mirror of
https://github.com/QuivrHQ/quivr.git
synced 2024-12-30 18:43:33 +03:00
1d33fbd3eb
* feat(queue): added * feat(crawling): added queue * fix(crawler): fixed github * feat(docker): simplified docker compose * feat(celery): added worker * feat(files): now uploaded * feat(files): missing routes * feat(delete): added * feat(storage): added policy and migrations * feat(sqs): implemented * feat(redis): added queue name variable * fix(task): updated * style(env): emoved unused env * ci(tests): removed broken tests
141 lines
4.4 KiB
Python
141 lines
4.4 KiB
Python
import os
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import tempfile
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from typing import Any, Optional
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from uuid import UUID
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from fastapi import UploadFile
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from logger import get_logger
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from models.brains import Brain
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from models.databases.supabase.supabase import SupabaseDB
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from models.settings import get_supabase_db
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from pydantic import BaseModel
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from utils.file import compute_sha1_from_file
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logger = get_logger(__name__)
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class File(BaseModel):
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id: Optional[UUID] = None
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file: Optional[UploadFile]
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file_name: Optional[str] = ""
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file_size: Optional[int] = None
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file_sha1: Optional[str] = ""
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vectors_ids: Optional[list] = []
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file_extension: Optional[str] = ""
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content: Optional[Any] = None
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chunk_size: int = 500
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chunk_overlap: int = 0
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documents: Optional[Any] = None
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@property
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def supabase_db(self) -> SupabaseDB:
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return get_supabase_db()
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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if self.file:
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self.file_name = self.file.filename
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self.file_size = self.file.size # pyright: ignore reportPrivateUsage=none
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self.file_extension = os.path.splitext(
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self.file.filename # pyright: ignore reportPrivateUsage=none
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)[-1].lower()
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async def compute_file_sha1(self):
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"""
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Compute the sha1 of the file using a temporary file
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"""
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with tempfile.NamedTemporaryFile(
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delete=False,
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suffix=self.file.filename, # pyright: ignore reportPrivateUsage=none
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) as tmp_file:
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await self.file.seek(0) # pyright: ignore reportPrivateUsage=none
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self.content = (
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await self.file.read() # pyright: ignore reportPrivateUsage=none
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)
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tmp_file.write(self.content)
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tmp_file.flush()
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self.file_sha1 = compute_sha1_from_file(tmp_file.name)
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os.remove(tmp_file.name)
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def compute_documents(self, loader_class):
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"""
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Compute the documents from the file
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Args:
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loader_class (class): The class of the loader to use to load the file
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"""
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logger.info(f"Computing documents from file {self.file_name}")
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documents = []
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with tempfile.NamedTemporaryFile(
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delete=False,
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suffix=self.file.filename, # pyright: ignore reportPrivateUsage=none
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) as tmp_file:
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tmp_file.write(self.content) # pyright: ignore reportPrivateUsage=none
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tmp_file.flush()
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loader = loader_class(tmp_file.name)
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documents = loader.load()
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os.remove(tmp_file.name)
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text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
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chunk_size=self.chunk_size, chunk_overlap=self.chunk_overlap
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)
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self.documents = text_splitter.split_documents(documents)
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def set_file_vectors_ids(self):
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"""
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Set the vectors_ids property with the ids of the vectors
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that are associated with the file in the vectors table
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"""
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self.vectors_ids = self.supabase_db.get_vectors_by_file_sha1(
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self.file_sha1
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).data
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def file_already_exists(self):
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"""
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Check if file already exists in vectors table
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"""
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self.set_file_vectors_ids()
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# if the file does not exist in vectors then no need to go check in brains_vectors
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if len(self.vectors_ids) == 0: # pyright: ignore reportPrivateUsage=none
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return False
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return True
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def file_already_exists_in_brain(self, brain_id):
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"""
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Check if file already exists in a brain
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Args:
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brain_id (str): Brain id
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"""
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response = self.supabase_db.get_brain_vectors_by_brain_id_and_file_sha1(
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brain_id, self.file_sha1 # type: ignore
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)
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if len(response.data) == 0:
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return False
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return True
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def file_is_empty(self):
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"""
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Check if file is empty by checking if the file pointer is at the beginning of the file
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"""
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return self.file.size < 1 # pyright: ignore reportPrivateUsage=none
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def link_file_to_brain(self, brain: Brain):
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self.set_file_vectors_ids()
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if self.vectors_ids is None:
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return
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for vector_id in self.vectors_ids: # pyright: ignore reportPrivateUsage=none
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brain.create_brain_vector(vector_id["id"], self.file_sha1)
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