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feat: Add function to validate models with tool calling function and related fixes in agent component (#5720)

Browse source 
* Update nvidia.py

* update agent experience with improving model selection

update agent experience with improving model selection  and making only the tool calling models available.

* variable clean up

* [autofix.ci] apply automated fixes

* Update src/backend/base/langflow/base/models/model_input_constants.py

Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>

* Update src/backend/base/langflow/base/models/model_input_constants.py

Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>

* added default models

* [autofix.ci] apply automated fixes

* [autofix.ci] apply automated fixes (attempt 2/3)

* format errors solved

* [autofix.ci] apply automated fixes

* Update model.py

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>
This commit is contained in:
Edwin Jose 2025-01-16 14:11:41 -05:00 • committed by GitHub
commit 778b74dfa8
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14 changed files with 270 additions and 106 deletions
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15
src/backend/base/langflow/base/models/model.py
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@ -3,6 +3,7 @@ import json
import warnings
from abc import abstractmethod
from langchain_core.language_models import BaseChatModel
from langchain_core.language_models.llms import LLM
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
from langchain_core.output_parsers import BaseOutputParser
@ -43,6 +44,20 @@ class LCModelComponent(Component):
def _get_exception_message(self, e: Exception):
return str(e)
def supports_tool_calling(self, model: LanguageModel) -> bool:
try:
# Check if the bind_tools method is the same as the base class's method
if model.bind_tools is BaseChatModel.bind_tools:
return False
def test_tool(x: int) -> int:
return x
model_with_tool = model.bind_tools([test_tool])
return hasattr(model_with_tool, "tools") and len(model_with_tool.tools) > 0
except (AttributeError, TypeError, ValueError):
return False
def _validate_outputs(self) -> None:
# At least these two outputs must be defined
required_output_methods = ["text_response", "build_model"]

10
src/backend/base/langflow/base/models/model_input_constants.py
View file

@ -28,7 +28,7 @@ def process_inputs(component_data):
if isinstance(component_data, SecretStrInput):
component_data.value = ""
component_data.load_from_db = False
elif component_data.name == "temperature":
elif component_data.name in {"temperature", "tool_model_enabled", "base_url"}:
component_data = set_advanced_true(component_data)
return component_data
@ -180,3 +180,11 @@ except ImportError:
MODEL_PROVIDERS = list(MODEL_PROVIDERS_DICT.keys())
ALL_PROVIDER_FIELDS: list[str] = [field for provider in MODEL_PROVIDERS_DICT.values() for field in provider["fields"]]
MODEL_DYNAMIC_UPDATE_FIELDS = [
"api_key",
"model",
"tool_model_enabled",
"base_url",
"model_name",
]

21
src/backend/base/langflow/components/agents/agent.py
View file

@ -3,6 +3,7 @@ from langchain_core.tools import StructuredTool
from langflow.base.agents.agent import LCToolsAgentComponent
from langflow.base.models.model_input_constants import (
ALL_PROVIDER_FIELDS,
MODEL_DYNAMIC_UPDATE_FIELDS,
MODEL_PROVIDERS_DICT,
)
from langflow.base.models.model_utils import get_model_name
@ -144,6 +145,16 @@ class AgentComponent(ToolCallingAgentComponent):
model_kwargs = {input_.name: getattr(self, f"{prefix}{input_.name}") for input_ in inputs}
return component.set(**model_kwargs).build_model()
def set_component_params(self, component):
provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)
if provider_info:
inputs = provider_info.get("inputs")
prefix = provider_info.get("prefix")
model_kwargs = {input_.name: getattr(self, f"{prefix}{input_.name}") for input_ in inputs}
return component.set(**model_kwargs)
return component
def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:
"""Delete specified fields from build_config."""
for field in fields:
@ -164,7 +175,7 @@ class AgentComponent(ToolCallingAgentComponent):
) -> dotdict:
# Iterate over all providers in the MODEL_PROVIDERS_DICT
# Existing logic for updating build_config
if field_name == "agent_llm":
if field_name in ("agent_llm",):
provider_info = MODEL_PROVIDERS_DICT.get(field_value)
if provider_info:
component_class = provider_info.get("component_class")
@ -233,10 +244,15 @@ class AgentComponent(ToolCallingAgentComponent):
if missing_keys:
msg = f"Missing required keys in build_config: {missing_keys}"
raise ValueError(msg)
if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:
if (
isinstance(self.agent_llm, str)
and self.agent_llm in MODEL_PROVIDERS_DICT
and field_name in MODEL_DYNAMIC_UPDATE_FIELDS
):
provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)
if provider_info:
component_class = provider_info.get("component_class")
component_class = self.set_component_params(component_class)
prefix = provider_info.get("prefix")
if component_class and hasattr(component_class, "update_build_config"):
# Call each component class's update_build_config method
@ -246,5 +262,4 @@ class AgentComponent(ToolCallingAgentComponent):
build_config = await update_component_build_config(
component_class, build_config, field_value, field_name
)
return build_config

106
src/backend/base/langflow/components/models/anthropic.py
View file

@ -1,9 +1,14 @@
from pydantic.v1 import SecretStr
from typing import Any
import requests
from loguru import logger
from langflow.base.models.anthropic_constants import ANTHROPIC_MODELS
from langflow.base.models.model import LCModelComponent
from langflow.field_typing import LanguageModel
from langflow.io import DropdownInput, FloatInput, IntInput, MessageTextInput, SecretStrInput
from langflow.field_typing.range_spec import RangeSpec
from langflow.io import BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput, SliderInput
from langflow.schema.dotdict import dotdict
class AnthropicModelComponent(LCModelComponent):
@ -22,19 +27,42 @@ class AnthropicModelComponent(LCModelComponent):
info="The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
),
DropdownInput(
name="model",
name="model_name",
display_name="Model Name",
options=ANTHROPIC_MODELS,
info="https://python.langchain.com/docs/integrations/chat/anthropic",
value="claude-3-5-sonnet-latest",
options=[],
refresh_button=True,
real_time_refresh=True,
),
SecretStrInput(
name="api_key",
display_name="Anthropic API Key",
info="Your Anthropic API key.",
value=None,
real_time_refresh=True,
),
SliderInput(
name="temperature",
display_name="Temperature",
value=0.1,
info="Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
range_spec=RangeSpec(min=0, max=1, step=0.01),
),
SecretStrInput(name="anthropic_api_key", display_name="Anthropic API Key", info="Your Anthropic API key."),
FloatInput(name="temperature", display_name="Temperature", value=0.1),
MessageTextInput(
name="anthropic_api_url",
name="base_url",
display_name="Anthropic API URL",
advanced=True,
info="Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
value="https://api.anthropic.com",
real_time_refresh=True,
),
BoolInput(
name="tool_model_enabled",
display_name="Enable Tool Models",
info=(
"Select if you want to use models that can work with tools. If yes, only those models will be shown."
),
advanced=False,
value=True,
real_time_refresh=True,
),
MessageTextInput(
name="prefill", display_name="Prefill", info="Prefill text to guide the model's response.", advanced=True
@ -47,19 +75,13 @@ class AnthropicModelComponent(LCModelComponent):
except ImportError as e:
msg = "langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`."
raise ImportError(msg) from e
model = self.model
anthropic_api_key = self.anthropic_api_key
max_tokens = self.max_tokens
temperature = self.temperature
anthropic_api_url = self.anthropic_api_url or "https://api.anthropic.com"
try:
output = ChatAnthropic(
model=model,
anthropic_api_key=(SecretStr(anthropic_api_key).get_secret_value() if anthropic_api_key else None),
max_tokens_to_sample=max_tokens,
temperature=temperature,
anthropic_api_url=anthropic_api_url,
model=self.model_name,
anthropic_api_key=self.api_key,
max_tokens_to_sample=self.max_tokens,
temperature=self.temperature,
anthropic_api_url=self.base_url,
streaming=self.stream,
)
except Exception as e:
@ -68,6 +90,32 @@ class AnthropicModelComponent(LCModelComponent):
return output
def get_models(self, tool_model_enabled: bool | None = None) -> list[str]:
try:
import anthropic
client = anthropic.Anthropic(api_key=self.api_key)
models = client.models.list(limit=20).data
model_ids = [model.id for model in models]
except (ImportError, ValueError, requests.exceptions.RequestException) as e:
logger.exception(f"Error getting model names: {e}")
model_ids = ANTHROPIC_MODELS
if tool_model_enabled:
try:
from langchain_anthropic.chat_models import ChatAnthropic
except ImportError as e:
msg = "langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`."
raise ImportError(msg) from e
for model in model_ids:
model_with_tool = ChatAnthropic(
model=self.model_name,
anthropic_api_key=self.api_key,
anthropic_api_url=self.base_url,
)
if not self.supports_tool_calling(model_with_tool):
model_ids.remove(model)
return model_ids
def _get_exception_message(self, exception: Exception) -> str | None:
"""Get a message from an Anthropic exception.
@ -86,3 +134,19 @@ class AnthropicModelComponent(LCModelComponent):
if message:
return message
return None
def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
if field_name in ("base_url", "model_name", "tool_model_enabled", "api_key") and field_value:
try:
if len(self.api_key) != 0:
try:
ids = self.get_models(tool_model_enabled=self.tool_model_enabled)
except (ImportError, ValueError, requests.exceptions.RequestException) as e:
logger.exception(f"Error getting model names: {e}")
ids = ANTHROPIC_MODELS
build_config["model_name"]["options"] = ids
build_config["model_name"]["value"] = ids[0]
except Exception as e:
msg = f"Error getting model names: {e}"
raise ValueError(msg) from e
return build_config

107
src/backend/base/langflow/components/models/groq.py
View file

@ -1,11 +1,12 @@
import requests
from loguru import logger
from pydantic.v1 import SecretStr
from typing_extensions import override
from langflow.base.models.groq_constants import GROQ_MODELS
from langflow.base.models.model import LCModelComponent
from langflow.field_typing import LanguageModel
from langflow.io import DropdownInput, FloatInput, IntInput, MessageTextInput, SecretStrInput
from langflow.field_typing.range_spec import RangeSpec
from langflow.io import BoolInput, DropdownInput, FloatInput, IntInput, MessageTextInput, SecretStrInput, SliderInput
class GroqModel(LCModelComponent):
@ -16,13 +17,16 @@ class GroqModel(LCModelComponent):
inputs = [
*LCModelComponent._base_inputs,
SecretStrInput(name="groq_api_key", display_name="Groq API Key", info="API key for the Groq API."),
SecretStrInput(
name="api_key", display_name="Groq API Key", info="API key for the Groq API.", real_time_refresh=True
),
MessageTextInput(
name="groq_api_base",
name="base_url",
display_name="Groq API Base",
info="Base URL path for API requests, leave blank if not using a proxy or service emulator.",
advanced=True,
value="https://api.groq.com",
real_time_refresh=True,
),
IntInput(
name="max_tokens",
@ -36,6 +40,13 @@ class GroqModel(LCModelComponent):
info="Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
value=0.1,
),
SliderInput(
name="temperature",
display_name="Temperature",
value=0.1,
info="Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
range_spec=RangeSpec(min=0, max=1, step=0.01),
),
IntInput(
name="n",
display_name="N",
@ -47,33 +58,65 @@ class GroqModel(LCModelComponent):
name="model_name",
display_name="Model",
info="The name of the model to use.",
options=GROQ_MODELS,
value="llama-3.1-8b-instant",
options=[],
refresh_button=True,
real_time_refresh=True,
),
BoolInput(
name="tool_model_enabled",
display_name="Enable Tool Models",
info=(
"Select if you want to use models that can work with tools. If yes, only those models will be shown."
),
advanced=False,
value=True,
real_time_refresh=True,
),
]
def get_models(self) -> list[str]:
api_key = self.groq_api_key
base_url = self.groq_api_base or "https://api.groq.com"
url = f"{base_url}/openai/v1/models"
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
def get_models(self, tool_model_enabled: bool | None = None) -> list[str]:
try:
url = f"{self.base_url}/openai/v1/models"
headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
response = requests.get(url, headers=headers, timeout=10)
response.raise_for_status()
model_list = response.json()
return [model["id"] for model in model_list.get("data", [])]
except requests.RequestException as e:
self.status = f"Error fetching models: {e}"
return GROQ_MODELS
model_ids = [model["id"] for model in model_list.get("data", [])]
except (ImportError, ValueError, requests.exceptions.RequestException) as e:
logger.exception(f"Error getting model names: {e}")
model_ids = GROQ_MODELS
if tool_model_enabled:
try:
from langchain_groq import ChatGroq
except ImportError as e:
msg = "langchain_groq is not installed. Please install it with `pip install langchain_groq`."
raise ImportError(msg) from e
for model in model_ids:
model_with_tool = ChatGroq(
model=model,
api_key=self.api_key,
base_url=self.base_url,
)
if not self.supports_tool_calling(model_with_tool):
model_ids.remove(model)
return model_ids
return model_ids
@override
def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):
if field_name in {"groq_api_key", "groq_api_base", "model_name"}:
models = self.get_models()
build_config["model_name"]["options"] = models
if field_name in ("base_url", "model_name", "tool_model_enabled", "api_key") and field_value:
try:
if len(self.api_key) != 0:
try:
ids = self.get_models(tool_model_enabled=self.tool_model_enabled)
except (ImportError, ValueError, requests.exceptions.RequestException) as e:
logger.exception(f"Error getting model names: {e}")
ids = GROQ_MODELS
build_config["model_name"]["options"] = ids
build_config["model_name"]["value"] = ids[0]
except Exception as e:
msg = f"Error getting model names: {e}"
raise ValueError(msg) from e
return build_config
def build_model(self) -> LanguageModel: # type: ignore[type-var]
@ -83,20 +126,12 @@ class GroqModel(LCModelComponent):
msg = "langchain-groq is not installed. Please install it with `pip install langchain-groq`."
raise ImportError(msg) from e
groq_api_key = self.groq_api_key
model_name = self.model_name
max_tokens = self.max_tokens
temperature = self.temperature
groq_api_base = self.groq_api_base
n = self.n
stream = self.stream
return ChatGroq(
model=model_name,
max_tokens=max_tokens or None,
temperature=temperature,
base_url=groq_api_base,
n=n or 1,
api_key=SecretStr(groq_api_key).get_secret_value(),
streaming=stream,
model=self.model_name,
max_tokens=self.max_tokens or None,
temperature=self.temperature,
base_url=self.base_url,
n=self.n or 1,
api_key=SecretStr(self.api_key).get_secret_value(),
streaming=self.stream,
)

29
src/backend/base/langflow/components/models/nvidia.py
View file

@ -2,7 +2,8 @@ from typing import Any
from langflow.base.models.model import LCModelComponent
from langflow.field_typing import LanguageModel
from langflow.inputs import BoolInput, DropdownInput, FloatInput, IntInput, SecretStrInput, StrInput
from langflow.field_typing.range_spec import RangeSpec
from langflow.inputs import BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput, SliderInput
from langflow.schema.dotdict import dotdict
@ -23,15 +24,17 @@ class NVIDIAModelComponent(LCModelComponent):
name="model_name",
display_name="Model Name",
advanced=False,
options=["mistralai/mixtral-8x7b-instruct-v0.1"],
value="mistralai/mixtral-8x7b-instruct-v0.1",
options=[],
real_time_refresh=True,
refresh_button=True,
),
StrInput(
MessageTextInput(
name="base_url",
display_name="NVIDIA Base URL",
value="https://integrate.api.nvidia.com/v1",
refresh_button=True,
info="The base URL of the NVIDIA API. Defaults to https://integrate.api.nvidia.com/v1.",
real_time_refresh=True,
),
BoolInput(
name="tool_model_enabled",
@ -41,15 +44,23 @@ class NVIDIAModelComponent(LCModelComponent):
),
advanced=False,
value=True,
real_time_refresh=True,
),
SecretStrInput(
name="nvidia_api_key",
name="api_key",
display_name="NVIDIA API Key",
info="The NVIDIA API Key.",
advanced=False,
value="NVIDIA_API_KEY",
real_time_refresh=True,
),
SliderInput(
name="temperature",
display_name="Temperature",
value=0.1,
info="Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
range_spec=RangeSpec(min=0, max=1, step=0.01),
),
FloatInput(name="temperature", display_name="Temperature", value=0.1),
IntInput(
name="seed",
display_name="Seed",
@ -67,7 +78,7 @@ class NVIDIAModelComponent(LCModelComponent):
return [model.id for model in build_model.available_models]
def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
if field_name == "base_url" and field_value:
if field_name in ("base_url", "model_name", "tool_model_enabled", "api_key") and field_value:
try:
ids = self.get_models(self.tool_model_enabled)
build_config["model_name"]["options"] = ids
@ -83,7 +94,7 @@ class NVIDIAModelComponent(LCModelComponent):
except ImportError as e:
msg = "Please install langchain-nvidia-ai-endpoints to use the NVIDIA model."
raise ImportError(msg) from e
nvidia_api_key = self.nvidia_api_key
api_key = self.api_key
temperature = self.temperature
model_name: str = self.model_name
max_tokens = self.max_tokens
@ -92,7 +103,7 @@ class NVIDIAModelComponent(LCModelComponent):
max_tokens=max_tokens or None,
model=model_name,
base_url=self.base_url,
api_key=nvidia_api_key,
api_key=api_key,
temperature=temperature or 0.1,
seed=seed,
)

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src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json
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