mirror of
https://git.mirrors.martin98.com/https://github.com/langgenius/dify.git
synced 2025-07-23 21:24:26 +08:00
fix: baichuan max chunks (#1990)
This commit is contained in:
parent
94626487db
commit
ebd11e7482
@ -1,4 +1,4 @@
|
|||||||
from typing import Optional
|
from typing import Optional, Tuple
|
||||||
|
|
||||||
from core.model_runtime.entities.model_entities import PriceType
|
from core.model_runtime.entities.model_entities import PriceType
|
||||||
from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult, EmbeddingUsage
|
from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult, EmbeddingUsage
|
||||||
@ -38,6 +38,50 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
|
|||||||
raise ValueError('Invalid model name')
|
raise ValueError('Invalid model name')
|
||||||
if not api_key:
|
if not api_key:
|
||||||
raise CredentialsValidateFailedError('api_key is required')
|
raise CredentialsValidateFailedError('api_key is required')
|
||||||
|
|
||||||
|
# split into chunks of batch size 16
|
||||||
|
chunks = []
|
||||||
|
for i in range(0, len(texts), 16):
|
||||||
|
chunks.append(texts[i:i + 16])
|
||||||
|
|
||||||
|
embeddings = []
|
||||||
|
token_usage = 0
|
||||||
|
|
||||||
|
for chunk in chunks:
|
||||||
|
# embeding chunk
|
||||||
|
chunk_embeddings, chunk_usage = self.embedding(
|
||||||
|
model=model,
|
||||||
|
api_key=api_key,
|
||||||
|
texts=chunk,
|
||||||
|
user=user
|
||||||
|
)
|
||||||
|
|
||||||
|
embeddings.extend(chunk_embeddings)
|
||||||
|
token_usage += chunk_usage
|
||||||
|
|
||||||
|
result = TextEmbeddingResult(
|
||||||
|
model=model,
|
||||||
|
embeddings=embeddings,
|
||||||
|
usage=self._calc_response_usage(
|
||||||
|
model=model,
|
||||||
|
credentials=credentials,
|
||||||
|
tokens=token_usage
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
return result
|
||||||
|
|
||||||
|
def embedding(self, model: str, api_key, texts: list[str], user: Optional[str] = None) \
|
||||||
|
-> Tuple[list[list[float]], int]:
|
||||||
|
"""
|
||||||
|
Embed given texts
|
||||||
|
|
||||||
|
:param model: model name
|
||||||
|
:param credentials: model credentials
|
||||||
|
:param texts: texts to embed
|
||||||
|
:param user: unique user id
|
||||||
|
:return: embeddings result
|
||||||
|
"""
|
||||||
url = self.api_base
|
url = self.api_base
|
||||||
headers = {
|
headers = {
|
||||||
'Authorization': 'Bearer ' + api_key,
|
'Authorization': 'Bearer ' + api_key,
|
||||||
@ -85,17 +129,10 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
|
|||||||
except Exception as e:
|
except Exception as e:
|
||||||
raise InternalServerError(f"Failed to convert response to json: {e} with text: {response.text}")
|
raise InternalServerError(f"Failed to convert response to json: {e} with text: {response.text}")
|
||||||
|
|
||||||
usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage['total_tokens'])
|
return [
|
||||||
|
data['embedding'] for data in embeddings
|
||||||
|
], usage['total_tokens']
|
||||||
|
|
||||||
result = TextEmbeddingResult(
|
|
||||||
model=model,
|
|
||||||
embeddings=[[
|
|
||||||
float(data) for data in x['embedding']
|
|
||||||
] for x in embeddings],
|
|
||||||
usage=usage
|
|
||||||
)
|
|
||||||
|
|
||||||
return result
|
|
||||||
|
|
||||||
def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
|
def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
|
||||||
"""
|
"""
|
||||||
|
@ -59,3 +59,40 @@ def test_get_num_tokens():
|
|||||||
)
|
)
|
||||||
|
|
||||||
assert num_tokens == 2
|
assert num_tokens == 2
|
||||||
|
|
||||||
|
def test_max_chunks():
|
||||||
|
model = BaichuanTextEmbeddingModel()
|
||||||
|
|
||||||
|
result = model.invoke(
|
||||||
|
model='baichuan-text-embedding',
|
||||||
|
credentials={
|
||||||
|
'api_key': os.environ.get('BAICHUAN_API_KEY'),
|
||||||
|
},
|
||||||
|
texts=[
|
||||||
|
"hello",
|
||||||
|
"world",
|
||||||
|
"hello",
|
||||||
|
"world",
|
||||||
|
"hello",
|
||||||
|
"world",
|
||||||
|
"hello",
|
||||||
|
"world",
|
||||||
|
"hello",
|
||||||
|
"world",
|
||||||
|
"hello",
|
||||||
|
"world",
|
||||||
|
"hello",
|
||||||
|
"world",
|
||||||
|
"hello",
|
||||||
|
"world",
|
||||||
|
"hello",
|
||||||
|
"world",
|
||||||
|
"hello",
|
||||||
|
"world",
|
||||||
|
"hello",
|
||||||
|
"world",
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
|
assert isinstance(result, TextEmbeddingResult)
|
||||||
|
assert len(result.embeddings) == 22
|
Loading…
x
Reference in New Issue
Block a user