pip3 install openai, statsig
```text
After that, we can begin coding in a python file:
```python
import openai
from statsig import statsig, StatsigEvent, StatsigUser
import time
openai.api_key = "your_openai_key" # Replace with your own key
statsig.initialize("your_statsig_secret") # Replace with your Statsig secret
user = StatsigUser("user-id") #This is a placeholder ID - in a normal experiment Statsig recommends using a user's actual unique ID for consistency in targeting. See https://docs.statsig.com/concepts/user
```javascript
### The ask_question Function
The following code all occurs in one function titled ask_question (see the [final code](#final-code))
1. Get User Input
```python
#ask the user for a question to query GPT with
question = input("\nWhat is your question? ")
```text
First, we prompt the user with the question they'd like to ask ChatGPT
2. Query OpenAI's GPT
```python
#track the start time so we can check response time later
start_time = time.time()
completion = openai.ChatCompletion.create(
model=statsig.get_experiment(user, "statsig_openai_integration").get("model", 'gpt-4'),
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": question}
]
)
```text
Next, we request a completion that queries the GPT model specified by the Statsig experiment (either gpt-3.5-turbo or gpt-4). We also start a timer so we can track the response time in our events later on.
3. Display Response & Log Implicit Feedback
```python
#log "implicit" indicators to Statsig
c = completion.choices[0]
stats = completion.usage #completion object has the number of tokens used - which is that what GPT usage is charged on.
statsig.log_event(StatsigEvent(user, "chat_completion", value=c.finish_reason, metadata={"response_time": time.time() - start_time, "completion_tokens": stats["completion_tokens"], "prompt_tokens": stats["prompt_tokens"], "total_tokens": stats["total_tokens"]}))
#print the message back to the user
print(f"\nAnswer: {c.message['content']}")
```python
With the response from OpenAI we have our first set of useful information to log to Statsig - like the response time and tokens used. We log this information with Statsig's SDK, storing them in the metadata of the request.
4. Collect User Feedback & Log to Statsig
```python
#track explicit feedback: if the user is satisfied with the answer
satisfaction = input("\nDid this answer your question? (y/n): ")
# Log "explicit" indicators to Statsig
if satisfaction == 'y':
statsig.log_event(StatsigEvent(user, "satisfaction"))
elif satisfaction == 'n':
statsig.log_event(StatsigEvent(user, "dissatisfaction"))
```text
Next, we log a more explicit indicator of feedback, the user's self-reported satisfaction or dissatisfaction. The satisfaction metric can provide a strong indicator of the model's overall power.
And we're done - we can run this Python program with the following code, now outside of our ask_question function.
```python
if __name__ == "__main__":
while input("Would you like to ask a question? (y/n): ").lower() == 'y':
ask_question()
```sql
## Tips for Using Statsig with AI
1. Experimentation: You can also test other model parameters like temperature, top_p, or initial prompts.
2. Log Useful Data: Consider logging other interesting user interactions or feedback.
3. Analyze and Iterate: After collecting enough data, analyze the results on the Statsig dashboard.
4. User Identification: Consider integrating a mechanism to uniquely identify each user/session.
<Note>
Always ensure you're in compliance with user privacy regulations and that you have user consent where necessary.
</Note>
### Useful Links
- [OpenAI Documentation](https://platform.openai.com/docs/api-reference/chat/create)
## Final code
```python
import openai
from statsig import statsig, StatsigEvent, StatsigUser
import time
openai.api_key = "your_openai_key"
statsig.initialize("your_statsig_secret")
user = StatsigUser("user-id") #This is a placeholder ID - in a normal experiment Statsig recommends using a user's actual unique ID for consistency in targeting. See https://docs.statsig.com/concepts/user
def ask_question():
#ask the user for a question to query GPT with
question = input("\nWhat is your question? ")
#track the start time so we can check response time later
start_time = time.time()
#query GPT with the OpenAI Python library for chat completions
completion = openai.ChatCompletion.create(
model=statsig.get_experiment(user, "statsig_openai_collab").get("model", 'gpt-4'), #experiment is setup to return either "gpt-3.5-turbo" or "gpt-4".
#other than varying the model selected, other attributes could be varied like "temperature", "top_p", "presence_penalty" and more.
#See the "Create chat completions" section of the OpenAI documentation for more: https://platform.openai.com/docs/api-reference/chat/create
messages=[
{"role": "system", "content": "You are a helpful assistant."}, #Initial prompts are another candidate for experimentation
{"role": "user", "content": question}
]
)
#log "implicit" indicators to Statsig
c = completion.choices[0] #we've only requested one choice, so selecting the first
stats = completion.usage #completion object has the number of tokens used - which is that what GPT usage is charged on.
statsig.log_event(StatsigEvent(user, "chat_completion", value=c.finish_reason, metadata={"response_time": time.time() - start_time, "completion_tokens": stats["completion_tokens"], "prompt_tokens": stats["prompt_tokens"], "total_tokens": stats["total_tokens"]}))
#print the message back to the user
print(f"\nAnswer: {c.message['content']}")
#track explicit feedback: if the user is satisfied with the answer
satisfaction = input("\nDid this answer your question? (y/n): ")
#log "explicit" indicators to Statsig
if satisfaction == 'y':
statsig.log_event(StatsigEvent(user, "satisfaction"))
elif satisfaction == 'n':
statsig.log_event(StatsigEvent(user, "dissatisfaction"))
if __name__ == "__main__":
#Let the user abandon the question-asking process when they are done
while input("Would you like to ask a question? (y/n): ").lower() == 'y':
ask_question()
