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chore: migrate legacy langchain imports to maintain compatibility #6643

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99 changes: 68 additions & 31 deletions vertexai/agent_engines/templates/langchain.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -43,11 +43,12 @@
RunnableSerializable = Any

try:
from langchain_google_vertexai.functions_utils import _ToolsType

_ToolsType = _ToolsType
from langchain_google_genai.functions_utils import _ToolsType
except ImportError:
_ToolsType = Any
try:
from langchain_google_vertexai.functions_utils import _ToolsType
except ImportError:
_ToolsType = Any

try:
from opentelemetry.sdk import trace
Expand Down Expand Up @@ -81,13 +82,15 @@ def _default_runnable_kwargs(has_history: bool) -> Mapping[str, Any]:

def _default_output_parser():
try:
from langchain.agents.output_parsers.tools import ToolsAgentOutputParser
from langchain_classic.agents.output_parsers.tools import ToolsAgentOutputParser
except (ModuleNotFoundError, ImportError):
# Fallback to an older version if needed.
from langchain.agents.output_parsers.openai_tools import (
OpenAIToolsAgentOutputParser as ToolsAgentOutputParser,
)

try:
from langchain.agents.output_parsers.tools import ToolsAgentOutputParser
except (ModuleNotFoundError, ImportError):
# Fallback to an older version if needed.
from langchain.agents.output_parsers.openai_tools import (
OpenAIToolsAgentOutputParser as ToolsAgentOutputParser,
)
return ToolsAgentOutputParser()


Expand All @@ -98,17 +101,29 @@ def _default_model_builder(
location: str,
model_kwargs: Optional[Mapping[str, Any]] = None,
) -> "BaseLanguageModel":
import vertexai
from google.cloud.aiplatform import initializer
from langchain_google_vertexai import ChatVertexAI

model_kwargs = model_kwargs or {}
current_project = initializer.global_config.project
current_location = initializer.global_config.location
vertexai.init(project=project, location=location)
model = ChatVertexAI(model_name=model_name, **model_kwargs)
vertexai.init(project=current_project, location=current_location)
return model
try:
from langchain_google_genai import ChatGoogleGenerativeAI

model = ChatGoogleGenerativeAI(
model=model_name,
project=project,
location=location,
vertexai=True,
**model_kwargs,
)
return model
except ImportError:
import vertexai
from google.cloud.aiplatform import initializer
from langchain_google_vertexai import ChatVertexAI

current_project = initializer.global_config.project
current_location = initializer.global_config.location
vertexai.init(project=project, location=location)
model = ChatVertexAI(model_name=model_name, **model_kwargs)
vertexai.init(project=current_project, location=current_location)
return model


def _default_runnable_builder(
Expand All @@ -124,8 +139,16 @@ def _default_runnable_builder(
runnable_kwargs: Optional[Mapping[str, Any]] = None,
) -> "RunnableSerializable":
from langchain_core import tools as lc_tools
from langchain.agents import AgentExecutor
from langchain.tools.base import StructuredTool

try:
from langchain_classic.agents import AgentExecutor
except ImportError:
from langchain.agents import AgentExecutor

try:
from langchain_core.tools import StructuredTool
except ImportError:
from langchain.tools.base import StructuredTool

# The prompt template and runnable_kwargs needs to be customized depending
# on whether the user intends for the agent to have history. The way the
Expand Down Expand Up @@ -261,12 +284,16 @@ def _default_prompt(
from langchain_core import prompts

try:
from langchain.agents.format_scratchpad.tools import format_to_tool_messages
except (ModuleNotFoundError, ImportError):
# Fallback to an older version if needed.
from langchain.agents.format_scratchpad.openai_tools import (
format_to_openai_tool_messages as format_to_tool_messages,
from langchain_classic.agents.format_scratchpad.tools import (
format_to_tool_messages,
)
except (ModuleNotFoundError, ImportError):
try:
from langchain.agents.format_scratchpad.tools import format_to_tool_messages
except (ModuleNotFoundError, ImportError):
from langchain.agents.format_scratchpad.openai_tools import (
format_to_openai_tool_messages as format_to_tool_messages,
)

system_instructions = []
if system_instruction:
Expand Down Expand Up @@ -629,13 +656,18 @@ def query(
Returns:
The output of querying the Agent with the given input and config.
"""
from langchain.load import dump as langchain_load_dump
try:
from langchain_core.load import dumpd
except ImportError:
from langchain.load import dump as langchain_load_dump

dumpd = langchain_load_dump.dumpd

if isinstance(input, str):
input = {"input": input}
if not self._tmpl_attrs.get("runnable"):
self.set_up()
return langchain_load_dump.dumpd(
return dumpd(
self._tmpl_attrs.get("runnable").invoke(
input=input, config=config, **kwargs
)
Expand All @@ -662,7 +694,12 @@ def stream_query(
Yields:
The output of querying the Agent with the given input and config.
"""
from langchain.load import dump as langchain_load_dump
try:
from langchain_core.load import dumpd
except ImportError:
from langchain.load import dump as langchain_load_dump

dumpd = langchain_load_dump.dumpd

if isinstance(input, str):
input = {"input": input}
Expand All @@ -673,4 +710,4 @@ def stream_query(
config=config,
**kwargs,
):
yield langchain_load_dump.dumpd(chunk)
yield dumpd(chunk)
101 changes: 68 additions & 33 deletions vertexai/preview/reasoning_engines/templates/langchain.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -43,11 +43,12 @@
RunnableSerializable = Any

try:
from langchain_google_vertexai.functions_utils import _ToolsType

_ToolsType = _ToolsType
from langchain_google_genai.functions_utils import _ToolsType
except ImportError:
_ToolsType = Any
try:
from langchain_google_vertexai.functions_utils import _ToolsType
except ImportError:
_ToolsType = Any

try:
from opentelemetry.sdk import trace
Expand Down Expand Up @@ -81,13 +82,15 @@ def _default_runnable_kwargs(has_history: bool) -> Mapping[str, Any]:

def _default_output_parser():
try:
from langchain.agents.output_parsers.tools import ToolsAgentOutputParser
from langchain_classic.agents.output_parsers.tools import ToolsAgentOutputParser
except (ModuleNotFoundError, ImportError):
# Fallback to an older version if needed.
from langchain.agents.output_parsers.openai_tools import (
OpenAIToolsAgentOutputParser as ToolsAgentOutputParser,
)

try:
from langchain.agents.output_parsers.tools import ToolsAgentOutputParser
except (ModuleNotFoundError, ImportError):
# Fallback to an older version if needed.
from langchain.agents.output_parsers.openai_tools import (
OpenAIToolsAgentOutputParser as ToolsAgentOutputParser,
)
return ToolsAgentOutputParser()


Expand All @@ -98,17 +101,29 @@ def _default_model_builder(
location: str,
model_kwargs: Optional[Mapping[str, Any]] = None,
) -> "BaseLanguageModel":
import vertexai
from google.cloud.aiplatform import initializer
from langchain_google_vertexai import ChatVertexAI

model_kwargs = model_kwargs or {}
current_project = initializer.global_config.project
current_location = initializer.global_config.location
vertexai.init(project=project, location=location)
model = ChatVertexAI(model_name=model_name, **model_kwargs)
vertexai.init(project=current_project, location=current_location)
return model
try:
from langchain_google_genai import ChatGoogleGenerativeAI

model = ChatGoogleGenerativeAI(
model=model_name,
project=project,
location=location,
vertexai=True,
**model_kwargs,
)
return model
except ImportError:
import vertexai
from google.cloud.aiplatform import initializer
from langchain_google_vertexai import ChatVertexAI

current_project = initializer.global_config.project
current_location = initializer.global_config.location
vertexai.init(project=project, location=location)
model = ChatVertexAI(model_name=model_name, **model_kwargs)
vertexai.init(project=current_project, location=current_location)
return model


def _default_runnable_builder(
Expand All @@ -124,8 +139,16 @@ def _default_runnable_builder(
runnable_kwargs: Optional[Mapping[str, Any]] = None,
) -> "RunnableSerializable":
from langchain_core import tools as lc_tools
from langchain.agents import AgentExecutor
from langchain.tools.base import StructuredTool

try:
from langchain_classic.agents import AgentExecutor
except ImportError:
from langchain.agents import AgentExecutor

try:
from langchain_core.tools import StructuredTool
except ImportError:
from langchain.tools.base import StructuredTool

# The prompt template and runnable_kwargs needs to be customized depending
# on whether the user intends for the agent to have history. The way the
Expand Down Expand Up @@ -175,12 +198,16 @@ def _default_prompt(
from langchain_core import prompts

try:
from langchain.agents.format_scratchpad.tools import format_to_tool_messages
except (ModuleNotFoundError, ImportError):
# Fallback to an older version if needed.
from langchain.agents.format_scratchpad.openai_tools import (
format_to_openai_tool_messages as format_to_tool_messages,
from langchain_classic.agents.format_scratchpad.tools import (
format_to_tool_messages,
)
except (ModuleNotFoundError, ImportError):
try:
from langchain.agents.format_scratchpad.tools import format_to_tool_messages
except (ModuleNotFoundError, ImportError):
from langchain.agents.format_scratchpad.openai_tools import (
format_to_openai_tool_messages as format_to_tool_messages,
)

system_instructions = []
if system_instruction:
Expand Down Expand Up @@ -605,15 +632,18 @@ def query(
Returns:
The output of querying the Agent with the given input and config.
"""
from langchain.load import dump as langchain_load_dump
try:
from langchain_core.load import dumpd
except ImportError:
from langchain.load import dump as langchain_load_dump

dumpd = langchain_load_dump.dumpd

if isinstance(input, str):
input = {"input": input}
if not self._runnable:
self.set_up()
return langchain_load_dump.dumpd(
self._runnable.invoke(input=input, config=config, **kwargs)
)
return dumpd(self._runnable.invoke(input=input, config=config, **kwargs))

def stream_query(
self,
Expand All @@ -636,11 +666,16 @@ def stream_query(
Yields:
The output of querying the Agent with the given input and config.
"""
from langchain.load import dump as langchain_load_dump
try:
from langchain_core.load import dumpd
except ImportError:
from langchain.load import dump as langchain_load_dump

dumpd = langchain_load_dump.dumpd

if isinstance(input, str):
input = {"input": input}
if not self._runnable:
self.set_up()
for chunk in self._runnable.stream(input=input, config=config, **kwargs):
yield langchain_load_dump.dumpd(chunk)
yield dumpd(chunk)
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