Description
Description of the bug:
When using the google-generativeai library in a Google Colab environment (Python 3.11), calling model.generate_content_async() consistently results in a TypeError: object GenerateContentResponse can't be used in 'await' expression. This error originates from within the google.ai.generativelanguage_v1beta.services.generative_service.async_client.py file, specifically at the line response = await rpc(...).
This issue persists even after:
Ensuring the latest versions of google-generativeai and its dependencies (google-ai-generativelanguage, google-api-core, grpcio, protobuf) are installed.
Performing aggressive reinstalls of these libraries (including uninstall, --force-reinstall, --no-cache-dir).
Restarting the Colab runtime after each installation/reinstallation.
Using nest_asyncio.apply() when running in a notebook environment where an event loop might already be present.
Importantly, synchronous calls using model.generate_content() work correctly, indicating that API key configuration, network access, and model accessibility are not the primary issues. The problem appears to be specific to the asynchronous pathway.
Environment
Platform: Google Colab
Python Version: 3.11 (as provided by Google Colab)
google-generativeai version: 0.8.5 (and tested with >=0.5.0)
google-ai-generativelanguage version: 0.6.15
google-api-core version: 2.24.2
grpcio version: 1.71.0
protobuf version: 5.29.4
Steps to Reproduce (Minimal Example):
Cell 1: Installation (Run this cell, then Restart Colab Runtime)
!pip uninstall google-generativeai google-ai-generativelanguage google-api-core grpcio -y
!pip install \
--upgrade \
--force-reinstall \
--no-cache-dir \
google-generativeai>=0.8.0 \
google-ai-generativelanguage \
google-api-core \
grpcio \
protobuf \
nest-asyncio \
--quiet
print("--- Installed Library Versions ---")
Cell 2: Minimal Test Code (Run this cell AFTER restarting the runtime from Cell 1)
import asyncio
import google.generativeai as genai
import os
import logging
import importlib.metadata
import nest_asyncio # Ensure nest_asyncio is imported
# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - [%(funcName)s] %(message)s")
logger = logging.getLogger(__name__)
# --- Library version check ---
try:
logger.info(f"google-generativeai version: {importlib.metadata.version('google-generativeai')}")
logger.info(f"google-ai-generativelanguage version: {importlib.metadata.version('google-ai-generativelanguage')}")
logger.info(f"google-api-core version: {importlib.metadata.version('google-api-core')}")
logger.info(f"grpcio version: {importlib.metadata.version('grpcio')}")
logger.info(f"protobuf version: {importlib.metadata.version('protobuf')}")
except importlib.metadata.PackageNotFoundError as e:
logger.warning(f"Could not find version for a package during check: {e}")
# --- Get API Key ---
GOOGLE_API_KEY = None
try:
from google.colab import userdata
GOOGLE_API_KEY = userdata.get('GOOGLE_API_KEY') # Ensure your API key is set in Colab Secrets
logger.info("Attempted to get API key from Colab userdata.")
except ImportError:
logger.info("google.colab.userdata not available. Will try environment variable.")
except Exception as e: # Catch if userdata.get fails for other reasons
logger.error(f"Error getting API key from userdata: {e}")
if not GOOGLE_API_KEY:
logger.error("GOOGLE_API_KEY not found in Colab userdata. Please set it for this test.")
# For testing, you might temporarily hardcode it here, but REMOVE IT before sharing widely.
# GOOGLE_API_KEY = "YOUR_ACTUAL_API_KEY"
# if "YOUR_ACTUAL_API_KEY" == GOOGLE_API_KEY:
# logger.warning("USING HARDCODED API KEY FOR TEST.")
# else:
# raise ValueError("API Key not set.")
# --- Synchronous Call Test ---
def synchronous_gemini_call():
if not GOOGLE_API_KEY: return "API Key not available for sync test."
try:
logger.info("Configuring Gemini for SYNCHRONOUS call.")
genai.configure(api_key=GOOGLE_API_KEY)
model = genai.GenerativeModel('gemini-1.5-flash-latest')
prompt = "Translate 'good morning' to German."
response = model.generate_content(prompt)
logger.info("SYNCHRONOUS call successful.")
return response.text
except Exception as e:
logger.error(f"Error in synchronous_gemini_call: {e}", exc_info=True)
return f"Error in synchronous call: {e}"
# --- Asynchronous Call Test ---
async def asynchronous_gemini_call():
if not GOOGLE_API_KEY: return "API Key not available for async test."
try:
logger.info("Configuring Gemini for ASYNCHRONOUS call.")
genai.configure(api_key=GOOGLE_API_KEY) # Re-configure for clarity, though it's global
model = genai.GenerativeModel('gemini-1.5-flash-latest')
prompt = "Translate 'hello world' to Spanish."
logger.info(f"Attempting ASYNCHRONOUS call with prompt: '{prompt}'")
response = await model.generate_content_async(prompt)
logger.info("ASYNCHRONOUS call successful.")
return response.text
except Exception as e:
logger.error(f"Error in asynchronous_gemini_call: {e}", exc_info=True)
raise
# --- Main execution for the cell ---
print("Starting revised minimal test execution...")
sync_success = False
print("\n--- TESTING SYNCHRONOUS CALL ---")
sync_result = "Not run (API key missing)"
if GOOGLE_API_KEY:
sync_result = synchronous_gemini_call()
if not sync_result.startswith("Error in synchronous call"):
sync_success = True
print(f"Synchronous call result: {sync_result}")
print("\n--- TESTING ASYNCHRONOUS CALL ---")
async_result = "Not run (API key missing or sync test failed)"
if GOOGLE_API_KEY and sync_success:
nest_asyncio.apply() # Apply nest_asyncio for Colab environment
logger.info("nest_asyncio applied. Attempting asyncio.run() for asynchronous_gemini_call.")
try:
async_result = asyncio.run(asynchronous_gemini_call())
except Exception as e:
logger.error(f"Asynchronous call FAILED (asyncio.run wrapper): {e}", exc_info=True)
async_result = f"Error in async call: {e}"
elif not GOOGLE_API_KEY:
print("Async test skipped: API Key missing.")
elif not sync_success:
print("Async test skipped: Sync test failed.")
print(f"Asynchronous call result: {async_result}")
print("\nRevised minimal test cell finished.")
Actual vs expected behavior:
Actual Behavior
The call to await model.generate_content_async(prompt) raises a TypeError.
Full traceback:
ERROR:main:Error in minimal_gemini_test: object GenerateContentResponse can't be used in 'await' expression
Traceback (most recent call last):
File "<ipython-input-1-19e42bece22b>", line 42, in minimal_gemini_test
response = await model.generate_content_async(prompt)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/google/generativeai/generative_models.py", line 385, in generate_content_async
response = await self._async_client.generate_content(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/google/ai/generativelanguage_v1beta/services/generative_service/async_client.py", line 440, in generate_content
response = await rpc(
^^^^^^^^^^
TypeError: object GenerateContentResponse can't be used in 'await' expression
ERROR:main:main_test caught an exception.
Minimal test finished.
Expected Behavior
The await model.generate_content_async(prompt) call should successfully complete, and response should be a GenerateContentResponse object whose attributes (e.g., response.text) can then be accessed. No TypeError should be raised during the await operation.
Any other information you'd like to share?
This issue seems specific to the asynchronous gRPC pathway in the google-ai-generativelanguage client or its interaction with the underlying google-api-core[grpc_async] and grpcio in the Colab Python 3.11 environment.
The fact that synchronous calls model.generate_content() work correctly with the same API key, model, and prompt suggests that the core communication and authentication are fine.
The problem is consistently reproducible in a standard Google Colab notebook environment using the provided steps.
A workaround using await asyncio.to_thread(model.generate_content, prompt) for the synchronous call within an async function does work, but this is not ideal and defeats the purpose of a native async method.