更新python后端

2025-09-18 17:18:18 +08:00
parent 2fc209e6e6
commit de6a350da8
45 changed files with 2524 additions and 89 deletions
@@ -0,0 +1,109 @@
+from config.milvus import knVectorstore,memVectorstore
+from langchain.schema import Document
+from datetime import datetime
+from typing import List
+
+from typing import List, Dict, Any
+
+def get_knowledge_by_key_words(key_words: str, kn_ids: List[str]) -> str:
+    """
+    根据关键词和 kn_ids 列表，在知识库中检索相关内容，并返回整理后的文本字符串
+    """
+    # 构建过滤表达式：只查 kn_ids 范围内的
+    if kn_ids:
+        ids_expr = " or ".join([f'kn_id == "{kid}"' for kid in kn_ids])
+        expr = f"({ids_expr})"
+    else:
+        return "未找到相关的知识。"
+    
+    result = knVectorstore.similarity_search(
+        query=key_words,
+        k=3,  # 可调节返回条数
+        expr=expr
+    )
+    
+    # 整理成字符串
+    doc_texts = []
+    for idx, doc in enumerate(result, start=1):
+        text = doc.page_content.strip()
+        if text:
+            # 可以加个编号，便于LLM区分
+            doc_texts.append(f"[文档{idx}]: {text}")
+    
+    # 拼成一个大字符串，用换行隔开
+    combined_text = "\n\n".join(doc_texts)
+    return combined_text
+
+
+def get_memory_by_key_words(key_words: str, ai_ids: List[str]) -> str:
+    print("ai_id是：" , ai_ids)
+    """
+    根据关键词和 ai_ids 列表，在知识库中检索相关内容，并返回整理后的文本字符串
+    """
+    # 构建过滤表达式：只查 kn_ids 范围内的
+    if ai_ids:
+        ids_expr = " or ".join([f'ai_id == "{kid}"' for kid in ai_ids])
+        expr = f"({ids_expr})"
+    else:
+        expr = ""  # 不限制 kn_id todo 实际上应该不反悔任何内容
+    
+    result = memVectorstore.similarity_search(
+        query=key_words,
+        k=5,  # 可调节返回条数
+        expr=expr
+    )
+    
+    # 整理成字符串
+    doc_texts = []
+    for idx, doc in enumerate(result, start=1):
+        text = doc.page_content.strip()
+        if text:
+            # 可以加个编号，便于LLM区分
+            doc_texts.append(f"[记忆{idx}]: {text}")
+    
+    # 拼成一个大字符串，用换行隔开
+    combined_text = "\n\n".join(doc_texts)
+    return combined_text
+def get_knowledge_by_base_id(base_id: str):
+    expr = f'kn_id == "{base_id}"'  # base_id 会被替换
+    result =  knVectorstore.similarity_search(
+        query="",  # 如果只想用过滤条件，可以传空字符串
+        k=100,
+        expr=expr
+    )
+    return [
+        {
+            "id": str(doc.metadata["id"]),
+            "text": doc.page_content,
+            "is_active": doc.metadata["is_active"],
+        }
+        for doc in result
+    ]
+
+def add_knowledge(text: str, is_active: bool, base_id: str, user_id: str):
+    docs = [
+        Document(
+            page_content=text,
+            metadata={
+                "kn_id": str(base_id),
+                "created_by": str(user_id),
+                "created_at": datetime.now().isoformat(),
+                "is_active": is_active,
+            }
+        )
+    ]
+    return knVectorstore.add_documents(docs)
+
+def add_memory(ai_id:str,mem: str, user_id: str,is_active: bool):
+    docs = [
+        Document(
+            page_content=mem,
+            metadata={
+                "ai_id": str(ai_id),
+                "created_by": str(user_id),
+                "created_at": datetime.now().isoformat(),
+                "is_active": is_active,
+            }
+        )
+    ]
+    return memVectorstore.add_documents(docs)