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refactor: 移除 ChromaDB 相关代码和依赖

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移除项目中所有与 ChromaDB 相关的代码、依赖和文档引用，统一使用 Milvus 作为向量数据库实现
This commit is contained in:
Wenjie Zhang 2026-01-02 14:44:00 +08:00
parent 9c48eee7dc
commit 302175e0b0
17 changed files with 51 additions and 1305 deletions
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1
docs/latest/intro/knowledge-base.md
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@ -8,7 +8,6 @@
| 存储类型 | 特点 | 适用场景 |
|----------|------|----------|
| **Chroma** | 轻量级向量数据库 | 已弃用，建议使用 Milvus |
| **Milvus** | 高性能向量数据库 | 大规模生产环境、高性能查询 |
| **LightRAG** | 图增强检索 | 复杂知识关系，构建成本较高 |

4
docs/latest/intro/project-overview.md
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@ -9,7 +9,7 @@ Yuxi-Know（语析）是一个基于知识图谱和向量数据库的智能知
- **后端服务**: [FastAPI](https://github.com/tiangolo/fastapi) + Python 3.12+
- **前端界面**: [Vue.js 3](https://github.com/vuejs/vue) + [Ant Design Vue](https://github.com/vueComponent/ant-design-vue)
- **数据库存储**: [SQLite](https://github.com/sqlite/sqlite) + [MinIO](https://github.com/minio/minio)
- **知识存储**: [Milvus](https://github.com/milvus-io/milvus)、[Chroma](https://github.com/chroma-core/chroma)（向量数据库）+ [Neo4j](https://github.com/neo4j/neo4j)（图数据库）
- **知识存储**: [Milvus](https://github.com/milvus-io/milvus)（向量数据库）+ [Neo4j](https://github.com/neo4j/neo4j)（图数据库）
- **智能体框架**: [LangGraph](https://github.com/langchain-ai/langgraph)
- **文档解析**: [LightRAG](https://github.com/HKUDS/LightRAG) + [MinerU](https://github.com/HKUDS/MinerU) + [PP-Structure-V3](https://github.com/PaddlePaddle/PaddleOCR)
- **容器编排**: [Docker Compose](https://github.com/docker/compose)
@ -17,7 +17,7 @@ Yuxi-Know（语析）是一个基于知识图谱和向量数据库的智能知
### 核心功能
- **智能问答**: 支持多种大语言模型，提供智能对话和问答服务
- **知识库管理**: 支持多种存储形式（Chroma、Milvus、LightRAG）
- **知识库管理**: 支持多种存储形式（Milvus、LightRAG）
- **知识图谱**: 自动构建和可视化知识图谱，支持图查询
- **文档解析**: 支持 PDF、Word、图片等多种格式的智能解析
- **权限管理**: 三级权限体系（超级管理员、管理员、普通用户）

1
pyproject.toml
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@ -10,7 +10,6 @@ dependencies = [
"sqlalchemy[asyncio]>=2.0.0",
"langchain>=1.2.0",
"langchain-core >= 1.2.5",
"chromadb>=1.3",
"colorlog>=6.9.0",
"dashscope>=1.23.2",
"docx2txt>=0.9",

1
server/routers/dashboard_router.py
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@ -452,7 +452,6 @@ async def get_knowledge_stats(
kb_type = kb_info.get("kb_type", "unknown")
display_type = {
"lightrag": "LightRAG",
"chroma": "Chroma",
"faiss": "FAISS",
"milvus": "Milvus",
"qdrant": "Qdrant",

64
server/routers/knowledge_router.py
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@ -95,7 +95,7 @@ async def create_database(
def normalize_reranker_config(kb: str, params: dict) -> None:
reranker_cfg = params.get("reranker_config")
if kb not in {"chroma", "milvus"}:
if kb not in {"milvus"}:
if kb == "lightrag" and reranker_cfg:
logger.warning("LightRAG does not support reranker, ignoring reranker_config")
params.pop("reranker_config", None)
@ -786,68 +786,6 @@ async def get_knowledge_base_query_params(db_id: str, current_user: User = Depen
},
],
}
elif kb_type == "chroma":
top_k_default = reranker_config.get("final_top_k", 10)
params_list = [
{
"key": "top_k",
"label": "TopK",
"type": "number",
"default": top_k_default,
"min": 1,
"max": 100,
"description": "返回的最大结果数量",
},
{
"key": "similarity_threshold",
"label": "相似度阈值",
"type": "number",
"default": 0.0,
"min": 0.0,
"max": 1.0,
"step": 0.1,
"description": "过滤相似度低于此值的结果",
},
{
"key": "include_distances",
"label": "显示相似度",
"type": "boolean",
"default": True,
"description": "在结果中显示相似度分数",
},
{
"key": "use_reranker",
"label": "启用重排序",
"type": "boolean",
"default": reranker_enabled,
"description": "是否使用精排模型对检索结果进行重排序",
},
{
"key": "recall_top_k",
"label": "召回数量",
"type": "number",
"default": reranker_config.get("recall_top_k", 50),
"min": 10,
"max": 200,
"description": "启用重排序时向量检索的候选数量",
},
]
if config.reranker_names:
params_list.append(
{
"key": "reranker_model",
"label": "重排序模型",
"type": "select",
"default": reranker_config.get("model", ""),
"options": [
{"label": info.name, "value": model_id} for model_id, info in config.reranker_names.items()
],
"description": "覆盖默认配置，选择用于本次查询的重排序模型",
}
)
params = {"type": "chroma", "options": params_list}
elif kb_type == "milvus":
top_k_default = reranker_config.get("final_top_k", 10)
params_list = [

2
src/knowledge/__init__.py
View File

@ -2,14 +2,12 @@ import os
from ..config import config
from .factory import KnowledgeBaseFactory
from .implementations.chroma import ChromaKB
from .implementations.lightrag import LightRagKB
from .implementations.milvus import MilvusKB
from .manager import KnowledgeBaseManager
from .services.upload_graph_service import UploadGraphService
# 注册知识库类型
KnowledgeBaseFactory.register("chroma", ChromaKB, {"description": "基于 ChromaDB 的轻量级向量知识库，适合开发和小规模"})
KnowledgeBaseFactory.register("milvus", MilvusKB, {"description": "基于 Milvus 的生产级向量知识库，适合高性能部署"})
KnowledgeBaseFactory.register("lightrag", LightRagKB, {"description": "基于图检索的知识库，支持实体关系构建和复杂查询"})

4
src/knowledge/implementations/__init__.py
View File

@ -1,13 +1,11 @@
"""知识库具体实现模块
包含各种知识库的具体实现：
- ChromaKB: 基于 ChromaDB 的向量知识库
- MilvusKB: 基于 Milvus 的向量知识库
- LightRagKB: 基于 LightRAG 的图检索知识库
"""
from .chroma import ChromaKB
from .lightrag import LightRagKB
from .milvus import MilvusKB
__all__ = ["ChromaKB", "MilvusKB", "LightRagKB"]
__all__ = ["MilvusKB", "LightRagKB"]

530
src/knowledge/implementations/chroma.py
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@ -1,530 +0,0 @@
import asyncio
import os
import time
import traceback
from typing import Any
import chromadb
from chromadb.config import Settings
from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
from src.knowledge.base import KnowledgeBase
from src.knowledge.indexing import process_file_to_markdown
from src.knowledge.utils.kb_utils import (
get_embedding_config,
prepare_item_metadata,
split_text_into_chunks,
)
from src.utils import logger
from src.utils.datetime_utils import utc_isoformat
class ChromaKB(KnowledgeBase):
"""基于 ChromaDB 的向量库"""
def __init__(self, work_dir: str, **kwargs):
"""
初始化 ChromaDB 知识库
Args:
work_dir: 工作目录
**kwargs: 其他配置参数
"""
super().__init__(work_dir)
if chromadb is None:
raise ImportError("chromadb is not installed. Please install it with: pip install chromadb")
# ChromaDB 配置
self.chroma_db_path = os.path.join(work_dir, "chromadb")
os.makedirs(self.chroma_db_path, exist_ok=True)
# 初始化 ChromaDB 客户端
self.chroma_client = chromadb.PersistentClient(
path=self.chroma_db_path, settings=Settings(anonymized_telemetry=False)
)
# 存储集合映射 {db_id: collection}
self.collections: dict[str, Any] = {}
logger.info("ChromaKB initialized")
@property
def kb_type(self) -> str:
"""知识库类型标识"""
return "chroma"
async def _create_kb_instance(self, db_id: str, kb_config: dict) -> Any:
"""创建向量数据库集合"""
logger.info(f"Creating ChromaDB collection for {db_id}")
if db_id not in self.databases_meta:
raise ValueError(f"Database {db_id} not found")
embed_info = self.databases_meta[db_id].get("embed_info", {})
embedding_function = self._get_embedding_function(embed_info)
# 创建或获取集合
collection_name = db_id
try:
# 尝试获取现有集合
collection = self.chroma_client.get_collection(name=collection_name, embedding_function=embedding_function)
logger.info(f"Retrieved existing collection: {collection_name}")
# 检查现有集合的配置是否匹配当前的 embed_info
expected_model = getattr(embed_info, "name", None) if embed_info else None
if expected_model is None and hasattr(embed_info, "get"):
expected_model = embed_info.get("name")
elif embed_info and isinstance(embed_info, dict):
expected_model = embed_info.get("name")
expected_model = expected_model or "default"
collection_metadata = collection.metadata or {}
current_model = collection_metadata.get("embedding_model", "unknown")
logger.debug(f"Collection {collection_name} uses model '{current_model}', but expected '{expected_model}'.")
# 如果模型不匹配，删除现有集合并重新创建
if current_model != expected_model:
logger.warning(
f"Collection {collection_name} uses model '{current_model}', "
f"but expected '{expected_model}'. Recreating collection."
)
self.chroma_client.delete_collection(name=collection_name)
raise Exception("Model mismatch, recreating collection")
except Exception:
# 创建新集合
model_name = getattr(embed_info, "name", None) if embed_info else None
if model_name is None and hasattr(embed_info, "get"):
model_name = embed_info.get("name")
elif embed_info and isinstance(embed_info, dict):
model_name = embed_info.get("name")
model_name = model_name or "default"
logger.info(f"Creating new collection with embedding model: {model_name}")
collection_metadata = {
"db_id": db_id,
"created_at": utc_isoformat(),
"embedding_model": model_name,
}
collection = self.chroma_client.create_collection(
name=collection_name, embedding_function=embedding_function, metadata=collection_metadata
)
logger.info(f"Created new collection: {collection_name}")
return collection
async def _initialize_kb_instance(self, instance: Any) -> None:
"""初始化向量数据库集合（无需特殊初始化）"""
pass
def _get_embedding_function(self, embed_info: dict):
"""获取 embedding 函数"""
config_dict = get_embedding_config(embed_info)
return OpenAIEmbeddingFunction(
model_name=config_dict["model"],
api_key=config_dict["api_key"],
api_base=config_dict["base_url"].replace("/embeddings", ""),
)
async def _get_chroma_collection(self, db_id: str):
"""获取或创建 ChromaDB 集合"""
if db_id in self.collections:
return self.collections[db_id]
if db_id not in self.databases_meta:
return None
try:
# 创建集合
collection = await self._create_kb_instance(db_id, {})
await self._initialize_kb_instance(collection)
self.collections[db_id] = collection
return collection
except Exception as e:
logger.error(f"Failed to create vector collection for {db_id}: {e}")
logger.error(f"Traceback: {traceback.format_exc()}")
return None
def _split_text_into_chunks(self, text: str, file_id: str, filename: str, params: dict) -> list[dict]:
"""将文本分割成块"""
chunks = split_text_into_chunks(text, file_id, filename, params)
# 为 ChromaDB 添加特定的 metadata 格式
for chunk in chunks:
chunk["metadata"] = {
"source": chunk["source"],
"chunk_id": chunk["chunk_id"],
"full_doc_id": file_id,
"chunk_type": chunk.get("chunk_type", "normal"), # 添加chunk类型标识
}
return chunks
async def add_content(self, db_id: str, items: list[str], params: dict | None = None) -> list[dict]:
"""添加内容（文件/URL）"""
if db_id not in self.databases_meta:
raise ValueError(f"Database {db_id} not found")
collection = await self._get_chroma_collection(db_id)
if not collection:
raise ValueError(f"Failed to get ChromaDB collection for {db_id}")
content_type = params.get("content_type", "file") if params else "file"
processed_items_info = []
for item in items:
# 准备文件元数据
metadata = await prepare_item_metadata(item, content_type, db_id, params=params)
file_id = metadata["file_id"]
filename = metadata["filename"]
# 添加文件记录
file_record = metadata.copy()
self.files_meta[file_id] = file_record
self._save_metadata()
self._add_to_processing_queue(file_id)
try:
# 确保params中包含db_id（ZIP文件处理需要）
if params is None:
params = {}
params["db_id"] = db_id
# 根据内容类型处理内容
if content_type != "file":
raise ValueError("URL 内容解析已禁用")
markdown_content = await process_file_to_markdown(item, params=params)
# 分割文本成块
chunks = self._split_text_into_chunks(markdown_content, file_id, filename, params)
logger.info(f"Split {filename} into {len(chunks)} chunks")
# 准备向量数据库插入的数据
if chunks:
documents = [chunk["content"] for chunk in chunks]
metadatas = [chunk["metadata"] for chunk in chunks]
ids = [chunk["id"] for chunk in chunks]
# 插入到 ChromaDB - 分批处理以避免超出 OpenAI 批次大小限制
batch_size = 64 # OpenAI 的最大批次大小限制
total_batches = (len(chunks) + batch_size - 1) // batch_size
for i in range(0, len(chunks), batch_size):
batch_documents = documents[i : i + batch_size]
batch_metadatas = metadatas[i : i + batch_size]
batch_ids = ids[i : i + batch_size]
await asyncio.to_thread(
collection.add,
documents=batch_documents,
metadatas=batch_metadatas,
ids=batch_ids,
)
batch_num = i // batch_size + 1
logger.info(f"Processed batch {batch_num}/{total_batches} for {filename}")
logger.info(f"Inserted {content_type} {item} into ChromaDB. Done.")
# 更新状态为完成
self.files_meta[file_id]["status"] = "done"
self._save_metadata()
file_record["status"] = "done"
except Exception as e:
logger.error(f"处理{content_type} {item} 失败: {e}, {traceback.format_exc()}")
self.files_meta[file_id]["status"] = "failed"
self._save_metadata()
file_record["status"] = "failed"
finally:
self._remove_from_processing_queue(file_id)
processed_items_info.append(file_record)
return processed_items_info
async def update_content(self, db_id: str, file_ids: list[str], params: dict | None = None) -> list[dict]:
"""更新内容 - 根据file_ids重新解析文件并更新向量库"""
if db_id not in self.databases_meta:
raise ValueError(f"Database {db_id} not found")
collection = await self._get_chroma_collection(db_id)
if not collection:
raise ValueError(f"Failed to get ChromaDB collection for {db_id}")
# 处理默认参数
if params is None:
params = {}
content_type = params.get("content_type", "file")
processed_items_info = []
for file_id in file_ids:
# 从元数据中获取文件信息
if file_id not in self.files_meta:
logger.warning(f"File {file_id} not found in metadata, skipping")
continue
file_meta = self.files_meta[file_id]
file_path = file_meta.get("path")
filename = file_meta.get("filename")
if not file_path:
logger.warning(f"File path not found for {file_id}, skipping")
continue
# 添加到处理队列
self._add_to_processing_queue(file_id)
try:
# 更新状态为处理中
self.files_meta[file_id]["processing_params"] = params.copy()
self.files_meta[file_id]["status"] = "processing"
self._save_metadata()
# 重新解析文件为 markdown
if content_type != "file":
raise ValueError("URL 内容解析已禁用")
markdown_content = await process_file_to_markdown(file_path, params=params)
# 先删除现有的 ChromaDB 数据（仅删除chunks，保留元数据）
await self.delete_file_chunks_only(db_id, file_id)
# 重新生成 chunks
chunks = self._split_text_into_chunks(markdown_content, file_id, filename, params)
logger.info(f"Split {filename} into {len(chunks)} chunks")
if chunks:
documents = [chunk["content"] for chunk in chunks]
metadatas = [chunk["metadata"] for chunk in chunks]
ids = [chunk["id"] for chunk in chunks]
# 插入到 ChromaDB - 分批处理以避免超出 OpenAI 批次大小限制
batch_size = 64 # OpenAI 的最大批次大小限制
total_batches = (len(chunks) + batch_size - 1) // batch_size
for i in range(0, len(chunks), batch_size):
batch_documents = documents[i : i + batch_size]
batch_metadatas = metadatas[i : i + batch_size]
batch_ids = ids[i : i + batch_size]
await asyncio.to_thread(
collection.add,
documents=batch_documents,
metadatas=batch_metadatas,
ids=batch_ids,
)
batch_num = i // batch_size + 1
logger.info(f"Processed batch {batch_num}/{total_batches} for {filename}")
logger.info(f"Updated {content_type} {file_path} in ChromaDB. Done.")
# 更新元数据状态
self.files_meta[file_id]["status"] = "done"
self._save_metadata()
# 从处理队列中移除
self._remove_from_processing_queue(file_id)
# 返回更新后的文件信息
updated_file_meta = file_meta.copy()
updated_file_meta["status"] = "done"
updated_file_meta["file_id"] = file_id
processed_items_info.append(updated_file_meta)
except Exception as e:
logger.error(f"更新{content_type} {file_path} 失败: {e}, {traceback.format_exc()}")
self.files_meta[file_id]["status"] = "failed"
self._save_metadata()
# 从处理队列中移除
self._remove_from_processing_queue(file_id)
# 返回失败的文件信息
failed_file_meta = file_meta.copy()
failed_file_meta["status"] = "failed"
failed_file_meta["file_id"] = file_id
processed_items_info.append(failed_file_meta)
return processed_items_info
async def aquery(self, query_text: str, db_id: str, **kwargs) -> list[dict]:
"""异步查询知识库"""
collection = await self._get_chroma_collection(db_id)
if not collection:
raise ValueError(f"Database {db_id} not found")
try:
db_meta = self.databases_meta.get(db_id, {})
db_metadata = db_meta.get("metadata", {}) or {}
reranker_config = db_metadata.get("reranker_config", {}) or {}
requested_top_k = int(kwargs.get("top_k", reranker_config.get("final_top_k", 10)))
requested_top_k = max(requested_top_k, 1)
similarity_threshold = float(kwargs.get("similarity_threshold", 0.0))
include_distances = bool(kwargs.get("include_distances", True))
use_reranker = bool(kwargs.get("use_reranker", reranker_config.get("enabled", False)))
if use_reranker:
recall_top_k = int(kwargs.get("recall_top_k", reranker_config.get("recall_top_k", 50)))
recall_top_k = max(recall_top_k, requested_top_k)
final_top_k = requested_top_k
else:
recall_top_k = requested_top_k
final_top_k = requested_top_k
results = collection.query(
query_texts=[query_text],
n_results=recall_top_k,
include=["documents", "metadatas", "distances"],
)
if not results or not results.get("documents") or not results["documents"][0]:
return []
documents = results["documents"][0]
metadatas = results["metadatas"][0] if results.get("metadatas") else []
distances = results["distances"][0] if results.get("distances") else []
retrieved_chunks = []
for i, doc in enumerate(documents):
similarity = 1 - distances[i] if i < len(distances) else 1.0
if similarity < similarity_threshold:
continue
metadata = metadatas[i] if i < len(metadatas) else {}
# 确保 file_id 在元数据中，并使用统一的键名
if "full_doc_id" in metadata:
metadata["file_id"] = metadata.pop("full_doc_id")
chunk = {"content": doc, "metadata": metadata, "score": similarity}
if include_distances and i < len(distances):
chunk["distance"] = distances[i]
retrieved_chunks.append(chunk)
logger.debug(f"ChromaDB query response: {len(retrieved_chunks)} chunks found (after similarity filtering)")
if use_reranker and retrieved_chunks:
try:
reranker_model = kwargs.get("reranker_model", reranker_config.get("model"))
if not reranker_model:
logger.warning("Reranker enabled but no model specified, skipping reranking")
else:
from src.models.rerank import get_reranker
reranker = get_reranker(reranker_model)
try:
rerank_start = time.time()
documents_text = [chunk["content"] for chunk in retrieved_chunks]
rerank_scores = await reranker.acompute_score([query_text, documents_text], normalize=True)
for chunk, rerank_score in zip(retrieved_chunks, rerank_scores):
chunk["rerank_score"] = float(rerank_score)
retrieved_chunks.sort(
key=lambda item: item.get("rerank_score", item.get("score", 0.0)), reverse=True
)
elapsed = time.time() - rerank_start
logger.info(
f"Reranking completed for {db_id} in {elapsed:.3f}s with model {reranker_model}"
)
finally:
await reranker.aclose()
except Exception as exc: # noqa: BLE001
logger.error(f"Reranking failed: {exc}, falling back to vector scores")
return retrieved_chunks[:final_top_k]
except Exception as e:
logger.error(f"ChromaDB query error: {e}, {traceback.format_exc()}")
return []
async def delete_file_chunks_only(self, db_id: str, file_id: str) -> None:
"""仅删除文件的chunks数据，保留元数据（用于更新操作）"""
collection = await self._get_chroma_collection(db_id)
if collection:
try:
# 查找所有相关的chunks
results = collection.get(where={"full_doc_id": file_id}, include=["metadatas"])
# 删除所有相关chunks
if results and results.get("ids"):
collection.delete(ids=results["ids"])
logger.info(f"Deleted {len(results['ids'])} chunks for file {file_id}")
except Exception as e:
logger.error(f"Error deleting file {file_id} from ChromaDB: {e}")
# 注意：这里不删除 files_meta[file_id]，保留元数据用于后续操作
async def delete_file(self, db_id: str, file_id: str) -> None:
"""删除文件（包括元数据）"""
# 先删除 ChromaDB 中的 chunks 数据
await self.delete_file_chunks_only(db_id, file_id)
# 删除文件记录
if file_id in self.files_meta:
del self.files_meta[file_id]
self._save_metadata()
async def get_file_basic_info(self, db_id: str, file_id: str) -> dict:
"""获取文件基本信息（仅元数据）"""
if file_id not in self.files_meta:
raise Exception(f"File not found: {file_id}")
return {"meta": self.files_meta[file_id]}
async def get_file_content(self, db_id: str, file_id: str) -> dict:
"""获取文件内容信息（chunks和lines）"""
if file_id not in self.files_meta:
raise Exception(f"File not found: {file_id}")
# 使用 ChromaDB 获取chunks
content_info = {"lines": []}
collection = await self._get_chroma_collection(db_id)
if collection:
try:
# 获取文档的所有chunks
results = collection.get(where={"full_doc_id": file_id}, include=["documents", "metadatas"])
# 构建chunks数据
doc_chunks = []
if results and results.get("ids"):
for i, chunk_id in enumerate(results["ids"]):
chunk_data = {
"id": chunk_id,
"content": results["documents"][i] if i < len(results["documents"]) else "",
"metadata": results["metadatas"][i] if i < len(results["metadatas"]) else {},
"chunk_order_index": results["metadatas"][i].get("chunk_index", i)
if i < len(results["metadatas"])
else i,
}
doc_chunks.append(chunk_data)
# 按 chunk_order_index 排序
doc_chunks.sort(key=lambda x: x.get("chunk_order_index", 0))
content_info["lines"] = doc_chunks
return content_info
except Exception as e:
logger.error(f"Failed to get file content from ChromaDB: {e}")
content_info["lines"] = []
return content_info
return content_info
async def get_file_info(self, db_id: str, file_id: str) -> dict:
"""获取文件完整信息（基本信息+内容信息）- 保持向后兼容"""
if file_id not in self.files_meta:
raise Exception(f"File not found: {file_id}")
# 合并基本信息和内容信息
basic_info = await self.get_file_basic_info(db_id, file_id)
content_info = await self.get_file_content(db_id, file_id)
return {**basic_info, **content_info}

99
src/knowledge/manager.py
View File

@ -626,7 +626,6 @@ class KnowledgeBaseManager:
包含不一致信息的字典，按知识库类型分组
"""
inconsistencies = {
"chroma": {"missing_collections": [], "missing_files": []},
"milvus": {"missing_collections": [], "missing_files": []},
"total_missing_collections": 0,
"total_missing_files": 0,
@ -634,16 +633,6 @@ class KnowledgeBaseManager:
logger.info("开始检测向量数据库与元数据的一致性...")
# 检测 ChromaDB 数据不一致
if "chroma" in self.kb_instances:
try:
chroma_inconsistencies = await self._detect_chroma_inconsistencies()
inconsistencies["chroma"] = chroma_inconsistencies
inconsistencies["total_missing_collections"] += len(chroma_inconsistencies["missing_collections"])
inconsistencies["total_missing_files"] += len(chroma_inconsistencies["missing_files"])
except Exception as e:
logger.error(f"检测 ChromaDB 数据不一致时出错: {e}")
# 检测 Milvus 数据不一致
if "milvus" in self.kb_instances:
try:
@ -659,79 +648,6 @@ class KnowledgeBaseManager:
return inconsistencies
async def _detect_chroma_inconsistencies(self) -> dict:
"""检测 ChromaDB 中的数据不一致"""
inconsistencies = {"missing_collections": [], "missing_files": []}
chroma_kb = self.kb_instances["chroma"]
# 获取 ChromaDB 中所有实际的集合
try:
actual_collections = chroma_kb.chroma_client.list_collections()
actual_collection_names = {col.name for col in actual_collections}
# 获取 metadata 中记录的数据库ID
metadata_collection_names = set()
for db_id, db_meta in chroma_kb.databases_meta.items():
metadata_collection_names.add(db_id)
# 找出存在于 ChromaDB 但不在 metadata 中的集合
missing_collections = actual_collection_names - metadata_collection_names
for collection_name in missing_collections:
# 跳过一些系统集合
if not collection_name.startswith("kb_"):
continue
collection_info = {"collection_name": collection_name, "detected_at": utc_isoformat()}
# 尝试获取集合的基本信息
try:
collection = chroma_kb.chroma_client.get_collection(name=collection_name)
collection_info["count"] = collection.count()
collection_info["metadata"] = collection.metadata
except Exception as e:
logger.warning(f"无法获取集合 {collection_name} 的详细信息: {e}")
collection_info["count"] = "unknown"
inconsistencies["missing_collections"].append(collection_info)
logger.warning(
f"发现 ChromaDB 中存在但 metadata 中缺失的集合: {collection_name} "
f"(文档数: {collection_info['count']})"
)
# 检查文件级别的不一致（针对已知的数据库）
for db_id in metadata_collection_names:
try:
collection = chroma_kb.chroma_client.get_collection(name=db_id)
actual_count = collection.count()
# 获取 metadata 中记录的文件数量
metadata_files_count = sum(
1 for file_info in chroma_kb.files_meta.values() if file_info.get("database_id") == db_id
)
# 如果向量数据库中有数据但 metadata 中没有文件记录，可能存在文件缺失
if actual_count > 0 and metadata_files_count == 0:
inconsistencies["missing_files"].append(
{
"database_id": db_id,
"vector_count": actual_count,
"metadata_files_count": metadata_files_count,
"detected_at": utc_isoformat(),
}
)
logger.warning(
f"发现数据库 {db_id} 在 ChromaDB 中有 {actual_count} 条向量数据，但 metadata 中没有文件记录"
)
except Exception as e:
logger.debug(f"检查数据库 {db_id} 的文件一致性时出错: {e}")
except Exception as e:
logger.error(f"检测 ChromaDB 数据不一致时出错: {e}")
return inconsistencies
async def _detect_milvus_inconsistencies(self) -> dict:
"""检测 Milvus 中的数据不一致"""
inconsistencies = {"missing_collections": [], "missing_files": []}
@ -823,21 +739,6 @@ class KnowledgeBaseManager:
logger.warning("数据一致性检测完成，发现以下不一致情况：")
logger.warning("=" * 80)
# ChromaDB 不一致情况
chroma_missing = inconsistencies["chroma"]["missing_collections"]
chroma_files_missing = inconsistencies["chroma"]["missing_files"]
if chroma_missing or chroma_files_missing:
logger.warning("ChromaDB 不一致情况：")
logger.warning(f" 缺失集合数量: {len(chroma_missing)}")
for collection_info in chroma_missing:
logger.warning(f" - 集合: {collection_info['collection_name']}, 向量数: {collection_info['count']}")
logger.warning(f" 缺失文件记录数量: {len(chroma_files_missing)}")
for file_info in chroma_files_missing:
logger.warning(
f" - 数据库: {file_info['database_id']}, 向量数: {file_info['vector_count']}, "
f"元数据文件数: {file_info['metadata_files_count']}"
)
# Milvus 不一致情况
milvus_missing = inconsistencies["milvus"]["missing_collections"]
milvus_files_missing = inconsistencies["milvus"]["missing_files"]

2
src/knowledge/utils/kb_utils.py
View File

@ -119,7 +119,7 @@ def split_text_into_chunks(text: str, file_id: str, filename: str, params: dict
# 如果设置了分隔符，先分割后以当前的分割逻辑处理
if separator:
# 转换分隔符为可视格式（换行符显示为 \n）
separator_display = separator.replace('\n', '\\n').replace('\r', '\\r').replace('\t', '\\t')
separator_display = separator.replace("\n", "\\n").replace("\r", "\\r").replace("\t", "\\t")
logger.debug(f"启用预分割模式，使用分隔符: '{separator_display}'")
pre_chunks = text.split(separator)
text_chunks = []

2
test/api/test_knowledge_router.py
View File

@ -59,7 +59,7 @@ async def test_admin_can_create_vector_db_with_reranker(test_client, admin_heade
"database_name": db_name,
"description": "Vector DB with reranker",
"embed_model_name": "siliconflow/BAAI/bge-m3",
"kb_type": "chroma",
"kb_type": "milvus",
"additional_params": {
"reranker_config": {
"enabled": True,

598
uv.lock
View File

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[[package]]
name = "pyclipper"
version = "1.4.0"
@ -5451,12 +4955,6 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/be/7a/097801205b991bc3115e8af1edb850d30aeaf0118520b016354cf5ccd3f6/pypdfium2-4.30.0-py3-none-win_arm64.whl", hash = "sha256:119b2969a6d6b1e8d55e99caaf05290294f2d0fe49c12a3f17102d01c441bd29", size = 2752118, upload-time = "2024-05-09T18:33:15.489Z" },
]
[[package]]
name = "pypika"
version = "0.48.9"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/c7/2c/94ed7b91db81d61d7096ac8f2d325ec562fc75e35f3baea8749c85b28784/PyPika-0.48.9.tar.gz", hash = "sha256:838836a61747e7c8380cd1b7ff638694b7a7335345d0f559b04b2cd832ad5378", size = 67259, upload-time = "2022-03-15T11:22:57.066Z" }
[[package]]
name = "pypinyin"
version = "0.55.0"
@ -5466,15 +4964,6 @@ wheels = [
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]
[[package]]
name = "pyproject-hooks"
version = "1.2.0"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/e7/82/28175b2414effca1cdac8dc99f76d660e7a4fb0ceefa4b4ab8f5f6742925/pyproject_hooks-1.2.0.tar.gz", hash = "sha256:1e859bd5c40fae9448642dd871adf459e5e2084186e8d2c2a79a824c970da1f8", size = 19228, upload-time = "2024-09-29T09:24:13.293Z" }
wheels = [
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[[package]]
name = "pyreadline3"
version = "3.5.4"
@ -5965,19 +5454,6 @@ wheels = [
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]
[[package]]
name = "requests-oauthlib"
version = "2.0.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "oauthlib" },
{ name = "requests" },
]
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[[package]]
name = "requests-toolbelt"
version = "1.0.0"
@ -6587,8 +6063,8 @@ name = "soundfile"
version = "0.13.1"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "cffi" },
{ name = "numpy" },
{ name = "cffi", marker = "sys_platform != 'win32'" },
{ name = "numpy", marker = "sys_platform != 'win32'" },
]
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wheels = [
@ -7858,7 +7334,6 @@ dependencies = [
{ name = "aiohttp" },
{ name = "aiosqlite" },
{ name = "asyncpg" },
{ name = "chromadb" },
{ name = "colorlog" },
{ name = "dashscope" },
{ name = "deepagents" },
@ -7933,7 +7408,6 @@ requires-dist = [
{ name = "aiohttp", specifier = ">=3.9.0" },
{ name = "aiosqlite", specifier = ">=0.20.0" },
{ name = "asyncpg", specifier = ">=0.30.0" },
{ name = "chromadb", specifier = ">=1.3" },
{ name = "colorlog", specifier = ">=6.9.0" },
{ name = "dashscope", specifier = ">=1.23.2" },
{ name = "deepagents", specifier = ">=0.2.5" },

2
web/src/components/FileUploadModal.vue
View File

@ -381,7 +381,7 @@ const tempChunkParams = ref({
// 计算属性：是否支持QA分割
const isQaSplitSupported = computed(() => {
const type = kbType.value?.toLowerCase();
return type === 'chroma' || type === 'milvus';
return type === 'milvus';
});
const isGraphBased = computed(() => {

2
web/src/components/QuerySection.vue
View File

@ -95,7 +95,7 @@
{{ queryResult }}
</div>
<!-- Chroma/Milvus 返回列表格式 -->
<!-- Milvus 返回列表格式 -->
<div v-else-if="Array.isArray(queryResult)" class="result-list">
<div v-if="queryResult.length === 0" class="no-results">
<p>未找到相关结果</p>

2
web/src/stores/database.js
View File

@ -78,7 +78,7 @@ export const useDatabaseStore = defineStore('database', () => {
}
// 向量数据库的重排序模型验证
if (['chroma', 'milvus'].includes(formData.kb_type)) {
if (['milvus'].includes(formData.kb_type)) {
if (formData.reranker_config?.enabled && !formData.reranker_config?.model) {
message.error('请选择重排序模型');
return false;

2
web/src/utils/kb_utils.js
View File

@ -2,7 +2,6 @@
export const getKbTypeLabel = (type) => {
const labels = {
lightrag: 'LightRAG',
chroma: 'Chroma',
milvus: 'Milvus'
};
return labels[type] || type;
@ -11,7 +10,6 @@ export const getKbTypeLabel = (type) => {
export const getKbTypeColor = (type) => {
const colors = {
lightrag: 'purple',
chroma: 'orange',
milvus: 'red'
};
return colors[type] || 'blue';

40
web/src/views/DataBaseView.vue
View File

@ -24,13 +24,6 @@
<div class="card-header">
<component :is="getKbTypeIcon(typeKey)" class="type-icon" />
<span class="type-title">{{ getKbTypeLabel(typeKey) }}</span>
<a-tooltip
v-if="typeKey === 'chroma'"
title="Chroma 已标记为弃用状态，建议使用 Milvus 替代。同时会在下个正式版本中移除。"
placement="top"
>
<span class="deprecated-badge">弃用</span>
</a-tooltip>
</div>
<div class="card-description">{{ typeInfo.description }}</div>
</div>
@ -103,7 +96,7 @@
</div>
<div
v-if="['chroma', 'milvus'].includes(newDatabase.kb_type)"
v-if="['milvus'].includes(newDatabase.kb_type)"
class="reranker-config"
>
<div class="reranker-row">
@ -304,7 +297,7 @@ const rerankerOptions = computed(() =>
}))
)
const isVectorKb = computed(() => ['chroma', 'milvus'].includes(newDatabase.kb_type))
const isVectorKb = computed(() => ['milvus'].includes(newDatabase.kb_type))
const llmModelSpec = computed(() => {
const provider = newDatabase.llm_info?.provider || ''
@ -318,26 +311,8 @@ const llmModelSpec = computed(() => {
// 支持的知识库类型
const supportedKbTypes = ref({})
// 有序的知识库类型（Chroma 排在最后）
const orderedKbTypes = computed(() => {
const types = { ...supportedKbTypes.value }
const ordered = {}
const chromaData = types.chroma
// 先添加除了 Chroma 之外的所有类型
Object.keys(types).forEach(key => {
if (key !== 'chroma') {
ordered[key] = types[key]
}
})
// 最后添加 Chroma（如果存在）
if (chromaData) {
ordered.chroma = chromaData
}
return ordered
})
// 有序的知识库类型
const orderedKbTypes = computed(() => supportedKbTypes.value)
// 加载支持的知识库类型
const loadSupportedKbTypes = async () => {
@ -371,7 +346,6 @@ const cancelCreateDatabase = () => {
const getKbTypeLabel = (type) => {
const labels = {
lightrag: 'LightRAG',
chroma: 'Chroma',
milvus: 'CommonRAG'
}
return labels[type] || type
@ -380,7 +354,6 @@ const getKbTypeLabel = (type) => {
const getKbTypeIcon = (type) => {
const icons = {
lightrag: Waypoints,
chroma: FileDigit,
milvus: DatabaseZap
}
return icons[type] || Database
@ -389,7 +362,6 @@ const getKbTypeIcon = (type) => {
const getKbTypeColor = (type) => {
const colors = {
lightrag: 'purple',
chroma: 'orange',
milvus: 'red'
}
return colors[type] || 'blue'
@ -431,7 +403,7 @@ const handleKbTypeChange = (type) => {
console.log('知识库类型改变:', type)
resetNewDatabase()
newDatabase.kb_type = type
if (!['chroma', 'milvus'].includes(type)) {
if (!['milvus'].includes(type)) {
newDatabase.reranker.enabled = false
}
}
@ -462,7 +434,7 @@ const buildRequestData = () => {
}
// 根据类型添加特定配置
if (['chroma', 'milvus'].includes(newDatabase.kb_type)) {
if (['milvus'].includes(newDatabase.kb_type)) {
if (newDatabase.storage) {
requestData.additional_params.storage = newDatabase.storage
}