From 7dda1bf45a23c6ebc33fb82462894e21e8bda25d Mon Sep 17 00:00:00 2001 From: Bagatur Date: Fri, 13 Oct 2023 15:38:35 -0700 Subject: [PATCH] more --- .../langchain/embeddings/dashscope.py | 10 ++++----- .../langchain/embeddings/google_palm.py | 8 +++---- .../langchain/langchain/embeddings/localai.py | 22 +++++++++---------- .../langchain/langchain/embeddings/minimax.py | 10 ++++----- libs/langchain/langchain/embeddings/openai.py | 20 ++++++++--------- 5 files changed, 35 insertions(+), 35 deletions(-) diff --git a/libs/langchain/langchain/embeddings/dashscope.py b/libs/langchain/langchain/embeddings/dashscope.py index 31d38e52b15..4b23af5a113 100644 --- a/libs/langchain/langchain/embeddings/dashscope.py +++ b/libs/langchain/langchain/embeddings/dashscope.py @@ -40,12 +40,12 @@ def _create_retry_decorator(embeddings: DashScopeEmbeddings) -> Callable[[Any], ) -def embed_with_retry(embeddings: DashScopeEmbeddings, **kwargs: Any) -> Any: +def _embed_with_retry(embeddings: DashScopeEmbeddings, **kwargs: Any) -> Any: """Use tenacity to retry the embedding call.""" retry_decorator = _create_retry_decorator(embeddings) @retry_decorator - def _embed_with_retry(**kwargs: Any) -> Any: + def __embed_with_retry(**kwargs: Any) -> Any: resp = embeddings.client.call(**kwargs) if resp.status_code == 200: return resp.output["embeddings"] @@ -61,7 +61,7 @@ def embed_with_retry(embeddings: DashScopeEmbeddings, **kwargs: Any) -> Any: response=resp, ) - return _embed_with_retry(**kwargs) + return __embed_with_retry(**kwargs) class DashScopeEmbeddings(BaseModel, Embeddings): @@ -135,7 +135,7 @@ class DashScopeEmbeddings(BaseModel, Embeddings): Returns: List of embeddings, one for each text. """ - embeddings = embed_with_retry( + embeddings = _embed_with_retry( self, input=texts, text_type="document", model=self.model ) embedding_list = [item["embedding"] for item in embeddings] @@ -150,7 +150,7 @@ class DashScopeEmbeddings(BaseModel, Embeddings): Returns: Embedding for the text. """ - embedding = embed_with_retry( + embedding = _embed_with_retry( self, input=text, text_type="query", model=self.model )[0]["embedding"] return embedding diff --git a/libs/langchain/langchain/embeddings/google_palm.py b/libs/langchain/langchain/embeddings/google_palm.py index fcf83b36692..2aaeb7d57fe 100644 --- a/libs/langchain/langchain/embeddings/google_palm.py +++ b/libs/langchain/langchain/embeddings/google_palm.py @@ -40,17 +40,17 @@ def _create_retry_decorator() -> Callable[[Any], Any]: ) -def embed_with_retry( +def _embed_with_retry( embeddings: GooglePalmEmbeddings, *args: Any, **kwargs: Any ) -> Any: """Use tenacity to retry the completion call.""" retry_decorator = _create_retry_decorator() @retry_decorator - def _embed_with_retry(*args: Any, **kwargs: Any) -> Any: + def __embed_with_retry(*args: Any, **kwargs: Any) -> Any: return embeddings.client.generate_embeddings(*args, **kwargs) - return _embed_with_retry(*args, **kwargs) + return __embed_with_retry(*args, **kwargs) class GooglePalmEmbeddings(BaseModel, Embeddings): @@ -83,5 +83,5 @@ class GooglePalmEmbeddings(BaseModel, Embeddings): def embed_query(self, text: str) -> List[float]: """Embed query text.""" - embedding = embed_with_retry(self, self.model_name, text) + embedding = _embed_with_retry(self, self.model_name, text) return embedding["embedding"] diff --git a/libs/langchain/langchain/embeddings/localai.py b/libs/langchain/langchain/embeddings/localai.py index f63a6a66a18..e15575e50e8 100644 --- a/libs/langchain/langchain/embeddings/localai.py +++ b/libs/langchain/langchain/embeddings/localai.py @@ -94,27 +94,27 @@ def _check_response(response: dict) -> dict: return response -def embed_with_retry(embeddings: LocalAIEmbeddings, **kwargs: Any) -> Any: +def _embed_with_retry(embeddings: LocalAIEmbeddings, **kwargs: Any) -> Any: """Use tenacity to retry the embedding call.""" retry_decorator = _create_retry_decorator(embeddings) @retry_decorator - def _embed_with_retry(**kwargs: Any) -> Any: + def __embed_with_retry(**kwargs: Any) -> Any: response = embeddings.client.create(**kwargs) return _check_response(response) - return _embed_with_retry(**kwargs) + return __embed_with_retry(**kwargs) -async def async_embed_with_retry(embeddings: LocalAIEmbeddings, **kwargs: Any) -> Any: +async def _async_embed_with_retry(embeddings: LocalAIEmbeddings, **kwargs: Any) -> Any: """Use tenacity to retry the embedding call.""" @_async_retry_decorator(embeddings) - async def _async_embed_with_retry(**kwargs: Any) -> Any: + async def __async_embed_with_retry(**kwargs: Any) -> Any: response = await embeddings.client.acreate(**kwargs) return _check_response(response) - return await _async_embed_with_retry(**kwargs) + return await __async_embed_with_retry(**kwargs) class LocalAIEmbeddings(BaseModel, Embeddings): @@ -265,13 +265,13 @@ class LocalAIEmbeddings(BaseModel, Embeddings): # See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500 # replace newlines, which can negatively affect performance. text = text.replace("\n", " ") - return embed_with_retry( + return _embed_with_retry( self, input=[text], **self._invocation_params, - )["data"][ - 0 - ]["embedding"] + )[ + "data" + ][0]["embedding"] async def _aembedding_func(self, text: str, *, engine: str) -> List[float]: """Call out to LocalAI's embedding endpoint.""" @@ -281,7 +281,7 @@ class LocalAIEmbeddings(BaseModel, Embeddings): # replace newlines, which can negatively affect performance. text = text.replace("\n", " ") return ( - await async_embed_with_retry( + await _async_embed_with_retry( self, input=[text], **self._invocation_params, diff --git a/libs/langchain/langchain/embeddings/minimax.py b/libs/langchain/langchain/embeddings/minimax.py index 9b8035d904f..10c2fc5cbc0 100644 --- a/libs/langchain/langchain/embeddings/minimax.py +++ b/libs/langchain/langchain/embeddings/minimax.py @@ -34,15 +34,15 @@ def _create_retry_decorator() -> Callable[[Any], Any]: ) -def embed_with_retry(embeddings: MiniMaxEmbeddings, *args: Any, **kwargs: Any) -> Any: +def _embed_with_retry(embeddings: MiniMaxEmbeddings, *args: Any, **kwargs: Any) -> Any: """Use tenacity to retry the completion call.""" retry_decorator = _create_retry_decorator() @retry_decorator - def _embed_with_retry(*args: Any, **kwargs: Any) -> Any: + def __embed_with_retry(*args: Any, **kwargs: Any) -> Any: return embeddings.embed(*args, **kwargs) - return _embed_with_retry(*args, **kwargs) + return __embed_with_retry(*args, **kwargs) class MiniMaxEmbeddings(BaseModel, Embeddings): @@ -144,7 +144,7 @@ class MiniMaxEmbeddings(BaseModel, Embeddings): Returns: List of embeddings, one for each text. """ - embeddings = embed_with_retry(self, texts=texts, embed_type=self.embed_type_db) + embeddings = _embed_with_retry(self, texts=texts, embed_type=self.embed_type_db) return embeddings def embed_query(self, text: str) -> List[float]: @@ -156,7 +156,7 @@ class MiniMaxEmbeddings(BaseModel, Embeddings): Returns: Embeddings for the text. """ - embeddings = embed_with_retry( + embeddings = _embed_with_retry( self, texts=[text], embed_type=self.embed_type_query ) return embeddings[0] diff --git a/libs/langchain/langchain/embeddings/openai.py b/libs/langchain/langchain/embeddings/openai.py index 274788a1a8c..0448c2b8ad6 100644 --- a/libs/langchain/langchain/embeddings/openai.py +++ b/libs/langchain/langchain/embeddings/openai.py @@ -95,27 +95,27 @@ def _check_response(response: dict, skip_empty: bool = False) -> dict: return response -def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any: +def _embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any: """Use tenacity to retry the embedding call.""" retry_decorator = _create_retry_decorator(embeddings) @retry_decorator - def _embed_with_retry(**kwargs: Any) -> Any: + def __embed_with_retry(**kwargs: Any) -> Any: response = embeddings.client.create(**kwargs) return _check_response(response, skip_empty=embeddings.skip_empty) - return _embed_with_retry(**kwargs) + return __embed_with_retry(**kwargs) -async def async_embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any: +async def _async_embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any: """Use tenacity to retry the embedding call.""" @_async_retry_decorator(embeddings) - async def _async_embed_with_retry(**kwargs: Any) -> Any: + async def __async_embed_with_retry(**kwargs: Any) -> Any: response = await embeddings.client.acreate(**kwargs) return _check_response(response, skip_empty=embeddings.skip_empty) - return await _async_embed_with_retry(**kwargs) + return await __async_embed_with_retry(**kwargs) class OpenAIEmbeddings(BaseModel, Embeddings): @@ -371,7 +371,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): _iter = range(0, len(tokens), _chunk_size) for i in _iter: - response = embed_with_retry( + response = _embed_with_retry( self, input=tokens[i : i + _chunk_size], **self._invocation_params, @@ -389,7 +389,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): for i in range(len(texts)): _result = results[i] if len(_result) == 0: - average = embed_with_retry( + average = _embed_with_retry( self, input="", **self._invocation_params, @@ -443,7 +443,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): batched_embeddings: List[List[float]] = [] _chunk_size = chunk_size or self.chunk_size for i in range(0, len(tokens), _chunk_size): - response = await async_embed_with_retry( + response = await _async_embed_with_retry( self, input=tokens[i : i + _chunk_size], **self._invocation_params, @@ -460,7 +460,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): _result = results[i] if len(_result) == 0: average = ( - await async_embed_with_retry( + await _async_embed_with_retry( self, input="", **self._invocation_params,