解决冲突（这啥呀）

ai_review/agent/rag_store.py

@@ -1,117 +0,0 @@
-import hashlib
-import json
-import os
-import chromadb
-from chromadb.utils import embedding_functions
-
-basedir = os.path.dirname(os.path.abspath(__file__))
-
-
-def _md5(text: str) -> str:
-    return hashlib.md5(text.encode("utf-8")).hexdigest()
-
-
-class RAGStore:
-    """RAG vector store for content moderation examples with MD5 dedup."""
-
-    def __init__(self, persist_dir=None):
-        persist_dir = persist_dir or os.path.join(basedir, "chroma_db")
-        self.client = chromadb.PersistentClient(path=persist_dir)
-        self.ef = embedding_functions.DefaultEmbeddingFunction()
-
-        existing = [c.name for c in self.client.list_collections()]
-        if "moderation_examples" in existing:
-            self.collection = self.client.get_collection(
-                name="moderation_examples",
-                embedding_function=self.ef,
-            )
-        else:
-            self.collection = self.client.create_collection(
-                name="moderation_examples",
-                embedding_function=self.ef,
-            )
-        # 无论首次还是后续启动，都增量同步 knowledge_base.json
-        self._sync_knowledge_base()
-
-    def _md5_exists(self, md5_hash: str) -> bool:
-        result = self.collection.get(where={"md5": md5_hash})
-        return len(result["ids"]) > 0
-
-    def _sync_knowledge_base(self):
-        """增量同步：只加入 knowledge_base.json 中尚未入库的条目。"""
-        kb_path = os.path.join(basedir, "knowledge_base.json")
-        if not os.path.exists(kb_path):
-            return
-
-        with open(kb_path, "r", encoding="utf-8") as f:
-            entries = json.load(f)
-
-        new_entries = []
-        for e in entries:
-            h = _md5(e["text"])
-            if not self._md5_exists(h):
-                new_entries.append(e)
-
-        if new_entries:
-            self.collection.add(
-                ids=[e["id"] for e in new_entries],
-                documents=[e["text"] for e in new_entries],
-                metadatas=[
-                    {
-                        "type": e["type"],
-                        "category": e["category"],
-                        "md5": _md5(e["text"]),
-                    }
-                    for e in new_entries
-                ],
-            )
-
-    def query(self, text: str, n_results: int = 3) -> list[dict]:
-        results = self.collection.query(
-            query_texts=[text],
-            n_results=n_results,
-        )
-        output = []
-        ids = results["ids"][0]
-        docs = results["documents"][0]
-        metas = results["metadatas"][0]
-        distances = results["distances"][0]
-
-        for i, doc, meta, dist in zip(ids, docs, metas, distances):
-            output.append({
-                "id": i,
-                "text": doc,
-                "type": meta.get("type", ""),
-                "category": meta.get("category", ""),
-                "distance": dist,
-            })
-        return output
-
-    def add_example(self, entry_id: str, text: str, entry_type: str, category: str):
-        """运行时动态添加，MD5 去重。"""
-        h = _md5(text)
-        if self._md5_exists(h):
-            return False
-
-        self.collection.add(
-            ids=[entry_id],
-            documents=[text],
-            metadatas=[{"type": entry_type, "category": category, "md5": h}],
-        )
-        return True
-
-    def list_all(self) -> list[dict]:
-        """返回 RAG 库中所有条目。"""
-        if self.collection.count() == 0:
-            return []
-
-        result = self.collection.get()
-        output = []
-        for i, doc, meta in zip(result["ids"], result["documents"], result["metadatas"]):
-            output.append({
-                "id": i,
-                "text": doc,
-                "type": meta.get("type", ""),
-                "category": meta.get("category", ""),
-            })
-        return output