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community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
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163
libs/community/langchain_community/embeddings/clarifai.py
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163
libs/community/langchain_community/embeddings/clarifai.py
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import logging
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from typing import 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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logger = logging.getLogger(__name__)
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class ClarifaiEmbeddings(BaseModel, Embeddings):
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"""Clarifai embedding models.
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To use, you should have the ``clarifai`` python package installed, and the
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environment variable ``CLARIFAI_PAT`` set with your personal access token or pass it
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as a named parameter to the constructor.
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Example:
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.. code-block:: python
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from langchain_community.embeddings import ClarifaiEmbeddings
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clarifai = ClarifaiEmbeddings(user_id=USER_ID,
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app_id=APP_ID,
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model_id=MODEL_ID)
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(or)
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clarifai_llm = Clarifai(model_url=EXAMPLE_URL)
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"""
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model_url: Optional[str] = None
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"""Model url to use."""
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model_id: Optional[str] = None
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"""Model id to use."""
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model_version_id: Optional[str] = None
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"""Model version id to use."""
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app_id: Optional[str] = None
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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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"""Clarifai personal access token to use."""
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api_base: str = "https://api.clarifai.com"
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@root_validator()
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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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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_url = values.get("model_url")
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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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return values
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Call out to Clarifai's embedding models.
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Args:
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texts: The list of texts to embed.
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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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input_obj = Inputs(pat=pat)
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batch_size = 32
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embeddings = []
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try:
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for i in range(0, len(texts), batch_size):
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batch = texts[i : i + batch_size]
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input_batch = [
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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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embeddings.extend(
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[
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list(output.data.embeddings[0].vector)
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for output in predict_response.outputs
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]
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)
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except Exception as e:
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logger.error(f"Predict failed, exception: {e}")
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return embeddings
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def embed_query(self, text: str) -> List[float]:
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"""Call out to Clarifai's embedding models.
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Args:
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text: The text to embed.
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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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bytes(text, "utf-8"), input_type="text"
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)
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embeddings = [
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list(op.data.embeddings[0].vector) for op in predict_response.outputs
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]
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except Exception as e:
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logger.error(f"Predict failed, exception: {e}")
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return embeddings[0]
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