Source code for langchain_community.embeddings.clarifai

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__)

[docs]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 = "" 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 = "" 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
[docs] 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([0].vector) for output in predict_response.outputs ] ) except Exception as e: logger.error(f"Predict failed, exception: {e}") return embeddings
[docs] 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([0].vector) for op in predict_response.outputs ] except Exception as e: logger.error(f"Predict failed, exception: {e}") return embeddings[0]