mirror of
https://github.com/hwchase17/langchain
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Solar is a large language model developed by [Upstage](https://upstage.ai/). It's a powerful and purpose-trained LLM. You can visit the embedding service provided by Solar within this pr. You may get **SOLAR_API_KEY** from https://console.upstage.ai/services/embedding You can refer to more details about accepted llm integration at https://python.langchain.com/docs/integrations/llms/solar.
140 lines
4.0 KiB
Python
140 lines
4.0 KiB
Python
from __future__ import annotations
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import logging
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from typing import Any, Callable, Dict, List, Optional
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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, SecretStr, root_validator
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
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from tenacity import (
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before_sleep_log,
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retry,
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stop_after_attempt,
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wait_exponential,
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)
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logger = logging.getLogger(__name__)
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def _create_retry_decorator() -> Callable[[Any], Any]:
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"""Returns a tenacity retry decorator."""
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multiplier = 1
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min_seconds = 1
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max_seconds = 4
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max_retries = 6
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return retry(
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reraise=True,
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stop=stop_after_attempt(max_retries),
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wait=wait_exponential(multiplier=multiplier, min=min_seconds, max=max_seconds),
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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def embed_with_retry(embeddings: SolarEmbeddings, *args: Any, **kwargs: Any) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator()
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@retry_decorator
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def _embed_with_retry(*args: Any, **kwargs: Any) -> Any:
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return embeddings.embed(*args, **kwargs)
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return _embed_with_retry(*args, **kwargs)
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class SolarEmbeddings(BaseModel, Embeddings):
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"""Solar's embedding service.
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To use, you should have the environment variable``SOLAR_API_KEY`` set
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with your API token, or pass 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.embeddings import SolarEmbeddings
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embeddings = SolarEmbeddings()
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query_text = "This is a test query."
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query_result = embeddings.embed_query(query_text)
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document_text = "This is a test document."
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document_result = embeddings.embed_documents([document_text])
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"""
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endpoint_url: str = "https://api.upstage.ai/v1/solar/embeddings"
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"""Endpoint URL to use."""
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model: str = "solar-1-mini-embedding-query"
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"""Embeddings model name to use."""
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solar_api_key: Optional[SecretStr] = None
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"""API Key for Solar API."""
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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 api key exists in environment."""
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solar_api_key = convert_to_secret_str(
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get_from_dict_or_env(values, "solar_api_key", "SOLAR_API_KEY")
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)
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values["solar_api_key"] = solar_api_key
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return values
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def embed(
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self,
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text: str,
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) -> List[List[float]]:
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payload = {
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"model": self.model,
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"input": text,
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}
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# HTTP headers for authorization
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headers = {
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"Authorization": f"Bearer {self.solar_api_key.get_secret_value()}", # type: ignore[union-attr]
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"Content-Type": "application/json",
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}
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# send request
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response = requests.post(self.endpoint_url, headers=headers, json=payload)
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parsed_response = response.json()
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# check for errors
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if len(parsed_response["data"]) == 0:
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raise ValueError(
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f"Solar API returned an error: {parsed_response['base_resp']}"
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)
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embedding = parsed_response["data"][0]["embedding"]
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return embedding
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed documents using a Solar embedding endpoint.
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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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embeddings = [embed_with_retry(self, text=text) for text in texts]
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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 Solar embedding endpoint.
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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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embedding = embed_with_retry(self, text=text)
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return embedding
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