app.py

import asyncio
import logging
import os
import sys
import uvicorn
from fastapi import FastAPI
from schemas import _SolutionCriticismOutput, CriticizeSolutionsRequest, CritiqueResponse, RequirementInfo, ReqGroupingCategory, ReqGroupingResponse, ReqGroupingRequest, _ReqGroupingCategory, _ReqGroupingOutput, SolutionCriticism
from jinja2 import Environment, FileSystemLoader
from litellm.router import Router
from dotenv import load_dotenv

logging.basicConfig(
    level=logging.INFO,
    format='[%(asctime)s][%(levelname)s][%(filename)s:%(lineno)d]: %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S'
)

# Load .env files
load_dotenv()

if "LLM_MODEL" not in os.environ or "LLM_API_KEY" not in os.environ:
    logging.error(
        "No LLM token (`LLM_API_KEY`) and/or LLM model (`LLM_MODEL`) were provided in the env vars. Exiting")
    sys.exit(-1)

# LiteLLM router
llm_router = Router(model_list=[
    {
        "model_name": "chat",
        "litellm_params": {
            "model": os.environ.get("LLM_MODEL"),
            "api_key": os.environ.get("LLM_API_KEY"),
            "max_retries": 5
        }
    }
])

# Jinja2 environment to load prompt templates
prompt_env = Environment(loader=FileSystemLoader('prompts'), enable_async=True)

api = FastAPI()


@api.post("/categorize_requirements")
async def categorize_reqs(params: ReqGroupingRequest) -> ReqGroupingResponse:
    """Categorize the given service requirements into categories"""

    MAX_ATTEMPTS = 5

    categories: list[_ReqGroupingCategory] = []
    messages = []

    # categorize the requirements using their indices
    req_prompt = await prompt_env.get_template("classify.txt").render_async(**{
        "requirements": [rq.model_dump() for rq in params.requirements],
        "max_n_categories": params.max_n_categories,
        "response_schema": _ReqGroupingOutput.model_json_schema()})

    # add system prompt with requirements
    messages.append({"role": "user", "content": req_prompt})

    # ensure all requirements items are processed
    for attempt in range(MAX_ATTEMPTS):
        req_completion = await llm_router.acompletion(model="chat", messages=messages, response_format=_ReqGroupingOutput)
        output = _ReqGroupingOutput.model_validate_json(
            req_completion.choices[0].message.content)

        # # quick check to ensure no requirement was left out by the LLM by checking all IDs are contained in at least a single category
        valid_ids_universe = set(range(1, len(params.requirements)))
        assigned_ids = {
            req_id for cat in output.categories for req_id in cat.items}

        # keep only non-hallucinated, valid assigned ids
        valid_assigned_ids = assigned_ids.intersection(valid_ids_universe)

        # check for remaining requirements assigned to none of the categories
        unassigned_ids = valid_ids_universe - valid_assigned_ids

        if len(unassigned_ids) == 0:
            categories.extend(output.categories)
            break
        else:
            messages.append(req_completion.choices[0].message)
            messages.append(
                {"role": "user", "content": f"You haven't categorized the following requirements in at least one category {unassigned_ids}. Please do so."})

            if attempt == MAX_ATTEMPTS - 1:
                raise Exception("Failed to classify all requirements")

    # build the final category objects
    # remove the invalid (likely hallucinated) requirement IDs
    final_categories = []
    for idx, cat in enumerate(output.categories):
        final_categories.append(ReqGroupingCategory(
            id=idx,
            title=cat.title,
            requirements=[params.requirements[i]
                          for i in cat.items if i < len(params.requirements)]
        ))

    return ReqGroupingResponse(categories=final_categories)


@api.post("/criticize_solution")
async def criticize_solution(params: CriticizeSolutionsRequest) -> CritiqueResponse:
    """Criticize the challenges, weaknesses and limitations of the provided solutions."""
    req_prompt = await prompt_env.get_template("criticize.txt").render_async(**{
        "solutions": [sol.model_dump() for sol in params.solutions],
        "response_schema": _SolutionCriticismOutput.model_json_schema()
    })
    req_completion = await llm_router.acompletion(model="chat", messages=[{"role": "user", "content": req_prompt}], response_format=_SolutionCriticismOutput)
    criticism_out = _SolutionCriticismOutput.model_validate_json(
        req_completion.choices[0].message.content)

    return CritiqueResponse(critiques=[
        SolutionCriticism(solution=sol, criticism=crit) for (sol, crit) in zip(params.solutions, criticism_out.criticisms)
    ])

uvicorn.run(api, host="0.0.0.0", port=8000)