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import chainlit as cl
from pydantic import BaseModel, Field
from dotenv import load_dotenv
import instructor
from openai import OpenAI
import os
load_dotenv()
# Patch the OpenAI client with Instructor
client = instructor.from_openai(OpenAI(api_key=os.environ['OPENAI_API_KEY']))
# Define the Pydantic models
class UserProposal(BaseModel):
proposal: str = Field(description="This is the proposal of the original user prompt. It should be a clear concise detailed plan to use simple ai software tools to solve specific problem.")
is_clear: str = Field(description="Is the proposed plan clear? It specifies which tools it needs to use and how. It lays out each component and how they all connect.")
is_detailed: str = Field(description="Is the proposed plan detailed? Each component should have a description of what it does.")
is_explicit: str = Field(description="Is the proposed plan explicit? Each component should have a data model to describe their input and output schema.")
class ProposedArchitecture(BaseModel):
proposed_architecture: str = Field(description="A detailed AI application architecture with all the tools required for the plan proposed. (e.g. Python packages)")
class PropositionWithRevision(BaseModel):
revised_proposed_architecture: str = Field(description="Step by step implementation of software solution.")
# Define functions
def extract_user_proposal_details(user_proposal: str) -> UserProposal:
return client.chat.completions.create(
model="gpt-4-turbo-preview",
response_model=UserProposal,
messages=[
{"role": "user", "content": user_proposal},
],
)
def generate_proposed_architecture(proposal: str) -> ProposedArchitecture:
return client.chat.completions.create(
model="gpt-4-turbo-preview",
response_model=ProposedArchitecture,
messages=[
{"role": "user", "content": f"Write a detailed AI application architecture with all the tools required for the plan proposed: \n\n{proposal}"},
],
)
def revise_architecture(proposed_architecture: str) -> PropositionWithRevision:
return client.chat.completions.create(
model="gpt-4-turbo-preview",
response_model=PropositionWithRevision,
messages=[
{"role": "user", "content": f"Revise the plan proposed: \n\n{proposed_architecture}\n\nThe plan should be a step by step implementation of software solution."},
],
)
# Define the Chainlit message handler
@cl.on_message
async def main(message: cl.Message):
user_proposal = message.content
user_proposal_details = extract_user_proposal_details(user_proposal)
proposed_architecture = generate_proposed_architecture(user_proposal_details.proposal)
await cl.Message(
content=f"Proposed Architecture:\n{proposed_architecture.proposed_architecture}"
).send()
feedback_message = await cl.AskUserMessage(content="What do you think about this proposed plan and alleged architecture?", timeout=300).send()
if feedback_message:
human_feedback_of_proposed_plan = feedback_message['output']
revised_architecture = revise_architecture(proposed_architecture.proposed_architecture)
await cl.Message(
content=f"Revised Architecture:\n{revised_architecture.revised_proposed_architecture}"
).send()
with open("output.md", "w") as output_file:
output_file.write("# User Proposal\n")
output_file.write(user_proposal_details.proposal + "\n\n")
output_file.write("# Proposed Architecture\n")
output_file.write(proposed_architecture.proposed_architecture + "\n\n")
output_file.write("# Revised Architecture\n")
output_file.write(revised_architecture.revised_proposed_architecture + "\n")
await cl.Message(
content="The results have been saved to output.md"
).send()
else:
await cl.Message(
content="No feedback received. Exiting."
).send()
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