mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
3ecb903d49
Description : * Tidy up, add missing docstring and fix unused params * Enable using session token
142 lines
4.6 KiB
Python
142 lines
4.6 KiB
Python
import logging
|
|
from typing import Any, Dict, List, Optional
|
|
|
|
from langchain_core.embeddings import Embeddings
|
|
from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class ClarifaiEmbeddings(BaseModel, Embeddings):
|
|
"""Clarifai embedding models.
|
|
|
|
To use, you should have the ``clarifai`` python package installed, and the
|
|
environment variable ``CLARIFAI_PAT`` set with your personal access token or pass it
|
|
as a named parameter to the constructor.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.embeddings import ClarifaiEmbeddings
|
|
clarifai = ClarifaiEmbeddings(user_id=USER_ID,
|
|
app_id=APP_ID,
|
|
model_id=MODEL_ID)
|
|
(or)
|
|
Example_URL = "https://clarifai.com/clarifai/main/models/BAAI-bge-base-en-v15"
|
|
clarifai = ClarifaiEmbeddings(model_url=EXAMPLE_URL)
|
|
"""
|
|
|
|
model_url: Optional[str] = None
|
|
"""Model url to use."""
|
|
model_id: Optional[str] = None
|
|
"""Model id to use."""
|
|
model_version_id: Optional[str] = None
|
|
"""Model version id to use."""
|
|
app_id: Optional[str] = None
|
|
"""Clarifai application id to use."""
|
|
user_id: Optional[str] = None
|
|
"""Clarifai user id to use."""
|
|
pat: Optional[str] = Field(default=None, exclude=True)
|
|
"""Clarifai personal access token to use."""
|
|
token: Optional[str] = Field(default=None, exclude=True)
|
|
"""Clarifai session token to use."""
|
|
model: Any = Field(default=None, exclude=True) #: :meta private:
|
|
api_base: str = "https://api.clarifai.com"
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
extra = Extra.forbid
|
|
|
|
@root_validator()
|
|
def validate_environment(cls, values: Dict) -> Dict:
|
|
"""Validate that we have all required info to access Clarifai
|
|
platform and python package exists in environment."""
|
|
|
|
try:
|
|
from clarifai.client.model import Model
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Could not import clarifai python package. "
|
|
"Please install it with `pip install clarifai`."
|
|
)
|
|
user_id = values.get("user_id")
|
|
app_id = values.get("app_id")
|
|
model_id = values.get("model_id")
|
|
model_version_id = values.get("model_version_id")
|
|
model_url = values.get("model_url")
|
|
api_base = values.get("api_base")
|
|
pat = values.get("pat")
|
|
token = values.get("token")
|
|
|
|
values["model"] = Model(
|
|
url=model_url,
|
|
app_id=app_id,
|
|
user_id=user_id,
|
|
model_version=dict(id=model_version_id),
|
|
pat=pat,
|
|
token=token,
|
|
model_id=model_id,
|
|
base_url=api_base,
|
|
)
|
|
|
|
return values
|
|
|
|
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
|
"""Call out to Clarifai's embedding models.
|
|
|
|
Args:
|
|
texts: The list of texts to embed.
|
|
|
|
Returns:
|
|
List of embeddings, one for each text.
|
|
"""
|
|
from clarifai.client.input import Inputs
|
|
|
|
input_obj = Inputs.from_auth_helper(self.model.auth_helper)
|
|
batch_size = 32
|
|
embeddings = []
|
|
|
|
try:
|
|
for i in range(0, len(texts), batch_size):
|
|
batch = texts[i : i + batch_size]
|
|
input_batch = [
|
|
input_obj.get_text_input(input_id=str(id), raw_text=inp)
|
|
for id, inp in enumerate(batch)
|
|
]
|
|
predict_response = self.model.predict(input_batch)
|
|
embeddings.extend(
|
|
[
|
|
list(output.data.embeddings[0].vector)
|
|
for output in predict_response.outputs
|
|
]
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Predict failed, exception: {e}")
|
|
|
|
return embeddings
|
|
|
|
def embed_query(self, text: str) -> List[float]:
|
|
"""Call out to Clarifai's embedding models.
|
|
|
|
Args:
|
|
text: The text to embed.
|
|
|
|
Returns:
|
|
Embeddings for the text.
|
|
"""
|
|
|
|
try:
|
|
predict_response = self.model.predict_by_bytes(
|
|
bytes(text, "utf-8"), input_type="text"
|
|
)
|
|
embeddings = [
|
|
list(op.data.embeddings[0].vector) for op in predict_response.outputs
|
|
]
|
|
|
|
except Exception as e:
|
|
logger.error(f"Predict failed, exception: {e}")
|
|
|
|
return embeddings[0]
|