Merge branch 'master' into sqldocstore_postgres_compat

docs/docs/integrations/tools/awslambda.ipynb

@@ -62,23 +62,25 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.agents import AgentType, initialize_agent, load_tools\n",
+    "from langchain.agents import AgentExecutor, OpenAIFunctionsAgent\n",
+    "from langchain.tools import Tool\n",
     "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "\n",
-    "tools = load_tools(\n",
-    "    [\"awslambda\"],\n",
-    "    awslambda_tool_name=\"email-sender\",\n",
-    "    awslambda_tool_description=\"sends an email with the specified content to test@testing123.com\",\n",
-    "    function_name=\"testFunction1\",\n",
+    "tools = Tool(\n",
+    "    name=\"email-sender\",\n",
+    "    description=\"Sends an email with the specified content to test@testing123.com\",\n",
+    "    func=lambda input_text: f\"Email sent to test@testing123.com with content: {input_text}\",\n",
     ")\n",
     "\n",
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")\n",
+    "agent = OpenAIFunctionsAgent(llm=llm, tools=[tools])\n",
     "\n",
-    "agent.run(\"Send an email to test@testing123.com saying hello world.\")"
+    "agent_executor = AgentExecutor(agent=agent, tools=[tools], verbose=True)\n",
+    "\n",
+    "agent_executor.invoke(\n",
+    "    {\"input\": \" Send an email to test@testing123.com saying hello world.\"}\n",
+    ")"
    ]
   },
   {

libs/community/langchain_community/document_loaders/parsers/images.py

@@ -45,24 +45,24 @@ class BaseImageBlobParser(BaseBlobParser):
         """
         try:
             from PIL import Image as Img
-
-            with blob.as_bytes_io() as buf:
-                if blob.mimetype == "application/x-npy":
-                    img = Img.fromarray(numpy.load(buf))
-                else:
-                    img = Img.open(buf)
-                content = self._analyze_image(img)
-                logger.debug("Image text: %s", content.replace("\n", "\\n"))
-                yield Document(
-                    page_content=content,
-                    metadata={**blob.metadata, **{"source": blob.source}},
-                )
         except ImportError:
             raise ImportError(
                 "`Pillow` package not found, please install it with "
                 "`pip install Pillow`"
             )
 
+        with blob.as_bytes_io() as buf:
+            if blob.mimetype == "application/x-npy":
+                img = Img.fromarray(numpy.load(buf))
+            else:
+                img = Img.open(buf)
+            content = self._analyze_image(img)
+            logger.debug("Image text: %s", content.replace("\n", "\\n"))
+            yield Document(
+                page_content=content,
+                metadata={**blob.metadata, **{"source": blob.source}},
+            )
+
 
 class RapidOCRBlobParser(BaseImageBlobParser):
     """Parser for extracting text from images using the RapidOCR library.

libs/partners/groq/langchain_groq/chat_models.py

@@ -88,8 +88,6 @@ from typing_extensions import Self
 
 from langchain_groq.version import __version__
 
-WARNED_DEFAULT_MODEL = False
-
 
 class ChatGroq(BaseChatModel):
     """`Groq` Chat large language models API.
@@ -305,7 +303,7 @@ class ChatGroq(BaseChatModel):
 
     client: Any = Field(default=None, exclude=True)  #: :meta private:
     async_client: Any = Field(default=None, exclude=True)  #: :meta private:
-    model_name: str = Field(default="mixtral-8x7b-32768", alias="model")
+    model_name: str = Field(alias="model")
     """Model name to use."""
     temperature: float = 0.7
     """What sampling temperature to use."""
@@ -353,27 +351,6 @@ class ChatGroq(BaseChatModel):
         populate_by_name=True,
     )
 
-    @model_validator(mode="before")
-    @classmethod
-    def warn_default_model(cls, values: Dict[str, Any]) -> Any:
-        """Warning anticipating removal of default model."""
-        # TODO(ccurme): remove this warning in 0.3.0 when default model is removed
-        global WARNED_DEFAULT_MODEL
-        if (
-            "model" not in values
-            and "model_name" not in values
-            and not WARNED_DEFAULT_MODEL
-        ):
-            warnings.warn(
-                "Groq is retiring the default model for ChatGroq, mixtral-8x7b-32768, "
-                "on March 20, 2025. Requests with the default model will start failing "
-                "on that date. Version 0.3.0 of langchain-groq will remove the "
-                "default. Please specify `model` explicitly, e.g., "
-                "`model='mistral-saba-24b'` or `model='llama-3.3-70b-versatile'`.",
-            )
-            WARNED_DEFAULT_MODEL = True
-        return values
-
     @model_validator(mode="before")
     @classmethod
     def build_extra(cls, values: Dict[str, Any]) -> Any:

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

@@ -2,7 +2,6 @@
 
 import json
 import os
-import warnings
 from typing import Any
 from unittest.mock import AsyncMock, MagicMock, patch
 
@@ -280,23 +279,3 @@ def test_groq_serialization() -> None:
 
     # Ensure a None was preserved
     assert llm.groq_api_base == llm2.groq_api_base
-
-
-def test_groq_warns_default_model() -> None:
-    """Test that a warning is raised if a default model is used."""
-
-    # Delete this test in 0.3 release, when the default model is removed.
-
-    # Test no warning if model is specified
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")
-        ChatGroq(model="foo")
-
-    # Test warns if default model is used
-    with pytest.warns(match="default model"):
-        ChatGroq()
-
-    # Test only warns once
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")
-        ChatGroq()