Update component display names and descriptions

This commit is contained in:
Rodrigo Nader 2024-03-29 22:10:45 -03:00
commit 30145a0067
11 changed files with 15 additions and 31 deletions

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@ -7,12 +7,8 @@ from langflow.interface.custom.custom_component import CustomComponent
class AmazonBedrockEmeddingsComponent(CustomComponent): class AmazonBedrockEmeddingsComponent(CustomComponent):
"""
A custom component for implementing an Embeddings Model using Amazon Bedrock.
"""
display_name: str = "Amazon Bedrock Embeddings" display_name: str = "Amazon Bedrock Embeddings"
description: str = "Embeddings model from Amazon Bedrock." description: str = "Generate embeddings using Amazon Bedrock models."
documentation = "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock" documentation = "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock"
def build_config(self): def build_config(self):

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@ -5,8 +5,8 @@ from langflow.interface.custom.custom_component import CustomComponent
class AzureOpenAIEmbeddingsComponent(CustomComponent): class AzureOpenAIEmbeddingsComponent(CustomComponent):
display_name: str = "AzureOpenAIEmbeddings" display_name: str = "Azure OpenAI Embeddings"
description: str = "Embeddings model from Azure OpenAI." description: str = "Generate embeddings using Azure OpenAI models."
documentation: str = "https://python.langchain.com/docs/integrations/text_embedding/azureopenai" documentation: str = "https://python.langchain.com/docs/integrations/text_embedding/azureopenai"
beta = False beta = False
icon = "Azure" icon = "Azure"

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@ -6,8 +6,8 @@ from langflow.custom import CustomComponent
class CohereEmbeddingsComponent(CustomComponent): class CohereEmbeddingsComponent(CustomComponent):
display_name = "CohereEmbeddings" display_name = "Cohere Embeddings"
description = "Cohere embedding models." description = "Generate embeddings using Cohere models."
def build_config(self): def build_config(self):
return { return {

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@ -6,8 +6,8 @@ from langflow.interface.custom.custom_component import CustomComponent
class HuggingFaceEmbeddingsComponent(CustomComponent): class HuggingFaceEmbeddingsComponent(CustomComponent):
display_name = "HuggingFaceEmbeddings" display_name = "Hugging Face Embeddings"
description = "HuggingFace sentence_transformers embedding models." description = "Generate embeddings using HuggingFace models."
documentation = ( documentation = (
"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers" "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers"
) )

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@ -7,8 +7,8 @@ from langflow.interface.custom.custom_component import CustomComponent
class HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent): class HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):
display_name = "HuggingFaceInferenceAPIEmbeddings" display_name = "Hugging Face API Embeddings"
description = "HuggingFace sentence_transformers embedding models, API version." description = "Generate embeddings using Hugging Face Inference API models."
documentation = "https://github.com/huggingface/text-embeddings-inference" documentation = "https://github.com/huggingface/text-embeddings-inference"
icon = "HuggingFace" icon = "HuggingFace"

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@ -7,12 +7,8 @@ from langflow.interface.custom.custom_component import CustomComponent
class OllamaEmbeddingsComponent(CustomComponent): class OllamaEmbeddingsComponent(CustomComponent):
"""
A custom component for implementing an Embeddings Model using Ollama.
"""
display_name: str = "Ollama Embeddings" display_name: str = "Ollama Embeddings"
description: str = "Embeddings model from Ollama." description: str = "Generate embeddings using Ollama models."
documentation = "https://python.langchain.com/docs/integrations/text_embedding/ollama" documentation = "https://python.langchain.com/docs/integrations/text_embedding/ollama"
def build_config(self): def build_config(self):

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@ -7,8 +7,8 @@ from langflow.interface.custom.custom_component import CustomComponent
class OpenAIEmbeddingsComponent(CustomComponent): class OpenAIEmbeddingsComponent(CustomComponent):
display_name = "OpenAIEmbeddings" display_name = "OpenAI Embeddings"
description = "OpenAI embedding models" description = "Generate embeddings using OpenAI models."
def build_config(self): def build_config(self):
return { return {

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@ -6,8 +6,8 @@ from langflow.interface.custom.custom_component import CustomComponent
class VertexAIEmbeddingsComponent(CustomComponent): class VertexAIEmbeddingsComponent(CustomComponent):
display_name = "VertexAIEmbeddings" display_name = "VertexAI Embeddings"
description = "Google Cloud VertexAI embedding models." description = "Generate embeddings using Google Cloud VertexAI models."
def build_config(self): def build_config(self):
return { return {

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@ -6,7 +6,7 @@ from langflow.interface.custom.custom_component import CustomComponent
class PromptComponent(CustomComponent): class PromptComponent(CustomComponent):
display_name: str = "Prompt" display_name: str = "Prompt"
description: str = "Create prompt templates with dynamic variables. Prompts can help guide the behavior of a Language Model." description: str = "Create a prompt template with dynamic variables."
icon = "terminal-square" icon = "terminal-square"
def build_config(self): def build_config(self):

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@ -9,10 +9,6 @@ from langflow.schema import Record
class ChromaSearchComponent(LCVectorStoreComponent): class ChromaSearchComponent(LCVectorStoreComponent):
"""
A custom component for implementing a Vector Store using Chroma.
"""
display_name: str = "Chroma Search" display_name: str = "Chroma Search"
description: str = "Search a Chroma collection for similar documents." description: str = "Search a Chroma collection for similar documents."
icon = "Chroma" icon = "Chroma"

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@ -9,10 +9,6 @@ from langflow.schema import Record
class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent): class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):
"""
A custom component for implementing a Vector Store using PostgreSQL.
"""
display_name: str = "PGVector Search" display_name: str = "PGVector Search"
description: str = "Search a PGVector Store for similar documents." description: str = "Search a PGVector Store for similar documents."
documentation = "https://python.langchain.com/docs/integrations/vectorstores/pgvector" documentation = "https://python.langchain.com/docs/integrations/vectorstores/pgvector"