Integrate plugin.handle_text_embedding hook (#2804)

* add feature custom text embedding in plugin

* black code format

* _get_embedding_with_plugin()

* Fix docstring & type hint

---------

Co-authored-by: Reinier van der Leer <github@pwuts.nl>

autogpt/memory/vector/utils.py

@@ -1,3 +1,4 @@
+from contextlib import suppress
 from typing import Any, overload
 
 import numpy as np
@@ -12,12 +13,12 @@ Embedding = list[np.float32] | np.ndarray[Any, np.dtype[np.float32]]
 
 
 @overload
-def get_embedding(input: str | TText) -> Embedding:
+def get_embedding(input: str | TText, config: Config) -> Embedding:
     ...
 
 
 @overload
-def get_embedding(input: list[str] | list[TText]) -> list[Embedding]:
+def get_embedding(input: list[str] | list[TText], config: Config) -> list[Embedding]:
     ...
 
 
@@ -37,9 +38,16 @@ def get_embedding(
 
     if isinstance(input, str):
         input = input.replace("\n", " ")
+
+        with suppress(NotImplementedError):
+            return _get_embedding_with_plugin(input, config)
+
     elif multiple and isinstance(input[0], str):
         input = [text.replace("\n", " ") for text in input]
 
+        with suppress(NotImplementedError):
+            return [_get_embedding_with_plugin(i, config) for i in input]
+
     model = config.embedding_model
     kwargs = {"model": model}
     kwargs.update(config.get_openai_credentials(model))
@@ -62,3 +70,13 @@ def get_embedding(
 
     embeddings = sorted(embeddings, key=lambda x: x["index"])
     return [d["embedding"] for d in embeddings]
+
+
+def _get_embedding_with_plugin(text: str, config: Config) -> Embedding:
+    for plugin in config.plugins:
+        if plugin.can_handle_text_embedding(text):
+            embedding = plugin.handle_text_embedding(text)
+            if embedding is not None:
+                return embedding
+
+    raise NotImplementedError

autogpt/models/base_open_ai_plugin.py

@@ -198,18 +198,20 @@ class BaseOpenAIPlugin(AutoGPTPluginTemplate):
     def can_handle_text_embedding(self, text: str) -> bool:
         """This method is called to check that the plugin can
           handle the text_embedding method.
-        Args:
-            text (str): The text to be convert to embedding.
-          Returns:
-              bool: True if the plugin can handle the text_embedding method."""
-        return False
 
-    def handle_text_embedding(self, text: str) -> list:
-        """This method is called when the chat completion is done.
         Args:
             text (str): The text to be convert to embedding.
         Returns:
-            list: The text embedding.
+            bool: True if the plugin can handle the text_embedding method."""
+        return False
+
+    def handle_text_embedding(self, text: str) -> list[float]:
+        """This method is called to create a text embedding.
+
+        Args:
+            text (str): The text to be convert to embedding.
+        Returns:
+            list[float]: The created embedding vector.
         """
 
     def can_handle_user_input(self, user_input: str) -> bool:

tests/unit/models/test_base_open_api_plugin.py

@@ -54,6 +54,7 @@ def test_dummy_plugin_default_methods(dummy_plugin):
     assert not dummy_plugin.can_handle_pre_command()
     assert not dummy_plugin.can_handle_post_command()
     assert not dummy_plugin.can_handle_chat_completion(None, None, None, None)
+    assert not dummy_plugin.can_handle_text_embedding(None)
 
     assert dummy_plugin.on_response("hello") == "hello"
     assert dummy_plugin.post_prompt(None) is None
@@ -77,3 +78,4 @@ def test_dummy_plugin_default_methods(dummy_plugin):
     assert isinstance(post_command, str)
     assert post_command == "upgraded successfully!"
     assert dummy_plugin.handle_chat_completion(None, None, None, None) is None
+    assert dummy_plugin.handle_text_embedding(None) is None