mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
from __future__ import annotations
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import warnings
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from typing import Any, Iterator, List, Optional
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel
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def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
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for i in range(0, len(texts), size):
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yield texts[i : i + size]
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class MlflowAIGatewayEmbeddings(Embeddings, BaseModel):
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"""MLflow AI Gateway embeddings.
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To use, you should have the ``mlflow[gateway]`` python package installed.
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For more information, see https://mlflow.org/docs/latest/gateway/index.html.
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Example:
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.. code-block:: python
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from langchain_community.embeddings import MlflowAIGatewayEmbeddings
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embeddings = MlflowAIGatewayEmbeddings(
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gateway_uri="<your-mlflow-ai-gateway-uri>",
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route="<your-mlflow-ai-gateway-embeddings-route>"
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)
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"""
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route: str
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"""The route to use for the MLflow AI Gateway API."""
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gateway_uri: Optional[str] = None
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"""The URI for the MLflow AI Gateway API."""
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def __init__(self, **kwargs: Any):
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warnings.warn(
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"`MlflowAIGatewayEmbeddings` is deprecated. Use `MlflowEmbeddings` or "
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"`DatabricksEmbeddings` instead.",
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DeprecationWarning,
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)
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try:
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import mlflow.gateway
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except ImportError as e:
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raise ImportError(
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"Could not import `mlflow.gateway` module. "
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"Please install it with `pip install mlflow[gateway]`."
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) from e
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super().__init__(**kwargs)
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if self.gateway_uri:
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mlflow.gateway.set_gateway_uri(self.gateway_uri)
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def _query(self, texts: List[str]) -> List[List[float]]:
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try:
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import mlflow.gateway
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except ImportError as e:
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raise ImportError(
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"Could not import `mlflow.gateway` module. "
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"Please install it with `pip install mlflow[gateway]`."
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) from e
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embeddings = []
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for txt in _chunk(texts, 20):
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resp = mlflow.gateway.query(self.route, data={"text": txt})
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embeddings.append(resp["embeddings"])
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return embeddings
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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return self._query(texts)
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def embed_query(self, text: str) -> List[float]:
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return self._query([text])[0]
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