vk_hackathon/index/main.py

import asyncio
import logging
import os
from functools import lru_cache
from typing import Any

from fastapi import FastAPI, Request
from fastapi.exceptions import RequestValidationError
from fastapi.responses import JSONResponse
from pydantic import BaseModel

HOST = os.getenv("HOST", "0.0.0.0")
PORT = int(os.getenv("PORT", "8000"))
UVICORN_WORKERS = 8

logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
logger = logging.getLogger("index-service")


class Chat(BaseModel):
    id: str
    name: str
    sn: str
    type: str
    is_public: bool | None = None
    members_count: int | None = None
    members: list[dict[str, Any]] | None = None


class Message(BaseModel):
    id: str
    thread_sn: str | None = None
    time: int
    text: str
    sender_id: str
    file_snippets: str
    parts: list[dict[str, Any]] | None = None
    mentions: list[str] | None = None
    member_event: dict[str, Any] | None = None
    is_system: bool
    is_hidden: bool
    is_forward: bool
    is_quote: bool


class ChatData(BaseModel):
    chat: Chat
    overlap_messages: list[Message]
    new_messages: list[Message]


class IndexAPIRequest(BaseModel):
    data: ChatData


class IndexAPIItem(BaseModel):
    page_content: str
    dense_content: str
    sparse_content: str
    message_ids: list[str]


class IndexAPIResponse(BaseModel):
    results: list[IndexAPIItem]


class SparseEmbeddingRequest(BaseModel):
    texts: list[str]


class SparseVector(BaseModel):
    indices: list[int]
    values: list[float]


CHUNK_SIZE = 256
OVERLAP_SIZE = 128
SPARSE_MODEL_NAME = "Qdrant/bm25"
FASTEMBED_CACHE_PATH = "/models/fastembed"


def render_message(message: Message) -> str:
    parts_list: list[str] = []

    if message.sender_id:
        sender_name = message.sender_id.split("@")[0].replace(".", " ")
        parts_list.append(f"[{sender_name}]:")

    if message.text:
        parts_list.append(message.text)

    if message.parts:
        for part in message.parts:
            media_type = part.get("mediaType", "text")
            part_text = part.get("text")
            if isinstance(part_text, str) and part_text:
                if media_type == "forward":
                    parts_list.append(f"[Пересланное]: {part_text}")
                elif media_type == "quote":
                    parts_list.append(f"[Цитата]: {part_text}")
                else:
                    parts_list.append(part_text)

    if message.file_snippets:
        parts_list.append(f"[Файл]: {message.file_snippets}")

    return " ".join(parts_list).strip()


def build_chunks(
    chat: Chat,
    overlap_messages: list[Message],
    new_messages: list[Message],
) -> list[IndexAPIItem]:
    new_messages = [m for m in new_messages if not m.is_system and not m.is_hidden]
    overlap_messages = [m for m in overlap_messages if not m.is_system and not m.is_hidden]

    result: list[IndexAPIItem] = []

    def build_text_and_ranges(messages: list[Message]) -> tuple[str, list[tuple[int, int, str]]]:
        text_parts: list[str] = []
        message_ranges: list[tuple[int, int, str]] = []
        position = 0

        for index, message in enumerate(messages):
            text = render_message(message)
            if not text:
                continue

            if index > 0 and text_parts:
                text_parts.append("\n")
                position += 1

            start = position
            text_parts.append(text)
            position += len(text)
            message_ranges.append((start, position, message.id))

        return "".join(text_parts), message_ranges

    def slice_tail(text: str, tail_size: int) -> str:
        if tail_size <= 0:
            return ""
        tail_start = max(0, len(text) - tail_size)
        return text[tail_start:]

    overlap_text, _ = build_text_and_ranges(overlap_messages)
    previous_chunk_text = slice_tail(overlap_text, OVERLAP_SIZE)

    new_text, new_message_ranges = build_text_and_ranges(new_messages)

    for start in range(0, len(new_text), CHUNK_SIZE):
        chunk_body = new_text[start: start + CHUNK_SIZE]
        if not chunk_body:
            continue

        chunk_body_ranges = [
            (
                max(message_start, start) - start,
                min(message_end, start + len(chunk_body)) - start,
                message_id,
            )
            for message_start, message_end, message_id in new_message_ranges
            if message_end > start and message_start < start + len(chunk_body)
        ]

        chunk_overlap = previous_chunk_text
        chunk_text = chunk_overlap
        if chunk_text and chunk_body:
            chunk_text += "\n"
        chunk_text += chunk_body

        dense_text = f"[{chat.name}] {chunk_text}"
        sparse_text = chunk_body

        result.append(
            IndexAPIItem(
                page_content=chunk_text,
                dense_content=dense_text,
                sparse_content=sparse_text,
                message_ids=[message_id for _, _, message_id in chunk_body_ranges],
            )
        )
        previous_chunk_text = slice_tail(chunk_text, OVERLAP_SIZE)

    return result


app = FastAPI(title="Index Service", version="0.1.0")


@app.get("/health")
async def health() -> dict[str, str]:
    return {"status": "ok"}


@app.post("/index", response_model=IndexAPIResponse)
async def index(payload: IndexAPIRequest) -> IndexAPIResponse:
    return IndexAPIResponse(
        results=build_chunks(
            payload.data.chat,
            payload.data.overlap_messages,
            payload.data.new_messages,
        )
    )


@lru_cache(maxsize=1)
def get_sparse_model():
    from fastembed import SparseTextEmbedding

    logger.info("Loading sparse model %s from cache %s", SPARSE_MODEL_NAME, FASTEMBED_CACHE_PATH)
    return SparseTextEmbedding(model_name=SPARSE_MODEL_NAME)


def embed_sparse_texts(texts: list[str]) -> list[dict]:
    model = get_sparse_model()
    vectors = []
    for item in model.embed(texts):
        vectors.append(
            {
                "indices": item.indices.tolist(),
                "values": item.values.tolist(),
            }
        )
    return vectors


@app.post("/sparse_embedding")
async def sparse_embedding(payload: SparseEmbeddingRequest) -> dict[str, Any]:
    vectors = await asyncio.to_thread(embed_sparse_texts, payload.texts)
    return {"vectors": vectors}


@app.exception_handler(Exception)
async def exception_handler(request: Request, exc: Exception) -> JSONResponse:
    logger.exception(exc)
    if isinstance(exc, RequestValidationError):
        return JSONResponse(status_code=422, content={"detail": exc.errors()})
    return JSONResponse(status_code=500, content={"detail": str(exc)})


def main() -> None:
    import uvicorn

    uvicorn.run("main:app", host=HOST, port=PORT, reload=False, workers=UVICORN_WORKERS)


if __name__ == "__main__":
    main()