routes/llm.py

import logging
import os
from typing import Annotated, Optional
from uuid import UUID

from components.services.dataset import DatasetService
from components.services.entity import EntityService
from fastapi import APIRouter, Depends, HTTPException

import common.dependencies as DI
from common.configuration import Configuration, Query
from components.llm.common import ChatRequest, LlmParams, LlmPredictParams, Message
from components.llm.deepinfra_api import DeepInfraApi
from components.llm.utils import append_llm_response_to_history
from components.services.llm_config import LLMConfigService
from components.services.llm_prompt import LlmPromptService

router = APIRouter(prefix='/llm')
logger = logging.getLogger(__name__)

conf = DI.get_config()
llm_params = LlmParams(
    **{
        "url": conf.llm_config.base_url,
        "model": conf.llm_config.model,
        "tokenizer": "unsloth/Llama-3.3-70B-Instruct",
        "type": "deepinfra",
        "default": True,
        "predict_params": LlmPredictParams(
            temperature=0.15,
            top_p=0.95,
            min_p=0.05,
            seed=42,
            repetition_penalty=1.2,
            presence_penalty=1.1,
            n_predict=2000,
        ),
        "api_key": os.environ.get(conf.llm_config.api_key_env),
        "context_length": 128000,
    }
)
# TODO: унести в DI
llm_api = DeepInfraApi(params=llm_params)


@router.post("/chat")
async def chat(
    request: ChatRequest,
    config: Annotated[Configuration, Depends(DI.get_config)],
    llm_api: Annotated[DeepInfraApi, Depends(DI.get_llm_service)],
    prompt_service: Annotated[LlmPromptService, Depends(DI.get_llm_prompt_service)],
    llm_config_service: Annotated[LLMConfigService, Depends(DI.get_llm_config_service)],
    entity_service: Annotated[EntityService, Depends(DI.get_entity_service)],
    dataset_service: Annotated[DatasetService, Depends(DI.get_dataset_service)],
):
    try:
        p = llm_config_service.get_default()
        system_prompt = prompt_service.get_default()

        predict_params = LlmPredictParams(
            temperature=p.temperature,
            top_p=p.top_p,
            min_p=p.min_p,
            seed=p.seed,
            frequency_penalty=p.frequency_penalty,
            presence_penalty=p.presence_penalty,
            n_predict=p.n_predict,
            stop=[],
        )

        # TODO: Вынести
        def get_last_user_message(chat_request: ChatRequest) -> Optional[Message]:
            return next(
                (
                    msg
                    for msg in reversed(chat_request.history)
                    if msg.role == "user"
                    and (msg.searchResults is None or not msg.searchResults)
                ),
                None,
            )

        def insert_search_results_to_message(
            chat_request: ChatRequest, new_content: str
        ) -> bool:
            for msg in reversed(chat_request.history):
                if msg.role == "user" and (
                    msg.searchResults is None or not msg.searchResults
                ):
                    msg.content = new_content
                    return True
            return False

        last_query = get_last_user_message(request)
        search_result = None
        
        logger.info(f"last_query: {last_query}")

        if last_query:
            dataset = dataset_service.get_current_dataset()
            if dataset is None:
                raise HTTPException(status_code=400, detail="Dataset not found")
            logger.info(f"last_query: {last_query.content}")
            _, scores, chunk_ids = entity_service.search_similar(last_query.content, dataset.id)
            
            chunks = entity_service.chunk_repository.get_chunks_by_ids(chunk_ids)
            
            logger.info(f"chunk_ids: {chunk_ids[:3]}...{chunk_ids[-3:]}")
            logger.info(f"scores: {scores[:3]}...{scores[-3:]}")
            
            text_chunks = entity_service.build_text(chunks, scores)
            
            logger.info(f"text_chunks: {text_chunks[:3]}...{text_chunks[-3:]}")

            new_message = f'{last_query.content} /n<search-results>/n{text_chunks}/n</search-results>'
            insert_search_results_to_message(request, new_message)
            
        logger.info(f"request: {request}")

        response = await llm_api.predict_chat_stream(
            request, system_prompt.text, predict_params
        )
        result = append_llm_response_to_history(request, response)
        return result
    except Exception as e:
        logger.error(
            f"Error processing LLM request: {str(e)}", stack_info=True, stacklevel=10
        )
        return {"error": str(e)}