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Improvement[Community]Improve Embeddings API (#28038)

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- Fix `BaichuanTextEmbeddings` api url
- Remove unused params in api doc
- Fix word spelling
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ZhangShenao 2024-11-13 02:57:35 +08:00 committed by GitHub
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3 changed files with 4 additions and 5 deletions
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4
libs/community/langchain_community/embeddings/baichuan.py
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@ -15,7 +15,7 @@ from pydantic import (
from requests import RequestException from requests import RequestException
from typing_extensions import Self from typing_extensions import Self
BAICHUAN_API_URL: str = "http://api.baichuan-ai.com/v1/embeddings" BAICHUAN_API_URL: str = "https://api.baichuan-ai.com/v1/embeddings"
# BaichuanTextEmbeddings is an embedding model provided by Baichuan Inc. (https://www.baichuan-ai.com/home). # BaichuanTextEmbeddings is an embedding model provided by Baichuan Inc. (https://www.baichuan-ai.com/home).
# As of today (Jan 25th, 2024) BaichuanTextEmbeddings ranks #1 in C-MTEB # As of today (Jan 25th, 2024) BaichuanTextEmbeddings ranks #1 in C-MTEB
@ -25,7 +25,7 @@ BAICHUAN_API_URL: str = "http://api.baichuan-ai.com/v1/embeddings"
# Official Website: https://platform.baichuan-ai.com/docs/text-Embedding # Official Website: https://platform.baichuan-ai.com/docs/text-Embedding
# An API-key is required to use this embedding model. You can get one by registering # An API-key is required to use this embedding model. You can get one by registering
# at https://platform.baichuan-ai.com/docs/text-Embedding. # at https://platform.baichuan-ai.com/docs/text-Embedding.
# BaichuanTextEmbeddings support 512 token window and preduces vectors with # BaichuanTextEmbeddings support 512 token window and produces vectors with
# 1024 dimensions. # 1024 dimensions.

2
libs/community/langchain_community/embeddings/dashscope.py
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@ -143,8 +143,6 @@ class DashScopeEmbeddings(BaseModel, Embeddings):
Args: Args:
texts: The list of texts to embed. texts: The list of texts to embed.
chunk_size: The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns: Returns:
List of embeddings, one for each text. List of embeddings, one for each text.

3
libs/community/langchain_community/embeddings/fake.py
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@ -36,7 +36,8 @@ class DeterministicFakeEmbedding(Embeddings, BaseModel):
np.random.seed(seed) np.random.seed(seed)
return list(np.random.normal(size=self.size)) return list(np.random.normal(size=self.size))
def _get_seed(self, text: str) -> int: @staticmethod
def _get_seed(text: str) -> int:
""" """
Get a seed for the random generator, using the hash of the text. Get a seed for the random generator, using the hash of the text.
""" """