E5Encoder, documentation dependencies, mkdocstrings

docs/encoders.md

@@ -33,3 +33,11 @@ Turftopic currently supports OpenAI, Voyage and Cohere embeddings.
 :::turftopic.encoders.OpenAIEmbeddings
 
 :::turftopic.encoders.VoyageEmbeddings
+
+## E5 Embeddings
+
+Most E5 models expect the input to be prefixed with something like `"query: "` (see the [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) model card).  
+In instructional E5 models, it is also possible to add an instruction, following the format `f"Instruct: {task_description} \nQuery: {document}"` (see the [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) model card).  
+In Turftopic, E5 embeddings including the prefixing is handled by the `E5Encoder`.
+
+:::turftopic.encoders.E5Encoder

mkdocs.yml

@@ -42,7 +42,6 @@ plugins:
           show_symbol_type_heading: true
           docstring_style: numpy
           heading_level: 3
-    custom_templates: templates    - content.code.select
 
 markdown_extensions:
   - pymdownx.highlight:

pyproject.toml

@@ -22,6 +22,13 @@ pyro-ppl = { version = "^1.8.0", optional = true }
 [tool.poetry.extras]
 pyro-ppl = ["pyro-ppl"]
 
+[tool.poetry.group.docs]
+optional = true
+
+[tool.poetry.group.docs.dependencies]
+mkdocs = "^1.5.2"
+mkdocs-material = "^9.5.12"
+mkdocstrings = { version = "^0.24.0", extras = ["python"] }
 
 [build-system]
 requires = ["poetry-core"]

turftopic/encoders/__init__.py

@@ -2,10 +2,12 @@
 from turftopic.encoders.cohere import CohereEmbeddings
 from turftopic.encoders.openai import OpenAIEmbeddings
 from turftopic.encoders.voyage import VoyageEmbeddings
+from turftopic.encoders.e5 import E5Encoder
 
 __all__ = [
     "CohereEmbeddings",
     "OpenAIEmbeddings",
     "VoyageEmbeddings",
     "ExternalEncoder",
+    "E5Encoder"
 ]

turftopic/encoders/e5.py

@@ -0,0 +1,122 @@
+import warnings
+from typing import Callable, Optional
+
+import numpy as np
+from sentence_transformers import SentenceTransformer
+
+
+class E5Encoder(SentenceTransformer):
+    """Encoder model oriented at using E5 models.
+    ```python
+    from turftopic.encoders import E5Encoder
+    from turftopic import GMM
+    model = GMM(10, encoder=E5Encoder(model_name="intfloat/multilingual-e5-small", prefix="query: "))
+    ```
+    Parameters
+    ----------
+    model_name: str
+        Embedding model to use.
+        Either a SentenceTransformers pre-trained models or a model from HuggingFace Hub.
+    prefix : Optional[str]
+        A string that gets added to the start of each document (formats each document followingly: `f"{prefix}{text}"`).
+        Expected by most E5 models. Consult model cards on Hugging Face to see what prefix is expected by your specific model.
+    preprocessor : Optional[Callable]
+        A function that formats documents as desired.
+        Overwrites `prefix` and only applies if `prefix == None`.
+        Both input and output must be string.
+        First argument must be input text.
+        By default `None`.
+    Examples
+    --------
+    Instructional models can also be used.
+    In this case, the documents should be prefixed with a one-sentence instruction that describes the task.
+    See Notes for available models and instruction suggestions.
+    ```python
+    from turftopic.encoders import E5Encoder
+    def add_instruct_prefix(document: str) -> str:
+        task_description = "YOUR_INSTRUCTION"
+        return f'Instruct: {task_description}\nQuery: {document}'
+    encoder = E5Encoder(model_name="intfloat/multilingual-e5-large-instruct", preprocessor=add_instruct_prefix)
+    model = GMM(10, encoder=encoder)
+    ```
+    Or the same can be done using a `prefix` argument:
+    ```python
+    from turftopic.encoders import E5Encoder
+    from turftopic import GMM
+    prefix = "Instruct: YOUR_INSTRUCTION\nQuery: "
+    encoder = E5Encoder(model_name="intfloat/multilingual-e5-large-instruct", prefix=prefix)
+    model = GMM(10, encoder=encoder)
+    ```
+    Notes
+    -----
+    See available E5-based sentence transformers on Hugging Face Hub:
+    https://huggingface.co/models?library=sentence-transformers&sort=trending&search=e5
+    Instruction templates:
+    https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106
+    """
+
+    def __init__(
+        self,
+        model_name: str,
+        prefix: Optional[str] = None,
+        preprocessor: Optional[Callable] = None,
+        **kwargs,
+    ):
+        super().__init__(model_name, **kwargs)
+
+        # check for both prefix and preprocessor being specified
+        if prefix is not None and preprocessor is not None:
+            warnings.warn(
+                "Both `prefix` and `preprocessor` are specified. `preprocessor` will be ignored! "
+                "To avoid this warning, specify only one of them.",
+            )
+
+        # pick either prefix or preprocessor to do the job
+        if prefix is not None:
+            self.preprocessor = lambda x: f"{prefix}{x}"
+        else:
+            if preprocessor is not None:
+                try:
+                    assert self._is_preprocessor_valid(
+                        preprocessor=preprocessor
+                    )
+                    self.preprocessor = preprocessor
+                except:
+                    raise AssertionError(
+                        "`preprocessor` failed vaildation. Please make sure your preprocessor returns type `str`."
+                    )
+            else:
+                raise ValueError(
+                    "Either `prefix` or `preprocessor` must be specified."
+                )
+
+    @staticmethod
+    def _is_preprocessor_valid(preprocessor: Callable) -> bool:
+        """Check if preprocessor returns string."""
+        input_0 = None
+        input_1 = "What are assertions? and why would you use them?"
+        output_0 = preprocessor(input_0)
+        output_1 = preprocessor(input_1)
+
+        return all(
+            [
+                isinstance(output_0, str),
+                isinstance(output_1, str),
+            ]
+        )
+
+    def encode(self, sentences: list[str], **kwargs) -> np.ndarray:
+        """
+        Parameters
+        ----------
+        sentences: list[str]
+            Input text.
+        Notes
+        -----
+        See docs for `SentenceTransformer.encode` for available **kwargs
+        https://www.sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode
+        """
+
+        sentences = [self.preprocessor(sentence) for sentence in sentences]
+
+        return super().encode(sentences, **kwargs)