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Chats enable multi-turn conversations where the model remembers previous messages and can build on the conversation context. This is perfect for building chatbots, assistants, and interactive applications.
Creating a Chat Session
Create a chat session using client.chats.create() to start a conversation:
chat = client.chats.create(model='gemini-2.5-flash')
Synchronous Non-Streaming
Send messages and receive complete responses:
chat = client.chats.create(model='gemini-2.5-flash')
response = chat.send_message('tell me a story')
print(response.text)
response = chat.send_message('summarize the story you told me in 1 sentence')
print(response.text)
send_message() retains the conversation context, so the model can reference previous messages.
Synchronous Streaming
Stream responses as they’re generated for real-time user experiences:
chat = client.chats.create(model='gemini-2.5-flash')
for chunk in chat.send_message_stream('tell me a story'):
print(chunk.text, end='')
Asynchronous Non-Streaming
Use async/await for non-blocking chat operations:
import asyncio
async def chat_example():
chat = client.aio.chats.create(model='gemini-2.5-flash')
response = await chat.send_message('tell me a story')
print(response.text)
response = await chat.send_message('summarize the story you told me in 1 sentence')
print(response.text)
asyncio.run(chat_example())
Asynchronous Streaming
Combine async operations with streaming for the best performance:
import asyncio
async def chat_stream_example():
chat = client.aio.chats.create(model='gemini-2.5-flash')
async for chunk in await chat.send_message_stream('tell me a story'):
print(chunk.text, end='')
asyncio.run(chat_stream_example())
Context Retention
Chat sessions automatically maintain conversation history. The model can:
- Reference previous messages
- Build on earlier responses
- Maintain consistency across turns
- Use pronouns and context from prior exchanges
chat = client.chats.create(model='gemini-2.5-flash')
# First turn
response = chat.send_message('My name is Alice and I love hiking.')
print(response.text) # Model acknowledges the information
# Second turn - model remembers your name
response = chat.send_message('What was my name again?')
print(response.text) # "Your name is Alice"
# Third turn - model remembers your interests
response = chat.send_message('Recommend an activity for me.')
print(response.text) # Model suggests hiking-related activities
Configuration Options
You can configure chat sessions with the same options as generate_content:
from google.genai import types
chat = client.chats.create(
model='gemini-2.5-flash',
config=types.GenerateContentConfig(
temperature=0.7,
max_output_tokens=1024,
system_instruction='You are a helpful assistant that speaks concisely.',
)
)
response = chat.send_message('tell me about Python')
print(response.text)
Accessing Chat History
You can access the full conversation history from a chat session:
chat = client.chats.create(model='gemini-2.5-flash')
chat.send_message('Hello!')
chat.send_message('How are you?')
# Access the conversation history
for message in chat.history:
print(f"{message.role}: {message.parts[0].text}")
Best Practices
- Use streaming for better user experience in chat interfaces
- Use async for handling multiple concurrent chat sessions
- Set system instructions to define the assistant’s personality and behavior
- Monitor context length - long conversations may exceed token limits
- Create new sessions when starting a new topic or conversation
Common Patterns
Chat with History Clearing
chat = client.chats.create(model='gemini-2.5-flash')
# Have a conversation
chat.send_message('Remember this number: 42')
chat.send_message('What number did I tell you?') # Returns 42
# Start fresh by creating a new chat session
chat = client.chats.create(model='gemini-2.5-flash')
chat.send_message('What number did I tell you?') # No memory of 42
Chat with Function Calling
from google.genai import types
def get_weather(location: str) -> str:
"""Get the current weather for a location."""
return "sunny"
chat = client.chats.create(
model='gemini-2.5-flash',
config=types.GenerateContentConfig(
tools=[get_weather],
)
)
response = chat.send_message('What is the weather in Boston?')
print(response.text)
response = chat.send_message('How about in New York?')
print(response.text)