[全量] 初始化项目代码、配置、文档及Agent协同harness

langchain-chat/server/knowledge_base/kb_service/milvus_kb_service.py

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+from typing import List, Dict, Optional
+
+from langchain.schema import Document
+from langchain.vectorstores.milvus import Milvus
+import os
+import logging
+
+from configs import kbs_config
+from server.db.repository import list_file_num_docs_id_by_kb_name_and_file_name
+
+from server.knowledge_base.kb_service.base import KBService, SupportedVSType, EmbeddingsFunAdapter, \
+    score_threshold_process
+from server.knowledge_base.utils import KnowledgeFile
+
+import numpy as np
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+class MilvusKBService(KBService):
+    milvus: Milvus
+
+    @staticmethod
+    def get_collection(milvus_name):
+        from pymilvus import Collection
+        return Collection(milvus_name)
+
+    def get_doc_by_ids(self, ids: List[str]) -> List[Document]:
+        result = []
+        if self.milvus and self.milvus.col:
+            # ids = [int(id) for id in ids]  # for milvus if needed #pr 2725
+            data_list = self.milvus.col.query(expr=f'pk in {[int(_id) for _id in ids]}', output_fields=["*"])
+            for data in data_list:
+                text = data.pop("text")
+                result.append(Document(page_content=text, metadata=data))
+        return result
+
+    def get_doc_by_sources_name(self, source_name_list: List[str]) -> List[Document]:
+        result = []
+        if self.milvus and self.milvus.col:
+            # ids = [int(id) for id in ids]  # for milvus if needed #pr 2725
+            data_list = self.milvus.col.query(expr=f'source in {source_name_list}', output_fields=["*"])
+            for data in data_list:
+                text = data.pop("text")
+                result.append(Document(page_content=text, metadata=data))
+        return result
+
+
+    def del_doc_by_ids(self, ids: List[str]) -> bool:
+        if self.milvus and self.milvus.col:
+            self.milvus.col.delete(expr=f'pk in {ids}')
+
+    @staticmethod
+    def search(milvus_name, content, limit=3):
+        search_params = {
+            "metric_type": "L2",
+            "params": {"nprobe": 10},
+        }
+        c = MilvusKBService.get_collection(milvus_name)
+        return c.search(content, "embeddings", search_params, limit=limit, output_fields=["content"])
+
+    def do_create_kb(self):
+        pass
+
+    def vs_type(self) -> str:
+        return SupportedVSType.MILVUS
+
+    def _load_milvus(self):
+        try:
+            self.milvus = Milvus(embedding_function=EmbeddingsFunAdapter(self.embed_model),
+                                collection_name=self.kb_name,
+                                connection_args=kbs_config.get("milvus"),
+                                index_params=kbs_config.get("milvus_kwargs")["index_params"],
+                                search_params=kbs_config.get("milvus_kwargs")["search_params"],
+                                auto_id=True
+                                )
+            logger.info("成功加载 Milvus 实例 'milvus'。")
+
+            # -------- 兼容不同 schema 的文本字段 --------
+            # 新库尚无 Milvus 集合时 langchain_community.Milvus.col 为 None，
+            # 会在首次 add_documents 建表后再有 schema，此处勿访问 .col.schema。
+            try:
+                col = self.milvus.col
+                if col is None:
+                    logger.debug(
+                        "集合 %s 尚未在 Milvus 中建表，跳过文本字段探测（首次写入时会自动创建）",
+                        self.kb_name,
+                    )
+                else:
+                    field_names = [f.name for f in col.schema.fields]
+                    if self.milvus._text_field not in field_names:
+                        if "page_content" in field_names:
+                            self.milvus._text_field = "page_content"
+                        elif "content" in field_names:
+                            self.milvus._text_field = "content"
+                        else:
+                            for f in col.schema.fields:
+                                if hasattr(f, "dtype") and str(f.dtype).startswith("DataType.VARCHAR"):
+                                    self.milvus._text_field = f.name
+                                    break
+                        logger.info(f"集合 {self.kb_name} 使用文本字段: {self.milvus._text_field}")
+            except Exception as e:
+                logger.warning(f"检测并设置文本字段失败: {e}")
+        except Exception as e:
+            logger.error(f"加载 Milvus 实例 'milvus' 失败: {e}")
+            self._create_collection_if_not_exists()
+            # 重新加载
+            # self._load_milvus()
+
+    def _create_collection_if_not_exists(self):
+      """根据传入字段创建 Milvus 集合"""
+      from pymilvus import Collection, CollectionSchema, FieldSchema, DataType
+      from langchain_community.vectorstores import Milvus
+      
+      # 定义你的字段（根据你的需求修改）
+      fields = [
+          FieldSchema(name="pk", dtype=DataType.Int64, is_primary=True, auto_id=True),
+          FieldSchema(name="vector", dtype=DataType.FloatVector, dim=768),  # dim 根据 embedding 模型调整
+          FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=65535),
+          FieldSchema(name="source", dtype=DataType.VARCHAR, max_length=1024),
+          FieldSchema(name="metadata", dtype=DataType.VARCHAR, max_length=65535),
+          # 添加其他自定义字段...
+      ]
+      
+      schema = CollectionSchema(fields=fields, description=self.kb_name)
+      
+      # 创建集合
+      collection = Collection(name=self.kb_name, schema=schema, using="default")
+      
+      # 创建索引
+      index_params = kbs_config.get("milvus_kwargs")["index_params"]
+      collection.create_index(field_name="vector", index_params=index_params)
+      
+      logger.info(f"成功创建 Milvus 集合: {self.kb_name}")
+
+    def do_init(self):
+        self._load_milvus()
+
+    def do_drop_kb(self):
+        if self.milvus and self.milvus.col:
+            self.milvus.col.release()
+            # self.milvus.col.drop() # 禁用从chatchat删除集合
+
+    def do_search(self, query: str, top_k: int, score_threshold: float, expr: str, custom_strategy_config: dict = {}):
+        self._load_milvus()
+        embed_func = EmbeddingsFunAdapter(self.embed_model)
+        try:
+            embeddings = embed_func.embed_query(query)
+            if top_k > 50:
+                # 按顺序返回全文内容
+                docs = self.milvus.similarity_search_by_vector(embeddings, top_k, expr = expr)
+                docs = sorted(docs, key=lambda doc: doc.metadata['pk'])  # 根据 pk 从小到大排序
+                # return score_threshold_process(query,score_threshold, top_k, docs)
+                return docs
+            else:
+                docs = self.milvus.similarity_search_with_score_by_vector(embeddings, top_k, expr = expr)
+                # TODO 动态score_threshold
+            return score_threshold_process(query,score_threshold, top_k, docs)
+        except Exception as e:
+            logger.error(f"搜索 Milvus 集合 '{self.kb_name}' 失败: {e}")
+            return []
+
+    def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
+        for doc in docs:
+            for k, v in doc.metadata.items():
+                doc.metadata[k] = str(v)
+            for field in self.milvus.fields:
+                doc.metadata.setdefault(field, "")
+            doc.metadata.pop(self.milvus._text_field, None)
+            doc.metadata.pop(self.milvus._vector_field, None)
+
+        ids = self.milvus.add_documents(docs)
+        doc_infos = [{"id": id, "metadata": doc.metadata} for id, doc in zip(ids, docs)]
+        return doc_infos
+
+    def do_delete_doc(self, kb_file: KnowledgeFile, **kwargs):
+        id_list = list_file_num_docs_id_by_kb_name_and_file_name(kb_file.kb_name, kb_file.filename)
+        if self.milvus and self.milvus.col:
+            self.milvus.col.delete(expr=f'pk in {id_list}')
+
+        # Issue 2846, for windows
+        # if self.milvus.col:
+        #     file_path = kb_file.filepath.replace("\\", "\\\\")
+        #     file_name = os.path.basename(file_path)
+        #     id_list = [item.get("pk") for item in
+        #                self.milvus.col.query(expr=f'source == "{file_name}"', output_fields=["pk"])]
+        #     self.milvus.col.delete(expr=f'pk in {id_list}')
+
+    def do_clear_vs(self):
+        if self.milvus and self.milvus.col:
+            self.do_drop_kb()
+            self.do_init()
+
+
+if __name__ == '__main__':
+    # 测试建表使用
+    from server.db.base import Base, engine
+
+    Base.metadata.create_all(bind=engine)
+    milvusService = MilvusKBService("t_policy_total_bce_v1")
+    # milvusService.add_doc(KnowledgeFile("README.md", "test"))
+
+    # print(milvusService.get_doc_by_ids(["444022434274215486"]))
+    # milvusService.delete_doc(KnowledgeFile("README.md", "test"))
+    # milvusService.do_drop_kb()
+    # print(milvusService.search_docs("如何启动api服务"))