core: release 0.3.0.dev2 (#26120)

.github/scripts/check_diff.py

@@ -106,7 +106,7 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
     if dir_ == "libs/core":
         return [
             {"working-directory": dir_, "python-version": f"3.{v}"}
-            for v in range(9, 13)
+            for v in range(8, 13)
         ]
     min_python = "3.9"
     max_python = "3.12"

.github/scripts/get_min_versions.py

@@ -15,7 +15,6 @@ MIN_VERSION_LIBS = [
     "langchain",
     "langchain-text-splitters",
     "SQLAlchemy",
-    "pydantic",
 ]
 
 SKIP_IF_PULL_REQUEST = ["langchain-core"]

.github/workflows/_integration_test.yml

@@ -67,7 +67,6 @@ jobs:
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
           GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
           GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
-          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
           EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
           WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}

.github/workflows/_release.yml

@@ -273,7 +273,6 @@ jobs:
           GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
           GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
           GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
           EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
           WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}

.github/workflows/check_diffs.yml

@@ -46,7 +46,6 @@ jobs:
     strategy:
       matrix:
         job-configs: ${{ fromJson(needs.build.outputs.lint) }}
-      fail-fast: false
     uses: ./.github/workflows/_lint.yml
     with:
       working-directory: ${{ matrix.job-configs.working-directory }}
@@ -60,7 +59,6 @@ jobs:
     strategy:
       matrix:
         job-configs: ${{ fromJson(needs.build.outputs.test) }}
-      fail-fast: false
     uses: ./.github/workflows/_test.yml
     with:
       working-directory: ${{ matrix.job-configs.working-directory }}
@@ -73,7 +71,6 @@ jobs:
     strategy:
       matrix:
         job-configs: ${{ fromJson(needs.build.outputs.test-doc-imports) }}
-      fail-fast: false
     uses: ./.github/workflows/_test_doc_imports.yml
     secrets: inherit
     with:
@@ -86,7 +83,6 @@ jobs:
     strategy:
       matrix:
         job-configs: ${{ fromJson(needs.build.outputs.compile-integration-tests) }}
-      fail-fast: false
     uses: ./.github/workflows/_compile_integration_test.yml
     with:
       working-directory: ${{ matrix.job-configs.working-directory }}
@@ -101,7 +97,6 @@ jobs:
       matrix:
         # note different variable for extended test dirs
         job-configs: ${{ fromJson(needs.build.outputs.extended-tests) }}
-      fail-fast: false
     runs-on: ubuntu-latest
     defaults:
       run:

.github/workflows/codespell.yml

@@ -5,6 +5,7 @@ on:
   push:
     branches: [master, v0.1]
   pull_request:
+    branches: [master, v0.1]
 
 permissions:
   contents: read

.github/workflows/scheduled_test.yml

@@ -90,7 +90,6 @@ jobs:
           AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
           GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}

Makefile

@@ -36,6 +36,7 @@ api_docs_build:
 API_PKG ?= text-splitters
 
 api_docs_quick_preview:
+	poetry run pip install "pydantic<2"
 	poetry run python docs/api_reference/create_api_rst.py $(API_PKG)
 	cd docs/api_reference && poetry run make html
 	poetry run python docs/api_reference/scripts/custom_formatter.py docs/api_reference/_build/html/

docs/api_reference/scripts/custom_formatter.py

@@ -17,10 +17,7 @@ def process_toc_h3_elements(html_content: str) -> str:
 
     # Process each element
     for element in toc_h3_elements:
-        try:
-            element = element.a.code.span
-        except Exception:
-            continue
+        element = element.a.code.span
         # Get the text content of the element
         content = element.get_text()
 

docs/api_reference/templates/pydantic.rst

@@ -15,7 +15,7 @@
     :member-order: groupwise
     :show-inheritance: True
     :special-members: __call__
-    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, model_construct, model_copy, model_dump, model_dump_json, model_parametrized_name, model_post_init, model_rebuild, model_validate, model_validate_json, model_validate_strings, model_extra, model_fields_set, model_json_schema
+    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace
 
 
     {% block attributes %}

docs/api_reference/templates/runnable_pydantic.rst

@@ -15,7 +15,7 @@
     :member-order: groupwise
     :show-inheritance: True
     :special-members: __call__
-    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, astream_log, transform, atransform, get_output_schema, get_prompts, config_schema, map, pick, pipe, with_listeners, with_alisteners, with_config, with_fallbacks, with_types, with_retry, InputType, OutputType, config_specs, output_schema, get_input_schema, get_graph, get_name, input_schema, name, bind, assign, as_tool, get_config_jsonschema, get_input_jsonschema, get_output_jsonschema, model_construct, model_copy, model_dump, model_dump_json, model_parametrized_name, model_post_init, model_rebuild, model_validate, model_validate_json, model_validate_strings, to_json, model_extra, model_fields_set, model_json_schema
+    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, astream_log, transform, atransform, get_output_schema, get_prompts, config_schema, map, pick, pipe, with_listeners, with_alisteners, with_config, with_fallbacks, with_types, with_retry, InputType, OutputType, config_specs, output_schema, get_input_schema, get_graph, get_name, input_schema, name, bind, assign, as_tool
 
     .. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
 

docs/docs/concepts.mdx

@@ -945,7 +945,7 @@ Here's an example:
 ```python
 from typing import Optional
 
-from pydantic import BaseModel, Field
+from langchain_core.pydantic_v1 import BaseModel, Field
 
 
 class Joke(BaseModel):
@@ -1062,7 +1062,7 @@ a `tool_calls` field containing `args` that match the desired shape.
 There are several acceptable formats you can use to bind tools to a model in LangChain. Here's one example:
 
 ```python
-from pydantic import BaseModel, Field
+from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_openai import ChatOpenAI
 
 class ResponseFormatter(BaseModel):

docs/docs/how_to/MultiQueryRetriever.ipynb

@@ -18,23 +18,8 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "994d6c74",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:08:00.190093Z",
-     "iopub.status.busy": "2024-09-10T20:08:00.189665Z",
-     "iopub.status.idle": "2024-09-10T20:08:05.438015Z",
-     "shell.execute_reply": "2024-09-10T20:08:05.437685Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Build a sample vectorDB\n",
     "from langchain_chroma import Chroma\n",
@@ -69,14 +54,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "edbca101",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:08:05.439930Z",
-     "iopub.status.busy": "2024-09-10T20:08:05.439810Z",
-     "iopub.status.idle": "2024-09-10T20:08:05.553766Z",
-     "shell.execute_reply": "2024-09-10T20:08:05.553520Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.retrievers.multi_query import MultiQueryRetriever\n",
@@ -93,14 +71,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "9e6d3b69",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:08:05.555359Z",
-     "iopub.status.busy": "2024-09-10T20:08:05.555262Z",
-     "iopub.status.idle": "2024-09-10T20:08:05.557046Z",
-     "shell.execute_reply": "2024-09-10T20:08:05.556825Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Set logging for the queries\n",
@@ -114,20 +85,13 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "bc93dc2b-9407-48b0-9f9a-338247e7eb69",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:08:05.558176Z",
-     "iopub.status.busy": "2024-09-10T20:08:05.558100Z",
-     "iopub.status.idle": "2024-09-10T20:08:07.250342Z",
-     "shell.execute_reply": "2024-09-10T20:08:07.249711Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:langchain.retrievers.multi_query:Generated queries: ['1. How can Task Decomposition be achieved through different methods?', '2. What strategies are commonly used for Task Decomposition?', '3. What are the various ways to break down tasks in Task Decomposition?']\n"
+      "INFO:langchain.retrievers.multi_query:Generated queries: ['1. How can Task Decomposition be achieved through different methods?', '2. What strategies are commonly used for Task Decomposition?', '3. What are the various techniques for breaking down tasks in Task Decomposition?']\n"
      ]
     },
     {
@@ -173,21 +137,14 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "d9afb0ca",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:08:07.253875Z",
-     "iopub.status.busy": "2024-09-10T20:08:07.253600Z",
-     "iopub.status.idle": "2024-09-10T20:08:07.277848Z",
-     "shell.execute_reply": "2024-09-10T20:08:07.277487Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from typing import List\n",
     "\n",
     "from langchain_core.output_parsers import BaseOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "# Output parser will split the LLM result into a list of queries\n",
@@ -223,20 +180,13 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "59c75c56-dbd7-4887-b9ba-0b5b21069f51",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:08:07.280001Z",
-     "iopub.status.busy": "2024-09-10T20:08:07.279861Z",
-     "iopub.status.idle": "2024-09-10T20:08:09.579525Z",
-     "shell.execute_reply": "2024-09-10T20:08:09.578837Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:langchain.retrievers.multi_query:Generated queries: ['1. Can you provide insights on regression from the course material?', '2. How is regression discussed in the course content?', '3. What information does the course offer regarding regression?', '4. In what way is regression covered in the course?', \"5. What are the course's teachings on regression?\"]\n"
+      "INFO:langchain.retrievers.multi_query:Generated queries: ['1. Can you provide insights on regression from the course material?', '2. How is regression discussed in the course content?', '3. What information does the course offer about regression?', '4. In what way is regression covered in the course?', '5. What are the teachings of the course regarding regression?']\n"
      ]
     },
     {
@@ -278,7 +228,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/agent_executor.ipynb

@@ -461,7 +461,7 @@
    "id": "f8014c9d",
    "metadata": {},
    "source": [
-    "Now, we can initialize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts/#agents).\n",
+    "Now, we can initalize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts/#agents).\n",
     "\n",
     "Note that we are passing in the `model`, not `model_with_tools`. That is because `create_tool_calling_agent` will call `.bind_tools` for us under the hood."
    ]

docs/docs/how_to/binding.ipynb

@@ -183,7 +183,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_z0OU2CytqENVrRTI6T8DkI3u', 'function': {'arguments': '{\"location\": \"San Francisco, CA\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}, {'id': 'call_ft96IJBh0cMKkQWrZjNg4bsw', 'function': {'arguments': '{\"location\": \"New York, NY\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}, {'id': 'call_tfbtGgCLmuBuWgZLvpPwvUMH', 'function': {'arguments': '{\"location\": \"Los Angeles, CA\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 84, 'prompt_tokens': 85, 'total_tokens': 169}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_77a673219d', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-d57ad5fa-b52a-4822-bc3e-74f838697e18-0', tool_calls=[{'name': 'get_current_weather', 'args': {'location': 'San Francisco, CA', 'unit': 'celsius'}, 'id': 'call_z0OU2CytqENVrRTI6T8DkI3u'}, {'name': 'get_current_weather', 'args': {'location': 'New York, NY', 'unit': 'celsius'}, 'id': 'call_ft96IJBh0cMKkQWrZjNg4bsw'}, {'name': 'get_current_weather', 'args': {'location': 'Los Angeles, CA', 'unit': 'celsius'}, 'id': 'call_tfbtGgCLmuBuWgZLvpPwvUMH'}])"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_z0OU2CytqENVrRTI6T8DkI3u', 'function': {'arguments': '{\"location\": \"San Francisco, CA\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}, {'id': 'call_ft96IJBh0cMKkQWrZjNg4bsw', 'function': {'arguments': '{\"location\": \"New York, NY\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}, {'id': 'call_tfbtGgCLmuBuWgZLvpPwvUMH', 'function': {'arguments': '{\"location\": \"Los Angeles, CA\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 84, 'prompt_tokens': 85, 'total_tokens': 169}, 'model_name': 'gpt-3.5-turbo-1106', 'system_fingerprint': 'fp_77a673219d', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-d57ad5fa-b52a-4822-bc3e-74f838697e18-0', tool_calls=[{'name': 'get_current_weather', 'args': {'location': 'San Francisco, CA', 'unit': 'celsius'}, 'id': 'call_z0OU2CytqENVrRTI6T8DkI3u'}, {'name': 'get_current_weather', 'args': {'location': 'New York, NY', 'unit': 'celsius'}, 'id': 'call_ft96IJBh0cMKkQWrZjNg4bsw'}, {'name': 'get_current_weather', 'args': {'location': 'Los Angeles, CA', 'unit': 'celsius'}, 'id': 'call_tfbtGgCLmuBuWgZLvpPwvUMH'}])"
       ]
      },
      "execution_count": 5,
@@ -192,7 +192,7 @@
     }
    ],
    "source": [
-    "model = ChatOpenAI(model=\"gpt-4o-mini\").bind(tools=tools)\n",
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo-1106\").bind(tools=tools)\n",
     "model.invoke(\"What's the weather in SF, NYC and LA?\")"
    ]
   },

docs/docs/how_to/chat_models_universal_init.ipynb

@@ -26,32 +26,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "165b0de6-9ae3-4e3d-aa98-4fc8a97c4a06",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:22:32.858670Z",
-     "iopub.status.busy": "2024-09-10T20:22:32.858278Z",
-     "iopub.status.idle": "2024-09-10T20:22:33.009452Z",
-     "shell.execute_reply": "2024-09-10T20:22:33.007022Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "zsh:1: 0.2.8 not found\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%pip install -qU langchain>=0.2.8 langchain-openai langchain-anthropic langchain-google-vertexai"
    ]
@@ -66,48 +44,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "id": "79e14913-803c-4382-9009-5c6af3d75d35",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:22:33.015729Z",
-     "iopub.status.busy": "2024-09-10T20:22:33.015241Z",
-     "iopub.status.idle": "2024-09-10T20:22:39.391716Z",
-     "shell.execute_reply": "2024-09-10T20:22:39.390438Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/4j/2rz3865x6qg07tx43146py8h0000gn/T/ipykernel_95293/571506279.py:4: LangChainBetaWarning: The function `init_chat_model` is in beta. It is actively being worked on, so the API may change.\n",
-      "  gpt_4o = init_chat_model(\"gpt-4o\", model_provider=\"openai\", temperature=0)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "GPT-4o: I'm an AI created by OpenAI, and I don't have a personal name. How can I assist you today?\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Claude Opus: My name is Claude. It's nice to meet you!\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gemini 1.5: I am a large language model, trained by Google. \n",
+      "GPT-4o: I'm an AI created by OpenAI, and I don't have a personal name. You can call me Assistant! How can I help you today?\n",
       "\n",
-      "I don't have a name like a person does. You can call me Bard if you like! 😊 \n",
+      "Claude Opus: My name is Claude. It's nice to meet you!\n",
+      "\n",
+      "Gemini 1.5: I am a large language model, trained by Google. I do not have a name. \n",
       "\n",
       "\n"
      ]
@@ -145,16 +94,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "0378ccc6-95bc-4d50-be50-fccc193f0a71",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:22:39.396908Z",
-     "iopub.status.busy": "2024-09-10T20:22:39.396563Z",
-     "iopub.status.idle": "2024-09-10T20:22:39.444959Z",
-     "shell.execute_reply": "2024-09-10T20:22:39.444646Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "gpt_4o = init_chat_model(\"gpt-4o\", temperature=0)\n",
@@ -174,24 +116,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "6c037f27-12d7-4e83-811e-4245c0e3ba58",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:22:39.446901Z",
-     "iopub.status.busy": "2024-09-10T20:22:39.446773Z",
-     "iopub.status.idle": "2024-09-10T20:22:40.301906Z",
-     "shell.execute_reply": "2024-09-10T20:22:40.300918Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"I'm an AI created by OpenAI, and I don't have a personal name. How can I assist you today?\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 11, 'total_tokens': 34}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_25624ae3a5', 'finish_reason': 'stop', 'logprobs': None}, id='run-b41df187-4627-490d-af3c-1c96282d3eb0-0', usage_metadata={'input_tokens': 11, 'output_tokens': 23, 'total_tokens': 34})"
+       "AIMessage(content=\"I'm an AI language model created by OpenAI, and I don't have a personal name. You can call me Assistant or any other name you prefer! How can I assist you today?\", response_metadata={'token_usage': {'completion_tokens': 37, 'prompt_tokens': 11, 'total_tokens': 48}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d576307f90', 'finish_reason': 'stop', 'logprobs': None}, id='run-5428ab5c-b5c0-46de-9946-5d4ca40dbdc8-0', usage_metadata={'input_tokens': 11, 'output_tokens': 37, 'total_tokens': 48})"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -206,24 +141,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "321e3036-abd2-4e1f-bcc6-606efd036954",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:22:40.316030Z",
-     "iopub.status.busy": "2024-09-10T20:22:40.315628Z",
-     "iopub.status.idle": "2024-09-10T20:22:41.199134Z",
-     "shell.execute_reply": "2024-09-10T20:22:41.198173Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"My name is Claude. It's nice to meet you!\", additional_kwargs={}, response_metadata={'id': 'msg_01Fx9P74A7syoFkwE73CdMMY', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 11, 'output_tokens': 15}}, id='run-a0fd2bbd-3b7e-46bf-8d69-a48c7e60b03c-0', usage_metadata={'input_tokens': 11, 'output_tokens': 15, 'total_tokens': 26})"
+       "AIMessage(content=\"My name is Claude. It's nice to meet you!\", response_metadata={'id': 'msg_012XvotUJ3kGLXJUWKBVxJUi', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 11, 'output_tokens': 15}}, id='run-1ad1eefe-f1c6-4244-8bc6-90e2cb7ee554-0', usage_metadata={'input_tokens': 11, 'output_tokens': 15, 'total_tokens': 26})"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -246,24 +174,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "id": "814a2289-d0db-401e-b555-d5116112b413",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:22:41.203346Z",
-     "iopub.status.busy": "2024-09-10T20:22:41.203004Z",
-     "iopub.status.idle": "2024-09-10T20:22:41.891450Z",
-     "shell.execute_reply": "2024-09-10T20:22:41.890539Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"I'm an AI created by OpenAI, and I don't have a personal name. How can I assist you today?\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 11, 'total_tokens': 34}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_25624ae3a5', 'finish_reason': 'stop', 'logprobs': None}, id='run-3380f977-4b89-4f44-bc02-b64043b3166f-0', usage_metadata={'input_tokens': 11, 'output_tokens': 23, 'total_tokens': 34})"
+       "AIMessage(content=\"I'm an AI language model created by OpenAI, and I don't have a personal name. You can call me Assistant or any other name you prefer! How can I assist you today?\", response_metadata={'token_usage': {'completion_tokens': 37, 'prompt_tokens': 11, 'total_tokens': 48}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_ce0793330f', 'finish_reason': 'stop', 'logprobs': None}, id='run-3923e328-7715-4cd6-b215-98e4b6bf7c9d-0', usage_metadata={'input_tokens': 11, 'output_tokens': 37, 'total_tokens': 48})"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -281,24 +202,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "6c8755ba-c001-4f5a-a497-be3f1db83244",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:22:41.896413Z",
-     "iopub.status.busy": "2024-09-10T20:22:41.895967Z",
-     "iopub.status.idle": "2024-09-10T20:22:42.767565Z",
-     "shell.execute_reply": "2024-09-10T20:22:42.766619Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"My name is Claude. It's nice to meet you!\", additional_kwargs={}, response_metadata={'id': 'msg_01EFKSWpmsn2PSYPQa4cNHWb', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 11, 'output_tokens': 15}}, id='run-3c58f47c-41b9-4e56-92e7-fb9602e3787c-0', usage_metadata={'input_tokens': 11, 'output_tokens': 15, 'total_tokens': 26})"
+       "AIMessage(content=\"My name is Claude. It's nice to meet you!\", response_metadata={'id': 'msg_01RyYR64DoMPNCfHeNnroMXm', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 11, 'output_tokens': 15}}, id='run-22446159-3723-43e6-88df-b84797e7751d-0', usage_metadata={'input_tokens': 11, 'output_tokens': 15, 'total_tokens': 26})"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -328,37 +242,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "067dabee-1050-4110-ae24-c48eba01e13b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:22:42.771941Z",
-     "iopub.status.busy": "2024-09-10T20:22:42.771606Z",
-     "iopub.status.idle": "2024-09-10T20:22:43.909206Z",
-     "shell.execute_reply": "2024-09-10T20:22:43.908496Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[{'name': 'GetPopulation',\n",
        "  'args': {'location': 'Los Angeles, CA'},\n",
-       "  'id': 'call_Ga9m8FAArIyEjItHmztPYA22',\n",
-       "  'type': 'tool_call'},\n",
+       "  'id': 'call_sYT3PFMufHGWJD32Hi2CTNUP'},\n",
        " {'name': 'GetPopulation',\n",
        "  'args': {'location': 'New York, NY'},\n",
-       "  'id': 'call_jh2dEvBaAHRaw5JUDthOs7rt',\n",
-       "  'type': 'tool_call'}]"
+       "  'id': 'call_j1qjhxRnD3ffQmRyqjlI1Lnk'}]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class GetWeather(BaseModel):\n",
@@ -383,31 +288,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "e57dfe9f-cd24-4e37-9ce9-ccf8daf78f89",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:22:43.912746Z",
-     "iopub.status.busy": "2024-09-10T20:22:43.912447Z",
-     "iopub.status.idle": "2024-09-10T20:22:46.437049Z",
-     "shell.execute_reply": "2024-09-10T20:22:46.436093Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[{'name': 'GetPopulation',\n",
        "  'args': {'location': 'Los Angeles, CA'},\n",
-       "  'id': 'toolu_01JMufPf4F4t2zLj7miFeqXp',\n",
-       "  'type': 'tool_call'},\n",
+       "  'id': 'toolu_01CxEHxKtVbLBrvzFS7GQ5xR'},\n",
        " {'name': 'GetPopulation',\n",
        "  'args': {'location': 'New York City, NY'},\n",
-       "  'id': 'toolu_01RQBHcE8kEEbYTuuS8WqY1u',\n",
-       "  'type': 'tool_call'}]"
+       "  'id': 'toolu_013A79qt5toWSsKunFBDZd5S'}]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/docs/how_to/chat_token_usage_tracking.ipynb

@@ -71,7 +71,7 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\")\n",
     "openai_response = llm.invoke(\"hello\")\n",
     "openai_response.usage_metadata"
    ]
@@ -182,13 +182,13 @@
       "content=' you' id='run-adb20c31-60c7-43a2-99b2-d4a53ca5f623'\n",
       "content=' today' id='run-adb20c31-60c7-43a2-99b2-d4a53ca5f623'\n",
       "content='?' id='run-adb20c31-60c7-43a2-99b2-d4a53ca5f623'\n",
-      "content='' response_metadata={'finish_reason': 'stop', 'model_name': 'gpt-4o-mini'} id='run-adb20c31-60c7-43a2-99b2-d4a53ca5f623'\n",
+      "content='' response_metadata={'finish_reason': 'stop', 'model_name': 'gpt-3.5-turbo-0125'} id='run-adb20c31-60c7-43a2-99b2-d4a53ca5f623'\n",
       "content='' id='run-adb20c31-60c7-43a2-99b2-d4a53ca5f623' usage_metadata={'input_tokens': 8, 'output_tokens': 9, 'total_tokens': 17}\n"
      ]
     }
    ],
    "source": [
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\")\n",
     "\n",
     "aggregate = None\n",
     "for chunk in llm.stream(\"hello\", stream_usage=True):\n",
@@ -252,7 +252,7 @@
       "content=' you' id='run-8e758550-94b0-4cca-a298-57482793c25d'\n",
       "content=' today' id='run-8e758550-94b0-4cca-a298-57482793c25d'\n",
       "content='?' id='run-8e758550-94b0-4cca-a298-57482793c25d'\n",
-      "content='' response_metadata={'finish_reason': 'stop', 'model_name': 'gpt-4o-mini'} id='run-8e758550-94b0-4cca-a298-57482793c25d'\n"
+      "content='' response_metadata={'finish_reason': 'stop', 'model_name': 'gpt-3.5-turbo-0125'} id='run-8e758550-94b0-4cca-a298-57482793c25d'\n"
      ]
     }
    ],
@@ -289,7 +289,7 @@
     }
    ],
    "source": [
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class Joke(BaseModel):\n",
@@ -300,7 +300,7 @@
     "\n",
     "\n",
     "llm = ChatOpenAI(\n",
-    "    model=\"gpt-4o-mini\",\n",
+    "    model=\"gpt-3.5-turbo-0125\",\n",
     "    stream_usage=True,\n",
     ")\n",
     "# Under the hood, .with_structured_output binds tools to the\n",
@@ -362,7 +362,7 @@
     "from langchain_community.callbacks.manager import get_openai_callback\n",
     "\n",
     "llm = ChatOpenAI(\n",
-    "    model=\"gpt-4o-mini\",\n",
+    "    model=\"gpt-3.5-turbo-0125\",\n",
     "    temperature=0,\n",
     "    stream_usage=True,\n",
     ")\n",

docs/docs/how_to/chatbots_memory.ipynb

@@ -77,7 +77,7 @@
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "chat = ChatOpenAI(model=\"gpt-4o-mini\")"
+    "chat = ChatOpenAI(model=\"gpt-3.5-turbo-0125\")"
    ]
   },
   {
@@ -191,7 +191,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='You just asked me to translate the sentence \"I love programming\" from English to French.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 61, 'total_tokens': 79}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5cbb21c2-9c30-4031-8ea8-bfc497989535-0', usage_metadata={'input_tokens': 61, 'output_tokens': 18, 'total_tokens': 79})"
+       "AIMessage(content='You just asked me to translate the sentence \"I love programming\" from English to French.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 61, 'total_tokens': 79}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5cbb21c2-9c30-4031-8ea8-bfc497989535-0', usage_metadata={'input_tokens': 61, 'output_tokens': 18, 'total_tokens': 79})"
       ]
      },
      "execution_count": 5,
@@ -312,7 +312,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='\"J\\'adore la programmation.\"', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 39, 'total_tokens': 48}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-648b0822-b0bb-47a2-8e7d-7d34744be8f2-0', usage_metadata={'input_tokens': 39, 'output_tokens': 9, 'total_tokens': 48})"
+       "AIMessage(content='\"J\\'adore la programmation.\"', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 39, 'total_tokens': 48}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-648b0822-b0bb-47a2-8e7d-7d34744be8f2-0', usage_metadata={'input_tokens': 39, 'output_tokens': 9, 'total_tokens': 48})"
       ]
      },
      "execution_count": 8,
@@ -342,7 +342,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='You asked me to translate the sentence \"I love programming\" from English to French.', response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 63, 'total_tokens': 80}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5950435c-1dc2-43a6-836f-f989fd62c95e-0', usage_metadata={'input_tokens': 63, 'output_tokens': 17, 'total_tokens': 80})"
+       "AIMessage(content='You asked me to translate the sentence \"I love programming\" from English to French.', response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 63, 'total_tokens': 80}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5950435c-1dc2-43a6-836f-f989fd62c95e-0', usage_metadata={'input_tokens': 63, 'output_tokens': 17, 'total_tokens': 80})"
       ]
      },
      "execution_count": 9,
@@ -421,7 +421,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Your name is Nemo.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 66, 'total_tokens': 72}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f8aabef8-631a-4238-a39b-701e881fbe47-0', usage_metadata={'input_tokens': 66, 'output_tokens': 6, 'total_tokens': 72})"
+       "AIMessage(content='Your name is Nemo.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 66, 'total_tokens': 72}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f8aabef8-631a-4238-a39b-701e881fbe47-0', usage_metadata={'input_tokens': 66, 'output_tokens': 6, 'total_tokens': 72})"
       ]
      },
      "execution_count": 22,
@@ -501,7 +501,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='P. Sherman is a fictional character from the animated movie \"Finding Nemo\" who lives at 42 Wallaby Way, Sydney.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 53, 'total_tokens': 80}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5642ef3a-fdbe-43cf-a575-d1785976a1b9-0', usage_metadata={'input_tokens': 53, 'output_tokens': 27, 'total_tokens': 80})"
+       "AIMessage(content='P. Sherman is a fictional character from the animated movie \"Finding Nemo\" who lives at 42 Wallaby Way, Sydney.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 53, 'total_tokens': 80}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5642ef3a-fdbe-43cf-a575-d1785976a1b9-0', usage_metadata={'input_tokens': 53, 'output_tokens': 27, 'total_tokens': 80})"
       ]
      },
      "execution_count": 24,
@@ -529,9 +529,9 @@
        " HumanMessage(content='How are you today?'),\n",
        " AIMessage(content='Fine thanks!'),\n",
        " HumanMessage(content=\"What's my name?\"),\n",
-       " AIMessage(content='Your name is Nemo.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 66, 'total_tokens': 72}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f8aabef8-631a-4238-a39b-701e881fbe47-0', usage_metadata={'input_tokens': 66, 'output_tokens': 6, 'total_tokens': 72}),\n",
+       " AIMessage(content='Your name is Nemo.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 66, 'total_tokens': 72}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f8aabef8-631a-4238-a39b-701e881fbe47-0', usage_metadata={'input_tokens': 66, 'output_tokens': 6, 'total_tokens': 72}),\n",
        " HumanMessage(content='Where does P. Sherman live?'),\n",
-       " AIMessage(content='P. Sherman is a fictional character from the animated movie \"Finding Nemo\" who lives at 42 Wallaby Way, Sydney.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 53, 'total_tokens': 80}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5642ef3a-fdbe-43cf-a575-d1785976a1b9-0', usage_metadata={'input_tokens': 53, 'output_tokens': 27, 'total_tokens': 80})]"
+       " AIMessage(content='P. Sherman is a fictional character from the animated movie \"Finding Nemo\" who lives at 42 Wallaby Way, Sydney.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 53, 'total_tokens': 80}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5642ef3a-fdbe-43cf-a575-d1785976a1b9-0', usage_metadata={'input_tokens': 53, 'output_tokens': 27, 'total_tokens': 80})]"
       ]
      },
      "execution_count": 25,
@@ -565,7 +565,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"I'm sorry, but I don't have access to your personal information, so I don't know your name. How else may I assist you today?\", response_metadata={'token_usage': {'completion_tokens': 31, 'prompt_tokens': 74, 'total_tokens': 105}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-0ab03495-1f7c-4151-9070-56d2d1c565ff-0', usage_metadata={'input_tokens': 74, 'output_tokens': 31, 'total_tokens': 105})"
+       "AIMessage(content=\"I'm sorry, but I don't have access to your personal information, so I don't know your name. How else may I assist you today?\", response_metadata={'token_usage': {'completion_tokens': 31, 'prompt_tokens': 74, 'total_tokens': 105}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-0ab03495-1f7c-4151-9070-56d2d1c565ff-0', usage_metadata={'input_tokens': 74, 'output_tokens': 31, 'total_tokens': 105})"
       ]
      },
      "execution_count": 27,

docs/docs/how_to/chatbots_retrieval.ipynb

@@ -71,7 +71,7 @@
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "chat = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0.2)"
+    "chat = ChatOpenAI(model=\"gpt-3.5-turbo-1106\", temperature=0.2)"
    ]
   },
   {

docs/docs/how_to/chatbots_tools.ipynb

@@ -70,7 +70,7 @@
     "\n",
     "# Choose the LLM that will drive the agent\n",
     "# Only certain models support this\n",
-    "chat = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
+    "chat = ChatOpenAI(model=\"gpt-3.5-turbo-1106\", temperature=0)"
    ]
   },
   {

docs/docs/how_to/contextual_compression.ipynb

@@ -258,7 +258,7 @@
     "from langchain.retrievers.document_compressors import LLMListwiseRerank\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "\n",
     "_filter = LLMListwiseRerank.from_llm(llm, top_n=1)\n",
     "compression_retriever = ContextualCompressionRetriever(\n",

docs/docs/how_to/convert_runnable_to_tool.ipynb

@@ -190,7 +190,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class GSchema(BaseModel):\n",
@@ -285,7 +285,7 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
    ]
   },
   {
@@ -362,11 +362,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_W8cnfOjwqEn4cFcg19LN9mYD', 'function': {'arguments': '{\"__arg1\":\"dogs\"}', 'name': 'pet_info_retriever'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 60, 'total_tokens': 79}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-d7f81de9-1fb7-4caf-81ed-16dcdb0b2ab4-0', tool_calls=[{'name': 'pet_info_retriever', 'args': {'__arg1': 'dogs'}, 'id': 'call_W8cnfOjwqEn4cFcg19LN9mYD'}], usage_metadata={'input_tokens': 60, 'output_tokens': 19, 'total_tokens': 79})]}}\n",
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_W8cnfOjwqEn4cFcg19LN9mYD', 'function': {'arguments': '{\"__arg1\":\"dogs\"}', 'name': 'pet_info_retriever'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 60, 'total_tokens': 79}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-d7f81de9-1fb7-4caf-81ed-16dcdb0b2ab4-0', tool_calls=[{'name': 'pet_info_retriever', 'args': {'__arg1': 'dogs'}, 'id': 'call_W8cnfOjwqEn4cFcg19LN9mYD'}], usage_metadata={'input_tokens': 60, 'output_tokens': 19, 'total_tokens': 79})]}}\n",
       "----\n",
       "{'tools': {'messages': [ToolMessage(content=\"[Document(id='86f835fe-4bbe-4ec6-aeb4-489a8b541707', page_content='Dogs are great companions, known for their loyalty and friendliness.')]\", name='pet_info_retriever', tool_call_id='call_W8cnfOjwqEn4cFcg19LN9mYD')]}}\n",
       "----\n",
-      "{'agent': {'messages': [AIMessage(content='Dogs are known for being great companions, known for their loyalty and friendliness.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 134, 'total_tokens': 152}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-9ca5847a-a5eb-44c0-a774-84cc2c5bbc5b-0', usage_metadata={'input_tokens': 134, 'output_tokens': 18, 'total_tokens': 152})]}}\n",
+      "{'agent': {'messages': [AIMessage(content='Dogs are known for being great companions, known for their loyalty and friendliness.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 134, 'total_tokens': 152}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-9ca5847a-a5eb-44c0-a774-84cc2c5bbc5b-0', usage_metadata={'input_tokens': 134, 'output_tokens': 18, 'total_tokens': 152})]}}\n",
       "----\n"
      ]
     }
@@ -497,11 +497,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_17iLPWvOD23zqwd1QVQ00Y63', 'function': {'arguments': '{\"question\":\"What are dogs known for according to pirates?\",\"answer_style\":\"quote\"}', 'name': 'pet_expert'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 28, 'prompt_tokens': 59, 'total_tokens': 87}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-7fef44f3-7bba-4e63-8c51-2ad9c5e65e2e-0', tool_calls=[{'name': 'pet_expert', 'args': {'question': 'What are dogs known for according to pirates?', 'answer_style': 'quote'}, 'id': 'call_17iLPWvOD23zqwd1QVQ00Y63'}], usage_metadata={'input_tokens': 59, 'output_tokens': 28, 'total_tokens': 87})]}}\n",
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_17iLPWvOD23zqwd1QVQ00Y63', 'function': {'arguments': '{\"question\":\"What are dogs known for according to pirates?\",\"answer_style\":\"quote\"}', 'name': 'pet_expert'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 28, 'prompt_tokens': 59, 'total_tokens': 87}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-7fef44f3-7bba-4e63-8c51-2ad9c5e65e2e-0', tool_calls=[{'name': 'pet_expert', 'args': {'question': 'What are dogs known for according to pirates?', 'answer_style': 'quote'}, 'id': 'call_17iLPWvOD23zqwd1QVQ00Y63'}], usage_metadata={'input_tokens': 59, 'output_tokens': 28, 'total_tokens': 87})]}}\n",
       "----\n",
       "{'tools': {'messages': [ToolMessage(content='\"Dogs are known for their loyalty and friendliness, making them great companions for pirates on long sea voyages.\"', name='pet_expert', tool_call_id='call_17iLPWvOD23zqwd1QVQ00Y63')]}}\n",
       "----\n",
-      "{'agent': {'messages': [AIMessage(content='According to pirates, dogs are known for their loyalty and friendliness, making them great companions for pirates on long sea voyages.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 119, 'total_tokens': 146}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5a30edc3-7be0-4743-b980-ca2f8cad9b8d-0', usage_metadata={'input_tokens': 119, 'output_tokens': 27, 'total_tokens': 146})]}}\n",
+      "{'agent': {'messages': [AIMessage(content='According to pirates, dogs are known for their loyalty and friendliness, making them great companions for pirates on long sea voyages.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 119, 'total_tokens': 146}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5a30edc3-7be0-4743-b980-ca2f8cad9b8d-0', usage_metadata={'input_tokens': 119, 'output_tokens': 27, 'total_tokens': 146})]}}\n",
       "----\n"
      ]
     }

docs/docs/how_to/custom_tools.ipynb

@@ -13,7 +13,7 @@
     "|---------------|---------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
     "| name          | str                             | Must be unique within a set of tools provided to an LLM or agent.                                                                                                              |\n",
     "| description   | str                             | Describes what the tool does. Used as context by the LLM or agent.                                                                                                             |\n",
-    "| args_schema   | pydantic.BaseModel | Optional but recommended, and required if using callback handlers. It can be used to provide more information (e.g., few-shot examples) or validation for expected parameters. |\n",
+    "| args_schema   | langchain.pydantic_v1.BaseModel | Optional but recommended, and required if using callback handlers. It can be used to provide more information (e.g., few-shot examples) or validation for expected parameters. |\n",
     "| return_direct | boolean                         | Only relevant for agents. When True, after invoking the given tool, the agent will stop and return the result direcly to the user.                                             |\n",
     "\n",
     "LangChain supports the creation of tools from:\n",
@@ -48,14 +48,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "cc7005cd-072f-4d37-8453-6297468e5192",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:52.645451Z",
-     "iopub.status.busy": "2024-09-10T20:25:52.645081Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.030958Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.030669Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -95,14 +88,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "0c0991db-b997-4611-be37-4346e660506b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.032544Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.032420Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.035349Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.035123Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.tools import tool\n",
@@ -126,29 +112,22 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "5626423f-053e-4a66-adca-1d794d835397",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.036658Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.036574Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.041154Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.040964Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'description': 'Multiply a by the maximum of b.',\n",
-       " 'properties': {'a': {'description': 'scale factor',\n",
-       "   'title': 'A',\n",
+       "{'title': 'multiply_by_maxSchema',\n",
+       " 'description': 'Multiply a by the maximum of b.',\n",
+       " 'type': 'object',\n",
+       " 'properties': {'a': {'title': 'A',\n",
+       "   'description': 'scale factor',\n",
        "   'type': 'string'},\n",
-       "  'b': {'description': 'list of ints over which to take maximum',\n",
-       "   'items': {'type': 'integer'},\n",
-       "   'title': 'B',\n",
-       "   'type': 'array'}},\n",
-       " 'required': ['a', 'b'],\n",
-       " 'title': 'multiply_by_maxSchema',\n",
-       " 'type': 'object'}"
+       "  'b': {'title': 'B',\n",
+       "   'description': 'list of ints over which to take maximum',\n",
+       "   'type': 'array',\n",
+       "   'items': {'type': 'integer'}}},\n",
+       " 'required': ['a', 'b']}"
       ]
      },
      "execution_count": 3,
@@ -184,14 +163,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "9216d03a-f6ea-4216-b7e1-0661823a4c0b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.042516Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.042427Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.045217Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.045010Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -199,13 +171,13 @@
      "text": [
       "multiplication-tool\n",
       "Multiply two numbers.\n",
-      "{'a': {'description': 'first number', 'title': 'A', 'type': 'integer'}, 'b': {'description': 'second number', 'title': 'B', 'type': 'integer'}}\n",
+      "{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}\n",
       "True\n"
      ]
     }
    ],
    "source": [
-    "from pydantic import BaseModel, Field\n",
+    "from langchain.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class CalculatorInput(BaseModel):\n",
@@ -246,26 +218,19 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "336f5538-956e-47d5-9bde-b732559f9e61",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.046526Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.046456Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.050045Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.049836Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'description': 'The foo.',\n",
-       " 'properties': {'bar': {'description': 'The bar.',\n",
-       "   'title': 'Bar',\n",
+       "{'title': 'fooSchema',\n",
+       " 'description': 'The foo.',\n",
+       " 'type': 'object',\n",
+       " 'properties': {'bar': {'title': 'Bar',\n",
+       "   'description': 'The bar.',\n",
        "   'type': 'string'},\n",
-       "  'baz': {'description': 'The baz.', 'title': 'Baz', 'type': 'integer'}},\n",
-       " 'required': ['bar', 'baz'],\n",
-       " 'title': 'fooSchema',\n",
-       " 'type': 'object'}"
+       "  'baz': {'title': 'Baz', 'description': 'The baz.', 'type': 'integer'}},\n",
+       " 'required': ['bar', 'baz']}"
       ]
      },
      "execution_count": 5,
@@ -312,14 +277,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "564fbe6f-11df-402d-b135-ef6ff25e1e63",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.051302Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.051218Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.059704Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.059490Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -362,14 +320,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "6bc055d4-1fbe-4db5-8881-9c382eba6b1b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.060971Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.060883Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.064615Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.064408Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -378,7 +329,7 @@
       "6\n",
       "Calculator\n",
       "multiply numbers\n",
-      "{'a': {'description': 'first number', 'title': 'A', 'type': 'integer'}, 'b': {'description': 'second number', 'title': 'B', 'type': 'integer'}}\n"
+      "{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}\n"
      ]
     }
    ],
@@ -422,32 +373,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "8ef593c5-cf72-4c10-bfc9-7d21874a0c24",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.065797Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.065733Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.130458Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.130229Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/4j/2rz3865x6qg07tx43146py8h0000gn/T/ipykernel_95770/2548361071.py:14: LangChainBetaWarning: This API is in beta and may change in the future.\n",
-      "  as_tool = chain.as_tool(\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "{'answer_style': {'title': 'Answer Style', 'type': 'string'}}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -492,26 +428,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "1dad8f8e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.131904Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.131803Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.136797Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.136563Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from typing import Optional, Type\n",
     "\n",
+    "from langchain.pydantic_v1 import BaseModel\n",
     "from langchain_core.callbacks import (\n",
     "    AsyncCallbackManagerForToolRun,\n",
     "    CallbackManagerForToolRun,\n",
     ")\n",
     "from langchain_core.tools import BaseTool\n",
-    "from pydantic import BaseModel\n",
     "\n",
     "\n",
     "class CalculatorInput(BaseModel):\n",
@@ -519,11 +448,9 @@
     "    b: int = Field(description=\"second number\")\n",
     "\n",
     "\n",
-    "# Note: It's important that every field has type hints. BaseTool is a\n",
-    "# Pydantic class and not having type hints can lead to unexpected behavior.\n",
     "class CustomCalculatorTool(BaseTool):\n",
-    "    name: str = \"Calculator\"\n",
-    "    description: str = \"useful for when you need to answer questions about math\"\n",
+    "    name = \"Calculator\"\n",
+    "    description = \"useful for when you need to answer questions about math\"\n",
     "    args_schema: Type[BaseModel] = CalculatorInput\n",
     "    return_direct: bool = True\n",
     "\n",
@@ -550,16 +477,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "bb551c33",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.138074Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.138007Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.141360Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.141158Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -567,7 +487,7 @@
      "text": [
       "Calculator\n",
       "useful for when you need to answer questions about math\n",
-      "{'a': {'description': 'first number', 'title': 'A', 'type': 'integer'}, 'b': {'description': 'second number', 'title': 'B', 'type': 'integer'}}\n",
+      "{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}\n",
       "True\n",
       "6\n",
       "6\n"
@@ -608,16 +528,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "6615cb77-fd4c-4676-8965-f92cc71d4944",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.142587Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.142504Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.147205Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.146995Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -647,16 +560,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "bb2af583-eadd-41f4-a645-bf8748bd3dcd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.148383Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.148307Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.152684Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.152486Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -699,16 +605,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "4ad0932c-8610-4278-8c57-f9218f654c8a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.153849Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.153773Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.158312Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.158130Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -751,16 +650,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "7094c0e8-6192-4870-a942-aad5b5ae48fd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.159440Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.159364Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.160922Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.160712Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.tools import ToolException\n",
@@ -781,16 +673,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "b4d22022-b105-4ccc-a15b-412cb9ea3097",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.162046Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.161968Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.165236Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.165052Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -798,7 +683,7 @@
        "'Error: There is no city by the name of foobar.'"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -822,16 +707,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "3fad1728-d367-4e1b-9b54-3172981271cf",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.166372Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.166294Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.169739Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.169553Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -839,7 +717,7 @@
        "\"There is no such city, but it's probably above 0K there!\""
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -863,16 +741,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "ebfe7c1f-318d-4e58-99e1-f31e69473c46",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.170937Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.170859Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.174498Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.174304Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -880,7 +751,7 @@
        "'The following errors occurred during tool execution: `Error: There is no city by the name of foobar.`'"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -920,16 +791,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 1,
    "id": "14905425-0334-43a0-9de9-5bcf622ede0e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.175683Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.175605Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.178798Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.178601Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import random\n",
@@ -956,16 +820,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 9,
    "id": "0f2e1528-404b-46e6-b87c-f0957c4b9217",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.179881Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.179807Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.182100Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.181940Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -973,7 +830,7 @@
        "'Successfully generated array of 10 random ints in [0, 9].'"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -992,24 +849,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 3,
    "id": "cc197777-26eb-46b3-a83b-c2ce116c6311",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.183238Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.183170Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.185752Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.185567Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "ToolMessage(content='Successfully generated array of 10 random ints in [0, 9].', name='generate_random_ints', tool_call_id='123', artifact=[4, 8, 2, 4, 1, 0, 9, 5, 8, 1])"
+       "ToolMessage(content='Successfully generated array of 10 random ints in [0, 9].', name='generate_random_ints', tool_call_id='123', artifact=[1, 4, 2, 5, 3, 9, 0, 4, 7, 7])"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1035,16 +885,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 6,
    "id": "fe1a09d1-378b-4b91-bb5e-0697c3d7eb92",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.186884Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.186803Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.190718Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.190494Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.tools import BaseTool\n",
@@ -1074,24 +917,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 8,
    "id": "8c3d16f6-1c4a-48ab-b05a-38547c592e79",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:25:53.191872Z",
-     "iopub.status.busy": "2024-09-10T20:25:53.191794Z",
-     "iopub.status.idle": "2024-09-10T20:25:53.194396Z",
-     "shell.execute_reply": "2024-09-10T20:25:53.194184Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "ToolMessage(content='Generated 3 floats in [0.1, 3.3333], rounded to 4 decimals.', name='generate_random_floats', tool_call_id='123', artifact=[1.5566, 0.5134, 2.7914])"
+       "ToolMessage(content='Generated 3 floats in [0.1, 3.3333], rounded to 4 decimals.', name='generate_random_floats', tool_call_id='123', artifact=[1.4277, 0.7578, 2.4871])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/docs/how_to/document_loader_pdf.ipynb

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

docs/docs/how_to/extraction_examples.ipynb

@@ -29,16 +29,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "89579144-bcb3-490a-8036-86a0a6bcd56b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:26:41.780410Z",
-     "iopub.status.busy": "2024-09-10T20:26:41.780102Z",
-     "iopub.status.idle": "2024-09-10T20:26:42.147112Z",
-     "shell.execute_reply": "2024-09-10T20:26:42.146838Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
@@ -74,24 +67,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "610c3025-ea63-4cd7-88bd-c8cbcb4d8a3f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:26:42.148746Z",
-     "iopub.status.busy": "2024-09-10T20:26:42.148621Z",
-     "iopub.status.idle": "2024-09-10T20:26:42.162044Z",
-     "shell.execute_reply": "2024-09-10T20:26:42.161794Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "ChatPromptValue(messages=[SystemMessage(content=\"You are an expert extraction algorithm. Only extract relevant information from the text. If you do not know the value of an attribute asked to extract, return null for the attribute's value.\", additional_kwargs={}, response_metadata={}), HumanMessage(content='testing 1 2 3', additional_kwargs={}, response_metadata={}), HumanMessage(content='this is some text', additional_kwargs={}, response_metadata={})])"
+       "ChatPromptValue(messages=[SystemMessage(content=\"You are an expert extraction algorithm. Only extract relevant information from the text. If you do not know the value of an attribute asked to extract, return null for the attribute's value.\"), HumanMessage(content='testing 1 2 3'), HumanMessage(content='this is some text')])"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -118,22 +104,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "d875a49a-d2cb-4b9e-b5bf-41073bc3905c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:26:42.163477Z",
-     "iopub.status.busy": "2024-09-10T20:26:42.163391Z",
-     "iopub.status.idle": "2024-09-10T20:26:42.324449Z",
-     "shell.execute_reply": "2024-09-10T20:26:42.324206Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from typing import List, Optional\n",
     "\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
-    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",
@@ -183,16 +162,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "08356810-77ce-4e68-99d9-faa0326f2cee",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:26:42.326100Z",
-     "iopub.status.busy": "2024-09-10T20:26:42.326016Z",
-     "iopub.status.idle": "2024-09-10T20:26:42.329260Z",
-     "shell.execute_reply": "2024-09-10T20:26:42.329014Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import uuid\n",
@@ -205,7 +177,7 @@
     "    SystemMessage,\n",
     "    ToolMessage,\n",
     ")\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class Example(TypedDict):\n",
@@ -266,16 +238,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "7f59a745-5c81-4011-a4c5-a33ec1eca7ef",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:26:42.330580Z",
-     "iopub.status.busy": "2024-09-10T20:26:42.330488Z",
-     "iopub.status.idle": "2024-09-10T20:26:42.332813Z",
-     "shell.execute_reply": "2024-09-10T20:26:42.332598Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "examples = [\n",
@@ -308,29 +273,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "976bb7b8-09c4-4a3e-80df-49a483705c08",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:26:42.333955Z",
-     "iopub.status.busy": "2024-09-10T20:26:42.333876Z",
-     "iopub.status.idle": "2024-09-10T20:26:42.336841Z",
-     "shell.execute_reply": "2024-09-10T20:26:42.336635Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "system: content=\"You are an expert extraction algorithm. Only extract relevant information from the text. If you do not know the value of an attribute asked to extract, return null for the attribute's value.\" additional_kwargs={} response_metadata={}\n",
-      "human: content=\"The ocean is vast and blue. It's more than 20,000 feet deep. There are many fish in it.\" additional_kwargs={} response_metadata={}\n",
-      "ai: content='' additional_kwargs={} response_metadata={} tool_calls=[{'name': 'Data', 'args': {'people': []}, 'id': '240159b1-1405-4107-a07c-3c6b91b3d5b7', 'type': 'tool_call'}]\n",
-      "tool: content='You have correctly called this tool.' tool_call_id='240159b1-1405-4107-a07c-3c6b91b3d5b7'\n",
-      "human: content='Fiona traveled far from France to Spain.' additional_kwargs={} response_metadata={}\n",
-      "ai: content='' additional_kwargs={} response_metadata={} tool_calls=[{'name': 'Data', 'args': {'people': [{'name': 'Fiona', 'hair_color': None, 'height_in_meters': None}]}, 'id': '3fc521e4-d1d2-4c20-bf40-e3d72f1068da', 'type': 'tool_call'}]\n",
-      "tool: content='You have correctly called this tool.' tool_call_id='3fc521e4-d1d2-4c20-bf40-e3d72f1068da'\n",
-      "human: content='this is some text' additional_kwargs={} response_metadata={}\n"
+      "system: content=\"You are an expert extraction algorithm. Only extract relevant information from the text. If you do not know the value of an attribute asked to extract, return null for the attribute's value.\"\n",
+      "human: content=\"The ocean is vast and blue. It's more than 20,000 feet deep. There are many fish in it.\"\n",
+      "ai: content='' tool_calls=[{'name': 'Person', 'args': {'name': None, 'hair_color': None, 'height_in_meters': None}, 'id': 'b843ba77-4c9c-48ef-92a4-54e534f24521'}]\n",
+      "tool: content='You have correctly called this tool.' tool_call_id='b843ba77-4c9c-48ef-92a4-54e534f24521'\n",
+      "human: content='Fiona traveled far from France to Spain.'\n",
+      "ai: content='' tool_calls=[{'name': 'Person', 'args': {'name': 'Fiona', 'hair_color': None, 'height_in_meters': None}, 'id': '46f00d6b-50e5-4482-9406-b07bb10340f6'}]\n",
+      "tool: content='You have correctly called this tool.' tool_call_id='46f00d6b-50e5-4482-9406-b07bb10340f6'\n",
+      "human: content='this is some text'\n"
      ]
     }
    ],
@@ -362,16 +320,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "df2e1ee1-69e8-4c4d-b349-95f2e320317b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:26:42.338001Z",
-     "iopub.status.busy": "2024-09-10T20:26:42.337915Z",
-     "iopub.status.idle": "2024-09-10T20:26:42.349121Z",
-     "shell.execute_reply": "2024-09-10T20:26:42.348908Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# | output: false\n",
@@ -392,16 +343,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "dbfea43d-769b-42e9-a76f-ce722f7d6f93",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:26:42.350335Z",
-     "iopub.status.busy": "2024-09-10T20:26:42.350264Z",
-     "iopub.status.idle": "2024-09-10T20:26:42.424894Z",
-     "shell.execute_reply": "2024-09-10T20:26:42.424623Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "runnable = prompt | llm.with_structured_output(\n",
@@ -423,49 +367,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "66545cab-af2a-40a4-9dc9-b4110458b7d3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:26:42.426258Z",
-     "iopub.status.busy": "2024-09-10T20:26:42.426187Z",
-     "iopub.status.idle": "2024-09-10T20:26:46.151633Z",
-     "shell.execute_reply": "2024-09-10T20:26:46.150690Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "people=[]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "people=[]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "people=[]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "people=[]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "people=[Person(name='earth', hair_color='null', height_in_meters='null')]\n",
+      "people=[Person(name='earth', hair_color='null', height_in_meters='null')]\n",
+      "people=[]\n",
+      "people=[Person(name='earth', hair_color='null', height_in_meters='null')]\n",
       "people=[]\n"
      ]
     }
@@ -488,49 +401,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "1c09d805-ec16-4123-aef9-6a5b59499b5c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:26:46.155346Z",
-     "iopub.status.busy": "2024-09-10T20:26:46.155110Z",
-     "iopub.status.idle": "2024-09-10T20:26:51.810359Z",
-     "shell.execute_reply": "2024-09-10T20:26:51.809636Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "people=[]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "people=[]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "people=[]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "people=[]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "people=[]\n",
+      "people=[]\n",
+      "people=[]\n",
+      "people=[]\n",
       "people=[]\n"
      ]
     }
@@ -553,16 +435,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "a9b7a762-1b75-4f9f-b9d9-6732dd05802c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:26:51.813309Z",
-     "iopub.status.busy": "2024-09-10T20:26:51.813150Z",
-     "iopub.status.idle": "2024-09-10T20:26:53.474153Z",
-     "shell.execute_reply": "2024-09-10T20:26:53.473522Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -570,7 +445,7 @@
        "Data(people=[Person(name='Harrison', hair_color='black', height_in_meters=None)])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -601,7 +476,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/extraction_long_text.ipynb

@@ -23,56 +23,16 @@
    "id": "57969139-ad0a-487e-97d8-cb30e2af9742",
    "metadata": {},
    "source": [
-    "## Setup\n",
+    "## Set up\n",
     "\n",
-    "First we'll install the dependencies needed for this guide:"
+    "We need some example data! Let's download an article about [cars from wikipedia](https://en.wikipedia.org/wiki/Car) and load it as a LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "a3b4d838-5be4-4207-8a4a-9ef5624c48f2",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:19.850767Z",
-     "iopub.status.busy": "2024-09-10T20:35:19.850427Z",
-     "iopub.status.idle": "2024-09-10T20:35:21.432233Z",
-     "shell.execute_reply": "2024-09-10T20:35:21.431606Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -qU langchain-community lxml faiss-cpu langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac000b03-33fc-414f-8f2c-3850df621a35",
-   "metadata": {},
-   "source": [
-    "Now we need some example data! Let's download an article about [cars from wikipedia](https://en.wikipedia.org/wiki/Car) and load it as a LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
    "id": "84460db2-36e1-4037-bfa6-2a11883c2ba5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:21.434882Z",
-     "iopub.status.busy": "2024-09-10T20:35:21.434571Z",
-     "iopub.status.idle": "2024-09-10T20:35:22.214545Z",
-     "shell.execute_reply": "2024-09-10T20:35:22.214253Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import re\n",
@@ -95,22 +55,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "fcb6917b-123d-4630-a0ce-ed8b293d482d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:22.216143Z",
-     "iopub.status.busy": "2024-09-10T20:35:22.216039Z",
-     "iopub.status.idle": "2024-09-10T20:35:22.218117Z",
-     "shell.execute_reply": "2024-09-10T20:35:22.217854Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "80427\n"
+      "79174\n"
      ]
     }
    ],
@@ -134,20 +87,13 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "a3b288ed-87a6-4af0-aac8-20921dc370d4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:22.219468Z",
-     "iopub.status.busy": "2024-09-10T20:35:22.219395Z",
-     "iopub.status.idle": "2024-09-10T20:35:22.340594Z",
-     "shell.execute_reply": "2024-09-10T20:35:22.340319Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from typing import List, Optional\n",
     "\n",
     "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class KeyDevelopment(BaseModel):\n",
@@ -210,14 +156,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "109f4f05-d0ff-431d-93d9-8f5aa34979a6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:22.342277Z",
-     "iopub.status.busy": "2024-09-10T20:35:22.342171Z",
-     "iopub.status.idle": "2024-09-10T20:35:22.532302Z",
-     "shell.execute_reply": "2024-09-10T20:35:22.532034Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# | output: false\n",
@@ -232,14 +171,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "aa4ae224-6d3d-4fe2-b210-7db19a9fe580",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:22.533795Z",
-     "iopub.status.busy": "2024-09-10T20:35:22.533708Z",
-     "iopub.status.idle": "2024-09-10T20:35:22.610573Z",
-     "shell.execute_reply": "2024-09-10T20:35:22.610307Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "extractor = prompt | llm.with_structured_output(\n",
@@ -262,14 +194,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "27b8a373-14b3-45ea-8bf5-9749122ad927",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:22.612123Z",
-     "iopub.status.busy": "2024-09-10T20:35:22.612052Z",
-     "iopub.status.idle": "2024-09-10T20:35:22.753493Z",
-     "shell.execute_reply": "2024-09-10T20:35:22.753179Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_text_splitters import TokenTextSplitter\n",
@@ -302,14 +227,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "6ba766b5-8d6c-48e6-8d69-f391a66b65d2",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:22.755067Z",
-     "iopub.status.busy": "2024-09-10T20:35:22.754987Z",
-     "iopub.status.idle": "2024-09-10T20:35:36.691130Z",
-     "shell.execute_reply": "2024-09-10T20:35:36.690500Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Limit just to the first 3 chunks\n",
@@ -336,27 +254,21 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "c3f77470-ce6c-477f-8957-650913218632",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:36.694799Z",
-     "iopub.status.busy": "2024-09-10T20:35:36.694458Z",
-     "iopub.status.idle": "2024-09-10T20:35:36.701416Z",
-     "shell.execute_reply": "2024-09-10T20:35:36.700993Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[KeyDevelopment(year=1769, description='Nicolas-Joseph Cugnot built the first full-scale, self-propelled mechanical vehicle, a steam-powered tricycle.', evidence='Nicolas-Joseph Cugnot is widely credited with building the first full-scale, self-propelled mechanical vehicle in about 1769; he created a steam-powered tricycle.'),\n",
-       " KeyDevelopment(year=1807, description=\"Nicéphore Niépce and his brother Claude created what was probably the world's first internal combustion engine.\", evidence=\"In 1807, Nicéphore Niépce and his brother Claude created what was probably the world's first internal combustion engine (which they called a Pyréolophore), but installed it in a boat on the river Saone in France.\"),\n",
-       " KeyDevelopment(year=1886, description='Carl Benz patented the Benz Patent-Motorwagen, marking the birth of the modern car.', evidence='In November 1881, French inventor Gustave Trouvé demonstrated a three-wheeled car powered by electricity at the International Exposition of Electricity. Although several other German engineers (including Gottlieb Daimler, Wilhelm Maybach, and Siegfried Marcus) were working on cars at about the same time, the year 1886 is regarded as the birth year of the modern car—a practical, marketable automobile for everyday use—when the German Carl Benz patented his Benz Patent-Motorwagen; he is generally acknowledged as the inventor of the car.'),\n",
-       " KeyDevelopment(year=1886, description='Carl Benz began promotion of his vehicle, marking the introduction of the first commercially available automobile.', evidence='Benz began promotion of the vehicle on 3 July 1886.'),\n",
-       " KeyDevelopment(year=1888, description=\"Bertha Benz undertook the first road trip by car to prove the road-worthiness of her husband's invention.\", evidence=\"In August 1888, Bertha Benz, the wife and business partner of Carl Benz, undertook the first road trip by car, to prove the road-worthiness of her husband's invention.\"),\n",
+       "[KeyDevelopment(year=1966, description='The Toyota Corolla began production, becoming the best-selling series of automobile in history.', evidence='The Toyota Corolla, which has been in production since 1966, is the best-selling series of automobile in history.'),\n",
+       " KeyDevelopment(year=1769, description='Nicolas-Joseph Cugnot built the first steam-powered road vehicle.', evidence='The French inventor Nicolas-Joseph Cugnot built the first steam-powered road vehicle in 1769.'),\n",
+       " KeyDevelopment(year=1808, description='François Isaac de Rivaz designed and constructed the first internal combustion-powered automobile.', evidence='the Swiss inventor François Isaac de Rivaz designed and constructed the first internal combustion-powered automobile in 1808.'),\n",
+       " KeyDevelopment(year=1886, description='Carl Benz patented his Benz Patent-Motorwagen, inventing the modern car.', evidence='The modern car—a practical, marketable automobile for everyday use—was invented in 1886, when the German inventor Carl Benz patented his Benz Patent-Motorwagen.'),\n",
+       " KeyDevelopment(year=1908, description='Ford Model T, one of the first cars affordable by the masses, began production.', evidence='One of the first cars affordable by the masses was the Ford Model T, begun in 1908, an American car manufactured by the Ford Motor Company.'),\n",
+       " KeyDevelopment(year=1888, description=\"Bertha Benz undertook the first road trip by car to prove the road-worthiness of her husband's invention.\", evidence=\"In August 1888, Bertha Benz, the wife of Carl Benz, undertook the first road trip by car, to prove the road-worthiness of her husband's invention.\"),\n",
        " KeyDevelopment(year=1896, description='Benz designed and patented the first internal-combustion flat engine, called boxermotor.', evidence='In 1896, Benz designed and patented the first internal-combustion flat engine, called boxermotor.'),\n",
-       " KeyDevelopment(year=1897, description='The first motor car in central Europe and one of the first factory-made cars in the world, the Präsident automobil, was produced by Nesselsdorfer Wagenbau.', evidence='The first motor car in central Europe and one of the first factory-made cars in the world, was produced by Czech company Nesselsdorfer Wagenbau (later renamed to Tatra) in 1897, the Präsident automobil.'),\n",
-       " KeyDevelopment(year=1901, description='Ransom Olds started large-scale, production-line manufacturing of affordable cars at his Oldsmobile factory in Lansing, Michigan.', evidence='Large-scale, production-line manufacturing of affordable cars was started by Ransom Olds in 1901 at his Oldsmobile factory in Lansing, Michigan.'),\n",
-       " KeyDevelopment(year=1913, description=\"Henry Ford introduced the world's first moving assembly line for cars at the Highland Park Ford Plant.\", evidence=\"This concept was greatly expanded by Henry Ford, beginning in 1913 with the world's first moving assembly line for cars at the Highland Park Ford Plant.\")]"
+       " KeyDevelopment(year=1897, description='Nesselsdorfer Wagenbau produced the Präsident automobil, one of the first factory-made cars in the world.', evidence='The first motor car in central Europe and one of the first factory-made cars in the world, was produced by Czech company Nesselsdorfer Wagenbau (later renamed to Tatra) in 1897, the Präsident automobil.'),\n",
+       " KeyDevelopment(year=1890, description='Daimler Motoren Gesellschaft (DMG) was founded by Daimler and Maybach in Cannstatt.', evidence='Daimler and Maybach founded Daimler Motoren Gesellschaft (DMG) in Cannstatt in 1890.'),\n",
+       " KeyDevelopment(year=1891, description='Auguste Doriot and Louis Rigoulot completed the longest trip by a petrol-driven vehicle with a Daimler powered Peugeot Type 3.', evidence='In 1891, Auguste Doriot and his Peugeot colleague Louis Rigoulot completed the longest trip by a petrol-driven vehicle when their self-designed and built Daimler powered Peugeot Type 3 completed 2,100 kilometres (1,300 mi) from Valentigney to Paris and Brest and back again.')]"
       ]
      },
      "execution_count": 9,
@@ -403,14 +315,7 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "aaf37c82-625b-4fa1-8e88-73303f08ac16",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:36.703897Z",
-     "iopub.status.busy": "2024-09-10T20:35:36.703718Z",
-     "iopub.status.idle": "2024-09-10T20:35:38.451523Z",
-     "shell.execute_reply": "2024-09-10T20:35:38.450925Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_community.vectorstores import FAISS\n",
@@ -439,14 +344,7 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "47aad00b-7013-4f7f-a1b0-02ef269093bf",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:38.455094Z",
-     "iopub.status.busy": "2024-09-10T20:35:38.454851Z",
-     "iopub.status.idle": "2024-09-10T20:35:38.458315Z",
-     "shell.execute_reply": "2024-09-10T20:35:38.457940Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "rag_extractor = {\n",
@@ -458,14 +356,7 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "68f2de01-0cd8-456e-a959-db236189d41b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:38.460115Z",
-     "iopub.status.busy": "2024-09-10T20:35:38.459949Z",
-     "iopub.status.idle": "2024-09-10T20:35:43.195532Z",
-     "shell.execute_reply": "2024-09-10T20:35:43.194254Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "results = rag_extractor.invoke(\"Key developments associated with cars\")"
@@ -475,21 +366,15 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "1788e2d6-77bb-417f-827c-eb96c035164e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:43.200497Z",
-     "iopub.status.busy": "2024-09-10T20:35:43.200037Z",
-     "iopub.status.idle": "2024-09-10T20:35:43.206773Z",
-     "shell.execute_reply": "2024-09-10T20:35:43.205426Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "year=2006 description='Car-sharing services in the US experienced double-digit growth in revenue and membership.' evidence='in the US, some car-sharing services have experienced double-digit growth in revenue and membership growth between 2006 and 2007.'\n",
-      "year=2020 description='56 million cars were manufactured worldwide, with China producing the most.' evidence='In 2020, there were 56 million cars manufactured worldwide, down from 67 million the previous year. The automotive industry in China produces by far the most (20 million in 2020).'\n"
+      "year=1869 description='Mary Ward became one of the first documented car fatalities in Parsonstown, Ireland.' evidence='Mary Ward became one of the first documented car fatalities in 1869 in Parsonstown, Ireland,'\n",
+      "year=1899 description=\"Henry Bliss one of the US's first pedestrian car casualties in New York City.\" evidence=\"Henry Bliss one of the US's first pedestrian car casualties in 1899 in New York City.\"\n",
+      "year=2030 description='All fossil fuel vehicles will be banned in Amsterdam.' evidence='all fossil fuel vehicles will be banned in Amsterdam from 2030.'\n"
      ]
     }
    ],
@@ -531,7 +416,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/extraction_parse.ipynb

@@ -27,16 +27,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "25487939-8713-4ec7-b774-e4a761ac8298",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:44.442501Z",
-     "iopub.status.busy": "2024-09-10T20:35:44.442044Z",
-     "iopub.status.idle": "2024-09-10T20:35:44.872217Z",
-     "shell.execute_reply": "2024-09-10T20:35:44.871897Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# | output: false\n",
@@ -69,23 +62,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "497eb023-c043-443d-ac62-2d4ea85fe1b0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:44.873979Z",
-     "iopub.status.busy": "2024-09-10T20:35:44.873840Z",
-     "iopub.status.idle": "2024-09-10T20:35:44.878966Z",
-     "shell.execute_reply": "2024-09-10T20:35:44.878718Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from typing import List, Optional\n",
     "\n",
     "from langchain_core.output_parsers import PydanticOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from pydantic import BaseModel, Field, validator\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field, validator\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",
@@ -128,16 +114,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "20b99ffb-a114-49a9-a7be-154c525f8ada",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:44.880355Z",
-     "iopub.status.busy": "2024-09-10T20:35:44.880277Z",
-     "iopub.status.idle": "2024-09-10T20:35:44.881834Z",
-     "shell.execute_reply": "2024-09-10T20:35:44.881601Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "query = \"Anna is 23 years old and she is 6 feet tall\""
@@ -145,16 +124,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "4f3a66ce-de19-4571-9e54-67504ae3fba7",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:44.883138Z",
-     "iopub.status.busy": "2024-09-10T20:35:44.883049Z",
-     "iopub.status.idle": "2024-09-10T20:35:44.885139Z",
-     "shell.execute_reply": "2024-09-10T20:35:44.884801Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -168,7 +140,7 @@
       "\n",
       "Here is the output schema:\n",
       "```\n",
-      "{\"$defs\": {\"Person\": {\"description\": \"Information about a person.\", \"properties\": {\"name\": {\"description\": \"The name of the person\", \"title\": \"Name\", \"type\": \"string\"}, \"height_in_meters\": {\"description\": \"The height of the person expressed in meters.\", \"title\": \"Height In Meters\", \"type\": \"number\"}}, \"required\": [\"name\", \"height_in_meters\"], \"title\": \"Person\", \"type\": \"object\"}}, \"description\": \"Identifying information about all people in a text.\", \"properties\": {\"people\": {\"items\": {\"$ref\": \"#/$defs/Person\"}, \"title\": \"People\", \"type\": \"array\"}}, \"required\": [\"people\"]}\n",
+      "{\"description\": \"Identifying information about all people in a text.\", \"properties\": {\"people\": {\"title\": \"People\", \"type\": \"array\", \"items\": {\"$ref\": \"#/definitions/Person\"}}}, \"required\": [\"people\"], \"definitions\": {\"Person\": {\"title\": \"Person\", \"description\": \"Information about a person.\", \"type\": \"object\", \"properties\": {\"name\": {\"title\": \"Name\", \"description\": \"The name of the person\", \"type\": \"string\"}, \"height_in_meters\": {\"title\": \"Height In Meters\", \"description\": \"The height of the person expressed in meters.\", \"type\": \"number\"}}, \"required\": [\"name\", \"height_in_meters\"]}}}\n",
       "```\n",
       "Human: Anna is 23 years old and she is 6 feet tall\n"
      ]
@@ -188,16 +160,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "7e0041eb-37dc-4384-9fe3-6dd8c356371e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:44.886765Z",
-     "iopub.status.busy": "2024-09-10T20:35:44.886675Z",
-     "iopub.status.idle": "2024-09-10T20:35:46.835960Z",
-     "shell.execute_reply": "2024-09-10T20:35:46.835282Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -205,7 +170,7 @@
        "People(people=[Person(name='Anna', height_in_meters=1.83)])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -244,16 +209,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "b1f11912-c1bb-4a2a-a482-79bf3996961f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:46.839577Z",
-     "iopub.status.busy": "2024-09-10T20:35:46.839233Z",
-     "iopub.status.idle": "2024-09-10T20:35:46.849663Z",
-     "shell.execute_reply": "2024-09-10T20:35:46.849177Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import json\n",
@@ -263,7 +221,7 @@
     "from langchain_anthropic.chat_models import ChatAnthropic\n",
     "from langchain_core.messages import AIMessage\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from pydantic import BaseModel, Field, validator\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field, validator\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",
@@ -321,23 +279,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "9260d5e8-3b6c-4639-9f3b-fb2f90239e4b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:46.851870Z",
-     "iopub.status.busy": "2024-09-10T20:35:46.851698Z",
-     "iopub.status.idle": "2024-09-10T20:35:46.854786Z",
-     "shell.execute_reply": "2024-09-10T20:35:46.854424Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "System: Answer the user query. Output your answer as JSON that  matches the given schema: ```json\n",
-      "{'$defs': {'Person': {'description': 'Information about a person.', 'properties': {'name': {'description': 'The name of the person', 'title': 'Name', 'type': 'string'}, 'height_in_meters': {'description': 'The height of the person expressed in meters.', 'title': 'Height In Meters', 'type': 'number'}}, 'required': ['name', 'height_in_meters'], 'title': 'Person', 'type': 'object'}}, 'description': 'Identifying information about all people in a text.', 'properties': {'people': {'items': {'$ref': '#/$defs/Person'}, 'title': 'People', 'type': 'array'}}, 'required': ['people'], 'title': 'People', 'type': 'object'}\n",
+      "{'title': 'People', 'description': 'Identifying information about all people in a text.', 'type': 'object', 'properties': {'people': {'title': 'People', 'type': 'array', 'items': {'$ref': '#/definitions/Person'}}}, 'required': ['people'], 'definitions': {'Person': {'title': 'Person', 'description': 'Information about a person.', 'type': 'object', 'properties': {'name': {'title': 'Name', 'description': 'The name of the person', 'type': 'string'}, 'height_in_meters': {'title': 'Height In Meters', 'description': 'The height of the person expressed in meters.', 'type': 'number'}}, 'required': ['name', 'height_in_meters']}}}\n",
       "```. Make sure to wrap the answer in ```json and ``` tags\n",
       "Human: Anna is 23 years old and she is 6 feet tall\n"
      ]
@@ -350,32 +301,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "c523301d-ae0e-45e3-b195-7fd28c67a5c4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:35:46.856945Z",
-     "iopub.status.busy": "2024-09-10T20:35:46.856769Z",
-     "iopub.status.idle": "2024-09-10T20:35:48.373728Z",
-     "shell.execute_reply": "2024-09-10T20:35:48.373079Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/bagatur/langchain/.venv/lib/python3.11/site-packages/pydantic/_internal/_fields.py:201: UserWarning: Field name \"schema\" in \"PromptInput\" shadows an attribute in parent \"BaseModel\"\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "[{'people': [{'name': 'Anna', 'height_in_meters': 1.83}]}]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -413,7 +349,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/fallbacks.ipynb

@@ -90,7 +90,7 @@
    "outputs": [],
    "source": [
     "# Note that we set max_retries = 0 to avoid retrying on RateLimits, etc\n",
-    "openai_llm = ChatOpenAI(model=\"gpt-4o-mini\", max_retries=0)\n",
+    "openai_llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", max_retries=0)\n",
     "anthropic_llm = ChatAnthropic(model=\"claude-3-haiku-20240307\")\n",
     "llm = openai_llm.with_fallbacks([anthropic_llm])"
    ]

docs/docs/how_to/few_shot_examples_chat.ipynb

@@ -86,7 +86,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='The expression \"2 🦜 9\" is not a standard mathematical operation or equation. It appears to be a combination of the number 2 and the parrot emoji 🦜 followed by the number 9. It does not have a specific mathematical meaning.', response_metadata={'token_usage': {'completion_tokens': 54, 'prompt_tokens': 17, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-aad12dda-5c47-4a1e-9949-6fe94e03242a-0', usage_metadata={'input_tokens': 17, 'output_tokens': 54, 'total_tokens': 71})"
+       "AIMessage(content='The expression \"2 🦜 9\" is not a standard mathematical operation or equation. It appears to be a combination of the number 2 and the parrot emoji 🦜 followed by the number 9. It does not have a specific mathematical meaning.', response_metadata={'token_usage': {'completion_tokens': 54, 'prompt_tokens': 17, 'total_tokens': 71}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-aad12dda-5c47-4a1e-9949-6fe94e03242a-0', usage_metadata={'input_tokens': 17, 'output_tokens': 54, 'total_tokens': 71})"
       ]
      },
      "execution_count": 4,
@@ -97,7 +97,7 @@
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0.0)\n",
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0.0)\n",
     "\n",
     "model.invoke(\"What is 2 🦜 9?\")"
    ]
@@ -212,7 +212,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='11', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 60, 'total_tokens': 61}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5ec4e051-262f-408e-ad00-3f2ebeb561c3-0', usage_metadata={'input_tokens': 60, 'output_tokens': 1, 'total_tokens': 61})"
+       "AIMessage(content='11', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 60, 'total_tokens': 61}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5ec4e051-262f-408e-ad00-3f2ebeb561c3-0', usage_metadata={'input_tokens': 60, 'output_tokens': 1, 'total_tokens': 61})"
       ]
      },
      "execution_count": 8,
@@ -418,7 +418,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='6', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 60, 'total_tokens': 61}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-d1863e5e-17cd-4e9d-bf7a-b9f118747a65-0', usage_metadata={'input_tokens': 60, 'output_tokens': 1, 'total_tokens': 61})"
+       "AIMessage(content='6', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 60, 'total_tokens': 61}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-d1863e5e-17cd-4e9d-bf7a-b9f118747a65-0', usage_metadata={'input_tokens': 60, 'output_tokens': 1, 'total_tokens': 61})"
       ]
      },
      "execution_count": 13,
@@ -427,7 +427,7 @@
     }
    ],
    "source": [
-    "chain = final_prompt | ChatOpenAI(model=\"gpt-4o-mini\", temperature=0.0)\n",
+    "chain = final_prompt | ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0.0)\n",
     "\n",
     "chain.invoke({\"input\": \"What's 3 🦜 3?\"})"
    ]

docs/docs/how_to/function_calling.ipynb

@@ -136,7 +136,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "# Note that the docstrings here are crucial, as they will be passed along\n",
@@ -191,7 +191,7 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
    ]
   },
   {
@@ -696,7 +696,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/how_to/graph_constructing.ipynb

@@ -24,7 +24,7 @@
     "\n",
     "## Architecture\n",
     "\n",
-    "At a high-level, the steps of constructing a knowledge graph from text are:\n",
+    "At a high-level, the steps of constructing a knowledge are from text are:\n",
     "\n",
     "1. **Extracting structured information from text**: Model is used to extract structured graph information from text.\n",
     "2. **Storing into graph database**: Storing the extracted structured graph information into a graph database enables downstream RAG applications\n",

docs/docs/how_to/graph_mapping.ipynb

@@ -163,8 +163,8 @@
     "from typing import List, Optional\n",
     "\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
-    "from pydantic import BaseModel, Field\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
     "\n",

docs/docs/how_to/graph_semantic.ipynb

@@ -177,15 +177,14 @@
    "source": [
     "from typing import Optional, Type\n",
     "\n",
+    "# Import things that are needed generically\n",
+    "from langchain.pydantic_v1 import BaseModel, Field\n",
     "from langchain_core.callbacks import (\n",
     "    AsyncCallbackManagerForToolRun,\n",
     "    CallbackManagerForToolRun,\n",
     ")\n",
     "from langchain_core.tools import BaseTool\n",
     "\n",
-    "# Import things that are needed generically\n",
-    "from pydantic import BaseModel, Field\n",
-    "\n",
     "description_query = \"\"\"\n",
     "MATCH (m:Movie|Person)\n",
     "WHERE m.title CONTAINS $candidate OR m.name CONTAINS $candidate\n",
@@ -227,15 +226,14 @@
    "source": [
     "from typing import Optional, Type\n",
     "\n",
+    "# Import things that are needed generically\n",
+    "from langchain.pydantic_v1 import BaseModel, Field\n",
     "from langchain_core.callbacks import (\n",
     "    AsyncCallbackManagerForToolRun,\n",
     "    CallbackManagerForToolRun,\n",
     ")\n",
     "from langchain_core.tools import BaseTool\n",
     "\n",
-    "# Import things that are needed generically\n",
-    "from pydantic import BaseModel, Field\n",
-    "\n",
     "\n",
     "class InformationInput(BaseModel):\n",
     "    entity: str = Field(description=\"movie or a person mentioned in the question\")\n",

docs/docs/how_to/logprobs.ipynb

@@ -94,7 +94,7 @@
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\").bind(logprobs=True)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\").bind(logprobs=True)\n",
     "\n",
     "msg = llm.invoke((\"human\", \"how are you today\"))\n",
     "\n",

docs/docs/how_to/multi_vector.ipynb

@@ -440,7 +440,7 @@
    "source": [
     "from typing import List\n",
     "\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class HypotheticalQuestions(BaseModel):\n",

docs/docs/how_to/output_parser_fixing.ipynb

@@ -24,8 +24,8 @@
     "from typing import List\n",
     "\n",
     "from langchain_core.output_parsers import PydanticOutputParser\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "from pydantic import BaseModel, Field"
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

docs/docs/how_to/output_parser_json.ipynb

@@ -71,8 +71,8 @@
    "source": [
     "from langchain_core.output_parsers import JsonOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
-    "from pydantic import BaseModel, Field\n",
     "\n",
     "model = ChatOpenAI(temperature=0)\n",
     "\n",

docs/docs/how_to/output_parser_retry.ipynb

@@ -20,8 +20,8 @@
     "from langchain.output_parsers import OutputFixingParser\n",
     "from langchain_core.output_parsers import PydanticOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
-    "from langchain_openai import ChatOpenAI, OpenAI\n",
-    "from pydantic import BaseModel, Field"
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_openai import ChatOpenAI, OpenAI"
    ]
   },
   {

docs/docs/how_to/output_parser_structured.ipynb

@@ -35,17 +35,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "id": "1594b2bf-2a6f-47bb-9a81-38930f8e606b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Joke(setup='Why did the tomato turn red?', punchline='Because it saw the salad dressing!')"
+       "Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -53,8 +53,8 @@
    "source": [
     "from langchain_core.output_parsers import PydanticOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field, validator\n",
     "from langchain_openai import OpenAI\n",
-    "from pydantic import BaseModel, Field, model_validator\n",
     "\n",
     "model = OpenAI(model_name=\"gpt-3.5-turbo-instruct\", temperature=0.0)\n",
     "\n",
@@ -65,13 +65,11 @@
     "    punchline: str = Field(description=\"answer to resolve the joke\")\n",
     "\n",
     "    # You can add custom validation logic easily with Pydantic.\n",
-    "    @model_validator(mode=\"before\")\n",
-    "    @classmethod\n",
-    "    def question_ends_with_question_mark(cls, values: dict) -> dict:\n",
-    "        setup = values[\"setup\"]\n",
-    "        if setup[-1] != \"?\":\n",
+    "    @validator(\"setup\")\n",
+    "    def question_ends_with_question_mark(cls, field):\n",
+    "        if field[-1] != \"?\":\n",
     "            raise ValueError(\"Badly formed question!\")\n",
-    "        return values\n",
+    "        return field\n",
     "\n",
     "\n",
     "# Set up a parser + inject instructions into the prompt template.\n",
@@ -241,9 +239,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv-311",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv-311"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -255,7 +253,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/docs/how_to/output_parser_yaml.ipynb

@@ -70,8 +70,8 @@
    "source": [
     "from langchain.output_parsers import YamlOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
-    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "# Define your desired data structure.\n",

docs/docs/how_to/qa_citations.ipynb

@@ -269,7 +269,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class CitedAnswer(BaseModel):\n",

docs/docs/how_to/query_constructing_filters.ipynb

@@ -24,16 +24,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 13,
    "id": "8ca446a0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:32:35.834087Z",
-     "iopub.status.busy": "2024-09-11T02:32:35.833763Z",
-     "iopub.status.idle": "2024-09-11T02:32:36.588973Z",
-     "shell.execute_reply": "2024-09-11T02:32:36.588677Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from typing import Optional\n",
@@ -47,7 +40,7 @@
     ")\n",
     "from langchain_community.query_constructors.chroma import ChromaTranslator\n",
     "from langchain_community.query_constructors.elasticsearch import ElasticsearchTranslator\n",
-    "from pydantic import BaseModel"
+    "from langchain_core.pydantic_v1 import BaseModel"
    ]
   },
   {
@@ -60,16 +53,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 11,
    "id": "64055006",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:32:36.590665Z",
-     "iopub.status.busy": "2024-09-11T02:32:36.590527Z",
-     "iopub.status.idle": "2024-09-11T02:32:36.592985Z",
-     "shell.execute_reply": "2024-09-11T02:32:36.592763Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class Search(BaseModel):\n",
@@ -80,16 +66,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 12,
    "id": "44eb6d98",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:32:36.594147Z",
-     "iopub.status.busy": "2024-09-11T02:32:36.594072Z",
-     "iopub.status.idle": "2024-09-11T02:32:36.595777Z",
-     "shell.execute_reply": "2024-09-11T02:32:36.595563Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "search_query = Search(query=\"RAG\", start_year=2022, author=\"LangChain\")"
@@ -97,16 +76,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "id": "e8ba6705",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:32:36.596902Z",
-     "iopub.status.busy": "2024-09-11T02:32:36.596824Z",
-     "iopub.status.idle": "2024-09-11T02:32:36.598805Z",
-     "shell.execute_reply": "2024-09-11T02:32:36.598629Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def construct_comparisons(query: Search):\n",
@@ -132,16 +104,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
    "id": "6a79c9da",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:32:36.599989Z",
-     "iopub.status.busy": "2024-09-11T02:32:36.599909Z",
-     "iopub.status.idle": "2024-09-11T02:32:36.601521Z",
-     "shell.execute_reply": "2024-09-11T02:32:36.601306Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "comparisons = construct_comparisons(search_query)"
@@ -149,16 +114,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 17,
    "id": "2d0e9689",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:32:36.602688Z",
-     "iopub.status.busy": "2024-09-11T02:32:36.602603Z",
-     "iopub.status.idle": "2024-09-11T02:32:36.604171Z",
-     "shell.execute_reply": "2024-09-11T02:32:36.603981Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "_filter = Operation(operator=Operator.AND, arguments=comparisons)"
@@ -166,16 +124,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 18,
    "id": "e4c0b2ce",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:32:36.605267Z",
-     "iopub.status.busy": "2024-09-11T02:32:36.605190Z",
-     "iopub.status.idle": "2024-09-11T02:32:36.607993Z",
-     "shell.execute_reply": "2024-09-11T02:32:36.607796Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -184,7 +135,7 @@
        "   {'term': {'metadata.author.keyword': 'LangChain'}}]}}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -195,16 +146,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 19,
    "id": "d75455ae",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:32:36.609091Z",
-     "iopub.status.busy": "2024-09-11T02:32:36.609012Z",
-     "iopub.status.idle": "2024-09-11T02:32:36.611075Z",
-     "shell.execute_reply": "2024-09-11T02:32:36.610869Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -212,7 +156,7 @@
        "{'$and': [{'start_year': {'$gt': 2022}}, {'author': {'$eq': 'LangChain'}}]}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -238,7 +182,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/docs/how_to/query_few_shot.ipynb

@@ -35,14 +35,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:33:48.329739Z",
-     "iopub.status.busy": "2024-09-11T02:33:48.329033Z",
-     "iopub.status.idle": "2024-09-11T02:33:48.334555Z",
-     "shell.execute_reply": "2024-09-11T02:33:48.334086Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# %pip install -qU langchain-core langchain-openai"
@@ -60,23 +53,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "40e2979e-a818-4b96-ac25-039336f94319",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:33:48.337140Z",
-     "iopub.status.busy": "2024-09-11T02:33:48.336958Z",
-     "iopub.status.idle": "2024-09-11T02:33:48.342671Z",
-     "shell.execute_reply": "2024-09-11T02:33:48.342281Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
     "\n",
     "# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
@@ -95,21 +80,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 37,
    "id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:33:48.345004Z",
-     "iopub.status.busy": "2024-09-11T02:33:48.344838Z",
-     "iopub.status.idle": "2024-09-11T02:33:48.413166Z",
-     "shell.execute_reply": "2024-09-11T02:33:48.412908Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from typing import List, Optional\n",
     "\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "sub_queries_description = \"\"\"\\\n",
     "If the original question contains multiple distinct sub-questions, \\\n",
@@ -143,16 +121,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 64,
    "id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:33:48.414805Z",
-     "iopub.status.busy": "2024-09-11T02:33:48.414700Z",
-     "iopub.status.idle": "2024-09-11T02:33:49.023858Z",
-     "shell.execute_reply": "2024-09-11T02:33:49.023547Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
@@ -172,7 +143,7 @@
     "        (\"human\", \"{question}\"),\n",
     "    ]\n",
     ")\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "structured_llm = llm.with_structured_output(Search)\n",
     "query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
    ]
@@ -187,24 +158,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 65,
    "id": "0bcfce06-6f0c-4f9d-a1fc-dc29342d2aae",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:33:49.025536Z",
-     "iopub.status.busy": "2024-09-11T02:33:49.025437Z",
-     "iopub.status.idle": "2024-09-11T02:33:50.170550Z",
-     "shell.execute_reply": "2024-09-11T02:33:50.169835Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='difference between web voyager and reflection agents', sub_queries=['what is web voyager', 'what are reflection agents', 'do both web voyager and reflection agents use langgraph?'], publish_year=None)"
+       "Search(query='web voyager vs reflection agents', sub_queries=['difference between web voyager and reflection agents', 'do web voyager and reflection agents use langgraph'], publish_year=None)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -229,16 +193,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 53,
    "id": "15b4923d-a08e-452d-8889-9a09a57d1095",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:33:50.180367Z",
-     "iopub.status.busy": "2024-09-11T02:33:50.173961Z",
-     "iopub.status.idle": "2024-09-11T02:33:50.186703Z",
-     "shell.execute_reply": "2024-09-11T02:33:50.186090Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "examples = []"
@@ -246,16 +203,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 54,
    "id": "da5330e6-827a-40e5-982b-b23b6286b758",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:33:50.189822Z",
-     "iopub.status.busy": "2024-09-11T02:33:50.189617Z",
-     "iopub.status.idle": "2024-09-11T02:33:50.195116Z",
-     "shell.execute_reply": "2024-09-11T02:33:50.194617Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "question = \"What's chat langchain, is it a langchain template?\"\n",
@@ -268,16 +218,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 55,
    "id": "580e857a-27df-4ecf-a19c-458dc9244ec8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:33:50.198178Z",
-     "iopub.status.busy": "2024-09-11T02:33:50.198002Z",
-     "iopub.status.idle": "2024-09-11T02:33:50.204115Z",
-     "shell.execute_reply": "2024-09-11T02:33:50.202534Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "question = \"How to build multi-agent system and stream intermediate steps from it\"\n",
@@ -295,16 +238,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 56,
    "id": "fa63310d-69e3-4701-825c-fbb01f8a5a16",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:33:50.207416Z",
-     "iopub.status.busy": "2024-09-11T02:33:50.207196Z",
-     "iopub.status.idle": "2024-09-11T02:33:50.212484Z",
-     "shell.execute_reply": "2024-09-11T02:33:50.211974Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "question = \"LangChain agents vs LangGraph?\"\n",
@@ -330,16 +266,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 57,
    "id": "68b03709-9a60-4acf-b96c-cafe1056c6f3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:33:50.215540Z",
-     "iopub.status.busy": "2024-09-11T02:33:50.215250Z",
-     "iopub.status.idle": "2024-09-11T02:33:50.224108Z",
-     "shell.execute_reply": "2024-09-11T02:33:50.223490Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import uuid\n",
@@ -384,16 +313,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 58,
    "id": "d9bf9f87-3e6b-4fc2-957b-949b077fab54",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:33:50.227215Z",
-     "iopub.status.busy": "2024-09-11T02:33:50.226993Z",
-     "iopub.status.idle": "2024-09-11T02:33:50.231333Z",
-     "shell.execute_reply": "2024-09-11T02:33:50.230742Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.prompts import MessagesPlaceholder\n",
@@ -407,24 +329,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 62,
    "id": "e565ccb0-3530-4782-b56b-d1f6d0a8e559",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:33:50.233833Z",
-     "iopub.status.busy": "2024-09-11T02:33:50.233646Z",
-     "iopub.status.idle": "2024-09-11T02:33:51.318133Z",
-     "shell.execute_reply": "2024-09-11T02:33:51.317640Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query=\"What's the difference between web voyager and reflection agents? Do both use langgraph?\", sub_queries=['What is web voyager', 'What are reflection agents', 'Do web voyager and reflection agents use langgraph?'], publish_year=None)"
+       "Search(query='Difference between web voyager and reflection agents, do they both use LangGraph?', sub_queries=['What is Web Voyager', 'What are Reflection agents', 'Do Web Voyager and Reflection agents use LangGraph'], publish_year=None)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -462,7 +377,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/docs/how_to/query_high_cardinality.ipynb

@@ -33,20 +33,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%pip install -qU langchain langchain-community langchain-openai faker langchain-chroma"
+    "# %pip install -qU langchain langchain-community langchain-openai faker langchain-chroma"
    ]
   },
   {
@@ -61,23 +53,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "40e2979e-a818-4b96-ac25-039336f94319",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:34:54.036110Z",
-     "iopub.status.busy": "2024-09-11T02:34:54.035829Z",
-     "iopub.status.idle": "2024-09-11T02:34:54.038746Z",
-     "shell.execute_reply": "2024-09-11T02:34:54.038430Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
     "\n",
     "# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
@@ -96,16 +80,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "e5ba65c2",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:34:54.040738Z",
-     "iopub.status.busy": "2024-09-11T02:34:54.040515Z",
-     "iopub.status.idle": "2024-09-11T02:34:54.622643Z",
-     "shell.execute_reply": "2024-09-11T02:34:54.622382Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from faker import Faker\n",
@@ -125,24 +102,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "c901ea97",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:34:54.624195Z",
-     "iopub.status.busy": "2024-09-11T02:34:54.624106Z",
-     "iopub.status.idle": "2024-09-11T02:34:54.627231Z",
-     "shell.execute_reply": "2024-09-11T02:34:54.626971Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Jacob Adams'"
+       "'Hayley Gonzalez'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -153,24 +123,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "b0d42ae2",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:34:54.628545Z",
-     "iopub.status.busy": "2024-09-11T02:34:54.628460Z",
-     "iopub.status.idle": "2024-09-11T02:34:54.630474Z",
-     "shell.execute_reply": "2024-09-11T02:34:54.630282Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Eric Acevedo'"
+       "'Jesse Knight'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -191,33 +154,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "0ae69afc",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:34:54.631758Z",
-     "iopub.status.busy": "2024-09-11T02:34:54.631678Z",
-     "iopub.status.idle": "2024-09-11T02:34:54.666448Z",
-     "shell.execute_reply": "2024-09-11T02:34:54.666216Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from pydantic import BaseModel, Field, model_validator"
+    "from langchain_core.pydantic_v1 import BaseModel, Field"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "6c9485ce",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:34:54.667852Z",
-     "iopub.status.busy": "2024-09-11T02:34:54.667733Z",
-     "iopub.status.idle": "2024-09-11T02:34:54.700224Z",
-     "shell.execute_reply": "2024-09-11T02:34:54.700004Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class Search(BaseModel):\n",
@@ -227,17 +176,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "aebd704a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:34:54.701556Z",
-     "iopub.status.busy": "2024-09-11T02:34:54.701465Z",
-     "iopub.status.idle": "2024-09-11T02:34:55.179986Z",
-     "shell.execute_reply": "2024-09-11T02:34:55.179640Z"
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harrisonchase/workplace/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: The function `with_structured_output` is in beta. It is actively being worked on, so the API may change.\n",
+      "  warn_beta(\n"
+     ]
     }
-   },
-   "outputs": [],
+   ],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.runnables import RunnablePassthrough\n",
@@ -250,7 +201,7 @@
     "        (\"human\", \"{question}\"),\n",
     "    ]\n",
     ")\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "structured_llm = llm.with_structured_output(Search)\n",
     "query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
    ]
@@ -265,24 +216,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 33,
    "id": "cc0d344b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:34:55.181603Z",
-     "iopub.status.busy": "2024-09-11T02:34:55.181500Z",
-     "iopub.status.idle": "2024-09-11T02:34:55.778884Z",
-     "shell.execute_reply": "2024-09-11T02:34:55.778324Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='aliens', author='Jesse Knight')"
+       "Search(query='books about aliens', author='Jesse Knight')"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -301,24 +245,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 34,
    "id": "82b6b2ad",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:34:55.784266Z",
-     "iopub.status.busy": "2024-09-11T02:34:55.782603Z",
-     "iopub.status.idle": "2024-09-11T02:34:56.206779Z",
-     "shell.execute_reply": "2024-09-11T02:34:56.206068Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='aliens', author='Jess Knight')"
+       "Search(query='books about aliens', author='Jess Knight')"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -339,16 +276,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 35,
    "id": "98788a94",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:34:56.210043Z",
-     "iopub.status.busy": "2024-09-11T02:34:56.209657Z",
-     "iopub.status.idle": "2024-09-11T02:34:56.213962Z",
-     "shell.execute_reply": "2024-09-11T02:34:56.213413Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "system = \"\"\"Generate a relevant search query for a library system.\n",
@@ -369,16 +299,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 36,
    "id": "e65412f5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:34:56.216144Z",
-     "iopub.status.busy": "2024-09-11T02:34:56.216005Z",
-     "iopub.status.idle": "2024-09-11T02:34:56.218754Z",
-     "shell.execute_reply": "2024-09-11T02:34:56.218416Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "query_analyzer_all = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
@@ -394,17 +317,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 37,
    "id": "696b000f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:34:56.220827Z",
-     "iopub.status.busy": "2024-09-11T02:34:56.220680Z",
-     "iopub.status.idle": "2024-09-11T02:34:58.846872Z",
-     "shell.execute_reply": "2024-09-11T02:34:58.846273Z"
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error code: 400 - {'error': {'message': \"This model's maximum context length is 16385 tokens. However, your messages resulted in 33885 tokens (33855 in the messages, 30 in the functions). Please reduce the length of the messages or functions.\", 'type': 'invalid_request_error', 'param': 'messages', 'code': 'context_length_exceeded'}}\n"
+     ]
     }
-   },
-   "outputs": [],
+   ],
    "source": [
     "try:\n",
     "    res = query_analyzer_all.invoke(\"what are books about aliens by jess knight\")\n",
@@ -422,16 +346,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 38,
    "id": "0f0d0757",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:34:58.850318Z",
-     "iopub.status.busy": "2024-09-11T02:34:58.850100Z",
-     "iopub.status.idle": "2024-09-11T02:34:58.873883Z",
-     "shell.execute_reply": "2024-09-11T02:34:58.873525Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "llm_long = ChatOpenAI(model=\"gpt-4-turbo-preview\", temperature=0)\n",
@@ -441,24 +358,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 39,
    "id": "03e5b7b2",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:34:58.875940Z",
-     "iopub.status.busy": "2024-09-11T02:34:58.875811Z",
-     "iopub.status.idle": "2024-09-11T02:35:02.947273Z",
-     "shell.execute_reply": "2024-09-11T02:35:02.946220Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='aliens', author='jess knight')"
+       "Search(query='aliens', author='Kevin Knight')"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -479,16 +389,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 25,
    "id": "32b19e07",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:35:02.951939Z",
-     "iopub.status.busy": "2024-09-11T02:35:02.951583Z",
-     "iopub.status.idle": "2024-09-11T02:35:41.777839Z",
-     "shell.execute_reply": "2024-09-11T02:35:41.777392Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_chroma import Chroma\n",
@@ -500,16 +403,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 51,
    "id": "774cb7b0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:35:41.780883Z",
-     "iopub.status.busy": "2024-09-11T02:35:41.780774Z",
-     "iopub.status.idle": "2024-09-11T02:35:41.782739Z",
-     "shell.execute_reply": "2024-09-11T02:35:41.782498Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def select_names(question):\n",
@@ -520,16 +416,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 52,
    "id": "1173159c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:35:41.783992Z",
-     "iopub.status.busy": "2024-09-11T02:35:41.783913Z",
-     "iopub.status.idle": "2024-09-11T02:35:41.785911Z",
-     "shell.execute_reply": "2024-09-11T02:35:41.785632Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "create_prompt = {\n",
@@ -540,16 +429,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 53,
    "id": "0a892607",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:35:41.787082Z",
-     "iopub.status.busy": "2024-09-11T02:35:41.787008Z",
-     "iopub.status.idle": "2024-09-11T02:35:41.788543Z",
-     "shell.execute_reply": "2024-09-11T02:35:41.788362Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "query_analyzer_select = create_prompt | structured_llm"
@@ -557,24 +439,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 54,
    "id": "8195d7cd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:35:41.789624Z",
-     "iopub.status.busy": "2024-09-11T02:35:41.789551Z",
-     "iopub.status.idle": "2024-09-11T02:35:42.099839Z",
-     "shell.execute_reply": "2024-09-11T02:35:42.099042Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "ChatPromptValue(messages=[SystemMessage(content='Generate a relevant search query for a library system.\\n\\n`author` attribute MUST be one of:\\n\\nJennifer Knight, Jill Knight, John Knight, Dr. Jeffrey Knight, Christopher Knight, Andrea Knight, Brandy Knight, Jennifer Keller, Becky Chambers, Sarah Knapp\\n\\nDo NOT hallucinate author name!'), HumanMessage(content='what are books by jess knight')])"
+       "ChatPromptValue(messages=[SystemMessage(content='Generate a relevant search query for a library system.\\n\\n`author` attribute MUST be one of:\\n\\nJesse Knight, Kelly Knight, Scott Knight, Richard Knight, Andrew Knight, Katherine Knight, Erica Knight, Ashley Knight, Becky Knight, Kevin Knight\\n\\nDo NOT hallucinate author name!'), HumanMessage(content='what are books by jess knight')])"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -585,24 +460,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 55,
    "id": "d3228b4e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:35:42.106571Z",
-     "iopub.status.busy": "2024-09-11T02:35:42.105861Z",
-     "iopub.status.idle": "2024-09-11T02:35:42.909738Z",
-     "shell.execute_reply": "2024-09-11T02:35:42.908875Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='books about aliens', author='Jennifer Knight')"
+       "Search(query='books about aliens', author='Jesse Knight')"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -624,45 +492,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 47,
    "id": "a2e8b434",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:35:42.915376Z",
-     "iopub.status.busy": "2024-09-11T02:35:42.914923Z",
-     "iopub.status.idle": "2024-09-11T02:35:42.923958Z",
-     "shell.execute_reply": "2024-09-11T02:35:42.922391Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain_core.pydantic_v1 import validator\n",
+    "\n",
+    "\n",
     "class Search(BaseModel):\n",
     "    query: str\n",
     "    author: str\n",
     "\n",
-    "    @model_validator(mode=\"before\")\n",
-    "    @classmethod\n",
-    "    def double(cls, values: dict) -> dict:\n",
-    "        author = values[\"author\"]\n",
-    "        closest_valid_author = vectorstore.similarity_search(author, k=1)[\n",
-    "            0\n",
-    "        ].page_content\n",
-    "        values[\"author\"] = closest_valid_author\n",
-    "        return values"
+    "    @validator(\"author\")\n",
+    "    def double(cls, v: str) -> str:\n",
+    "        return vectorstore.similarity_search(v, k=1)[0].page_content"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 48,
    "id": "919c0601",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:35:42.927718Z",
-     "iopub.status.busy": "2024-09-11T02:35:42.927428Z",
-     "iopub.status.idle": "2024-09-11T02:35:42.933784Z",
-     "shell.execute_reply": "2024-09-11T02:35:42.933344Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "system = \"\"\"Generate a relevant search query for a library system\"\"\"\n",
@@ -680,24 +531,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 50,
    "id": "6c4f3e9a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:35:42.936506Z",
-     "iopub.status.busy": "2024-09-11T02:35:42.936186Z",
-     "iopub.status.idle": "2024-09-11T02:35:43.711754Z",
-     "shell.execute_reply": "2024-09-11T02:35:43.710695Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='aliens', author='John Knight')"
+       "Search(query='books about aliens', author='Jesse Knight')"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -708,16 +552,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "a309cb11",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:35:43.717567Z",
-     "iopub.status.busy": "2024-09-11T02:35:43.717189Z",
-     "iopub.status.idle": "2024-09-11T02:35:43.722339Z",
-     "shell.execute_reply": "2024-09-11T02:35:43.720537Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# TODO: show trigram similarity"
@@ -726,9 +563,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv-311",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv-311"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -740,7 +577,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/docs/how_to/query_multiple_queries.ipynb

@@ -33,25 +33,10 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:41:53.160868Z",
-     "iopub.status.busy": "2024-09-11T02:41:53.160512Z",
-     "iopub.status.idle": "2024-09-11T02:41:57.605370Z",
-     "shell.execute_reply": "2024-09-11T02:41:57.604888Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "%pip install -qU langchain langchain-community langchain-openai langchain-chroma"
+    "# %pip install -qU langchain langchain-community langchain-openai langchain-chroma"
    ]
   },
   {
@@ -66,23 +51,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "40e2979e-a818-4b96-ac25-039336f94319",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:41:57.607874Z",
-     "iopub.status.busy": "2024-09-11T02:41:57.607697Z",
-     "iopub.status.idle": "2024-09-11T02:41:57.610422Z",
-     "shell.execute_reply": "2024-09-11T02:41:57.610012Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
     "\n",
     "# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
@@ -101,16 +78,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "1f621694",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:41:57.612276Z",
-     "iopub.status.busy": "2024-09-11T02:41:57.612146Z",
-     "iopub.status.idle": "2024-09-11T02:41:59.074590Z",
-     "shell.execute_reply": "2024-09-11T02:41:59.074052Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_chroma import Chroma\n",
@@ -138,21 +108,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:41:59.077712Z",
-     "iopub.status.busy": "2024-09-11T02:41:59.077514Z",
-     "iopub.status.idle": "2024-09-11T02:41:59.081509Z",
-     "shell.execute_reply": "2024-09-11T02:41:59.081112Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from typing import List, Optional\n",
     "\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class Search(BaseModel):\n",
@@ -166,17 +129,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:41:59.083613Z",
-     "iopub.status.busy": "2024-09-11T02:41:59.083492Z",
-     "iopub.status.idle": "2024-09-11T02:41:59.204636Z",
-     "shell.execute_reply": "2024-09-11T02:41:59.204377Z"
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harrisonchase/workplace/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: The function `with_structured_output` is in beta. It is actively being worked on, so the API may change.\n",
+      "  warn_beta(\n"
+     ]
     }
-   },
-   "outputs": [],
+   ],
    "source": [
     "from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
@@ -194,7 +159,7 @@
     "        (\"human\", \"{question}\"),\n",
     "    ]\n",
     ")\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "structured_llm = llm.with_structured_output(Search)\n",
     "query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
    ]
@@ -209,24 +174,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "bc1d3863",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:41:59.206178Z",
-     "iopub.status.busy": "2024-09-11T02:41:59.206101Z",
-     "iopub.status.idle": "2024-09-11T02:41:59.817758Z",
-     "shell.execute_reply": "2024-09-11T02:41:59.817310Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(queries=['Harrison Work', 'Harrison employment history'])"
+       "Search(queries=['Harrison work location'])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -237,24 +195,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "af62af17-4f90-4dbd-a8b4-dfff51f1db95",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:41:59.820168Z",
-     "iopub.status.busy": "2024-09-11T02:41:59.819990Z",
-     "iopub.status.idle": "2024-09-11T02:42:00.309034Z",
-     "shell.execute_reply": "2024-09-11T02:42:00.308578Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(queries=['Harrison work history', 'Ankush work history'])"
+       "Search(queries=['Harrison work place', 'Ankush work place'])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -275,16 +226,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "1e047d87",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:00.311131Z",
-     "iopub.status.busy": "2024-09-11T02:42:00.310972Z",
-     "iopub.status.idle": "2024-09-11T02:42:00.313365Z",
-     "shell.execute_reply": "2024-09-11T02:42:00.313025Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.runnables import chain"
@@ -292,16 +236,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 31,
    "id": "8dac7866",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:00.315138Z",
-     "iopub.status.busy": "2024-09-11T02:42:00.315016Z",
-     "iopub.status.idle": "2024-09-11T02:42:00.317427Z",
-     "shell.execute_reply": "2024-09-11T02:42:00.317088Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "@chain\n",
@@ -318,25 +255,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 33,
    "id": "232ad8a7-7990-4066-9228-d35a555f7293",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:00.318951Z",
-     "iopub.status.busy": "2024-09-11T02:42:00.318829Z",
-     "iopub.status.idle": "2024-09-11T02:42:01.512855Z",
-     "shell.execute_reply": "2024-09-11T02:42:01.512321Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='Harrison worked at Kensho'),\n",
-       " Document(page_content='Harrison worked at Kensho')]"
+       "[Document(page_content='Harrison worked at Kensho')]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -347,16 +276,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 34,
    "id": "28e14ba5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:01.515743Z",
-     "iopub.status.busy": "2024-09-11T02:42:01.515400Z",
-     "iopub.status.idle": "2024-09-11T02:42:02.349930Z",
-     "shell.execute_reply": "2024-09-11T02:42:02.349382Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -365,7 +287,7 @@
        " Document(page_content='Ankush worked at Facebook')]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -399,7 +321,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/docs/how_to/query_multiple_retrievers.ipynb

@@ -33,25 +33,10 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:13.105266Z",
-     "iopub.status.busy": "2024-09-11T02:42:13.104556Z",
-     "iopub.status.idle": "2024-09-11T02:42:17.936922Z",
-     "shell.execute_reply": "2024-09-11T02:42:17.936478Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "%pip install -qU langchain langchain-community langchain-openai langchain-chroma"
+    "# %pip install -qU langchain langchain-community langchain-openai langchain-chroma"
    ]
   },
   {
@@ -66,23 +51,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "40e2979e-a818-4b96-ac25-039336f94319",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:17.939072Z",
-     "iopub.status.busy": "2024-09-11T02:42:17.938929Z",
-     "iopub.status.idle": "2024-09-11T02:42:17.941266Z",
-     "shell.execute_reply": "2024-09-11T02:42:17.940968Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
     "\n",
     "# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
@@ -101,16 +78,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 16,
    "id": "1f621694",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:17.942794Z",
-     "iopub.status.busy": "2024-09-11T02:42:17.942674Z",
-     "iopub.status.idle": "2024-09-11T02:42:19.939459Z",
-     "shell.execute_reply": "2024-09-11T02:42:19.938842Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_chroma import Chroma\n",
@@ -140,21 +110,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 17,
    "id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:19.942780Z",
-     "iopub.status.busy": "2024-09-11T02:42:19.942567Z",
-     "iopub.status.idle": "2024-09-11T02:42:19.947709Z",
-     "shell.execute_reply": "2024-09-11T02:42:19.947252Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from typing import List, Optional\n",
     "\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class Search(BaseModel):\n",
@@ -172,16 +135,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 18,
    "id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:19.949936Z",
-     "iopub.status.busy": "2024-09-11T02:42:19.949778Z",
-     "iopub.status.idle": "2024-09-11T02:42:20.073883Z",
-     "shell.execute_reply": "2024-09-11T02:42:20.073556Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
@@ -198,7 +154,7 @@
     "        (\"human\", \"{question}\"),\n",
     "    ]\n",
     ")\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "structured_llm = llm.with_structured_output(Search)\n",
     "query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
    ]
@@ -213,24 +169,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 19,
    "id": "bc1d3863",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:20.075511Z",
-     "iopub.status.busy": "2024-09-11T02:42:20.075428Z",
-     "iopub.status.idle": "2024-09-11T02:42:20.902011Z",
-     "shell.execute_reply": "2024-09-11T02:42:20.901558Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='work history', person='HARRISON')"
+       "Search(query='workplace', person='HARRISON')"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -241,24 +190,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 20,
    "id": "af62af17-4f90-4dbd-a8b4-dfff51f1db95",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:20.904384Z",
-     "iopub.status.busy": "2024-09-11T02:42:20.904195Z",
-     "iopub.status.idle": "2024-09-11T02:42:21.468172Z",
-     "shell.execute_reply": "2024-09-11T02:42:21.467639Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='work history', person='ANKUSH')"
+       "Search(query='workplace', person='ANKUSH')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -279,16 +221,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 21,
    "id": "1e047d87",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:21.470953Z",
-     "iopub.status.busy": "2024-09-11T02:42:21.470736Z",
-     "iopub.status.idle": "2024-09-11T02:42:21.473544Z",
-     "shell.execute_reply": "2024-09-11T02:42:21.473064Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.runnables import chain"
@@ -296,16 +231,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 22,
    "id": "4ec0c7fe",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:21.476024Z",
-     "iopub.status.busy": "2024-09-11T02:42:21.475835Z",
-     "iopub.status.idle": "2024-09-11T02:42:21.478359Z",
-     "shell.execute_reply": "2024-09-11T02:42:21.477932Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "retrievers = {\n",
@@ -316,16 +244,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 23,
    "id": "8dac7866",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:21.480247Z",
-     "iopub.status.busy": "2024-09-11T02:42:21.480084Z",
-     "iopub.status.idle": "2024-09-11T02:42:21.482732Z",
-     "shell.execute_reply": "2024-09-11T02:42:21.482382Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "@chain\n",
@@ -337,16 +258,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 24,
    "id": "232ad8a7-7990-4066-9228-d35a555f7293",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:21.484480Z",
-     "iopub.status.busy": "2024-09-11T02:42:21.484361Z",
-     "iopub.status.idle": "2024-09-11T02:42:22.136704Z",
-     "shell.execute_reply": "2024-09-11T02:42:22.136244Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -354,7 +268,7 @@
        "[Document(page_content='Harrison worked at Kensho')]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -365,16 +279,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 25,
    "id": "28e14ba5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:22.139305Z",
-     "iopub.status.busy": "2024-09-11T02:42:22.139106Z",
-     "iopub.status.idle": "2024-09-11T02:42:23.479739Z",
-     "shell.execute_reply": "2024-09-11T02:42:23.479170Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -382,7 +289,7 @@
        "[Document(page_content='Ankush worked at Facebook')]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -416,7 +323,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/docs/how_to/query_no_queries.ipynb

@@ -35,25 +35,10 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:33.121714Z",
-     "iopub.status.busy": "2024-09-11T02:42:33.121392Z",
-     "iopub.status.idle": "2024-09-11T02:42:36.998607Z",
-     "shell.execute_reply": "2024-09-11T02:42:36.998126Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "%pip install -qU langchain langchain-community langchain-openai langchain-chroma"
+    "# %pip install -qU langchain langchain-community langchain-openai langchain-chroma"
    ]
   },
   {
@@ -68,23 +53,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "40e2979e-a818-4b96-ac25-039336f94319",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:37.001017Z",
-     "iopub.status.busy": "2024-09-11T02:42:37.000859Z",
-     "iopub.status.idle": "2024-09-11T02:42:37.003704Z",
-     "shell.execute_reply": "2024-09-11T02:42:37.003335Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
     "\n",
     "# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
@@ -103,16 +80,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "1f621694",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:37.005644Z",
-     "iopub.status.busy": "2024-09-11T02:42:37.005493Z",
-     "iopub.status.idle": "2024-09-11T02:42:38.288481Z",
-     "shell.execute_reply": "2024-09-11T02:42:38.287904Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_chroma import Chroma\n",
@@ -140,21 +110,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:38.291700Z",
-     "iopub.status.busy": "2024-09-11T02:42:38.291468Z",
-     "iopub.status.idle": "2024-09-11T02:42:38.295796Z",
-     "shell.execute_reply": "2024-09-11T02:42:38.295205Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from typing import Optional\n",
     "\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class Search(BaseModel):\n",
@@ -168,16 +131,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:38.297840Z",
-     "iopub.status.busy": "2024-09-11T02:42:38.297712Z",
-     "iopub.status.idle": "2024-09-11T02:42:38.420456Z",
-     "shell.execute_reply": "2024-09-11T02:42:38.420140Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
@@ -193,7 +149,7 @@
     "        (\"human\", \"{question}\"),\n",
     "    ]\n",
     ")\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "structured_llm = llm.bind_tools([Search])\n",
     "query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
    ]
@@ -208,24 +164,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "bc1d3863",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:38.421934Z",
-     "iopub.status.busy": "2024-09-11T02:42:38.421831Z",
-     "iopub.status.idle": "2024-09-11T02:42:39.048915Z",
-     "shell.execute_reply": "2024-09-11T02:42:39.048519Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_korLZrh08PTRL94f4L7rFqdj', 'function': {'arguments': '{\"query\":\"Harrison\"}', 'name': 'Search'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 95, 'total_tokens': 109}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_483d39d857', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-ea94d376-37bf-4f80-abe6-e3b42b767ea0-0', tool_calls=[{'name': 'Search', 'args': {'query': 'Harrison'}, 'id': 'call_korLZrh08PTRL94f4L7rFqdj', 'type': 'tool_call'}], usage_metadata={'input_tokens': 95, 'output_tokens': 14, 'total_tokens': 109})"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_ZnoVX4j9Mn8wgChaORyd1cvq', 'function': {'arguments': '{\"query\":\"Harrison\"}', 'name': 'Search'}, 'type': 'function'}]})"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -236,24 +185,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "af62af17-4f90-4dbd-a8b4-dfff51f1db95",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:39.050923Z",
-     "iopub.status.busy": "2024-09-11T02:42:39.050785Z",
-     "iopub.status.idle": "2024-09-11T02:42:40.090421Z",
-     "shell.execute_reply": "2024-09-11T02:42:40.089454Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 93, 'total_tokens': 103}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_483d39d857', 'finish_reason': 'stop', 'logprobs': None}, id='run-ebdfc44a-455a-4ca6-be85-84559886b1e1-0', usage_metadata={'input_tokens': 93, 'output_tokens': 10, 'total_tokens': 103})"
+       "AIMessage(content='Hello! How can I assist you today?')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -274,16 +216,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "1e047d87",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:40.093716Z",
-     "iopub.status.busy": "2024-09-11T02:42:40.093472Z",
-     "iopub.status.idle": "2024-09-11T02:42:40.097732Z",
-     "shell.execute_reply": "2024-09-11T02:42:40.097274Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
@@ -294,16 +229,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "8dac7866",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:40.100028Z",
-     "iopub.status.busy": "2024-09-11T02:42:40.099882Z",
-     "iopub.status.idle": "2024-09-11T02:42:40.103105Z",
-     "shell.execute_reply": "2024-09-11T02:42:40.102734Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "@chain\n",
@@ -320,16 +248,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "232ad8a7-7990-4066-9228-d35a555f7293",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:40.105092Z",
-     "iopub.status.busy": "2024-09-11T02:42:40.104917Z",
-     "iopub.status.idle": "2024-09-11T02:42:41.341967Z",
-     "shell.execute_reply": "2024-09-11T02:42:41.341455Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -344,7 +265,7 @@
        "[Document(page_content='Harrison worked at Kensho')]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -355,24 +276,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "id": "28e14ba5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:42:41.344639Z",
-     "iopub.status.busy": "2024-09-11T02:42:41.344411Z",
-     "iopub.status.idle": "2024-09-11T02:42:41.798332Z",
-     "shell.execute_reply": "2024-09-11T02:42:41.798054Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 93, 'total_tokens': 103}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_483d39d857', 'finish_reason': 'stop', 'logprobs': None}, id='run-e87f058d-30c0-4075-8a89-a01b982d557e-0', usage_metadata={'input_tokens': 93, 'output_tokens': 10, 'total_tokens': 103})"
+       "AIMessage(content='Hello! How can I assist you today?')"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -406,7 +320,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/docs/how_to/serialization.ipynb

@@ -44,7 +44,7 @@
     "    ],\n",
     ")\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", api_key=\"llm-api-key\")\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", api_key=\"llm-api-key\")\n",
     "\n",
     "chain = prompt | llm"
    ]

docs/docs/how_to/sql_large_db.ipynb

@@ -136,7 +136,7 @@
    "source": [
     "from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class Table(BaseModel):\n",

docs/docs/how_to/structured_output.ipynb

@@ -101,7 +101,7 @@
    "source": [
     "from typing import Optional\n",
     "\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "# Pydantic\n",
@@ -667,7 +667,7 @@
     "\n",
     "from langchain_core.output_parsers import PydanticOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",
@@ -794,7 +794,7 @@
     "\n",
     "from langchain_core.messages import AIMessage\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",

docs/docs/how_to/tool_calling.ipynb

@@ -64,14 +64,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:43:40.609832Z",
-     "iopub.status.busy": "2024-09-11T02:43:40.609565Z",
-     "iopub.status.idle": "2024-09-11T02:43:40.617860Z",
-     "shell.execute_reply": "2024-09-11T02:43:40.617391Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# The function name, type hints, and docstring are all part of the tool\n",
@@ -116,17 +109,10 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:43:40.620257Z",
-     "iopub.status.busy": "2024-09-11T02:43:40.620084Z",
-     "iopub.status.idle": "2024-09-11T02:43:40.689214Z",
-     "shell.execute_reply": "2024-09-11T02:43:40.688938Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class add(BaseModel):\n",
@@ -158,14 +144,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:43:40.690850Z",
-     "iopub.status.busy": "2024-09-11T02:43:40.690739Z",
-     "iopub.status.idle": "2024-09-11T02:43:40.693436Z",
-     "shell.execute_reply": "2024-09-11T02:43:40.693199Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from typing_extensions import Annotated, TypedDict\n",
@@ -222,8 +201,7 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "if \"OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
    ]
@@ -231,19 +209,12 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:43:42.447839Z",
-     "iopub.status.busy": "2024-09-11T02:43:42.447760Z",
-     "iopub.status.idle": "2024-09-11T02:43:43.181171Z",
-     "shell.execute_reply": "2024-09-11T02:43:43.180680Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_iXj4DiW1p7WLjTAQMRO0jxMs', 'function': {'arguments': '{\"a\":3,\"b\":12}', 'name': 'multiply'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 80, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_483d39d857', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-0b620986-3f62-4df7-9ba3-4595089f9ad4-0', tool_calls=[{'name': 'multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_iXj4DiW1p7WLjTAQMRO0jxMs', 'type': 'tool_call'}], usage_metadata={'input_tokens': 80, 'output_tokens': 17, 'total_tokens': 97})"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_BwYJ4UgU5pRVCBOUmiu7NhF9', 'function': {'arguments': '{\"a\":3,\"b\":12}', 'name': 'multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 80, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_ba606877f9', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-7f05e19e-4561-40e2-a2d0-8f4e28e9a00f-0', tool_calls=[{'name': 'multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_BwYJ4UgU5pRVCBOUmiu7NhF9', 'type': 'tool_call'}], usage_metadata={'input_tokens': 80, 'output_tokens': 17, 'total_tokens': 97})"
       ]
      },
      "execution_count": 5,
@@ -288,25 +259,18 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:43:43.184004Z",
-     "iopub.status.busy": "2024-09-11T02:43:43.183777Z",
-     "iopub.status.idle": "2024-09-11T02:43:43.743024Z",
-     "shell.execute_reply": "2024-09-11T02:43:43.742171Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[{'name': 'multiply',\n",
        "  'args': {'a': 3, 'b': 12},\n",
-       "  'id': 'call_1fyhJAbJHuKQe6n0PacubGsL',\n",
+       "  'id': 'call_rcdMie7E89Xx06lEKKxJyB5N',\n",
        "  'type': 'tool_call'},\n",
        " {'name': 'add',\n",
        "  'args': {'a': 11, 'b': 49},\n",
-       "  'id': 'call_fc2jVkKzwuPWyU7kS9qn1hyG',\n",
+       "  'id': 'call_nheGN8yfvSJsnIuGZaXihou3',\n",
        "  'type': 'tool_call'}]"
       ]
      },
@@ -342,14 +306,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:43:43.746273Z",
-     "iopub.status.busy": "2024-09-11T02:43:43.746020Z",
-     "iopub.status.idle": "2024-09-11T02:43:44.586236Z",
-     "shell.execute_reply": "2024-09-11T02:43:44.585619Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -364,7 +321,7 @@
    ],
    "source": [
     "from langchain_core.output_parsers import PydanticToolsParser\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class add(BaseModel):\n",

docs/docs/how_to/tool_calling_parallel.ipynb

@@ -59,7 +59,7 @@
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
    ]
   },
   {

docs/docs/how_to/tool_choice.ipynb

@@ -59,7 +59,7 @@
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
    ]
   },
   {
@@ -77,7 +77,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_9cViskmLvPnHjXk9tbVla5HA', 'function': {'arguments': '{\"a\":2,\"b\":4}', 'name': 'Multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 103, 'total_tokens': 112}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-095b827e-2bdd-43bb-8897-c843f4504883-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 2, 'b': 4}, 'id': 'call_9cViskmLvPnHjXk9tbVla5HA'}], usage_metadata={'input_tokens': 103, 'output_tokens': 9, 'total_tokens': 112})"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_9cViskmLvPnHjXk9tbVla5HA', 'function': {'arguments': '{\"a\":2,\"b\":4}', 'name': 'Multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 103, 'total_tokens': 112}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-095b827e-2bdd-43bb-8897-c843f4504883-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 2, 'b': 4}, 'id': 'call_9cViskmLvPnHjXk9tbVla5HA'}], usage_metadata={'input_tokens': 103, 'output_tokens': 9, 'total_tokens': 112})"
       ]
      },
      "metadata": {},
@@ -111,7 +111,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_mCSiJntCwHJUBfaHZVUB2D8W', 'function': {'arguments': '{\"a\":1,\"b\":2}', 'name': 'Add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 94, 'total_tokens': 109}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-28f75260-9900-4bed-8cd3-f1579abb65e5-0', tool_calls=[{'name': 'Add', 'args': {'a': 1, 'b': 2}, 'id': 'call_mCSiJntCwHJUBfaHZVUB2D8W'}], usage_metadata={'input_tokens': 94, 'output_tokens': 15, 'total_tokens': 109})"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_mCSiJntCwHJUBfaHZVUB2D8W', 'function': {'arguments': '{\"a\":1,\"b\":2}', 'name': 'Add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 94, 'total_tokens': 109}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-28f75260-9900-4bed-8cd3-f1579abb65e5-0', tool_calls=[{'name': 'Add', 'args': {'a': 1, 'b': 2}, 'id': 'call_mCSiJntCwHJUBfaHZVUB2D8W'}], usage_metadata={'input_tokens': 94, 'output_tokens': 15, 'total_tokens': 109})"
       ]
      },
      "metadata": {},

docs/docs/how_to/tool_runtime.ipynb

@@ -55,20 +55,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:51.802901Z",
-     "iopub.status.busy": "2024-09-11T02:52:51.802682Z",
-     "iopub.status.idle": "2024-09-11T02:52:52.398167Z",
-     "shell.execute_reply": "2024-09-11T02:52:52.397911Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# | output: false\n",
     "# | echo: false\n",
     "\n",
-    "%pip install -qU langchain langchain_openai\n",
+    "# %pip install -qU langchain langchain_openai\n",
     "\n",
     "import os\n",
     "from getpass import getpass\n",
@@ -78,7 +71,7 @@
     "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)"
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
    ]
   },
   {
@@ -93,14 +86,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:52.399922Z",
-     "iopub.status.busy": "2024-09-11T02:52:52.399796Z",
-     "iopub.status.idle": "2024-09-11T02:52:52.406349Z",
-     "shell.execute_reply": "2024-09-11T02:52:52.406077Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from typing import List\n",
@@ -155,29 +141,22 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:52.407763Z",
-     "iopub.status.busy": "2024-09-11T02:52:52.407691Z",
-     "iopub.status.idle": "2024-09-11T02:52:52.411761Z",
-     "shell.execute_reply": "2024-09-11T02:52:52.411512Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'description': 'Add the list of favorite pets.',\n",
-       " 'properties': {'pets': {'description': 'List of favorite pets to set.',\n",
-       "   'items': {'type': 'string'},\n",
-       "   'title': 'Pets',\n",
-       "   'type': 'array'},\n",
-       "  'user_id': {'description': \"User's ID.\",\n",
-       "   'title': 'User Id',\n",
+       "{'title': 'update_favorite_petsSchema',\n",
+       " 'description': 'Add the list of favorite pets.',\n",
+       " 'type': 'object',\n",
+       " 'properties': {'pets': {'title': 'Pets',\n",
+       "   'description': 'List of favorite pets to set.',\n",
+       "   'type': 'array',\n",
+       "   'items': {'type': 'string'}},\n",
+       "  'user_id': {'title': 'User Id',\n",
+       "   'description': \"User's ID.\",\n",
        "   'type': 'string'}},\n",
-       " 'required': ['pets', 'user_id'],\n",
-       " 'title': 'update_favorite_petsSchema',\n",
-       " 'type': 'object'}"
+       " 'required': ['pets', 'user_id']}"
       ]
      },
      "execution_count": 3,
@@ -199,26 +178,19 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:52.427826Z",
-     "iopub.status.busy": "2024-09-11T02:52:52.427691Z",
-     "iopub.status.idle": "2024-09-11T02:52:52.431791Z",
-     "shell.execute_reply": "2024-09-11T02:52:52.431574Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'description': 'Add the list of favorite pets.',\n",
-       " 'properties': {'pets': {'description': 'List of favorite pets to set.',\n",
-       "   'items': {'type': 'string'},\n",
-       "   'title': 'Pets',\n",
-       "   'type': 'array'}},\n",
-       " 'required': ['pets'],\n",
-       " 'title': 'update_favorite_pets',\n",
-       " 'type': 'object'}"
+       "{'title': 'update_favorite_pets',\n",
+       " 'description': 'Add the list of favorite pets.',\n",
+       " 'type': 'object',\n",
+       " 'properties': {'pets': {'title': 'Pets',\n",
+       "   'description': 'List of favorite pets to set.',\n",
+       "   'type': 'array',\n",
+       "   'items': {'type': 'string'}}},\n",
+       " 'required': ['pets']}"
       ]
      },
      "execution_count": 4,
@@ -240,14 +212,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:52.433096Z",
-     "iopub.status.busy": "2024-09-11T02:52:52.433014Z",
-     "iopub.status.idle": "2024-09-11T02:52:52.437499Z",
-     "shell.execute_reply": "2024-09-11T02:52:52.437239Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -275,21 +240,14 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:52.439148Z",
-     "iopub.status.busy": "2024-09-11T02:52:52.438742Z",
-     "iopub.status.idle": "2024-09-11T02:52:53.394524Z",
-     "shell.execute_reply": "2024-09-11T02:52:53.394005Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[{'name': 'update_favorite_pets',\n",
        "  'args': {'pets': ['cats', 'parrots']},\n",
-       "  'id': 'call_pZ6XVREGh1L0BBSsiGIf1xVm',\n",
+       "  'id': 'call_W3cn4lZmJlyk8PCrKN4PRwqB',\n",
        "  'type': 'tool_call'}]"
       ]
      },
@@ -326,21 +284,14 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:53.397134Z",
-     "iopub.status.busy": "2024-09-11T02:52:53.396972Z",
-     "iopub.status.idle": "2024-09-11T02:52:53.403332Z",
-     "shell.execute_reply": "2024-09-11T02:52:53.402787Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[{'name': 'update_favorite_pets',\n",
        "  'args': {'pets': ['cats', 'parrots'], 'user_id': '123'},\n",
-       "  'id': 'call_pZ6XVREGh1L0BBSsiGIf1xVm',\n",
+       "  'id': 'call_W3cn4lZmJlyk8PCrKN4PRwqB',\n",
        "  'type': 'tool_call'}]"
       ]
      },
@@ -378,19 +329,12 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:53.405183Z",
-     "iopub.status.busy": "2024-09-11T02:52:53.405048Z",
-     "iopub.status.idle": "2024-09-11T02:52:54.248576Z",
-     "shell.execute_reply": "2024-09-11T02:52:54.248107Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[ToolMessage(content='null', name='update_favorite_pets', tool_call_id='call_oYCD0THSedHTbwNAY3NW6uUj')]"
+       "[ToolMessage(content='null', name='update_favorite_pets', tool_call_id='call_HUyF6AihqANzEYxQnTUKxkXj')]"
       ]
      },
      "execution_count": 8,
@@ -421,14 +365,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:54.251169Z",
-     "iopub.status.busy": "2024-09-11T02:52:54.250948Z",
-     "iopub.status.idle": "2024-09-11T02:52:54.254279Z",
-     "shell.execute_reply": "2024-09-11T02:52:54.253889Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -457,29 +394,22 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:54.256425Z",
-     "iopub.status.busy": "2024-09-11T02:52:54.256279Z",
-     "iopub.status.idle": "2024-09-11T02:52:54.262533Z",
-     "shell.execute_reply": "2024-09-11T02:52:54.262228Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'description': 'Update list of favorite pets',\n",
-       " 'properties': {'pets': {'description': 'List of favorite pets to set.',\n",
-       "   'items': {'type': 'string'},\n",
-       "   'title': 'Pets',\n",
-       "   'type': 'array'},\n",
-       "  'user_id': {'description': \"User's ID.\",\n",
-       "   'title': 'User Id',\n",
+       "{'title': 'UpdateFavoritePetsSchema',\n",
+       " 'description': 'Update list of favorite pets',\n",
+       " 'type': 'object',\n",
+       " 'properties': {'pets': {'title': 'Pets',\n",
+       "   'description': 'List of favorite pets to set.',\n",
+       "   'type': 'array',\n",
+       "   'items': {'type': 'string'}},\n",
+       "  'user_id': {'title': 'User Id',\n",
+       "   'description': \"User's ID.\",\n",
        "   'type': 'string'}},\n",
-       " 'required': ['pets', 'user_id'],\n",
-       " 'title': 'UpdateFavoritePetsSchema',\n",
-       " 'type': 'object'}"
+       " 'required': ['pets', 'user_id']}"
       ]
      },
      "execution_count": 10,
@@ -488,8 +418,8 @@
     }
    ],
    "source": [
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_core.tools import BaseTool\n",
-    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class UpdateFavoritePetsSchema(BaseModel):\n",
@@ -510,26 +440,19 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:54.264192Z",
-     "iopub.status.busy": "2024-09-11T02:52:54.264074Z",
-     "iopub.status.idle": "2024-09-11T02:52:54.267400Z",
-     "shell.execute_reply": "2024-09-11T02:52:54.267113Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'description': 'Update list of favorite pets',\n",
-       " 'properties': {'pets': {'description': 'List of favorite pets to set.',\n",
-       "   'items': {'type': 'string'},\n",
-       "   'title': 'Pets',\n",
-       "   'type': 'array'}},\n",
-       " 'required': ['pets'],\n",
-       " 'title': 'update_favorite_pets',\n",
-       " 'type': 'object'}"
+       "{'title': 'update_favorite_pets',\n",
+       " 'description': 'Update list of favorite pets',\n",
+       " 'type': 'object',\n",
+       " 'properties': {'pets': {'title': 'Pets',\n",
+       "   'description': 'List of favorite pets to set.',\n",
+       "   'type': 'array',\n",
+       "   'items': {'type': 'string'}}},\n",
+       " 'required': ['pets']}"
       ]
      },
      "execution_count": 11,
@@ -543,33 +466,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:54.269027Z",
-     "iopub.status.busy": "2024-09-11T02:52:54.268905Z",
-     "iopub.status.idle": "2024-09-11T02:52:54.276123Z",
-     "shell.execute_reply": "2024-09-11T02:52:54.275876Z"
-    }
-   },
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'description': 'Update list of favorite pets',\n",
-       " 'properties': {'pets': {'description': 'List of favorite pets to set.',\n",
-       "   'items': {'type': 'string'},\n",
-       "   'title': 'Pets',\n",
-       "   'type': 'array'},\n",
-       "  'user_id': {'description': \"User's ID.\",\n",
-       "   'title': 'User Id',\n",
+       "{'title': 'UpdateFavoritePetsSchema',\n",
+       " 'description': 'Update list of favorite pets',\n",
+       " 'type': 'object',\n",
+       " 'properties': {'pets': {'title': 'Pets',\n",
+       "   'description': 'List of favorite pets to set.',\n",
+       "   'type': 'array',\n",
+       "   'items': {'type': 'string'}},\n",
+       "  'user_id': {'title': 'User Id',\n",
+       "   'description': \"User's ID.\",\n",
        "   'type': 'string'}},\n",
-       " 'required': ['pets', 'user_id'],\n",
-       " 'title': 'UpdateFavoritePetsSchema',\n",
-       " 'type': 'object'}"
+       " 'required': ['pets', 'user_id']}"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -592,30 +508,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:54.277497Z",
-     "iopub.status.busy": "2024-09-11T02:52:54.277400Z",
-     "iopub.status.idle": "2024-09-11T02:52:54.280323Z",
-     "shell.execute_reply": "2024-09-11T02:52:54.280072Z"
-    }
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'description': 'Update list of favorite pets',\n",
-       " 'properties': {'pets': {'description': 'List of favorite pets to set.',\n",
-       "   'items': {'type': 'string'},\n",
-       "   'title': 'Pets',\n",
-       "   'type': 'array'}},\n",
-       " 'required': ['pets'],\n",
-       " 'title': 'update_favorite_pets',\n",
-       " 'type': 'object'}"
+       "{'title': 'update_favorite_pets',\n",
+       " 'description': 'Update list of favorite pets',\n",
+       " 'type': 'object',\n",
+       " 'properties': {'pets': {'title': 'Pets',\n",
+       "   'description': 'List of favorite pets to set.',\n",
+       "   'type': 'array',\n",
+       "   'items': {'type': 'string'}}},\n",
+       " 'required': ['pets']}"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -626,30 +535,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:54.281741Z",
-     "iopub.status.busy": "2024-09-11T02:52:54.281642Z",
-     "iopub.status.idle": "2024-09-11T02:52:54.288857Z",
-     "shell.execute_reply": "2024-09-11T02:52:54.288632Z"
-    }
-   },
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'description': 'Use the tool.\\n\\nAdd run_manager: Optional[CallbackManagerForToolRun] = None\\nto child implementations to enable tracing.',\n",
-       " 'properties': {'pets': {'items': {'type': 'string'},\n",
-       "   'title': 'Pets',\n",
-       "   'type': 'array'},\n",
+       "{'title': 'update_favorite_petsSchema',\n",
+       " 'description': 'Use the tool.\\n\\nAdd run_manager: Optional[CallbackManagerForToolRun] = None\\nto child implementations to enable tracing.',\n",
+       " 'type': 'object',\n",
+       " 'properties': {'pets': {'title': 'Pets',\n",
+       "   'type': 'array',\n",
+       "   'items': {'type': 'string'}},\n",
        "  'user_id': {'title': 'User Id', 'type': 'string'}},\n",
-       " 'required': ['pets', 'user_id'],\n",
-       " 'title': 'update_favorite_petsSchema',\n",
-       " 'type': 'object'}"
+       " 'required': ['pets', 'user_id']}"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -668,29 +570,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T02:52:54.290237Z",
-     "iopub.status.busy": "2024-09-11T02:52:54.290145Z",
-     "iopub.status.idle": "2024-09-11T02:52:54.294273Z",
-     "shell.execute_reply": "2024-09-11T02:52:54.294053Z"
-    }
-   },
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'description': 'Update list of favorite pets',\n",
-       " 'properties': {'pets': {'items': {'type': 'string'},\n",
-       "   'title': 'Pets',\n",
-       "   'type': 'array'}},\n",
-       " 'required': ['pets'],\n",
-       " 'title': 'update_favorite_pets',\n",
-       " 'type': 'object'}"
+       "{'title': 'update_favorite_pets',\n",
+       " 'description': 'Update list of favorite pets',\n",
+       " 'type': 'object',\n",
+       " 'properties': {'pets': {'title': 'Pets',\n",
+       "   'type': 'array',\n",
+       "   'items': {'type': 'string'}}},\n",
+       " 'required': ['pets']}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -702,7 +597,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -716,7 +611,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tool_streaming.ipynb

@@ -60,7 +60,7 @@
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "llm_with_tools = llm.bind_tools(tools)"
    ]
   },

docs/docs/how_to/tools_builtin.ipynb

@@ -46,33 +46,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "84f70856-b865-4658-9930-7577fb4712ce",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T03:01:11.847104Z",
-     "iopub.status.busy": "2024-09-11T03:01:11.846727Z",
-     "iopub.status.idle": "2024-09-11T03:01:13.200038Z",
-     "shell.execute_reply": "2024-09-11T03:01:13.199355Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -qU langchain-community wikipedia"
+    "!pip install -qU wikipedia"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 51,
    "id": "b4eaed85-c5a6-4ba9-b401-40258b0131c2",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T03:01:13.203356Z",
-     "iopub.status.busy": "2024-09-11T03:01:13.202996Z",
-     "iopub.status.idle": "2024-09-11T03:01:14.740686Z",
-     "shell.execute_reply": "2024-09-11T03:01:14.739748Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -103,25 +89,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 55,
    "id": "7f094f01-2e98-4947-acc4-0846963a96e0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T03:01:14.745018Z",
-     "iopub.status.busy": "2024-09-11T03:01:14.744347Z",
-     "iopub.status.idle": "2024-09-11T03:01:14.752527Z",
-     "shell.execute_reply": "2024-09-11T03:01:14.752112Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Name: wikipedia\n",
-      "Description: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.\n",
-      "args schema: {'query': {'description': 'query to look up on wikipedia', 'title': 'Query', 'type': 'string'}}\n",
-      "returns directly?: False\n"
+      "Name: wiki-tool\n",
+      "Description: look up things in wikipedia\n",
+      "args schema: {'query': {'title': 'Query', 'description': 'query to look up in Wikipedia, should be 3 or less words', 'type': 'string'}}\n",
+      "returns directly?: True\n"
      ]
     }
    ],
@@ -145,16 +124,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 56,
    "id": "1365784c-e666-41c8-a1bb-e50f822b5936",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T03:01:14.755274Z",
-     "iopub.status.busy": "2024-09-11T03:01:14.755068Z",
-     "iopub.status.idle": "2024-09-11T03:01:15.375704Z",
-     "shell.execute_reply": "2024-09-11T03:01:15.374841Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -168,7 +140,7 @@
    "source": [
     "from langchain_community.tools import WikipediaQueryRun\n",
     "from langchain_community.utilities import WikipediaAPIWrapper\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class WikiInputs(BaseModel):\n",
@@ -192,16 +164,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 57,
    "id": "6e8850d6-6840-443e-a2be-adf64b30975c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T03:01:15.378598Z",
-     "iopub.status.busy": "2024-09-11T03:01:15.378414Z",
-     "iopub.status.idle": "2024-09-11T03:01:15.382248Z",
-     "shell.execute_reply": "2024-09-11T03:01:15.381801Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -209,7 +174,7 @@
      "text": [
       "Name: wiki-tool\n",
       "Description: look up things in wikipedia\n",
-      "args schema: {'query': {'description': 'query to look up in Wikipedia, should be 3 or less words', 'title': 'Query', 'type': 'string'}}\n",
+      "args schema: {'query': {'title': 'Query', 'description': 'query to look up in Wikipedia, should be 3 or less words', 'type': 'string'}}\n",
       "returns directly?: True\n"
      ]
     }
@@ -247,9 +212,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv-311",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv-311"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

docs/docs/how_to/tools_chain.ipynb

@@ -166,7 +166,7 @@
     "\n",
     "from langchain_openai.chat_models import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
    ]
   },
   {

docs/docs/how_to/tools_error.ipynb

@@ -53,14 +53,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "08785b6d-722d-4620-b6ec-36deb3842c69",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T03:10:25.005243Z",
-     "iopub.status.busy": "2024-09-11T03:10:25.005074Z",
-     "iopub.status.idle": "2024-09-11T03:10:25.007679Z",
-     "shell.execute_reply": "2024-09-11T03:10:25.007361Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import getpass\n",
@@ -88,16 +81,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "86258950-5e61-4340-81b9-84a5d26e8773",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T03:10:25.009496Z",
-     "iopub.status.busy": "2024-09-11T03:10:25.009371Z",
-     "iopub.status.idle": "2024-09-11T03:10:25.552917Z",
-     "shell.execute_reply": "2024-09-11T03:10:25.552592Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# | echo: false\n",
@@ -105,24 +91,16 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "if \"OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "1d20604e-c4d1-4d21-841b-23e4f61aec36",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T03:10:25.554543Z",
-     "iopub.status.busy": "2024-09-11T03:10:25.554439Z",
-     "iopub.status.idle": "2024-09-11T03:10:25.631610Z",
-     "shell.execute_reply": "2024-09-11T03:10:25.631346Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Define tool\n",
@@ -153,33 +131,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "d354664c-ac44-4967-a35f-8912b3ad9477",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T03:10:25.633050Z",
-     "iopub.status.busy": "2024-09-11T03:10:25.632978Z",
-     "iopub.status.idle": "2024-09-11T03:10:26.556508Z",
-     "shell.execute_reply": "2024-09-11T03:10:26.556233Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "ename": "ValidationError",
-     "evalue": "1 validation error for complex_toolSchema\ndict_arg\n  Field required [type=missing, input_value={'int_arg': 5, 'float_arg': 2.1}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.8/v/missing",
+     "evalue": "1 validation error for complex_toolSchema\ndict_arg\n  field required (type=value_error.missing)",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muse complex tool. the args are 5, 2.1, empty dictionary. don\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt forget dict_arg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      3\u001b[0m \u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/langchain/.venv/lib/python3.11/site-packages/langchain_core/runnables/base.py:2998\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   2996\u001b[0m             \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(step\u001b[38;5;241m.\u001b[39minvoke, \u001b[38;5;28minput\u001b[39m, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   2997\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2998\u001b[0m             \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2999\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   3000\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "File \u001b[0;32m~/langchain/.venv/lib/python3.11/site-packages/langchain_core/tools/base.py:456\u001b[0m, in \u001b[0;36mBaseTool.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    449\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    450\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    451\u001b[0m     \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mstr\u001b[39m, Dict, ToolCall],\n\u001b[1;32m    452\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    453\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    454\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    455\u001b[0m     tool_input, kwargs \u001b[38;5;241m=\u001b[39m _prep_run_args(\u001b[38;5;28minput\u001b[39m, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 456\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/langchain/.venv/lib/python3.11/site-packages/langchain_core/tools/base.py:659\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, config, tool_call_id, **kwargs)\u001b[0m\n\u001b[1;32m    657\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error_to_raise:\n\u001b[1;32m    658\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_tool_error(error_to_raise)\n\u001b[0;32m--> 659\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m error_to_raise\n\u001b[1;32m    660\u001b[0m output \u001b[38;5;241m=\u001b[39m _format_output(content, artifact, tool_call_id, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, status)\n\u001b[1;32m    661\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_tool_end(output, color\u001b[38;5;241m=\u001b[39mcolor, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[0;32m~/langchain/.venv/lib/python3.11/site-packages/langchain_core/tools/base.py:622\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, config, tool_call_id, **kwargs)\u001b[0m\n\u001b[1;32m    620\u001b[0m context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[1;32m    621\u001b[0m context\u001b[38;5;241m.\u001b[39mrun(_set_config_context, child_config)\n\u001b[0;32m--> 622\u001b[0m tool_args, tool_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_to_args_and_kwargs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    623\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m signature(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    624\u001b[0m     tool_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n",
-      "File \u001b[0;32m~/langchain/.venv/lib/python3.11/site-packages/langchain_core/tools/base.py:545\u001b[0m, in \u001b[0;36mBaseTool._to_args_and_kwargs\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m    544\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_to_args_and_kwargs\u001b[39m(\u001b[38;5;28mself\u001b[39m, tool_input: Union[\u001b[38;5;28mstr\u001b[39m, Dict]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[Tuple, Dict]:\n\u001b[0;32m--> 545\u001b[0m     tool_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    546\u001b[0m     \u001b[38;5;66;03m# For backwards compatibility, if run_input is a string,\u001b[39;00m\n\u001b[1;32m    547\u001b[0m     \u001b[38;5;66;03m# pass as a positional argument.\u001b[39;00m\n\u001b[1;32m    548\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tool_input, \u001b[38;5;28mstr\u001b[39m):\n",
-      "File \u001b[0;32m~/langchain/.venv/lib/python3.11/site-packages/langchain_core/tools/base.py:487\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m    485\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m input_args \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    486\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(input_args, BaseModel):\n\u001b[0;32m--> 487\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43minput_args\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_validate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    488\u001b[0m         result_dict \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39mmodel_dump()\n\u001b[1;32m    489\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(input_args, BaseModelV1):\n",
-      "File \u001b[0;32m~/langchain/.venv/lib/python3.11/site-packages/pydantic/main.py:568\u001b[0m, in \u001b[0;36mBaseModel.model_validate\u001b[0;34m(cls, obj, strict, from_attributes, context)\u001b[0m\n\u001b[1;32m    566\u001b[0m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m    567\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 568\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pydantic_validator__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_python\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    569\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_attributes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_attributes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontext\u001b[49m\n\u001b[1;32m    570\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for complex_toolSchema\ndict_arg\n  Field required [type=missing, input_value={'int_arg': 5, 'float_arg': 2.1}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.8/v/missing"
+      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muse complex tool. the args are 5, 2.1, empty dictionary. don\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt forget dict_arg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      3\u001b[0m \u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/runnables/base.py:2572\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   2570\u001b[0m             \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m step\u001b[38;5;241m.\u001b[39minvoke(\u001b[38;5;28minput\u001b[39m, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   2571\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2572\u001b[0m             \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2573\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   2574\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/tools.py:380\u001b[0m, in \u001b[0;36mBaseTool.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    373\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    374\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    375\u001b[0m     \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mstr\u001b[39m, Dict],\n\u001b[1;32m    376\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    377\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    378\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    379\u001b[0m     config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[0;32m--> 380\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    381\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    382\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    383\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtags\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    384\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    385\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    386\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    387\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    388\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    389\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/tools.py:537\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, config, **kwargs)\u001b[0m\n\u001b[1;32m    535\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ValidationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    536\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_validation_error:\n\u001b[0;32m--> 537\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    538\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_validation_error, \u001b[38;5;28mbool\u001b[39m):\n\u001b[1;32m    539\u001b[0m         observation \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTool input validation error\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/tools.py:526\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, config, **kwargs)\u001b[0m\n\u001b[1;32m    524\u001b[0m context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[1;32m    525\u001b[0m context\u001b[38;5;241m.\u001b[39mrun(_set_config_context, child_config)\n\u001b[0;32m--> 526\u001b[0m parsed_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    527\u001b[0m tool_args, tool_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_to_args_and_kwargs(parsed_input)\n\u001b[1;32m    528\u001b[0m observation \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    529\u001b[0m     context\u001b[38;5;241m.\u001b[39mrun(\n\u001b[1;32m    530\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run, \u001b[38;5;241m*\u001b[39mtool_args, run_manager\u001b[38;5;241m=\u001b[39mrun_manager, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtool_kwargs\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    533\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m context\u001b[38;5;241m.\u001b[39mrun(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run, \u001b[38;5;241m*\u001b[39mtool_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtool_kwargs)\n\u001b[1;32m    534\u001b[0m )\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/tools.py:424\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m    422\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    423\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m input_args \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 424\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43minput_args\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    425\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m    426\u001b[0m             k: \u001b[38;5;28mgetattr\u001b[39m(result, k)\n\u001b[1;32m    427\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdict()\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m    428\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m tool_input\n\u001b[1;32m    429\u001b[0m         }\n\u001b[1;32m    430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tool_input\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/pydantic/main.py:526\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.parse_obj\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for complex_toolSchema\ndict_arg\n  field required (type=value_error.missing)"
      ]
     }
    ],
@@ -201,16 +172,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "8fedb550-683d-45ae-8876-ae7acb332019",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T03:10:26.558131Z",
-     "iopub.status.busy": "2024-09-11T03:10:26.558031Z",
-     "iopub.status.idle": "2024-09-11T03:10:27.399844Z",
-     "shell.execute_reply": "2024-09-11T03:10:27.399201Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -222,10 +186,9 @@
       "\n",
       "raised the following error:\n",
       "\n",
-      "<class 'pydantic_core._pydantic_core.ValidationError'>: 1 validation error for complex_toolSchema\n",
+      "<class 'pydantic.error_wrappers.ValidationError'>: 1 validation error for complex_toolSchema\n",
       "dict_arg\n",
-      "  Field required [type=missing, input_value={'int_arg': 5, 'float_arg': 2.1}, input_type=dict]\n",
-      "    For further information visit https://errors.pydantic.dev/2.8/v/missing\n"
+      "  field required (type=value_error.missing)\n"
      ]
     }
    ],
@@ -263,16 +226,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "02cc4223-35fa-4240-976a-012299ca703c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T03:10:27.404122Z",
-     "iopub.status.busy": "2024-09-11T03:10:27.403539Z",
-     "iopub.status.idle": "2024-09-11T03:10:38.080547Z",
-     "shell.execute_reply": "2024-09-11T03:10:38.079955Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -280,7 +236,7 @@
        "10.5"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -321,16 +277,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "id": "b5659956-9454-468a-9753-a3ff9052b8f5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T03:10:38.083810Z",
-     "iopub.status.busy": "2024-09-11T03:10:38.083623Z",
-     "iopub.status.idle": "2024-09-11T03:10:38.090089Z",
-     "shell.execute_reply": "2024-09-11T03:10:38.089682Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.messages import AIMessage, HumanMessage, ToolCall, ToolMessage\n",
@@ -386,16 +335,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "id": "4c45f5bd-cbb4-47d5-b4b6-aec50673c750",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-11T03:10:38.092152Z",
-     "iopub.status.busy": "2024-09-11T03:10:38.092021Z",
-     "iopub.status.idle": "2024-09-11T03:10:39.592443Z",
-     "shell.execute_reply": "2024-09-11T03:10:39.591990Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -403,7 +345,7 @@
        "10.5"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -459,7 +401,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tools_few_shot.ipynb

@@ -48,7 +48,7 @@
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "llm_with_tools = llm.bind_tools(tools)"
    ]
   },

docs/docs/how_to/tools_human.ipynb

@@ -129,13 +129,13 @@
     "\n",
     "@tool\n",
     "def count_emails(last_n_days: int) -> int:\n",
-    "    \"\"\"Dummy function to count number of e-mails. Returns 2 * last_n_days.\"\"\"\n",
+    "    \"\"\"Multiply two integers together.\"\"\"\n",
     "    return last_n_days * 2\n",
     "\n",
     "\n",
     "@tool\n",
     "def send_email(message: str, recipient: str) -> str:\n",
-    "    \"\"\"Dummy function for sending an e-mail.\"\"\"\n",
+    "    \"Add two integers.\"\n",
     "    return f\"Successfully sent email to {recipient}.\"\n",
     "\n",
     "\n",

docs/docs/integrations/chat/ai21.ipynb

@@ -50,18 +50,18 @@
   },
   {
    "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
+   ]
   },
   {
    "cell_type": "markdown",
@@ -73,14 +73,14 @@
   },
   {
    "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 +115,15 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 2,
    "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 +135,21 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 3,
    "id": "46b982dc-5d8a-46da-a711-81c03ccd6adc",
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore programmer.\", id='run-2e8d16d6-a06e-45cb-8d0c-1c8208645033-0')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "messages = [\n",
     "    (\n",
@@ -147,9 +160,7 @@
     "]\n",
     "ai_msg = llm.invoke(messages)\n",
     "ai_msg"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
@@ -163,6 +174,7 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 4,
    "id": "39353473fce5dd2e",
    "metadata": {
     "collapsed": false,
@@ -170,6 +182,18 @@
      "outputs_hidden": false
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe das Programmieren.', id='run-e1bd82dc-1a7e-4b2e-bde9-ac995929ac0f-0')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
@@ -191,95 +215,7 @@
     "        \"input\": \"I love programming.\",\n",
     "    }\n",
     ")"
-   ],
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "# Tool Calls / Function Calling",
-   "id": "39c0ccd229927eab"
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "This example shows how to use tool calling with AI21 models:",
-   "id": "2bf6b40be07fe2d4"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "from langchain_ai21.chat_models import ChatAI21\n",
-    "from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage\n",
-    "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",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def get_weather(location: str, date: str) -> str:\n",
-    "    \"\"\"“Provide the weather for the specified location on the given date.”\"\"\"\n",
-    "    if location == \"New York\" and date == \"2024-12-05\":\n",
-    "        return \"25 celsius\"\n",
-    "    elif location == \"New York\" and date == \"2024-12-06\":\n",
-    "        return \"27 celsius\"\n",
-    "    elif location == \"London\" and date == \"2024-12-05\":\n",
-    "        return \"22 celsius\"\n",
-    "    return \"32 celsius\"\n",
-    "\n",
-    "\n",
-    "llm = ChatAI21(model=\"jamba-1.5-mini\")\n",
-    "\n",
-    "llm_with_tools = llm.bind_tools([convert_to_openai_tool(get_weather)])\n",
-    "\n",
-    "chat_messages = [\n",
-    "    SystemMessage(\n",
-    "        content=\"You are a helpful assistant. You can use the provided tools \"\n",
-    "        \"to assist with various tasks and provide accurate information\"\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "human_messages = [\n",
-    "    HumanMessage(\n",
-    "        content=\"What is the forecast for the weather in New York on December 5, 2024?\"\n",
-    "    ),\n",
-    "    HumanMessage(content=\"And what about the 2024-12-06?\"),\n",
-    "    HumanMessage(content=\"OK, thank you.\"),\n",
-    "    HumanMessage(content=\"What is the expected weather in London on December 5, 2024?\"),\n",
-    "]\n",
-    "\n",
-    "\n",
-    "for human_message in human_messages:\n",
-    "    print(f\"User: {human_message.content}\")\n",
-    "    chat_messages.append(human_message)\n",
-    "    response = llm_with_tools.invoke(chat_messages)\n",
-    "    chat_messages.append(response)\n",
-    "    if response.tool_calls:\n",
-    "        tool_call = response.tool_calls[0]\n",
-    "        if tool_call[\"name\"] == \"get_weather\":\n",
-    "            weather = get_weather.invoke(\n",
-    "                {\n",
-    "                    \"location\": tool_call[\"args\"][\"location\"],\n",
-    "                    \"date\": tool_call[\"args\"][\"date\"],\n",
-    "                }\n",
-    "            )\n",
-    "            chat_messages.append(\n",
-    "                ToolMessage(content=weather, tool_call_id=tool_call[\"id\"])\n",
-    "            )\n",
-    "            llm_answer = llm_with_tools.invoke(chat_messages)\n",
-    "            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/anthropic.ipynb

@@ -59,8 +59,7 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"ANTHROPIC_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass(\"Enter your Anthropic API key: \")"
+    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass(\"Enter your Anthropic API key: \")"
    ]
   },
   {
@@ -275,7 +274,7 @@
     }
    ],
    "source": [
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class GetWeather(BaseModel):\n",

docs/docs/integrations/chat/anthropic_functions.ipynb

@@ -69,7 +69,7 @@
     }
    ],
    "source": [
-    "from pydantic import BaseModel\n",
+    "from langchain_core.pydantic_v1 import BaseModel\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",

docs/docs/integrations/chat/azure_chat_openai.ipynb

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

docs/docs/integrations/chat/cerebras.ipynb

@@ -76,8 +76,7 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"CEREBRAS_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"CEREBRAS_API_KEY\"] = getpass.getpass(\"Enter your Cerebras API key: \")"
+    "os.environ[\"CEREBRAS_API_KEY\"] = getpass.getpass(\"Enter your Cerebras API key: \")"
    ]
   },
   {

docs/docs/integrations/chat/databricks.ipynb

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

docs/docs/integrations/chat/deepinfra.ipynb

@@ -123,8 +123,8 @@
     "from dotenv import find_dotenv, load_dotenv\n",
     "from langchain_community.chat_models import ChatDeepInfra\n",
     "from langchain_core.messages import HumanMessage\n",
+    "from langchain_core.pydantic_v1 import BaseModel\n",
     "from langchain_core.tools import tool\n",
-    "from pydantic import BaseModel\n",
     "\n",
     "model_name = \"meta-llama/Meta-Llama-3-70B-Instruct\"\n",
     "\n",

docs/docs/integrations/chat/edenai.ipynb

@@ -264,7 +264,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "llm = ChatEdenAI(provider=\"openai\", temperature=0.2, max_tokens=500)\n",
     "\n",

docs/docs/integrations/chat/fireworks.ipynb

@@ -52,8 +52,7 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"FIREWORKS_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Enter your Fireworks API key: \")"
+    "os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Enter your Fireworks API key: \")"
    ]
   },
   {

docs/docs/integrations/chat/friendli.ipynb

@@ -44,8 +44,7 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"FRIENDLI_TOKEN\" not in os.environ:\n",
-    "    os.environ[\"FRIENDLI_TOKEN\"] = getpass.getpass(\"Friendi Personal Access Token: \")"
+    "os.environ[\"FRIENDLI_TOKEN\"] = getpass.getpass(\"Friendi Personal Access Token: \")"
    ]
   },
   {

docs/docs/integrations/chat/google_generative_ai.ipynb

@@ -60,8 +60,7 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"GOOGLE_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"GOOGLE_API_KEY\"] = getpass.getpass(\"Enter your Google AI API key: \")"
+    "os.environ[\"GOOGLE_API_KEY\"] = getpass.getpass(\"Enter your Google AI API key: \")"
    ]
   },
   {

docs/docs/integrations/chat/groq.ipynb

@@ -50,8 +50,7 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"GROQ_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"GROQ_API_KEY\"] = getpass.getpass(\"Enter your Groq API key: \")"
+    "os.environ[\"GROQ_API_KEY\"] = getpass.getpass(\"Enter your Groq API key: \")"
    ]
   },
   {

docs/docs/integrations/chat/ibm_watsonx.ipynb

@@ -483,7 +483,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class GetWeather(BaseModel):\n",

docs/docs/integrations/chat/llamacpp.ipynb

@@ -226,8 +226,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_core.tools import tool\n",
-    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class WeatherInput(BaseModel):\n",
@@ -343,8 +343,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain_core.pydantic_v1 import BaseModel\n",
     "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
-    "from pydantic import BaseModel\n",
     "\n",
     "\n",
     "class Joke(BaseModel):\n",

docs/docs/integrations/chat/mistralai.ipynb

@@ -52,8 +52,7 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"MISTRAL_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"MISTRAL_API_KEY\"] = getpass.getpass(\"Enter your Mistral API key: \")"
+    "os.environ[\"MISTRAL_API_KEY\"] = getpass.getpass(\"Enter your Mistral API key: \")"
    ]
   },
   {

docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb

@@ -658,8 +658,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain_core.pydantic_v1 import Field\n",
     "from langchain_core.tools import tool\n",
-    "from pydantic import Field\n",
     "\n",
     "\n",
     "@tool\n",

docs/docs/integrations/chat/premai.ipynb

@@ -426,8 +426,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_core.tools import tool\n",
-    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "# Define the schema for function arguments\n",

docs/docs/integrations/chat/yi.ipynb

@@ -41,8 +41,7 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"YI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"YI_API_KEY\"] = getpass.getpass(\"Enter your Yi API key: \")"
+    "os.environ[\"YI_API_KEY\"] = getpass.getpass(\"Enter your Yi API key: \")"
    ]
   },
   {

docs/docs/integrations/chat_loaders/langsmith_llm_runs.ipynb

@@ -72,7 +72,7 @@
    "source": [
     "from enum import Enum\n",
     "\n",
-    "from pydantic import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class Operation(Enum):\n",
@@ -135,8 +135,8 @@
    "source": [
     "from pprint import pprint\n",
     "\n",
+    "from langchain_core.pydantic_v1 import BaseModel\n",
     "from langchain_core.utils.function_calling import convert_pydantic_to_openai_function\n",
-    "from pydantic import BaseModel\n",
     "\n",
     "openai_function_def = convert_pydantic_to_openai_function(Calculator)\n",
     "pprint(openai_function_def)"

docs/docs/integrations/document_loaders/scrapfly.ipynb

@@ -49,7 +49,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ScrapflyLoader also allows passing ScrapeConfig object for customizing the scrape request. See the documentation for the full feature details and their API params: https://scrapfly.io/docs/scrape-api/getting-started"
+    "The ScrapflyLoader also allows passigng ScrapeConfig object for customizing the scrape request. See the documentation for the full feature details and their API params: https://scrapfly.io/docs/scrape-api/getting-started"
    ]
   },
   {

docs/docs/integrations/document_loaders/unstructured_file.ipynb

@@ -39,10 +39,9 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"UNSTRUCTURED_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"UNSTRUCTURED_API_KEY\"] = getpass.getpass(\n",
-    "        \"Enter your Unstructured API key: \"\n",
-    "    )"
+    "os.environ[\"UNSTRUCTURED_API_KEY\"] = getpass.getpass(\n",
+    "    \"Enter your Unstructured API key: \"\n",
+    ")"
    ]
   },
   {

docs/docs/integrations/document_transformers/dashscope_rerank.ipynb

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

docs/docs/integrations/document_transformers/jina_rerank.ipynb

@@ -214,7 +214,7 @@
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "combine_docs_chain = create_stuff_documents_chain(llm, retrieval_qa_chat_prompt)\n",
     "chain = create_retrieval_chain(compression_retriever, combine_docs_chain)"
    ]

docs/docs/integrations/document_transformers/rankllm-reranker.ipynb

@@ -50,8 +50,7 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
    ]
   },
   {

docs/docs/integrations/document_transformers/volcengine_rerank.ipynb

@@ -44,10 +44,8 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"VOLC_API_AK\" not in os.environ:\n",
-    "    os.environ[\"VOLC_API_AK\"] = getpass.getpass(\"Volcengine API AK:\")\n",
-    "if \"VOLC_API_SK\" not in os.environ:\n",
-    "    os.environ[\"VOLC_API_SK\"] = getpass.getpass(\"Volcengine API SK:\")"
+    "os.environ[\"VOLC_API_AK\"] = getpass.getpass(\"Volcengine API AK:\")\n",
+    "os.environ[\"VOLC_API_SK\"] = getpass.getpass(\"Volcengine API SK:\")"
    ]
   },
   {

docs/docs/integrations/document_transformers/voyageai-reranker.ipynb

@@ -51,8 +51,7 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "if \"VOYAGE_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"VOYAGE_API_KEY\"] = getpass.getpass(\"Voyage AI API Key:\")"
+    "os.environ[\"VOYAGE_API_KEY\"] = getpass.getpass(\"Voyage AI API Key:\")"
    ]
   },
   {

docs/docs/integrations/memory/couchbase_chat_message_history.ipynb

@@ -267,7 +267,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Hello Bob! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 22, 'total_tokens': 32}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-a0f8a29e-ddf4-4e06-a1fe-cf8c325a2b72-0', usage_metadata={'input_tokens': 22, 'output_tokens': 10, 'total_tokens': 32})"
+       "AIMessage(content='Hello Bob! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 22, 'total_tokens': 32}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-a0f8a29e-ddf4-4e06-a1fe-cf8c325a2b72-0', usage_metadata={'input_tokens': 22, 'output_tokens': 10, 'total_tokens': 32})"
       ]
      },
      "execution_count": 11,
@@ -288,7 +288,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Your name is Bob.', response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 43, 'total_tokens': 48}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f764a9eb-999e-4042-96b6-fe47b7ae4779-0', usage_metadata={'input_tokens': 43, 'output_tokens': 5, 'total_tokens': 48})"
+       "AIMessage(content='Your name is Bob.', response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 43, 'total_tokens': 48}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f764a9eb-999e-4042-96b6-fe47b7ae4779-0', usage_metadata={'input_tokens': 43, 'output_tokens': 5, 'total_tokens': 48})"
       ]
      },
      "execution_count": 12,

docs/docs/integrations/memory/tidb_chat_message_history.ipynb

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

docs/docs/integrations/memory/xata_chat_message_history.ipynb

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

docs/docs/integrations/platforms/huggingface.mdx

@@ -12,7 +12,7 @@ pip install langchain-huggingface
 
 ## Chat models
 
-### ChatHuggingFace
+### Models from Hugging Face
 
 We can use the `Hugging Face` LLM classes or directly use the `ChatHuggingFace` class.
 
@@ -24,16 +24,7 @@ from langchain_huggingface import ChatHuggingFace
 
 ## LLMs
 
-### HuggingFaceEndpoint
-
-
-See a [usage example](/docs/integrations/llms/huggingface_endpoint).
-
-```python
-from langchain_huggingface import HuggingFaceEndpoint
-```
-
-### HuggingFacePipeline
+### Hugging Face Local Pipelines
 
 Hugging Face models can be run locally through the `HuggingFacePipeline` class.
 
@@ -53,22 +44,6 @@ See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
 from langchain_huggingface import HuggingFaceEmbeddings
 ```
 
-### HuggingFaceEndpointEmbeddings
-
-See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
-
-```python
-from langchain_huggingface import HuggingFaceEndpointEmbeddings
-```
-
-### HuggingFaceInferenceAPIEmbeddings
-
-See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
-
-```python
-from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
-```
-
 ### HuggingFaceInstructEmbeddings
 
 See a [usage example](/docs/integrations/text_embedding/instruct_embeddings).
@@ -88,6 +63,25 @@ See a [usage example](/docs/integrations/text_embedding/bge_huggingface).
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings
 ```
 
+### Hugging Face Text Embeddings Inference (TEI)
+
+>[Hugging Face Text Embeddings Inference (TEI)](https://huggingface.co/docs/text-generation-inference/index) is a toolkit for deploying and serving open-source
+> text embeddings and sequence classification models. `TEI` enables high-performance extraction for the most popular models,
+>including `FlagEmbedding`, `Ember`, `GTE` and `E5`.
+
+We need to install `huggingface-hub` python package.
+
+```bash
+pip install huggingface-hub
+```
+
+See a [usage example](/docs/integrations/text_embedding/text_embeddings_inference).
+
+```python
+from langchain_community.embeddings import HuggingFaceHubEmbeddings
+```
+
+
 ## Document Loaders
 
 ### Hugging Face dataset
@@ -110,34 +104,7 @@ See a [usage example](/docs/integrations/document_loaders/hugging_face_dataset).
 from langchain_community.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
 ```
 
-### Hugging Face model loader
 
->Load model information from `Hugging Face Hub`, including README content.
->
->This loader interfaces with the `Hugging Face Models API` to fetch 
-> and load model metadata and README files. 
-> The API allows you to search and filter models based on 
-> specific criteria such as model tags, authors, and more.
-
-```python
-from langchain_community.document_loaders import HuggingFaceModelLoader
-```
-
-### Image captions
-
-It uses the Hugging Face models to generate image captions.
-
-We need to install several python packages.
-
-```bash
-pip install transformers pillow
-```
-
-See a [usage example](/docs/integrations/document_loaders/image_captions).
-
-```python
-from langchain_community.document_loaders import ImageCaptionLoader
-```
 
 ## Tools
 
@@ -157,12 +124,3 @@ See a [usage example](/docs/integrations/tools/huggingface_tools).
 ```python
 from langchain_community.agent_toolkits.load_tools import load_huggingface_tool
 ```
-
-### Hugging Face Text-to-Speech Model Inference.
-
-> It is a wrapper around `OpenAI Text-to-Speech API`.
-
-```python
-from langchain_community.tools.audio import HuggingFaceTextToSpeechModelInference
-```
-

docs/docs/integrations/platforms/microsoft.mdx

@@ -436,8 +436,6 @@ See a [usage example](/docs/integrations/tools/azure_ai_services).
 from langchain_community.agent_toolkits import azure_ai_services
 ```
 
-#### Azure AI Services individual tools
-
 The `azure_ai_services` toolkit includes the following tools:
 
 - Image Analysis: [AzureAiServicesImageAnalysisTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.azure_ai_services.image_analysis.AzureAiServicesImageAnalysisTool.html)
@@ -462,23 +460,6 @@ See a [usage example](/docs/integrations/tools/office365).
 from langchain_community.agent_toolkits import O365Toolkit
 ```
 
-#### Office 365 individual tools
-
-You can use individual tools from the Office 365 Toolkit:
-- `O365CreateDraftMessage`: tool for creating a draft email in Office 365
-- `O365SearchEmails`: tool for searching email messages in Office 365
-- `O365SearchEvents`: tool for searching calendar events in Office 365
-- `O365SendEvent`: tool for sending calendar events in Office 365
-- `O365SendMessage`: tool for sending an email in Office 365
-
-```python
-from langchain_community.tools.office365 import O365CreateDraftMessage
-from langchain_community.tools.office365 import O365SearchEmails
-from langchain_community.tools.office365 import O365SearchEvents
-from langchain_community.tools.office365 import O365SendEvent
-from langchain_community.tools.office365 import O365SendMessage
-```
-
 ### Microsoft Azure PowerBI
 
 We need to install `azure-identity` python package.
@@ -494,20 +475,6 @@ from langchain_community.agent_toolkits import PowerBIToolkit
 from langchain_community.utilities.powerbi import PowerBIDataset
 ```
 
-#### PowerBI individual tools
-
-You can use individual tools from the Azure PowerBI Toolkit:
-- `InfoPowerBITool`: tool for getting metadata about a PowerBI Dataset
-- `ListPowerBITool`: tool for getting tables names
-- `QueryPowerBITool`: tool for querying a PowerBI Dataset
-
-```python
-from langchain_community.tools.powerbi.tool import InfoPowerBITool
-from langchain_community.tools.powerbi.tool import ListPowerBITool
-from langchain_community.tools.powerbi.tool import QueryPowerBITool
-```
-
-
 ### PlayWright Browser Toolkit
 
 >[Playwright](https://github.com/microsoft/playwright) is an open-source automation tool 

docs/docs/integrations/providers/apache.mdx

@@ -1,63 +0,0 @@
-# Apache Software Foundation
-
->[The Apache Software Foundation (Wikipedia)](https://en.wikipedia.org/wiki/The_Apache_Software_Foundation) 
-> is a decentralized open source community of developers. The software they 
-> produce is distributed under the terms of the Apache License, a permissive 
-> open-source license for free and open-source software (FOSS). The Apache projects 
-> are characterized by a collaborative, consensus-based development process 
-> and an open and pragmatic software license, which is to say that it 
-> allows developers, who receive the software freely, to redistribute 
-> it under non-free terms. Each project is managed by a self-selected 
-> team of technical experts who are active contributors to the project.
-
-## Apache AGE
-
->[Apache AGE](https://age.apache.org/) is a `PostgreSQL` extension that provides 
-> graph database functionality. `AGE` is an acronym for `A Graph Extension`, and 
-> is inspired by Bitnine’s fork of `PostgreSQL 10`, `AgensGraph`, which is 
-> a multimodal database. The goal of the project is to create single 
-> storage that can handle both relational and graph model data so that users 
-> can use standard ANSI SQL along with `openCypher`, the Graph query language. 
-> The data elements `Apache AGE` stores are nodes, edges connecting them, and 
-> attributes of nodes and edges.
- 
-See more about [integrating with Apache AGE](/docs/integrations/graphs/apache_age).
-
-## Apache Cassandra
-
->[Apache Cassandra](https://cassandra.apache.org/) is a NoSQL, row-oriented, 
-> highly scalable and highly available database. Starting with version 5.0, 
-> the database ships with vector search capabilities.
- 
-See more about [integrating with Apache Cassandra](/docs/integrations/providers/cassandra/).
-
-## Apache Doris
-
->[Apache Doris](https://doris.apache.org/) is a modern data warehouse for 
-> real-time analytics. It delivers lightning-fast analytics on real-time data at scale.
->
->Usually `Apache Doris` is categorized into OLAP, and it has showed excellent 
-> performance in ClickBench — a Benchmark For Analytical DBMS. Since it has 
-> a super-fast vectorized execution engine, it could also be used as a fast vectordb.
- 
-See more about [integrating with Apache Doris](/docs/integrations/providers/apache_doris/).
-
-## Apache Kafka
-
->[Apache Kafka](https://github.com/apache/kafka) is a distributed messaging system 
-> that is used to publish and subscribe to streams of records.
- 
-See more about [integrating with Apache Kafka](/docs/integrations/memory/kafka_chat_message_history).
-
-
-## Apache Spark
-
->[Apache Spark](https://spark.apache.org/) is a unified analytics engine for 
-> large-scale data processing. It provides high-level APIs in Scala, Java, 
-> Python, and R, and an optimized engine that supports general computation 
-> graphs for data analysis. It also supports a rich set of higher-level 
-> tools including `Spark SQL` for SQL and DataFrames, `pandas API on Spark` 
-> for pandas workloads, `MLlib` for machine learning, 
-> `GraphX` for graph processing, and `Structured Streaming` for stream processing.
-
-See more about [integrating with Apache Spark](/docs/integrations/providers/spark).

docs/docs/integrations/providers/apple.mdx

@@ -1,22 +0,0 @@
-# Apple
-
->[Apple Inc. (Wikipedia)](https://en.wikipedia.org/wiki/Apple_Inc.) is an American 
-> multinational corporation and technology company.
->
-> [iMessage (Wikipedia)](https://en.wikipedia.org/wiki/IMessage) is an instant 
-> messaging service developed by Apple Inc. and launched in 2011. 
-> `iMessage` functions exclusively on Apple platforms.
-
-## Installation and Setup
-
-See [setup instructions](/docs/integrations/chat_loaders/imessage).
-
-## Chat loader
-
-It loads chat sessions from the `iMessage` `chat.db` `SQLite` file.
-
-See a [usage example](/docs/integrations/chat_loaders/imessage).
-
-```python
-from langchain_community.chat_loaders.imessage import IMessageChatLoader
-```

docs/docs/integrations/providers/nomic.ipynb

@@ -0,0 +1,69 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nomic\n",
+    "\n",
+    "Nomic currently offers two products:\n",
+    "\n",
+    "- Atlas: their Visual Data Engine\n",
+    "- GPT4All: their Open Source Edge Language Model Ecosystem\n",
+    "\n",
+    "The Nomic integration exists in its own [partner package](https://pypi.org/project/langchain-nomic/). You can install it with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-nomic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Currently, you can import their hosted [embedding model](/docs/integrations/text_embedding/nomic) as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "y8ku6X96sebl"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_nomic import NomicEmbeddings"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/docs/integrations/providers/nomic.mdx

@@ -1,58 +0,0 @@
-# Nomic
-
->[Nomic](https://www.nomic.ai/) builds tools that enable everyone to interact with AI scale datasets and run AI models on consumer computers.
->
->`Nomic` currently offers two products:
->
->- `Atlas`: the Visual Data Engine
->- `GPT4All`: the Open Source Edge Language Model Ecosystem
-
-The Nomic integration exists in two partner packages: [langchain-nomic](https://pypi.org/project/langchain-nomic/)
-and in [langchain-community](https://pypi.org/project/langchain-community/). 
-
-## Installation
-
-You can install them with:
-
-```bash
-pip install -U langchain-nomic
-pip install -U langchain-community
-```
-
-## LLMs
-
-### GPT4All
-
-See [a usage example](/docs/integrations/llms/gpt4all).
-
-```python
-from langchain_community.llms import GPT4All
-```
-
-## Embedding models
-
-### NomicEmbeddings
-
-See [a usage example](/docs/integrations/text_embedding/nomic).
-
-```python
-from langchain_nomic import NomicEmbeddings
-```
-
-### GPT4All
-
-See [a usage example](/docs/integrations/text_embedding/gpt4all).
-
-```python
-from langchain_community.embeddings import GPT4AllEmbeddings
-```
-
-## Vector store
-
-### Atlas
-
-See [a usage example and installation instructions](/docs/integrations/vectorstores/atlas).
-
-```python
-from langchain_community.vectorstores import AtlasDB
-```

docs/docs/integrations/providers/premai.md

@@ -286,7 +286,7 @@ In order to pass tools and let the LLM choose the tool it needs to call, we need
 
 ```python
 from langchain_core.tools import tool
-from pydantic import BaseModel, Field 
+from langchain_core.pydantic_v1 import BaseModel, Field 
 
 # Define the schema for function arguments
 class OperationInput(BaseModel):

docs/docs/integrations/providers/spark.mdx

@@ -1,49 +0,0 @@
-# Spark
-
->[Apache Spark](https://spark.apache.org/) is a unified analytics engine for 
-> large-scale data processing. It provides high-level APIs in Scala, Java, 
-> Python, and R, and an optimized engine that supports general computation 
-> graphs for data analysis. It also supports a rich set of higher-level 
-> tools including `Spark SQL` for SQL and DataFrames, `pandas API on Spark` 
-> for pandas workloads, `MLlib` for machine learning, 
-> `GraphX` for graph processing, and `Structured Streaming` for stream processing.
-
-## Document loaders
-
-### PySpark
-
-It loads data from a `PySpark` DataFrame.
-
-See a [usage example](/docs/integrations/document_loaders/pyspark_dataframe).
-
-```python
-from langchain_community.document_loaders import PySparkDataFrameLoader
-```
-
-## Tools/Toolkits
-
-### Spark SQL toolkit
-
-Toolkit for interacting with `Spark SQL`.
-
-See a [usage example](/docs/integrations/tools/spark_sql).
-
-```python
-from langchain_community.agent_toolkits import SparkSQLToolkit, create_spark_sql_agent
-from langchain_community.utilities.spark_sql import SparkSQL
-```
-
-#### Spark SQL individual tools
-
-You can use individual tools from the Spark SQL Toolkit:
-- `InfoSparkSQLTool`: tool for getting metadata about a Spark SQL
-- `ListSparkSQLTool`: tool for getting tables names
-- `QueryCheckerTool`: tool uses an LLM to check if a query is correct
-- `QuerySparkSQLTool`: tool for querying a Spark SQL
-
-```python
-from langchain_community.tools.spark_sql.tool import InfoSparkSQLTool
-from langchain_community.tools.spark_sql.tool import ListSparkSQLTool
-from langchain_community.tools.spark_sql.tool import QueryCheckerTool
-from langchain_community.tools.spark_sql.tool import QuerySparkSQLTool
-```

docs/docs/integrations/providers/sparkllm.mdx

@@ -4,26 +4,11 @@
 It has cross-domain knowledge and language understanding ability by learning a large amount of texts, codes and images.
 It can understand and perform tasks based on natural dialogue.
 
-## Chat models
+## SparkLLM LLM Model
+An example is available at [example](/docs/integrations/llms/sparkllm).
 
-See a [usage example](/docs/integrations/chat/sparkllm).
+## SparkLLM Chat Model
+An example is available at [example](/docs/integrations/chat/sparkllm).
 
-```python
-from langchain_community.chat_models import ChatSparkLLM
-```
-
-## LLMs
-
-See a [usage example](/docs/integrations/llms/sparkllm).
-
-```python
-from langchain_community.llms import SparkLLM
-```
-
-## Embedding models
-
-See a [usage example](/docs/integrations/text_embedding/sparkllm)
-
-```python
-from langchain_community.embeddings import SparkLLMTextEmbeddings
-```
+## SparkLLM Text Embedding Model
+An example is available at [example](/docs/integrations/text_embedding/sparkllm)

docs/docs/integrations/providers/transwarp.mdx

@@ -1,34 +0,0 @@
-# Transwarp
-
->[Transwarp](https://www.transwarp.cn/en/introduction) aims to build 
-> enterprise-level big data and AI infrastructure software, 
-> to shape the future of data world. It provides enterprises with 
-> infrastructure software and services around the whole data lifecycle, 
-> including integration, storage, governance, modeling, analysis, 
-> mining and circulation. 
-> 
-> `Transwarp` focuses on technology research and 
-> development and has accumulated core technologies in these aspects: 
-> distributed computing, SQL compilations, database technology, 
-> unification for multi-model data management, container-based cloud computing, 
-> and big data analytics and intelligence.
-
-## Installation
-
-You have to install several python packages:
-
-```bash
-pip install -U tiktoken hippo-api
-```
-
-and get the connection configuration.
-
-## Vector stores
-
-### Hippo
-
-See [a usage example and installation instructions](/docs/integrations/vectorstores/hippo).
-
-```python
-from langchain_community.vectorstores.hippo import Hippo
-```

docs/docs/integrations/providers/upstage.ipynb

@@ -6,18 +6,45 @@
    "source": [
     "# Upstage\n",
     "\n",
-    ">[Upstage](https://upstage.ai) is a leading artificial intelligence (AI) company specializing in delivering above-human-grade performance LLM components.\n",
-    ">\n",
-    ">**Solar Mini Chat** is a fast yet powerful advanced large language model focusing on English and Korean. It has been specifically fine-tuned for multi-turn chat purposes, showing enhanced performance across a wide range of natural language processing tasks, like multi-turn conversation or tasks that require an understanding of long contexts, such as RAG (Retrieval-Augmented Generation), compared to other models of a similar size. This fine-tuning equips it with the ability to handle longer conversations more effectively, making it particularly adept for interactive applications.\n",
-    "\n",
-    ">Other than Solar, Upstage also offers features for real-world RAG (retrieval-augmented generation), such as **Groundedness Check** and **Layout Analysis**. \n"
+    "[Upstage](https://upstage.ai) is a leading artificial intelligence (AI) company specializing in delivering above-human-grade performance LLM components. \n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Upstage LangChain integrations\n",
+    "## Solar LLM\n",
+    "\n",
+    "**Solar Mini Chat** is a fast yet powerful advanced large language model focusing on English and Korean. It has been specifically fine-tuned for multi-turn chat purposes, showing enhanced performance across a wide range of natural language processing tasks, like multi-turn conversation or tasks that require an understanding of long contexts, such as RAG (Retrieval-Augmented Generation), compared to other models of a similar size. This fine-tuning equips it with the ability to handle longer conversations more effectively, making it particularly adept for interactive applications.\n",
+    "\n",
+    "Other than Solar, Upstage also offers features for real-world RAG (retrieval-augmented generation), such as **Groundedness Check** and **Layout Analysis**. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation and Setup\n",
+    "\n",
+    "Install `langchain-upstage` package:\n",
+    "\n",
+    "```bash\n",
+    "pip install -qU langchain-core langchain-upstage\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get [API Keys](https://console.upstage.ai) and set environment variable `UPSTAGE_API_KEY`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upstage LangChain integrations\n",
     "\n",
     "| API | Description | Import | Example usage |\n",
     "| --- | --- | --- | --- |\n",
@@ -33,20 +60,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Installation and Setup\n",
+    "## Quick Examples\n",
     "\n",
-    "Install `langchain-upstage` package:\n",
-    "\n",
-    "```bash\n",
-    "pip install -qU langchain-core langchain-upstage\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get [API Keys](https://console.upstage.ai) and set environment variable `UPSTAGE_API_KEY`."
+    "### Environment Setup"
    ]
   },
   {
@@ -64,11 +80,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Chat models\n",
     "\n",
-    "### Solar LLM\n",
-    "\n",
-    "See [a usage example](/docs/integrations/chat/upstage)."
+    "### Chat\n"
    ]
   },
   {
@@ -88,9 +101,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Embedding models\n",
     "\n",
-    "See [a usage example](/docs/integrations/text_embedding/upstage)."
+    "\n",
+    "### Text embedding\n",
+    "\n"
    ]
   },
   {
@@ -120,45 +134,7 @@
     }
    },
    "source": [
-    "## Document loader\n",
-    "\n",
-    "### Layout Analysis\n",
-    "\n",
-    "See [a usage example](/docs/integrations/document_loaders/upstage)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_upstage import UpstageLayoutAnalysisLoader\n",
-    "\n",
-    "file_path = \"/PATH/TO/YOUR/FILE.pdf\"\n",
-    "layzer = UpstageLayoutAnalysisLoader(file_path, split=\"page\")\n",
-    "\n",
-    "# For improved memory efficiency, consider using the lazy_load method to load documents page by page.\n",
-    "docs = layzer.load()  # or layzer.lazy_load()\n",
-    "\n",
-    "for doc in docs[:3]:\n",
-    "    print(doc)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    "## Tools\n",
-    "\n",
-    "### Groundedness Check\n",
-    "\n",
-    "See [a usage example](/docs/integrations/tools/upstage_groundedness_check)."
+    "### Groundedness Check"
    ]
   },
   {
@@ -183,6 +159,36 @@
     "response = groundedness_check.invoke(request_input)\n",
     "print(response)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "### Layout Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_upstage import UpstageLayoutAnalysisLoader\n",
+    "\n",
+    "file_path = \"/PATH/TO/YOUR/FILE.pdf\"\n",
+    "layzer = UpstageLayoutAnalysisLoader(file_path, split=\"page\")\n",
+    "\n",
+    "# For improved memory efficiency, consider using the lazy_load method to load documents page by page.\n",
+    "docs = layzer.load()  # or layzer.lazy_load()\n",
+    "\n",
+    "for doc in docs[:3]:\n",
+    "    print(doc)"
+   ]
   }
  ],
  "metadata": {
@@ -204,7 +210,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,