Source code for langchain_core.vectorstores.utils
"""Internal utilities for the in memory implementation of VectorStore.
These are part of a private API, and users should not use them directly
as they can change without notice.
"""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
import numpy as np
Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
logger = logging.getLogger(__name__)
def _cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
"""Row-wise cosine similarity between two equal-width matrices.
Args:
X: A matrix of shape (n, m).
Y: A matrix of shape (k, m).
Returns:
A matrix of shape (n, k) where each element (i, j) is the cosine similarity
between the ith row of X and the jth row of Y.
Raises:
ValueError: If the number of columns in X and Y are not the same.
ImportError: If numpy is not installed.
"""
try:
import numpy as np
except ImportError as e:
raise ImportError(
"cosine_similarity requires numpy to be installed. "
"Please install numpy with `pip install numpy`."
) from e
if len(X) == 0 or len(Y) == 0:
return np.array([])
X = np.array(X)
Y = np.array(Y)
if X.shape[1] != Y.shape[1]:
raise ValueError(
f"Number of columns in X and Y must be the same. X has shape {X.shape} "
f"and Y has shape {Y.shape}."
)
try:
import simsimd as simd
X = np.array(X, dtype=np.float32)
Y = np.array(Y, dtype=np.float32)
Z = 1 - np.array(simd.cdist(X, Y, metric="cosine"))
return Z
except ImportError:
logger.debug(
"Unable to import simsimd, defaulting to NumPy implementation. If you want "
"to use simsimd please install with `pip install simsimd`."
)
X_norm = np.linalg.norm(X, axis=1)
Y_norm = np.linalg.norm(Y, axis=1)
# Ignore divide by zero errors run time warnings as those are handled below.
with np.errstate(divide="ignore", invalid="ignore"):
similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
return similarity
[docs]def maximal_marginal_relevance(
query_embedding: np.ndarray,
embedding_list: list,
lambda_mult: float = 0.5,
k: int = 4,
) -> List[int]:
"""Calculate maximal marginal relevance.
Args:
query_embedding: The query embedding.
embedding_list: A list of embeddings.
lambda_mult: The lambda parameter for MMR. Default is 0.5.
k: The number of embeddings to return. Default is 4.
Returns:
A list of indices of the embeddings to return.
Raises:
ImportError: If numpy is not installed.
"""
try:
import numpy as np
except ImportError as e:
raise ImportError(
"maximal_marginal_relevance requires numpy to be installed. "
"Please install numpy with `pip install numpy`."
) from e
if min(k, len(embedding_list)) <= 0:
return []
if query_embedding.ndim == 1:
query_embedding = np.expand_dims(query_embedding, axis=0)
similarity_to_query = _cosine_similarity(query_embedding, embedding_list)[0]
most_similar = int(np.argmax(similarity_to_query))
idxs = [most_similar]
selected = np.array([embedding_list[most_similar]])
while len(idxs) < min(k, len(embedding_list)):
best_score = -np.inf
idx_to_add = -1
similarity_to_selected = _cosine_similarity(embedding_list, selected)
for i, query_score in enumerate(similarity_to_query):
if i in idxs:
continue
redundant_score = max(similarity_to_selected[i])
equation_score = (
lambda_mult * query_score - (1 - lambda_mult) * redundant_score
)
if equation_score > best_score:
best_score = equation_score
idx_to_add = i
idxs.append(idx_to_add)
selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
return idxs