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| # You can find this code for Chainlit python streaming here (https://docs.chainlit.io/concepts/streaming/python) | |
| # OpenAI Chat completion | |
| import os | |
| from openai import AsyncOpenAI # importing openai for API usage | |
| import chainlit as cl # importing chainlit for our app | |
| from chainlit.prompt import Prompt, PromptMessage # importing prompt tools | |
| from chainlit.playground.providers import ChatOpenAI # importing ChatOpenAI tools | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| # ChatOpenAI Templates | |
| system_template = """You are a versatile, knowledgeable assistant with strong capabilities in: | |
| 1. Explaining technical concepts in simple terms | |
| 2. Summarizing and extracting key information | |
| 3. Creative writing and storytelling | |
| 4. Problem-solving and logical reasoning | |
| 5. Adapting your tone and style to different contexts | |
| Always maintain a helpful, pleasant tone while providing comprehensive responses. | |
| """ | |
| # User template | |
| user_template = """{input} | |
| Think through your response step by step. | |
| """ | |
| # 1. Technical concept explanation template | |
| explanation_template = """For explaining technical concepts to beginners: | |
| - Start with a simple, jargon-free definition | |
| - Use a relatable real-world analogy or metaphor | |
| - Break down complex ideas into simpler components | |
| - Provide concrete examples that illustrate the concept | |
| - Explain practical benefits or applications | |
| - End with a memorable summary comparison | |
| Apply this approach to explain this concept: {input}""" | |
| # 2. Summarization template | |
| summary_template = """For summarization tasks: | |
| - Read the full text carefully first | |
| - Identify only the most important points (usually 3-7 key points) | |
| - Use concise, clear language | |
| - Organize points logically (chronological, importance, or topic-based) | |
| - Use bullet points for clarity and scannability | |
| - Ensure no critical information is lost | |
| - Avoid adding your own interpretations or opinions | |
| Summarize this text: {input}""" | |
| # 3. Creative writing template | |
| creative_template = """For creative writing tasks: | |
| - Create a complete story with beginning, middle, and end | |
| - Develop a clear central character with a goal or challenge | |
| - Establish a vivid setting with sensory details | |
| - Include an interesting complication or twist | |
| - Resolve the story in a satisfying way | |
| - Use descriptive language efficiently | |
| - Stick precisely to the required word count | |
| - Incorporate the specific theme or elements requested | |
| Write a creative story with these requirements: {input}""" | |
| # 4. Problem-solving template | |
| problem_solving_template = """For math or logical problems: | |
| - Read the problem carefully to identify what's being asked | |
| - List all given information and constraints | |
| - Break down the problem into smaller steps | |
| - Show your work for each step with clear explanations dont use latex | |
| - Check your solution against the original constraints | |
| - Present the final answer clearly | |
| - Verify the answer with a different approach if possible | |
| Solve this problem step by step: {input}""" | |
| # 5. Tone transformation template | |
| tone_template = """For changing the tone of text: | |
| - Identify the target tone (formal, casual, enthusiastic, etc.) | |
| - Note key characteristics of that tone (vocabulary level, sentence structure, expressions) | |
| - Preserve all important information from the original | |
| - Replace informal phrases with more formal alternatives (or vice versa) | |
| - Adjust sentence structure to match the desired tone | |
| - Revise for consistency in tone throughout | |
| - Ensure the message remains clear despite tone changes | |
| Transform this text to the specified tone: {input}""" | |
| # marks a function that will be executed at the start of a user session | |
| async def start_chat(): | |
| settings = { | |
| "model": "gpt-4o-mini", | |
| "temperature": 0, | |
| "max_tokens": 1000, | |
| "top_p": 1, | |
| "frequency_penalty": 0, | |
| "presence_penalty": 0, | |
| } | |
| cl.user_session.set("settings", settings) | |
| # marks a function that should be run each time the chatbot receives a message from a user | |
| async def main(message: cl.Message): | |
| settings = cl.user_session.get("settings").copy() | |
| client = AsyncOpenAI() | |
| print(message.content) | |
| # Detect task type and select appropriate template | |
| if any(term in message.content.lower() for term in ["explain", "concept", "explain this", "explain the concept"]): | |
| template_to_use = explanation_template | |
| # For explanations, lower temperature for clarity | |
| settings["temperature"] = 0.1 | |
| elif any(term in message.content.lower() for term in ["summary", "summarize", "key points"]): | |
| template_to_use = summary_template | |
| # For summaries, low temperature for factual accuracy | |
| settings["temperature"] = 0.1 | |
| elif any(term in message.content.lower() for term in ["story", "creative", "imaginative"]): | |
| template_to_use = creative_template | |
| # For creative writing, higher temperature | |
| settings["temperature"] = 0.7 | |
| settings["max_tokens"] = 300 # Ensure enough space for creativity | |
| elif any(term in message.content.lower() for term in ["problem", "solve", "math", "how many"]): | |
| template_to_use = problem_solving_template | |
| # For math problems, zero temperature for accuracy | |
| settings["temperature"] = 0 | |
| elif any(term in message.content.lower() for term in ["tone", "formal", "professional", "rewrite"]): | |
| template_to_use = tone_template | |
| # Moderate temperature for tone transformation | |
| settings["temperature"] = 0.3 | |
| else: | |
| # Default template if no specific type is detected | |
| template_to_use = user_template | |
| # Create prompt with the selected template | |
| prompt = Prompt( | |
| provider=ChatOpenAI.id, | |
| messages=[ | |
| PromptMessage( | |
| role="system", | |
| template=system_template, | |
| formatted=system_template, | |
| ), | |
| PromptMessage( | |
| role="user", | |
| template=template_to_use, | |
| formatted=template_to_use.format(input=message.content), | |
| ), | |
| ], | |
| inputs={"input": message.content}, | |
| settings=settings, | |
| ) | |
| print([m.to_openai() for m in prompt.messages]) | |
| msg = cl.Message(content="") | |
| # Call OpenAI | |
| async for stream_resp in await client.chat.completions.create( | |
| messages=[m.to_openai() for m in prompt.messages], stream=True, **settings | |
| ): | |
| token = stream_resp.choices[0].delta.content | |
| if not token: | |
| token = "" | |
| await msg.stream_token(token) | |
| # Update the prompt object with the completion | |
| prompt.completion = msg.content | |
| msg.prompt = prompt | |
| # Send and close the message stream | |
| await msg.send() | |