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207 | class DeepInfraEmbeddingModel(BaseEmbedding):
"""
A wrapper class for accessing embedding models available via the DeepInfra API. This class allows for easy integration
of DeepInfra embeddings into your projects, supporting both synchronous and asynchronous retrieval of text embeddings.
Args:
model_id (str): Identifier for the model to be used for embeddings. Defaults to 'sentence-transformers/clip-ViT-B-32'.
normalize (bool): Flag to normalize embeddings post retrieval. Defaults to False.
api_token (str): DeepInfra API token. If not provided,
the token is fetched from the environment variable 'DEEPINFRA_API_TOKEN'.
Examples:
>>> from llama_index.embeddings.deepinfra import DeepInfraEmbeddingModel
>>> model = DeepInfraEmbeddingModel()
>>> print(model.get_text_embedding("Hello, world!"))
[0.1, 0.2, 0.3, ...]
"""
"""model_id can be obtained from the DeepInfra website."""
_model_id: str = PrivateAttr()
"""normalize flag to normalize embeddings post retrieval."""
_normalize: bool = PrivateAttr()
"""api_token should be obtained from the DeepInfra website."""
_api_token: str = PrivateAttr()
"""query_prefix is used to add a prefix to queries."""
_query_prefix: str = PrivateAttr()
"""text_prefix is used to add a prefix to texts."""
_text_prefix: str = PrivateAttr()
def __init__(
self,
model_id: str = DEFAULT_MODEL_ID,
normalize: bool = False,
api_token: str = None,
callback_manager: Optional[CallbackManager] = None,
query_prefix: str = "",
text_prefix: str = "",
embed_batch_size: int = MAX_BATCH_SIZE,
) -> None:
"""
Init params.
"""
super().__init__(
callback_manager=callback_manager, embed_batch_size=embed_batch_size
)
self._model_id = model_id
self._normalize = normalize
self._api_token = api_token or os.getenv(ENV_VARIABLE, None)
self._query_prefix = query_prefix
self._text_prefix = text_prefix
def _post(self, data: List[str]) -> List[List[float]]:
"""
Sends a POST request to the DeepInfra Inference API with the given data and returns the API response.
Input data is chunked into batches to avoid exceeding the maximum batch size (1024).
Args:
data (List[str]): A list of strings to be embedded.
Returns:
dict: A dictionary containing embeddings from the API.
"""
url = self.get_url()
chunked_data = _chunk(data, self.embed_batch_size)
embeddings = []
for chunk in chunked_data:
response = requests.post(
url,
json={
"inputs": chunk,
},
headers={
"Authorization": f"Bearer {self._api_token}",
"Content-Type": "application/json",
},
)
response.raise_for_status()
embeddings.extend(response.json()["embeddings"])
return embeddings
def get_url(self):
"""
Get DeepInfra API URL.
"""
return f"{INFERENCE_URL}/{self._model_id}"
async def _apost(self, data: List[str]) -> List[List[float]]:
"""
Sends a POST request to the DeepInfra Inference API with the given data and returns the API response.
Input data is chunked into batches to avoid exceeding the maximum batch size (1024).
Args:
data (List[str]): A list of strings to be embedded.
Output:
List[float]: A list of embeddings from the API.
"""
url = self.get_url()
chunked_data = _chunk(data, self.embed_batch_size)
embeddings = []
for chunk in chunked_data:
async with aiohttp.ClientSession() as session:
async with session.post(
url,
json={
"inputs": chunk,
},
headers={
"Authorization": f"Bearer {self._api_token}",
"Content-Type": "application/json",
},
) as resp:
response = await resp.json()
embeddings.extend(response["embeddings"])
return embeddings
def _get_query_embedding(self, query: str) -> List[float]:
"""
Get query embedding.
"""
return self._post(self._add_query_prefix([query]))[0]
async def _aget_query_embedding(self, query: str) -> List[float]:
"""
Async get query embedding.
"""
response = await self._apost(self._add_query_prefix([query]))
return response[0]
def _get_query_embeddings(self, queries: List[str]) -> List[List[float]]:
"""
Get query embeddings.
"""
return self._post(self._add_query_prefix(queries))
async def _aget_query_embeddings(self, queries: List[str]) -> List[List[float]]:
"""
Async get query embeddings.
"""
return await self._apost(self._add_query_prefix(queries))
def _get_text_embedding(self, text: str) -> List[float]:
"""
Get text embedding.
"""
return self._post(self._add_text_prefix([text]))[0]
async def _aget_text_embedding(self, text: str) -> List[float]:
"""
Async get text embedding.
"""
response = await self._apost(self._add_text_prefix([text]))
return response[0]
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""
Get text embedding.
"""
return self._post(self._add_text_prefix(texts))
async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""
Async get text embeddings.
"""
return await self._apost(self._add_text_prefix(texts))
def _add_query_prefix(self, queries: List[str]) -> List[str]:
"""
Add query prefix to queries.
"""
return (
[self._query_prefix + query for query in queries]
if self._query_prefix
else queries
)
def _add_text_prefix(self, texts: List[str]) -> List[str]:
"""
Add text prefix to texts.
"""
return (
[self._text_prefix + text for text in texts] if self._text_prefix else texts
)
|