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https://github.com/hwchase17/langchain
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6b2a57161a
- **Description:** add support for kwargs in`MlflowEmbeddings` `embed_document()` and `embed_query()` so that all the arguments required by Cohere API (and others?) can be passed down to the server. - **Issue:** #15234 - **Dependencies:** MLflow with MLflow Deployments (`pip install mlflow[genai]`) **Tests** Now this code [adapted from the docs](https://python.langchain.com/docs/integrations/providers/mlflow#embeddings-example) for the Cohere API works locally. ```python """ Setup ----- export COHERE_API_KEY=... mlflow deployments start-server --config-path examples/deployments/cohere/config.yaml Run --- python /path/to/this/file.py """ embeddings = MlflowCohereEmbeddings(target_uri="http://127.0.0.1:5000", endpoint="embeddings") print(embeddings.embed_query("hello")[:3]) print(embeddings.embed_documents(["hello", "world"])[0][:3]) ``` Output ``` [0.060455322, 0.028793335, -0.025848389] [0.031707764, 0.021057129, -0.009361267] ```
89 lines
2.9 KiB
Python
89 lines
2.9 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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"""Wrapper around embeddings 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/server.html.
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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, inputs={"input": txt, **params}
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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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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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