Type Mismatch For using OpenAI Interface

System Info

I use OpenAIEmbeddings from langchain.embedding, and using openAI function.

However I find it's a problem that when we call OpenAI Interface, the input is a 2D list, not a 1D list.

If the input is a 1D list, it could work for embedding.create func

like this:

and I found out that openai api does not support 2D list as input.

how could I solve this problem

the error is shown below:

Traceback (most recent call last):
  File "..\main.py", line 47, in <module>
    langchain_openai()
  File "..\main.py", line 42, in langchain_openai
    query_result = embeddings.embed_query(text)
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\langchain\embeddings\openai.py", line 501, in embed_query
    return self.embed_documents([text])[0]
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\langchain\embeddings\openai.py", line 473, in embed_documents
    return self._get_len_safe_embeddings(texts, engine=self.deployment)
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\langchain\embeddings\openai.py", line 359, in _get_len_safe_embeddings
    response = embed_with_retry(
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\langchain\embeddings\openai.py", line 108, in embed_with_retry
    return _embed_with_retry(**kwargs)
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\tenacity\__init__.py", line 289, in wrapped_f
    return self(f, *args, **kw)
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\tenacity\__init__.py", line 379, in __call__
    do = self.iter(retry_state=retry_state)
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\tenacity\__init__.py", line 314, in iter
    return fut.result()
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\concurrent\futures\_base.py", line 437, in result
    return self.__get_result()
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\concurrent\futures\_base.py", line 389, in __get_result
    raise self._exception
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\tenacity\__init__.py", line 382, in __call__
    result = fn(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\langchain\embeddings\openai.py", line 105, in _embed_with_retry
    response = embeddings.client.create(**kwargs)
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\openai\api_resources\embedding.py", line 33, in create
    response = super().create(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 153, in create
    response, _, api_key = requestor.request(
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\openai\api_requestor.py", line 298, in request
    resp, got_stream = self._interpret_response(result, stream)
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\openai\api_requestor.py", line 700, in _interpret_response
    self._interpret_response_line(
  File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\openai\api_requestor.py", line 763, in _interpret_response_line
    raise self.handle_error_response(
openai.error.InvalidRequestError: parse parameter error: type mismatch

Who can help?

@hwchase17 @agola11

Information

[x] The official example notebooks/scripts
[ ] My own modified scripts

Related Components

[ ] LLMs/Chat Models
[X] Embedding Models
[ ] Prompts / Prompt Templates / Prompt Selectors
[ ] Output Parsers
[ ] Document Loaders
[ ] Vector Stores / Retrievers
[ ] Memory
[ ] Agents / Agent Executors
[ ] Tools / Toolkits
[ ] Chains
[ ] Callbacks/Tracing
[ ] Async

Reproduction

def langchain_openai():
    from langchain.embeddings import OpenAIEmbeddings

    embeddings = OpenAIEmbeddings(
        model="text-embedding-ada-002",
        openai_api_base="",
        openai_api_key=""
    )
    text = "This is a test query."
    query_result = embeddings.embed_query(text)
    print(query_result[:5])

Expected behavior

I think it's a bug or something to fix

Answer generated by a 🤖

Answer

Hello,

Thank you for reaching out and providing detailed information about the issue you're facing.

From your description, it seems like you're trying to use the embed_query method with a single string input, which should work as expected. The embed_query method is designed to handle a single string and internally wraps it into a list before passing it to the embed_documents method. The embed_documents method is designed to handle a list of strings.

Here's the correct way to use these methods:

from langchain.embeddings import OpenAIEmbeddings

# Initialize the OpenAIEmbeddings object
embeddings = OpenAIEmbeddings(
    model="text-embedding-ada-002",
    openai_api_base="",
    openai_api_key=""
)

# For a single string input
text = "This is a test query."
query_result = embeddings.embed_query(text)
print(query_result[:5])

If you're still encountering the same error, it might be due to an issue with the OpenAI API or the specific model you're using. Please ensure that your OpenAI API key is correct and that the model "text-embedding-ada-002" is available and supports the operations you're trying to perform.

If the issue persists, could you please provide more information about the version of LangChain you're using and the exact steps to reproduce the error? This will help us better understand the issue and provide a more accurate solution.

Best regards, Dosu

Sources

libs/langchain/langchain/embeddings/openai.py

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.

Answer generated by a 🤖

Answer

Hello,

Thank you for reaching out and providing detailed information about the issue you're facing.

From your description, it seems like you're trying to use the embed_query method with a single string input, which should work as expected. The embed_query method is designed to handle a single string and internally wraps it into a list before passing it to the embed_documents method. The embed_documents method is designed to handle a list of strings.

Here's the correct way to use these methods:
from langchain.embeddings import OpenAIEmbeddings

# Initialize the OpenAIEmbeddings object
embeddings = OpenAIEmbeddings(
    model="text-embedding-ada-002",
    openai_api_base="",
    openai_api_key=""
)

# For a single string input
text = "This is a test query."
query_result = embeddings.embed_query(text)
print(query_result[:5])
If you're still encountering the same error, it might be due to an issue with the OpenAI API or the specific model you're using. Please ensure that your OpenAI API key is correct and that the model "text-embedding-ada-002" is available and supports the operations you're trying to perform.

If the issue persists, could you please provide more information about the version of LangChain you're using and the exact steps to reproduce the error? This will help us better understand the issue and provide a more accurate solution.

Best regards, Dosu

Sources

libs/langchain/langchain/embeddings/openai.py

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.

I try your way, however it's still type mismatch.

If I use offical openai interface.if input is a array, it could work.

if input is a 2D array, it will type mismatch.

However, your input to _embed_with_retry is 2D like pic bleow

Answer generated by a 🤖

Answer

Hello,

Thank you for reaching out and providing a detailed description of the issue you're facing. Based on your description, it seems like the embed_query method from the OpenAIEmbeddings class in the LangChain Python framework is expecting a string as input, but the _embed_with_retry method might be receiving a 2D array instead, which is causing the type mismatch error.

The embed_query method in the OpenAIEmbeddings class has the following signature:

def embed_query(self, text: str) -> List[float]:

This method expects a single argument text which should be of type str. This argument represents the text to be embedded. The method returns a list of floating point numbers (List[float]), which represents the embedding for the input text.

However, the _embed_with_retry method, which is used internally by the embed_query method, expects a dictionary as input. This dictionary should contain the arguments to be passed to the embeddings.client.create method.

If you're calling the embed_query method with a single string as input and still encountering the error, it's possible that there's an issue with how the LangChain framework is handling the input internally. Specifically, it might be converting the input into a 2D array before passing it to the _embed_with_retry method, which is causing the type mismatch error.

Unfortunately, I can't see the screenshots you've provided, but based on your description, it seems like the OpenAI interface works fine when the input is a 1D array but throws a type mismatch error when the input is a 2D array. This further suggests that the issue might be with how the LangChain framework is handling the input internally.

To resolve this issue, I would recommend checking the internal implementation of the embed_query method and the _embed_with_retry method in the LangChain framework to ensure that they're handling the input correctly. If you find that the input is being incorrectly converted into a 2D array, you might need to adjust the code to ensure that a dictionary is passed to the _embed_with_retry method instead.

If you're comfortable with it, you could also consider submitting a pull request to the LangChain repository with the necessary changes. This would not only resolve the issue for you, but also for other users who might be facing the same problem.

I hope this helps! If you have any further questions or need more clarification, please don't hesitate to ask.

Best, Dosu

Sources

libs/langchain/langchain/embeddings/openai.py

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.

Answer generated by a 🤖

Answer

Hello,

Thank you for reaching out and providing a detailed description of the issue you're facing. Based on your description, it seems like the embed_query method from the OpenAIEmbeddings class in the LangChain Python framework is expecting a string as input, but the _embed_with_retry method might be receiving a 2D array instead, which is causing the type mismatch error.

The embed_query method in the OpenAIEmbeddings class has the following signature:
def embed_query(self, text: str) -> List[float]:
This method expects a single argument text which should be of type str. This argument represents the text to be embedded. The method returns a list of floating point numbers (List[float]), which represents the embedding for the input text.

However, the _embed_with_retry method, which is used internally by the embed_query method, expects a dictionary as input. This dictionary should contain the arguments to be passed to the embeddings.client.create method.

If you're calling the embed_query method with a single string as input and still encountering the error, it's possible that there's an issue with how the LangChain framework is handling the input internally. Specifically, it might be converting the input into a 2D array before passing it to the _embed_with_retry method, which is causing the type mismatch error.

Unfortunately, I can't see the screenshots you've provided, but based on your description, it seems like the OpenAI interface works fine when the input is a 1D array but throws a type mismatch error when the input is a 2D array. This further suggests that the issue might be with how the LangChain framework is handling the input internally.

To resolve this issue, I would recommend checking the internal implementation of the embed_query method and the _embed_with_retry method in the LangChain framework to ensure that they're handling the input correctly. If you find that the input is being incorrectly converted into a 2D array, you might need to adjust the code to ensure that a dictionary is passed to the _embed_with_retry method instead.

If you're comfortable with it, you could also consider submitting a pull request to the LangChain repository with the necessary changes. This would not only resolve the issue for you, but also for other users who might be facing the same problem.

I hope this helps! If you have any further questions or need more clarification, please don't hesitate to ask.

Best, Dosu

Sources

libs/langchain/langchain/embeddings/openai.py

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.

Could you ask some real person?

langchain-ai / langchain