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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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147
libs/community/langchain_community/embeddings/mosaicml.py
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147
libs/community/langchain_community/embeddings/mosaicml.py
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from typing import Any, Dict, List, Mapping, Optional, Tuple
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import requests
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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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class MosaicMLInstructorEmbeddings(BaseModel, Embeddings):
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"""MosaicML embedding service.
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To use, you should have the
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environment variable ``MOSAICML_API_TOKEN`` set with your API token, or pass
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it 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.llms import MosaicMLInstructorEmbeddings
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endpoint_url = (
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"https://models.hosted-on.mosaicml.hosting/instructor-large/v1/predict"
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)
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mosaic_llm = MosaicMLInstructorEmbeddings(
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endpoint_url=endpoint_url,
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mosaicml_api_token="my-api-key"
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)
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"""
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endpoint_url: str = (
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"https://models.hosted-on.mosaicml.hosting/instructor-xl/v1/predict"
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)
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"""Endpoint URL to use."""
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embed_instruction: str = "Represent the document for retrieval: "
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"""Instruction used to embed documents."""
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query_instruction: str = (
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"Represent the question for retrieving supporting documents: "
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)
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"""Instruction used to embed the query."""
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retry_sleep: float = 1.0
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"""How long to try sleeping for if a rate limit is encountered"""
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mosaicml_api_token: Optional[str] = None
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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 api key and python package exists in environment."""
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mosaicml_api_token = get_from_dict_or_env(
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values, "mosaicml_api_token", "MOSAICML_API_TOKEN"
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)
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values["mosaicml_api_token"] = mosaicml_api_token
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return values
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {"endpoint_url": self.endpoint_url}
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def _embed(
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self, input: List[Tuple[str, str]], is_retry: bool = False
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) -> List[List[float]]:
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payload = {"inputs": input}
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# HTTP headers for authorization
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headers = {
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"Authorization": f"{self.mosaicml_api_token}",
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"Content-Type": "application/json",
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}
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# send request
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try:
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response = requests.post(self.endpoint_url, headers=headers, json=payload)
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except requests.exceptions.RequestException as e:
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raise ValueError(f"Error raised by inference endpoint: {e}")
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try:
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if response.status_code == 429:
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if not is_retry:
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import time
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time.sleep(self.retry_sleep)
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return self._embed(input, is_retry=True)
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raise ValueError(
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f"Error raised by inference API: rate limit exceeded.\nResponse: "
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f"{response.text}"
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)
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parsed_response = response.json()
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# The inference API has changed a couple of times, so we add some handling
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# to be robust to multiple response formats.
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if isinstance(parsed_response, dict):
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output_keys = ["data", "output", "outputs"]
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for key in output_keys:
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if key in parsed_response:
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output_item = parsed_response[key]
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break
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else:
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raise ValueError(
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f"No key data or output in response: {parsed_response}"
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)
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if isinstance(output_item, list) and isinstance(output_item[0], list):
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embeddings = output_item
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else:
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embeddings = [output_item]
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else:
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raise ValueError(f"Unexpected response type: {parsed_response}")
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except requests.exceptions.JSONDecodeError as e:
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raise ValueError(
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f"Error raised by inference API: {e}.\nResponse: {response.text}"
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)
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return embeddings
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed documents using a MosaicML deployed instructor embedding model.
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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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instruction_pairs = [(self.embed_instruction, text) for text in texts]
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embeddings = self._embed(instruction_pairs)
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return embeddings
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def embed_query(self, text: str) -> List[float]:
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"""Embed a query using a MosaicML deployed instructor embedding model.
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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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instruction_pair = (self.query_instruction, text)
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embedding = self._embed([instruction_pair])[0]
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return embedding
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