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fix(embedding): 修复ollama model 不起作用的 bug，并修复model info 由于 getattr 不生效的 bug # 361

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- 在 EmbedModelInfo 中添加 model_id 字段
- 修改 get_embedding_config 以优先使用 model_id 选择模型
- 重构 MilvusKB 的嵌入函数获取逻辑
- 添加调试日志并优化错误处理
This commit is contained in:
Wenjie Zhang 2025-12-03 18:28:31 +08:00
parent 001751dd46
commit 49253cbc04
6 changed files with 50 additions and 27 deletions
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10
server/routers/knowledge_router.py
View File

@ -87,10 +87,12 @@ async def create_database(
"""创建知识库"""
logger.debug(
f"Create database {database_name} with kb_type {kb_type}, "
f"additional_params {additional_params}, llm_info {llm_info}"
f"additional_params {additional_params}, llm_info {llm_info}, "
f"embed_model_name {embed_model_name}"
)
try:
additional_params = {**(additional_params or {})}
additional_params["auto_generate_questions"] = False # 默认不生成问题
def normalize_reranker_config(kb: str, params: dict) -> None:
reranker_cfg = params.get("reranker_config")
@ -112,12 +114,12 @@ async def create_database(
if not isinstance(reranker_cfg, Mapping):
raise HTTPException(status_code=400, detail="reranker_config must be an object")
enabled = bool(reranker_cfg.get("enabled", False))
reranker_enabled = bool(reranker_cfg.get("enabled", False))
model = (reranker_cfg.get("model") or "").strip()
recall_top_k = max(1, int(reranker_cfg.get("recall_top_k", 50)))
final_top_k = max(1, int(reranker_cfg.get("final_top_k", 10)))
if enabled:
if reranker_enabled:
if not model:
raise HTTPException(status_code=400, detail="reranker_config.model is required when enabled")
if model not in config.reranker_names:
@ -132,7 +134,7 @@ async def create_database(
model = model if model in config.reranker_names else ""
params["reranker_config"] = {
"enabled": enabled,
"enabled": reranker_enabled,
"model": model,
"recall_top_k": recall_top_k,
"final_top_k": final_top_k,

9
src/config/static/models.py
View File

@ -29,7 +29,7 @@ class EmbedModelInfo(BaseModel):
dimension: int = Field(..., description="向量维度")
base_url: str = Field(..., description="API 基础 URL")
api_key: str = Field(..., description="API Key 或环境变量名")
model_id: str | None = Field(None, description="可选的模型 ID")
class RerankerInfo(BaseModel):
"""重排序模型配置"""
@ -158,42 +158,49 @@ DEFAULT_CHAT_MODEL_PROVIDERS: dict[str, ChatModelProvider] = {
DEFAULT_EMBED_MODELS: dict[str, EmbedModelInfo] = {
"siliconflow/BAAI/bge-m3": EmbedModelInfo(
model_id="siliconflow/BAAI/bge-m3",
name="BAAI/bge-m3",
dimension=1024,
base_url="https://api.siliconflow.cn/v1/embeddings",
api_key="SILICONFLOW_API_KEY",
),
"siliconflow/Pro/BAAI/bge-m3": EmbedModelInfo(
model_id="siliconflow/Pro/BAAI/bge-m3",
name="Pro/BAAI/bge-m3",
dimension=1024,
base_url="https://api.siliconflow.cn/v1/embeddings",
api_key="SILICONFLOW_API_KEY",
),
"siliconflow/Qwen/Qwen3-Embedding-0.6B": EmbedModelInfo(
model_id="siliconflow/Qwen/Qwen3-Embedding-0.6B",
name="Qwen/Qwen3-Embedding-0.6B",
dimension=1024,
base_url="https://api.siliconflow.cn/v1/embeddings",
api_key="SILICONFLOW_API_KEY",
),
"vllm/Qwen/Qwen3-Embedding-0.6B": EmbedModelInfo(
model_id="vllm/Qwen/Qwen3-Embedding-0.6B",
name="Qwen3-Embedding-0.6B",
dimension=1024,
base_url="http://localhost:8000/v1/embeddings",
api_key="no_api_key",
),
"ollama/nomic-embed-text": EmbedModelInfo(
model_id="ollama/nomic-embed-text",
name="nomic-embed-text",
dimension=768,
base_url="http://localhost:11434/api/embed",
api_key="no_api_key",
),
"ollama/bge-m3": EmbedModelInfo(
model_id="ollama/bge-m3",
name="bge-m3",
dimension=1024,
base_url="http://localhost:11434/api/embed",
api_key="no_api_key",
),
"dashscope/text-embedding-v4": EmbedModelInfo(
model_id="dashscope/text-embedding-v4",
name="text-embedding-v4",
dimension=1024,
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings",

35
src/knowledge/implementations/milvus.py
View File

@ -7,6 +7,7 @@ from typing import Any
from pymilvus import Collection, CollectionSchema, DataType, FieldSchema, connections, db, utility
from src import config
from src.knowledge.base import KnowledgeBase
from src.knowledge.indexing import process_file_to_markdown
from src.knowledge.utils.kb_utils import (
@ -91,10 +92,14 @@ class MilvusKB(KnowledgeBase):
"""创建 Milvus 集合"""
logger.info(f"Creating Milvus collection for {db_id}")
if db_id not in self.databases_meta:
if not (metadata := self.databases_meta.get(db_id)):
raise ValueError(f"Database {db_id} not found")
embed_info = self.databases_meta[db_id].get("embed_info", {})
# embed_info = metadata.get("embed_info", {})
if not (embed_info := metadata.get("embed_info")):
logger.error(f"Embedding info not found for database {db_id}, using default model")
embed_info = config.embed_model_names[config.embed_model]
collection_name = db_id
try:
@ -117,8 +122,8 @@ class MilvusKB(KnowledgeBase):
except Exception:
# 创建新集合
embedding_dim = getattr(embed_info, "dimension", 1024) if embed_info else 1024
model_name = getattr(embed_info, "name", "default") if embed_info else "default"
embedding_dim = embed_info.get("dimension", 1024)
model_name = embed_info.get("name", "default")
# 定义集合Schema
fields = [
@ -142,7 +147,7 @@ class MilvusKB(KnowledgeBase):
index_params = {"metric_type": "COSINE", "index_type": "IVF_FLAT", "params": {"nlist": 1024}}
collection.create_index("embedding", index_params)
logger.info(f"Created new Milvus collection: {collection_name}")
logger.info(f"Created new Milvus collection: {collection_name}: {model_name=}, {embedding_dim=}")
return collection
@ -154,25 +159,29 @@ class MilvusKB(KnowledgeBase):
except Exception as e:
logger.warning(f"Failed to load collection into memory: {e}")
def _get_async_embedding_function(self, embed_info: dict):
def _get_async_embedding(self, embed_info: dict):
"""获取 embedding 函数"""
# 检查是否有 model_id 字段，优先使用 select_embedding_model
if embed_info and "model_id" in embed_info:
from src.models.embed import select_embedding_model
return select_embedding_model(embed_info["model_id"])
# 使用原有的逻辑（兼容模式））
config_dict = get_embedding_config(embed_info)
embedding_model = OtherEmbedding(
return OtherEmbedding(
model=config_dict.get("model"),
base_url=config_dict.get("base_url"),
api_key=config_dict.get("api_key"),
)
def _get_async_embedding_function(self, embed_info: dict):
"""获取 embedding 函数"""
embedding_model = self._get_async_embedding(embed_info)
return partial(embedding_model.abatch_encode, batch_size=40)
def _get_embedding_function(self, embed_info: dict):
"""获取 embedding 函数"""
config_dict = get_embedding_config(embed_info)
embedding_model = OtherEmbedding(
model=config_dict.get("model"),
base_url=config_dict.get("base_url"),
api_key=config_dict.get("api_key"),
)
embedding_model = self._get_async_embedding(embed_info)
return partial(embedding_model.batch_encode, batch_size=40)

6
src/knowledge/manager.py
View File

@ -246,16 +246,12 @@ class KnowledgeBaseManager:
db_id = db_info["db_id"]
async with self._metadata_lock:
# 准备 additional_params，包含 auto_generate_questions
saved_params = kwargs.copy()
saved_params["auto_generate_questions"] = False
self.global_databases_meta[db_id] = {
"name": database_name,
"description": description,
"kb_type": kb_type,
"created_at": utc_isoformat(),
"additional_params": saved_params,
"additional_params": kwargs.copy(),
}
self._save_global_metadata()

14
src/knowledge/utils/kb_utils.py
View File

@ -247,15 +247,23 @@ def get_embedding_config(embed_info: dict) -> dict:
try:
if embed_info:
# 处理 embed_info 可能是字典或 EmbedModelInfo 对象的情况
if hasattr(embed_info, "name"):
# 优先检查是否有 model_id 字段
if "model_id" in embed_info:
from src.models.embed import select_embedding_model
model = select_embedding_model(embed_info["model_id"])
config_dict["model"] = model.model
config_dict["api_key"] = model.api_key
config_dict["base_url"] = model.base_url
config_dict["dimension"] = getattr(model, "dimension", 1024)
elif hasattr(embed_info, "name"):
# EmbedModelInfo 对象
config_dict["model"] = embed_info.name
config_dict["api_key"] = os.getenv(embed_info.api_key) or embed_info.api_key
config_dict["base_url"] = embed_info.base_url
config_dict["dimension"] = embed_info.dimension
else:
# 字典形式
# 字典形式（保持向后兼容）
config_dict["model"] = embed_info["name"]
config_dict["api_key"] = os.getenv(embed_info["api_key"]) or embed_info["api_key"]
config_dict["base_url"] = embed_info["base_url"]

3
src/models/embed.py
View File

@ -11,7 +11,7 @@ from src.utils import get_docker_safe_url, hashstr, logger
class BaseEmbeddingModel(ABC):
def __init__(self, model=None, name=None, dimension=None, url=None, base_url=None, api_key=None):
def __init__(self, model=None, name=None, dimension=None, url=None, base_url=None, api_key=None, model_id=None):
"""
Args:
model: 模型名称，冗余设计，同name
@ -140,6 +140,7 @@ class OllamaEmbedding(BaseEmbeddingModel):
payload = {"model": self.model, "input": message}
async with httpx.AsyncClient() as client:
try:
print(f"\n\n\nOllama Embedding request: {payload}\n\n\n")
response = await client.post(self.base_url, json=payload, timeout=60)
response.raise_for_status()
result = response.json()