mirror of
https://github.com/microsoft/semantic-kernel.git
synced 2026-10-12 02:19:06 +00:00
Julio Carlos MenendezandEvan Mattson
d97f9536c4
### Motivation and Context Delete all *_async where *_sync doesn't exist to make the library more Pythonic. Fixes #4629 ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [X] The code builds clean without any errors or warnings - [X] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [X] All unit tests pass, and I have added new tests where possible - [X] I didn't break anyone 😄 --------- Co-authored-by: Evan Mattson <[email protected]>
89 lines
3.1 KiB
Python
89 lines
3.1 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
|
|
|
|
import os
|
|
|
|
from dotenv import load_dotenv
|
|
|
|
import semantic_kernel as sk
|
|
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
|
|
from semantic_kernel.connectors.search_engine import GoogleConnector
|
|
from semantic_kernel.core_plugins import WebSearchEnginePlugin
|
|
|
|
load_dotenv()
|
|
|
|
|
|
async def main():
|
|
kernel = sk.Kernel()
|
|
api_key, org_id = sk.openai_settings_from_dot_env()
|
|
kernel.add_chat_service("chat-gpt", OpenAIChatCompletion("gpt-3.5-turbo", api_key, org_id))
|
|
|
|
"""
|
|
Instantiate a Google Connector
|
|
Make sure to have the following keys in a .env file or set as environment variables
|
|
- GOOGLE_API_KEY
|
|
- GOOGLE_SEARCH_ENGINE_ID
|
|
|
|
A Google Custom Search API has to be created in order to have an API key and a search engine ID.
|
|
To create a Google Custom Search API, follow the guide - https://developers.google.com/custom-search/v1/overview.
|
|
If you have already created the service, the credentials can be found in the Credentials tab on the page
|
|
https://console.cloud.google.com/apis/api/customsearch.googleapis.com
|
|
"""
|
|
connector = GoogleConnector(
|
|
api_key=os.getenv("GOOGLE_API_KEY"),
|
|
search_engine_id=os.getenv("GOOGLE_SEARCH_ENGINE_ID"),
|
|
)
|
|
|
|
# Import the WebSearchEnginePlugin and pass the Google Connector to it.
|
|
web_plugin = kernel.import_plugin(WebSearchEnginePlugin(connector), "WebSearch")
|
|
|
|
# The search query
|
|
prompt = "Who is Leonardo DiCaprio's current girlfriend?"
|
|
search = web_plugin["searchAsync"]
|
|
|
|
# By default, only one search result is provided
|
|
result = await search.invoke_async(prompt)
|
|
print(result)
|
|
|
|
"""
|
|
Output:
|
|
["Celebrity Celebrity News Everything You Need to Know About Leonardo DiCaprio and Camila Morrone's
|
|
Relationship From the beginning of their romance to today, we track their relationship here. By..."]
|
|
"""
|
|
|
|
# Following example demonstrates the use of the plugin within a semantic function
|
|
prompt = """
|
|
Answer the question using only the data that is provided in the data section.
|
|
Do not use any prior knowledge to answer the question.
|
|
Data: {{WebSearch.SearchAsync "What is semantic kernel?"}}
|
|
Question: What is semantic kernel?
|
|
Answer:
|
|
"""
|
|
|
|
qna = kernel.create_semantic_function(prompt, temperature=0.2)
|
|
context = kernel.create_new_context()
|
|
|
|
"""
|
|
Two context parameters can be passed to the search engine plugin.
|
|
- num_results controls the number of results returned by the web search.
|
|
- offset controls the number of results to omit.
|
|
"""
|
|
context["num_results"] = "10"
|
|
context["offset"] = "0"
|
|
|
|
result = await qna.invoke_async(context=context)
|
|
print(result)
|
|
|
|
"""
|
|
Output:
|
|
Semantic Kernel is an open-source SDK that lets you easily combine AI services like OpenAI,
|
|
Azure OpenAI, and Hugging Face with conventional programming languages like C# and Python.
|
|
By doing so, you can create AI apps that combine the best of both worlds.
|
|
Semantic Kernel is at the center of the copilot stack.
|
|
"""
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import asyncio
|
|
|
|
asyncio.run(main())
|