Source code for langchain_community.embeddings.mlflow

from __future__ import annotations

from typing import Any, Dict, Iterator, List
from urllib.parse import urlparse

from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, PrivateAttr

def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
    for i in range(0, len(texts), size):
        yield texts[i : i + size]

[docs]class MlflowEmbeddings(Embeddings, BaseModel): """Embedding LLMs in MLflow. To use, you should have the `mlflow[genai]` python package installed. For more information, see Example: .. code-block:: python from langchain_community.embeddings import MlflowEmbeddings embeddings = MlflowEmbeddings( target_uri="http://localhost:5000", endpoint="embeddings", ) """ endpoint: str """The endpoint to use.""" target_uri: str """The target URI to use.""" _client: Any = PrivateAttr() """The parameters to use for queries.""" query_params: Dict[str, str] = {} """The parameters to use for documents.""" documents_params: Dict[str, str] = {} def __init__(self, **kwargs: Any): super().__init__(**kwargs) self._validate_uri() try: from mlflow.deployments import get_deploy_client self._client = get_deploy_client(self.target_uri) except ImportError as e: raise ImportError( "Failed to create the client. " f"Please run `pip install mlflow{self._mlflow_extras}` to install " "required dependencies." ) from e @property def _mlflow_extras(self) -> str: return "[genai]" def _validate_uri(self) -> None: if self.target_uri == "databricks": return allowed = ["http", "https", "databricks"] if urlparse(self.target_uri).scheme not in allowed: raise ValueError( f"Invalid target URI: {self.target_uri}. " f"The scheme must be one of {allowed}." )
[docs] def embed(self, texts: List[str], params: Dict[str, str]) -> List[List[float]]: embeddings: List[List[float]] = [] for txt in _chunk(texts, 20): resp = self._client.predict( endpoint=self.endpoint, inputs={"input": txt, **params}, # type: ignore[arg-type] ) embeddings.extend(r["embedding"] for r in resp["data"]) return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: return self.embed(texts, params=self.documents_params)
[docs] def embed_query(self, text: str) -> List[float]: return self.embed([text], params=self.query_params)[0]
[docs]class MlflowCohereEmbeddings(MlflowEmbeddings): """Cohere embedding LLMs in MLflow.""" query_params: Dict[str, str] = {"input_type": "search_query"} documents_params: Dict[str, str] = {"input_type": "search_document"}