Source code for langchain_community.embeddings.jina

from typing import Any, Dict, List, Optional

import requests
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

JINA_API_URL: str = ""

[docs]class JinaEmbeddings(BaseModel, Embeddings): """Jina embedding models.""" session: Any #: :meta private: model_name: str = "jina-embeddings-v2-base-en" jina_api_key: Optional[SecretStr] = None @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that auth token exists in environment.""" try: jina_api_key = convert_to_secret_str( get_from_dict_or_env(values, "jina_api_key", "JINA_API_KEY") ) except ValueError as original_exc: try: jina_api_key = convert_to_secret_str( get_from_dict_or_env(values, "jina_auth_token", "JINA_AUTH_TOKEN") ) except ValueError: raise original_exc session = requests.Session() session.headers.update( { "Authorization": f"Bearer {jina_api_key.get_secret_value()}", "Accept-Encoding": "identity", "Content-type": "application/json", } ) values["session"] = session return values def _embed(self, texts: List[str]) -> List[List[float]]: # Call Jina AI Embedding API resp = # type: ignore JINA_API_URL, json={"input": texts, "model": self.model_name} ).json() if "data" not in resp: raise RuntimeError(resp["detail"]) embeddings = resp["data"] # Sort resulting embeddings by index sorted_embeddings = sorted(embeddings, key=lambda e: e["index"]) # type: ignore # Return just the embeddings return [result["embedding"] for result in sorted_embeddings]
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Call out to Jina's embedding endpoint. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ return self._embed(texts)
[docs] def embed_query(self, text: str) -> List[float]: """Call out to Jina's embedding endpoint. Args: text: The text to embed. Returns: Embeddings for the text. """ return self._embed([text])[0]