Merge branch 'master' into pgvectorstore-docs

docs/api_reference/conf.py

@@ -11,6 +11,7 @@
 import json
 import os
 import sys
+from datetime import datetime
 from pathlib import Path
 
 import toml
@@ -104,7 +105,7 @@ def skip_private_members(app, what, name, obj, skip, options):
 # -- Project information -----------------------------------------------------
 
 project = "🦜🔗 LangChain"
-copyright = "2023, LangChain Inc"
+copyright = f"{datetime.now().year}, LangChain Inc"
 author = "LangChain, Inc"
 
 html_favicon = "_static/img/brand/favicon.png"

docs/docs/integrations/document_loaders/oracleadb_loader.ipynb

@@ -36,10 +36,7 @@
     "pip install oracledb"
    ],
    "metadata": {
-    "collapsed": false,
+    "collapsed": false
-    "pycharm": {
-     "is_executing": true
-    }
    }
   },
   {
@@ -51,10 +48,7 @@
     "from settings import s"
    ],
    "metadata": {
-    "collapsed": false,
+    "collapsed": false
-    "pycharm": {
-     "is_executing": true
-    }
    }
   },
   {
@@ -97,16 +91,14 @@
     "doc_2 = doc_loader_2.load()"
    ],
    "metadata": {
-    "collapsed": false,
+    "collapsed": false
-    "pycharm": {
-     "is_executing": true
-    }
    }
   },
   {
    "cell_type": "markdown",
    "source": [
-    "With TLS authentication, wallet_location and wallet_password are not required."
+    "With TLS authentication, wallet_location and wallet_password are not required.\n",
+    "Bind variable option is provided by argument \"parameters\"."
    ],
    "metadata": {
     "collapsed": false
@@ -117,6 +109,8 @@
    "execution_count": null,
    "outputs": [],
    "source": [
+    "SQL_QUERY = \"select channel_id, channel_desc from sh.channels where channel_desc = :1 fetch first 5 rows only\"\n",
+    "\n",
     "doc_loader_3 = OracleAutonomousDatabaseLoader(\n",
     "    query=SQL_QUERY,\n",
     "    user=s.USERNAME,\n",
@@ -124,6 +118,7 @@
     "    schema=s.SCHEMA,\n",
     "    config_dir=s.CONFIG_DIR,\n",
     "    tns_name=s.TNS_NAME,\n",
+    "    parameters=[\"Direct Sales\"],\n",
     ")\n",
     "doc_3 = doc_loader_3.load()\n",
     "\n",
@@ -133,6 +128,7 @@
     "    password=s.PASSWORD,\n",
     "    schema=s.SCHEMA,\n",
     "    connection_string=s.CONNECTION_STRING,\n",
+    "    parameters=[\"Direct Sales\"],\n",
     ")\n",
     "doc_4 = doc_loader_4.load()"
    ],

docs/docs/integrations/tools/tableau.ipynb

@@ -183,7 +183,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Initalize simple_datasource_qa for querying Tableau Datasources through VDS\n",
+    "# Initialize simple_datasource_qa for querying Tableau Datasources through VDS\n",
     "analyze_datasource = initialize_simple_datasource_qa(\n",
     "    domain=tableau_server,\n",
     "    site=tableau_site,\n",

libs/community/langchain_community/callbacks/openai_info.py

@@ -10,6 +10,38 @@ from langchain_core.messages import AIMessage
 from langchain_core.outputs import ChatGeneration, LLMResult
 
 MODEL_COST_PER_1K_TOKENS = {
+    # GPT-4.1 input
+    "gpt-4.1": 0.002,
+    "gpt-4.1-2025-04-14": 0.002,
+    "gpt-4.1-cached": 0.0005,
+    "gpt-4.1-2025-04-14-cached": 0.0005,
+    # GPT-4.1 output
+    "gpt-4.1-completion": 0.008,
+    "gpt-4.1-2025-04-14-completion": 0.008,
+    # GPT-4.1-mini input
+    "gpt-4.1-mini": 0.0004,
+    "gpt-4.1-mini-2025-04-14": 0.0004,
+    "gpt-4.1-mini-cached": 0.0001,
+    "gpt-4.1-mini-2025-04-14-cached": 0.0001,
+    # GPT-4.1-mini output
+    "gpt-4.1-mini-completion": 0.0016,
+    "gpt-4.1-mini-2025-04-14-completion": 0.0016,
+    # GPT-4.1-nano input
+    "gpt-4.1-nano": 0.0001,
+    "gpt-4.1-nano-2025-04-14": 0.0001,
+    "gpt-4.1-nano-cached": 0.000025,
+    "gpt-4.1-nano-2025-04-14-cached": 0.000025,
+    # GPT-4.1-nano output
+    "gpt-4.1-nano-completion": 0.0004,
+    "gpt-4.1-nano-2025-04-14-completion": 0.0004,
+    # GPT-4.5-preview input
+    "gpt-4.5-preview": 0.075,
+    "gpt-4.5-preview-2025-02-27": 0.075,
+    "gpt-4.5-preview-cached": 0.0375,
+    "gpt-4.5-preview-2025-02-27-cached": 0.0375,
+    # GPT-4.5-preview output
+    "gpt-4.5-preview-completion": 0.15,
+    "gpt-4.5-preview-2025-02-27-completion": 0.15,
     # OpenAI o1 input
     "o1": 0.015,
     "o1-2024-12-17": 0.015,
@@ -18,6 +50,28 @@ MODEL_COST_PER_1K_TOKENS = {
     # OpenAI o1 output
     "o1-completion": 0.06,
     "o1-2024-12-17-completion": 0.06,
+    # OpenAI o1-pro input
+    "o1-pro": 0.15,
+    "o1-pro-2025-03-19": 0.15,
+    # OpenAI o1-pro output
+    "o1-pro-completion": 0.6,
+    "o1-pro-2025-03-19-completion": 0.6,
+    # OpenAI o3 input
+    "o3": 0.01,
+    "o3-2025-04-16": 0.01,
+    "o3-cached": 0.0025,
+    "o3-2025-04-16-cached": 0.0025,
+    # OpenAI o3 output
+    "o3-completion": 0.04,
+    "o3-2025-04-16-completion": 0.04,
+    # OpenAI o4-mini input
+    "o4-mini": 0.0011,
+    "o4-mini-2025-04-16": 0.0011,
+    "o4-mini-cached": 0.000275,
+    "o4-mini-2025-04-16-cached": 0.000275,
+    # OpenAI o4-mini output
+    "o4-mini-completion": 0.0044,
+    "o4-mini-2025-04-16-completion": 0.0044,
     # OpenAI o3-mini input
     "o3-mini": 0.0011,
     "o3-mini-2025-01-31": 0.0011,
@@ -26,6 +80,14 @@ MODEL_COST_PER_1K_TOKENS = {
     # OpenAI o3-mini output
     "o3-mini-completion": 0.0044,
     "o3-mini-2025-01-31-completion": 0.0044,
+    # OpenAI o1-mini input (updated pricing)
+    "o1-mini": 0.0011,
+    "o1-mini-cached": 0.00055,
+    "o1-mini-2024-09-12": 0.0011,
+    "o1-mini-2024-09-12-cached": 0.00055,
+    # OpenAI o1-mini output (updated pricing)
+    "o1-mini-completion": 0.0044,
+    "o1-mini-2024-09-12-completion": 0.0044,
     # OpenAI o1-preview input
     "o1-preview": 0.015,
     "o1-preview-cached": 0.0075,
@@ -34,22 +96,6 @@ MODEL_COST_PER_1K_TOKENS = {
     # OpenAI o1-preview output
     "o1-preview-completion": 0.06,
     "o1-preview-2024-09-12-completion": 0.06,
-    # OpenAI o1-mini input
-    "o1-mini": 0.003,
-    "o1-mini-cached": 0.0015,
-    "o1-mini-2024-09-12": 0.003,
-    "o1-mini-2024-09-12-cached": 0.0015,
-    # OpenAI o1-mini output
-    "o1-mini-completion": 0.012,
-    "o1-mini-2024-09-12-completion": 0.012,
-    # GPT-4o-mini input
-    "gpt-4o-mini": 0.00015,
-    "gpt-4o-mini-cached": 0.000075,
-    "gpt-4o-mini-2024-07-18": 0.00015,
-    "gpt-4o-mini-2024-07-18-cached": 0.000075,
-    # GPT-4o-mini output
-    "gpt-4o-mini-completion": 0.0006,
-    "gpt-4o-mini-2024-07-18-completion": 0.0006,
     # GPT-4o input
     "gpt-4o": 0.0025,
     "gpt-4o-cached": 0.00125,
@@ -63,6 +109,65 @@ MODEL_COST_PER_1K_TOKENS = {
     "gpt-4o-2024-05-13-completion": 0.015,
     "gpt-4o-2024-08-06-completion": 0.01,
     "gpt-4o-2024-11-20-completion": 0.01,
+    # GPT-4o-audio-preview input
+    "gpt-4o-audio-preview": 0.0025,
+    "gpt-4o-audio-preview-2024-12-17": 0.0025,
+    "gpt-4o-audio-preview-2024-10-01": 0.0025,
+    # GPT-4o-audio-preview output
+    "gpt-4o-audio-preview-completion": 0.01,
+    "gpt-4o-audio-preview-2024-12-17-completion": 0.01,
+    "gpt-4o-audio-preview-2024-10-01-completion": 0.01,
+    # GPT-4o-realtime-preview input
+    "gpt-4o-realtime-preview": 0.005,
+    "gpt-4o-realtime-preview-2024-12-17": 0.005,
+    "gpt-4o-realtime-preview-2024-10-01": 0.005,
+    "gpt-4o-realtime-preview-cached": 0.0025,
+    "gpt-4o-realtime-preview-2024-12-17-cached": 0.0025,
+    "gpt-4o-realtime-preview-2024-10-01-cached": 0.0025,
+    # GPT-4o-realtime-preview output
+    "gpt-4o-realtime-preview-completion": 0.02,
+    "gpt-4o-realtime-preview-2024-12-17-completion": 0.02,
+    "gpt-4o-realtime-preview-2024-10-01-completion": 0.02,
+    # GPT-4o-mini input
+    "gpt-4o-mini": 0.00015,
+    "gpt-4o-mini-cached": 0.000075,
+    "gpt-4o-mini-2024-07-18": 0.00015,
+    "gpt-4o-mini-2024-07-18-cached": 0.000075,
+    # GPT-4o-mini output
+    "gpt-4o-mini-completion": 0.0006,
+    "gpt-4o-mini-2024-07-18-completion": 0.0006,
+    # GPT-4o-mini-audio-preview input
+    "gpt-4o-mini-audio-preview": 0.00015,
+    "gpt-4o-mini-audio-preview-2024-12-17": 0.00015,
+    # GPT-4o-mini-audio-preview output
+    "gpt-4o-mini-audio-preview-completion": 0.0006,
+    "gpt-4o-mini-audio-preview-2024-12-17-completion": 0.0006,
+    # GPT-4o-mini-realtime-preview input
+    "gpt-4o-mini-realtime-preview": 0.0006,
+    "gpt-4o-mini-realtime-preview-2024-12-17": 0.0006,
+    "gpt-4o-mini-realtime-preview-cached": 0.0003,
+    "gpt-4o-mini-realtime-preview-2024-12-17-cached": 0.0003,
+    # GPT-4o-mini-realtime-preview output
+    "gpt-4o-mini-realtime-preview-completion": 0.0024,
+    "gpt-4o-mini-realtime-preview-2024-12-17-completion": 0.0024,
+    # GPT-4o-mini-search-preview input
+    "gpt-4o-mini-search-preview": 0.00015,
+    "gpt-4o-mini-search-preview-2025-03-11": 0.00015,
+    # GPT-4o-mini-search-preview output
+    "gpt-4o-mini-search-preview-completion": 0.0006,
+    "gpt-4o-mini-search-preview-2025-03-11-completion": 0.0006,
+    # GPT-4o-search-preview input
+    "gpt-4o-search-preview": 0.0025,
+    "gpt-4o-search-preview-2025-03-11": 0.0025,
+    # GPT-4o-search-preview output
+    "gpt-4o-search-preview-completion": 0.01,
+    "gpt-4o-search-preview-2025-03-11-completion": 0.01,
+    # Computer-use-preview input
+    "computer-use-preview": 0.003,
+    "computer-use-preview-2025-03-11": 0.003,
+    # Computer-use-preview output
+    "computer-use-preview-completion": 0.012,
+    "computer-use-preview-2025-03-11-completion": 0.012,
     # GPT-4 input
     "gpt-4": 0.03,
     "gpt-4-0314": 0.03,
@@ -219,6 +324,7 @@ def standardize_model_name(
         or model_name.startswith("gpt-35")
         or model_name.startswith("o1-")
         or model_name.startswith("o3-")
+        or model_name.startswith("o4-")
         or ("finetuned" in model_name and "legacy" not in model_name)
     ):
         return model_name + "-completion"
@@ -226,8 +332,10 @@ def standardize_model_name(
         token_type == TokenType.PROMPT_CACHED
         and (
             model_name.startswith("gpt-4o")
+            or model_name.startswith("gpt-4.1")
             or model_name.startswith("o1")
             or model_name.startswith("o3")
+            or model_name.startswith("o4")
         )
         and not (model_name.startswith("gpt-4o-2024-05-13"))
     ):

libs/community/langchain_community/document_loaders/oracleadb_loader.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
 from langchain_core.documents import Document
 
@@ -31,6 +31,7 @@ class OracleAutonomousDatabaseLoader(BaseLoader):
         wallet_password: Optional[str] = None,
         connection_string: Optional[str] = None,
         metadata: Optional[List[str]] = None,
+        parameters: Optional[Union[list, tuple, dict]] = None,
     ):
         """
         init method
@@ -44,6 +45,7 @@ class OracleAutonomousDatabaseLoader(BaseLoader):
         :param wallet_password: password of wallet
         :param connection_string: connection string to connect to adb instance
         :param metadata: metadata used in document
+        :param parameters: bind variable to use in query
         """
         # Mandatory required arguments.
         self.query = query
@@ -67,6 +69,9 @@ class OracleAutonomousDatabaseLoader(BaseLoader):
         # metadata column
         self.metadata = metadata
 
+        # parameters, e.g bind variable
+        self.parameters = parameters
+
         # dsn
         self.dsn: Optional[str]
         self._set_dsn()
@@ -96,7 +101,10 @@ class OracleAutonomousDatabaseLoader(BaseLoader):
             cursor = connection.cursor()
             if self.schema:
                 cursor.execute(f"alter session set current_schema={self.schema}")
-            cursor.execute(self.query)
+            if self.parameters:
+                cursor.execute(self.query, self.parameters)
+            else:
+                cursor.execute(self.query)
             columns = [col[0] for col in cursor.description]
             data = cursor.fetchall()
             data = [

libs/community/langchain_community/embeddings/openai.py

@@ -668,7 +668,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         # NOTE: to keep things simple, we assume the list may contain texts longer
         #       than the maximum context and use length-safe embedding function.
         engine = cast(str, self.deployment)
-        return self._get_len_safe_embeddings(texts, engine=engine)
+        return self._get_len_safe_embeddings(
+            texts, engine=engine, chunk_size=chunk_size
+        )
 
     async def aembed_documents(
         self, texts: List[str], chunk_size: Optional[int] = 0
@@ -686,7 +688,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         # NOTE: to keep things simple, we assume the list may contain texts longer
         #       than the maximum context and use length-safe embedding function.
         engine = cast(str, self.deployment)
-        return await self._aget_len_safe_embeddings(texts, engine=engine)
+        return self._get_len_safe_embeddings(
+            texts, engine=engine, chunk_size=chunk_size
+        )
 
     def embed_query(self, text: str) -> List[float]:
         """Call out to OpenAI's embedding endpoint for embedding query text.

libs/community/tests/unit_tests/embeddings/test_openai.py

@@ -1,7 +1,12 @@
+import os
+from unittest.mock import patch
+
 import pytest
 
 from langchain_community.embeddings.openai import OpenAIEmbeddings
 
+os.environ["OPENAI_API_KEY"] = "foo"
+
 
 @pytest.mark.requires("openai")
 def test_openai_invalid_model_kwargs() -> None:
@@ -14,3 +19,20 @@ def test_openai_incorrect_field() -> None:
     with pytest.warns(match="not default parameter"):
         llm = OpenAIEmbeddings(foo="bar", openai_api_key="foo")  # type: ignore[call-arg]
     assert llm.model_kwargs == {"foo": "bar"}
+
+
+@pytest.mark.requires("openai")
+def test_embed_documents_with_custom_chunk_size() -> None:
+    embeddings = OpenAIEmbeddings(chunk_size=2)
+    texts = ["text1", "text2", "text3", "text4"]
+    custom_chunk_size = 3
+
+    with patch.object(embeddings.client, "create") as mock_create:
+        mock_create.side_effect = [
+            {"data": [{"embedding": [0.1, 0.2]}, {"embedding": [0.3, 0.4]}]},
+            {"data": [{"embedding": [0.5, 0.6]}, {"embedding": [0.7, 0.8]}]},
+        ]
+
+        embeddings.embed_documents(texts, chunk_size=custom_chunk_size)
+        mock_create.assert_any_call(input=[[1342, 19]], **embeddings._invocation_params)
+        mock_create.assert_any_call(input=[[1342, 19]], **embeddings._invocation_params)

libs/core/langchain_core/output_parsers/openai_tools.py

@@ -2,6 +2,7 @@
 
 import copy
 import json
+import logging
 from json import JSONDecodeError
 from typing import Annotated, Any, Optional
 
@@ -16,6 +17,8 @@ from langchain_core.outputs import ChatGeneration, Generation
 from langchain_core.utils.json import parse_partial_json
 from langchain_core.utils.pydantic import TypeBaseModel
 
+logger = logging.getLogger(__name__)
+
 
 def parse_tool_call(
     raw_tool_call: dict[str, Any],
@@ -250,6 +253,14 @@ class JsonOutputKeyToolsParser(JsonOutputToolsParser):
         return parsed_result
 
 
+# Common cause of ValidationError is truncated output due to max_tokens.
+_MAX_TOKENS_ERROR = (
+    "Output parser received a `max_tokens` stop reason. "
+    "The output is likely incomplete—please increase `max_tokens` "
+    "or shorten your prompt."
+)
+
+
 class PydanticToolsParser(JsonOutputToolsParser):
     """Parse tools from OpenAI response."""
 
@@ -296,6 +307,14 @@ class PydanticToolsParser(JsonOutputToolsParser):
             except (ValidationError, ValueError):
                 if partial:
                     continue
+                has_max_tokens_stop_reason = any(
+                    generation.message.response_metadata.get("stop_reason")
+                    == "max_tokens"
+                    for generation in result
+                    if isinstance(generation, ChatGeneration)
+                )
+                if has_max_tokens_stop_reason:
+                    logger.exception(_MAX_TOKENS_ERROR)
                 raise
         if self.first_tool_only:
             return pydantic_objects[0] if pydantic_objects else None

libs/core/langchain_core/runnables/graph.py

@@ -108,7 +108,7 @@ class Node(NamedTuple):
 
     id: str
     name: str
-    data: Union[type[BaseModel], RunnableType]
+    data: Union[type[BaseModel], RunnableType, None]
     metadata: Optional[dict[str, Any]]
 
     def copy(self, *, id: Optional[str] = None, name: Optional[str] = None) -> Node:
@@ -181,7 +181,7 @@ class MermaidDrawMethod(Enum):
     API = "api"  # Uses Mermaid.INK API to render the graph
 
 
-def node_data_str(id: str, data: Union[type[BaseModel], RunnableType]) -> str:
+def node_data_str(id: str, data: Union[type[BaseModel], RunnableType, None]) -> str:
     """Convert the data of a node to a string.
 
     Args:
@@ -193,7 +193,7 @@ def node_data_str(id: str, data: Union[type[BaseModel], RunnableType]) -> str:
     """
     from langchain_core.runnables.base import Runnable
 
-    if not is_uuid(id):
+    if not is_uuid(id) or data is None:
         return id
     data_str = data.get_name() if isinstance(data, Runnable) else data.__name__
     return data_str if not data_str.startswith("Runnable") else data_str[8:]
@@ -215,8 +215,10 @@ def node_data_json(
     from langchain_core.load.serializable import to_json_not_implemented
     from langchain_core.runnables.base import Runnable, RunnableSerializable
 
-    if isinstance(node.data, RunnableSerializable):
+    if node.data is None:
-        json: dict[str, Any] = {
+        json: dict[str, Any] = {}
+    elif isinstance(node.data, RunnableSerializable):
+        json = {
             "type": "runnable",
             "data": {
                 "id": node.data.lc_id(),
@@ -317,7 +319,7 @@ class Graph:
 
     def add_node(
         self,
-        data: Union[type[BaseModel], RunnableType],
+        data: Union[type[BaseModel], RunnableType, None],
         id: Optional[str] = None,
         *,
         metadata: Optional[dict[str, Any]] = None,

libs/core/tests/unit_tests/output_parsers/test_openai_tools.py

@@ -2,7 +2,7 @@ from collections.abc import AsyncIterator, Iterator
 from typing import Any
 
 import pytest
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, ValidationError
 
 from langchain_core.messages import (
     AIMessage,
@@ -635,3 +635,24 @@ def test_parse_with_different_pydantic_1_proper() -> None:
             forecast="Sunny",
         )
     ]
+
+
+def test_max_tokens_error(caplog: Any) -> None:
+    parser = PydanticToolsParser(tools=[NameCollector], first_tool_only=True)
+    input = AIMessage(
+        content="",
+        tool_calls=[
+            {
+                "id": "call_OwL7f5PE",
+                "name": "NameCollector",
+                "args": {"names": ["suz", "jerm"]},
+            }
+        ],
+        response_metadata={"stop_reason": "max_tokens"},
+    )
+    with pytest.raises(ValidationError):
+        _ = parser.invoke(input)
+    assert any(
+        "`max_tokens` stop reason" in msg and record.levelname == "ERROR"
+        for record, msg in zip(caplog.records, caplog.messages)
+    )

libs/partners/anthropic/langchain_anthropic/chat_models.py

@@ -82,8 +82,8 @@ class AnthropicTool(TypedDict):
     """Anthropic tool definition."""
 
     name: str
-    description: str
     input_schema: dict[str, Any]
+    description: NotRequired[str]
     cache_control: NotRequired[dict[str, str]]
 
 
@@ -1675,9 +1675,10 @@ def convert_to_anthropic_tool(
         oai_formatted = convert_to_openai_tool(tool)["function"]
         anthropic_formatted = AnthropicTool(
             name=oai_formatted["name"],
-            description=oai_formatted["description"],
             input_schema=oai_formatted["parameters"],
         )
+        if "description" in oai_formatted:
+            anthropic_formatted["description"] = oai_formatted["description"]
     return anthropic_formatted
 
 

libs/partners/anthropic/tests/unit_tests/test_chat_models.py

@@ -931,3 +931,12 @@ def test_anthropic_bind_tools_tool_choice() -> None:
     assert cast(RunnableBinding, chat_model_with_tools).kwargs["tool_choice"] == {
         "type": "any"
     }
+
+
+def test_optional_description() -> None:
+    llm = ChatAnthropic(model="claude-3-5-haiku-latest")
+
+    class SampleModel(BaseModel):
+        sample_field: str
+
+    _ = llm.with_structured_output(SampleModel.model_json_schema())