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Thank you for contributing to LangChain! - [x] **PR title**: "package: description" - Where "package" is whichever of langchain, community, core, etc. is being modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI changes. - Example: "community: add foobar LLM" - [ ] **PR message**: ***Delete this entire checklist*** and replace with - **Description:** a description of the change - **Issue:** the issue # it fixes, if applicable - **Dependencies:** any dependencies required for this change - **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out! - [ ] **Add tests and docs**: If you're adding a new integration, please include 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. - [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
from __future__ import annotations
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from typing import Iterator, List
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from urllib.parse import urlparse
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from langchain_core._api import deprecated
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from langchain_community.embeddings.mlflow import MlflowEmbeddings
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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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@deprecated(
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since="0.3.3",
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removal="1.0",
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alternative_import="databricks_langchain.DatabricksEmbeddings",
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)
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class DatabricksEmbeddings(MlflowEmbeddings):
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"""Databricks embeddings.
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To use, you should have the ``mlflow`` 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 DatabricksEmbeddings
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embeddings = DatabricksEmbeddings(
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target_uri="databricks",
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endpoint="embeddings",
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)
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"""
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target_uri: str = "databricks"
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"""The target URI to use. Defaults to ``databricks``."""
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@property
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def _mlflow_extras(self) -> str:
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return ""
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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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if urlparse(self.target_uri).scheme != "databricks":
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raise ValueError(
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"Invalid target URI. The target URI must be a valid databricks URI."
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)
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