Conversation

pip install -U swarms

def add(
    self,
    role: str,
    content: Union[str, dict, list, Any],
    metadata: Optional[dict] = None,
    category: Optional[str] = None,
)

def return_history_as_string(self) -> str:

def export(self, force: bool = True)

def load(self, filename: str)

def search(self, keyword: str) -> list

def truncate_memory_with_tokenizer(self)

def export_and_count_categories(self) -> Dict[str, int]

from swarms.structs import Conversation

# Create a conversation
conversation = Conversation(
    name="my-conversation",
    system_prompt="You are a helpful assistant.",
    time_enabled=True,
    autosave=True,
    token_count=True,
    context_length=8192
)

# Add messages
conversation.add("user", "Hello, how are you?")
conversation.add("assistant", "I am doing well, thanks.")
conversation.add("user", "What is the weather in Tokyo?")

# Get conversation as string
print(conversation.return_history_as_string())

# Export to JSON
conversation.export_method = "json"
conversation.export()

# Load from file
conversation = Conversation.load_conversation(
    name="my-conversation",
    load_filepath="conversation_my-conversation.json"
)

# Enable token counting and context management
conversation = Conversation(
    token_count=True,
    context_length=4096,
    dynamic_context_window=True
)

# Add messages with categories for tracking
conversation.add("user", "My input", category="input")
conversation.add("assistant", "My response", category="output")

# Count tokens by category
tokens = conversation.export_and_count_categories()
print(f"Input tokens: {tokens['input_tokens']}")
print(f"Output tokens: {tokens['output_tokens']}")
print(f"Total tokens: {tokens['total_tokens']}")

# Search for messages
results = conversation.search("weather")

# Get last message
last_message = conversation.get_last_message_as_string()

# Get specific message by index
message = conversation.query(0)