Update component display names and descriptions
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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
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class AmazonBedrockEmeddingsComponent(CustomComponent):
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class AmazonBedrockEmeddingsComponent(CustomComponent):
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"""
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A custom component for implementing an Embeddings Model using Amazon Bedrock.
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"""
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display_name: str = "Amazon Bedrock Embeddings"
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display_name: str = "Amazon Bedrock Embeddings"
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description: str = "Embeddings model from Amazon Bedrock."
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description: str = "Generate embeddings using Amazon Bedrock models."
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documentation = "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock"
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documentation = "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock"
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def build_config(self):
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def build_config(self):
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@ -5,8 +5,8 @@ from langflow.interface.custom.custom_component import CustomComponent
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class AzureOpenAIEmbeddingsComponent(CustomComponent):
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class AzureOpenAIEmbeddingsComponent(CustomComponent):
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display_name: str = "AzureOpenAIEmbeddings"
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display_name: str = "Azure OpenAI Embeddings"
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description: str = "Embeddings model from Azure OpenAI."
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description: str = "Generate embeddings using Azure OpenAI models."
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documentation: str = "https://python.langchain.com/docs/integrations/text_embedding/azureopenai"
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documentation: str = "https://python.langchain.com/docs/integrations/text_embedding/azureopenai"
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beta = False
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beta = False
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icon = "Azure"
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icon = "Azure"
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@ -6,8 +6,8 @@ from langflow.custom import CustomComponent
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class CohereEmbeddingsComponent(CustomComponent):
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class CohereEmbeddingsComponent(CustomComponent):
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display_name = "CohereEmbeddings"
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display_name = "Cohere Embeddings"
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description = "Cohere embedding models."
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description = "Generate embeddings using Cohere models."
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def build_config(self):
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def build_config(self):
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return {
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return {
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@ -6,8 +6,8 @@ from langflow.interface.custom.custom_component import CustomComponent
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class HuggingFaceEmbeddingsComponent(CustomComponent):
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class HuggingFaceEmbeddingsComponent(CustomComponent):
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display_name = "HuggingFaceEmbeddings"
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display_name = "Hugging Face Embeddings"
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description = "HuggingFace sentence_transformers embedding models."
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description = "Generate embeddings using HuggingFace models."
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documentation = (
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documentation = (
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"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers"
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"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers"
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)
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)
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@ -7,8 +7,8 @@ from langflow.interface.custom.custom_component import CustomComponent
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class HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):
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class HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):
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display_name = "HuggingFaceInferenceAPIEmbeddings"
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display_name = "Hugging Face API Embeddings"
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description = "HuggingFace sentence_transformers embedding models, API version."
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description = "Generate embeddings using Hugging Face Inference API models."
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documentation = "https://github.com/huggingface/text-embeddings-inference"
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documentation = "https://github.com/huggingface/text-embeddings-inference"
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icon = "HuggingFace"
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icon = "HuggingFace"
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@ -7,12 +7,8 @@ from langflow.interface.custom.custom_component import CustomComponent
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class OllamaEmbeddingsComponent(CustomComponent):
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class OllamaEmbeddingsComponent(CustomComponent):
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"""
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A custom component for implementing an Embeddings Model using Ollama.
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"""
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display_name: str = "Ollama Embeddings"
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display_name: str = "Ollama Embeddings"
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description: str = "Embeddings model from Ollama."
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description: str = "Generate embeddings using Ollama models."
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documentation = "https://python.langchain.com/docs/integrations/text_embedding/ollama"
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documentation = "https://python.langchain.com/docs/integrations/text_embedding/ollama"
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def build_config(self):
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def build_config(self):
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@ -7,8 +7,8 @@ from langflow.interface.custom.custom_component import CustomComponent
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class OpenAIEmbeddingsComponent(CustomComponent):
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class OpenAIEmbeddingsComponent(CustomComponent):
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display_name = "OpenAIEmbeddings"
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display_name = "OpenAI Embeddings"
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description = "OpenAI embedding models"
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description = "Generate embeddings using OpenAI models."
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def build_config(self):
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def build_config(self):
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return {
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return {
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@ -6,8 +6,8 @@ from langflow.interface.custom.custom_component import CustomComponent
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class VertexAIEmbeddingsComponent(CustomComponent):
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class VertexAIEmbeddingsComponent(CustomComponent):
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display_name = "VertexAIEmbeddings"
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display_name = "VertexAI Embeddings"
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description = "Google Cloud VertexAI embedding models."
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description = "Generate embeddings using Google Cloud VertexAI models."
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def build_config(self):
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def build_config(self):
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return {
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return {
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@ -6,7 +6,7 @@ from langflow.interface.custom.custom_component import CustomComponent
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class PromptComponent(CustomComponent):
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class PromptComponent(CustomComponent):
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display_name: str = "Prompt"
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display_name: str = "Prompt"
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description: str = "Create prompt templates with dynamic variables. Prompts can help guide the behavior of a Language Model."
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description: str = "Create a prompt template with dynamic variables."
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icon = "terminal-square"
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icon = "terminal-square"
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def build_config(self):
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def build_config(self):
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@ -9,10 +9,6 @@ from langflow.schema import Record
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class ChromaSearchComponent(LCVectorStoreComponent):
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class ChromaSearchComponent(LCVectorStoreComponent):
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"""
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A custom component for implementing a Vector Store using Chroma.
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"""
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display_name: str = "Chroma Search"
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display_name: str = "Chroma Search"
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description: str = "Search a Chroma collection for similar documents."
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description: str = "Search a Chroma collection for similar documents."
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icon = "Chroma"
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icon = "Chroma"
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@ -9,10 +9,6 @@ from langflow.schema import Record
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class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):
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class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):
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"""
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A custom component for implementing a Vector Store using PostgreSQL.
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"""
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display_name: str = "PGVector Search"
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display_name: str = "PGVector Search"
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description: str = "Search a PGVector Store for similar documents."
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description: str = "Search a PGVector Store for similar documents."
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documentation = "https://python.langchain.com/docs/integrations/vectorstores/pgvector"
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documentation = "https://python.langchain.com/docs/integrations/vectorstores/pgvector"
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