1 files changed, 0 insertions, 67 deletions
diff --git a/autogpt/memory/vector/utils.py b/autogpt/memory/vector/utils.py
deleted file mode 100644
index 74438f28c..000000000
--- a/autogpt/memory/vector/utils.py
+++ /dev/null
@@ -1,67 +0,0 @@
-from typing import Any, overload
-
-import numpy as np
-
-from autogpt.config import Config
-from autogpt.llm.base import TText
-from autogpt.llm.providers import openai as iopenai
-from autogpt.logs import logger
-
-Embedding = list[np.float32] | np.ndarray[Any, np.dtype[np.float32]]
-"""Embedding vector"""
-
-
-@overload
-def get_embedding(input: str | TText) -> Embedding:
-    ...
-
-
-@overload
-def get_embedding(input: list[str] | list[TText]) -> list[Embedding]:
-    ...
-
-
-def get_embedding(
-    input: str | TText | list[str] | list[TText], config: Config
-) -> Embedding | list[Embedding]:
-    """Get an embedding from the ada model.
-
-    Args:
-        input: Input text to get embeddings for, encoded as a string or array of tokens.
-            Multiple inputs may be given as a list of strings or token arrays.
-
-    Returns:
-        List[float]: The embedding.
-    """
-    multiple = isinstance(input, list) and all(not isinstance(i, int) for i in input)
-
-    if isinstance(input, str):
-        input = input.replace("\n", " ")
-    elif multiple and isinstance(input[0], str):
-        input = [text.replace("\n", " ") for text in input]
-
-    model = config.embedding_model
-    if config.use_azure:
-        kwargs = config.get_azure_kwargs(model)
-    else:
-        kwargs = {"model": model}
-
-    logger.debug(
-        f"Getting embedding{f's for {len(input)} inputs' if multiple else ''}"
-        f" with model '{model}'"
-        + (f" via Azure deployment '{kwargs['engine']}'" if config.use_azure else "")
-    )
-    if config.use_azure:
-        breakpoint()
-
-    embeddings = iopenai.create_embedding(
-        input,
-        **kwargs,
-        api_key=config.openai_api_key,
-    ).data
-
-    if not multiple:
-        return embeddings[0]["embedding"]
-
-    embeddings = sorted(embeddings, key=lambda x: x["index"])
-    return [d["embedding"] for d in embeddings]