Wfh/ref links (#8454)

libs/langchain/langchain/agents/agent_toolkits/__init__.py

@@ -11,6 +11,7 @@ from langchain.agents.agent_toolkits.gmail.toolkit import GmailToolkit
 from langchain.agents.agent_toolkits.jira.toolkit import JiraToolkit
 from langchain.agents.agent_toolkits.json.base import create_json_agent
 from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
+from langchain.agents.agent_toolkits.multion.base import create_multion_agent
 from langchain.agents.agent_toolkits.nla.toolkit import NLAToolkit
 from langchain.agents.agent_toolkits.office365.toolkit import O365Toolkit
 from langchain.agents.agent_toolkits.openapi.base import create_openapi_agent
@@ -63,6 +64,7 @@ __all__ = [
     "create_pbi_agent",
     "create_pbi_chat_agent",
     "create_python_agent",
+    "create_multion_agent",
     "create_spark_dataframe_agent",
     "create_spark_sql_agent",
     "create_sql_agent",

libs/langchain/langchain/prompts/chat.py

@@ -457,6 +457,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
         messages: Sequence[
             Union[
                 BaseMessagePromptTemplate,
+                BaseChatPromptTemplate,
                 BaseMessage,
                 Tuple[str, str],
                 Tuple[Type, str],
@@ -515,7 +516,9 @@ class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
         # Automatically infer input variables from messages
         input_vars = set()
         for _message in _messages:
-            if isinstance(_message, BaseMessagePromptTemplate):
+            if isinstance(
+                _message, (BaseChatPromptTemplate, BaseMessagePromptTemplate)
+            ):
                 input_vars.update(_message.input_variables)
 
         return cls(input_variables=sorted(input_vars), messages=_messages)
@@ -643,12 +646,13 @@ def _create_template_from_message_type(
 def _convert_to_message(
     message: Union[
         BaseMessagePromptTemplate,
+        BaseChatPromptTemplate,
         BaseMessage,
         Tuple[str, str],
         Tuple[Type, str],
         str,
     ]
-) -> Union[BaseMessage, BaseMessagePromptTemplate]:
+) -> Union[BaseMessage, BaseMessagePromptTemplate, BaseChatPromptTemplate]:
     """Instantiate a message from a variety of message formats.
 
     The message format can be one of the following:
@@ -665,8 +669,10 @@ def _convert_to_message(
     Returns:
         an instance of a message or a message template
     """
-    if isinstance(message, BaseMessagePromptTemplate):
-        _message: Union[BaseMessage, BaseMessagePromptTemplate] = message
+    if isinstance(message, (BaseMessagePromptTemplate, BaseChatPromptTemplate)):
+        _message: Union[
+            BaseMessage, BaseMessagePromptTemplate, BaseChatPromptTemplate
+        ] = message
     elif isinstance(message, BaseMessage):
         _message = message
     elif isinstance(message, str):

libs/langchain/tests/integration_tests/chains/test_cpal.py

@@ -7,17 +7,15 @@ from unittest import mock
 
 import pydantic
 import pytest
-
-from langchain import OpenAI
-from langchain.experimental.cpal.base import (
+from langchain_experimental.cpal.base import (
     CausalChain,
     CPALChain,
     InterventionChain,
     NarrativeChain,
     QueryChain,
 )
-from langchain.experimental.cpal.constants import Constant
-from langchain.experimental.cpal.models import (
+from langchain_experimental.cpal.constants import Constant
+from langchain_experimental.cpal.models import (
     CausalModel,
     EntityModel,
     EntitySettingModel,
@@ -25,18 +23,20 @@ from langchain.experimental.cpal.models import (
     NarrativeModel,
     QueryModel,
 )
-from langchain.experimental.cpal.templates.univariate.causal import (
+from langchain_experimental.cpal.templates.univariate.causal import (
     template as causal_template,
 )
-from langchain.experimental.cpal.templates.univariate.intervention import (
+from langchain_experimental.cpal.templates.univariate.intervention import (
     template as intervention_template,
 )
-from langchain.experimental.cpal.templates.univariate.narrative import (
+from langchain_experimental.cpal.templates.univariate.narrative import (
     template as narrative_template,
 )
-from langchain.experimental.cpal.templates.univariate.query import (
+from langchain_experimental.cpal.templates.univariate.query import (
     template as query_template,
 )
+
+from langchain import OpenAI
 from langchain.output_parsers import PydanticOutputParser
 from langchain.prompts.prompt import PromptTemplate
 from tests.unit_tests.llms.fake_llm import FakeLLM
@@ -274,7 +274,7 @@ class TestUnitCPALChain_MathWordProblems(unittest.TestCase):
         patch required since `networkx` package is not part of unit test environment
         """
         with mock.patch(
-            "langchain.experimental.cpal.models.NetworkxEntityGraph"
+            "langchain_experimental.cpal.models.NetworkxEntityGraph"
         ) as mock_networkx:
             graph_instance = mock_networkx.return_value
             graph_instance.get_topological_sort.return_value = [