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Added **kwargs for embedding funcs #7664

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Added **kwargs for embedding funcs #7664

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14 changes: 8 additions & 6 deletions langchain/embeddings/openai.py
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Original file line number Diff line number Diff line change
Expand Up @@ -276,7 +276,7 @@ def _invocation_params(self) -> Dict:
# please refer to
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
def _get_len_safe_embeddings(
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None, **kwargs
) -> List[List[float]]:
embeddings: List[List[float]] = [[] for _ in range(len(texts))]
try:
Expand Down Expand Up @@ -329,6 +329,7 @@ def _get_len_safe_embeddings(
self,
input=tokens[i : i + _chunk_size],
**self._invocation_params,
**kwargs
)
batched_embeddings += [r["embedding"] for r in response["data"]]

Expand Down Expand Up @@ -424,7 +425,7 @@ async def _aget_len_safe_embeddings(

return embeddings

def _embedding_func(self, text: str, *, engine: str) -> List[float]:
def _embedding_func(self, text: str, *, engine: str, **kwargs) -> List[float]:
"""Call out to OpenAI's embedding endpoint."""
# handle large input text
if len(text) > self.embedding_ctx_length:
Expand All @@ -438,6 +439,7 @@ def _embedding_func(self, text: str, *, engine: str) -> List[float]:
self,
input=[text],
**self._invocation_params,
**kwargs
)[
"data"
][0]["embedding"]
Expand All @@ -461,7 +463,7 @@ async def _aembedding_func(self, text: str, *, engine: str) -> List[float]:
)["data"][0]["embedding"]

def embed_documents(
self, texts: List[str], chunk_size: Optional[int] = 0
self, texts: List[str], chunk_size: Optional[int] = 0, **kwargs
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this signature is defined on base embeddings class, probably don't want to break the interface just here and im not sure we should add it to the base class.

what if added something like this instead? https://github.com/hwchase17/langchain/blob/c7b687e944883df972cabdf00064112587306daf/langchain/llms/openai.py#L137

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The issue is that I need to also pass a parameter model. If I place stuff in model_kwargs (in other places where I can)

llm = AzureOpenAI(deployment_name="text-davinci-003",model_name="text-davinci-003", model_kwargs={ "user" : "JL", "model" : "text-davinci-003" })

__root__
  Parameters {'model'} should be specified explicitly. Instead they were passed in as part of `model_kwargs` parameter. (type=value_error)

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for embeddings you can already pass model in directly, no?

OpenAIEmbeddings(model=...)

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yes but I need to place it also on the request body because that is parsed by our custom proxy 😢

) -> List[List[float]]:
"""Call out to OpenAI's embedding endpoint for embedding search docs.

Expand All @@ -475,7 +477,7 @@ def embed_documents(
"""
# NOTE: to keep things simple, we assume the list may contain texts longer
# than the maximum context and use length-safe embedding function.
return self._get_len_safe_embeddings(texts, engine=self.deployment)
return self._get_len_safe_embeddings(texts, engine=self.deployment, **kwargs)

async def aembed_documents(
self, texts: List[str], chunk_size: Optional[int] = 0
Expand All @@ -494,7 +496,7 @@ async def aembed_documents(
# than the maximum context and use length-safe embedding function.
return await self._aget_len_safe_embeddings(texts, engine=self.deployment)

def embed_query(self, text: str) -> List[float]:
def embed_query(self, text: str, **kwargs) -> List[float]:
"""Call out to OpenAI's embedding endpoint for embedding query text.

Args:
Expand All @@ -503,7 +505,7 @@ def embed_query(self, text: str) -> List[float]:
Returns:
Embedding for the text.
"""
embedding = self._embedding_func(text, engine=self.deployment)
embedding = self._embedding_func(text, engine=self.deployment, **kwargs)
return embedding

async def aembed_query(self, text: str) -> List[float]:
Expand Down
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