mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
92 lines
3.0 KiB
Python
92 lines
3.0 KiB
Python
from __future__ import annotations
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from typing import Any, Dict, Iterator, List
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from urllib.parse import urlparse
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel, PrivateAttr
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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 MlflowEmbeddings(Embeddings, BaseModel):
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"""Embedding LLMs in MLflow.
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To use, you should have the `mlflow[genai]` python package installed.
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For more information, see https://mlflow.org/docs/latest/llms/deployments.
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Example:
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.. code-block:: python
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from langchain_community.embeddings import MlflowEmbeddings
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embeddings = MlflowEmbeddings(
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target_uri="http://localhost:5000",
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endpoint="embeddings",
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)
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"""
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endpoint: str
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"""The endpoint to use."""
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target_uri: str
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"""The target URI to use."""
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_client: Any = PrivateAttr()
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"""The parameters to use for queries."""
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query_params: Dict[str, str] = {}
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"""The parameters to use for documents."""
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documents_params: Dict[str, str] = {}
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def __init__(self, **kwargs: Any):
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super().__init__(**kwargs)
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self._validate_uri()
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try:
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from mlflow.deployments import get_deploy_client
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self._client = get_deploy_client(self.target_uri)
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except ImportError as e:
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raise ImportError(
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"Failed to create the client. "
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f"Please run `pip install mlflow{self._mlflow_extras}` to install "
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"required dependencies."
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) from e
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@property
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def _mlflow_extras(self) -> str:
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return "[genai]"
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def _validate_uri(self) -> None:
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if self.target_uri == "databricks":
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return
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allowed = ["http", "https", "databricks"]
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if urlparse(self.target_uri).scheme not in allowed:
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raise ValueError(
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f"Invalid target URI: {self.target_uri}. "
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f"The scheme must be one of {allowed}."
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)
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def embed(self, texts: List[str], params: Dict[str, str]) -> List[List[float]]:
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embeddings: List[List[float]] = []
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for txt in _chunk(texts, 20):
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resp = self._client.predict(
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endpoint=self.endpoint,
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inputs={"input": txt, **params}, # type: ignore[arg-type]
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)
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embeddings.extend(r["embedding"] for r in resp["data"])
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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.embed(texts, params=self.documents_params)
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def embed_query(self, text: str) -> List[float]:
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return self.embed([text], params=self.query_params)[0]
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class MlflowCohereEmbeddings(MlflowEmbeddings):
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"""Cohere embedding LLMs in MLflow."""
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query_params: Dict[str, str] = {"input_type": "search_query"}
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documents_params: Dict[str, str] = {"input_type": "search_document"}
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