langchain: Bug after the openai updated in Embedding

🤖

The error you’re encountering is due to a recent change in the EmbeddingFunction interface. The EmbeddingFunction.__call__ method now expects two arguments: self and input. However, it seems that in your code, the EmbeddingFunction.__call__ method is being called with self, args, and kwargs.

To resolve this issue, you need to update the way you’re calling the EmbeddingFunction.__call__ method. Instead of passing args and kwargs, you should pass a single input argument.

Here’s how you can do it:

class SagemakerEndpointEmbeddings(Embeddings):
    # ...

    def embed_query(self, query: str) -> List[float]:
        input = self.prepare_input(query)
        return self.embedding_function(input)

    def embed_documents(self, documents: List[str]) -> List[List[float]]:
        input = self.prepare_input(documents)
        return self.embedding_function(input)

In the prepare_input method, you should prepare the input argument in a way that is compatible with the new EmbeddingFunction.__call__ interface.

Also, you might need to adjust the predict_fn() function within the custom inference.py script to handle batched requests. Change the return line from return {"vectors": sentence_embeddings[0].tolist()} to return {"vectors": sentence_embeddings.tolist()}.

Please refer to the following sources for more information:

• libs/langchain/langchain/embeddings/sagemaker_endpoint.py
• docs/docs/integrations/text_embedding/sagemaker-endpoint.ipynb
• docs/extras/integrations/text_embedding/sagemaker-endpoint.ipynb

I hope this helps! If you have any further questions or if something is unclear, please let me know.

Sources

• libs/langchain/langchain/embeddings/sagemaker_endpoint.py
• docs/docs/integrations/text_embedding/sagemaker-endpoint.ipynb
• docs/extras/integrations/text_embedding/sagemaker-endpoint.ipynb

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Problem seems to appear with chroma update to 0.4.16

Pinning to previous version seems to help.

chromadb==0.4.15

Langchain still implements the old interface. See: https://docs.trychroma.com/migration#migration-to-0416---november-7-2023

Langchain 0.0.332 released with the chroma team’s fix for compatibility with chromadb>=0.4.16

Can now use latest of both pip install -U langchain chromadb

I still met the problem, all the library are the latest version, here are my code:

from langchain.document_loaders import TextLoader
loader = TextLoader('/tanglg/lx/code/Qwen/forest_file.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
 
embeddings = OpenAIEmbeddings(
    openai_api_key="EMPTY",
    openai_api_base="http://localhost:8000/v1", 
)

forest_file_store = Chroma.from_documents(documents=texts, embedding=embeddings, collection_name="forest_file")

and here is the error info:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tanglg/lx/code/Qwen/xly_vectordb_test.ipynb 单元格 3 line 1
      [5](vscode-notebook-cell://ssh-remote%2B172.30.254.229/tanglg/lx/code/Qwen/xly_vectordb_test.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4) texts = text_splitter.split_documents(documents)
      [7](vscode-notebook-cell://ssh-remote%2B172.30.254.229/tanglg/lx/code/Qwen/xly_vectordb_test.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=6) embeddings = OpenAIEmbeddings(
      [8](vscode-notebook-cell://ssh-remote%2B172.30.254.229/tanglg/lx/code/Qwen/xly_vectordb_test.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=7)     openai_api_key="EMPTY",
      [9](vscode-notebook-cell://ssh-remote%2B172.30.254.229/tanglg/lx/code/Qwen/xly_vectordb_test.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=8)     openai_api_base="http://localhost:8000/v1", 
     [10](vscode-notebook-cell://ssh-remote%2B172.30.254.229/tanglg/lx/code/Qwen/xly_vectordb_test.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=9) )
---> [12](vscode-notebook-cell://ssh-remote%2B172.30.254.229/tanglg/lx/code/Qwen/xly_vectordb_test.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=11) forest_file_store = Chroma.from_documents(documents=texts, embedding=embeddings, collection_name="forest_file")

File /tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:684, in Chroma.from_documents(cls, documents, embedding, ids, collection_name, persist_directory, client_settings, client, collection_metadata, **kwargs)
    [682](https://vscode-remote+ssh-002dremote-002b172-002e30-002e254-002e229.vscode-resource.vscode-cdn.net/tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:682) texts = [doc.page_content for doc in documents]
    [683](https://vscode-remote+ssh-002dremote-002b172-002e30-002e254-002e229.vscode-resource.vscode-cdn.net/tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:683) metadatas = [doc.metadata for doc in documents]
--> [684](https://vscode-remote+ssh-002dremote-002b172-002e30-002e254-002e229.vscode-resource.vscode-cdn.net/tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:684) return cls.from_texts(
    [685](https://vscode-remote+ssh-002dremote-002b172-002e30-002e254-002e229.vscode-resource.vscode-cdn.net/tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:685)     texts=texts,
    [686](https://vscode-remote+ssh-002dremote-002b172-002e30-002e254-002e229.vscode-resource.vscode-cdn.net/tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:686)     embedding=embedding,
    [687](https://vscode-remote+ssh-002dremote-002b172-002e30-002e254-002e229.vscode-resource.vscode-cdn.net/tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:687)     metadatas=metadatas,
    [688](https://vscode-remote+ssh-002dremote-002b172-002e30-002e254-002e229.vscode-resource.vscode-cdn.net/tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:688)     ids=ids,
    [689](https://vscode-remote+ssh-002dremote-002b172-002e30-002e254-002e229.vscode-resource.vscode-cdn.net/tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:689)     collection_name=collection_name,
    [690](https://vscode-remote+ssh-002dremote-002b172-002e30-002e254-002e229.vscode-resource.vscode-cdn.net/tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:690)     persist_directory=persist_directory,
    [691](https://vscode-remote+ssh-002dremote-002b172-002e30-002e254-002e229.vscode-resource.vscode-cdn.net/tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:691)     client_settings=client_settings,
    [692](https://vscode-remote+ssh-002dremote-002b172-002e30-002e254-002e229.vscode-resource.vscode-cdn.net/tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:692)     client=client,
    [693](https://vscode-remote+ssh-002dremote-002b172-002e30-002e254-002e229.vscode-resource.vscode-cdn.net/tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:693)     collection_metadata=collection_metadata,
    [694](https://vscode-remote+ssh-002dremote-002b172-002e30-002e254-002e229.vscode-resource.vscode-cdn.net/tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:694)     **kwargs,
    [695](https://vscode-remote+ssh-002dremote-002b172-002e30-002e254-002e229.vscode-resource.vscode-cdn.net/tanglg/commonsoft/miniconda3/envs/qwen/lib/python3.10/site-packages/langchain/vectorstores/chroma.py:695) )
...

ValueError: Expected EmbeddingFunction.__call__ to have the following signature: odict_keys(['self', 'input']), got odict_keys(['self', 'args', 'kwargs'])
Please see https://docs.trychroma.com/embeddings for details of the EmbeddingFunction interface.
Please note the recent change to the EmbeddingFunction interface: https://docs.trychroma.com/migration#migration-to-0416---november-7-2023

Problem seems to appear with chroma update to 0.4.16

Pinning to previous version seems to help.

chromadb==0.4.15

Langchain still implements the old interface. See: https://docs.trychroma.com/migration#migration-to-0416---november-7-2023

Thank you 😂

O problema parece aparecer com a atualização do chroma para 0.4.16

Fixar na versão anterior parece ajudar.

chromadb==0.4.15

Langchain ainda implementa a interface antiga. Consulte: https://docs.trychroma.com/migration#migration-to-0416---november-7-2023

My hero!