chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Chat History manipulation samples
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This folder contains samples that demonstrate how to manipulate chat history in Semantic Kernel.
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## [Serialize Chat History](./serialize_chat_history.py)
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This sample demonstrates how to build a conversational chatbot using Semantic Kernel, it features auto function calling, but with file-based serialization of the chat history. This sample stores and reads the chat history at every turn. This is not the best way to do it, but clearly demonstrates the mechanics.
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To run this sample a environment with keys for the chosen chat service is required. In line 61 you can change the model used. This sample uses a temporary file to store the chat history, so no additional setup is required.
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## [Store Chat History in Cosmos DB](./store_chat_history_in_cosmosdb.py)
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This a more complex version of the sample above, it uses Azure CosmosDB NoSQL to store the chat messages.
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In order to do that a simple datamodel is defined. And then a class is created that extends ChatHistory, this class adds `store` and `read` methods, as well as a `create_collection` method that creates a collection in CosmosDB.
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This samples further uses the same chat service setup as the sample above, so the keys and other parameters for the chosen model should be in the environment. Next to that a AZURE_COSMOS_DB_NO_SQL_URL and optionally a AZURE_COSMOS_DB_NO_SQL_KEY should be set in the environment, you can also rely on Entra ID Auth instead of the key. The database name can also be put in the environment.
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import tempfile
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from samples.concepts.setup.chat_completion_services import Services, get_chat_completion_service_and_request_settings
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from semantic_kernel.contents import ChatHistory
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"""
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This sample demonstrates how to build a conversational chatbot
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using Semantic Kernel, it features auto function calling,
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but with file-based serialization of the chat history.
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This sample stores and reads the chat history at every turn.
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This is not the best way to do it, but clearly demonstrates the mechanics.
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More optimal would for instance be to only write once when a conversation is done.
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And writing to something other then a file is also usually better.
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"""
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# You can select from the following chat completion services that support function calling:
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# - Services.OPENAI
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# - Services.AZURE_OPENAI
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# - Services.AZURE_AI_INFERENCE
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# - Services.ANTHROPIC
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# - Services.BEDROCK
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# - Services.GOOGLE_AI
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# - Services.MISTRAL_AI
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# - Services.OLLAMA
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# - Services.ONNX
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# - Services.VERTEX_AI
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# - Services.DEEPSEEK
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# Please make sure you have configured your environment correctly for the selected chat completion service.
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chat_completion_service, request_settings = get_chat_completion_service_and_request_settings(Services.OPENAI)
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async def chat(file) -> bool:
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"""
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Continuously prompt the user for input and show the assistant's response.
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Type 'exit' to exit.
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"""
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try:
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# Try to load the chat history from a file.
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history = ChatHistory.load_chat_history_from_file(file_path=file)
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print(f"Chat history successfully loaded {len(history.messages)} messages.")
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except Exception:
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# Create a new chat history to store the system message, initial messages, and the conversation.
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print("Chat history file not found. Starting a new conversation.")
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history = ChatHistory()
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history.add_system_message(
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"You are a chat bot. Your name is Mosscap and you have one goal: figure out what people need."
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)
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try:
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# Get the user input
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user_input = input("User:> ")
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except (KeyboardInterrupt, EOFError):
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print("\n\nExiting chat...")
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return False
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if user_input.lower().strip() == "exit":
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print("\n\nExiting chat...")
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return False
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# Add the user input to the chat history
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history.add_user_message(user_input)
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# Get a response from the chat completion service
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result = await chat_completion_service.get_chat_message_content(history, request_settings)
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# Update the chat history with the user's input and the assistant's response
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if result:
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print(f"Mosscap:> {result}")
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history.add_message(result)
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# Save the chat history to a file.
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print(f"Saving {len(history.messages)} messages to the file.")
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history.store_chat_history_to_file(file_path=file)
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return True
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"""
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Sample output:
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Welcome to the chat bot!
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Type 'exit' to exit.
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Try a math question to see function calling in action (e.g. 'what is 3+3?').
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Your chat history will be saved in: <local working directory>/tmpq1n1f6qk.json
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Chat history file not found. Starting a new conversation.
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User:> Hello, how are you?
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Mosscap:> Hello! I'm here and ready to help. What do you need today?
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Saving 3 messages to the file.
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Chat history successfully loaded 3 messages.
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User:> exit
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"""
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async def main() -> None:
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chatting = True
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with tempfile.NamedTemporaryFile(mode="w+", dir=".", suffix=".json", delete=True) as file:
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print(
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"Welcome to the chat bot!\n"
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" Type 'exit' to exit.\n"
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" Try a math question to see function calling in action (e.g. 'what is 3+3?')."
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f" Your chat history will be saved in: {file.name}"
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)
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try:
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while chatting:
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chatting = await chat(file.name)
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except Exception:
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print("Closing and removing the file.")
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if __name__ == "__main__":
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asyncio.run(main())
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from dataclasses import dataclass
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from typing import Annotated
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from samples.concepts.setup.chat_completion_services import Services, get_chat_completion_service_and_request_settings
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from semantic_kernel import Kernel
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from semantic_kernel.connectors.ai import FunctionChoiceBehavior
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from semantic_kernel.connectors.azure_cosmos_db import CosmosNoSqlStore
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from semantic_kernel.contents import ChatHistory, ChatMessageContent
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from semantic_kernel.core_plugins.math_plugin import MathPlugin
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from semantic_kernel.core_plugins.time_plugin import TimePlugin
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from semantic_kernel.data.vector import VectorStore, VectorStoreCollection, VectorStoreField, vectorstoremodel
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"""
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This sample demonstrates how to build a conversational chatbot
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using Semantic Kernel, it features auto function calling,
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but with Azure CosmosDB as storage for the chat history.
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This sample stores and reads the chat history at every turn.
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This is not the best way to do it, but clearly demonstrates the mechanics.
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Further refinement would be to only write once when a conversation is done.
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And there is also no logic to see if there is something to write.
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You could also enhance the ChatHistoryModel with a summary and a vector for that
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in order to search for similar conversations.
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"""
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# 1. We first create simple datamodel for the chat history.
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# Note that this model does not contain any vectors,
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# those can be added, for instance to store a summary of the conversation.
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@vectorstoremodel
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@dataclass
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class ChatHistoryModel:
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session_id: Annotated[str, VectorStoreField("key")]
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user_id: Annotated[str, VectorStoreField("data", is_indexed=True)]
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messages: Annotated[list[dict[str, str]], VectorStoreField("data", is_indexed=True)]
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# 2. We then create a class that extends the ChatHistory class
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# and implements the methods to store and read the chat history.
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# This could also use one of the history reducers to make
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# sure the database doesn't grow too large.
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# It adds a `store` attribute and a couple of methods.
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class ChatHistoryInCosmosDB(ChatHistory):
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"""This class extends the ChatHistory class to store the chat history in a Cosmos DB."""
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session_id: str
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user_id: str
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store: VectorStore
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collection: VectorStoreCollection[str, ChatHistoryModel] | None = None
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async def create_collection(self, collection_name: str) -> None:
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"""Create a collection with the inbuild data model using the vector store.
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First create the collection, then call this method to create the collection itself.
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"""
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self.collection = self.store.get_collection(
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collection_name=collection_name,
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record_type=ChatHistoryModel,
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)
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await self.collection.ensure_collection_exists()
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async def store_messages(self) -> None:
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"""Store the chat history in the Cosmos DB.
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Note that we use model_dump to convert the chat message content into a serializable format.
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"""
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if self.collection:
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await self.collection.upsert(
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ChatHistoryModel(
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session_id=self.session_id,
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user_id=self.user_id,
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messages=[msg.model_dump() for msg in self.messages],
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)
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)
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async def read_messages(self) -> None:
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"""Read the chat history from the Cosmos DB.
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Note that we use the model_validate method to convert the serializable format back into a ChatMessageContent.
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"""
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if self.collection:
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record = await self.collection.get(self.session_id)
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if record:
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for message in record.messages:
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self.messages.append(ChatMessageContent.model_validate(message))
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# 3. We now create a fairly standard kernel, with functions and a chat service.
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# Create and configure the kernel.
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kernel = Kernel()
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# Load some sample plugins (for demonstration of function calling).
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kernel.add_plugin(MathPlugin(), plugin_name="math")
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kernel.add_plugin(TimePlugin(), plugin_name="time")
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# You can select from the following chat completion services that support function calling:
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# - Services.OPENAI
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# - Services.AZURE_OPENAI
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# - Services.AZURE_AI_INFERENCE
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# - Services.ANTHROPIC
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# - Services.BEDROCK
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# - Services.GOOGLE_AI
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# - Services.MISTRAL_AI
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# - Services.OLLAMA
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# - Services.ONNX
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# - Services.VERTEX_AI
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# - Services.DEEPSEEK
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# Please make sure you have configured your environment correctly for the selected chat completion service.
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chat_completion_service, request_settings = get_chat_completion_service_and_request_settings(Services.AZURE_OPENAI)
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# Configure the function choice behavior. Here, we set it to Auto, where auto_invoke=True by default.
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# With `auto_invoke=True`, the model will automatically choose and call functions as needed.
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request_settings.function_choice_behavior = FunctionChoiceBehavior.Auto(filters={"excluded_plugins": ["ChatBot"]})
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kernel.add_service(chat_completion_service)
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# 4. The main chat loop, which takes a history object and prompts the user for input.
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# It then adds the user input to the history and gets a response from the chat completion service.
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# Finally, it prints the response and saves the chat history to the Cosmos DB.
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async def chat(history: ChatHistoryInCosmosDB) -> bool:
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"""
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Continuously prompt the user for input and show the assistant's response.
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Type 'exit' to exit.
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"""
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await history.read_messages()
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print(f"Chat history successfully loaded {len(history.messages)} messages.")
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if len(history.messages) == 0:
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# if it is a new conversation, add the system message and a couple of initial messages.
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history.add_system_message(
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"You are a chat bot. Your name is Mosscap and you have one goal: figure out what people need."
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)
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history.add_user_message("Hi there, who are you?")
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history.add_assistant_message("I am Mosscap, a chat bot. I'm trying to figure out what people need.")
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try:
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user_input = input("User:> ")
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except (KeyboardInterrupt, EOFError):
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print("\n\nExiting chat...")
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return False
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if user_input.lower().strip() == "exit":
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print("\n\nExiting chat...")
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return False
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# add the user input to the chat history
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history.add_user_message(user_input)
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result = await chat_completion_service.get_chat_message_content(history, request_settings, kernel=kernel)
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if result:
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print(f"Mosscap:> {result}")
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history.add_message(result)
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# Save the chat history to CosmosDB.
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print(f"Saving {len(history.messages)} messages to AzureCosmosDB.")
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await history.store_messages()
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return True
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async def main() -> None:
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delete_when_done = True
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session_id = "session1"
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chatting = True
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# 5. We now create the store, ChatHistory and collection and start the chat loop.
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# First we enter the store context manager to connect.
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# The create_database flag will create the database if it does not exist.
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async with CosmosNoSqlStore(create_database=True) as store:
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# Then we create the chat history in CosmosDB.
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history = ChatHistoryInCosmosDB(store=store, session_id=session_id, user_id="user")
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# Finally we create the collection.
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await history.create_collection(collection_name="chat_history")
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print(
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"Welcome to the chat bot!\n"
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" Type 'exit' to exit.\n"
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" Try a math question to see function calling in action (e.g. 'what is 3+3?')."
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)
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try:
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while chatting:
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chatting = await chat(history)
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except Exception:
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print("Closing chat...")
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if delete_when_done and history.collection:
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await history.collection.ensure_collection_deleted()
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if __name__ == "__main__":
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asyncio.run(main())
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