1 files changed, 0 insertions, 77 deletions
diff --git a/autogpt/memory/vector/providers/base.py b/autogpt/memory/vector/providers/base.py
deleted file mode 100644
index aa233228f..000000000
--- a/autogpt/memory/vector/providers/base.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import abc
-import functools
-from typing import MutableSet, Sequence
-
-import numpy as np
-
-from autogpt.config.config import Config
-from autogpt.logs import logger
-
-from .. import MemoryItem, MemoryItemRelevance
-from ..utils import Embedding, get_embedding
-
-
-class VectorMemoryProvider(MutableSet[MemoryItem]):
-    @abc.abstractmethod
-    def __init__(self, config: Config):
-        pass
-
-    def get(self, query: str, config: Config) -> MemoryItemRelevance | None:
-        """
-        Gets the data from the memory that is most relevant to the given query.
-
-        Args:
-            query: The query used to retrieve information.
-            config: The config Object.
-
-        Returns: The most relevant Memory
-        """
-        result = self.get_relevant(query, 1, config)
-        return result[0] if result else None
-
-    def get_relevant(
-        self, query: str, k: int, config: Config
-    ) -> Sequence[MemoryItemRelevance]:
-        """
-        Returns the top-k most relevant memories for the given query
-
-        Args:
-            query: the query to compare stored memories to
-            k: the number of relevant memories to fetch
-            config: The config Object.
-
-        Returns:
-            list[MemoryItemRelevance] containing the top [k] relevant memories
-        """
-        if len(self) < 1:
-            return []
-
-        logger.debug(
-            f"Searching for {k} relevant memories for query '{query}'; "
-            f"{len(self)} memories in index"
-        )
-
-        relevances = self.score_memories_for_relevance(query, config)
-        logger.debug(f"Memory relevance scores: {[str(r) for r in relevances]}")
-
-        # take last k items and reverse
-        top_k_indices = np.argsort([r.score for r in relevances])[-k:][::-1]
-
-        return [relevances[i] for i in top_k_indices]
-
-    def score_memories_for_relevance(
-        self, for_query: str, config: Config
-    ) -> Sequence[MemoryItemRelevance]:
-        """
-        Returns MemoryItemRelevance for every memory in the index.
-        Implementations may override this function for performance purposes.
-        """
-        e_query: Embedding = get_embedding(for_query, config)
-        return [m.relevance_for(for_query, e_query) for m in self]
-
-    def get_stats(self) -> tuple[int, int]:
-        """
-        Returns:
-            tuple (n_memories: int, n_chunks: int): the stats of the memory index
-        """
-        return len(self), functools.reduce(lambda t, m: t + len(m.e_chunks), self, 0)