Get Started with the Semantic Kernel Python Integration

Install and import dependencies.

1. Run the following command in your notebook to install the semantic kernel in your environment.

   pip install --quiet --upgrade semantic-kernel openai motor

2. Run the following code to import the required packages:

   import getpass, openai
   import semantic_kernel as sk
   from semantic_kernel.connectors.ai.open_ai import (OpenAIChatCompletion, OpenAITextEmbedding)
   from semantic_kernel.connectors.memory.mongodb_atlas import MongoDBAtlasMemoryStore
   from semantic_kernel.core_plugins.text_memory_plugin import TextMemoryPlugin
   from semantic_kernel.memory.semantic_text_memory import SemanticTextMemory
   from semantic_kernel.prompt_template.input_variable import InputVariable
   from semantic_kernel.prompt_template.prompt_template_config import PromptTemplateConfig
   from pymongo import MongoClient
   from pymongo.operations import SearchIndexModel

Define environmental variables.

Run the following code and provide the following when prompted:

• Your OpenAI API Key.

• Your Atlas cluster's SRV connection string.

OPENAI_API_KEY = getpass.getpass("OpenAI API Key:")
ATLAS_CONNECTION_STRING = getpass.getpass("MongoDB Atlas SRV Connection String:")

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

Initialize the kernel.

Run the following code to initialize the kernel.

kernel = sk.Kernel()

Add the AI services to the kernel.

Run the following code to configure the OpenAI embedding model and chat model used in this tutorial and add these services to the kernel. This code specifies the following:

• OpenAI's text-embedding-ada-002 as the embedding model used to convert text into vector embeddings.

• OpenAI's gpt-3.5-turbo as the chat model used to generate responses.

chat_service = OpenAIChatCompletion(
   service_id="chat",
   ai_model_id="gpt-3.5-turbo",
   api_key=OPENAI_API_KEY
)
embedding_service = OpenAITextEmbedding(
   ai_model_id="text-embedding-ada-002",
   api_key=OPENAI_API_KEY
)
kernel.add_service(chat_service)
kernel.add_service(embedding_service)

Instantiate Atlas as a memory store.

Run the following code to instantiate Atlas as a memory store and add it to the kernel. This code establishes a connection to your Atlas cluster and specifies the following:

• semantic_kernel_db as the Atlas database used to store the documents.

• vector_index as the index used to run semantic search queries.

It also imports a plugin called TextMemoryPlugin, which provides a group of native functions to help you store and retrieve text in memory.

mongodb_atlas_memory_store = MongoDBAtlasMemoryStore(
   connection_string=ATLAS_CONNECTION_STRING,
   database_name="semantic_kernel_db",
   index_name="vector_index"
)
memory = SemanticTextMemory(
   storage=mongodb_atlas_memory_store,
   embeddings_generator=embedding_service
)
kernel.add_plugin(TextMemoryPlugin(memory), "TextMemoryPlugin")

Load sample data on your Atlas cluster.

This code defines and runs a function to populate the semantic_kernel_db.test collection with some sample documents. These documents contain personalized data that the LLM did not originally have access to.

 async def populate_memory(kernel: sk.Kernel) -> None:
    await memory.save_information(
       collection="test", id="1", text="I am a developer"
    )
    await memory.save_information(
       collection="test", id="2", text="I started using MongoDB two years ago"
    )
    await memory.save_information(
       collection="test", id="3", text="I'm using MongoDB Vector Search with Semantic Kernel to implement RAG"
    )
    await memory.save_information(
       collection="test", id="4", text="I like coffee"
    )
 print("Populating memory...")
 await populate_memory(kernel)
 print(kernel)

Populating memory...
plugins=KernelPluginCollection(plugins={'TextMemoryPlugin': KernelPlugin(name='TextMemoryPlugin', description=None, functions={'recall': KernelFunctionFromMethod(metadata=KernelFunctionMetadata(name='recall', plugin_name='TextMemoryPlugin', description='Recall a fact from the long term memory', parameters=[KernelParameterMetadata(name='ask', description='The information to retrieve', default_value=None, type_='str', is_required=True, type_object=<class 'str'>), KernelParameterMetadata(name='collection', description='The collection to search for information.', default_value='generic', type_='str', is_required=False, type_object=<class 'str'>), KernelParameterMetadata(name='relevance', description='The relevance score, from 0.0 to 1.0; 1.0 means perfect match', default_value=0.75, type_='float', is_required=False, type_object=<class 'float'>), KernelParameterMetadata(name='limit', description='The maximum number of relevant memories to recall.', default_value=1, type_='int', is_required=False, type_object=<class 'int'>)], is_prompt=False, is_asynchronous=True, return_parameter=KernelParameterMetadata(name='return', description='', default_value=None, type_='str', is_required=True, type_object=None)), method=<bound method TextMemoryPlugin.recall of TextMemoryPlugin(memory=SemanticTextMemory())>, stream_method=None), 'save': KernelFunctionFromMethod(metadata=KernelFunctionMetadata(name='save', plugin_name='TextMemoryPlugin', description='Save information to semantic memory', parameters=[KernelParameterMetadata(name='text', description='The information to save.', default_value=None, type_='str', is_required=True, type_object=<class 'str'>), KernelParameterMetadata(name='key', description='The unique key to associate with the information.', default_value=None, type_='str', is_required=True, type_object=<class 'str'>), KernelParameterMetadata(name='collection', description='The collection to save the information.', default_value='generic', type_='str', is_required=False, type_object=<class 'str'>)], is_prompt=False, is_asynchronous=True, return_parameter=KernelParameterMetadata(name='return', description='', default_value=None, type_='', is_required=True, type_object=None)), method=<bound method TextMemoryPlugin.save of TextMemoryPlugin(memory=SemanticTextMemory())>, stream_method=None)})}) services={'chat': OpenAIChatCompletion(ai_model_id='gpt-3.5-turbo', service_id='chat', client=<openai.AsyncOpenAI object at 0x7999971c8fa0>, ai_model_type=<OpenAIModelTypes.CHAT: 'chat'>, prompt_tokens=0, completion_tokens=0, total_tokens=0), 'text-embedding-ada-002': OpenAITextEmbedding(ai_model_id='text-embedding-ada-002', service_id='text-embedding-ada-002', client=<openai.AsyncOpenAI object at 0x7999971c8fd0>, ai_model_type=<OpenAIModelTypes.EMBEDDING: 'embedding'>, prompt_tokens=32, completion_tokens=0, total_tokens=32)} ai_service_selector=<semantic_kernel.services.ai_service_selector.AIServiceSelector object at 0x7999971cad70> retry_mechanism=PassThroughWithoutRetry() function_invoking_handlers={} function_invoked_handlers={}

In Atlas, go to the Clusters page for your project.

1. If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.

2. If it's not already displayed, select your desired project from the Projects menu in the navigation bar.

3. If it's not already displayed, click Clusters in the sidebar.

   The Clusters page displays.

Go to the Atlas Search page for your cluster.

You can go the Atlas Search page from the sidebar, the Data Explorer, or your cluster details page.

Define the Atlas Vector Search index.

1. Click Create Search Index.

2. Under Atlas Vector Search, select JSON Editor and then click Next.

3. In the Database and Collection section, find the semantic_kernel_db database, and select the test collection.

4. In the Index Name field, enter vector_index.

5. Replace the default definition with the following index definition and then click Next.

   This index definition specifies indexing the following fields in an index of the vectorSearch type:

   • embedding field as the vector type. The embedding field contains the embeddings created using OpenAI's text-embedding-ada-002 embedding model. The index definition specifies 1536 vector dimensions and measures similarity using cosine.

   1
   {
   2
      "fields": [
   3
         {
   4
            "type": "vector",
   5
            "path": "embedding",
   6
            "numDimensions": 1536,
   7
            "similarity": "cosine"
   8
         }
   9
      ]
   10
   }

Review the index definition and then click Create Search Index.

A modal window displays to let you know that your index is building.

Click Close to close the You're All Set! modal window and wait for the index to finish building.

The index should take about one minute to build. While it builds, the Status column reads Initial Sync. When it finishes building, the Status column reads Active.