Gabriel Luiz Freitas Almeida
GitHub
commit
977e72bb9b
13 changed files with 1071 additions and 38 deletions
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@ -2,7 +2,7 @@ from typing import Optional
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from langchain_google_genai import ChatGoogleGenerativeAI # type: ignore
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from langflow import CustomComponent
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from langflow.field_typing import BaseLanguageModel, RangeSpec, TemplateField
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from langflow.field_typing import BaseLanguageModel, RangeSpec
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from pydantic.v1.types import SecretStr
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@ -13,42 +13,42 @@ class GoogleGenerativeAIComponent(CustomComponent):
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def build_config(self):
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return {
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"google_api_key": TemplateField(
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display_name="Google API Key",
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info="The Google API Key to use for the Google Generative AI.",
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),
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"max_output_tokens": TemplateField(
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display_name="Max Output Tokens",
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info="The maximum number of tokens to generate.",
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),
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"temperature": TemplateField(
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display_name="Temperature",
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info="Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
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),
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"top_k": TemplateField(
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display_name="Top K",
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info="Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.",
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range_spec=RangeSpec(min=0, max=2, step=0.1),
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advanced=True,
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),
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"top_p": TemplateField(
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display_name="Top P",
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info="The maximum cumulative probability of tokens to consider when sampling.",
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advanced=True,
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),
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"n": TemplateField(
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display_name="N",
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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.",
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advanced=True,
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),
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"model": TemplateField(
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display_name="Model",
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info="The name of the model to use. Supported examples: gemini-pro",
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options=["gemini-pro", "gemini-pro-vision"],
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),
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"code": TemplateField(
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advanced=True,
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),
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"google_api_key": {
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"display_name": "Google API Key",
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"info": "The Google API Key to use for the Google Generative AI.",
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},
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"max_output_tokens": {
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"display_name": "Max Output Tokens",
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"info": "The maximum number of tokens to generate.",
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},
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"temperature": {
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"display_name": "Temperature",
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"info": "Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
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},
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"top_k": {
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"display_name": "Top K",
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"info": "Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.",
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"range_spec": RangeSpec(min=0, max=2, step=0.1),
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"advanced": True,
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},
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"top_p": {
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"display_name": "Top P",
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"info": "The maximum cumulative probability of tokens to consider when sampling.",
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"advanced": True,
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},
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"n": {
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"display_name": "N",
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"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.",
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"advanced": True,
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},
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"model": {
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"display_name": "Model",
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"info": "The name of the model to use. Supported examples: gemini-pro",
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"options": ["gemini-pro", "gemini-pro-vision"],
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},
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"code": {
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"advanced": True,
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},
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}
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def build(
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66
src/backend/langflow/components/models/AmazonBedrock.py
Normal file
66
src/backend/langflow/components/models/AmazonBedrock.py
Normal file
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@ -0,0 +1,66 @@
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from typing import Optional
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from langchain_community.chat_models.bedrock import BedrockChat
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from langflow.field_typing import Text
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from langflow import CustomComponent
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class AmazonBedrockComponent(CustomComponent):
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display_name: str = "Amazon Bedrock model"
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description: str = "Generate text using LLM model from Amazon Bedrock."
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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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"inputs": {"display_name": "Input"},
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}
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def build(
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self,
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inputs: str,
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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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) -> Text:
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try:
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output = BedrockChat(
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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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message = output.invoke(inputs)
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result = message.content if hasattr(message, "content") else message
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self.status = result
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return result
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81
src/backend/langflow/components/models/Anthropic.py
Normal file
81
src/backend/langflow/components/models/Anthropic.py
Normal file
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@ -0,0 +1,81 @@
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from typing import Optional
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from langchain_community.chat_models.anthropic import ChatAnthropic
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from pydantic.v1 import SecretStr
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from langflow.field_typing import Text
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from langflow import CustomComponent
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class AnthropicLLM(CustomComponent):
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display_name: str = "Anthropic model"
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description: str = "Generate text using Anthropic Chat&Completion large language models."
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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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"field_type": "int",
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"value": 256,
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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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"inputs": {"display_name": "Input"},
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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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inputs:str,
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anthropic_api_key: Optional[str] = None,
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max_tokens: Optional[int] = None,
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temperature: Optional[float] = None,
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api_endpoint: Optional[str] = None,
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) -> Text:
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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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message = output.invoke(inputs)
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result = message.content if hasattr(message, "content") else message
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self.status = result
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return result
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104
src/backend/langflow/components/models/AzureOpenAI.py
Normal file
104
src/backend/langflow/components/models/AzureOpenAI.py
Normal file
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@ -0,0 +1,104 @@
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from typing import Optional
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from langflow import CustomComponent
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from langchain.llms.base import BaseLanguageModel
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from langchain_openai import AzureChatOpenAI
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class AzureChatOpenAIComponent(CustomComponent):
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display_name: str = "AzureOpenAI model"
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description: str = "Generate text using LLM model from Azure OpenAI."
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documentation: str = (
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"https://python.langchain.com/docs/integrations/llms/azure_openai"
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)
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beta = False
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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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"value": 1000,
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"required": False,
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"field_type": "int",
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"advanced": True,
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"info": "Maximum number of tokens to generate.",
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},
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"code": {"show": False},
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"inputs": {"display_name": "Input"},
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"inputs": {"display_name": "Input"},
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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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inputs: 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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) -> BaseLanguageModel:
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try:
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output = 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=api_key,
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temperature=temperature,
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max_tokens=max_tokens,
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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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message = output.invoke(inputs)
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result = message.content if hasattr(message, "content") else message
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self.status = result
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return result
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100
src/backend/langflow/components/models/BaiduQianfanChat.py
Normal file
100
src/backend/langflow/components/models/BaiduQianfanChat.py
Normal file
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@ -0,0 +1,100 @@
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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 import CustomComponent
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from langflow.field_typing import Text
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class QianfanChatEndpointComponent(CustomComponent):
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display_name: str = "QianfanChat Model"
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description: str = (
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"Generate text using 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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"inputs": {"display_name": "Input"},
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}
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def build(
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self,
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inputs: str,
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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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) -> Text:
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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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message = output.invoke(inputs)
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result = message.content if hasattr(message, "content") else message
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self.status = result
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return result
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39
src/backend/langflow/components/models/CTransformers.py
Normal file
39
src/backend/langflow/components/models/CTransformers.py
Normal file
|
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@ -0,0 +1,39 @@
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from typing import Dict, Optional
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from langchain_community.llms.ctransformers import CTransformers
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from langflow.field_typing import Text
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from langflow import CustomComponent
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class CTransformersComponent(CustomComponent):
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display_name = "CTransformers model"
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||||
description = "Generate text using CTransformers LLM models"
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documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers"
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def build_config(self):
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return {
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"model": {"display_name": "Model", "required": True},
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"model_file": {
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"display_name": "Model File",
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||||
"required": False,
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"field_type": "file",
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"file_types": [".bin"],
|
||||
},
|
||||
"model_type": {"display_name": "Model Type", "required": True},
|
||||
"config": {
|
||||
"display_name": "Config",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
"field_type": "dict",
|
||||
"value": '{"top_k":40,"top_p":0.95,"temperature":0.8,"repetition_penalty":1.1,"last_n_tokens":64,"seed":-1,"max_new_tokens":256,"stop":"","stream":"False","reset":"True","batch_size":8,"threads":-1,"context_length":-1,"gpu_layers":0}',
|
||||
},
|
||||
"inputs": {"display_name": "Input"},
|
||||
}
|
||||
|
||||
def build(self, model: str, model_file: str,inputs:str, model_type: str, config: Optional[Dict] = None) -> Text:
|
||||
output = CTransformers(model=model, model_file=model_file, model_type=model_type, config=config)
|
||||
message = output.invoke(inputs)
|
||||
result = message.content if hasattr(message, "content") else message
|
||||
self.status = result
|
||||
return result
|
||||
30
src/backend/langflow/components/models/Cohere.py
Normal file
30
src/backend/langflow/components/models/Cohere.py
Normal file
|
|
@ -0,0 +1,30 @@
|
|||
from langchain_community.chat_models.cohere import ChatCohere
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class CohereComponent(CustomComponent):
|
||||
display_name = "Cohere model"
|
||||
description = "Generate text using Cohere large language models."
|
||||
documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"cohere_api_key": {"display_name": "Cohere API Key", "type": "password", "password": True},
|
||||
"max_tokens": {"display_name": "Max Tokens", "default": 256, "type": "int", "show": True},
|
||||
"temperature": {"display_name": "Temperature", "default": 0.75, "type": "float", "show": True},
|
||||
"inputs": {"display_name": "Input"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
cohere_api_key: str,
|
||||
inputs: str,
|
||||
max_tokens: int = 256,
|
||||
temperature: float = 0.75,
|
||||
) -> Text:
|
||||
output = ChatCohere(cohere_api_key=cohere_api_key, max_tokens=max_tokens, temperature=temperature)
|
||||
message = output.invoke(inputs)
|
||||
result = message.content if hasattr(message, "content") else message
|
||||
self.status = result
|
||||
return result
|
||||
79
src/backend/langflow/components/models/GoogleGenerativeAI.py
Normal file
79
src/backend/langflow/components/models/GoogleGenerativeAI.py
Normal file
|
|
@ -0,0 +1,79 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_google_genai import ChatGoogleGenerativeAI # type: ignore
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import RangeSpec
|
||||
from pydantic.v1.types import SecretStr
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class GoogleGenerativeAIComponent(CustomComponent):
|
||||
display_name: str = "Google Generative AI model"
|
||||
description: str = "Generate text using Google Generative AI to generate text."
|
||||
documentation: str = "http://docs.langflow.org/components/custom"
|
||||
|
||||
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.",
|
||||
"range_spec":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,
|
||||
},
|
||||
"inputs": {"display_name": "Input"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
google_api_key: str,
|
||||
model: str,
|
||||
inputs: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,
|
||||
) -> Text:
|
||||
output = 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),
|
||||
)
|
||||
message = output.invoke(inputs)
|
||||
result = message.content if hasattr(message, "content") else message
|
||||
self.status = result
|
||||
return result
|
||||
50
src/backend/langflow/components/models/HuggingFace.py
Normal file
50
src/backend/langflow/components/models/HuggingFace.py
Normal file
|
|
@ -0,0 +1,50 @@
|
|||
from typing import Optional
|
||||
from langflow import CustomComponent
|
||||
from langchain.llms.huggingface_endpoint import HuggingFaceEndpoint
|
||||
from langchain_community.chat_models.huggingface import ChatHuggingFace
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class HuggingFaceEndpointsComponent(CustomComponent):
|
||||
display_name: str = "Hugging Face Inference API models"
|
||||
description: str = "Generate text using LLM model from Hugging Face Inference API."
|
||||
|
||||
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},
|
||||
"inputs": {"display_name": "Input"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
inputs: str,
|
||||
endpoint_url: str,
|
||||
task: str = "text2text-generation",
|
||||
huggingfacehub_api_token: Optional[str] = None,
|
||||
model_kwargs: Optional[dict] = None,
|
||||
) -> Text:
|
||||
try:
|
||||
llm = HuggingFaceEndpoint(
|
||||
endpoint_url=endpoint_url,
|
||||
task=task,
|
||||
huggingfacehub_api_token=huggingfacehub_api_token,
|
||||
model_kwargs=model_kwargs,
|
||||
)
|
||||
except Exception as e:
|
||||
raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
|
||||
output = ChatHuggingFace(llm=llm)
|
||||
message = output.invoke(inputs)
|
||||
result = message.content if hasattr(message, "content") else message
|
||||
self.status = result
|
||||
return result
|
||||
|
||||
136
src/backend/langflow/components/models/LlamaCpp.py
Normal file
136
src/backend/langflow/components/models/LlamaCpp.py
Normal file
|
|
@ -0,0 +1,136 @@
|
|||
from typing import Optional, List, Dict, Any
|
||||
from langflow import CustomComponent
|
||||
from langchain_community.llms.llamacpp import LlamaCpp
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class LlamaCppComponent(CustomComponent):
|
||||
display_name = "LlamaCpp model"
|
||||
description = "Generate text using llama.cpp model."
|
||||
documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"grammar": {"display_name": "Grammar", "advanced": True},
|
||||
"cache": {"display_name": "Cache", "advanced": True},
|
||||
"client": {"display_name": "Client", "advanced": True},
|
||||
"echo": {"display_name": "Echo", "advanced": True},
|
||||
"f16_kv": {"display_name": "F16 KV", "advanced": True},
|
||||
"grammar_path": {"display_name": "Grammar Path", "advanced": True},
|
||||
"last_n_tokens_size": {"display_name": "Last N Tokens Size", "advanced": True},
|
||||
"logits_all": {"display_name": "Logits All", "advanced": True},
|
||||
"logprobs": {"display_name": "Logprobs", "advanced": True},
|
||||
"lora_base": {"display_name": "Lora Base", "advanced": True},
|
||||
"lora_path": {"display_name": "Lora Path", "advanced": True},
|
||||
"max_tokens": {"display_name": "Max Tokens", "advanced": True},
|
||||
"metadata": {"display_name": "Metadata", "advanced": True},
|
||||
"model_kwargs": {"display_name": "Model Kwargs", "advanced": True},
|
||||
"model_path": {
|
||||
"display_name": "Model Path",
|
||||
"field_type": "file",
|
||||
"file_types": [".bin"],
|
||||
"required": True,
|
||||
},
|
||||
"n_batch": {"display_name": "N Batch", "advanced": True},
|
||||
"n_ctx": {"display_name": "N Ctx", "advanced": True},
|
||||
"n_gpu_layers": {"display_name": "N GPU Layers", "advanced": True},
|
||||
"n_parts": {"display_name": "N Parts", "advanced": True},
|
||||
"n_threads": {"display_name": "N Threads", "advanced": True},
|
||||
"repeat_penalty": {"display_name": "Repeat Penalty", "advanced": True},
|
||||
"rope_freq_base": {"display_name": "Rope Freq Base", "advanced": True},
|
||||
"rope_freq_scale": {"display_name": "Rope Freq Scale", "advanced": True},
|
||||
"seed": {"display_name": "Seed", "advanced": True},
|
||||
"stop": {"display_name": "Stop", "advanced": True},
|
||||
"streaming": {"display_name": "Streaming", "advanced": True},
|
||||
"suffix": {"display_name": "Suffix", "advanced": True},
|
||||
"tags": {"display_name": "Tags", "advanced": True},
|
||||
"temperature": {"display_name": "Temperature"},
|
||||
"top_k": {"display_name": "Top K", "advanced": True},
|
||||
"top_p": {"display_name": "Top P", "advanced": True},
|
||||
"use_mlock": {"display_name": "Use Mlock", "advanced": True},
|
||||
"use_mmap": {"display_name": "Use Mmap", "advanced": True},
|
||||
"verbose": {"display_name": "Verbose", "advanced": True},
|
||||
"vocab_only": {"display_name": "Vocab Only", "advanced": True},
|
||||
"inputs": {"display_name": "Input"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
model_path: str,
|
||||
inputs:str,
|
||||
grammar: Optional[str] = None,
|
||||
cache: Optional[bool] = None,
|
||||
client: Optional[Any] = None,
|
||||
echo: Optional[bool] = False,
|
||||
f16_kv: bool = True,
|
||||
grammar_path: Optional[str] = None,
|
||||
last_n_tokens_size: Optional[int] = 64,
|
||||
logits_all: bool = False,
|
||||
logprobs: Optional[int] = None,
|
||||
lora_base: Optional[str] = None,
|
||||
lora_path: Optional[str] = None,
|
||||
max_tokens: Optional[int] = 256,
|
||||
metadata: Optional[Dict] = None,
|
||||
model_kwargs: Dict = {},
|
||||
n_batch: Optional[int] = 8,
|
||||
n_ctx: int = 512,
|
||||
n_gpu_layers: Optional[int] = 1,
|
||||
n_parts: int = -1,
|
||||
n_threads: Optional[int] = 1,
|
||||
repeat_penalty: Optional[float] = 1.1,
|
||||
rope_freq_base: float = 10000.0,
|
||||
rope_freq_scale: float = 1.0,
|
||||
seed: int = -1,
|
||||
stop: Optional[List[str]] = [],
|
||||
streaming: bool = True,
|
||||
suffix: Optional[str] = "",
|
||||
tags: Optional[List[str]] = [],
|
||||
temperature: Optional[float] = 0.8,
|
||||
top_k: Optional[int] = 40,
|
||||
top_p: Optional[float] = 0.95,
|
||||
use_mlock: bool = False,
|
||||
use_mmap: Optional[bool] = True,
|
||||
verbose: bool = True,
|
||||
vocab_only: bool = False,
|
||||
) -> Text:
|
||||
output = LlamaCpp(
|
||||
model_path=model_path,
|
||||
grammar=grammar,
|
||||
cache=cache,
|
||||
client=client,
|
||||
echo=echo,
|
||||
f16_kv=f16_kv,
|
||||
grammar_path=grammar_path,
|
||||
last_n_tokens_size=last_n_tokens_size,
|
||||
logits_all=logits_all,
|
||||
logprobs=logprobs,
|
||||
lora_base=lora_base,
|
||||
lora_path=lora_path,
|
||||
max_tokens=max_tokens,
|
||||
metadata=metadata,
|
||||
model_kwargs=model_kwargs,
|
||||
n_batch=n_batch,
|
||||
n_ctx=n_ctx,
|
||||
n_gpu_layers=n_gpu_layers,
|
||||
n_parts=n_parts,
|
||||
n_threads=n_threads,
|
||||
repeat_penalty=repeat_penalty,
|
||||
rope_freq_base=rope_freq_base,
|
||||
rope_freq_scale=rope_freq_scale,
|
||||
seed=seed,
|
||||
stop=stop,
|
||||
streaming=streaming,
|
||||
suffix=suffix,
|
||||
tags=tags,
|
||||
temperature=temperature,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
use_mlock=use_mlock,
|
||||
use_mmap=use_mmap,
|
||||
verbose=verbose,
|
||||
vocab_only=vocab_only,
|
||||
)
|
||||
message = output.invoke(inputs)
|
||||
result = message.content if hasattr(message, "content") else message
|
||||
self.status = result
|
||||
return result
|
||||
256
src/backend/langflow/components/models/Ollama.py
Normal file
256
src/backend/langflow/components/models/Ollama.py
Normal file
|
|
@ -0,0 +1,256 @@
|
|||
from typing import Any, Dict, List, Optional
|
||||
|
||||
# from langchain_community.chat_models import ChatOllama
|
||||
from langchain_community.chat_models import ChatOllama
|
||||
|
||||
# from langchain.chat_models import ChatOllama
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import Text
|
||||
|
||||
# whe When a callback component is added to Langflow, the comment must be uncommented.
|
||||
# from langchain.callbacks.manager import CallbackManager
|
||||
|
||||
|
||||
class ChatOllamaComponent(CustomComponent):
|
||||
display_name = "ChatOllama model"
|
||||
description = "Generate text using 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,
|
||||
},
|
||||
"inputs": {"display_name": "Input"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
base_url: Optional[str],
|
||||
model: str,
|
||||
inputs: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[str, Any]] = 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,
|
||||
) -> Text:
|
||||
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
|
||||
message = output.invoke(inputs)
|
||||
result = message.content if hasattr(message, "content") else message
|
||||
self.status = result
|
||||
return result
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_community.chat_models.openai import ChatOpenAI
|
||||
from langchain_openai import ChatOpenAI
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import NestedDict, Text
|
||||
|
||||
|
|
|
|||
92
src/backend/langflow/components/models/VertexAi.py
Normal file
92
src/backend/langflow/components/models/VertexAi.py
Normal file
|
|
@ -0,0 +1,92 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langchain_google_vertexai import ChatVertexAI
|
||||
from langchain_core.messages.base import BaseMessage
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class ChatVertexAIComponent(CustomComponent):
|
||||
display_name = "ChatVertexAI model"
|
||||
description = "Generate text using Vertex AI Chat large language models API."
|
||||
|
||||
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,
|
||||
},
|
||||
"inputs": {"display_name": "Input"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
inputs:str,
|
||||
credentials: Optional[str],
|
||||
project: str,
|
||||
examples: Optional[List[BaseMessage]] = [],
|
||||
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,
|
||||
) -> Text:
|
||||
output = ChatVertexAI(
|
||||
credentials=credentials,
|
||||
examples=examples,
|
||||
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,
|
||||
)
|
||||
message = output.invoke(inputs)
|
||||
result = message.content if hasattr(message, "content") else message
|
||||
self.status = result
|
||||
return result
|
||||
Loading…
Add table
Add a link
Reference in a new issue