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🤖 自动格式化代码 [skip ci]

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github-actions[bot]
2025-05-14 15:11:33 +00:00
parent 17d19e7cac
commit fb6094d269
17 changed files with 278 additions and 254 deletions
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26
src/chat/focus_chat/heartflow_prompt_builder.py
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@@ -7,12 +7,14 @@ from src.chat.person_info.relationship_manager import relationship_manager
from src.chat.utils.utils import get_embedding
import time
from typing import Union, Optional
# from common.database.database import db
from src.chat.utils.utils import get_recent_group_speaker
from src.manager.mood_manager import mood_manager
from src.chat.memory_system.Hippocampus import HippocampusManager
from src.chat.knowledge.knowledge_lib import qa_manager
from src.chat.focus_chat.expressors.exprssion_learner import expression_learner
# import traceback
import random
import json
@@ -614,7 +616,7 @@ class PromptBuilder:
return "" if not return_raw else []
query_embedding_magnitude = math.sqrt(sum(x * x for x in query_embedding))
if query_embedding_magnitude == 0: # Avoid division by zero
if query_embedding_magnitude == 0: # Avoid division by zero
return "" if not return_raw else []
for knowledge_item in all_knowledges:
@@ -623,35 +625,35 @@ class PromptBuilder:
db_embedding = json.loads(db_embedding_str)
if len(db_embedding) != len(query_embedding):
logger.warning(f"Embedding length mismatch for knowledge ID {knowledge_item.id if hasattr(knowledge_item, 'id') else 'N/A'}. Skipping.")
logger.warning(
f"Embedding length mismatch for knowledge ID {knowledge_item.id if hasattr(knowledge_item, 'id') else 'N/A'}. Skipping."
)
continue
# Calculate Cosine Similarity
dot_product = sum(q * d for q, d in zip(query_embedding, db_embedding))
db_embedding_magnitude = math.sqrt(sum(x * x for x in db_embedding))
if db_embedding_magnitude == 0: # Avoid division by zero
if db_embedding_magnitude == 0: # Avoid division by zero
similarity = 0.0
else:
similarity = dot_product / (query_embedding_magnitude * db_embedding_magnitude)
if similarity >= threshold:
results_with_similarity.append({
"content": knowledge_item.content,
"similarity": similarity
})
results_with_similarity.append({"content": knowledge_item.content, "similarity": similarity})
except json.JSONDecodeError:
logger.error(f"Failed to parse embedding for knowledge ID {knowledge_item.id if hasattr(knowledge_item, 'id') else 'N/A'}")
logger.error(
f"Failed to parse embedding for knowledge ID {knowledge_item.id if hasattr(knowledge_item, 'id') else 'N/A'}"
)
except Exception as e:
logger.error(f"Error processing knowledge item: {e}")
# Sort by similarity in descending order
results_with_similarity.sort(key=lambda x: x["similarity"], reverse=True)
# Limit results
limited_results = results_with_similarity[:limit]
logger.debug(f"知识库查询结果数量 (after Peewee processing): {len(limited_results)}")
if not limited_results: