Corrupted pickle file and loss data

[sonhm3029]

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Example Code

class FaissVectorDB:
    def __init__(
        self,
        vector_db_path="vectorstores/faiss",
        embed_model="text-embedding-3-large"
    ):
        # self.embedding_model = HuggingFaceEmbeddings(model_name=embed_model)
        self.embedding_model = OpenAIEmbeddings(model=embed_model, api_key=os.environ["OPENAI_API_KEY"])
        
        self.vector_db_path = vector_db_path
        
        if os.path.exists(vector_db_path) and os.path.exists(f"{vector_db_path}/index.faiss"):
            self.db = FAISS.load_local(vector_db_path, self.embedding_model)
        else:
            self.db = None

Description

I am using FAISS in my flask app. When start the app, i will init the FaissVectorDB and read it into memory. But recently, sometime my linux server die unexpectedly, then when restart the server, i found that the index.pkl file that store data and index is corrupted. I get the error: Ran out of input. That lose all my data.

Anyone met the same problem as me. Any idea and suggestion for me to find best way to run my vectordb.

I use my vectordb for RAG pipeline and because i think it will time consuming if each time use vectordb, i have to load again so i load the vectordb just one time at the starting falsk app.

System Info

python==3.9.13
angchain==0.1.9
langchain-community==0.0.24

[dosubot[bot]]

Hey @sonhm3029! I'm here to help you with any bugs, questions, or contributions while you wait for a human maintainer. Let's tackle this issue together!

To address the issue of corrupted index.pkl files and data loss, you can implement a backup and recovery mechanism. This will help ensure that you have a backup of your data in case of corruption. Here is an example implementation for your FaissVectorDB class:

1. Backup the index.pkl file regularly: Before saving a new version of the index.pkl file, create a backup of the existing file.
2. Implement a recovery mechanism: In case the index.pkl file is corrupted, restore it from the backup.

Here is an example implementation in Python:

import os
import shutil
import pickle
from pathlib import Path
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings

class FaissVectorDB:
    def __init__(self, vector_db_path="vectorstores/faiss", embed_model="text-embedding-3-large"):
        self.embedding_model = OpenAIEmbeddings(model=embed_model, api_key=os.environ["OPENAI_API_KEY"])
        self.vector_db_path = vector_db_path

        if os.path.exists(vector_db_path) and os.path.exists(f"{vector_db_path}/index.faiss"):
            self.db = self.load_local_with_backup(vector_db_path, self.embedding_model)
        else:
            self.db = None

    def save_local_with_backup(self, folder_path: str, index_name: str = "index") -> None:
        path = Path(folder_path)
        path.mkdir(exist_ok=True, parents=True)

        # Backup existing index.pkl file
        pkl_file_path = path / f"{index_name}.pkl"
        backup_file_path = path / f"{index_name}_backup.pkl"
        if pkl_file_path.exists():
            shutil.copy(pkl_file_path, backup_file_path)

        # Save index separately since it is not picklable
        faiss = FAISS.dependable_faiss_import()
        faiss.write_index(self.db.index, str(path / f"{index_name}.faiss"))

        # Save docstore and index_to_docstore_id
        with open(pkl_file_path, "wb") as f:
            pickle.dump((self.db.docstore, self.db.index_to_docstore_id), f)

    def load_local_with_backup(self, folder_path: str, embeddings: OpenAIEmbeddings, index_name: str = "index") -> FAISS:
        path = Path(folder_path)
        pkl_file_path = path / f"{index_name}.pkl"
        backup_file_path = path / f"{index_name}_backup.pkl"

        # Load index separately since it is not picklable
        faiss = FAISS.dependable_faiss_import()
        index = faiss.read_index(str(path / f"{index_name}.faiss"))

        # Attempt to load docstore and index_to_docstore_id
        try:
            with open(pkl_file_path, "rb") as f:
                docstore, index_to_docstore_id = pickle.load(f)
        except (pickle.UnpicklingError, EOFError):
            # If loading fails, restore from backup
            if backup_file_path.exists():
                shutil.copy(backup_file_path, pkl_file_path)
                with open(pkl_file_path, "rb") as f:
                    docstore, index_to_docstore_id = pickle.load(f)
            else:
                raise RuntimeError("Both index.pkl and its backup are corrupted or missing.")

        return FAISS(embeddings, index, docstore, index_to_docstore_id)

This implementation ensures that a backup of the index.pkl file is created before saving a new version, and it provides a recovery mechanism to restore from the backup in case of corruption. This should help mitigate the issue of data loss due to file corruption ^[1][2].

To continue talking to Dosu, mention @dosu.

[sonhm3029]

The problem is i haven't find out what make my file corrupted yet. I think that it because The server down while I were working on the file