Removes redundant splitters file

semantic_router/utils/splitters.py

-from typing import Any, List, Literal, Optional
-
-import numpy as np
-from pydantic.v1 import BaseModel
-
-from semantic_router.encoders import BaseEncoder
-
-
-class DocumentSplit(BaseModel):
-    docs: List[Any]
-    is_triggered: bool = False
-    triggered_score: Optional[float] = None
-
-
-def semantic_splitter(
-    encoder: BaseEncoder,
-    docs: List[Any],
-    threshold: float,
-    split_method: Literal[
-        "consecutive_similarity_drop", "cumulative_similarity_drop"
-    ] = "consecutive_similarity_drop",
-) -> List[DocumentSplit]:
-    """
-    Splits a list of documents base on semantic similarity changes.
-
-    Method 1: "consecutive_similarity_drop" - This method splits documents based on
-    the changes in similarity scores between consecutive documents.
-    Method 2: "cumulative_similarity_drop" - This method segments the documents based
-    on the changes in cumulative similarity score of the documents within the same
-    split.
-
-    Args:
-        encoder (BaseEncoder): Encoder for document embeddings.
-        docs (List[str]): Documents to split.
-        threshold (float): The similarity drop value that will trigger a new document
-        split.
-        split_method (str): The method to use for splitting.
-
-    Returns:
-        Dict[str, List[str]]: Splits with corresponding documents.
-    """
-    total_docs = len(docs)
-    splits = []
-    curr_split_start_idx = 0
-    curr_split_num = 1
-
-    if split_method == "consecutive_similarity_drop":
-        doc_embeds = encoder(docs)
-        norm_embeds = doc_embeds / np.linalg.norm(doc_embeds, axis=1, keepdims=True)
-        sim_matrix = np.matmul(norm_embeds, norm_embeds.T)
-
-        for idx in range(1, total_docs):
-            curr_sim_score = sim_matrix[idx - 1][idx]
-            if idx < len(sim_matrix) and curr_sim_score < threshold:
-                splits.append(
-                    DocumentSplit(
-                        docs=docs[curr_split_start_idx:idx],
-                        is_triggered=True,
-                        triggered_score=curr_sim_score,
-                    )
-                )
-                curr_split_start_idx = idx
-                curr_split_num += 1
-
-    elif split_method == "cumulative_similarity_drop":
-        for idx in range(1, total_docs):
-            if idx + 1 < total_docs:
-                curr_split_docs = "\n".join(docs[curr_split_start_idx : idx + 1])
-                next_doc = docs[idx + 1]
-
-                curr_split_docs_embed = encoder([curr_split_docs])[0]
-                next_doc_embed = encoder([next_doc])[0]
-
-                curr_sim_score = np.dot(curr_split_docs_embed, next_doc_embed) / (
-                    np.linalg.norm(curr_split_docs_embed)
-                    * np.linalg.norm(next_doc_embed)
-                )
-
-                if curr_sim_score < threshold:
-                    splits.append(
-                        DocumentSplit(
-                            docs=docs[curr_split_start_idx : idx + 1],
-                            is_triggered=True,
-                            triggered_score=curr_sim_score,
-                        )
-                    )
-                    curr_split_start_idx = idx + 1
-                    curr_split_num += 1
-
-    else:
-        raise ValueError(
-            "Invalid 'split_method'. Choose either 'consecutive_similarity_drop' or"
-            " 'cumulative_similarity_drop'."
-        )
-
-    splits.append(DocumentSplit(docs=docs[curr_split_start_idx:]))
-    return splits