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Duplicate ids passed into langchain\vectorstores\fais.__add will result in a mismatch between faiss and document's index #9019

Closed xia-huang-411303 closed 11 months ago

xia-huang-411303 commented 1 year ago

System Info

windows 10 python 3.10.12

Who can help?

@eyurtsev There is a BUG in the def __add function in langchain\vectorstores\faiss.py: When the parameter ids is provided and there are duplicates in it, index_to_docstore_id will report an error and the program will terminate. But the text vector embedding has been added to the index. This causes the id in the index to be inconsistent with the id in index_to_docstore_id, not aligned, and the text cannot be matched correctly.

langchain\vectorstores\faiss.py 中 def __add 函数存在BUG: 当参数 ids 已提供, 且其中存在重复时, index_to_docstore_id 新增会报错,程序运行终止. 但是文本向量 embedding 已经添加至 index 中.导致 index 中的 id 与 index_to_docstore_id 的中 id 对应不一致,文本无法正确匹配

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Related Components

Reproduction

None

Expected behavior

None

dosubot[bot] commented 1 year ago

Answer generated by a 🤖

Answer

Hello,

Thank you for bringing this issue to our attention. The problem you're facing is due to duplicate ids causing a mismatch between the faiss index and the document's index. This is because the __add function in FAISS class doesn't handle the case where the ids provided by the user are not unique.

To resolve this issue, you can add a check to ensure that the ids are unique before adding them to the index_to_docstore_id dictionary. Here's how you can modify the __add function to handle this:

def __add__(
        self,
        texts: Iterable[str],
        embeddings: Iterable[List[float]],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> List[str]:
        if not isinstance(self.docstore, AddableMixin):
            raise ValueError(
                "If trying to add texts, the underlying docstore should support "
                f"adding items, which {self.docstore} does not"
            )
        documents = []
        for i, text in enumerate(texts):
            metadata = metadatas[i] if metadatas else {}
            documents.append(Document(page_content=text, metadata=metadata))
        if ids is None:
            ids = [str(uuid.uuid4()) for _ in texts]
        else:
            # Check if ids are unique
            if len(ids) != len(set(ids)):
                raise ValueError("Ids should be unique")
        # Add to the index, the index_to_id mapping, and the docstore.
        starting_len = len(self.index_to_docstore_id)
        faiss = dependable_faiss_import()
        vector = np.array(embeddings, dtype=np.float32)
        if self._normalize_L2:
            faiss.normalize_L2(vector)
        self.index.add(vector)
        # Get list of index, id, and docs.
        full_info = [(starting_len + i, ids[i], doc) for i, doc in enumerate(documents)]
        # Add information to docstore and index.
        self.docstore.add({_id: doc for _, _id, doc in full_info})
        index_to_id = {index: _id for index, _id, _ in full_info}
        self.index_to_docstore_id.update(index_to_id)
        return [_id for _, _id, _ in full_info]

This modification will raise a ValueError if the ids provided by the user are not unique, thus preventing the mismatch issue.

Please let me know if this resolves your issue or if you need further assistance.

Best, Dosu

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xia-huang-411303 commented 1 year ago

CN: 这个解决方案不太行.因为相重复的ID可以是反复调用_add方法时传入的,单次调用的检查去重无法避免这个问题.可能需要在向FAISS中加入向量时,检查ID是否已经存在于index_to_docstore_id中,或者只是改变代码的顺序,如下. 总而言之,FAISS的ID不能跟文档的ID保持强一致性是一个硬伤

EN: This solution is not very good. Since duplicate ids can be passed in multiple calls to _add, single-call deduplication cannot avoid this problem. You may need to check if the ID already exists in index_to_docstore_id when adding a vector to FAISS, or just change the order of the code, as follows:

    def __add(
        self,
        texts: Iterable[str],
        embeddings: Iterable[List[float]],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> List[str]:
        if not isinstance(self.docstore, AddableMixin):
            raise ValueError(
                "If trying to add texts, the underlying docstore should support "
                f"adding items, which {self.docstore} does not"
            )
        documents = []
        for i, text in enumerate(texts):
            metadata = metadatas[i] if metadatas else {}
            documents.append(Document(page_content=text, metadata=metadata))
        if ids is None:
            ids = [str(uuid.uuid4()) for _ in texts]
        # Add to the index, the index_to_id mapping, and the docstore.
        starting_len = len(self.index_to_docstore_id)
        faiss = dependable_faiss_import()
        # Get list of index, id, and docs.
        full_info = [(starting_len + i, ids[i], doc) for i, doc in enumerate(documents)]
        # Add information to docstore and index.
        self.index_to_docstore_id.update({index: _id for index, _id, _ in full_info})
        self.docstore.add({_id: doc for _, _id, doc in full_info})
        vector = np.array(embeddings, dtype=np.float32)
        if self._normalize_L2:
            faiss.normalize_L2(vector)
        self.index.add(vector)
        return [_id for _, _id, _ in full_info]

In summary, the fact that FAISS ids are not strongly aligned with the document ids is a pain

dosubot[bot] commented 1 year ago

Answer generated by a 🤖

Answer

Thank you for your detailed explanation and proposed solution. You're correct in identifying that the current implementation of the _add method in the InMemoryDocstore class ensures that the ids in the dictionary and the document's index are always consistent, even across multiple calls to the _add method. This is achieved by checking for overlapping ids between the new texts to be added and the existing texts in the dictionary. If overlapping ids are found, a ValueError is raised.

However, I understand your concern about ensuring the consistency of ids between the FAISS index and the document's index. One possible solution could be to introduce a check before adding new vectors to the FAISS index. This check would ensure that the ids of the new vectors do not already exist in the index_to_docstore_id mapping. If duplicate ids are found, an error could be raised or the duplicates could be handled in a way that suits your application.

Here's a rough example of how this could be implemented:

# Check for duplicate ids before adding to the FAISS index
overlapping = set(ids).intersection(self.index_to_docstore_id.values())
if overlapping:
    raise ValueError(f"Tried to add ids that already exist in the FAISS index: {overlapping}")

This code should be added before the line where the new vectors are added to the FAISS index:

self.index.add(vector)

This solution should ensure that the ids in the FAISS index and the document's index are always consistent, even across multiple calls to the _add method.

Please note that this is just a suggestion and might need to be adjusted to fit your specific use case. If you feel this solution could be beneficial to others using the LangChain framework, we encourage you to contribute and make a PR. Your contributions are greatly appreciated!

Sources


This response is meant to be useful, save you time, and share context. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

dosubot[bot] commented 11 months ago

Hi, @xia-huang-411303! I'm Dosu, and I'm helping the LangChain team manage their backlog. I wanted to let you know that we are marking this issue as stale.

From what I understand, the issue is about a bug in the __add function in langchain\vectorstores\faiss.py where duplicate IDs cause a mismatch between the IDs in the index and index_to_docstore_id. Two proposed solutions have been suggested. The first solution suggests adding a check to ensure that the IDs are unique before adding them to the index_to_docstore_id dictionary. The second solution suggests introducing a check before adding new vectors to the FAISS index to ensure that the IDs of the new vectors do not already exist in the index_to_docstore_id mapping.

Before we close this issue, we wanted to check with you if it is still relevant to the latest version of the LangChain repository. If it is, please let us know by commenting on the issue. Otherwise, feel free to close the issue yourself or it will be automatically closed in 7 days.

Thank you for your contribution to LangChain!