chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,305 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
from enum import Enum
|
||||
|
||||
from semantic_kernel.connectors.ai.embedding_generator_base import EmbeddingGeneratorBase
|
||||
from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
|
||||
|
||||
|
||||
class Services(str, Enum):
|
||||
"""Enum for supported embedding services.
|
||||
|
||||
For service specific settings, refer to this documentation:
|
||||
https://github.com/microsoft/semantic-kernel/blob/main/python/samples/concepts/setup/ALL_SETTINGS.md
|
||||
"""
|
||||
|
||||
OPENAI = "openai"
|
||||
AZURE_OPENAI = "azure_openai"
|
||||
AZURE_AI_INFERENCE = "azure_ai_inference"
|
||||
BEDROCK = "bedrock"
|
||||
GOOGLE_AI = "google_ai"
|
||||
HUGGING_FACE = "huggingface"
|
||||
MISTRAL_AI = "mistral_ai"
|
||||
OLLAMA = "ollama"
|
||||
VERTEX_AI = "vertex_ai"
|
||||
|
||||
|
||||
def get_text_embedding_service_and_request_settings(
|
||||
service_name: Services,
|
||||
) -> tuple["EmbeddingGeneratorBase", "PromptExecutionSettings"]:
|
||||
"""Returns the embedding service and request settings for the given service name.
|
||||
|
||||
Args:
|
||||
service_name (Services): The service name.
|
||||
|
||||
Returns:
|
||||
Tuple[EmbeddingGeneratorBase, PromptExecutionSettings]: The embedding service and request settings.
|
||||
"""
|
||||
# Use lambdas to delay instantiation of the services until needed.
|
||||
embedding_services = {
|
||||
Services.OPENAI: lambda: get_openai_text_embedding_service_and_request_settings(),
|
||||
Services.AZURE_OPENAI: lambda: get_azure_openai_text_embedding_service_and_request_settings(),
|
||||
Services.AZURE_AI_INFERENCE: lambda: get_azure_ai_inference_text_embedding_service_and_request_settings(),
|
||||
Services.BEDROCK: lambda: get_bedrock_text_embedding_service_and_request_settings(),
|
||||
Services.GOOGLE_AI: lambda: get_google_ai_text_embedding_service_and_request_settings(),
|
||||
Services.HUGGING_FACE: lambda: get_hugging_face_text_embedding_service_and_request_settings(),
|
||||
Services.MISTRAL_AI: lambda: get_mistral_ai_text_embedding_service_and_request_settings(),
|
||||
Services.OLLAMA: lambda: get_ollama_text_embedding_service_and_request_settings(),
|
||||
Services.VERTEX_AI: lambda: get_vertex_ai_text_embedding_service_and_request_settings(),
|
||||
}
|
||||
|
||||
# Call the appropriate lambda or function based on the service name
|
||||
if service_name not in embedding_services:
|
||||
raise ValueError(f"Unsupported service name: {service_name}")
|
||||
return embedding_services[service_name]()
|
||||
|
||||
|
||||
def get_openai_text_embedding_service_and_request_settings() -> tuple[
|
||||
"EmbeddingGeneratorBase", "PromptExecutionSettings"
|
||||
]:
|
||||
"""Return OpenAI embedding service and request settings.
|
||||
|
||||
The service credentials can be read by 3 ways:
|
||||
1. Via the constructor
|
||||
2. Via the environment variables
|
||||
3. Via an environment file
|
||||
|
||||
The request settings control the behavior of the service. The default settings are sufficient to get started.
|
||||
However, you can adjust the settings to suit your needs.
|
||||
Note: Some of the settings are NOT meant to be set by the user.
|
||||
Please refer to the Semantic Kernel Python documentation for more information:
|
||||
https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel-python
|
||||
"""
|
||||
from semantic_kernel.connectors.ai.open_ai import OpenAIEmbeddingPromptExecutionSettings, OpenAITextEmbedding
|
||||
|
||||
embedding_service = OpenAITextEmbedding(ai_model_id="text-embedding-3-large")
|
||||
# Note: not all models support specifying the dimensions or there may be constraints on the dimensions
|
||||
request_settings = OpenAIEmbeddingPromptExecutionSettings(dimensions=3072)
|
||||
|
||||
return embedding_service, request_settings
|
||||
|
||||
|
||||
def get_azure_openai_text_embedding_service_and_request_settings() -> tuple[
|
||||
"EmbeddingGeneratorBase", "PromptExecutionSettings"
|
||||
]:
|
||||
"""Return Azure OpenAI embedding service and request settings.
|
||||
|
||||
The service credentials can be read by 3 ways:
|
||||
1. Via the constructor
|
||||
2. Via the environment variables
|
||||
3. Via an environment file
|
||||
|
||||
The request settings control the behavior of the service. The default settings are sufficient to get started.
|
||||
However, you can adjust the settings to suit your needs.
|
||||
Note: Some of the settings are NOT meant to be set by the user.
|
||||
Please refer to the Semantic Kernel Python documentation for more information:
|
||||
https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel-python
|
||||
"""
|
||||
from azure.identity import AzureCliCredential
|
||||
|
||||
from semantic_kernel.connectors.ai.open_ai import AzureTextEmbedding, OpenAIEmbeddingPromptExecutionSettings
|
||||
|
||||
embedding_service = AzureTextEmbedding(deployment_name="text-embedding-3-large", credential=AzureCliCredential())
|
||||
# Note: not all models support specifying the dimensions or there may be constraints on the dimensions
|
||||
request_settings = OpenAIEmbeddingPromptExecutionSettings(dimensions=3072)
|
||||
|
||||
return embedding_service, request_settings
|
||||
|
||||
|
||||
def get_azure_ai_inference_text_embedding_service_and_request_settings() -> tuple[
|
||||
"EmbeddingGeneratorBase", "PromptExecutionSettings"
|
||||
]:
|
||||
"""Return Azure AI Inference embedding service and request settings.
|
||||
|
||||
The service credentials can be read by 3 ways:
|
||||
1. Via the constructor
|
||||
2. Via the environment variables
|
||||
3. Via an environment file
|
||||
|
||||
The request settings control the behavior of the service. The default settings are sufficient to get started.
|
||||
However, you can adjust the settings to suit your needs.
|
||||
Note: Some of the settings are NOT meant to be set by the user.
|
||||
Please refer to the Semantic Kernel Python documentation for more information:
|
||||
https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel
|
||||
"""
|
||||
from semantic_kernel.connectors.ai.azure_ai_inference import (
|
||||
AzureAIInferenceEmbeddingPromptExecutionSettings,
|
||||
AzureAIInferenceTextEmbedding,
|
||||
)
|
||||
|
||||
# The AI model ID is used as an identifier for developers when they are using serverless endpoints
|
||||
# on AI Foundry. It is not actually used to identify the model in the service as the endpoint points
|
||||
# to only one model.
|
||||
# When developers are using one endpoint that can route to multiple models, the `ai_model_id` will be
|
||||
# used to identify the model. To use the latest routing feature on AI Foundry, please refer to the
|
||||
# following documentation:
|
||||
# https://learn.microsoft.com/en-us/azure/ai-services/multi-service-resource?%3Fcontext=%2Fazure%2Fai-services%2Fmodel-inference%2Fcontext%2Fcontext&pivots=azportal
|
||||
# https://learn.microsoft.com/en-us/azure/ai-foundry/model-inference/how-to/configure-project-connection?pivots=ai-foundry-portal
|
||||
# https://learn.microsoft.com/en-us/azure/ai-foundry/model-inference/how-to/inference?tabs=python
|
||||
|
||||
embedding_service = AzureAIInferenceTextEmbedding(ai_model_id="id")
|
||||
# Note: not all models support specifying the dimensions or there may be constraints on the dimensions
|
||||
request_settings = AzureAIInferenceEmbeddingPromptExecutionSettings(dimensions=1024)
|
||||
|
||||
return embedding_service, request_settings
|
||||
|
||||
|
||||
def get_bedrock_text_embedding_service_and_request_settings() -> tuple[
|
||||
"EmbeddingGeneratorBase", "PromptExecutionSettings"
|
||||
]:
|
||||
"""Return Bedrock embedding service and request settings.
|
||||
|
||||
The service credentials can be read by 3 ways:
|
||||
1. Via the constructor
|
||||
2. Via the environment variables
|
||||
3. Via an environment file
|
||||
|
||||
The request settings control the behavior of the service. The default settings are sufficient to get started.
|
||||
However, you can adjust the settings to suit your needs.
|
||||
Note: Some of the settings are NOT meant to be set by the user.
|
||||
Please refer to the Semantic Kernel Python documentation for more information:
|
||||
https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel
|
||||
"""
|
||||
from semantic_kernel.connectors.ai.bedrock import BedrockEmbeddingPromptExecutionSettings, BedrockTextEmbedding
|
||||
|
||||
embedding_service = BedrockTextEmbedding(model_id="amazon.titan-embed-text-v2:0")
|
||||
request_settings = BedrockEmbeddingPromptExecutionSettings(
|
||||
# For model specific settings, specify them in the extension_data dictionary.
|
||||
# For example, for Cohere Command specific settings, refer to:
|
||||
# https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html
|
||||
extension_data={"dimensions": 256},
|
||||
)
|
||||
|
||||
return embedding_service, request_settings
|
||||
|
||||
|
||||
def get_hugging_face_text_embedding_service_and_request_settings() -> tuple[
|
||||
"EmbeddingGeneratorBase", "PromptExecutionSettings"
|
||||
]:
|
||||
"""Return HuggingFace text embedding service and request settings.
|
||||
|
||||
The service credentials can be read by 3 ways:
|
||||
1. Via the constructor
|
||||
2. Via the environment variables
|
||||
3. Via an environment file
|
||||
|
||||
The request settings control the behavior of the service. The default settings are sufficient to get started.
|
||||
However, you can adjust the settings to suit your needs.
|
||||
Note: Some of the settings are NOT meant to be set by the user.
|
||||
Please refer to the Semantic Kernel Python documentation for more information:
|
||||
https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel
|
||||
"""
|
||||
from semantic_kernel.connectors.ai.hugging_face import HuggingFacePromptExecutionSettings, HuggingFaceTextEmbedding
|
||||
|
||||
embedding_service = HuggingFaceTextEmbedding(ai_model_id="sentence-transformers/all-MiniLM-L6-v2")
|
||||
request_settings = HuggingFacePromptExecutionSettings()
|
||||
|
||||
return embedding_service, request_settings
|
||||
|
||||
|
||||
def get_google_ai_text_embedding_service_and_request_settings() -> tuple[
|
||||
"EmbeddingGeneratorBase", "PromptExecutionSettings"
|
||||
]:
|
||||
"""Return Google AI text embedding service and request settings.
|
||||
|
||||
The service credentials can be read by 3 ways:
|
||||
1. Via the constructor
|
||||
2. Via the environment variables
|
||||
3. Via an environment file
|
||||
|
||||
The request settings control the behavior of the service. The default settings are sufficient to get started.
|
||||
However, you can adjust the settings to suit your needs.
|
||||
Note: Some of the settings are NOT meant to be set by the user.
|
||||
Please refer to the Semantic Kernel Python documentation for more information:
|
||||
https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel
|
||||
"""
|
||||
from semantic_kernel.connectors.ai.google.google_ai import (
|
||||
GoogleAIEmbeddingPromptExecutionSettings,
|
||||
GoogleAITextEmbedding,
|
||||
)
|
||||
|
||||
embedding_service = GoogleAITextEmbedding()
|
||||
# Note: not all models support specifying the dimensions or there may be constraints on the dimensions
|
||||
request_settings = GoogleAIEmbeddingPromptExecutionSettings(output_dimensionality=768)
|
||||
|
||||
return embedding_service, request_settings
|
||||
|
||||
|
||||
def get_mistral_ai_text_embedding_service_and_request_settings() -> tuple[
|
||||
"EmbeddingGeneratorBase", "PromptExecutionSettings"
|
||||
]:
|
||||
"""Return Mistral AI text embedding service and request settings.
|
||||
|
||||
The service credentials can be read by 3 ways:
|
||||
1. Via the constructor
|
||||
2. Via the environment variables
|
||||
3. Via an environment file
|
||||
|
||||
The request settings control the behavior of the service. The default settings are sufficient to get started.
|
||||
However, you can adjust the settings to suit your needs.
|
||||
Note: Some of the settings are NOT meant to be set by the user.
|
||||
Please refer to the Semantic Kernel Python documentation for more information:
|
||||
https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel
|
||||
"""
|
||||
from semantic_kernel.connectors.ai.mistral_ai import MistralAIPromptExecutionSettings, MistralAITextEmbedding
|
||||
|
||||
embedding_service = MistralAITextEmbedding()
|
||||
request_settings = MistralAIPromptExecutionSettings()
|
||||
|
||||
return embedding_service, request_settings
|
||||
|
||||
|
||||
def get_ollama_text_embedding_service_and_request_settings() -> tuple[
|
||||
"EmbeddingGeneratorBase", "PromptExecutionSettings"
|
||||
]:
|
||||
"""Return Ollama text embedding service and request settings.
|
||||
|
||||
The service credentials can be read by 3 ways:
|
||||
1. Via the constructor
|
||||
2. Via the environment variables
|
||||
3. Via an environment file
|
||||
|
||||
The request settings control the behavior of the service. The default settings are sufficient to get started.
|
||||
However, you can adjust the settings to suit your needs.
|
||||
Note: Some of the settings are NOT meant to be set by the user.
|
||||
Please refer to the Semantic Kernel Python documentation for more information:
|
||||
https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel
|
||||
"""
|
||||
from semantic_kernel.connectors.ai.ollama import OllamaEmbeddingPromptExecutionSettings, OllamaTextEmbedding
|
||||
|
||||
embedding_service = OllamaTextEmbedding()
|
||||
request_settings = OllamaEmbeddingPromptExecutionSettings(
|
||||
# For model specific settings, specify them in the options dictionary.
|
||||
# For more information on the available options, refer to the Ollama API documentation:
|
||||
# https://github.com/ollama/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values
|
||||
options={
|
||||
"temperature": 0.8,
|
||||
},
|
||||
)
|
||||
|
||||
return embedding_service, request_settings
|
||||
|
||||
|
||||
def get_vertex_ai_text_embedding_service_and_request_settings() -> tuple[
|
||||
"EmbeddingGeneratorBase", "PromptExecutionSettings"
|
||||
]:
|
||||
"""Return Vertex AI text embedding service and request settings.
|
||||
|
||||
The service credentials can be read by 3 ways:
|
||||
1. Via the constructor
|
||||
2. Via the environment variables
|
||||
3. Via an environment file
|
||||
|
||||
The request settings control the behavior of the service. The default settings are sufficient to get started.
|
||||
However, you can adjust the settings to suit your needs.
|
||||
Note: Some of the settings are NOT meant to be set by the user.
|
||||
Please refer to the Semantic Kernel Python documentation for more information:
|
||||
https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel
|
||||
"""
|
||||
from semantic_kernel.connectors.ai.google import GoogleAIEmbeddingPromptExecutionSettings, GoogleAITextEmbedding
|
||||
|
||||
embedding_service = GoogleAITextEmbedding()
|
||||
# Note: not all models support specifying the dimensions or there may be constraints on the dimensions
|
||||
request_settings = GoogleAIEmbeddingPromptExecutionSettings(output_dimensionality=768)
|
||||
|
||||
return embedding_service, request_settings
|
||||
Reference in New Issue
Block a user