diff --git a/semantic_router/schema.py b/semantic_router/schema.py
index 129d154a65e9d7dcbf7c3e37002d65d1c99f3f92..0876bae43a850ee231122f568140006a3f2ac948 100644
--- a/semantic_router/schema.py
+++ b/semantic_router/schema.py
@@ -11,9 +11,9 @@ from semantic_router.encoders import (
OpenAIEncoder,
)
# from semantic_router.utils.splitters import DocumentSplit, semantic_splitter
-from semantic_router.splitters.consecutive_sim import ConsecutiveSimSplitter
-from semantic_router.splitters.cumulative_sim import CumulativeSimSplitter
-from semantic_router.splitters.cav_sim import CAVSimSplitter
+# from semantic_router.splitters.consecutive_sim import ConsecutiveSimSplitter
+# from semantic_router.splitters.cumulative_sim import CumulativeSimSplitter
+# from semantic_router.splitters.cav_sim import CAVSimSplitter
class EncoderType(Enum):
@@ -77,60 +77,60 @@ class DocumentSplit(BaseModel):
class Conversation(BaseModel):
messages: List[Message]
- topics: List[Tuple[int, str]] = []
- splitter = None
+ # topics: List[Tuple[int, str]] = []
+ # splitter = None
def add_new_messages(self, new_messages: List[Message]):
self.messages.extend(new_messages)
- def configure_splitter(
- self,
- encoder: BaseEncoder,
- threshold: float = 0.5,
- split_method: Literal[
- "consecutive_similarity", "cumulative_similarity", "cav_similarity"
- ] = "consecutive_similarity",
- ):
- if split_method == "consecutive_similarity":
- self.splitter = ConsecutiveSimSplitter(encoder=encoder, similarity_threshold=threshold)
- elif split_method == "cumulative_similarity":
- self.splitter = CumulativeSimSplitter(encoder=encoder, similarity_threshold=threshold)
- elif split_method == "cav_similarity":
- self.splitter = CAVSimSplitter(encoder=encoder, similarity_threshold=threshold)
- else:
- raise ValueError(f"Invalid split method: {split_method}")
-
- def split_by_topic(self):
- if self.splitter is None:
- raise ValueError("Splitter is not configured. Please call configure_splitter first.")
+ # def configure_splitter(
+ # self,
+ # encoder: BaseEncoder,
+ # threshold: float = 0.5,
+ # split_method: Literal[
+ # "consecutive_similarity", "cumulative_similarity", "cav_similarity"
+ # ] = "consecutive_similarity",
+ # ):
+ # if split_method == "consecutive_similarity":
+ # self.splitter = ConsecutiveSimSplitter(encoder=encoder, similarity_threshold=threshold)
+ # elif split_method == "cumulative_similarity":
+ # self.splitter = CumulativeSimSplitter(encoder=encoder, similarity_threshold=threshold)
+ # elif split_method == "cav_similarity":
+ # self.splitter = CAVSimSplitter(encoder=encoder, similarity_threshold=threshold)
+ # else:
+ # raise ValueError(f"Invalid split method: {split_method}")
+
+ # def split_by_topic(self):
+ # if self.splitter is None:
+ # raise ValueError("Splitter is not configured. Please call configure_splitter first.")
- # Get the messages that haven't been clustered into topics yet
- unclustered_messages = self.messages[len(self.topics):]
+ # # Get the messages that haven't been clustered into topics yet
+ # unclustered_messages = self.messages[len(self.topics):]
- # Check if there are any messages that have been assigned topics
- if len(self.topics) >= 1:
- # Include the last message in the docs
- docs = [self.topics[-1][1]]
- else:
- # No messages have been assigned topics yet
- docs = []
+ # # Check if there are any messages that have been assigned topics
+ # if len(self.topics) >= 1:
+ # # Include the last message in the docs
+ # docs = [self.topics[-1][1]]
+ # else:
+ # # No messages have been assigned topics yet
+ # docs = []
- # Add the unclustered messages to the docs
- docs.extend([f"{m.role}: {m.content}" for m in unclustered_messages])
+ # # Add the unclustered messages to the docs
+ # docs.extend([f"{m.role}: {m.content}" for m in unclustered_messages])
- # Use the splitter to split the documents
- new_topics = self.splitter(docs)
+ # # Use the splitter to split the documents
+ # new_topics = self.splitter(docs)
- # Check if the first new topic includes the first new message.
- # This means that the first new message shares the same topic as the last old message to have been assigned a topic.
- if docs[-len(unclustered_messages)] in new_topics[0].docs:
- start = self.topics[-1][0]
- else:
- start = len(self.topics) + 1
+ # # Check if the first new topic includes the first new message.
+ # # This means that the first new message shares the same topic as the last old message to have been assigned a topic.
+ # if docs[-len(unclustered_messages)] in new_topics[0].docs:
+ # start = self.topics[-1][0]
+ # else:
+ # start = self.topics[-1][0] + 1
- # Add the new topics to the list of topics with unique IDs
- for i, topic in enumerate(new_topics, start=start):
- for message in topic.docs:
- self.topics.append((i, message))
+ # # Add the new topics to the list of topics with unique IDs
+ # for i, topic in enumerate(new_topics, start=start):
+ # for message in topic.docs:
+ # self.topics.append((i, message))
- return new_topics
\ No newline at end of file
+ # return new_topics
\ No newline at end of file
diff --git a/semantic_router/splitters/consecutive_sim.py b/semantic_router/splitters/consecutive_sim.py
index a9038750685a0c404027a2511dfda7b9df121619..b61610e585ce76be5aefcb4f4431dbbff12bda67 100644
--- a/semantic_router/splitters/consecutive_sim.py
+++ b/semantic_router/splitters/consecutive_sim.py
@@ -44,4 +44,103 @@ class ConsecutiveSimSplitter(BaseSplitter):
curr_split_start_idx = idx
curr_split_num += 1
splits.append(DocumentSplit(docs=list(docs[curr_split_start_idx:])))
+ return splits
+
+
+class ConsecutiveAvgSimSplitter(BaseSplitter):
+ def __init__(
+ self,
+ encoder: BaseEncoder,
+ name: str = "consecutive_similarity_splitter",
+ similarity_threshold: float = 0.45,
+ drop_threshold: float = 0.1 # Additional parameter to control the drop threshold
+ ):
+ super().__init__(
+ name=name,
+ similarity_threshold=similarity_threshold,
+ encoder=encoder
+ )
+
+ def __call__(self, docs: List[str], drop_threshold):
+ doc_embeds = self.encoder(docs)
+ norm_embeds = doc_embeds / np.linalg.norm(doc_embeds, axis=1, keepdims=True)
+ sim_matrix = np.matmul(norm_embeds, norm_embeds.T)
+ total_docs = len(docs)
+ splits = []
+ curr_split_start_idx = 0
+
+ # Calculate similarity scores between consecutive documents
+ sim_scores = [sim_matrix[i][i+1] for i in range(total_docs - 1)]
+
+ # Calculate running average of similarity scores
+ running_avg = [np.mean(sim_scores[:i+1]) for i in range(len(sim_scores))]
+
+ for idx, curr_sim_score in enumerate(sim_scores):
+ # Check for a significant drop in similarity compared to the running average
+ if idx > 0 and (running_avg[idx-1] - curr_sim_score) > drop_threshold:
+ splits.append(
+ DocumentSplit(
+ docs=list(docs[curr_split_start_idx:idx+1]), # Include current doc in the split
+ is_triggered=True,
+ triggered_score=curr_sim_score,
+ )
+ )
+ curr_split_start_idx = idx + 1 # Update the start index for the next split
+
+ # Add the last split
+ if curr_split_start_idx < total_docs:
+ splits.append(DocumentSplit(docs=list(docs[curr_split_start_idx:])))
+
+ return splits
+
+
+class ConsecutiveAvgSimSplitter2(BaseSplitter):
+ def __init__(
+ self,
+ encoder: BaseEncoder,
+ name: str = "consecutive_similarity_splitter",
+ similarity_threshold: float = 0.45,
+ drop_threshold: float = 0.1 # Additional parameter to control the drop threshold
+ ):
+ super().__init__(
+ name=name,
+ similarity_threshold=similarity_threshold,
+ encoder=encoder
+ )
+
+ def __call__(self, docs: List[str], drop_threshold):
+ doc_embeds = self.encoder(docs)
+ norm_embeds = doc_embeds / np.linalg.norm(doc_embeds, axis=1, keepdims=True)
+ sim_matrix = np.matmul(norm_embeds, norm_embeds.T)
+ total_docs = len(docs)
+ splits = []
+ curr_split_start_idx = 0
+
+ # Initialize an empty list to store similarity scores for the current topic segment
+ segment_sim_scores = []
+
+ for idx in range(total_docs - 1):
+ curr_sim_score = sim_matrix[idx][idx + 1]
+ segment_sim_scores.append(curr_sim_score)
+
+ # Calculate running average of similarity scores for the current segment
+ running_avg = np.mean(segment_sim_scores)
+
+ # Check for a significant drop in similarity compared to the running average
+ if idx > 0 and (running_avg - curr_sim_score) > drop_threshold:
+ splits.append(
+ DocumentSplit(
+ docs=list(docs[curr_split_start_idx:idx + 1]), # Include current doc in the split
+ is_triggered=True,
+ triggered_score=curr_sim_score,
+ )
+ )
+ curr_split_start_idx = idx + 1
+ # Reset the similarity scores for the new segment
+ segment_sim_scores = [curr_sim_score]
+
+ # Add the last split
+ if curr_split_start_idx < total_docs:
+ splits.append(DocumentSplit(docs=list(docs[curr_split_start_idx:])))
+
return splits
\ No newline at end of file