kotaemon/knowledgehub/embeddings/base.py
Nguyen Trung Duc (john) c339912312 [AUR-389] Add base interface and embedding model (#17)
This change provides the base interface of an embedding, and wrap the Langchain's OpenAI embedding. Usage as follow:

```python
from kotaemon.embeddings import AzureOpenAIEmbeddings

model = AzureOpenAIEmbeddings(
    model="text-embedding-ada-002",
    deployment="embedding-deployment",
    openai_api_base="https://test.openai.azure.com/",
    openai_api_key="some-key",
)
output = model("Hello world")
```
2023-09-14 14:08:58 +07:00

63 lines
1.9 KiB
Python

from typing import List, Type
from langchain.embeddings.base import Embeddings as LCEmbeddings
from theflow import Param
from ..components import BaseComponent
from ..documents.base import Document
class Embeddings(BaseComponent):
...
class LangchainEmbeddings(Embeddings):
_lc_class: Type[LCEmbeddings]
def __init__(self, **params):
if self._lc_class is None:
raise AttributeError(
"Should set _lc_class attribute to the LLM class from Langchain "
"if using LLM from Langchain"
)
self._kwargs: dict = {}
for param in list(params.keys()):
if param in self._lc_class.__fields__: # type: ignore
self._kwargs[param] = params.pop(param)
super().__init__(**params)
def __setattr__(self, name, value):
if name in self._lc_class.__fields__:
setattr(self.agent, name, value)
else:
super().__setattr__(name, value)
@Param.decorate(no_cache=True)
def agent(self):
return self._lc_class(**self._kwargs)
def run_raw(self, text: str) -> List[float]:
return self.agent.embed_query(text) # type: ignore
def run_batch_raw(self, text: List[str]) -> List[List[float]]:
return self.agent.embed_documents(text) # type: ignore
def run_document(self, text: Document) -> List[float]:
return self.agent.embed_query(text.text) # type: ignore
def run_batch_document(self, text: List[Document]):
return self.agent.embed_documents([each.text for each in text]) # type: ignore
def is_document(self, text) -> bool:
if isinstance(text, Document):
return True
elif isinstance(text, List) and isinstance(text[0], Document):
return True
return False
def is_batch(self, text) -> bool:
if isinstance(text, list):
return True
return False