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Title: Fix Bugs, Enhance Compatibility, and Improve Defaults in Pull Request #5

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Merged
limcheekin merged 11 commits into from
Nov 12, 2023
Merged

Title: Fix Bugs, Enhance Compatibility, and Improve Defaults in Pull Request #5

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e460fc9
change defalut HOST to 0.0.0.0
jayxuz Oct 23, 2023
417b63e
switch function when pydantic use different version
jayxuz Oct 23, 2023
19f90b7
fix class, embedding response in OpenAI format
jayxuz Oct 23, 2023
ba31de7
now HuggingFaceBgeEmbeddings work when load model use local path
jayxuz Oct 23, 2023
27e5ca7
set NORMALIZE_EMBEDDINGS defalut is True
jayxuz Oct 23, 2023
1e64d6d
set model_name default is DEFAULT_MODEL_NAME
jayxuz Oct 23, 2023
7a728ab
add dependency
jayxuz Oct 23, 2023
f70b19c
fix List<str> Post Error
jayxuz Oct 23, 2023
0775b84
class Embedding add attributes to fit OpenAI format
jayxuz Oct 23, 2023
4c5336d
Approximately calculate Embedding token values
jayxuz Oct 23, 2023
da186d6
openai embeding tokens is calculated as prompt_tokens
jayxuz Oct 23, 2023
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16 changes: 10 additions & 6 deletions open/text/embeddings/server/app.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@

from typing import List, Optional, Union
from starlette.concurrency import run_in_threadpool
from fastapi import FastAPI, APIRouter
Expand Down Expand Up @@ -66,10 +65,12 @@ class CreateEmbeddingRequest(BaseModel):
class Embedding(BaseModel):
embedding: List[float]


class Usage(BaseModel):
prompt_tokens: int
total_tokens: int


class CreateEmbeddingResponse(BaseModel):
data: List[Embedding]
model: str
Expand Down Expand Up @@ -127,25 +128,28 @@ def _create_embedding(input: Union[str, List[str]]):
model_name = DEFAULT_MODEL_NAME
model_name_short = model_name.split("/")[-1]
if isinstance(input, str):
return CreateEmbeddingResponse(data=[Embedding(embedding=embeddings.embed_query(input))],model=model_name_short,object='list',usage=Usage(prompt_tokens=5,total_tokens=5))
return CreateEmbeddingResponse(data=[Embedding(embedding=embeddings.embed_query(input))],
model=model_name_short, object='list',
usage=Usage(prompt_tokens=5, total_tokens=5))
else:
data = [Embedding(embedding=embedding)
for embedding in embeddings.embed_documents(input)]
return CreateEmbeddingResponse(data=[Embedding(embedding=embeddings.embed_query(input))],model=model_name_short,object='list',usage=Usage(prompt_tokens=5,total_tokens=5))
return CreateEmbeddingResponse(data=data, model=model_name_short, object='list',
usage=Usage(prompt_tokens=5, total_tokens=5))


@router.post(
"/v1/embeddings",
response_model=CreateEmbeddingResponse,
)
async def create_embedding(
request: CreateEmbeddingRequest
request: CreateEmbeddingRequest
):
if pydantic.__version__ > '2.0.0' :
if pydantic.__version__ > '2.0.0':
return await run_in_threadpool(
_create_embedding, **request.model_dump(exclude={"user", "model", "model_config"})
)
else :
else:
return await run_in_threadpool(
_create_embedding, **request.dict(exclude={"user", "model", "model_config"})
)