refactor: remove model_specs
This commit is contained in:
Gabriel Luiz Freitas Almeida
parent
67802d7c4b
commit
6b7dcaaef6
18 changed files with 0 additions and 1672 deletions
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@ -6,7 +6,6 @@ __all__ = [
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"experimental",
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"inputs",
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"memories",
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"model_specs",
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"outputs",
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"retrievers",
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"textsplitters",
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@ -1,62 +0,0 @@
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from typing import Optional
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from langchain_community.llms.bedrock import Bedrock
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from langflow.custom import CustomComponent
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from langflow.field_typing import LanguageModel
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class AmazonBedrockComponent(CustomComponent):
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display_name: str = "Amazon Bedrock"
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description: str = "LLM model from Amazon Bedrock."
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icon = "Amazon"
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def build_config(self):
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return {
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"model_id": {
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"display_name": "Model Id",
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"options": [
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"ai21.j2-grande-instruct",
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"ai21.j2-jumbo-instruct",
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"ai21.j2-mid",
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"ai21.j2-mid-v1",
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"ai21.j2-ultra",
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"ai21.j2-ultra-v1",
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"anthropic.claude-instant-v1",
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"anthropic.claude-v1",
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"anthropic.claude-v2",
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"cohere.command-text-v14",
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],
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},
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"credentials_profile_name": {"display_name": "Credentials Profile Name"},
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"streaming": {"display_name": "Streaming", "field_type": "bool"},
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"endpoint_url": {"display_name": "Endpoint URL"},
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"region_name": {"display_name": "Region Name"},
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"model_kwargs": {"display_name": "Model Kwargs"},
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"cache": {"display_name": "Cache"},
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"code": {"advanced": True},
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}
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def build(
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self,
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model_id: str = "anthropic.claude-instant-v1",
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credentials_profile_name: Optional[str] = None,
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region_name: Optional[str] = None,
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model_kwargs: Optional[dict] = None,
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endpoint_url: Optional[str] = None,
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streaming: bool = False,
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cache: Optional[bool] = None,
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) -> LanguageModel:
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try:
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output = Bedrock(
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credentials_profile_name=credentials_profile_name,
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model_id=model_id,
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region_name=region_name,
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model_kwargs=model_kwargs,
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endpoint_url=endpoint_url,
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streaming=streaming,
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cache=cache,
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) # type: ignore
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except Exception as e:
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raise ValueError("Could not connect to AmazonBedrock API.") from e
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return output
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@ -1,75 +0,0 @@
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from typing import Optional
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from langchain_anthropic import ChatAnthropic
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from pydantic.v1 import SecretStr
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from langflow.custom import CustomComponent
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from langflow.field_typing import LanguageModel
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class ChatAntropicSpecsComponent(CustomComponent):
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display_name: str = "Anthropic"
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description: str = "Anthropic Chat&Completion large language models."
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icon = "Anthropic"
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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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"claude-2.1",
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"claude-2.0",
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"claude-instant-1.2",
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"claude-instant-1",
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# Add more models as needed
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],
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"info": "https://python.langchain.com/docs/integrations/chat/anthropic",
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"required": True,
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"value": "claude-2.1",
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},
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"anthropic_api_key": {
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"display_name": "Anthropic API Key",
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"required": True,
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"password": True,
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"info": "Your Anthropic API key.",
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},
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"max_tokens": {
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"display_name": "Max Tokens",
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"advanced": True,
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"info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
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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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"value": 0.7,
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},
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"api_endpoint": {
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"display_name": "API Endpoint",
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"info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
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},
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"code": {"show": False},
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}
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def build(
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self,
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model: str,
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anthropic_api_key: Optional[str] = None,
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max_tokens: Optional[int] = 1000,
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temperature: Optional[float] = None,
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api_endpoint: Optional[str] = None,
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) -> LanguageModel:
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# Set default API endpoint if not provided
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if not api_endpoint:
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api_endpoint = "https://api.anthropic.com"
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try:
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output = ChatAnthropic(
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model_name=model,
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anthropic_api_key=SecretStr(anthropic_api_key) if anthropic_api_key else None,
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max_tokens_to_sample=max_tokens, # type: ignore
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temperature=temperature,
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anthropic_api_url=api_endpoint,
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)
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except Exception as e:
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raise ValueError("Could not connect to Anthropic API.") from e
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return output
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@ -1,101 +0,0 @@
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from typing import Optional
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from langchain_openai import AzureChatOpenAI
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from pydantic.v1 import SecretStr
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from langflow.custom import CustomComponent
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from langflow.field_typing import LanguageModel
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class AzureChatOpenAISpecsComponent(CustomComponent):
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display_name: str = "AzureChatOpenAI"
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description: str = "LLM model from Azure OpenAI."
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documentation: str = "https://python.langchain.com/docs/integrations/llms/azure_openai"
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beta = False
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icon = "Azure"
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AZURE_OPENAI_MODELS = [
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"gpt-35-turbo",
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"gpt-35-turbo-16k",
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"gpt-35-turbo-instruct",
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"gpt-4",
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"gpt-4-32k",
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"gpt-4-vision",
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]
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AZURE_OPENAI_API_VERSIONS = [
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"2023-03-15-preview",
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"2023-05-15",
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"2023-06-01-preview",
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"2023-07-01-preview",
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"2023-08-01-preview",
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"2023-09-01-preview",
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"2023-12-01-preview",
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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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"value": self.AZURE_OPENAI_MODELS[0],
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"options": self.AZURE_OPENAI_MODELS,
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"required": True,
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},
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"azure_endpoint": {
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"display_name": "Azure Endpoint",
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"required": True,
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"info": "Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`",
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},
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"azure_deployment": {
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"display_name": "Deployment Name",
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"required": True,
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},
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"api_version": {
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"display_name": "API Version",
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"options": self.AZURE_OPENAI_API_VERSIONS,
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"value": self.AZURE_OPENAI_API_VERSIONS[-1],
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"required": True,
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"advanced": True,
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},
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"api_key": {"display_name": "API Key", "required": True, "password": True},
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"temperature": {
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"display_name": "Temperature",
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"value": 0.7,
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"field_type": "float",
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"required": False,
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},
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"max_tokens": {
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"display_name": "Max Tokens",
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"advanced": True,
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"info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
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},
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"code": {"show": False},
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}
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def build(
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self,
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model: str,
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azure_endpoint: str,
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azure_deployment: str,
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api_key: str,
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api_version: str,
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temperature: float = 0.7,
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max_tokens: Optional[int] = 1000,
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) -> LanguageModel:
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if api_key:
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azure_api_key = SecretStr(api_key)
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else:
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azure_api_key = None
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try:
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llm = AzureChatOpenAI(
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model=model,
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azure_endpoint=azure_endpoint,
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azure_deployment=azure_deployment,
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api_version=api_version,
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api_key=azure_api_key,
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temperature=temperature,
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max_tokens=max_tokens or None,
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)
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except Exception as e:
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raise ValueError("Could not connect to AzureOpenAI API.") from e
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return llm
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@ -1,95 +0,0 @@
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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.custom import CustomComponent
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from langflow.field_typing import LanguageModel
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class QianfanChatEndpointComponent(CustomComponent):
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display_name: str = "QianfanChatEndpoint"
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description: str = (
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"Baidu Qianfan chat models. Get more detail from "
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"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint."
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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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"required": True,
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},
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"qianfan_ak": {
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"display_name": "Qianfan Ak",
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"required": True,
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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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"required": True,
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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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},
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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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},
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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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}
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def build(
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self,
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model: str = "ERNIE-Bot-turbo",
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qianfan_ak: Optional[str] = None,
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qianfan_sk: Optional[str] = None,
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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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) -> LanguageModel:
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try:
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output = QianfanChatEndpoint( # type: ignore
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model=model,
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qianfan_ak=SecretStr(qianfan_ak) if qianfan_ak else None,
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qianfan_sk=SecretStr(qianfan_sk) if qianfan_sk else None,
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top_p=top_p,
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temperature=temperature,
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penalty_score=penalty_score,
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endpoint=endpoint,
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)
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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 output # type: ignore
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@ -1,94 +0,0 @@
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from typing import Optional
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from langchain_community.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint
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from langflow.custom import CustomComponent
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from langflow.field_typing import LanguageModel
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class QianfanLLMEndpointComponent(CustomComponent):
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display_name: str = "QianfanLLMEndpoint"
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description: str = (
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"Baidu Qianfan hosted open source or customized models. "
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"Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint"
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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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"required": True,
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},
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"qianfan_ak": {
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"display_name": "Qianfan Ak",
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"required": True,
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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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"required": True,
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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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},
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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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},
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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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}
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def build(
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self,
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model: str = "ERNIE-Bot-turbo",
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qianfan_ak: Optional[str] = None,
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qianfan_sk: Optional[str] = None,
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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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) -> LanguageModel:
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try:
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output = QianfanLLMEndpoint( # type: ignore
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model=model,
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qianfan_ak=qianfan_ak,
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qianfan_sk=qianfan_sk,
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top_p=top_p,
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temperature=temperature,
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penalty_score=penalty_score,
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endpoint=endpoint,
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)
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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 output # type: ignore
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|
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@ -1,165 +0,0 @@
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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.custom import CustomComponent
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from langflow.field_typing import LanguageModel
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class ChatLiteLLMComponent(CustomComponent):
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display_name = "ChatLiteLLM"
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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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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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],
|
||||
},
|
||||
"streaming": {
|
||||
"display_name": "Streaming",
|
||||
"field_type": "bool",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
"default": True,
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
"advanced": False,
|
||||
"required": False,
|
||||
"default": 0.7,
|
||||
},
|
||||
"model_kwargs": {
|
||||
"display_name": "Model kwargs",
|
||||
"field_type": "dict",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
"default": {},
|
||||
},
|
||||
"top_p": {
|
||||
"display_name": "Top p",
|
||||
"field_type": "float",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
},
|
||||
"top_k": {
|
||||
"display_name": "Top k",
|
||||
"field_type": "int",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
},
|
||||
"n": {
|
||||
"display_name": "N",
|
||||
"field_type": "int",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
"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.",
|
||||
"default": 1,
|
||||
},
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"advanced": True,
|
||||
"info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
|
||||
},
|
||||
"max_retries": {
|
||||
"display_name": "Max retries",
|
||||
"field_type": "int",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
"default": 6,
|
||||
},
|
||||
"verbose": {
|
||||
"display_name": "Verbose",
|
||||
"field_type": "bool",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
"default": False,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
model: str,
|
||||
provider: str,
|
||||
api_key: Optional[str] = None,
|
||||
streaming: bool = True,
|
||||
temperature: Optional[float] = 0.7,
|
||||
model_kwargs: Optional[Dict[str, Any]] = {},
|
||||
top_p: Optional[float] = None,
|
||||
top_k: Optional[int] = None,
|
||||
n: int = 1,
|
||||
max_tokens: int = 256,
|
||||
max_retries: int = 6,
|
||||
verbose: bool = False,
|
||||
) -> LanguageModel:
|
||||
try:
|
||||
import litellm # type: ignore
|
||||
|
||||
litellm.drop_params = True
|
||||
litellm.set_verbose = verbose
|
||||
except ImportError:
|
||||
raise ChatLiteLLMException(
|
||||
"Could not import litellm python package. " "Please install it with `pip install litellm`"
|
||||
)
|
||||
provider_map = {
|
||||
"OpenAI": "openai_api_key",
|
||||
"Azure": "azure_api_key",
|
||||
"Anthropic": "anthropic_api_key",
|
||||
"Replicate": "replicate_api_key",
|
||||
"Cohere": "cohere_api_key",
|
||||
"OpenRouter": "openrouter_api_key",
|
||||
}
|
||||
# Set the API key based on the provider
|
||||
api_keys: dict[str, Optional[str]] = {v: None for v in provider_map.values()}
|
||||
|
||||
if variable_name := provider_map.get(provider):
|
||||
api_keys[variable_name] = api_key
|
||||
else:
|
||||
raise ChatLiteLLMException(
|
||||
f"Provider {provider} is not supported. Supported providers are: {', '.join(provider_map.keys())}"
|
||||
)
|
||||
|
||||
LLM = ChatLiteLLM(
|
||||
model=model,
|
||||
client=None,
|
||||
streaming=streaming,
|
||||
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,
|
||||
openai_api_key=api_keys["openai_api_key"],
|
||||
azure_api_key=api_keys["azure_api_key"],
|
||||
anthropic_api_key=api_keys["anthropic_api_key"],
|
||||
replicate_api_key=api_keys["replicate_api_key"],
|
||||
cohere_api_key=api_keys["cohere_api_key"],
|
||||
openrouter_api_key=api_keys["openrouter_api_key"],
|
||||
)
|
||||
return LLM
|
||||
|
|
@ -1,87 +0,0 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_mistralai import ChatMistralAI
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import LanguageModel
|
||||
|
||||
|
||||
class MistralAIModelComponent(CustomComponent):
|
||||
display_name: str = "MistralAI"
|
||||
description: str = "Generate text using MistralAI LLMs."
|
||||
icon = "MistralAI"
|
||||
|
||||
field_order = [
|
||||
"model",
|
||||
"mistral_api_key",
|
||||
"max_tokens",
|
||||
"temperature",
|
||||
"mistral_api_base",
|
||||
]
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"model": {
|
||||
"display_name": "Model Name",
|
||||
"options": [
|
||||
"open-mistral-7b",
|
||||
"open-mixtral-8x7b",
|
||||
"open-mixtral-8x22b",
|
||||
"mistral-small-latest",
|
||||
"mistral-medium-latest",
|
||||
"mistral-large-latest",
|
||||
],
|
||||
"info": "Name of the model to use.",
|
||||
"required": True,
|
||||
"value": "open-mistral-7b",
|
||||
},
|
||||
"mistral_api_key": {
|
||||
"display_name": "Mistral API Key",
|
||||
"required": True,
|
||||
"password": True,
|
||||
"info": "Your Mistral API key.",
|
||||
},
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"field_type": "int",
|
||||
"advanced": True,
|
||||
"value": 256,
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
"value": 0.1,
|
||||
},
|
||||
"mistral_api_base": {
|
||||
"display_name": "Mistral API Base",
|
||||
"advanced": True,
|
||||
"info": "Endpoint of the Mistral API. Defaults to 'https://api.mistral.ai' if not specified.",
|
||||
},
|
||||
"code": {"show": False},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
model: str,
|
||||
temperature: float = 0.1,
|
||||
mistral_api_key: Optional[str] = None,
|
||||
max_tokens: Optional[int] = None,
|
||||
mistral_api_base: Optional[str] = None,
|
||||
) -> LanguageModel:
|
||||
# Set default API endpoint if not provided
|
||||
if not mistral_api_base:
|
||||
mistral_api_base = "https://api.mistral.ai"
|
||||
|
||||
try:
|
||||
output = ChatMistralAI(
|
||||
model_name=model,
|
||||
api_key=(SecretStr(mistral_api_key) if mistral_api_key else None),
|
||||
max_tokens=max_tokens or None,
|
||||
temperature=temperature,
|
||||
endpoint=mistral_api_base,
|
||||
)
|
||||
except Exception as e:
|
||||
raise ValueError("Could not connect to Mistral API.") from e
|
||||
|
||||
return output
|
||||
|
|
@ -1,251 +0,0 @@
|
|||
from typing import Dict, List, Optional
|
||||
|
||||
# from langchain_community.chat_models import ChatOllama
|
||||
from langchain_community.chat_models import ChatOllama
|
||||
from langchain_core.language_models.chat_models import BaseChatModel
|
||||
|
||||
# from langchain.chat_models import ChatOllama
|
||||
from langflow.custom import CustomComponent
|
||||
|
||||
# from langchain.callbacks.manager import CallbackManager
|
||||
|
||||
|
||||
class ChatOllamaComponent(CustomComponent):
|
||||
display_name = "ChatOllama"
|
||||
description = "Local LLM for chat with Ollama."
|
||||
|
||||
def build_config(self) -> dict:
|
||||
return {
|
||||
"base_url": {
|
||||
"display_name": "Base URL",
|
||||
"info": "Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.",
|
||||
},
|
||||
"model": {
|
||||
"display_name": "Model Name",
|
||||
"value": "llama2",
|
||||
"info": "Refer to https://ollama.ai/library for more models.",
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
"value": 0.8,
|
||||
"info": "Controls the creativity of model responses.",
|
||||
},
|
||||
"cache": {
|
||||
"display_name": "Cache",
|
||||
"field_type": "bool",
|
||||
"info": "Enable or disable caching.",
|
||||
"advanced": True,
|
||||
"value": False,
|
||||
},
|
||||
### When a callback component is added to Langflow, the comment must be uncommented. ###
|
||||
# "callback_manager": {
|
||||
# "display_name": "Callback Manager",
|
||||
# "info": "Optional callback manager for additional functionality.",
|
||||
# "advanced": True,
|
||||
# },
|
||||
# "callbacks": {
|
||||
# "display_name": "Callbacks",
|
||||
# "info": "Callbacks to execute during model runtime.",
|
||||
# "advanced": True,
|
||||
# },
|
||||
########################################################################################
|
||||
"format": {
|
||||
"display_name": "Format",
|
||||
"field_type": "str",
|
||||
"info": "Specify the format of the output (e.g., json).",
|
||||
"advanced": True,
|
||||
},
|
||||
"metadata": {
|
||||
"display_name": "Metadata",
|
||||
"info": "Metadata to add to the run trace.",
|
||||
"advanced": True,
|
||||
},
|
||||
"mirostat": {
|
||||
"display_name": "Mirostat",
|
||||
"options": ["Disabled", "Mirostat", "Mirostat 2.0"],
|
||||
"info": "Enable/disable Mirostat sampling for controlling perplexity.",
|
||||
"value": "Disabled",
|
||||
"advanced": True,
|
||||
},
|
||||
"mirostat_eta": {
|
||||
"display_name": "Mirostat Eta",
|
||||
"field_type": "float",
|
||||
"info": "Learning rate for Mirostat algorithm. (Default: 0.1)",
|
||||
"advanced": True,
|
||||
},
|
||||
"mirostat_tau": {
|
||||
"display_name": "Mirostat Tau",
|
||||
"field_type": "float",
|
||||
"info": "Controls the balance between coherence and diversity of the output. (Default: 5.0)",
|
||||
"advanced": True,
|
||||
},
|
||||
"num_ctx": {
|
||||
"display_name": "Context Window Size",
|
||||
"field_type": "int",
|
||||
"info": "Size of the context window for generating tokens. (Default: 2048)",
|
||||
"advanced": True,
|
||||
},
|
||||
"num_gpu": {
|
||||
"display_name": "Number of GPUs",
|
||||
"field_type": "int",
|
||||
"info": "Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)",
|
||||
"advanced": True,
|
||||
},
|
||||
"num_thread": {
|
||||
"display_name": "Number of Threads",
|
||||
"field_type": "int",
|
||||
"info": "Number of threads to use during computation. (Default: detected for optimal performance)",
|
||||
"advanced": True,
|
||||
},
|
||||
"repeat_last_n": {
|
||||
"display_name": "Repeat Last N",
|
||||
"field_type": "int",
|
||||
"info": "How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)",
|
||||
"advanced": True,
|
||||
},
|
||||
"repeat_penalty": {
|
||||
"display_name": "Repeat Penalty",
|
||||
"field_type": "float",
|
||||
"info": "Penalty for repetitions in generated text. (Default: 1.1)",
|
||||
"advanced": True,
|
||||
},
|
||||
"tfs_z": {
|
||||
"display_name": "TFS Z",
|
||||
"field_type": "float",
|
||||
"info": "Tail free sampling value. (Default: 1)",
|
||||
"advanced": True,
|
||||
},
|
||||
"timeout": {
|
||||
"display_name": "Timeout",
|
||||
"field_type": "int",
|
||||
"info": "Timeout for the request stream.",
|
||||
"advanced": True,
|
||||
},
|
||||
"top_k": {
|
||||
"display_name": "Top K",
|
||||
"field_type": "int",
|
||||
"info": "Limits token selection to top K. (Default: 40)",
|
||||
"advanced": True,
|
||||
},
|
||||
"top_p": {
|
||||
"display_name": "Top P",
|
||||
"field_type": "float",
|
||||
"info": "Works together with top-k. (Default: 0.9)",
|
||||
"advanced": True,
|
||||
},
|
||||
"verbose": {
|
||||
"display_name": "Verbose",
|
||||
"field_type": "bool",
|
||||
"info": "Whether to print out response text.",
|
||||
},
|
||||
"tags": {
|
||||
"display_name": "Tags",
|
||||
"field_type": "list",
|
||||
"info": "Tags to add to the run trace.",
|
||||
"advanced": True,
|
||||
},
|
||||
"stop": {
|
||||
"display_name": "Stop Tokens",
|
||||
"field_type": "list",
|
||||
"info": "List of tokens to signal the model to stop generating text.",
|
||||
"advanced": True,
|
||||
},
|
||||
"system": {
|
||||
"display_name": "System",
|
||||
"field_type": "str",
|
||||
"info": "System to use for generating text.",
|
||||
"advanced": True,
|
||||
},
|
||||
"template": {
|
||||
"display_name": "Template",
|
||||
"field_type": "str",
|
||||
"info": "Template to use for generating text.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
base_url: Optional[str],
|
||||
model: str,
|
||||
mirostat: Optional[str],
|
||||
mirostat_eta: Optional[float] = None,
|
||||
mirostat_tau: Optional[float] = None,
|
||||
### When a callback component is added to Langflow, the comment must be uncommented.###
|
||||
# callback_manager: Optional[CallbackManager] = None,
|
||||
# callbacks: Optional[List[Callbacks]] = None,
|
||||
#######################################################################################
|
||||
repeat_last_n: Optional[int] = None,
|
||||
verbose: Optional[bool] = None,
|
||||
cache: Optional[bool] = None,
|
||||
num_ctx: Optional[int] = None,
|
||||
num_gpu: Optional[int] = None,
|
||||
format: Optional[str] = None,
|
||||
metadata: Optional[Dict] = None,
|
||||
num_thread: Optional[int] = None,
|
||||
repeat_penalty: Optional[float] = None,
|
||||
stop: Optional[List[str]] = None,
|
||||
system: Optional[str] = None,
|
||||
tags: Optional[List[str]] = None,
|
||||
temperature: Optional[float] = None,
|
||||
template: Optional[str] = None,
|
||||
tfs_z: Optional[float] = None,
|
||||
timeout: Optional[int] = None,
|
||||
top_k: Optional[int] = None,
|
||||
top_p: Optional[int] = None,
|
||||
) -> BaseChatModel:
|
||||
if not base_url:
|
||||
base_url = "http://localhost:11434"
|
||||
|
||||
# Mapping mirostat settings to their corresponding values
|
||||
mirostat_options = {"Mirostat": 1, "Mirostat 2.0": 2}
|
||||
|
||||
# Default to 0 for 'Disabled'
|
||||
mirostat_value = mirostat_options.get(mirostat, 0) # type: ignore
|
||||
|
||||
# Set mirostat_eta and mirostat_tau to None if mirostat is disabled
|
||||
if mirostat_value == 0:
|
||||
mirostat_eta = None
|
||||
mirostat_tau = None
|
||||
|
||||
# Mapping system settings to their corresponding values
|
||||
llm_params = {
|
||||
"base_url": base_url,
|
||||
"cache": cache,
|
||||
"model": model,
|
||||
"mirostat": mirostat_value,
|
||||
"format": format,
|
||||
"metadata": metadata,
|
||||
"tags": tags,
|
||||
## When a callback component is added to Langflow, the comment must be uncommented.##
|
||||
# "callback_manager": callback_manager,
|
||||
# "callbacks": callbacks,
|
||||
#####################################################################################
|
||||
"mirostat_eta": mirostat_eta,
|
||||
"mirostat_tau": mirostat_tau,
|
||||
"num_ctx": num_ctx,
|
||||
"num_gpu": num_gpu,
|
||||
"num_thread": num_thread,
|
||||
"repeat_last_n": repeat_last_n,
|
||||
"repeat_penalty": repeat_penalty,
|
||||
"temperature": temperature,
|
||||
"stop": stop,
|
||||
"system": system,
|
||||
"template": template,
|
||||
"tfs_z": tfs_z,
|
||||
"timeout": timeout,
|
||||
"top_k": top_k,
|
||||
"top_p": top_p,
|
||||
"verbose": verbose,
|
||||
}
|
||||
|
||||
# None Value remove
|
||||
llm_params = {k: v for k, v in llm_params.items() if v is not None}
|
||||
|
||||
try:
|
||||
output = ChatOllama(**llm_params) # type: ignore
|
||||
except Exception as e:
|
||||
raise ValueError("Could not initialize Ollama LLM.") from e
|
||||
|
||||
return output # type: ignore
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_openai import ChatOpenAI
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
from langflow.base.models.openai_constants import MODEL_NAMES
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import LanguageModel, NestedDict
|
||||
|
||||
|
||||
class ChatOpenAIComponent(CustomComponent):
|
||||
display_name = "ChatOpenAI"
|
||||
description = "`OpenAI` Chat large language models API."
|
||||
icon = "OpenAI"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"advanced": True,
|
||||
"info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
|
||||
},
|
||||
"model_kwargs": {
|
||||
"display_name": "Model Kwargs",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
},
|
||||
"model_name": {"display_name": "Model Name", "advanced": False, "options": MODEL_NAMES},
|
||||
"openai_api_base": {
|
||||
"display_name": "OpenAI API Base",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
"info": (
|
||||
"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\n"
|
||||
"You can change this to use other APIs like JinaChat, LocalAI and Prem."
|
||||
),
|
||||
},
|
||||
"openai_api_key": {
|
||||
"display_name": "OpenAI API Key",
|
||||
"advanced": False,
|
||||
"required": False,
|
||||
"password": True,
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"advanced": False,
|
||||
"required": False,
|
||||
"value": 0.7,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
max_tokens: Optional[int] = 0,
|
||||
model_kwargs: NestedDict = {},
|
||||
model_name: str = "gpt-3.5-turbo",
|
||||
openai_api_base: Optional[str] = None,
|
||||
openai_api_key: Optional[str] = None,
|
||||
temperature: float = 0.7,
|
||||
) -> LanguageModel:
|
||||
if not openai_api_base:
|
||||
openai_api_base = "https://api.openai.com/v1"
|
||||
if openai_api_key:
|
||||
api_key = SecretStr(openai_api_key)
|
||||
else:
|
||||
api_key = None
|
||||
return ChatOpenAI(
|
||||
max_tokens=max_tokens or None,
|
||||
model_kwargs=model_kwargs,
|
||||
model=model_name,
|
||||
base_url=openai_api_base,
|
||||
api_key=api_key,
|
||||
temperature=temperature,
|
||||
)
|
||||
|
|
@ -1,86 +0,0 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_community.chat_models.vertexai import ChatVertexAI
|
||||
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import LanguageModel
|
||||
|
||||
|
||||
class ChatVertexAIComponent(CustomComponent):
|
||||
display_name = "ChatVertexAI"
|
||||
description = "`Vertex AI` Chat large language models API."
|
||||
icon = "VertexAI"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"credentials": {
|
||||
"display_name": "Credentials",
|
||||
"field_type": "file",
|
||||
"file_types": ["json"],
|
||||
"file_path": None,
|
||||
},
|
||||
"examples": {
|
||||
"display_name": "Examples",
|
||||
"multiline": True,
|
||||
},
|
||||
"location": {
|
||||
"display_name": "Location",
|
||||
"value": "us-central1",
|
||||
},
|
||||
"max_output_tokens": {
|
||||
"display_name": "Max Output Tokens",
|
||||
"value": 128,
|
||||
"advanced": True,
|
||||
},
|
||||
"model_name": {
|
||||
"display_name": "Model Name",
|
||||
"value": "chat-bison",
|
||||
},
|
||||
"project": {
|
||||
"display_name": "Project",
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"value": 0.0,
|
||||
},
|
||||
"top_k": {
|
||||
"display_name": "Top K",
|
||||
"value": 40,
|
||||
"advanced": True,
|
||||
},
|
||||
"top_p": {
|
||||
"display_name": "Top P",
|
||||
"value": 0.95,
|
||||
"advanced": True,
|
||||
},
|
||||
"verbose": {
|
||||
"display_name": "Verbose",
|
||||
"value": False,
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
credentials: Optional[str],
|
||||
project: str,
|
||||
location: str = "us-central1",
|
||||
max_output_tokens: int = 128,
|
||||
model_name: str = "chat-bison",
|
||||
temperature: float = 0.0,
|
||||
top_k: int = 40,
|
||||
top_p: float = 0.95,
|
||||
verbose: bool = False,
|
||||
) -> LanguageModel:
|
||||
return ChatVertexAI(
|
||||
credentials=credentials,
|
||||
location=location,
|
||||
max_output_tokens=max_output_tokens,
|
||||
model_name=model_name,
|
||||
project=project,
|
||||
temperature=temperature,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
verbose=verbose,
|
||||
)
|
||||
|
|
@ -1,37 +0,0 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_cohere import ChatCohere
|
||||
from langflow.field_typing import LanguageModel
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
|
||||
|
||||
class CohereComponent(CustomComponent):
|
||||
display_name = "Cohere"
|
||||
description = "Cohere large language models."
|
||||
documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere"
|
||||
icon = "Cohere"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"cohere_api_key": {"display_name": "Cohere API Key", "type": "password", "password": True},
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"advanced": True,
|
||||
"info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
|
||||
},
|
||||
"temperature": {"display_name": "Temperature", "default": 0.75, "type": "float", "show": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
cohere_api_key: str,
|
||||
max_tokens: Optional[int] = 256,
|
||||
temperature: float = 0.75,
|
||||
) -> LanguageModel:
|
||||
if cohere_api_key:
|
||||
api_key = SecretStr(cohere_api_key)
|
||||
else:
|
||||
api_key = None
|
||||
return ChatCohere(cohere_api_key=api_key, max_tokens=max_tokens or None, temperature=temperature) # type: ignore
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_google_genai import ChatGoogleGenerativeAI # type: ignore
|
||||
from pydantic.v1.types import SecretStr
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import LanguageModel, RangeSpec
|
||||
|
||||
|
||||
class GoogleGenerativeAIComponent(CustomComponent):
|
||||
display_name: str = "Google Generative AI"
|
||||
description: str = "A component that uses Google Generative AI to generate text."
|
||||
documentation: str = "http://docs.langflow.org/components/custom"
|
||||
icon = "Google"
|
||||
|
||||
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.",
|
||||
},
|
||||
"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,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
google_api_key: str,
|
||||
model: str,
|
||||
max_output_tokens: Optional[int] = None,
|
||||
temperature: float = 0.1,
|
||||
top_k: Optional[int] = None,
|
||||
top_p: Optional[float] = None,
|
||||
n: Optional[int] = 1,
|
||||
) -> LanguageModel:
|
||||
return ChatGoogleGenerativeAI(
|
||||
model=model,
|
||||
max_output_tokens=max_output_tokens or None, # type: ignore
|
||||
temperature=temperature,
|
||||
top_k=top_k or None,
|
||||
top_p=top_p or None, # type: ignore
|
||||
n=n or 1,
|
||||
google_api_key=SecretStr(google_api_key),
|
||||
)
|
||||
|
|
@ -1,86 +0,0 @@
|
|||
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 langflow.base.models.model import LCModelComponent
|
||||
from langflow.field_typing import LanguageModel
|
||||
|
||||
|
||||
class GroqModelSpecs(LCModelComponent):
|
||||
display_name: str = "Groq"
|
||||
description: str = "Generate text using Groq."
|
||||
icon = "Groq"
|
||||
|
||||
field_order = [
|
||||
"groq_api_key",
|
||||
"model",
|
||||
"max_output_tokens",
|
||||
"temperature",
|
||||
"top_k",
|
||||
"top_p",
|
||||
"n",
|
||||
"input_value",
|
||||
"system_message",
|
||||
"stream",
|
||||
]
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"groq_api_key": {
|
||||
"display_name": "Groq API Key",
|
||||
"info": "API key for the Groq API.",
|
||||
"password": True,
|
||||
},
|
||||
"groq_api_base": {
|
||||
"display_name": "Groq API Base",
|
||||
"info": "Base URL path for API requests, leave blank if not using a proxy or service emulator.",
|
||||
"advanced": True,
|
||||
},
|
||||
"max_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].",
|
||||
},
|
||||
"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_name": {
|
||||
"display_name": "Model",
|
||||
"info": "The name of the model to use. Supported examples: gemini-pro",
|
||||
"options": MODEL_NAMES,
|
||||
},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
groq_api_key: str,
|
||||
model_name: str,
|
||||
groq_api_base: Optional[str] = None,
|
||||
max_tokens: Optional[int] = None,
|
||||
temperature: float = 0.1,
|
||||
n: Optional[int] = 1,
|
||||
stream: bool = False,
|
||||
) -> LanguageModel:
|
||||
return ChatGroq(
|
||||
model_name=model_name,
|
||||
max_tokens=max_tokens or None, # type: ignore
|
||||
temperature=temperature,
|
||||
groq_api_base=groq_api_base,
|
||||
n=n or 1,
|
||||
groq_api_key=SecretStr(groq_api_key),
|
||||
streaming=stream,
|
||||
)
|
||||
|
|
@ -1,45 +0,0 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import LanguageModel
|
||||
|
||||
|
||||
class HuggingFaceEndpointsComponent(CustomComponent):
|
||||
display_name: str = "Hugging Face Inference API"
|
||||
description: str = "LLM model from Hugging Face Inference API."
|
||||
icon = "HuggingFace"
|
||||
|
||||
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",
|
||||
},
|
||||
"code": {"show": False},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
endpoint_url: str,
|
||||
task: str = "text2text-generation",
|
||||
huggingfacehub_api_token: Optional[str] = None,
|
||||
model_kwargs: Optional[dict] = None,
|
||||
) -> LanguageModel:
|
||||
try:
|
||||
output = HuggingFaceEndpoint( # type: ignore
|
||||
endpoint_url=endpoint_url,
|
||||
task=task,
|
||||
huggingfacehub_api_token=huggingfacehub_api_token,
|
||||
model_kwargs=model_kwargs or {},
|
||||
)
|
||||
except Exception as e:
|
||||
raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
|
||||
return output
|
||||
|
|
@ -1,158 +0,0 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langchain_community.llms.ollama import Ollama
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import LanguageModel
|
||||
|
||||
|
||||
class OllamaLLM(CustomComponent):
|
||||
display_name = "Ollama"
|
||||
description = "Local LLM with Ollama."
|
||||
|
||||
def build_config(self) -> dict:
|
||||
return {
|
||||
"base_url": {
|
||||
"display_name": "Base URL",
|
||||
"info": "Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.",
|
||||
},
|
||||
"model": {
|
||||
"display_name": "Model Name",
|
||||
"value": "llama2",
|
||||
"info": "Refer to https://ollama.ai/library for more models.",
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
"value": 0.8,
|
||||
"info": "Controls the creativity of model responses.",
|
||||
},
|
||||
"mirostat": {
|
||||
"display_name": "Mirostat",
|
||||
"options": ["Disabled", "Mirostat", "Mirostat 2.0"],
|
||||
"info": "Enable/disable Mirostat sampling for controlling perplexity.",
|
||||
"value": "Disabled",
|
||||
"advanced": True,
|
||||
},
|
||||
"mirostat_eta": {
|
||||
"display_name": "Mirostat Eta",
|
||||
"field_type": "float",
|
||||
"info": "Learning rate influencing the algorithm's response to feedback.",
|
||||
"advanced": True,
|
||||
},
|
||||
"mirostat_tau": {
|
||||
"display_name": "Mirostat Tau",
|
||||
"field_type": "float",
|
||||
"info": "Controls balance between coherence and diversity.",
|
||||
"advanced": True,
|
||||
},
|
||||
"num_ctx": {
|
||||
"display_name": "Context Window Size",
|
||||
"field_type": "int",
|
||||
"info": "Size of the context window for generating the next token.",
|
||||
"advanced": True,
|
||||
},
|
||||
"num_gpu": {
|
||||
"display_name": "Number of GPUs",
|
||||
"field_type": "int",
|
||||
"info": "Number of GPUs to use for computation.",
|
||||
"advanced": True,
|
||||
},
|
||||
"num_thread": {
|
||||
"display_name": "Number of Threads",
|
||||
"field_type": "int",
|
||||
"info": "Number of threads to use during computation.",
|
||||
"advanced": True,
|
||||
},
|
||||
"repeat_last_n": {
|
||||
"display_name": "Repeat Last N",
|
||||
"field_type": "int",
|
||||
"info": "Sets how far back the model looks to prevent repetition.",
|
||||
"advanced": True,
|
||||
},
|
||||
"repeat_penalty": {
|
||||
"display_name": "Repeat Penalty",
|
||||
"field_type": "float",
|
||||
"info": "Penalty for repetitions in generated text.",
|
||||
"advanced": True,
|
||||
},
|
||||
"stop": {
|
||||
"display_name": "Stop Tokens",
|
||||
"info": "List of tokens to signal the model to stop generating text.",
|
||||
"advanced": True,
|
||||
},
|
||||
"tfs_z": {
|
||||
"display_name": "TFS Z",
|
||||
"field_type": "float",
|
||||
"info": "Tail free sampling to reduce impact of less probable tokens.",
|
||||
"advanced": True,
|
||||
},
|
||||
"top_k": {
|
||||
"display_name": "Top K",
|
||||
"field_type": "int",
|
||||
"info": "Limits token selection to top K for reducing nonsense generation.",
|
||||
"advanced": True,
|
||||
},
|
||||
"top_p": {
|
||||
"display_name": "Top P",
|
||||
"field_type": "int",
|
||||
"info": "Works with top-k to control diversity of generated text.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
base_url: Optional[str],
|
||||
model: str,
|
||||
temperature: Optional[float],
|
||||
mirostat: Optional[str],
|
||||
mirostat_eta: Optional[float] = None,
|
||||
mirostat_tau: Optional[float] = None,
|
||||
num_ctx: Optional[int] = None,
|
||||
num_gpu: Optional[int] = None,
|
||||
num_thread: Optional[int] = None,
|
||||
repeat_last_n: Optional[int] = None,
|
||||
repeat_penalty: Optional[float] = None,
|
||||
stop: Optional[List[str]] = None,
|
||||
tfs_z: Optional[float] = None,
|
||||
top_k: Optional[int] = None,
|
||||
top_p: Optional[int] = None,
|
||||
) -> LanguageModel:
|
||||
if not base_url:
|
||||
base_url = "http://localhost:11434"
|
||||
|
||||
# Mapping mirostat settings to their corresponding values
|
||||
mirostat_options = {"Mirostat": 1, "Mirostat 2.0": 2}
|
||||
|
||||
# Default to 0 for 'Disabled'
|
||||
mirostat_value = mirostat_options.get(mirostat, 0) # type: ignore
|
||||
|
||||
# Set mirostat_eta and mirostat_tau to None if mirostat is disabled
|
||||
if mirostat_value == 0:
|
||||
mirostat_eta = None
|
||||
mirostat_tau = None
|
||||
|
||||
try:
|
||||
llm = Ollama(
|
||||
base_url=base_url,
|
||||
model=model,
|
||||
mirostat=mirostat_value,
|
||||
mirostat_eta=mirostat_eta,
|
||||
mirostat_tau=mirostat_tau,
|
||||
num_ctx=num_ctx,
|
||||
num_gpu=num_gpu,
|
||||
num_thread=num_thread,
|
||||
repeat_last_n=repeat_last_n,
|
||||
repeat_penalty=repeat_penalty,
|
||||
temperature=temperature,
|
||||
stop=stop,
|
||||
tfs_z=tfs_z,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
raise ValueError("Could not connect to Ollama.") from e
|
||||
|
||||
return llm
|
||||
|
|
@ -1,151 +0,0 @@
|
|||
from typing import Dict, Optional
|
||||
|
||||
from langchain_community.llms.vertexai import VertexAI
|
||||
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import LanguageModel
|
||||
|
||||
|
||||
class VertexAIComponent(CustomComponent):
|
||||
display_name = "VertexAI"
|
||||
description = "Google Vertex AI large language models"
|
||||
icon = "VertexAI"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"credentials": {
|
||||
"display_name": "Credentials",
|
||||
"field_type": "file",
|
||||
"file_types": ["json"],
|
||||
"required": False,
|
||||
"value": None,
|
||||
},
|
||||
"location": {
|
||||
"display_name": "Location",
|
||||
"type": "str",
|
||||
"advanced": True,
|
||||
"value": "us-central1",
|
||||
"required": False,
|
||||
},
|
||||
"max_output_tokens": {
|
||||
"display_name": "Max Output Tokens",
|
||||
"field_type": "int",
|
||||
"value": 128,
|
||||
"required": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"max_retries": {
|
||||
"display_name": "Max Retries",
|
||||
"type": "int",
|
||||
"value": 6,
|
||||
"required": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"metadata": {
|
||||
"display_name": "Metadata",
|
||||
"field_type": "dict",
|
||||
"required": False,
|
||||
"default": {},
|
||||
},
|
||||
"model_name": {
|
||||
"display_name": "Model Name",
|
||||
"type": "str",
|
||||
"value": "text-bison",
|
||||
"required": False,
|
||||
},
|
||||
"n": {
|
||||
"advanced": True,
|
||||
"display_name": "N",
|
||||
"field_type": "int",
|
||||
"value": 1,
|
||||
"required": False,
|
||||
},
|
||||
"project": {
|
||||
"display_name": "Project",
|
||||
"type": "str",
|
||||
"required": False,
|
||||
"default": None,
|
||||
},
|
||||
"request_parallelism": {
|
||||
"display_name": "Request Parallelism",
|
||||
"field_type": "int",
|
||||
"value": 5,
|
||||
"required": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"streaming": {
|
||||
"display_name": "Streaming",
|
||||
"field_type": "bool",
|
||||
"value": False,
|
||||
"required": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
"value": 0.0,
|
||||
"required": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"top_k": {"display_name": "Top K", "type": "int", "default": 40, "required": False, "advanced": True},
|
||||
"top_p": {
|
||||
"display_name": "Top P",
|
||||
"field_type": "float",
|
||||
"value": 0.95,
|
||||
"required": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"tuned_model_name": {
|
||||
"display_name": "Tuned Model Name",
|
||||
"type": "str",
|
||||
"required": False,
|
||||
"value": None,
|
||||
"advanced": True,
|
||||
},
|
||||
"verbose": {
|
||||
"display_name": "Verbose",
|
||||
"field_type": "bool",
|
||||
"value": False,
|
||||
"required": False,
|
||||
},
|
||||
"name": {"display_name": "Name", "field_type": "str"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
credentials: Optional[str] = None,
|
||||
location: str = "us-central1",
|
||||
max_output_tokens: int = 128,
|
||||
max_retries: int = 6,
|
||||
metadata: Dict = {},
|
||||
model_name: str = "text-bison",
|
||||
n: int = 1,
|
||||
name: Optional[str] = None,
|
||||
project: Optional[str] = None,
|
||||
request_parallelism: int = 5,
|
||||
streaming: bool = False,
|
||||
temperature: float = 0.0,
|
||||
top_k: int = 40,
|
||||
top_p: float = 0.95,
|
||||
tuned_model_name: Optional[str] = None,
|
||||
verbose: bool = False,
|
||||
) -> LanguageModel:
|
||||
return VertexAI(
|
||||
credentials=credentials,
|
||||
location=location,
|
||||
max_output_tokens=max_output_tokens,
|
||||
max_retries=max_retries,
|
||||
metadata=metadata,
|
||||
model_name=model_name,
|
||||
n=n,
|
||||
name=name,
|
||||
project=project,
|
||||
request_parallelism=request_parallelism,
|
||||
streaming=streaming,
|
||||
temperature=temperature,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
tuned_model_name=tuned_model_name,
|
||||
verbose=verbose,
|
||||
)
|
||||
|
|
@ -1,30 +0,0 @@
|
|||
from .AmazonBedrockSpecs import AmazonBedrockComponent
|
||||
from .BaiduQianfanChatEndpointsSpecs import QianfanChatEndpointComponent
|
||||
from .BaiduQianfanLLMEndpointsSpecs import QianfanLLMEndpointComponent
|
||||
|
||||
from .ChatLiteLLMSpecs import ChatLiteLLMComponent
|
||||
from .ChatOllamaEndpointSpecs import ChatOllamaComponent
|
||||
from .ChatOpenAISpecs import ChatOpenAIComponent
|
||||
from .ChatVertexAISpecs import ChatVertexAIComponent
|
||||
from .CohereSpecs import CohereComponent
|
||||
from .GoogleGenerativeAISpecs import GoogleGenerativeAIComponent
|
||||
from .HuggingFaceEndpointsSpecs import HuggingFaceEndpointsComponent
|
||||
from .OllamaLLMSpecs import OllamaLLM
|
||||
from .VertexAISpecs import VertexAIComponent
|
||||
|
||||
__all__ = [
|
||||
"AmazonBedrockComponent",
|
||||
"AzureChatOpenAISpecsComponent",
|
||||
"QianfanChatEndpointComponent",
|
||||
"QianfanLLMEndpointComponent",
|
||||
"AnthropicLLM",
|
||||
"ChatLiteLLMComponent",
|
||||
"ChatOllamaComponent",
|
||||
"ChatOpenAIComponent",
|
||||
"ChatVertexAIComponent",
|
||||
"CohereComponent",
|
||||
"GoogleGenerativeAIComponent",
|
||||
"HuggingFaceEndpointsComponent",
|
||||
"OllamaLLM",
|
||||
"VertexAIComponent",
|
||||
]
|
||||
Loading…
Add table
Add a link
Reference in a new issue