mirror of
https://github.com/aljazceru/Auto-GPT.git
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bfbe613960
Additional changes: * Improve typing * Modularize message history memory & fix/refactor lots of things * Fix summarization * Move memory relevance calculation to MemoryItem & improve test * Fix import warnings in web_selenium.py * Remove `memory_add` ghost command * Implement overlap in `split_text` * Move memory tests into subdirectory * Remove deprecated `get_ada_embedding()` and helpers * Fix used token calculation in `chat_with_ai` * Replace Message TypedDict by dataclass * Fix AgentManager singleton issues in tests --------- Co-authored-by: Auto-GPT-Bot <github-bot@agpt.co>
72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
from typing import Any, overload
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import numpy as np
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import numpy.typing as npt
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import openai
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from autogpt.config import Config
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from autogpt.llm.utils import metered, retry_openai_api
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from autogpt.logs import logger
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Embedding = list[np.float32] | np.ndarray[Any, np.dtype[np.float32]]
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"""Embedding vector"""
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TText = list[int]
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"""Token array representing text"""
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@overload
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def get_embedding(input: str | TText) -> Embedding:
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...
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@overload
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def get_embedding(input: list[str] | list[TText]) -> list[Embedding]:
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...
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@metered
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@retry_openai_api()
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def get_embedding(
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input: str | TText | list[str] | list[TText],
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) -> Embedding | list[Embedding]:
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"""Get an embedding from the ada model.
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Args:
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input: Input text to get embeddings for, encoded as a string or array of tokens.
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Multiple inputs may be given as a list of strings or token arrays.
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Returns:
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List[float]: The embedding.
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"""
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cfg = Config()
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multiple = isinstance(input, list) and all(not isinstance(i, int) for i in input)
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if isinstance(input, str):
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input = input.replace("\n", " ")
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elif multiple and isinstance(input[0], str):
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input = [text.replace("\n", " ") for text in input]
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model = cfg.embedding_model
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if cfg.use_azure:
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kwargs = {"engine": cfg.get_azure_deployment_id_for_model(model)}
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else:
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kwargs = {"model": model}
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logger.debug(
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f"Getting embedding{f's for {len(input)} inputs' if multiple else ''}"
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f" with model '{model}'"
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+ (f" via Azure deployment '{kwargs['engine']}'" if cfg.use_azure else "")
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)
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embeddings = openai.Embedding.create(
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input=input,
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api_key=cfg.openai_api_key,
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**kwargs,
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).data
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if not multiple:
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return embeddings[0]["embedding"]
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embeddings = sorted(embeddings, key=lambda x: x["index"])
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return [d["embedding"] for d in embeddings]
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