upd: llm models
This commit is contained in:
namastex888
parent
0ff4694a16
commit
60806556b1
6 changed files with 484 additions and 527 deletions
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@ -1,112 +1,112 @@
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from typing import Optional
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from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
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from pydantic.v1 import SecretStr
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from langflow.base.constants import STREAM_INFO_TEXT
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from langflow.base.models.model import LCModelComponent
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from langflow.field_typing import Text
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from langflow.field_typing import BaseLanguageModel, Text
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from langflow.inputs import BoolInput, FloatInput, SecretStrInput, StrInput
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from langflow.template import Output
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class QianfanChatEndpointComponent(LCModelComponent):
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display_name: str = "Qianfan"
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description: str = "Generate text using Baidu Qianfan LLMs."
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documentation: str = "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint."
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documentation: str = "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint"
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icon = "BaiduQianfan"
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field_order = [
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"model",
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"qianfan_ak",
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"qianfan_sk",
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"top_p",
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"temperature",
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"penalty_score",
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"endpoint",
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"input_value",
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"system_message",
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"stream",
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inputs = [
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StrInput(
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name="input_value",
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display_name="Input",
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input_types=["Text", "Data", "Prompt"],
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),
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StrInput(
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name="model",
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display_name="Model Name",
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options=[
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"ERNIE-Bot",
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"ERNIE-Bot-turbo",
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"BLOOMZ-7B",
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"Llama-2-7b-chat",
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"Llama-2-13b-chat",
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"Llama-2-70b-chat",
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"Qianfan-BLOOMZ-7B-compressed",
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"Qianfan-Chinese-Llama-2-7B",
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"ChatGLM2-6B-32K",
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"AquilaChat-7B",
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],
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info="https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint",
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value="ERNIE-Bot-turbo",
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),
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SecretStrInput(
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name="qianfan_ak",
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display_name="Qianfan Ak",
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info="which you could get from https://cloud.baidu.com/product/wenxinworkshop",
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),
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SecretStrInput(
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name="qianfan_sk",
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display_name="Qianfan Sk",
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info="which you could get from https://cloud.baidu.com/product/wenxinworkshop",
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),
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FloatInput(
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name="top_p",
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display_name="Top p",
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info="Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
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value=0.8,
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advanced=True,
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),
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FloatInput(
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name="temperature",
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display_name="Temperature",
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info="Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
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value=0.95,
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),
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FloatInput(
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name="penalty_score",
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display_name="Penalty Score",
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info="Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
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value=1.0,
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advanced=True,
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),
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StrInput(
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name="endpoint",
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display_name="Endpoint",
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info="Endpoint of the Qianfan LLM, required if custom model used.",
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),
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BoolInput(
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name="stream",
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display_name="Stream",
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info=STREAM_INFO_TEXT,
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advanced=True,
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),
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StrInput(
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name="system_message",
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display_name="System Message",
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info="System message to pass to the model.",
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advanced=True,
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),
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]
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outputs = [
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Output(display_name="Text", name="text_output", method="text_response"),
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Output(display_name="Language Model", name="model_output", method="build_model"),
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]
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def build_config(self):
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return {
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"model": {
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"display_name": "Model Name",
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"options": [
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"ERNIE-Bot",
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"ERNIE-Bot-turbo",
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"BLOOMZ-7B",
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"Llama-2-7b-chat",
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"Llama-2-13b-chat",
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"Llama-2-70b-chat",
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"Qianfan-BLOOMZ-7B-compressed",
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"Qianfan-Chinese-Llama-2-7B",
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"ChatGLM2-6B-32K",
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"AquilaChat-7B",
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],
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"info": "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint",
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"value": "ERNIE-Bot-turbo",
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},
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"qianfan_ak": {
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"display_name": "Qianfan Ak",
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"password": True,
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"info": "which you could get from https://cloud.baidu.com/product/wenxinworkshop",
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},
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"qianfan_sk": {
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"display_name": "Qianfan Sk",
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"password": True,
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"info": "which you could get from https://cloud.baidu.com/product/wenxinworkshop",
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},
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"top_p": {
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"display_name": "Top p",
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"field_type": "float",
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"info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
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"value": 0.8,
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"advanced": True,
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},
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"temperature": {
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"display_name": "Temperature",
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"field_type": "float",
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"info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
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"value": 0.95,
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},
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"penalty_score": {
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"display_name": "Penalty Score",
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"field_type": "float",
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"info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
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"value": 1.0,
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"advanced": True,
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},
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"endpoint": {
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"display_name": "Endpoint",
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"info": "Endpoint of the Qianfan LLM, required if custom model used.",
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},
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"code": {"show": False},
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"input_value": {"display_name": "Input", "input_types": ["Text", "Data", "Prompt"]},
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"stream": {
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"display_name": "Stream",
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"info": STREAM_INFO_TEXT,
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"advanced": True,
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},
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"system_message": {
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"display_name": "System Message",
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"info": "System message to pass to the model.",
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"advanced": True,
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},
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}
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def text_response(self) -> Text:
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input_value = self.input_value
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stream = self.stream
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system_message = self.system_message
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output = self.build_model()
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result = self.get_chat_result(output, stream, input_value, system_message)
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self.status = result
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return result
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def build_model(self) -> BaseLanguageModel:
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model = self.model
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qianfan_ak = self.qianfan_ak
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qianfan_sk = self.qianfan_sk
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top_p = self.top_p
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temperature = self.temperature
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penalty_score = self.penalty_score
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endpoint = self.endpoint
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def build(
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self,
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input_value: Text,
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qianfan_ak: str,
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qianfan_sk: str,
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model: str,
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top_p: Optional[float] = None,
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temperature: Optional[float] = None,
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penalty_score: Optional[float] = None,
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endpoint: Optional[str] = None,
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stream: bool = False,
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system_message: Optional[str] = None,
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) -> Text:
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try:
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output = QianfanChatEndpoint( # type: ignore
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model=model,
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@ -120,4 +120,4 @@ class QianfanChatEndpointComponent(LCModelComponent):
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except Exception as e:
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raise ValueError("Could not connect to Baidu Qianfan API.") from e
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return self.get_chat_result(output, stream, input_value, system_message)
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return output
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@ -1,155 +1,143 @@
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from typing import Any, Dict, Optional
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from langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMException
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from langflow.base.constants import STREAM_INFO_TEXT
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from langflow.base.models.model import LCModelComponent
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from langflow.field_typing import Text
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from langflow.field_typing import BaseLanguageModel, Text
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from langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, StrInput
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from langflow.template import Output
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class ChatLiteLLMModelComponent(LCModelComponent):
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display_name = "LiteLLM"
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description = "`LiteLLM` collection of large language models."
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documentation = "https://python.langchain.com/docs/integrations/chat/litellm"
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field_order = [
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"model",
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"api_key",
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"provider",
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"temperature",
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"model_kwargs",
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"top_p",
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"top_k",
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"n",
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"max_tokens",
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"max_retries",
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"verbose",
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"stream",
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"input_value",
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"system_message",
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icon = "LiteLLM"
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inputs = [
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StrInput(name="input_value", display_name="Input", input_types=["Text", "Data", "Prompt"]),
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StrInput(
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name="model",
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display_name="Model name",
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advanced=False,
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required=True,
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info="The name of the model to use. For example, `gpt-3.5-turbo`.",
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),
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StrInput(
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name="api_key",
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display_name="API key",
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advanced=False,
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required=False,
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password=True,
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),
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DropdownInput(
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name="provider",
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display_name="Provider",
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info="The provider of the API key.",
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options=[
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"OpenAI",
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"Azure",
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"Anthropic",
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"Replicate",
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"Cohere",
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"OpenRouter",
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],
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),
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FloatInput(
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name="temperature",
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display_name="Temperature",
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advanced=False,
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required=False,
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default=0.7,
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),
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DictInput(
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name="model_kwargs",
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display_name="Model kwargs",
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advanced=True,
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required=False,
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default={},
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),
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FloatInput(
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name="top_p",
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display_name="Top p",
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advanced=True,
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required=False,
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),
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IntInput(
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name="top_k",
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display_name="Top k",
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advanced=True,
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required=False,
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),
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IntInput(
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name="n",
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display_name="N",
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advanced=True,
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required=False,
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info="Number of chat completions to generate for each prompt. "
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"Note that the API may not return the full n completions if duplicates are generated.",
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default=1,
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),
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IntInput(
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name="max_tokens",
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display_name="Max tokens",
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advanced=False,
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default=256,
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info="The maximum number of tokens to generate for each chat completion.",
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),
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IntInput(
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name="max_retries",
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display_name="Max retries",
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advanced=True,
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required=False,
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default=6,
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),
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BoolInput(
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name="verbose",
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display_name="Verbose",
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advanced=True,
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required=False,
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default=False,
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),
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BoolInput(
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name="stream",
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display_name="Stream",
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info=STREAM_INFO_TEXT,
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advanced=True,
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),
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StrInput(
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name="system_message",
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display_name="System Message",
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info="System message to pass to the model.",
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advanced=True,
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),
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]
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def build_config(self):
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return {
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"model": {
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"display_name": "Model name",
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"field_type": "str",
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"advanced": False,
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"required": True,
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"info": "The name of the model to use. For example, `gpt-3.5-turbo`.",
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},
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"api_key": {
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"display_name": "API key",
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"field_type": "str",
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"advanced": False,
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"required": False,
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"password": True,
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},
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"provider": {
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"display_name": "Provider",
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"info": "The provider of the API key.",
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"options": [
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"OpenAI",
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"Azure",
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"Anthropic",
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"Replicate",
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"Cohere",
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"OpenRouter",
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],
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},
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"temperature": {
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"display_name": "Temperature",
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"field_type": "float",
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"advanced": False,
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"required": False,
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"default": 0.7,
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},
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"model_kwargs": {
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"display_name": "Model kwargs",
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"field_type": "dict",
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"advanced": True,
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"required": False,
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"default": {},
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},
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"top_p": {
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"display_name": "Top p",
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"field_type": "float",
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"advanced": True,
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"required": False,
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},
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"top_k": {
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"display_name": "Top k",
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"field_type": "int",
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"advanced": True,
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"required": False,
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},
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"n": {
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"display_name": "N",
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"field_type": "int",
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"advanced": True,
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"required": False,
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"info": "Number of chat completions to generate for each prompt. "
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"Note that the API may not return the full n completions if duplicates are generated.",
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"default": 1,
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},
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"max_tokens": {
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"display_name": "Max tokens",
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"advanced": False,
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"default": 256,
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"info": "The maximum number of tokens to generate for each chat completion.",
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},
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"max_retries": {
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"display_name": "Max retries",
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"field_type": "int",
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"advanced": True,
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"required": False,
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"default": 6,
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},
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"verbose": {
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"display_name": "Verbose",
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"field_type": "bool",
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"advanced": True,
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"required": False,
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"default": False,
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},
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"input_value": {"display_name": "Input", "input_types": ["Text", "Data", "Prompt"]},
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"stream": {
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"display_name": "Stream",
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"info": STREAM_INFO_TEXT,
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"advanced": True,
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},
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"system_message": {
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"display_name": "System Message",
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"info": "System message to pass to the model.",
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"advanced": True,
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},
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}
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outputs = [
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Output(display_name="Text", name="text_output", method="text_response"),
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Output(display_name="Language Model", name="model_output", method="build_model"),
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]
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def build(
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self,
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input_value: Text,
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model: str,
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provider: str,
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api_key: Optional[str] = None,
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stream: bool = False,
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temperature: Optional[float] = 0.7,
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model_kwargs: Optional[Dict[str, Any]] = {},
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top_p: Optional[float] = None,
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top_k: Optional[int] = None,
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n: int = 1,
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max_tokens: int = 256,
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max_retries: int = 6,
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verbose: bool = False,
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system_message: Optional[str] = None,
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) -> Text:
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def text_response(self) -> Text:
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input_value = self.input_value
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stream = self.stream
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system_message = self.system_message
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output = self.build_model()
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result = self.get_chat_result(output, stream, input_value, system_message)
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self.status = result
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return result
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def build_model(self) -> BaseLanguageModel:
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try:
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import litellm # type: ignore
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litellm.drop_params = True
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litellm.set_verbose = verbose
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litellm.set_verbose = self.verbose
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except ImportError:
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raise ChatLiteLLMException(
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"Could not import litellm python package. " "Please install it with `pip install litellm`"
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)
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provider_map = {
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"OpenAI": "openai_api_key",
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"Azure": "azure_api_key",
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|
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@ -158,27 +146,28 @@ class ChatLiteLLMModelComponent(LCModelComponent):
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"Cohere": "cohere_api_key",
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"OpenRouter": "openrouter_api_key",
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}
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# Set the API key based on the provider
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api_keys: dict[str, Optional[str]] = {v: None for v in provider_map.values()}
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if variable_name := provider_map.get(provider):
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api_keys[variable_name] = api_key
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if variable_name := provider_map.get(self.provider):
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api_keys[variable_name] = self.api_key
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else:
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raise ChatLiteLLMException(
|
||||
f"Provider {provider} is not supported. Supported providers are: {', '.join(provider_map.keys())}"
|
||||
f"Provider {self.provider} is not supported. Supported providers are: {', '.join(provider_map.keys())}"
|
||||
)
|
||||
|
||||
output = ChatLiteLLM(
|
||||
model=model,
|
||||
model=self.model,
|
||||
client=None,
|
||||
streaming=stream,
|
||||
temperature=temperature,
|
||||
model_kwargs=model_kwargs if model_kwargs is not None else {},
|
||||
top_p=top_p,
|
||||
top_k=top_k,
|
||||
n=n,
|
||||
max_tokens=max_tokens,
|
||||
max_retries=max_retries,
|
||||
streaming=self.stream,
|
||||
temperature=self.temperature,
|
||||
model_kwargs=self.model_kwargs if self.model_kwargs is not None else {},
|
||||
top_p=self.top_p,
|
||||
top_k=self.top_k,
|
||||
n=self.n,
|
||||
max_tokens=self.max_tokens,
|
||||
max_retries=self.max_retries,
|
||||
openai_api_key=api_keys["openai_api_key"],
|
||||
azure_api_key=api_keys["azure_api_key"],
|
||||
anthropic_api_key=api_keys["anthropic_api_key"],
|
||||
|
|
@ -186,4 +175,6 @@ class ChatLiteLLMModelComponent(LCModelComponent):
|
|||
cohere_api_key=api_keys["cohere_api_key"],
|
||||
openrouter_api_key=api_keys["openrouter_api_key"],
|
||||
)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
|
||||
return output
|
||||
|
||||
|
|
@ -1,102 +1,100 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_google_genai import ChatGoogleGenerativeAI
|
||||
from pydantic.v1 import SecretStr
|
||||
from langflow.field_typing import Text, RangeSpec
|
||||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
|
||||
from langflow.field_typing import BaseLanguageModel, Text
|
||||
from langflow.inputs import BoolInput, FloatInput, IntInput, SecretStrInput, StrInput, DropdownInput
|
||||
from langflow.template import Output
|
||||
|
||||
class GoogleGenerativeAIComponent(LCModelComponent):
|
||||
display_name: str = "Google Generative AI"
|
||||
description: str = "Generate text using Google Generative AI."
|
||||
icon = "GoogleGenerativeAI"
|
||||
|
||||
field_order = [
|
||||
"google_api_key",
|
||||
"model",
|
||||
"max_output_tokens",
|
||||
"temperature",
|
||||
"top_k",
|
||||
"top_p",
|
||||
"n",
|
||||
"input_value",
|
||||
"system_message",
|
||||
"stream",
|
||||
inputs = [
|
||||
SecretStrInput(
|
||||
name="google_api_key",
|
||||
display_name="Google API Key",
|
||||
info="The Google API Key to use for the Google Generative AI.",
|
||||
),
|
||||
DropdownInput(
|
||||
name="model",
|
||||
display_name="Model",
|
||||
info="The name of the model to use.",
|
||||
options=["gemini-1.5-pro", "gemini-1.5-flash"],
|
||||
value="gemini-1.5-pro",
|
||||
),
|
||||
IntInput(
|
||||
name="max_output_tokens",
|
||||
display_name="Max Output Tokens",
|
||||
info="The maximum number of tokens to generate.",
|
||||
advanced=True,
|
||||
),
|
||||
FloatInput(
|
||||
name="temperature",
|
||||
display_name="Temperature",
|
||||
info="Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
|
||||
value=0.1,
|
||||
),
|
||||
IntInput(
|
||||
name="top_k",
|
||||
display_name="Top K",
|
||||
info="Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.",
|
||||
advanced=True,
|
||||
),
|
||||
FloatInput(
|
||||
name="top_p",
|
||||
display_name="Top P",
|
||||
info="The maximum cumulative probability of tokens to consider when sampling.",
|
||||
advanced=True,
|
||||
),
|
||||
IntInput(
|
||||
name="n",
|
||||
display_name="N",
|
||||
info="Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.",
|
||||
advanced=True,
|
||||
),
|
||||
StrInput(name="input_value", display_name="Input", info="The input to the model.", input_types=["Text", "Data", "Prompt"]),
|
||||
BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
|
||||
StrInput(
|
||||
name="system_message",
|
||||
display_name="System Message",
|
||||
info="System message to pass to the model.",
|
||||
advanced=True,
|
||||
),
|
||||
]
|
||||
outputs = [
|
||||
Output(display_name="Text", name="text_output", method="text_response"),
|
||||
Output(display_name="Language Model", name="model_output", method="build_model"),
|
||||
]
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"google_api_key": {
|
||||
"display_name": "Google API Key",
|
||||
"info": "The Google API Key to use for the Google Generative AI.",
|
||||
},
|
||||
"max_output_tokens": {
|
||||
"display_name": "Max Output Tokens",
|
||||
"info": "The maximum number of tokens to generate.",
|
||||
"advanced": True,
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"info": "Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
|
||||
},
|
||||
"top_k": {
|
||||
"display_name": "Top K",
|
||||
"info": "Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.",
|
||||
"rangeSpec": RangeSpec(min=0, max=2, step=0.1),
|
||||
"advanced": True,
|
||||
},
|
||||
"top_p": {
|
||||
"display_name": "Top P",
|
||||
"info": "The maximum cumulative probability of tokens to consider when sampling.",
|
||||
"advanced": True,
|
||||
},
|
||||
"n": {
|
||||
"display_name": "N",
|
||||
"info": "Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.",
|
||||
"advanced": True,
|
||||
},
|
||||
"model": {
|
||||
"display_name": "Model",
|
||||
"info": "The name of the model to use. Supported examples: gemini-pro",
|
||||
"options": ["gemini-pro", "gemini-pro-vision"],
|
||||
},
|
||||
"code": {
|
||||
"advanced": True,
|
||||
},
|
||||
"input_value": {"display_name": "Input", "info": "The input to the model."},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
"advanced": True,
|
||||
},
|
||||
"system_message": {
|
||||
"display_name": "System Message",
|
||||
"info": "System message to pass to the model.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
def text_response(self) -> Text:
|
||||
input_value = self.input_value
|
||||
stream = self.stream
|
||||
system_message = self.system_message
|
||||
output = self.build_model()
|
||||
result = self.get_chat_result(output, stream, input_value, system_message)
|
||||
self.status = result
|
||||
return result
|
||||
|
||||
def build_model(self) -> BaseLanguageModel:
|
||||
google_api_key = self.google_api_key
|
||||
model = self.model
|
||||
max_output_tokens = self.max_output_tokens
|
||||
temperature = self.temperature
|
||||
top_k = self.top_k
|
||||
top_p = self.top_p
|
||||
n = self.n
|
||||
|
||||
def build(
|
||||
self,
|
||||
google_api_key: str,
|
||||
model: str,
|
||||
input_value: Text,
|
||||
max_output_tokens: Optional[int] = None,
|
||||
temperature: float = 0.1,
|
||||
top_k: Optional[int] = None,
|
||||
top_p: Optional[float] = None,
|
||||
n: Optional[int] = 1,
|
||||
stream: bool = False,
|
||||
system_message: Optional[str] = None,
|
||||
) -> Text:
|
||||
output = ChatGoogleGenerativeAI(
|
||||
model=model,
|
||||
max_output_tokens=max_output_tokens or None, # type: ignore
|
||||
max_output_tokens=max_output_tokens or None,
|
||||
temperature=temperature,
|
||||
top_k=top_k or None,
|
||||
top_p=top_p or None, # type: ignore
|
||||
top_p=top_p or None,
|
||||
n=n or 1,
|
||||
google_api_key=SecretStr(google_api_key),
|
||||
)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
|
||||
return output
|
||||
|
|
@ -1,102 +1,106 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_groq import ChatGroq
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.groq_constants import MODEL_NAMES
|
||||
from pydantic.v1 import SecretStr
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
from langflow.field_typing import BaseLanguageModel, Text
|
||||
from langflow.template import Input, Output
|
||||
from langflow.inputs import BoolInput, FloatInput, IntInput, SecretStrInput, StrInput, DropdownInput
|
||||
from langflow.template import Output
|
||||
|
||||
|
||||
class GroqModelComponent(LCModelComponent):
|
||||
class GroqModel(LCModelComponent):
|
||||
display_name: str = "Groq"
|
||||
description: str = "Generate text using Groq."
|
||||
icon = "Groq"
|
||||
|
||||
inputs = [
|
||||
Input(
|
||||
SecretStrInput(
|
||||
name="groq_api_key",
|
||||
field_type=str,
|
||||
display_name="Groq API Key",
|
||||
info="API key for the Groq API.",
|
||||
password=True,
|
||||
),
|
||||
Input(
|
||||
StrInput(
|
||||
name="groq_api_base",
|
||||
field_type=Optional[str],
|
||||
display_name="Groq API Base",
|
||||
advanced=True,
|
||||
info="Base URL path for API requests, leave blank if not using a proxy or service emulator.",
|
||||
advanced=True,
|
||||
),
|
||||
Input(
|
||||
IntInput(
|
||||
name="max_tokens",
|
||||
field_type=Optional[int],
|
||||
display_name="Max Output Tokens",
|
||||
advanced=True,
|
||||
info="The maximum number of tokens to generate.",
|
||||
),
|
||||
Input(
|
||||
name="temperature",
|
||||
field_type=float,
|
||||
display_name="Temperature",
|
||||
info="Run inference with this temperature. Must be in the closed interval [0.0, 1.0].",
|
||||
),
|
||||
Input(
|
||||
name="n",
|
||||
field_type=Optional[int],
|
||||
display_name="N",
|
||||
advanced=True,
|
||||
info="Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.",
|
||||
),
|
||||
Input(
|
||||
FloatInput(
|
||||
name="temperature",
|
||||
display_name="Temperature",
|
||||
info="Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
|
||||
value=0.1,
|
||||
),
|
||||
IntInput(
|
||||
name="n",
|
||||
display_name="N",
|
||||
info="Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.",
|
||||
advanced=True,
|
||||
),
|
||||
DropdownInput(
|
||||
name="model_name",
|
||||
field_type=str,
|
||||
display_name="Model",
|
||||
info="The name of the model to use. Supported examples: gemini-pro",
|
||||
info="The name of the model to use.",
|
||||
options=MODEL_NAMES,
|
||||
),
|
||||
Input(name="input_value", field_type=str, display_name="Input", input_types=["Text", "Data", "Prompt"]),
|
||||
Input(name="stream", field_type=bool, display_name="Stream", advanced=True, info=STREAM_INFO_TEXT),
|
||||
Input(
|
||||
name="system_message",
|
||||
field_type=Optional[str],
|
||||
display_name="System Message",
|
||||
StrInput(
|
||||
name="input_value",
|
||||
display_name="Input",
|
||||
info="The input to the model.",
|
||||
input_types=["Text", "Data", "Prompt"],
|
||||
),
|
||||
BoolInput(
|
||||
name="stream",
|
||||
display_name="Stream",
|
||||
info=STREAM_INFO_TEXT,
|
||||
advanced=True,
|
||||
),
|
||||
StrInput(
|
||||
name="system_message",
|
||||
display_name="System Message",
|
||||
info="System message to pass to the model.",
|
||||
advanced=True,
|
||||
),
|
||||
]
|
||||
|
||||
outputs = [
|
||||
Output(display_name="Text", name="text_output", method="text_response"),
|
||||
Output(display_name="Language Model", name="model_output", method="model_response"),
|
||||
Output(display_name="Language Model", name="model_output", method="build_model"),
|
||||
]
|
||||
|
||||
def text_response(self) -> Text:
|
||||
input_value = self.input_value
|
||||
stream = self.stream
|
||||
system_message = self.system_message
|
||||
output = self.model_response()
|
||||
output = self.build_model()
|
||||
result = self.get_chat_result(output, stream, input_value, system_message)
|
||||
self.status = result
|
||||
return result
|
||||
|
||||
def model_response(self) -> BaseLanguageModel:
|
||||
def build_model(self) -> BaseLanguageModel:
|
||||
groq_api_key = self.groq_api_key
|
||||
model_name = self.model_name
|
||||
groq_api_base = self.groq_api_base or None
|
||||
max_tokens = self.max_tokens
|
||||
temperature = self.temperature
|
||||
n = self.n or 1
|
||||
groq_api_base = self.groq_api_base
|
||||
n = self.n
|
||||
stream = self.stream
|
||||
|
||||
output = ChatGroq(
|
||||
model_name=model_name,
|
||||
max_tokens=max_tokens or None, # type: ignore
|
||||
max_tokens=max_tokens or None,
|
||||
temperature=temperature,
|
||||
groq_api_base=groq_api_base,
|
||||
n=n,
|
||||
n=n or 1,
|
||||
groq_api_key=SecretStr(groq_api_key),
|
||||
streaming=stream,
|
||||
)
|
||||
return output
|
||||
|
||||
return output
|
||||
|
|
@ -5,7 +5,9 @@ from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
|
|||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.field_typing import BaseLanguageModel, Text
|
||||
from langflow.inputs import BoolInput, DictInput, DropdownInput, SecretStrInput, StrInput
|
||||
from langflow.template import Output
|
||||
|
||||
|
||||
class HuggingFaceEndpointsComponent(LCModelComponent):
|
||||
|
|
@ -13,64 +15,54 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
|
|||
description: str = "Generate text using Hugging Face Inference APIs."
|
||||
icon = "HuggingFace"
|
||||
|
||||
field_order = [
|
||||
"endpoint_url",
|
||||
"task",
|
||||
"huggingfacehub_api_token",
|
||||
"model_kwargs",
|
||||
"input_value",
|
||||
"system_message",
|
||||
"stream",
|
||||
inputs = [
|
||||
StrInput(name="input_value", display_name="Input", input_types=["Text", "Data", "Prompt"]),
|
||||
SecretStrInput(name="endpoint_url", display_name="Endpoint URL", password=True),
|
||||
DropdownInput(
|
||||
name="task",
|
||||
display_name="Task",
|
||||
options=["text2text-generation", "text-generation", "summarization"],
|
||||
),
|
||||
SecretStrInput(name="huggingfacehub_api_token", display_name="API token", password=True),
|
||||
DictInput(name="model_kwargs", display_name="Model Keyword Arguments", advanced=True),
|
||||
BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
|
||||
StrInput(
|
||||
name="system_message",
|
||||
display_name="System Message",
|
||||
info="System message to pass to the model.",
|
||||
advanced=True,
|
||||
),
|
||||
]
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"endpoint_url": {"display_name": "Endpoint URL", "password": True},
|
||||
"task": {
|
||||
"display_name": "Task",
|
||||
"options": ["text2text-generation", "text-generation", "summarization"],
|
||||
},
|
||||
"huggingfacehub_api_token": {"display_name": "API token", "password": True},
|
||||
"model_kwargs": {
|
||||
"display_name": "Model Keyword Arguments",
|
||||
"field_type": "code",
|
||||
"advanced": True,
|
||||
},
|
||||
"code": {"show": False},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Data", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
"advanced": True,
|
||||
},
|
||||
"system_message": {
|
||||
"display_name": "System Message",
|
||||
"info": "System message to pass to the model.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
outputs = [
|
||||
Output(display_name="Text", name="text_output", method="text_response"),
|
||||
Output(display_name="Language Model", name="model_output", method="build_model"),
|
||||
]
|
||||
|
||||
def text_response(self) -> Text:
|
||||
input_value = self.input_value
|
||||
stream = self.stream
|
||||
system_message = self.system_message
|
||||
output = self.build_model()
|
||||
result = self.get_chat_result(output, stream, input_value, system_message)
|
||||
self.status = result
|
||||
return result
|
||||
|
||||
def build_model(self) -> BaseLanguageModel:
|
||||
endpoint_url = self.endpoint_url
|
||||
task = self.task
|
||||
huggingfacehub_api_token = self.huggingfacehub_api_token
|
||||
model_kwargs = self.model_kwargs or {}
|
||||
|
||||
def build(
|
||||
self,
|
||||
input_value: Text,
|
||||
endpoint_url: str,
|
||||
model: Optional[str] = None,
|
||||
task: str = "text2text-generation",
|
||||
huggingfacehub_api_token: Optional[str] = None,
|
||||
model_kwargs: Optional[dict] = None,
|
||||
stream: bool = False,
|
||||
system_message: Optional[str] = None,
|
||||
) -> Text:
|
||||
try:
|
||||
llm = HuggingFaceEndpoint( # type: ignore
|
||||
llm = HuggingFaceEndpoint(
|
||||
endpoint_url=endpoint_url,
|
||||
task=task,
|
||||
huggingfacehub_api_token=huggingfacehub_api_token,
|
||||
model_kwargs=model_kwargs or {},
|
||||
model=model or "",
|
||||
model_kwargs=model_kwargs,
|
||||
)
|
||||
except Exception as e:
|
||||
raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
|
||||
|
||||
output = ChatHuggingFace(llm=llm)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
return output
|
||||
|
|
@ -1,131 +1,103 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_mistralai import ChatMistralAI
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
from langflow.field_typing import Text
|
||||
|
||||
from langflow.field_typing import BaseLanguageModel, Text
|
||||
from langflow.inputs import BoolInput, FloatInput, IntInput, SecretStrInput, StrInput, DropdownInput
|
||||
from langflow.template import Output
|
||||
|
||||
class MistralAIModelComponent(LCModelComponent):
|
||||
display_name = "MistralAI"
|
||||
description = "Generates text using MistralAI LLMs."
|
||||
icon = "MistralAI"
|
||||
|
||||
field_order = [
|
||||
"max_tokens",
|
||||
"model_kwargs",
|
||||
"model_name",
|
||||
"mistral_api_base",
|
||||
"mistral_api_key",
|
||||
"temperature",
|
||||
"input_value",
|
||||
"system_message",
|
||||
"stream",
|
||||
inputs = [
|
||||
StrInput(name="input_value", display_name="Input", input_types=["Text", "Data", "Prompt"]),
|
||||
IntInput(
|
||||
name="max_tokens",
|
||||
display_name="Max Tokens",
|
||||
advanced=True,
|
||||
info="The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
|
||||
),
|
||||
DropdownInput(
|
||||
name="model_name",
|
||||
display_name="Model Name",
|
||||
advanced=False,
|
||||
options=[
|
||||
"open-mixtral-8x7b",
|
||||
"open-mixtral-8x22b",
|
||||
"mistral-small-latest",
|
||||
"mistral-medium-latest",
|
||||
"mistral-large-latest",
|
||||
"codestral-latest",
|
||||
],
|
||||
value="codestral-latest",
|
||||
),
|
||||
StrInput(
|
||||
name="mistral_api_base",
|
||||
display_name="Mistral API Base",
|
||||
advanced=True,
|
||||
info=(
|
||||
"The base URL of the Mistral API. Defaults to https://api.mistral.ai/v1. "
|
||||
"You can change this to use other APIs like JinaChat, LocalAI and Prem."
|
||||
),
|
||||
),
|
||||
SecretStrInput(
|
||||
name="mistral_api_key",
|
||||
display_name="Mistral API Key",
|
||||
info="The Mistral API Key to use for the Mistral model.",
|
||||
advanced=False,
|
||||
),
|
||||
FloatInput(name="temperature", display_name="Temperature", advanced=False, value=0.1),
|
||||
BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
|
||||
StrInput(
|
||||
name="system_message",
|
||||
display_name="System Message",
|
||||
info="System message to pass to the model.",
|
||||
advanced=True,
|
||||
),
|
||||
IntInput(name="max_retries", display_name="Max Retries", advanced=True),
|
||||
IntInput(name="timeout", display_name="Timeout", advanced=True),
|
||||
IntInput(name="max_concurrent_requests", display_name="Max Concurrent Requests", advanced=True),
|
||||
FloatInput(name="top_p", display_name="Top P", advanced=True),
|
||||
IntInput(name="random_seed", display_name="Random Seed", value=1, advanced=True),
|
||||
BoolInput(name="safe_mode", display_name="Safe Mode", advanced=True),
|
||||
]
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Data", "Prompt"]},
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"advanced": True,
|
||||
"info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
|
||||
},
|
||||
"model_name": {
|
||||
"display_name": "Model Name",
|
||||
"advanced": False,
|
||||
"options": [
|
||||
"open-mistral-7b",
|
||||
"open-mixtral-8x7b",
|
||||
"open-mixtral-8x22b",
|
||||
"mistral-small-latest",
|
||||
"mistral-medium-latest",
|
||||
"mistral-large-latest",
|
||||
],
|
||||
"value": "open-mistral-7b",
|
||||
},
|
||||
"mistral_api_base": {
|
||||
"display_name": "Mistral API Base",
|
||||
"advanced": True,
|
||||
"info": (
|
||||
"The base URL of the Mistral API. Defaults to https://api.mistral.ai.\n\n"
|
||||
"You can change this to use other APIs like JinaChat, LocalAI and Prem."
|
||||
),
|
||||
},
|
||||
"mistral_api_key": {
|
||||
"display_name": "Mistral API Key",
|
||||
"info": "The Mistral API Key to use for the Mistral model.",
|
||||
"advanced": False,
|
||||
"password": True,
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"advanced": False,
|
||||
"value": 0.1,
|
||||
},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
"advanced": True,
|
||||
},
|
||||
"system_message": {
|
||||
"display_name": "System Message",
|
||||
"info": "System message to pass to the model.",
|
||||
"advanced": True,
|
||||
},
|
||||
"max_retries": {
|
||||
"display_name": "Max Retries",
|
||||
"advanced": True,
|
||||
},
|
||||
"timeout": {
|
||||
"display_name": "Timeout",
|
||||
"advanced": True,
|
||||
},
|
||||
"max_concurrent_requests": {
|
||||
"display_name": "Max Concurrent Requests",
|
||||
"advanced": True,
|
||||
},
|
||||
"top_p": {
|
||||
"display_name": "Top P",
|
||||
"advanced": True,
|
||||
},
|
||||
"random_seed": {
|
||||
"display_name": "Random Seed",
|
||||
"advanced": True,
|
||||
},
|
||||
"safe_mode": {
|
||||
"display_name": "Safe Mode",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
outputs = [
|
||||
Output(display_name="Text", name="text_output", method="text_response"),
|
||||
Output(display_name="Language Model", name="model_output", method="build_model"),
|
||||
]
|
||||
|
||||
def text_response(self) -> Text:
|
||||
input_value = self.input_value
|
||||
stream = self.stream
|
||||
system_message = self.system_message
|
||||
output = self.build_model()
|
||||
result = self.get_chat_result(output, stream, input_value, system_message)
|
||||
self.status = result
|
||||
return result
|
||||
|
||||
def build_model(self) -> BaseLanguageModel:
|
||||
mistral_api_key = self.mistral_api_key
|
||||
temperature = self.temperature
|
||||
model_name = self.model_name
|
||||
max_tokens = self.max_tokens
|
||||
mistral_api_base = self.mistral_api_base or "https://api.mistral.ai/v1"
|
||||
max_retries = self.max_retries
|
||||
timeout = self.timeout
|
||||
max_concurrent_requests = self.max_concurrent_requests
|
||||
top_p = self.top_p
|
||||
random_seed = self.random_seed
|
||||
safe_mode = self.safe_mode
|
||||
|
||||
def build(
|
||||
self,
|
||||
input_value: Text,
|
||||
mistral_api_key: str,
|
||||
model_name: str,
|
||||
temperature: float = 0.1,
|
||||
max_tokens: Optional[int] = 256,
|
||||
mistral_api_base: Optional[str] = None,
|
||||
stream: bool = False,
|
||||
system_message: Optional[str] = None,
|
||||
max_retries: int = 5,
|
||||
timeout: int = 120,
|
||||
max_concurrent_requests: int = 64,
|
||||
top_p: float = 1,
|
||||
random_seed: Optional[int] = None,
|
||||
safe_mode: bool = False,
|
||||
) -> Text:
|
||||
if not mistral_api_base:
|
||||
mistral_api_base = "https://api.mistral.ai"
|
||||
if mistral_api_key:
|
||||
api_key = SecretStr(mistral_api_key)
|
||||
else:
|
||||
api_key = None
|
||||
|
||||
chat_model = ChatMistralAI(
|
||||
output = ChatMistralAI(
|
||||
max_tokens=max_tokens or None,
|
||||
model_name=model_name,
|
||||
endpoint=mistral_api_base,
|
||||
|
|
@ -139,4 +111,4 @@ class MistralAIModelComponent(LCModelComponent):
|
|||
safe_mode=safe_mode,
|
||||
)
|
||||
|
||||
return self.get_chat_result(chat_model, stream, input_value, system_message)
|
||||
return output
|
||||
Loading…
Add table
Add a link
Reference in a new issue