.callbacks.manager import (
@@ -345,9 +345,9 @@ class Runnable(Generic[Input, Output], ABC):
         if inspect.isclass(root_type) and issubclass(root_type, BaseModel):
             return root_type
 
-        return create_model(
+        return create_model_v2(
             self.get_name("Input"),
-            __root__=root_type,
+            root=root_type,
             # create model needs access to appropriate type annotations to be
             # able to construct the pydantic model.
             # When we create the model, we pass information about the namespace
@@ -355,7 +355,7 @@ class Runnable(Generic[Input, Output], ABC):
             # be resolved correctly as well.
             # self.__class__.__module__ handles the case when the Runnable is
             # being sub-classed in a different module.
-            __module_name=self.__class__.__module__,
+            module_name=self.__class__.__module__,
         )
 
     def get_input_jsonschema(
@@ -413,9 +413,9 @@ class Runnable(Generic[Input, Output], ABC):
         if inspect.isclass(root_type) and issubclass(root_type, BaseModel):
             return root_type
 
-        return create_model(
+        return create_model_v2(
             self.get_name("Output"),
-            __root__=root_type,
+            root=root_type,
             # create model needs access to appropriate type annotations to be
             # able to construct the pydantic model.
             # When we create the model, we pass information about the namespace
@@ -423,7 +423,7 @@ class Runnable(Generic[Input, Output], ABC):
             # be resolved correctly as well.
             # self.__class__.__module__ handles the case when the Runnable is
             # being sub-classed in a different module.
-            __module_name=self.__class__.__module__,
+            module_name=self.__class__.__module__,
         )
 
     def get_output_jsonschema(
@@ -477,9 +477,9 @@ class Runnable(Generic[Input, Output], ABC):
         include = include or []
         config_specs = self.config_specs
         configurable = (
-            create_model(  # type: ignore[call-overload]
+            create_model_v2(  # type: ignore[call-overload]
                 "Configurable",
-                **{
+                field_definitions={
                     spec.id: (
                         spec.annotation,
                         Field(
@@ -502,8 +502,8 @@ class Runnable(Generic[Input, Output], ABC):
                 if field_name in [i for i in include if i != "configurable"]
             },
         }
-        model = create_model(  # type: ignore[call-overload]
-            self.get_name("Config"), **all_fields
+        model = create_model_v2(  # type: ignore[call-overload]
+            self.get_name("Config"), field_definitions=all_fields
         )
         return model
 
@@ -530,14 +530,14 @@ class Runnable(Generic[Input, Output], ABC):
         try:
             input_node = graph.add_node(self.get_input_schema(config))
         except TypeError:
-            input_node = graph.add_node(create_model(self.get_name("Input")))
+            input_node = graph.add_node(create_model_v2(self.get_name("Input")))
         runnable_node = graph.add_node(
             self, metadata=config.get("metadata") if config else None
         )
         try:
             output_node = graph.add_node(self.get_output_schema(config))
         except TypeError:
-            output_node = graph.add_node(create_model(self.get_name("Output")))
+            output_node = graph.add_node(create_model_v2(self.get_name("Output")))
         graph.add_edge(input_node, runnable_node)
         graph.add_edge(runnable_node, output_node)
         return graph
@@ -2583,9 +2583,9 @@ def _seq_input_schema(
         next_input_schema = _seq_input_schema(steps[1:], config)
         if not issubclass(next_input_schema, RootModel):
             # it's a dict as expected
-            return create_model(  # type: ignore[call-overload]
+            return create_model_v2(  # type: ignore[call-overload]
                 "RunnableSequenceInput",
-                **{
+                field_definitions={
                     k: (v.annotation, v.default)
                     for k, v in next_input_schema.model_fields.items()
                     if k not in first.mapper.steps__
@@ -2610,9 +2610,9 @@ def _seq_output_schema(
         prev_output_schema = _seq_output_schema(steps[:-1], config)
         if not issubclass(prev_output_schema, RootModel):
             # it's a dict as expected
-            return create_model(  # type: ignore[call-overload]
+            return create_model_v2(  # type: ignore[call-overload]
                 "RunnableSequenceOutput",
-                **{
+                field_definitions={
                     **{
                         k: (v.annotation, v.default)
                         for k, v in prev_output_schema.model_fields.items()
@@ -2628,9 +2628,9 @@ def _seq_output_schema(
         if not issubclass(prev_output_schema, RootModel):
             # it's a dict as expected
             if isinstance(last.keys, list):
-                return create_model(  # type: ignore[call-overload]
+                return create_model_v2(  # type: ignore[call-overload]
                     "RunnableSequenceOutput",
-                    **{
+                    field_definitions={
                         k: (v.annotation, v.default)
                         for k, v in prev_output_schema.model_fields.items()
                         if k in last.keys
@@ -2638,9 +2638,8 @@ def _seq_output_schema(
                 )
             else:
                 field = prev_output_schema.model_fields[last.keys]
-                return create_model(  # type: ignore[call-overload]
-                    "RunnableSequenceOutput",
-                    __root__=(field.annotation, field.default),
+                return create_model_v2(  # type: ignore[call-overload]
+                    "RunnableSequenceOutput", root=(field.annotation, field.default)
                 )
 
     return last.get_output_schema(config)
@@ -3582,9 +3581,9 @@ class RunnableParallel(RunnableSerializable[Input, Dict[str, Any]]):
             for s in self.steps__.values()
         ):
             # This is correct, but pydantic typings/mypy don't think so.
-            return create_model(  # type: ignore[call-overload]
+            return create_model_v2(  # type: ignore[call-overload]
                 self.get_name("Input"),
-                **{
+                field_definitions={
                     k: (v.annotation, v.default)
                     for step in self.steps__.values()
                     for k, v in step.get_input_schema(config).model_fields.items()
@@ -3606,7 +3605,7 @@ class RunnableParallel(RunnableSerializable[Input, Dict[str, Any]]):
             The output schema of the Runnable.
         """
         fields = {k: (v.OutputType, ...) for k, v in self.steps__.items()}
-        return create_model(self.get_name("Output"), **fields)
+        return create_model_v2(self.get_name("Output"), field_definitions=fields)
 
     @property
     def config_specs(self) -> List[ConfigurableFieldSpec]:
@@ -4076,13 +4075,13 @@ class RunnableGenerator(Runnable[Input, Output]):
         if inspect.isclass(root_type) and issubclass(root_type, BaseModel):
             return root_type
 
-        return create_model(
+        return create_model_v2(
             self.get_name("Input"),
-            __root__=root_type,
+            root=root_type,
             # To create the schema, we need to provide the module
             # where the underlying function is defined.
             # This allows pydantic to resolve type annotations appropriately.
-            __module_name=module,
+            module_name=module,
         )
 
     @property
@@ -4111,13 +4110,13 @@ class RunnableGenerator(Runnable[Input, Output]):
         if inspect.isclass(root_type) and issubclass(root_type, BaseModel):
             return root_type
 
-        return create_model(
+        return create_model_v2(
             self.get_name("Output"),
-            __root__=root_type,
+            root=root_type,
             # To create the schema, we need to provide the module
             # where the underlying function is defined.
             # This allows pydantic to resolve type annotations appropriately.
-            __module_name=module,
+            module_name=module,
         )
 
     def __eq__(self, other: Any) -> bool:
@@ -4366,25 +4365,25 @@ class RunnableLambda(Runnable[Input, Output]):
             ):
                 fields = {item[1:-1]: (Any, ...) for item in items}
                 # It's a dict, lol
-                return create_model(self.get_name("Input"), **fields)
+                return create_model_v2(self.get_name("Input"), field_definitions=fields)
             else:
                 module = getattr(func, "__module__", None)
-                return create_model(
+                return create_model_v2(
                     self.get_name("Input"),
-                    __root__=List[Any],
+                    root=List[Any],
                     # To create the schema, we need to provide the module
                     # where the underlying function is defined.
                     # This allows pydantic to resolve type annotations appropriately.
-                    __module_name=module,
+                    module_name=module,
                 )
 
         if self.InputType != Any:
             return super().get_input_schema(config)
 
         if dict_keys := get_function_first_arg_dict_keys(func):
-            return create_model(
+            return create_model_v2(
                 self.get_name("Input"),
-                **{key: (Any, ...) for key in dict_keys},  # type: ignore
+                field_definitions={key: (Any, ...) for key in dict_keys},
             )
 
         return super().get_input_schema(config)
@@ -4425,13 +4424,13 @@ class RunnableLambda(Runnable[Input, Output]):
         if inspect.isclass(root_type) and issubclass(root_type, BaseModel):
             return root_type
 
-        return create_model(
+        return create_model_v2(
             self.get_name("Output"),
-            __root__=root_type,
+            root=root_type,
             # To create the schema, we need to provide the module
             # where the underlying function is defined.
             # This allows pydantic to resolve type annotations appropriately.
-            __module_name=module,
+            module_name=module,
         )
 
     @property
@@ -4945,9 +4944,9 @@ class RunnableEachBase(RunnableSerializable[List[Input], List[Output]]):
     def get_input_schema(
         self, config: Optional[RunnableConfig] = None
     ) -> Type[BaseModel]:
-        return create_model(
+        return create_model_v2(
             self.get_name("Input"),
-            __root__=(
+            root=(
                 List[self.bound.get_input_schema(config)],  # type: ignore
                 None,
             ),
@@ -4958,7 +4957,7 @@ class RunnableEachBase(RunnableSerializable[List[Input], List[Output]]):
             # be resolved correctly as well.
             # self.__class__.__module__ handles the case when the Runnable is
             # being sub-classed in a different module.
-            __module_name=self.__class__.__module__,
+            module_name=self.__class__.__module__,
         )
 
     @property
@@ -4969,9 +4968,9 @@ class RunnableEachBase(RunnableSerializable[List[Input], List[Output]]):
         self, config: Optional[RunnableConfig] = None
     ) -> Type[BaseModel]:
         schema = self.bound.get_output_schema(config)
-        return create_model(
+        return create_model_v2(
             self.get_name("Output"),
-            __root__=List[schema],  # type: ignore[valid-type]
+            root=List[schema],  # type: ignore[valid-type]
             # create model needs access to appropriate type annotations to be
             # able to construct the pydantic model.
             # When we create the model, we pass information about the namespace
@@ -4979,7 +4978,7 @@ class RunnableEachBase(RunnableSerializable[List[Input], List[Output]]):
             # be resolved correctly as well.
             # self.__class__.__module__ handles the case when the Runnable is
             # being sub-classed in a different module.
-            __module_name=self.__class__.__module__,
+            module_name=self.__class__.__module__,
         )
 
     @property

libs/core/langchain_core/runnables/history.py

@@ -22,9 +22,9 @@ from langchain_core.runnables.passthrough import RunnablePassthrough
 from langchain_core.runnables.utils import (
     ConfigurableFieldSpec,
     Output,
-    create_model,
     get_unique_config_specs,
 )
+from langchain_core.utils.pydantic import create_model_v2
 
 if TYPE_CHECKING:
     from langchain_core.language_models.base import LanguageModelLike
@@ -386,10 +386,15 @@ class RunnableWithMessageHistory(RunnableBindingBase):
         elif self.input_messages_key:
             fields[self.input_messages_key] = (Sequence[BaseMessage], ...)
         else:
-            fields["__root__"] = (Sequence[BaseMessage], ...)
-        return create_model(  # type: ignore[call-overload]
+            return create_model_v2(
+                "RunnableWithChatHistoryInput",
+                module_name=self.__class__.__module__,
+                root=(Sequence[BaseMessage], ...),
+            )
+        return create_model_v2(  # type: ignore[call-overload]
             "RunnableWithChatHistoryInput",
-            **fields,
+            field_definitions=fields,
+            module_name=self.__class__.__module__,
         )
 
     @property
@@ -419,10 +424,10 @@ class RunnableWithMessageHistory(RunnableBindingBase):
         if inspect.isclass(root_type) and issubclass(root_type, BaseModel):
             return root_type
 
-        return create_model(
+        return create_model_v2(
             "RunnableWithChatHistoryOutput",
-            __root__=root_type,
-            __module_name=self.__class__.__module__,
+            root=root_type,
+            module_name=self.__class__.__module__,
         )
 
     def _is_not_async(self, *args: Sequence[Any], **kwargs: Dict[str, Any]) -> bool:

libs/core/langchain_core/runnables/passthrough.py

@@ -41,10 +41,10 @@ from langchain_core.runnables.graph import Graph
 from langchain_core.runnables.utils import (
     AddableDict,
     ConfigurableFieldSpec,
-    create_model,
 )
 from langchain_core.utils.aiter import atee, py_anext
 from langchain_core.utils.iter import safetee
+from langchain_core.utils.pydantic import create_model_v2
 
 if TYPE_CHECKING:
     from langchain_core.callbacks.manager import (
@@ -442,9 +442,8 @@ class RunnableAssign(RunnableSerializable[Dict[str, Any], Dict[str, Any]]):
             for name, field_info in map_output_schema.model_fields.items():
                 fields[name] = (field_info.annotation, field_info.default)
 
-            return create_model(  # type: ignore[call-overload]
-                "RunnableAssignOutput",
-                **fields,
+            return create_model_v2(  # type: ignore[call-overload]
+                "RunnableAssignOutput", field_definitions=fields
             )
         elif not issubclass(map_output_schema, RootModel):
             # ie. only map output is a dict

libs/core/langchain_core/runnables/utils.py

@@ -6,7 +6,6 @@ import ast
 import asyncio
 import inspect
 import textwrap
-import warnings
 from functools import lru_cache
 from inspect import signature
 from itertools import groupby
@@ -26,24 +25,17 @@ from typing import (
     Protocol,
     Sequence,
     Set,
-    Type,
     TypeVar,
     Union,
-    cast,
 )
 
-from pydantic import BaseModel, ConfigDict, PydanticDeprecationWarning, RootModel
-from pydantic import create_model as _create_model_base  # pydantic :ignore
-from pydantic.json_schema import (
-    DEFAULT_REF_TEMPLATE,
-    GenerateJsonSchema,
-    JsonSchemaMode,
-)
-from pydantic.v1 import BaseModel as BaseModelV1
 from typing_extensions import TypeGuard
 
 from langchain_core.runnables.schema import StreamEvent
 
+# Re-export create-model for backwards compatibility
+from langchain_core.utils.pydantic import create_model as create_model
+
 Input = TypeVar("Input", contravariant=True)
 # Output type should implement __concat__, as eg str, list, dict do
 Output = TypeVar("Output", covariant=True)
@@ -707,141 +699,6 @@ class _RootEventFilter:
         return include
 
 
-_SchemaConfig = ConfigDict(arbitrary_types_allowed=True, frozen=True)
-
-NO_DEFAULT = object()
-
-
-def _create_root_model(
-    name: str,
-    type_: Any,
-    module_name: Optional[str] = None,
-    default_: object = NO_DEFAULT,
-) -> Type[BaseModel]:
-    """Create a base class."""
-
-    def schema(
-        cls: Type[BaseModel],
-        by_alias: bool = True,
-        ref_template: str = DEFAULT_REF_TEMPLATE,
-    ) -> Dict[str, Any]:
-        # Complains about schema not being defined in superclass
-        schema_ = super(cls, cls).schema(  # type: ignore[misc]
-            by_alias=by_alias, ref_template=ref_template
-        )
-        schema_["title"] = name
-        return schema_
-
-    def model_json_schema(
-        cls: Type[BaseModel],
-        by_alias: bool = True,
-        ref_template: str = DEFAULT_REF_TEMPLATE,
-        schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
-        mode: JsonSchemaMode = "validation",
-    ) -> Dict[str, Any]:
-        # Complains about model_json_schema not being defined in superclass
-        schema_ = super(cls, cls).model_json_schema(  # type: ignore[misc]
-            by_alias=by_alias,
-            ref_template=ref_template,
-            schema_generator=schema_generator,
-            mode=mode,
-        )
-        schema_["title"] = name
-        return schema_
-
-    base_class_attributes = {
-        "__annotations__": {"root": type_},
-        "model_config": ConfigDict(arbitrary_types_allowed=True),
-        "schema": classmethod(schema),
-        "model_json_schema": classmethod(model_json_schema),
-        "__module__": module_name or "langchain_core.runnables.utils",
-    }
-
-    if default_ is not NO_DEFAULT:
-        base_class_attributes["root"] = default_
-    with warnings.catch_warnings():
-        if isinstance(type_, type) and issubclass(type_, BaseModelV1):
-            warnings.filterwarnings(
-                action="ignore", category=PydanticDeprecationWarning
-            )
-        custom_root_type = type(name, (RootModel,), base_class_attributes)
-    return cast(Type[BaseModel], custom_root_type)
-
-
-@lru_cache(maxsize=256)
-def _create_root_model_cached(
-    __model_name: str,
-    type_: Any,
-    default_: object = NO_DEFAULT,
-    module_name: Optional[str] = None,
-) -> Type[BaseModel]:
-    return _create_root_model(
-        __model_name, type_, default_=default_, module_name=module_name
-    )
-
-
-def create_model(
-    __model_name: str,
-    __module_name: Optional[str] = None,
-    **field_definitions: Any,
-) -> Type[BaseModel]:
-    """Create a pydantic model with the given field definitions.
-
-    Args:
-        __model_name: The name of the model.
-        __module_name: The name of the module where the model is defined.
-            This is used by Pydantic to resolve any forward references.
-        **field_definitions: The field definitions for the model.
-
-    Returns:
-        Type[BaseModel]: The created model.
-    """
-
-    # Move this to caching path
-    if "__root__" in field_definitions:
-        if len(field_definitions) > 1:
-            raise NotImplementedError(
-                "When specifying __root__ no other "
-                f"fields should be provided. Got {field_definitions}"
-            )
-
-        arg = field_definitions["__root__"]
-        if isinstance(arg, tuple):
-            kwargs = {"type_": arg[0], "default_": arg[1]}
-        else:
-            kwargs = {"type_": arg}
-
-        try:
-            named_root_model = _create_root_model_cached(
-                __model_name, module_name=__module_name, **kwargs
-            )
-        except TypeError:
-            # something in the arguments into _create_root_model_cached is not hashable
-            named_root_model = _create_root_model(
-                __model_name,
-                module_name=__module_name,
-                **kwargs,
-            )
-        return named_root_model
-    try:
-        return _create_model_cached(__model_name, **field_definitions)
-    except TypeError:
-        # something in field definitions is not hashable
-        return _create_model_base(
-            __model_name, __config__=_SchemaConfig, **field_definitions
-        )
-
-
-@lru_cache(maxsize=256)
-def _create_model_cached(
-    __model_name: str,
-    **field_definitions: Any,
-) -> Type[BaseModel]:
-    return _create_model_base(
-        __model_name, __config__=_SchemaConfig, **field_definitions
-    )
-
-
 def is_async_generator(
     func: Any,
 ) -> TypeGuard[Callable[..., AsyncIterator]]:

libs/core/langchain_core/tools/base.py

@@ -38,6 +38,7 @@ from pydantic import (
     validate_arguments,
 )
 from pydantic.v1 import BaseModel as BaseModelV1
+from pydantic.v1 import validate_arguments as validate_arguments_v1
 from typing_extensions import Annotated
 
 from langchain_core._api import deprecated
@@ -164,6 +165,35 @@ def _infer_arg_descriptions(
     return description, arg_descriptions
 
 
+def _is_pydantic_annotation(annotation: Any, pydantic_version: str = "v2") -> bool:
+    """Determine if a type annotation is a Pydantic model."""
+    base_model_class = BaseModelV1 if pydantic_version == "v1" else BaseModel
+    try:
+        return issubclass(annotation, base_model_class)
+    except TypeError:
+        return False
+
+
+def _function_annotations_are_pydantic_v1(
+    signature: inspect.Signature, func: Callable
+) -> bool:
+    """Determine if all Pydantic annotations in a function signature are from V1."""
+    any_v1_annotations = any(
+        _is_pydantic_annotation(parameter.annotation, pydantic_version="v1")
+        for parameter in signature.parameters.values()
+    )
+    any_v2_annotations = any(
+        _is_pydantic_annotation(parameter.annotation, pydantic_version="v2")
+        for parameter in signature.parameters.values()
+    )
+    if any_v1_annotations and any_v2_annotations:
+        raise NotImplementedError(
+            f"Function {func} contains a mix of Pydantic v1 and v2 annotations. "
+            "Only one version of Pydantic annotations per function is supported."
+        )
+    return any_v1_annotations and not any_v2_annotations
+
+
 class _SchemaConfig:
     """Configuration for the pydantic model.
 
@@ -208,16 +238,19 @@ def create_schema_from_function(
     Returns:
         A pydantic model with the same arguments as the function.
     """
-    # https://docs.pydantic.dev/latest/usage/validation_decorator/
-    with warnings.catch_warnings():
-        # We are using deprecated functionality here.
-        # This code should be re-written to simply construct a pydantic model
-        # using inspect.signature and create_model.
-        warnings.simplefilter("ignore", category=PydanticDeprecationWarning)
-        validated = validate_arguments(func, config=_SchemaConfig)  # type: ignore
-
     sig = inspect.signature(func)
 
+    if _function_annotations_are_pydantic_v1(sig, func):
+        validated = validate_arguments_v1(func, config=_SchemaConfig)  # type: ignore
+    else:
+        # https://docs.pydantic.dev/latest/usage/validation_decorator/
+        with warnings.catch_warnings():
+            # We are using deprecated functionality here.
+            # This code should be re-written to simply construct a pydantic model
+            # using inspect.signature and create_model.
+            warnings.simplefilter("ignore", category=PydanticDeprecationWarning)
+            validated = validate_arguments(func, config=_SchemaConfig)  # type: ignore
+
     # Let's ignore `self` and `cls` arguments for class and instance methods
     if func.__qualname__ and "." in func.__qualname__:
         # Then it likely belongs in a class namespace
@@ -274,7 +307,7 @@ def create_schema_from_function(
             valid_properties.append(field)
 
     return _create_subset_model(
-        f"{model_name}Schema",
+        model_name,
         inferred_model,
         list(valid_properties),
         descriptions=arg_descriptions,

libs/core/langchain_core/tools/convert.py

@@ -87,7 +87,7 @@ def tool(
         .. code-block:: python
 
             {
-                "title": "fooSchema",
+                "title": "foo",
                 "description": "The foo.",
                 "type": "object",
                 "properties": {

libs/core/langchain_core/utils/function_calling.py

@@ -288,7 +288,10 @@ def format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription:
     Returns:
         The function description.
     """
-    if tool.tool_call_schema:
+    from langchain_core.tools import simple
+
+    is_simple_oai_tool = isinstance(tool, simple.Tool) and not tool.args_schema
+    if tool.tool_call_schema and not is_simple_oai_tool:
         return convert_pydantic_to_openai_function(
             tool.tool_call_schema, name=tool.name, description=tool.description
         )

libs/core/langchain_core/utils/pydantic.py

@@ -5,12 +5,38 @@ from __future__ import annotations
 import inspect
 import textwrap
 import warnings
-from functools import wraps
-from typing import Any, Callable, Dict, List, Optional, Type, TypeVar, Union, overload
+from contextlib import nullcontext
+from functools import lru_cache, wraps
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+    overload,
+)
 
 import pydantic
-from pydantic import BaseModel, PydanticDeprecationWarning, root_validator
-from pydantic.json_schema import GenerateJsonSchema, JsonSchemaValue
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    PydanticDeprecationWarning,
+    RootModel,
+    root_validator,
+)
+from pydantic import (
+    create_model as _create_model_base,
+)
+from pydantic.json_schema import (
+    DEFAULT_REF_TEMPLATE,
+    GenerateJsonSchema,
+    JsonSchemaMode,
+    JsonSchemaValue,
+)
 from pydantic_core import core_schema
 
 
@@ -355,3 +381,237 @@ elif PYDANTIC_MAJOR_VERSION == 1:
         return model.__fields__  # type: ignore
 else:
     raise ValueError(f"Unsupported Pydantic version: {PYDANTIC_MAJOR_VERSION}")
+
+_SchemaConfig = ConfigDict(
+    arbitrary_types_allowed=True, frozen=True, protected_namespaces=()
+)
+
+NO_DEFAULT = object()
+
+
+def _create_root_model(
+    name: str,
+    type_: Any,
+    module_name: Optional[str] = None,
+    default_: object = NO_DEFAULT,
+) -> Type[BaseModel]:
+    """Create a base class."""
+
+    def schema(
+        cls: Type[BaseModel],
+        by_alias: bool = True,
+        ref_template: str = DEFAULT_REF_TEMPLATE,
+    ) -> Dict[str, Any]:
+        # Complains about schema not being defined in superclass
+        schema_ = super(cls, cls).schema(  # type: ignore[misc]
+            by_alias=by_alias, ref_template=ref_template
+        )
+        schema_["title"] = name
+        return schema_
+
+    def model_json_schema(
+        cls: Type[BaseModel],
+        by_alias: bool = True,
+        ref_template: str = DEFAULT_REF_TEMPLATE,
+        schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+        mode: JsonSchemaMode = "validation",
+    ) -> Dict[str, Any]:
+        # Complains about model_json_schema not being defined in superclass
+        schema_ = super(cls, cls).model_json_schema(  # type: ignore[misc]
+            by_alias=by_alias,
+            ref_template=ref_template,
+            schema_generator=schema_generator,
+            mode=mode,
+        )
+        schema_["title"] = name
+        return schema_
+
+    base_class_attributes = {
+        "__annotations__": {"root": type_},
+        "model_config": ConfigDict(arbitrary_types_allowed=True),
+        "schema": classmethod(schema),
+        "model_json_schema": classmethod(model_json_schema),
+        "__module__": module_name or "langchain_core.runnables.utils",
+    }
+
+    if default_ is not NO_DEFAULT:
+        base_class_attributes["root"] = default_
+    with warnings.catch_warnings():
+        if isinstance(type_, type) and issubclass(type_, BaseModelV1):
+            warnings.filterwarnings(
+                action="ignore", category=PydanticDeprecationWarning
+            )
+        custom_root_type = type(name, (RootModel,), base_class_attributes)
+    return cast(Type[BaseModel], custom_root_type)
+
+
+@lru_cache(maxsize=256)
+def _create_root_model_cached(
+    model_name: str,
+    type_: Any,
+    *,
+    module_name: Optional[str] = None,
+    default_: object = NO_DEFAULT,
+) -> Type[BaseModel]:
+    return _create_root_model(
+        model_name, type_, default_=default_, module_name=module_name
+    )
+
+
+@lru_cache(maxsize=256)
+def _create_model_cached(
+    __model_name: str,
+    **field_definitions: Any,
+) -> Type[BaseModel]:
+    return _create_model_base(
+        __model_name,
+        __config__=_SchemaConfig,
+        **_remap_field_definitions(field_definitions),
+    )
+
+
+def create_model(
+    __model_name: str,
+    __module_name: Optional[str] = None,
+    **field_definitions: Any,
+) -> Type[BaseModel]:
+    """Create a pydantic model with the given field definitions.
+
+    Please use create_model_v2 instead of this function.
+
+    Args:
+        __model_name: The name of the model.
+        __module_name: The name of the module where the model is defined.
+            This is used by Pydantic to resolve any forward references.
+        **field_definitions: The field definitions for the model.
+
+    Returns:
+        Type[BaseModel]: The created model.
+    """
+    kwargs = {}
+    if "__root__" in field_definitions:
+        kwargs["root"] = field_definitions.pop("__root__")
+
+    return create_model_v2(
+        __model_name,
+        module_name=__module_name,
+        field_definitions=field_definitions,
+        **kwargs,
+    )
+
+
+# Reserved names should capture all the `public` names / methods that are
+# used by BaseModel internally. This will keep the reserved names up-to-date.
+# For reference, the reserved names are:
+# "construct", "copy", "dict", "from_orm", "json", "parse_file", "parse_obj",
+# "parse_raw", "schema", "schema_json", "update_forward_refs", "validate",
+# "model_computed_fields", "model_config", "model_construct", "model_copy",
+# "model_dump", "model_dump_json", "model_extra", "model_fields",
+# "model_fields_set", "model_json_schema", "model_parametrized_name",
+# "model_post_init", "model_rebuild", "model_validate", "model_validate_json",
+# "model_validate_strings"
+_RESERVED_NAMES = {key for key in dir(BaseModel) if not key.startswith("_")}
+
+
+def _remap_field_definitions(field_definitions: Dict[str, Any]) -> Dict[str, Any]:
+    """This remaps fields to avoid colliding with internal pydantic fields."""
+    from pydantic import Field
+    from pydantic.fields import FieldInfo
+
+    remapped = {}
+    for key, value in field_definitions.items():
+        if key.startswith("_") or key in _RESERVED_NAMES:
+            # Let's add a prefix to avoid colliding with internal pydantic fields
+            if isinstance(value, FieldInfo):
+                raise NotImplementedError(
+                    f"Remapping for fields starting with '_' or fields with a name "
+                    f"matching a reserved name {_RESERVED_NAMES} is not supported if "
+                    f" the field is a pydantic Field instance. Got {key}."
+                )
+            type_, default_ = value
+            remapped[f"private_{key}"] = (
+                type_,
+                Field(
+                    default=default_,
+                    alias=key,
+                    serialization_alias=key,
+                    title=key.lstrip("_").replace("_", " ").title(),
+                ),
+            )
+        else:
+            remapped[key] = value
+    return remapped
+
+
+def create_model_v2(
+    model_name: str,
+    *,
+    module_name: Optional[str] = None,
+    field_definitions: Optional[Dict[str, Any]] = None,
+    root: Optional[Any] = None,
+) -> Type[BaseModel]:
+    """Create a pydantic model with the given field definitions.
+
+    Attention:
+        Please do not use outside of langchain packages. This API
+        is subject to change at any time.
+
+    Args:
+        model_name: The name of the model.
+        module_name: The name of the module where the model is defined.
+            This is used by Pydantic to resolve any forward references.
+        field_definitions: The field definitions for the model.
+        root: Type for a root model (RootModel)
+
+    Returns:
+        Type[BaseModel]: The created model.
+    """
+    field_definitions = cast(Dict[str, Any], field_definitions or {})  # type: ignore[no-redef]
+
+    if root:
+        if field_definitions:
+            raise NotImplementedError(
+                "When specifying __root__ no other "
+                f"fields should be provided. Got {field_definitions}"
+            )
+
+        if isinstance(root, tuple):
+            kwargs = {"type_": root[0], "default_": root[1]}
+        else:
+            kwargs = {"type_": root}
+
+        try:
+            named_root_model = _create_root_model_cached(
+                model_name, module_name=module_name, **kwargs
+            )
+        except TypeError:
+            # something in the arguments into _create_root_model_cached is not hashable
+            named_root_model = _create_root_model(
+                model_name,
+                module_name=module_name,
+                **kwargs,
+            )
+        return named_root_model
+
+    # No root, just field definitions
+    names = set(field_definitions.keys())
+
+    capture_warnings = False
+
+    for name in names:
+        # Also if any non-reserved name is used (e.g., model_id or model_name)
+        if name.startswith("model"):
+            capture_warnings = True
+
+    with warnings.catch_warnings() if capture_warnings else nullcontext():  # type: ignore[attr-defined]
+        if capture_warnings:
+            warnings.filterwarnings(action="ignore")
+        try:
+            return _create_model_cached(model_name, **field_definitions)
+        except TypeError:
+            # something in field definitions is not hashable
+            return _create_model_base(
+                model_name,
+                __config__=_SchemaConfig,
+                **_remap_field_definitions(field_definitions),
+            )

libs/core/poetry.lock

@@ -278,6 +278,37 @@ files = [
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     {file = "cffi-1.17.1-cp312-cp312-win_amd64.whl", hash = "sha256:51392eae71afec0d0c8fb1a53b204dbb3bcabcb3c9b807eedf3e1e6ccf2de903"},
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+    {file = "cffi-1.17.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:0984a4925a435b1da406122d4d7968dd861c1385afe3b45ba82b750f229811e2"},
+    {file = "cffi-1.17.1-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d01b12eeeb4427d3110de311e1774046ad344f5b1a7403101878976ecd7a10f3"},
+    {file = "cffi-1.17.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:706510fe141c86a69c8ddc029c7910003a17353970cff3b904ff0686a5927683"},
+    {file = "cffi-1.17.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:de55b766c7aa2e2a3092c51e0483d700341182f08e67c63630d5b6f200bb28e5"},
+    {file = "cffi-1.17.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c59d6e989d07460165cc5ad3c61f9fd8f1b4796eacbd81cee78957842b834af4"},
+    {file = "cffi-1.17.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd398dbc6773384a17fe0d3e7eeb8d1a21c2200473ee6806bb5e6a8e62bb73dd"},
+    {file = "cffi-1.17.1-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:3edc8d958eb099c634dace3c7e16560ae474aa3803a5df240542b305d14e14ed"},
+    {file = "cffi-1.17.1-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:72e72408cad3d5419375fc87d289076ee319835bdfa2caad331e377589aebba9"},
+    {file = "cffi-1.17.1-cp313-cp313-win32.whl", hash = "sha256:e03eab0a8677fa80d646b5ddece1cbeaf556c313dcfac435ba11f107ba117b5d"},
+    {file = "cffi-1.17.1-cp313-cp313-win_amd64.whl", hash = "sha256:f6a16c31041f09ead72d69f583767292f750d24913dadacf5756b966aacb3f1a"},
+    {file = "cffi-1.17.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:636062ea65bd0195bc012fea9321aca499c0504409f413dc88af450b57ffd03b"},
+    {file = "cffi-1.17.1-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c7eac2ef9b63c79431bc4b25f1cd649d7f061a28808cbc6c47b534bd789ef964"},
+    {file = "cffi-1.17.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e221cf152cff04059d011ee126477f0d9588303eb57e88923578ace7baad17f9"},
+    {file = "cffi-1.17.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:31000ec67d4221a71bd3f67df918b1f88f676f1c3b535a7eb473255fdc0b83fc"},
+    {file = "cffi-1.17.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6f17be4345073b0a7b8ea599688f692ac3ef23ce28e5df79c04de519dbc4912c"},
+    {file = "cffi-1.17.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0e2b1fac190ae3ebfe37b979cc1ce69c81f4e4fe5746bb401dca63a9062cdaf1"},
+    {file = "cffi-1.17.1-cp38-cp38-win32.whl", hash = "sha256:7596d6620d3fa590f677e9ee430df2958d2d6d6de2feeae5b20e82c00b76fbf8"},
+    {file = "cffi-1.17.1-cp38-cp38-win_amd64.whl", hash = "sha256:78122be759c3f8a014ce010908ae03364d00a1f81ab5c7f4a7a5120607ea56e1"},
+    {file = "cffi-1.17.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b2ab587605f4ba0bf81dc0cb08a41bd1c0a5906bd59243d56bad7668a6fc6c16"},
+    {file = "cffi-1.17.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:28b16024becceed8c6dfbc75629e27788d8a3f9030691a1dbf9821a128b22c36"},
+    {file = "cffi-1.17.1-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1d599671f396c4723d016dbddb72fe8e0397082b0a77a4fab8028923bec050e8"},
+    {file = "cffi-1.17.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ca74b8dbe6e8e8263c0ffd60277de77dcee6c837a3d0881d8c1ead7268c9e576"},
+    {file = "cffi-1.17.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f7f5baafcc48261359e14bcd6d9bff6d4b28d9103847c9e136694cb0501aef87"},
+    {file = "cffi-1.17.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:98e3969bcff97cae1b2def8ba499ea3d6f31ddfdb7635374834cf89a1a08ecf0"},
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+    {file = "cffi-1.17.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:9755e4345d1ec879e3849e62222a18c7174d65a6a92d5b346b1863912168b595"},
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+    {file = "cffi-1.17.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:c03e868a0b3bc35839ba98e74211ed2b05d2119be4e8a0f224fba9384f1fe02e"},
+    {file = "cffi-1.17.1-cp39-cp39-win32.whl", hash = "sha256:e31ae45bc2e29f6b2abd0de1cc3b9d5205aa847cafaecb8af1476a609a2f6eb7"},
+    {file = "cffi-1.17.1-cp39-cp39-win_amd64.whl", hash = "sha256:d016c76bdd850f3c626af19b0542c9677ba156e4ee4fccfdd7848803533ef662"},
+    {file = "cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824"},
 ]
 
 [package.dependencies]

libs/core/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-core"
-version = "0.3.0.dev4"
+version = "0.3.0.dev5"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"

libs/core/tests/unit_tests/prompts/test_chat.py

@@ -864,3 +864,48 @@ async def test_chat_tmpl_serdes(snapshot: SnapshotAssertion) -> None:
     )
     assert dumpd(template) == snapshot()
     assert load(dumpd(template)) == template
+
+
+def test_chat_prompt_template_variable_names() -> None:
+    """This test was written for an edge case that triggers a warning from Pydantic.
+
+    Verify that no run time warnings are raised.
+    """
+    with pytest.warns(None) as record:  # type: ignore
+        prompt = ChatPromptTemplate([("system", "{schema}")])
+        prompt.get_input_schema()
+
+    if record:
+        error_msg = []
+        for warning in record:
+            error_msg.append(
+                f"Warning type: {warning.category.__name__}, "
+                f"Warning message: {warning.message}, "
+                f"Warning location: {warning.filename}:{warning.lineno}"
+            )
+        msg = "\n".join(error_msg)
+    else:
+        msg = ""
+
+    assert list(record) == [], msg
+
+    # Verify value errors raised from illegal names
+    assert ChatPromptTemplate(
+        [("system", "{_private}")]
+    ).get_input_schema().model_json_schema() == {
+        "properties": {"_private": {"title": "Private", "type": "string"}},
+        "required": ["_private"],
+        "title": "PromptInput",
+        "type": "object",
+    }
+
+    assert ChatPromptTemplate(
+        [("system", "{model_json_schema}")]
+    ).get_input_schema().model_json_schema() == {
+        "properties": {
+            "model_json_schema": {"title": "Model Json Schema", "type": "string"}
+        },
+        "required": ["model_json_schema"],
+        "title": "PromptInput",
+        "type": "object",
+    }

libs/core/tests/unit_tests/runnables/test_imports.py

@@ -35,3 +35,9 @@ EXPECTED_ALL = [
 
 def test_all_imports() -> None:
     assert set(__all__) == set(EXPECTED_ALL)
+
+
+def test_imports_for_specific_funcs() -> None:
+    """Test that a few specific imports in more internal namespaces."""
+    # create_model implementation has been moved to langchain_core.utils.pydantic
+    from langchain_core.runnables.utils import create_model  # noqa

libs/core/tests/unit_tests/runnables/test_runnable.py

@@ -5392,3 +5392,36 @@ def test_pydantic_protected_namespaces() -> None:
 
         class CustomChatModel(RunnableSerializable):
             model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+
+
+def test_schema_for_prompt_and_chat_model() -> None:
+    """Testing that schema is generated properly when using variable names
+
+    that collide with pydantic attributes.
+    """
+    prompt = ChatPromptTemplate([("system", "{model_json_schema}, {_private}, {json}")])
+    chat_res = "i'm a chatbot"
+    # sleep to better simulate a real stream
+    chat = FakeListChatModel(responses=[chat_res], sleep=0.01)
+    chain = prompt | chat
+    assert (
+        chain.invoke(
+            {"model_json_schema": "hello", "_private": "goodbye", "json": "json"}
+        ).content
+        == chat_res
+    )
+
+    assert chain.get_input_jsonschema() == {
+        "properties": {
+            "model_json_schema": {"title": "Model Json Schema", "type": "string"},
+            "_private": {"title": "Private", "type": "string"},
+            "json": {"title": "Json", "type": "string"},
+        },
+        "required": [
+            "_private",
+            "json",
+            "model_json_schema",
+        ],
+        "title": "PromptInput",
+        "type": "object",
+    }

libs/core/tests/unit_tests/test_tools.py

@@ -23,7 +23,7 @@ from typing import (
 
 import pytest
 from pydantic import BaseModel, Field, ValidationError
-from pydantic import BaseModel as BaseModelProper
+from pydantic.v1 import BaseModel as BaseModelV1
 from typing_extensions import Annotated, TypedDict, TypeVar
 
 from langchain_core import tools
@@ -80,6 +80,14 @@ class _MockSchema(BaseModel):
     arg3: Optional[dict] = None
 
 
+class _MockSchemaV1(BaseModelV1):
+    """Return the arguments directly."""
+
+    arg1: int
+    arg2: bool
+    arg3: Optional[dict] = None
+
+
 class _MockStructuredTool(BaseTool):
     name: str = "structured_api"
     args_schema: Type[BaseModel] = _MockSchema
@@ -169,6 +177,13 @@ def test_decorator_with_specified_schema() -> None:
     assert isinstance(tool_func, BaseTool)
     assert tool_func.args_schema == _MockSchema
 
+    @tool(args_schema=_MockSchemaV1)
+    def tool_func_v1(arg1: int, arg2: bool, arg3: Optional[dict] = None) -> str:
+        return f"{arg1} {arg2} {arg3}"
+
+    assert isinstance(tool_func_v1, BaseTool)
+    assert tool_func_v1.args_schema == _MockSchemaV1
+
 
 def test_decorated_function_schema_equivalent() -> None:
     """Test that a BaseTool without a schema meets expectations."""
@@ -302,6 +317,51 @@ def test_structured_tool_types_parsed() -> None:
     assert result == expected
 
 
+def test_structured_tool_types_parsed_pydantic_v1() -> None:
+    """Test the non-primitive types are correctly passed to structured tools."""
+
+    class SomeBaseModel(BaseModelV1):
+        foo: str
+
+    class AnotherBaseModel(BaseModelV1):
+        bar: str
+
+    @tool
+    def structured_tool(some_base_model: SomeBaseModel) -> AnotherBaseModel:
+        """Return the arguments directly."""
+        return AnotherBaseModel(bar=some_base_model.foo)
+
+    assert isinstance(structured_tool, StructuredTool)
+
+    expected = AnotherBaseModel(bar="baz")
+    for arg in [
+        SomeBaseModel(foo="baz"),
+        SomeBaseModel(foo="baz").dict(),
+    ]:
+        args = {"some_base_model": arg}
+        result = structured_tool.run(args)
+        assert result == expected
+
+
+def test_structured_tool_types_parsed_pydantic_mixed() -> None:
+    """Test handling of tool with mixed Pydantic version arguments."""
+
+    class SomeBaseModel(BaseModelV1):
+        foo: str
+
+    class AnotherBaseModel(BaseModel):
+        bar: str
+
+    with pytest.raises(NotImplementedError):
+
+        @tool
+        def structured_tool(
+            some_base_model: SomeBaseModel, another_base_model: AnotherBaseModel
+        ) -> None:
+            """Return the arguments directly."""
+            pass
+
+
 def test_base_tool_inheritance_base_schema() -> None:
     """Test schema is correctly inferred when inheriting from BaseTool."""
 
@@ -359,7 +419,7 @@ def test_structured_tool_from_function_docstring() -> None:
             "baz": {"title": "Baz", "type": "string"},
         },
         "description": inspect.getdoc(foo),
-        "title": "fooSchema",
+        "title": "foo",
         "type": "object",
         "required": ["bar", "baz"],
     }
@@ -401,7 +461,7 @@ def test_structured_tool_from_function_docstring_complex_args() -> None:
             },
         },
         "description": inspect.getdoc(foo),
-        "title": "fooSchema",
+        "title": "foo",
         "type": "object",
         "required": ["bar", "baz"],
     }
@@ -502,7 +562,7 @@ def test_structured_tool_from_function_with_run_manager() -> None:
             "baz": {"title": "Baz", "type": "string"},
         },
         "description": inspect.getdoc(foo),
-        "title": "fooSchema",
+        "title": "foo",
         "type": "object",
         "required": ["bar", "baz"],
     }
@@ -734,7 +794,7 @@ def test_structured_tool_from_function() -> None:
     }
 
     assert _schema(structured_tool.args_schema) == {
-        "title": "fooSchema",
+        "title": "foo",
         "type": "object",
         "description": inspect.getdoc(foo),
         "properties": {
@@ -1063,7 +1123,7 @@ def test_tool_arg_descriptions() -> None:
     foo1 = tool(foo)
     args_schema = _schema(foo1.args_schema)  # type: ignore
     assert args_schema == {
-        "title": "fooSchema",
+        "title": "foo",
         "type": "object",
         "description": inspect.getdoc(foo),
         "properties": {
@@ -1077,7 +1137,7 @@ def test_tool_arg_descriptions() -> None:
     foo2 = tool(foo, parse_docstring=True)
     args_schema = _schema(foo2.args_schema)  # type: ignore
     expected = {
-        "title": "fooSchema",
+        "title": "foo",
         "description": "The foo.",
         "type": "object",
         "properties": {
@@ -1166,7 +1226,7 @@ def test_tool_annotated_descriptions() -> None:
     foo1 = tool(foo)
     args_schema = _schema(foo1.args_schema)  # type: ignore
     assert args_schema == {
-        "title": "fooSchema",
+        "title": "foo",
         "type": "object",
         "description": inspect.getdoc(foo),
         "properties": {
@@ -1389,7 +1449,7 @@ def injected_tool_with_schema(x: int, y: str) -> str:
 @pytest.mark.parametrize("tool_", [InjectedTool()])
 def test_tool_injected_arg_without_schema(tool_: BaseTool) -> None:
     assert _schema(tool_.get_input_schema()) == {
-        "title": "fooSchema",
+        "title": "foo",
         "description": "foo.\n\nArgs:\n    x: abc\n    y: 123",
         "type": "object",
         "properties": {
@@ -1428,7 +1488,7 @@ def test_tool_injected_arg_without_schema(tool_: BaseTool) -> None:
 
 @pytest.mark.parametrize(
     "tool_",
-    [injected_tool, injected_tool_with_schema, InjectedToolWithSchema()],
+    [injected_tool_with_schema, InjectedToolWithSchema()],
 )
 def test_tool_injected_arg_with_schema(tool_: BaseTool) -> None:
     assert _schema(tool_.get_input_schema()) == {
@@ -1469,6 +1529,46 @@ def test_tool_injected_arg_with_schema(tool_: BaseTool) -> None:
     }
 
 
+def test_tool_injected_arg() -> None:
+    tool_ = injected_tool
+    assert _schema(tool_.get_input_schema()) == {
+        "title": "foo",
+        "description": "foo.",
+        "type": "object",
+        "properties": {
+            "x": {"description": "abc", "title": "X", "type": "integer"},
+            "y": {"description": "123", "title": "Y", "type": "string"},
+        },
+        "required": ["x", "y"],
+    }
+    assert _schema(tool_.tool_call_schema) == {
+        "title": "foo",
+        "description": "foo.",
+        "type": "object",
+        "properties": {"x": {"description": "abc", "title": "X", "type": "integer"}},
+        "required": ["x"],
+    }
+    assert tool_.invoke({"x": 5, "y": "bar"}) == "bar"
+    assert tool_.invoke(
+        {"name": "foo", "args": {"x": 5, "y": "bar"}, "id": "123", "type": "tool_call"}
+    ) == ToolMessage("bar", tool_call_id="123", name="foo")
+    expected_error = (
+        ValidationError if not isinstance(tool_, InjectedTool) else TypeError
+    )
+    with pytest.raises(expected_error):
+        tool_.invoke({"x": 5})
+
+    assert convert_to_openai_function(tool_) == {
+        "name": "foo",
+        "description": "foo.",
+        "parameters": {
+            "type": "object",
+            "properties": {"x": {"type": "integer", "description": "abc"}},
+            "required": ["x"],
+        },
+    }
+
+
 def test_tool_inherited_injected_arg() -> None:
     class barSchema(BaseModel):
         """bar."""
@@ -1492,7 +1592,7 @@ def test_tool_inherited_injected_arg() -> None:
 
     tool_ = InheritedInjectedArgTool()
     assert tool_.get_input_schema().model_json_schema() == {
-        "title": "fooSchema",
+        "title": "fooSchema",  # Matches the title from the provided schema
         "description": "foo.",
         "type": "object",
         "properties": {
@@ -1501,6 +1601,7 @@ def test_tool_inherited_injected_arg() -> None:
         },
         "required": ["y", "x"],
     }
+    # Should not include `y` since it's annotated as an injected tool arg
     assert tool_.tool_call_schema.model_json_schema() == {
         "title": "foo",
         "description": "foo.",
@@ -1562,7 +1663,7 @@ def test_fn_injected_arg_with_schema(tool_: Callable) -> None:
 def generate_models() -> List[Any]:
     """Generate a list of base models depending on the pydantic version."""
 
-    class FooProper(BaseModelProper):
+    class FooProper(BaseModel):
         a: int
         b: str
 

libs/core/tests/unit_tests/utils/test_function_calling.py

@@ -38,7 +38,7 @@ from pydantic import BaseModel, Field
 
 from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
 from langchain_core.runnables import Runnable, RunnableLambda
-from langchain_core.tools import BaseTool, tool
+from langchain_core.tools import BaseTool, StructuredTool, Tool, tool
 from langchain_core.utils.function_calling import (
     _convert_typed_dict_to_openai_function,
     convert_to_openai_function,
@@ -112,6 +112,20 @@ def dummy_tool() -> BaseTool:
     return DummyFunction()
 
 
+@pytest.fixture()
+def dummy_structured_tool() -> StructuredTool:
+    class Schema(BaseModel):
+        arg1: int = Field(..., description="foo")
+        arg2: Literal["bar", "baz"] = Field(..., description="one of 'bar', 'baz'")
+
+    return StructuredTool.from_function(
+        lambda x: None,
+        name="dummy_function",
+        description="dummy function",
+        args_schema=Schema,
+    )
+
+
 @pytest.fixture()
 def dummy_pydantic() -> Type[BaseModel]:
     class dummy_function(BaseModel):
@@ -234,6 +248,7 @@ class DummyWithClassMethod:
 def test_convert_to_openai_function(
     pydantic: Type[BaseModel],
     function: Callable,
+    dummy_structured_tool: StructuredTool,
     dummy_tool: BaseTool,
     json_schema: Dict,
     Annotated_function: Callable,
@@ -264,6 +279,7 @@ def test_convert_to_openai_function(
     for fn in (
         pydantic,
         function,
+        dummy_structured_tool,
         dummy_tool,
         json_schema,
         expected,
@@ -296,6 +312,27 @@ def test_convert_to_openai_function(
     runnable_expected["parameters"] = parameters
     assert actual == runnable_expected
 
+    # Test simple Tool
+    def my_function(input_string: str) -> str:
+        pass
+
+    tool = Tool(
+        name="dummy_function",
+        func=my_function,
+        description="test description",
+    )
+    actual = convert_to_openai_function(tool)
+    expected = {
+        "name": "dummy_function",
+        "description": "test description",
+        "parameters": {
+            "properties": {"__arg1": {"title": "__arg1", "type": "string"}},
+            "required": ["__arg1"],
+            "type": "object",
+        },
+    }
+    assert actual == expected
+
 
 @pytest.mark.xfail(reason="Direct pydantic v2 models not yet supported")
 def test_convert_to_openai_function_nested_v2() -> None:

libs/core/tests/unit_tests/utils/test_pydantic.py

@@ -8,6 +8,7 @@ from pydantic import ConfigDict
 from langchain_core.utils.pydantic import (
     PYDANTIC_MAJOR_VERSION,
     _create_subset_model_v2,
+    create_model_v2,
     get_fields,
     is_basemodel_instance,
     is_basemodel_subclass,
@@ -194,3 +195,35 @@ def test_fields_pydantic_v1_from_2() -> None:
 
     fields = get_fields(Foo)
     assert fields == {"x": Foo.__fields__["x"]}
+
+
+def test_create_model_v2() -> None:
+    """Test that create model v2 works as expected."""
+
+    with pytest.warns(None) as record:  # type: ignore
+        foo = create_model_v2("Foo", field_definitions={"a": (int, None)})
+        foo.model_json_schema()
+
+    assert list(record) == []
+
+    # schema is used by pydantic, but OK to re-use
+    with pytest.warns(None) as record:  # type: ignore
+        foo = create_model_v2("Foo", field_definitions={"schema": (int, None)})
+        foo.model_json_schema()
+
+    assert list(record) == []
+
+    # From protected namespaces, but definitely OK to use.
+    with pytest.warns(None) as record:  # type: ignore
+        foo = create_model_v2("Foo", field_definitions={"model_id": (int, None)})
+        foo.model_json_schema()
+
+    assert list(record) == []
+
+    with pytest.warns(None) as record:  # type: ignore
+        # Verify that we can use non-English characters
+        field_name = "もしもし"
+        foo = create_model_v2("Foo", field_definitions={field_name: (int, None)})
+        foo.model_json_schema()
+
+    assert list(record) == []

libs/langchain/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
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+    {file = "cffi-1.17.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:c03e868a0b3bc35839ba98e74211ed2b05d2119be4e8a0f224fba9384f1fe02e"},
+    {file = "cffi-1.17.1-cp39-cp39-win32.whl", hash = "sha256:e31ae45bc2e29f6b2abd0de1cc3b9d5205aa847cafaecb8af1476a609a2f6eb7"},
+    {file = "cffi-1.17.1-cp39-cp39-win_amd64.whl", hash = "sha256:d016c76bdd850f3c626af19b0542c9677ba156e4ee4fccfdd7848803533ef662"},
+    {file = "cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824"},
 ]
 
 [package.dependencies]
@@ -1894,7 +1925,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.0.dev4"
+version = "0.3.0.dev5"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1903,7 +1934,7 @@ develop = true
 
 [package.dependencies]
 jsonpatch = "^1.33"
-langsmith = "^0.1.112"
+langsmith = "^0.1.117"
 packaging = ">=23.2,<25"
 pydantic = "^2.7.4"
 PyYAML = ">=5.3"
@@ -3815,7 +3846,9 @@ python-versions = ">=3.7"
 files = [
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@@ -3836,19 +3869,25 @@ files = [
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@@ -4200,20 +4239,6 @@ files = [
 cryptography = ">=35.0.0"
 types-pyOpenSSL = "*"
 
-[[package]]
-name = "types-requests"
-version = "2.31.0.6"
-description = "Typing stubs for requests"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "types-requests-2.31.0.6.tar.gz", hash = "sha256:cd74ce3b53c461f1228a9b783929ac73a666658f223e28ed29753771477b3bd0"},
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-]
-
-[package.dependencies]
-types-urllib3 = "*"
-
 [[package]]
 name = "types-requests"
 version = "2.32.0.20240907"
@@ -4250,17 +4275,6 @@ files = [
     {file = "types_toml-0.10.8.20240310-py3-none-any.whl", hash = "sha256:627b47775d25fa29977d9c70dc0cbab3f314f32c8d8d0c012f2ef5de7aaec05d"},
 ]
 
-[[package]]
-name = "types-urllib3"
-version = "1.26.25.14"
-description = "Typing stubs for urllib3"
-optional = false
-python-versions = "*"
-files = [
-    {file = "types-urllib3-1.26.25.14.tar.gz", hash = "sha256:229b7f577c951b8c1b92c1bc2b2fdb0b49847bd2af6d1cc2a2e3dd340f3bda8f"},
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-]
-
 [[package]]
 name = "typing-extensions"
 version = "4.12.2"
@@ -4297,22 +4311,6 @@ files = [
 [package.extras]
 dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
 
-[[package]]
-name = "urllib3"
-version = "1.26.20"
-description = "HTTP library with thread-safe connection pooling, file post, and more."
-optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
-files = [
-    {file = "urllib3-1.26.20-py2.py3-none-any.whl", hash = "sha256:0ed14ccfbf1c30a9072c7ca157e4319b70d65f623e91e7b32fadb2853431016e"},
-    {file = "urllib3-1.26.20.tar.gz", hash = "sha256:40c2dc0c681e47eb8f90e7e27bf6ff7df2e677421fd46756da1161c39ca70d32"},
-]
-
-[package.extras]
-brotli = ["brotli (==1.0.9)", "brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"]
-secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
-socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
-
 [[package]]
 name = "urllib3"
 version = "2.2.2"
@@ -4330,6 +4328,23 @@ h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "vcrpy"
+version = "4.3.0"
+description = "Automatically mock your HTTP interactions to simplify and speed up testing"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "vcrpy-4.3.0-py2.py3-none-any.whl", hash = "sha256:8fbd4be412e8a7f35f623dd61034e6380a1c8dbd0edf6e87277a3289f6e98093"},
+    {file = "vcrpy-4.3.0.tar.gz", hash = "sha256:49c270ce67e826dba027d83e20d25b67a5885487697e97bca6dbdf53d750a0ac"},
+]
+
+[package.dependencies]
+PyYAML = "*"
+six = ">=1.5"
+wrapt = "*"
+yarl = "*"
+
 [[package]]
 name = "vcrpy"
 version = "6.0.1"
@@ -4342,7 +4357,6 @@ files = [
 
 [package.dependencies]
 PyYAML = "*"
-urllib3 = {version = "<2", markers = "platform_python_implementation == \"PyPy\" or python_version < \"3.10\""}
 wrapt = "*"
 yarl = "*"
 
@@ -4661,4 +4675,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "d96302c2ca74bc4594b3aa702aea7c75fe55429c09bedbb114117d47a0ea072c"
+content-hash = "6285e2bb94bd02186bb92baa07d9af80829719f033c289f55af2a389a26758bc"

libs/langchain/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain"
-version = "0.3.0.dev1"
+version = "0.3.0.dev2"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
@@ -33,7 +33,7 @@ langchain-server = "langchain.server:main"
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = { version = "^0.3.0.dev2", allow-prereleases = true }
+langchain-core = { version = "^0.3.0.dev5", allow-prereleases = true }
 langchain-text-splitters = { version = "^0.3.0.dev1", allow-prereleases = true }
 langsmith = "^0.1.17"
 pydantic = "^2.7.4"

libs/partners/anthropic/langchain_anthropic/chat_models.py

@@ -802,21 +802,17 @@ class ChatAnthropic(BaseChatModel):
         ] = None,
         **kwargs: Any,
     ) -> Runnable[LanguageModelInput, BaseMessage]:
-        """Bind tool-like objects to this chat model.
+        r"""Bind tool-like objects to this chat model.
 
         Args:
             tools: A list of tool definitions to bind to this chat model.
                 Supports Anthropic format tool schemas and any tool definition handled
                 by :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`.
-            tool_choice: Which tool to require the model to call.
-                Options are:
-                    - name of the tool (str): calls corresponding tool;
-                    - ``"auto"`` or None: automatically selects a tool (including no tool);
-                    - ``"any"``: force at least one tool to be called;
-                    - or a dict of the form:
-                        ``{"type": "tool", "name": "tool_name"}``,
-                        or ``{"type: "any"}``,
-                        or ``{"type: "auto"}``;
+            tool_choice: Which tool to require the model to call. Options are:
+
+                - name of the tool as a string or as dict ``{"type": "tool", "name": "<<tool_name>>"}``: calls corresponding tool;
+                - ``"auto"``, ``{"type: "auto"}``, or None: automatically selects a tool (including no tool);
+                - ``"any"`` or ``{"type: "any"}``: force at least one tool to be called;
             kwargs: Any additional parameters are passed directly to
                 ``self.bind(**kwargs)``.
 
@@ -927,12 +923,14 @@ class ChatAnthropic(BaseChatModel):
                 llm_with_tools.invoke("what is the weather like in San Francisco",)
 
             This outputs:
-            .. code-block:: pycon
+
+            .. code-block:: python
 
                 AIMessage(content=[{'text': "Certainly! I can help you find out the current weather in San Francisco. To get this information, I'll use the GetWeather function. Let me fetch that data for you right away.", 'type': 'text'}, {'id': 'toolu_01TS5h8LNo7p5imcG7yRiaUM', 'input': {'location': 'San Francisco, CA'}, 'name': 'GetWeather', 'type': 'tool_use'}], response_metadata={'id': 'msg_01Xg7Wr5inFWgBxE5jH9rpRo', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 171, 'output_tokens': 96, 'cache_creation_input_tokens': 1470, 'cache_read_input_tokens': 0}}, id='run-b36a5b54-5d69-470e-a1b0-b932d00b089e-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'toolu_01TS5h8LNo7p5imcG7yRiaUM', 'type': 'tool_call'}], usage_metadata={'input_tokens': 171, 'output_tokens': 96, 'total_tokens': 267})
 
             If we invoke the tool again, we can see that the "usage" information in the AIMessage.response_metadata shows that we had a cache hit:
-            .. code-block:: pycon
+
+            .. code-block:: python
 
                 AIMessage(content=[{'text': 'To get the current weather in San Francisco, I can use the GetWeather function. Let me check that for you.', 'type': 'text'}, {'id': 'toolu_01HtVtY1qhMFdPprx42qU2eA', 'input': {'location': 'San Francisco, CA'}, 'name': 'GetWeather', 'type': 'tool_use'}], response_metadata={'id': 'msg_016RfWHrRvW6DAGCdwB6Ac64', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 171, 'output_tokens': 82, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 1470}}, id='run-88b1f825-dcb7-4277-ac27-53df55d22001-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'toolu_01HtVtY1qhMFdPprx42qU2eA', 'type': 'tool_call'}], usage_metadata={'input_tokens': 171, 'output_tokens': 82, 'total_tokens': 253})
 

libs/partners/fireworks/langchain_fireworks/chat_models.py

@@ -464,7 +464,7 @@ class ChatFireworks(BaseChatModel):
         default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
         for chunk in self.client.create(messages=message_dicts, **params):
             if not isinstance(chunk, dict):
-                chunk = chunk.dict()
+                chunk = chunk.model_dump()
             if len(chunk["choices"]) == 0:
                 continue
             choice = chunk["choices"][0]
@@ -520,7 +520,7 @@ class ChatFireworks(BaseChatModel):
     def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
         generations = []
         if not isinstance(response, dict):
-            response = response.dict()
+            response = response.model_dump()
         token_usage = response.get("usage", {})
         for res in response["choices"]:
             message = _convert_dict_to_message(res["message"])
@@ -558,7 +558,7 @@ class ChatFireworks(BaseChatModel):
         default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
         async for chunk in self.async_client.acreate(messages=message_dicts, **params):
             if not isinstance(chunk, dict):
-                chunk = chunk.dict()
+                chunk = chunk.model_dump()
             if len(chunk["choices"]) == 0:
                 continue
             choice = chunk["choices"][0]

libs/partners/fireworks/poetry.lock

@@ -667,7 +667,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.0.dev4"
+version = "0.3.0.dev5"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -676,7 +676,7 @@ develop = true
 
 [package.dependencies]
 jsonpatch = "^1.33"
-langsmith = "^0.1.112"
+langsmith = "^0.1.117"
 packaging = ">=23.2,<25"
 pydantic = "^2.7.4"
 PyYAML = ">=5.3"
@@ -1695,4 +1695,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "2a324e4a90491192ea7b6b846a799acbcbcf7c8ea8fa00e0534e00d447d3ecbc"
+content-hash = "9fd45aa6c8db9fc54fc39ac68a97c3c71d8903bb19a2fa706564569b2d1c76f6"

libs/partners/fireworks/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-fireworks"
-version = "0.2.0.dev1"
+version = "0.2.0.dev2"
 description = "An integration package connecting Fireworks and LangChain"
 authors = []
 readme = "README.md"
@@ -20,7 +20,7 @@ disallow_untyped_defs = "True"
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = { version = "^0.3.0.dev1", allow-prereleases = true }
+langchain-core = { version = "^0.3.0.dev5", allow-prereleases = true }
 fireworks-ai = ">=0.13.0"
 openai = "^1.10.0"
 requests = "^2"

libs/partners/pinecone/langchain_pinecone/embeddings.py

@@ -62,6 +62,7 @@ class PineconeEmbeddings(BaseModel, Embeddings):
     model_config = ConfigDict(
         extra="forbid",
         populate_by_name=True,
+        protected_namespaces=(),
     )
 
     @model_validator(mode="before")

libs/partners/pinecone/poetry.lock

@@ -1,118 +1,91 @@
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-optional = false
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-]
-
 [[package]]
 name = "aiohttp"
-version = "3.10.5"
+version = "3.9.5"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "aiohttp-3.9.5-cp39-cp39-win_amd64.whl", hash = "sha256:38d80498e2e169bc61418ff36170e0aad0cd268da8b38a17c4cf29d254a8b3f1"},
+    {file = "aiohttp-3.9.5.tar.gz", hash = "sha256:edea7d15772ceeb29db4aff55e482d4bcfb6ae160ce144f2682de02f6d693551"},
 ]
 
 [package.dependencies]
-aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
 async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
@@ -121,7 +94,7 @@ multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
@@ -639,7 +612,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.0.dev1"
+version = "0.3.0.dev5"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -648,7 +621,7 @@ develop = true
 
 [package.dependencies]
 jsonpatch = "^1.33"
-langsmith = "^0.1.75"
+langsmith = "^0.1.117"
 packaging = ">=23.2,<25"
 pydantic = "^2.7.4"
 PyYAML = ">=5.3"
@@ -661,7 +634,7 @@ url = "../../core"
 
 [[package]]
 name = "langchain-openai"
-version = "0.1.23"
+version = "0.2.0.dev2"
 description = "An integration package connecting OpenAI and LangChain"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -669,7 +642,7 @@ files = []
 develop = true
 
 [package.dependencies]
-langchain-core = "^0.3.0.dev"
+langchain-core = "^0.3.0.dev1"
 openai = "^1.40.0"
 tiktoken = ">=0.7,<1"
 
@@ -679,13 +652,13 @@ url = "../openai"
 
 [[package]]
 name = "langsmith"
-version = "0.1.115"
+version = "0.1.118"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.115-py3-none-any.whl", hash = "sha256:04e35cfd4c2d4ff1ea10bb577ff43957b05ebb3d9eb4e06e200701f4a2b4ac9f"},
-    {file = "langsmith-0.1.115.tar.gz", hash = "sha256:3b775377d858d32354f3ee0dd1ed637068cfe9a1f13e7b3bfa82db1615cdffc9"},
+    {file = "langsmith-0.1.118-py3-none-any.whl", hash = "sha256:f017127b3efb037da5e46ff4f8583e8192e7955191737240c327f3eadc144d7c"},
+    {file = "langsmith-0.1.118.tar.gz", hash = "sha256:ff1ca06c92c6081250244ebbce5d0bb347b9d898d2e9b60a13b11f0f0720f09f"},
 ]
 
 [package.dependencies]
@@ -1908,4 +1881,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "86a67d355f5c4f67f156c55dd85030ec0f4c0ce736d9122114f4dc166fdbdc80"
+content-hash = "c2f7f222f8c3c0c46e4feea598c66fc6efb1c0090353ee9bba2bd656a3854850"

libs/partners/pinecone/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-pinecone"
-version = "0.1.3"
+version = "0.2.0.dev1"
 description = "An integration package connecting Pinecone and LangChain"
 authors = []
 readme = "README.md"
@@ -20,9 +20,9 @@ disallow_untyped_defs = "True"
 
 [tool.poetry.dependencies]
 python = ">=3.9,<3.13"
-langchain-core = "^0.3.0.dev"
+langchain-core = { version = "^0.3.0.dev5", allow-prereleases = true }
 pinecone-client = "^5.0.0"
-aiohttp = "^3.9.5"
+aiohttp = ">=3.9.5,<3.10"
 
 [[tool.poetry.dependencies.numpy]]
 version = "^1"

libs/partners/pinecone/tests/integration_tests/test_vectorstores.py

@@ -91,6 +91,7 @@ class TestPinecone:
         )
         time.sleep(DEFAULT_SLEEP)  # prevent race condition
         output = docsearch.similarity_search(unique_id, k=1, namespace=NAMESPACE_NAME)
+        output[0].id = None  # overwrite ID for ease of comparison
         assert output == [Document(page_content=needs)]
 
     def test_from_texts_with_metadatas(
@@ -115,6 +116,7 @@ class TestPinecone:
         time.sleep(DEFAULT_SLEEP)  # prevent race condition
         output = docsearch.similarity_search(needs, k=1, namespace=namespace)
 
+        output[0].id = None
         # TODO: why metadata={"page": 0.0}) instead of {"page": 0}?
         assert output == [Document(page_content=needs, metadata={"page": 0.0})]
 
@@ -140,6 +142,8 @@ class TestPinecone:
         sorted_documents = sorted(docs, key=lambda x: x.metadata["page"])
         print(sorted_documents)  # noqa: T201
 
+        for document in sorted_documents:
+            document.id = None  # overwrite IDs for ease of comparison
         # TODO: why metadata={"page": 0.0}) instead of {"page": 0}, etc???
         assert sorted_documents == [
             Document(page_content="foo", metadata={"page": 0.0}),

libs/partners/qdrant/poetry.lock

@@ -620,7 +620,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.0.dev1"
+version = "0.3.0.dev5"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -629,7 +629,7 @@ develop = true
 
 [package.dependencies]
 jsonpatch = "^1.33"
-langsmith = "^0.1.75"
+langsmith = "^0.1.117"
 packaging = ">=23.2,<25"
 pydantic = "^2.7.4"
 PyYAML = ">=5.3"
@@ -642,13 +642,13 @@ url = "../../core"
 
 [[package]]
 name = "langsmith"
-version = "0.1.115"
+version = "0.1.118"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.115-py3-none-any.whl", hash = "sha256:04e35cfd4c2d4ff1ea10bb577ff43957b05ebb3d9eb4e06e200701f4a2b4ac9f"},
-    {file = "langsmith-0.1.115.tar.gz", hash = "sha256:3b775377d858d32354f3ee0dd1ed637068cfe9a1f13e7b3bfa82db1615cdffc9"},
+    {file = "langsmith-0.1.118-py3-none-any.whl", hash = "sha256:f017127b3efb037da5e46ff4f8583e8192e7955191737240c327f3eadc144d7c"},
+    {file = "langsmith-0.1.118.tar.gz", hash = "sha256:ff1ca06c92c6081250244ebbce5d0bb347b9d898d2e9b60a13b11f0f0720f09f"},
 ]
 
 [package.dependencies]
@@ -2056,4 +2056,4 @@ fastembed = ["fastembed"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "5ee9ce7ba2e1f6f2cc0ef47c529c25da7c1e67cf49768624789eeeddfe40be6d"
+content-hash = "97912fb88d52004e107efe17259b1e31e03bf5134d5b28694cfe6b65b8402c1a"

libs/partners/qdrant/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-qdrant"
-version = "0.1.3"
+version = "0.2.0.dev1"
 description = "An integration package connecting Qdrant and LangChain"
 authors = []
 readme = "README.md"
@@ -23,9 +23,8 @@ disallow_untyped_defs = true
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = "^0.3.0.dev"
+langchain-core = { version = "^0.3.0.dev5", allow-prereleases = true }
 qdrant-client = "^1.10.1"
-pydantic = "^2.7.4"
 fastembed = { version = "^0.3.3", python = ">=3.9,<3.13", optional = true }
 
 [tool.poetry.extras]

libs/standard-tests/langchain_standard_tests/integration_tests/chat_models.py

@@ -302,6 +302,28 @@ class ChatModelIntegrationTests(ChatModelTests):
         assert isinstance(chunk, dict)  # for mypy
         assert set(chunk.keys()) == {"setup", "punchline"}
 
+    def test_structured_output_optional_param(self, model: BaseChatModel) -> None:
+        """Test to verify structured output with an optional param."""
+        if not self.has_tool_calling:
+            pytest.skip("Test requires tool calling.")
+
+        class Joke(BaseModel):
+            """Joke to tell user."""
+
+            setup: str = Field(description="question to set up a joke")
+            punchline: Optional[str] = Field(
+                default=None, description="answer to resolve the joke"
+            )
+
+        chat = model.with_structured_output(Joke)  # type: ignore[arg-type]
+        setup_result = chat.invoke(
+            "Give me the setup to a joke about cats, no punchline."
+        )
+        assert isinstance(setup_result, Joke)
+
+        joke_result = chat.invoke("Give me a joke about cats, include the punchline.")
+        assert isinstance(joke_result, Joke)
+
     def test_tool_message_histories_string_content(self, model: BaseChatModel) -> None:
         """
         Test that message histories are compatible with string tool contents