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253 | class CohereEmbedding(BaseEmbedding):
"""CohereEmbedding uses the Cohere API to generate embeddings for text."""
# Instance variables initialized via Pydantic's mechanism
cohere_client: cohere.Client = Field(description="CohereAI client")
cohere_async_client: cohere.AsyncClient = Field(description="CohereAI Async client")
truncate: str = Field(description="Truncation type - START/ END/ NONE")
input_type: Optional[str] = Field(
description="Model Input type. If not provided, search_document and search_query are used when needed."
)
embedding_type: str = Field(
description="Embedding type. If not provided float embedding_type is used when needed."
)
def __init__(
self,
cohere_api_key: Optional[str] = None,
model_name: str = "embed-english-v3.0",
truncate: str = "END",
input_type: Optional[str] = None,
embedding_type: str = "float",
embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
callback_manager: Optional[CallbackManager] = None,
base_url: Optional[str] = None,
timeout: Optional[float] = None,
httpx_client: Optional[httpx.Client] = None,
httpx_async_client: Optional[httpx.AsyncClient] = None,
):
"""
A class representation for generating embeddings using the Cohere API.
Args:
cohere_client (Any): An instance of the Cohere client, which is used to communicate with the Cohere API.
truncate (str): A string indicating the truncation strategy to be applied to input text. Possible values
are 'START', 'END', or 'NONE'.
input_type (Optional[str]): An optional string that specifies the type of input provided to the model.
This is model-dependent and could be one of the following: 'search_query',
'search_document', 'classification', or 'clustering'.
model_name (str): The name of the model to be used for generating embeddings. The class ensures that
this model is supported and that the input type provided is compatible with the model.
"""
# Validate model_name and input_type
if model_name not in VALID_MODEL_INPUT_TYPES:
raise ValueError(f"{model_name} is not a valid model name")
if input_type not in VALID_MODEL_INPUT_TYPES[model_name]:
raise ValueError(
f"{input_type} is not a valid input type for the provided model."
)
if embedding_type not in VALID_MODEL_EMBEDDING_TYPES[model_name]:
raise ValueError(
f"{embedding_type} is not a embedding type for the provided model."
)
if truncate not in VALID_TRUNCATE_OPTIONS:
raise ValueError(f"truncate must be one of {VALID_TRUNCATE_OPTIONS}")
super().__init__(
cohere_client=cohere.Client(
cohere_api_key,
client_name="llama_index",
base_url=base_url,
timeout=timeout,
httpx_client=httpx_client,
),
cohere_async_client=cohere.AsyncClient(
cohere_api_key,
client_name="llama_index",
base_url=base_url,
timeout=timeout,
httpx_client=httpx_async_client,
),
cohere_api_key=cohere_api_key,
model_name=model_name,
input_type=input_type,
embedding_type=embedding_type,
truncate=truncate,
embed_batch_size=embed_batch_size,
callback_manager=callback_manager,
)
@classmethod
def class_name(cls) -> str:
return "CohereEmbedding"
def _embed(self, texts: List[str], input_type: str) -> List[List[float]]:
"""Embed sentences using Cohere."""
if self.model_name in V3_MODELS:
result = self.cohere_client.embed(
texts=texts,
input_type=self.input_type or input_type,
embedding_types=[self.embedding_type],
model=self.model_name,
truncate=self.truncate,
).embeddings
else:
result = self.cohere_client.embed(
texts=texts,
model=self.model_name,
embedding_types=[self.embedding_type],
truncate=self.truncate,
).embeddings
return getattr(result, self.embedding_type, None)
async def _aembed(self, texts: List[str], input_type: str) -> List[List[float]]:
"""Embed sentences using Cohere."""
if self.model_name in V3_MODELS:
result = (
await self.cohere_async_client.embed(
texts=texts,
input_type=self.input_type or input_type,
embedding_types=[self.embedding_type],
model=self.model_name,
truncate=self.truncate,
)
).embeddings
else:
result = (
await self.cohere_async_client.embed(
texts=texts,
model=self.model_name,
embedding_types=[self.embedding_type],
truncate=self.truncate,
)
).embeddings
return getattr(result, self.embedding_type, None)
def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding. For query embeddings, input_type='search_query'."""
return self._embed([query], input_type="search_query")[0]
async def _aget_query_embedding(self, query: str) -> List[float]:
"""Get query embedding async. For query embeddings, input_type='search_query'."""
return (await self._aembed([query], input_type="search_query"))[0]
def _get_text_embedding(self, text: str) -> List[float]:
"""Get text embedding."""
return self._embed([text], input_type="search_document")[0]
async def _aget_text_embedding(self, text: str) -> List[float]:
"""Get text embedding async."""
return (await self._aembed([text], input_type="search_document"))[0]
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Get text embeddings."""
return self._embed(texts, input_type="search_document")
async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Get text embeddings."""
return await self._aembed(texts, input_type="search_document")
|