Streaming Outputs with the Foundation Model API

This notebook covers how to stream responses from chat completion or text completion models. Streaming enables us to begin returning tokens as they are generated, before the entire response is complete. Streaming can dramatically reduce the time it takes for users to see a response.

%pip install --upgrade --force-reinstall databricks-genai-inference
dbutils.library.restartPython()

Streaming with the Python SDK

To enable streaming, include the argument stream=True in calls to chat completion or text completion endpoints. Here’s how to do it with the ChatCompletion.create method in the Python SDK:

from databricks_genai_inference import ChatCompletion

def stream_chat_reply(model, user_message, max_tokens=2048):
    messages = [{"role": "user", "content": user_message}]
    generator = ChatCompletion.create(
        model=model,
        max_tokens=max_tokens,
        stream=True,
        messages=messages,
    )

    for response in generator:
        # Using `end=''` to avoid adding a new line after each print statement
        print(response.message, end='')

With streaming enabled, the create method returns a generator that provides a sequence of ChatCompletionChunkObjects. The completion itself is included in the ChatCompletionChunkObject.message attribute, so we can iterate over generator and print each message in order to stream the results to the user.

stream_reply(model="mixtral-8x7b-instruct", user_message="Tell me in detail how to make chocolate chip cookies.")

1. Gather ingredients: 
   - 2 1/4 cups all-purpose flour
   - 1/2 teaspoon baking soda
   - 1 cup unsalted butter, room temperature
   - 1/2 cup granulated sugar
   - 1 cup packed light-brown sugar
   - 1 teaspoon salt
   - 2 teaspoons pure vanilla extract
   - 2 large eggs
   - 2 cups semisweet and/or milk chocolate chips

2. Preheat oven to 350°F (180°C). In a small bowl, whisk together flour and baking soda; set aside.

3. In the bowl of an electric mixer fitted with the paddle attachment, combine butter with both sugars; beat on medium speed until light and fluffy.

4. Reduce speed to low; add salt, vanilla, and eggs. Beat until well mixed, about 1 minute.

5. Add flour mixture; mix until just combined. Stir in chocolate chips.

6. Drop heaping tablespoon-size balls of dough about 2 inches apart on baking sheets lined with parchment paper.

7. Bake until cookies are golden around the edges, but still soft in the center, 8 to 10 minutes.

8. Remove from oven, and let cookies cool on baking sheet for 2 minutes. Transfer cookies to a wire rack to cool completely.

Tips:
- For a chewy cookie, take them out of the oven when they are slightly undercooked and let them finish cooking on the baking sheet.
- You can add other mix-ins like nuts or dried fruit if desired.
- Always preheat your oven before baking.
- Make sure your butter is at room temperature to ensure proper creaming with the sugars.
- Do not overmix the dough as it can lead to tough cookies.
- Store cooled cookies in an airtight container at room temperature for up to 1 week.

This works in much the same way for the text completion endpoint.

from databricks_genai_inference import Completion

def stream_text_reply(model, user_prompt, max_tokens=2048):
    generator = Completion.create(
        model=model,
        max_tokens=max_tokens,
        stream=True,
        prompt=user_prompt,
    )

    for response in generator:
        # Using `end=''` to avoid adding a new line after each print statement
        print(response.text, end='')

stream_text_reply(model="mpt-30b-instruct",
                  user_prompt="Tell me in detail how to make chocolate chip cookies.")

Ah, this is a very popular recipe.  Here are the basic ingredients, which you can always modify to your own tastes:
1 cup of butter, softened
1 cup of brown sugar
2 eggs
1 tsp vanilla
2 1/4 cups of flour
1 tsp of baking soda
1/4 tsp of salt
2 cups of chocolate chips

Some additional tips:  be sure to use softened butter and not melted butter, and also be sure to use a high-quality chocolate chip.  Also, be sure to cream the butter and sugar together well, and also be sure to mix in the eggs and vanilla well.  For the flour, baking soda, and salt, you want to mix them together dry, and then add them to the wet ingredients.  Finally, I recommend using a spoon to mix in the chocolate chips, since that gives a nice, even mixture.

Now, for baking, you want to preheat the oven to 375, and then spoon cookie dough onto a baking sheet, leaving about 2 inches of space between cookies.  You may want to use a cookie scoop for this, or you may want to just eyeball it.  You want to bake these cookies

Streaming responses with the REST API

Including "stream": true in the REST API request will result in series of ChatCompletionChunk objects when calling a Chat model endpoint.

%sh
curl \
-u token:$DATABRICKS_TOKEN \
-X POST \
-H "Content-Type: application/json" \
-d '{
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "Tell me in detail how to make chocolate chip cookies."
    }
  ],
  "max_tokens": 2048,
  "stream": true
}' \
https://e2-dogfood.staging.cloud.databricks.com/serving-endpoints/databricks-mixtral-8x7b-instruct/invocations
  

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed

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data: {"id":"604bbef0-44ac-4835-be63-70a4c0870278","object":"chat.completion.chunk","created":1707146947,"model":"mixtral-8x7b-instruct-v0.1","choices":[{"index":0,"delta":{"role":"assistant","content":" and"},"finish_reason":null}],"usage":{"prompt_tokens":28,"completion_tokens":701,"total_tokens":729}}

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data: {"id":"604bbef0-44ac-4835-be63-70a4c0870278","object":"chat.completion.chunk","created":1707146947,"model":"mixtral-8x7b-instruct-v0.1","choices":[{"index":0,"delta":{"role":"assistant","content":" as"},"finish_reason":null}],"usage":{"prompt_tokens":28,"completion_tokens":709,"total_tokens":737}}

data: {"id":"604bbef0-44ac-4835-be63-70a4c0870278","object":"chat.completion.chunk","created":1707146947,"model":"mixtral-8x7b-instruct-v0.1","choices":[{"index":0,"delta":{"role":"assistant","content":" they"},"finish_reason":null}],"usage":{"prompt_tokens":28,"completion_tokens":710,"total_tokens":738}}

data: {"id":"604bbef0-44ac-4835-be63-70a4c0870278","object":"chat.completion.chunk","created":1707146947,"model":"mixtral-8x7b-instruct-v0.1","choices":[{"index":0,"delta":{"role":"assistant","content":" are"},"finish_reason":null}],"usage":{"prompt_tokens":28,"completion_tokens":711,"total_tokens":739}}

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data: {"id":"604bbef0-44ac-4835-be63-70a4c0870278","object":"chat.completion.chunk","created":1707146947,"model":"mixtral-8x7b-instruct-v0.1","choices":[{"index":0,"delta":{"role":"assistant","content":" that"},"finish_reason":null}],"usage":{"prompt_tokens":28,"completion_tokens":721,"total_tokens":749}}

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100   254    0     0  100   254      0   1242 --:--:-- --:--:-- --:--:--  1239
100 29181    0 28927  100   254  25051    219  0:00:01  0:00:01 --:--:-- 25264
100 60401    0 60147  100   254  27892    117  0:00:02  0:00:02 --:--:-- 28002
100 91692    0 91438  100   254  28936     80  0:00:03  0:00:03 --:--:-- 29016
100  119k    0  119k  100   254  29412     61  0:00:04  0:00:04 --:--:-- 29472
100  149k    0  149k  100   254  29699     49  0:00:05  0:00:05 --:--:-- 30924
100  179k    0  179k  100   254  29863     41  0:00:06  0:00:06 --:--:-- 30975
100  209k    0  209k  100   254  29952     35  0:00:07  0:00:07 --:--:-- 30838
100  219k    0  219k  100   254  29995     33  0:00:07  0:00:07 --:--:-- 30770

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data: [DONE]

Calling on a completion model will return a series of text_completions.

%sh
curl \
 -u token:$DATABRICKS_TOKEN \
 -X POST \
 -H "Content-Type: application/json" \
 -d '{"prompt": "Tell me in detail how to make chocolate chip cookies.",
      "stream": true}' \
https://e2-dogfood.staging.cloud.databricks.com/serving-endpoints/databricks-mpt-30b-instruct/invocations

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed

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data: [DONE]

Conclusion

You are now ready to integrate streaming outputs into your next project with the Databricks Foundation Model API!

See the Databricks Foundation Model APIs Documentation for more detail.