mirror of
https://github.com/aljazceru/Auto-GPT.git
synced 2026-01-08 00:34:20 +01:00
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>
75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
import abc
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import functools
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from typing import MutableSet, Sequence
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import numpy as np
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from autogpt.config.config import Config
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from autogpt.logs import logger
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from autogpt.singleton import AbstractSingleton
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from .. import MemoryItem, MemoryItemRelevance
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from ..utils import Embedding, get_embedding
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class VectorMemoryProvider(MutableSet[MemoryItem], AbstractSingleton):
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@abc.abstractmethod
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def __init__(self, config: Config):
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pass
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def get(self, query: str) -> MemoryItemRelevance | None:
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"""
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Gets the data from the memory that is most relevant to the given query.
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Args:
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data: The data to compare to.
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Returns: The most relevant Memory
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"""
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result = self.get_relevant(query, 1)
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return result[0] if result else None
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def get_relevant(self, query: str, k: int) -> Sequence[MemoryItemRelevance]:
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"""
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Returns the top-k most relevant memories for the given query
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Args:
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query: the query to compare stored memories to
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k: the number of relevant memories to fetch
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Returns:
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list[MemoryItemRelevance] containing the top [k] relevant memories
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"""
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if len(self) < 1:
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return []
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logger.debug(
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f"Searching for {k} relevant memories for query '{query}'; "
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f"{len(self)} memories in index"
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)
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relevances = self.score_memories_for_relevance(query)
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logger.debug(f"Memory relevance scores: {[str(r) for r in relevances]}")
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# take last k items and reverse
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top_k_indices = np.argsort([r.score for r in relevances])[-k:][::-1]
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return [relevances[i] for i in top_k_indices]
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def score_memories_for_relevance(
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self, for_query: str
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) -> Sequence[MemoryItemRelevance]:
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"""
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Returns MemoryItemRelevance for every memory in the index.
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Implementations may override this function for performance purposes.
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"""
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e_query: Embedding = get_embedding(for_query)
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return [m.relevance_for(for_query, e_query) for m in self]
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def get_stats(self) -> tuple[int, int]:
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"""
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Returns:
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tuple (n_memories: int, n_chunks: int): the stats of the memory index
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"""
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return len(self), functools.reduce(lambda t, m: t + len(m.e_chunks), self, 0)
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