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update: test update_experience_pool in bfcl
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zouying
parent
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commit
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2 changed files with 0 additions and 162 deletions
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from typing import List
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from loguru import logger
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from experiencemaker.op import OP_REGISTRY
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from experiencemaker.op.base_op import BaseOp
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from experiencemaker.schema.experience import BaseExperience
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from experiencemaker.schema.request import ManagerRequest
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@OP_REGISTRY.register()
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class ExperienceDeletionOp(BaseOp):
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current_path: str = __file__
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def execute(self):
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"""Remove low-utility experiences"""
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request: ManagerRequest = self.context.request
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experiences: List[BaseExperience] = self.context.response.experience_list
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if not experiences:
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logger.info("No experiences found for deduplication")
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return
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logger.info(f"Starting deduplication for {len(experiences)} experiences")
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# Perform deduplication
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deduplicated_experiences = self._deduplicate_experiences(experiences)
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logger.info(f"Deduplication complete: {len(deduplicated_experiences)} deduplicated experiences out of {len(experiences)}")
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# Update context
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self.context.response.experience_list = deduplicated_experiences
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def _deduplicate_experiences(self, experiences: List[BaseExperience]) -> List[BaseExperience]:
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"""Remove duplicate experiences"""
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if not experiences:
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return experiences
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similarity_threshold = self.op_params.get("similarity_threshold", 0.5)
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workspace_id = self.context.request.workspace_id if hasattr(self.context, 'request') else None
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unique_experiences = []
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# Get existing experience embeddings
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existing_embeddings = self._get_existing_experience_embeddings(workspace_id)
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for experience in experiences:
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# Generate embedding for current experience
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current_embedding = self._get_experience_embedding(experience)
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if current_embedding is None:
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logger.warning(f"Failed to generate embedding for experience: {str(experience.when_to_use)[:50]}...")
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continue
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# Check similarity with existing experiences
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if self._is_similar_to_existing_experiences(current_embedding, existing_embeddings, similarity_threshold):
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logger.debug(f"Skipping similar experience: {str(experience.when_to_use)[:50]}...")
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continue
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# Check similarity with current batch experiences
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if self._is_similar_to_current_experiences(current_embedding, unique_experiences, similarity_threshold):
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logger.debug(f"Skipping duplicate in current batch: {str(experience.when_to_use)[:50]}...")
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continue
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# Add to unique experiences list
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unique_experiences.append(experience)
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logger.debug(f"Added unique experience: {str(experience.when_to_use)[:50]}...")
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return unique_experiences
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def _get_existing_experience_embeddings(self, workspace_id: str) -> List[List[float]]:
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"""Get embeddings of existing experiences"""
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try:
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if not hasattr(self, 'vector_store') or not self.vector_store or not workspace_id:
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return []
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# Query existing experience nodes
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existing_nodes = self.vector_store.search(
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query="...", # Empty query to get all
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workspace_id=workspace_id,
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top_k=self.op_params.get("max_existing_experiences", 1000)
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)
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# Extract embeddings
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existing_embeddings = []
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for node in existing_nodes:
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if hasattr(node, 'embedding') and node.embedding:
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existing_embeddings.append(node.embedding)
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logger.debug(f"Retrieved {len(existing_embeddings)} existing experience embeddings from workspace {workspace_id}")
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return existing_embeddings
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except Exception as e:
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logger.warning(f"Failed to retrieve existing experience embeddings: {e}")
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return []
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def _get_experience_embedding(self, experience: BaseExperience) -> List[float]:
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"""Generate embedding for experience"""
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try:
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if not hasattr(self, 'vector_store') or not self.vector_store:
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return None
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# Combine experience description and content for embedding
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text_for_embedding = f"{experience.when_to_use} {experience.content}"
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embeddings = self.vector_store.embedding_model.get_embeddings([text_for_embedding])
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if embeddings and len(embeddings) > 0:
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return embeddings[0]
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else:
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logger.warning("Empty embedding generated for experience")
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return None
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except Exception as e:
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logger.error(f"Error generating embedding for experience: {e}")
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return None
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def _is_similar_to_existing_experiences(self, current_embedding: List[float],
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existing_embeddings: List[List[float]],
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threshold: float) -> bool:
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"""Check if current embedding is similar to existing embeddings"""
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for existing_embedding in existing_embeddings:
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similarity = self._calculate_cosine_similarity(current_embedding, existing_embedding)
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if similarity > threshold:
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logger.debug(f"Found similar existing experience with similarity: {similarity:.3f}")
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return True
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return False
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def _is_similar_to_current_experiences(self, current_embedding: List[float],
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current_experiences: List[BaseExperience],
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threshold: float) -> bool:
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for existing_experience in current_experiences:
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existing_embedding = self._get_experience_embedding(existing_experience)
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if existing_embedding is None:
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continue
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similarity = self._calculate_cosine_similarity(current_embedding, existing_embedding)
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if similarity > threshold:
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logger.debug(f"Found similar experience in current batch with similarity: {similarity:.3f}")
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return True
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return False
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def _calculate_cosine_similarity(self, embedding1: List[float], embedding2: List[float]) -> float:
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"""Calculate cosine similarity"""
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try:
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import numpy as np
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vec1 = np.array(embedding1)
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vec2 = np.array(embedding2)
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# Calculate cosine similarity
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dot_product = np.dot(vec1, vec2)
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norm1 = np.linalg.norm(vec1)
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norm2 = np.linalg.norm(vec2)
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if norm1 == 0 or norm2 == 0:
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return 0.0
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return dot_product / (norm1 * norm2)
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except Exception as e:
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logger.error(f"Error calculating cosine similarity: {e}")
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return 0.0
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