LLM Chat Applications with Declarai, FastAPI, and Streamlit — Part 2 🚀

Author(s): Matan Kleyman

Originally published on Towards AI.

LLM Chat Applications with Declarai, FastAPI, and Streamlit — Part 2 U+1F680

Following the popularity of our previous Medium article (link U+1F517), which delved into deploying LLM Chat applications, we’ve taken your feedback on board. This second part introduces more advanced features, with a particular emphasis on streaming for chat applications.

Many of our readers expressed a desire for a streaming feature for chats. Streaming becomes crucial when dealing with extensive responses. Instead of making users wait for the entire response, streaming allows the reply to be sent as it’s being generated U+1F504. This ensures that users can access it instantly upon being dispatched from the webserver.

Thanks to recent enhancements in Declarai, we’re excited to leverage streaming, now available from version 0.1.10 onwards U+1F389.

Streaming in Action U+1F4F9

As demonstrated, streaming offers enhanced responsiveness and has become a standard in recent AI chat applications. Are you eager to see how we achieved this? Let’s dive into the code:

Declarai U+1F4BB

To utilize streaming, ensure the chatbot is declared with the streaming feature enabled by passing streaming=True .

import declarai

gpt_35 = declarai.openai(model="gpt-3.5-turbo")

@gpt_35.experimental.chat(streaming=True)
class StreamingSQLChat:
 """
 You are a sql assistant. You are helping a user to write a sql query.
 You should first know what sql syntax the user wants to use. It can be mysql, postgresql, sqllite, etc.
 If the user says something that is completely not related to SQL, you should say "I don't understand. I'm here to help you write a SQL query."
 After you provide the user with a query, you should ask the user if they need anything else.
 """
 greeting = "Hey dear SQL User. Hope you are doing well today. I am here to help you write a SQL query. Let's get started!. What SQL syntax would you like to use? It can be mysql, postgresql, sqllite, etc."

When interacting with the chatbot using streaming:

chat = StreamingSQLChat()

res = chat.send("I want to use mysql")

for chunk in res:
 print(chunk.response)


>>> Great
>>> Great!
>>> Great! MySQL
>>> Great! MySQL is
>>> Great! MySQL is a
>>> Great! MySQL is a popular
>>> Great! MySQL is a popular choice
>>> Great! MySQL is a popular choice for
>>> Great! MySQL is a popular choice for SQL
>>> Great! MySQL is a popular choice for SQL syntax
>>> Great! MySQL is a popular choice for SQL syntax.
>>> Great! MySQL is a popular choice for SQL syntax. How
>>> Great! MySQL is a popular choice for SQL syntax. How can
>>> Great! MySQL is a popular choice for SQL syntax. How can I
>>> Great! MySQL is a popular choice for SQL syntax. How can I assist
>>> Great! MySQL is a popular choice for SQL syntax. How can I assist you
>>> Great! MySQL is a popular choice for SQL syntax. How can I assist you with
>>> Great! MySQL is a popular choice for SQL syntax. How can I assist you with your
>>> Great! MySQL is a popular choice for SQL syntax. How can I assist you with your MySQL
>>> Great! MySQL is a popular choice for SQL syntax. How can I assist you with your MySQL query
>>> Great! MySQL is a popular choice for SQL syntax. How can I assist you with your MySQL query?
>>> Great! MySQL is a popular choice for SQL syntax. How can I assist you with your MySQL query?

What’s happening behind the scenes? By enabling streaming, OpenAI returns a generator, which produces items one at a time and only when required. Thus, as soon as a new chunk is available from OpenAI, it’s immediately processed.

FastAPI U+1F6E0️

For the streaming capability, we’ve introduced a distinct REST endpoint. This endpoint dispatches a StreamingResponse — essentially a generator where each chunk is a JSON containing the chunk’s details. It’s essential to specify the media_type as media_type="text/event-stream" which states the response type is of type stream.

@router.post("/chat/submit/{chat_id}/streaming")
def submit_chat_streaming(chat_id: str, request: str):
 chat = StreamingSQLChat(chat_history=FileMessageHistory(file_path=chat_id))
 response = chat.send(request)

 def stream():
 for llm_response in response:
 # Convert the LLMResponse to a JSON string
 data = json.dumps(jsonable_encoder(llm_response))
 yield data + "\n" # Yielding as newline-separated JSON strings

 return StreamingResponse(stream(), media_type="text/event-stream")

Streamlit App

On the front end, our goal is to display each new response chunk to the user as it’s received. This requires accessing the delta in response between each chunk of the response.

Here’s how it’s accomplished:

1. Start with an HTTP POST request with stream=True.
2. Iterate through each chunk and decode its JSON.
3. Extract the deltavalue and add it to the full response. This is basically the newly generated values.
4. Update the UI with the accumulated response.

res = requests.post(f"{BACKEND_URL}/api/v1/chat/submit/{session_name}/streaming",
 params={"request": prompt},
 stream=True)
with st.chat_message("assistant"):
 message_placeholder = st.empty()
 full_response = ""

 buffer = ""
 for chunk in res:
 decoded_chunk = chunk.decode('utf-8')
 buffer += decoded_chunk

 while "\n" in buffer:
 line, buffer = buffer.split("\n", 1)
 parsed_chunk = json.loads(line.strip())
 try:
 full_response += parsed_chunk["raw_response"]["choices"][0]["delta"]["content"]
 message_placeholder.markdown(full_response + "▌")
 except KeyError:
 pass

 message_placeholder.markdown(full_response)

Ready to deploy your streaming chatbot U+1F916? Dive into the complete code in this repository —

Stay in touch with Declarai developments U+1F48C. Connect with us on Linkedin Page , and give us a star ⭐️ on GitHub if you find our tools valuable!

You can also dive deeper into Declarai’s capabilities by exploring our documentation U+1F4D6

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Published via Towards AI