mirror of
https://github.com/hwchase17/langchain.git
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community[patch] : Tidy up and update Clarifai SDK functions (#18314)
Description : * Tidy up, add missing docstring and fix unused params * Enable using session token
This commit is contained in:
Phat Vo
GitHub
parent
93b87f2bfb
commit
3ecb903d49
@ -1,9 +1,8 @@
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import logging
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from typing import Dict, List, Optional
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from typing import Any, Dict, List, Optional
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
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from langchain_core.utils import get_from_dict_or_env
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from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
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logger = logging.getLogger(__name__)
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@ -37,8 +36,11 @@ class ClarifaiEmbeddings(BaseModel, Embeddings):
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"""Clarifai application id to use."""
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user_id: Optional[str] = None
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"""Clarifai user id to use."""
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pat: Optional[str] = None
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pat: Optional[str] = Field(default=None, exclude=True)
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"""Clarifai personal access token to use."""
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token: Optional[str] = Field(default=None, exclude=True)
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"""Clarifai session token to use."""
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model: Any = Field(default=None, exclude=True) #: :meta private:
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api_base: str = "https://api.clarifai.com"
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class Config:
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@ -51,21 +53,32 @@ class ClarifaiEmbeddings(BaseModel, Embeddings):
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"""Validate that we have all required info to access Clarifai
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platform and python package exists in environment."""
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values["pat"] = get_from_dict_or_env(values, "pat", "CLARIFAI_PAT")
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try:
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from clarifai.client.model import Model
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except ImportError:
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raise ImportError(
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"Could not import clarifai python package. "
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"Please install it with `pip install clarifai`."
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)
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user_id = values.get("user_id")
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app_id = values.get("app_id")
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model_id = values.get("model_id")
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model_version_id = values.get("model_version_id")
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model_url = values.get("model_url")
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api_base = values.get("api_base")
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pat = values.get("pat")
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token = values.get("token")
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if model_url is not None and model_id is not None:
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raise ValueError("Please provide either model_url or model_id, not both.")
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if model_url is None and model_id is None:
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raise ValueError("Please provide one of model_url or model_id.")
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if model_url is None and model_id is not None:
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if user_id is None or app_id is None:
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raise ValueError("Please provide a user_id and app_id.")
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values["model"] = Model(
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url=model_url,
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app_id=app_id,
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user_id=user_id,
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model_version=dict(id=model_version_id),
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pat=pat,
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token=token,
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model_id=model_id,
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base_url=api_base,
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)
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return values
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@ -78,27 +91,9 @@ class ClarifaiEmbeddings(BaseModel, Embeddings):
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Returns:
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List of embeddings, one for each text.
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"""
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try:
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from clarifai.client.input import Inputs
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from clarifai.client.model import Model
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except ImportError:
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raise ImportError(
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"Could not import clarifai python package. "
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"Please install it with `pip install clarifai`."
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)
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if self.pat is not None:
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pat = self.pat
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if self.model_url is not None:
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_model_init = Model(url=self.model_url, pat=pat)
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else:
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_model_init = Model(
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model_id=self.model_id,
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user_id=self.user_id,
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app_id=self.app_id,
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pat=pat,
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)
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from clarifai.client.input import Inputs
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input_obj = Inputs(pat=pat)
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input_obj = Inputs.from_auth_helper(self.model.auth_helper)
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batch_size = 32
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embeddings = []
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@ -109,7 +104,7 @@ class ClarifaiEmbeddings(BaseModel, Embeddings):
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input_obj.get_text_input(input_id=str(id), raw_text=inp)
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for id, inp in enumerate(batch)
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]
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predict_response = _model_init.predict(input_batch)
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predict_response = self.model.predict(input_batch)
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embeddings.extend(
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[
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list(output.data.embeddings[0].vector)
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@ -131,27 +126,9 @@ class ClarifaiEmbeddings(BaseModel, Embeddings):
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Returns:
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Embeddings for the text.
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"""
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try:
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from clarifai.client.model import Model
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except ImportError:
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raise ImportError(
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"Could not import clarifai python package. "
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"Please install it with `pip install clarifai`."
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)
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if self.pat is not None:
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pat = self.pat
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if self.model_url is not None:
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_model_init = Model(url=self.model_url, pat=pat)
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else:
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_model_init = Model(
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model_id=self.model_id,
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user_id=self.user_id,
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app_id=self.app_id,
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pat=pat,
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)
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try:
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predict_response = _model_init.predict_by_bytes(
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predict_response = self.model.predict_by_bytes(
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bytes(text, "utf-8"), input_type="text"
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)
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embeddings = [
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@ -4,8 +4,7 @@ from typing import Any, Dict, List, Optional
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.llms import LLM
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from langchain_core.outputs import Generation, LLMResult
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from langchain_core.pydantic_v1 import Extra, root_validator
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from langchain_core.utils import get_from_dict_or_env
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from langchain_core.pydantic_v1 import Extra, Field, root_validator
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from langchain_community.llms.utils import enforce_stop_tokens
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@ -42,8 +41,11 @@ class Clarifai(LLM):
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"""Clarifai application id to use."""
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user_id: Optional[str] = None
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"""Clarifai user id to use."""
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pat: Optional[str] = None
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pat: Optional[str] = Field(default=None, exclude=True) #: :meta private:
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"""Clarifai personal access token to use."""
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token: Optional[str] = Field(default=None, exclude=True) #: :meta private:
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"""Clarifai session token to use."""
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model: Any = Field(default=None, exclude=True) #: :meta private:
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api_base: str = "https://api.clarifai.com"
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class Config:
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@ -55,21 +57,32 @@ class Clarifai(LLM):
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that we have all required info to access Clarifai
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platform and python package exists in environment."""
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values["pat"] = get_from_dict_or_env(values, "pat", "CLARIFAI_PAT")
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try:
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from clarifai.client.model import Model
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except ImportError:
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raise ImportError(
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"Could not import clarifai python package. "
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"Please install it with `pip install clarifai`."
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)
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user_id = values.get("user_id")
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app_id = values.get("app_id")
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model_id = values.get("model_id")
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model_version_id = values.get("model_version_id")
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model_url = values.get("model_url")
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api_base = values.get("api_base")
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pat = values.get("pat")
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token = values.get("token")
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if model_url is not None and model_id is not None:
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raise ValueError("Please provide either model_url or model_id, not both.")
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if model_url is None and model_id is None:
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raise ValueError("Please provide one of model_url or model_id.")
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if model_url is None and model_id is not None:
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if user_id is None or app_id is None:
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raise ValueError("Please provide a user_id and app_id.")
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values["model"] = Model(
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url=model_url,
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app_id=app_id,
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user_id=user_id,
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model_version=dict(id=model_version_id),
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pat=pat,
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token=token,
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model_id=model_id,
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base_url=api_base,
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)
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return values
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@ -117,28 +130,10 @@ class Clarifai(LLM):
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response = clarifai_llm("Tell me a joke.")
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"""
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# If version_id None, Defaults to the latest model version
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try:
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from clarifai.client.model import Model
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except ImportError:
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raise ImportError(
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"Could not import clarifai python package. "
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"Please install it with `pip install clarifai`."
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)
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if self.pat is not None:
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pat = self.pat
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if self.model_url is not None:
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_model_init = Model(url=self.model_url, pat=pat)
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else:
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_model_init = Model(
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model_id=self.model_id,
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user_id=self.user_id,
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app_id=self.app_id,
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pat=pat,
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)
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try:
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(inference_params := {}) if inference_params is None else inference_params
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predict_response = _model_init.predict_by_bytes(
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predict_response = self.model.predict_by_bytes(
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bytes(prompt, "utf-8"),
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input_type="text",
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inference_params=inference_params,
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@ -165,27 +160,15 @@ class Clarifai(LLM):
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# TODO: add caching here.
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try:
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from clarifai.client.input import Inputs
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from clarifai.client.model import Model
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except ImportError:
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raise ImportError(
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"Could not import clarifai python package. "
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"Please install it with `pip install clarifai`."
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)
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if self.pat is not None:
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pat = self.pat
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if self.model_url is not None:
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_model_init = Model(url=self.model_url, pat=pat)
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else:
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_model_init = Model(
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model_id=self.model_id,
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user_id=self.user_id,
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app_id=self.app_id,
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pat=pat,
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)
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generations = []
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batch_size = 32
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input_obj = Inputs(pat=pat)
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input_obj = Inputs.from_auth_helper(self.model.auth_helper)
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try:
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for i in range(0, len(prompts), batch_size):
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batch = prompts[i : i + batch_size]
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@ -196,7 +179,7 @@ class Clarifai(LLM):
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(
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inference_params := {}
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) if inference_params is None else inference_params
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predict_response = _model_init.predict(
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predict_response = self.model.predict(
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inputs=input_batch, inference_params=inference_params
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)
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@ -36,8 +36,10 @@ class Clarifai(VectorStore):
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self,
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user_id: Optional[str] = None,
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app_id: Optional[str] = None,
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number_of_docs: Optional[int] = None,
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number_of_docs: Optional[int] = 4,
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pat: Optional[str] = None,
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token: Optional[str] = None,
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api_base: Optional[str] = "https://api.clarifai.com",
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) -> None:
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"""Initialize with Clarifai client.
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@ -45,6 +47,7 @@ class Clarifai(VectorStore):
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user_id (Optional[str], optional): User ID. Defaults to None.
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app_id (Optional[str], optional): App ID. Defaults to None.
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pat (Optional[str], optional): Personal access token. Defaults to None.
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token (Optional[str], optional): Session token. Defaults to None.
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number_of_docs (Optional[int], optional): Number of documents to return
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during vector search. Defaults to None.
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api_base (Optional[str], optional): API base. Defaults to None.
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@ -52,21 +55,33 @@ class Clarifai(VectorStore):
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Raises:
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ValueError: If user ID, app ID or personal access token is not provided.
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"""
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self._user_id = user_id or os.environ.get("CLARIFAI_USER_ID")
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self._app_id = app_id or os.environ.get("CLARIFAI_APP_ID")
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if pat:
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os.environ["CLARIFAI_PAT"] = pat
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self._pat = os.environ.get("CLARIFAI_PAT")
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if self._user_id is None or self._app_id is None or self._pat is None:
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_user_id = user_id or os.environ.get("CLARIFAI_USER_ID")
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_app_id = app_id or os.environ.get("CLARIFAI_APP_ID")
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if _user_id is None or _app_id is None:
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raise ValueError(
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"Could not find CLARIFAI_USER_ID, CLARIFAI_APP_ID or\
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CLARIFAI_PAT in your environment. "
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"Please set those env variables with a valid user ID, \
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app ID and personal access token \
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from https://clarifai.com/settings/security."
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"Could not find CLARIFAI_USER_ID "
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"or CLARIFAI_APP_ID in your environment. "
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"Please set those env variables with a valid user ID, app ID"
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)
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self._number_of_docs = number_of_docs
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try:
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from clarifai.client.search import Search
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except ImportError as e:
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raise ImportError(
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"Could not import clarifai python package. "
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"Please install it with `pip install clarifai`."
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) from e
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self._auth = Search(
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user_id=_user_id,
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app_id=_app_id,
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top_k=number_of_docs,
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pat=pat,
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token=token,
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base_url=api_base,
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).auth_helper
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def add_texts(
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self,
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texts: Iterable[str],
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@ -109,7 +124,7 @@ class Clarifai(VectorStore):
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ids
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), "Number of text inputs and input ids should be the same."
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input_obj = Inputs(app_id=self._app_id, user_id=self._user_id)
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input_obj = Inputs.from_auth_helper(auth=self._auth)
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batch_size = 32
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input_job_ids = []
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for idx in range(0, length, batch_size):
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@ -149,7 +164,7 @@ class Clarifai(VectorStore):
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def similarity_search_with_score(
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self,
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query: str,
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k: int = 4,
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k: Optional[int] = None,
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filters: Optional[dict] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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@ -157,7 +172,8 @@ class Clarifai(VectorStore):
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Args:
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query (str): Query text to search for.
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k (int): Number of results to return. Defaults to 4.
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k (Optional[int]): Number of results to return. If not set,
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it'll take _number_of_docs. Defaults to None.
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filter (Optional[Dict[str, str]]): Filter by metadata.
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Defaults to None.
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@ -175,10 +191,9 @@ class Clarifai(VectorStore):
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) from e
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# Get number of docs to return
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if self._number_of_docs is not None:
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k = self._number_of_docs
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top_k = k or self._number_of_docs
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search_obj = Search(user_id=self._user_id, app_id=self._app_id, top_k=k)
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search_obj = Search.from_auth_helper(auth=self._auth, top_k=top_k)
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rank = [{"text_raw": query}]
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# Add filter by metadata if provided.
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if filters is not None:
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@ -193,7 +208,7 @@ class Clarifai(VectorStore):
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def hit_to_document(hit: resources_pb2.Hit) -> Tuple[Document, float]:
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metadata = json_format.MessageToDict(hit.input.data.metadata)
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h = {"Authorization": f"Key {self._pat}"}
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h = dict(self._auth.metadata)
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request = requests.get(hit.input.data.text.url, headers=h)
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# override encoding by real educated guess as provided by chardet
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@ -215,19 +230,20 @@ class Clarifai(VectorStore):
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def similarity_search(
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self,
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query: str,
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k: int = 4,
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k: Optional[int] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Run similarity search using Clarifai.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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k: Number of Documents to return.
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If not set, it'll take _number_of_docs. Defaults to None.
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Returns:
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List of Documents most similar to the query and score for each
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"""
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docs_and_scores = self.similarity_search_with_score(query, **kwargs)
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docs_and_scores = self.similarity_search_with_score(query, k=k, **kwargs)
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return [doc for doc, _ in docs_and_scores]
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@classmethod
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@ -240,6 +256,7 @@ class Clarifai(VectorStore):
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app_id: Optional[str] = None,
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number_of_docs: Optional[int] = None,
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pat: Optional[str] = None,
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token: Optional[str] = None,
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**kwargs: Any,
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) -> Clarifai:
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"""Create a Clarifai vectorstore from a list of texts.
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@ -248,10 +265,14 @@ class Clarifai(VectorStore):
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user_id (str): User ID.
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app_id (str): App ID.
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texts (List[str]): List of texts to add.
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number_of_docs (Optional[int]): Number of documents to return
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during vector search. Defaults to None.
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metadatas (Optional[List[dict]]): Optional list of metadatas.
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number_of_docs (Optional[int]): Number of documents
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to return during vector search. Defaults to None.
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pat (Optional[str], optional): Personal access token.
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Defaults to None.
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token (Optional[str], optional): Session token. Defaults to None.
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metadatas (Optional[List[dict]]): Optional list
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of metadatas. Defaults to None.
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**kwargs: Additional keyword arguments to be passed to the Search.
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Returns:
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Clarifai: Clarifai vectorstore.
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@ -261,6 +282,8 @@ class Clarifai(VectorStore):
|
||||
app_id=app_id,
|
||||
number_of_docs=number_of_docs,
|
||||
pat=pat,
|
||||
token=token,
|
||||
**kwargs,
|
||||
)
|
||||
clarifai_vector_db.add_texts(texts=texts, metadatas=metadatas)
|
||||
return clarifai_vector_db
|
||||
@ -274,6 +297,7 @@ class Clarifai(VectorStore):
|
||||
app_id: Optional[str] = None,
|
||||
number_of_docs: Optional[int] = None,
|
||||
pat: Optional[str] = None,
|
||||
token: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> Clarifai:
|
||||
"""Create a Clarifai vectorstore from a list of documents.
|
||||
@ -282,8 +306,11 @@ class Clarifai(VectorStore):
|
||||
user_id (str): User ID.
|
||||
app_id (str): App ID.
|
||||
documents (List[Document]): List of documents to add.
|
||||
number_of_docs (Optional[int]): Number of documents to return
|
||||
during vector search. Defaults to None.
|
||||
number_of_docs (Optional[int]): Number of documents
|
||||
to return during vector search. Defaults to None.
|
||||
pat (Optional[str], optional): Personal access token. Defaults to None.
|
||||
token (Optional[str], optional): Session token. Defaults to None.
|
||||
**kwargs: Additional keyword arguments to be passed to the Search.
|
||||
|
||||
Returns:
|
||||
Clarifai: Clarifai vectorstore.
|
||||
@ -297,4 +324,6 @@ class Clarifai(VectorStore):
|
||||
number_of_docs=number_of_docs,
|
||||
pat=pat,
|
||||
metadatas=metadatas,
|
||||
token=token,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user