Make annoy an optional dependency

README.md

@@ -203,6 +203,12 @@ Note: as the model is not being re-trained, but uses the training data during in
 
 ### Dynamic Few-Shot Text Classification
 
+*To use this feature, you need to install `annoy` library:*
+
+```bash
+pip install scikit-llm[annoy]
+```
+
 `DynamicFewShotGPTClassifier` dynamically selects N samples per class to include in the prompt. This allows the few-shot classifier to scale to datasets that are too large for the standard context window of LLMs.
 
 *How does it work?*

pyproject.toml

@@ -8,11 +8,10 @@ dependencies = [
   "pandas>=1.5.0",
   "openai>=0.27.0",
   "tqdm>=4.60.0",
-  "annoy>=1.17.2",
   "google-cloud-aiplatform>=1.27.0"
 ]
 name = "scikit-llm"
-version = "0.3.3"
+version = "0.3.4"
 authors = [
   { name="Oleg Kostromin", email="kostromin97@gmail.com" },
   { name="Iryna Kondrashchenko", email="iryna230520@gmail.com" },
@@ -29,6 +28,7 @@ classifiers = [
 
 [project.optional-dependencies]
 gpt4all = ["gpt4all>=1.0.0"]
+annoy = ["annoy>=1.17.2"]
 
 [tool.ruff]
 select = [

skllm/memory/_annoy.py

@@ -2,7 +2,11 @@
 import tempfile
 from typing import Any, List
 
-from annoy import AnnoyIndex
+try:
+    from annoy import AnnoyIndex
+except (ImportError, ModuleNotFoundError):
+    AnnoyIndex = None
+
 from numpy import ndarray
 
 from skllm.memory.base import _BaseMemoryIndex
@@ -20,6 +24,10 @@ class AnnoyMemoryIndex(_BaseMemoryIndex):
     """
 
     def __init__(self, dim: int, metric: str = "euclidean", **kwargs: Any) -> None:
+        if AnnoyIndex is None:
+            raise ImportError(
+                "Annoy is not installed. Please install annoy by running `pip install scikit-llm[annoy]`."
+            )
         self._index = AnnoyIndex(dim, metric)
         self.metric = metric
         self.dim = dim