支持在线 Embeddings, Lite 模式支持所有知识库相关功能 (#1924)
新功能：
- 支持在线 Embeddings：zhipu-api, qwen-api, minimax-api, qianfan-api
- API 增加 /other/embed_texts 接口
- init_database.py 增加 --embed-model 参数，可以指定使用的嵌入模型（本地或在线均可）
- 对于 FAISS 知识库，支持多向量库，默认位置：{KB_PATH}/vector_store/{embed_model}
- Lite 模式支持所有知识库相关功能。此模式下最主要的限制是：
- 不能使用本地 LLM 和 Embeddings 模型
- 知识库不支持 PDF 文件
- init_database.py 重建知识库时不再默认情况数据库表，增加 clear-tables 参数手动控制。
- API 和 WEBUI 中 score_threshold 参数范围改为 [0, 2]，以更好的适应在线嵌入模型
问题修复：
- API 中 list_config_models 会删除 ONLINE_LLM_MODEL 中的敏感信息，导致第二轮API请求错误
开发者：
- 统一向量库的识别：以（kb_name,embed_model）为判断向量库唯一性的依据，避免 FAISS 知识库缓存加载逻辑错误
- KBServiceFactory.get_service_by_name 中添加 default_embed_model 参数，用于在构建新知识库时设置 embed_model
- 优化 kb_service 中 Embeddings 操作：
- 统一加载接口： server.utils.load_embeddings，利用全局缓存避免各处 Embeddings 传参
- 统一文本嵌入接口：server.knowledge_base.kb_service.base.[embed_texts, embed_documents]
- 重写 normalize 函数，去除对 scikit-learn/scipy 的依赖
This commit is contained in:
liunux4odoo
GitHub
parent
deed92169f
commit
65592a45c3
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@ -23,6 +23,11 @@ if __name__ == "__main__":
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'''
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)
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)
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parser.add_argument(
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"--clear-tables",
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action="store_true",
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help=("drop the database tables before recreate vector stores")
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)
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parser.add_argument(
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"-u",
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"--update-in-db",
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@ -74,7 +79,7 @@ if __name__ == "__main__":
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"--embed-model",
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type=str,
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default=EMBEDDING_MODEL,
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help=("specify knowledge base names to operate on. default is all folders exist in KB_ROOT_PATH.")
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help=("specify embeddings model.")
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)
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if len(sys.argv) <= 1:
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@ -84,9 +89,12 @@ if __name__ == "__main__":
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start_time = datetime.now()
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create_tables() # confirm tables exist
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if args.recreate_vs:
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if args.clear_tables:
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reset_tables()
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print("database talbes reseted")
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if args.recreate_vs:
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print("recreating all vector stores")
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folder2db(kb_names=args.kb_name, mode="recreate_vs", embed_model=args.embed_model)
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elif args.update_in_db:
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@ -7,18 +7,19 @@ openai
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# torchvision
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# torchaudio
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fastapi>=0.103.1
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python-multipart
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nltk~=3.8.1
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uvicorn~=0.23.1
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starlette~=0.27.0
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pydantic~=1.10.11
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# unstructured[all-docs]>=0.10.4
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# python-magic-bin; sys_platform == 'win32'
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unstructured[docx,csv]>=0.10.4 # add pdf if need
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python-magic-bin; sys_platform == 'win32'
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SQLAlchemy==2.0.19
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# faiss-cpu
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faiss-cpu
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# accelerate
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# spacy
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# PyMuPDF==1.22.5
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# rapidocr_onnxruntime>=1.3.2
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# PyMuPDF==1.22.5 # install if need pdf
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# rapidocr_onnxruntime>=1.3.2 # install if need pdf
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requests
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pathlib
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@ -74,7 +74,6 @@ def mount_app_routes(app: FastAPI, run_mode: str = None):
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)(search_engine_chat)
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# 知识库相关接口
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if run_mode != "lite":
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mount_knowledge_routes(app)
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# LLM模型相关接口
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@ -19,7 +19,7 @@ from server.knowledge_base.kb_doc_api import search_docs
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async def knowledge_base_chat(query: str = Body(..., description="用户输入", examples=["你好"]),
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knowledge_base_name: str = Body(..., description="知识库名称", examples=["samples"]),
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top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"),
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score_threshold: float = Body(SCORE_THRESHOLD, description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右", ge=0, le=1),
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score_threshold: float = Body(SCORE_THRESHOLD, description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右", ge=0, le=2),
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history: List[History] = Body([],
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description="历史对话",
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examples=[[
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@ -16,6 +16,7 @@ def embed_texts(
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) -> BaseResponse:
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'''
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对文本进行向量化。返回数据格式：BaseResponse(data=List[List[float]])
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TODO: 也许需要加入缓存机制，减少 token 消耗
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'''
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try:
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if embed_model in list_embed_models(): # 使用本地Embeddings模型
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@ -1,11 +1,8 @@
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.embeddings import HuggingFaceBgeEmbeddings
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from langchain.embeddings.base import Embeddings
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import threading
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from configs import (EMBEDDING_MODEL, CHUNK_SIZE,
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logger, log_verbose)
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from server.utils import embedding_device, get_model_path
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from server.utils import embedding_device, get_model_path, list_online_embed_models
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from contextlib import contextmanager
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from collections import OrderedDict
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from typing import List, Any, Union, Tuple
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@ -100,12 +97,21 @@ class CachePool:
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else:
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return cache
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def load_kb_embeddings(self, kb_name: str=None, embed_device: str = embedding_device()) -> Embeddings:
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def load_kb_embeddings(
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self,
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kb_name: str,
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embed_device: str = embedding_device(),
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default_embed_model: str = EMBEDDING_MODEL,
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) -> Embeddings:
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from server.db.repository.knowledge_base_repository import get_kb_detail
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from server.knowledge_base.kb_service.base import EmbeddingsFunAdapter
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kb_detail = get_kb_detail(kb_name=kb_name)
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print(kb_detail)
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embed_model = kb_detail.get("embed_model", EMBEDDING_MODEL)
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kb_detail = get_kb_detail(kb_name)
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embed_model = kb_detail.get("embed_model", default_embed_model)
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if embed_model in list_online_embed_models():
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return EmbeddingsFunAdapter(embed_model)
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else:
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return embeddings_pool.load_embeddings(model=embed_model, device=embed_device)
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@ -121,10 +127,12 @@ class EmbeddingsPool(CachePool):
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with item.acquire(msg="初始化"):
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self.atomic.release()
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if model == "text-embedding-ada-002": # openai text-embedding-ada-002
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from langchain.embeddings.openai import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings(model_name=model,
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openai_api_key=get_model_path(model),
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chunk_size=CHUNK_SIZE)
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elif 'bge-' in model:
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from langchain.embeddings import HuggingFaceBgeEmbeddings
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if 'zh' in model:
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# for chinese model
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query_instruction = "为这个句子生成表示以用于检索相关文章："
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@ -140,6 +148,7 @@ class EmbeddingsPool(CachePool):
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if model == "bge-large-zh-noinstruct": # bge large -noinstruct embedding
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embeddings.query_instruction = ""
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else:
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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embeddings = HuggingFaceEmbeddings(model_name=get_model_path(model), model_kwargs={'device': device})
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item.obj = embeddings
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item.finish_loading()
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@ -64,23 +64,24 @@ class KBFaissPool(_FaissPool):
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def load_vector_store(
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self,
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kb_name: str,
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vector_name: str = "vector_store",
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vector_name: str = None,
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create: bool = True,
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embed_model: str = EMBEDDING_MODEL,
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embed_device: str = embedding_device(),
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) -> ThreadSafeFaiss:
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self.atomic.acquire()
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vector_name = vector_name or embed_model
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cache = self.get((kb_name, vector_name)) # 用元组比拼接字符串好一些
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if cache is None:
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item = ThreadSafeFaiss((kb_name, vector_name), pool=self)
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self.set((kb_name, vector_name), item)
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with item.acquire(msg="初始化"):
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self.atomic.release()
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logger.info(f"loading vector store in '{kb_name}/{vector_name}' from disk.")
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logger.info(f"loading vector store in '{kb_name}/vector_store/{vector_name}' from disk.")
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vs_path = get_vs_path(kb_name, vector_name)
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if os.path.isfile(os.path.join(vs_path, "index.faiss")):
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embeddings = self.load_kb_embeddings(kb_name=kb_name, embed_device=embed_device)
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embeddings = self.load_kb_embeddings(kb_name=kb_name, embed_device=embed_device, default_embed_model=embed_model)
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vector_store = FAISS.load_local(vs_path, embeddings, normalize_L2=True)
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elif create:
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# create an empty vector store
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@ -6,7 +6,6 @@ import os
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import numpy as np
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from langchain.embeddings.base import Embeddings
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from langchain.docstore.document import Document
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from sklearn.preprocessing import normalize
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from server.db.repository.knowledge_base_repository import (
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add_kb_to_db, delete_kb_from_db, list_kbs_from_db, kb_exists,
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@ -31,6 +30,16 @@ from server.embeddings_api import embed_texts
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from server.embeddings_api import embed_documents
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def normalize(embeddings: List[List[float]]) -> np.ndarray:
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'''
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sklearn.preprocessing.normalize 的替代（使用 L2），避免安装 scipy, scikit-learn
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'''
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norm = np.linalg.norm(embeddings, axis=1)
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norm = np.reshape(norm, (norm.shape[0], 1))
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norm = np.tile(norm, (1, len(embeddings[0])))
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return np.divide(embeddings, norm)
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class SupportedVSType:
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FAISS = 'faiss'
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MILVUS = 'milvus'
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@ -52,6 +61,9 @@ class KBService(ABC):
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self.doc_path = get_doc_path(self.kb_name)
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self.do_init()
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def __repr__(self) -> str:
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return f"{self.kb_name} @ {self.embed_model}"
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def save_vector_store(self):
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'''
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保存向量库:FAISS保存到磁盘，milvus保存到数据库。PGVector暂未支持
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@ -209,7 +221,6 @@ class KBService(ABC):
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query: str,
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top_k: int,
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score_threshold: float,
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embeddings: Embeddings,
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) -> List[Document]:
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"""
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搜索知识库子类实自己逻辑
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@ -267,11 +278,15 @@ class KBServiceFactory:
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return DefaultKBService(kb_name)
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@staticmethod
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def get_service_by_name(kb_name: str
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def get_service_by_name(kb_name: str,
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default_vs_type: SupportedVSType = SupportedVSType.FAISS,
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default_embed_model: str = EMBEDDING_MODEL,
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) -> KBService:
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_, vs_type, embed_model = load_kb_from_db(kb_name)
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if vs_type is None and os.path.isdir(get_kb_path(kb_name)): # faiss knowledge base not in db
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vs_type = "faiss"
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if vs_type is None: # faiss knowledge base not in db
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vs_type = default_vs_type
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if embed_model is None:
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embed_model = default_embed_model
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return KBServiceFactory.get_service(kb_name, vs_type, embed_model)
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@staticmethod
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@ -357,7 +372,7 @@ class EmbeddingsFunAdapter(Embeddings):
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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embeddings = embed_texts(texts=texts, embed_model=self.embed_model, to_query=False).data
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return normalize(embeddings)
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return normalize(embeddings).tolist()
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def embed_query(self, text: str) -> List[float]:
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embeddings = embed_texts(texts=[text], embed_model=self.embed_model, to_query=True).data
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@ -1,14 +1,10 @@
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import os
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import shutil
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from configs import (
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KB_ROOT_PATH,
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SCORE_THRESHOLD,
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logger, log_verbose,
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)
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from server.knowledge_base.kb_service.base import KBService, SupportedVSType
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from configs import SCORE_THRESHOLD
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from server.knowledge_base.kb_service.base import KBService, SupportedVSType, EmbeddingsFunAdapter
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from server.knowledge_base.kb_cache.faiss_cache import kb_faiss_pool, ThreadSafeFaiss
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from server.knowledge_base.utils import KnowledgeFile
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from server.knowledge_base.utils import KnowledgeFile, get_kb_path, get_vs_path
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from server.utils import torch_gc
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from langchain.docstore.document import Document
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from typing import List, Dict, Optional
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@ -17,16 +13,16 @@ from typing import List, Dict, Optional
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class FaissKBService(KBService):
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vs_path: str
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kb_path: str
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vector_name: str = "vector_store"
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vector_name: str = None
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def vs_type(self) -> str:
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return SupportedVSType.FAISS
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def get_vs_path(self):
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return os.path.join(self.get_kb_path(), self.vector_name)
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return get_vs_path(self.kb_name, self.vector_name)
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def get_kb_path(self):
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return os.path.join(KB_ROOT_PATH, self.kb_name)
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return get_kb_path(self.kb_name)
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def load_vector_store(self) -> ThreadSafeFaiss:
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return kb_faiss_pool.load_vector_store(kb_name=self.kb_name,
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@ -41,6 +37,7 @@ class FaissKBService(KBService):
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return vs.docstore._dict.get(id)
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def do_init(self):
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self.vector_name = self.vector_name or self.embed_model
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self.kb_path = self.get_kb_path()
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self.vs_path = self.get_vs_path()
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@ -58,8 +55,10 @@ class FaissKBService(KBService):
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top_k: int,
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score_threshold: float = SCORE_THRESHOLD,
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) -> List[Document]:
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embed_func = EmbeddingsFunAdapter(self.embed_model)
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embeddings = embed_func.embed_query(query)
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with self.load_vector_store().acquire() as vs:
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docs = vs.similarity_search_with_score(query, k=top_k, score_threshold=score_threshold)
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docs = vs.similarity_search_with_score_by_vector(embeddings, k=top_k, score_threshold=score_threshold)
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return docs
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def do_add_doc(self,
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@ -59,7 +59,10 @@ class MilvusKBService(KBService):
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def do_search(self, query: str, top_k: int, score_threshold: float):
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self._load_milvus()
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return score_threshold_process(score_threshold, top_k, self.milvus.similarity_search_with_score(query, top_k))
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embed_func = EmbeddingsFunAdapter(self.embed_model)
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embeddings = embed_func.embed_query(query)
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docs = self.milvus.similarity_search_with_score_by_vector(embeddings, top_k)
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return score_threshold_process(score_threshold, top_k, docs)
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def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
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# TODO: workaround for bug #10492 in langchain
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@ -53,8 +53,10 @@ class PGKBService(KBService):
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def do_search(self, query: str, top_k: int, score_threshold: float):
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self._load_pg_vector()
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return score_threshold_process(score_threshold, top_k,
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self.pg_vector.similarity_search_with_score(query, top_k))
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embed_func = EmbeddingsFunAdapter(self.embed_model)
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embeddings = embed_func.embed_query(query)
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docs = self.pg_vector.similarity_search_with_score_by_vector(embeddings, top_k)
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return score_threshold_process(score_threshold, top_k, docs)
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def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
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ids = self.pg_vector.add_documents(docs)
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@ -59,7 +59,10 @@ class ZillizKBService(KBService):
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def do_search(self, query: str, top_k: int, score_threshold: float):
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self._load_zilliz()
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return score_threshold_process(score_threshold, top_k, self.zilliz.similarity_search_with_score(query, top_k))
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embed_func = EmbeddingsFunAdapter(self.embed_model)
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embeddings = embed_func.embed_query(query)
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docs = self.zilliz.similarity_search_with_score_by_vector(embeddings, top_k)
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return score_threshold_process(score_threshold, top_k, docs)
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def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
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for doc in docs:
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@ -67,6 +67,7 @@ def folder2db(
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kb_names = kb_names or list_kbs_from_folder()
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for kb_name in kb_names:
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kb = KBServiceFactory.get_service(kb_name, vs_type, embed_model)
|
||||
if not kb.exists():
|
||||
kb.create_kb()
|
||||
|
||||
# 清除向量库，从本地文件重建
|
||||
|
|
|
|||
|
|
@ -39,7 +39,7 @@ def get_doc_path(knowledge_base_name: str):
|
|||
|
||||
|
||||
def get_vs_path(knowledge_base_name: str, vector_name: str):
|
||||
return os.path.join(get_kb_path(knowledge_base_name), vector_name)
|
||||
return os.path.join(get_kb_path(knowledge_base_name), "vector_store", vector_name)
|
||||
|
||||
|
||||
def get_file_path(knowledge_base_name: str, doc_name: str):
|
||||
|
|
|
|||
|
|
@ -237,6 +237,7 @@ class ChatMessage(BaseModel):
|
|||
|
||||
|
||||
def torch_gc():
|
||||
try:
|
||||
import torch
|
||||
if torch.cuda.is_available():
|
||||
# with torch.cuda.device(DEVICE):
|
||||
|
|
@ -251,6 +252,8 @@ def torch_gc():
|
|||
"以支持及时清理 torch 产生的内存占用。")
|
||||
logger.error(f'{e.__class__.__name__}: {msg}',
|
||||
exc_info=e if log_verbose else None)
|
||||
except Exception:
|
||||
...
|
||||
|
||||
|
||||
def run_async(cor):
|
||||
|
|
@ -719,7 +722,7 @@ def list_online_embed_models() -> List[str]:
|
|||
|
||||
ret = []
|
||||
for k, v in list_config_llm_models()["online"].items():
|
||||
provider = v.get("provider")
|
||||
if provider := v.get("provider"):
|
||||
worker_class = getattr(model_workers, provider, None)
|
||||
if worker_class is not None and worker_class.can_embedding():
|
||||
ret.append(k)
|
||||
|
|
|
|||
7
webui.py
7
webui.py
|
|
@ -2,6 +2,7 @@ import streamlit as st
|
|||
from webui_pages.utils import *
|
||||
from streamlit_option_menu import option_menu
|
||||
from webui_pages.dialogue.dialogue import dialogue_page, chat_box
|
||||
from webui_pages.knowledge_base.knowledge_base import knowledge_base_page
|
||||
import os
|
||||
import sys
|
||||
from configs import VERSION
|
||||
|
|
@ -29,15 +30,11 @@ if __name__ == "__main__":
|
|||
"icon": "chat",
|
||||
"func": dialogue_page,
|
||||
},
|
||||
}
|
||||
if not is_lite:
|
||||
from webui_pages.knowledge_base.knowledge_base import knowledge_base_page
|
||||
pages.update({
|
||||
"知识库管理": {
|
||||
"icon": "hdd-stack",
|
||||
"func": knowledge_base_page,
|
||||
},
|
||||
})
|
||||
}
|
||||
|
||||
with st.sidebar:
|
||||
st.image(
|
||||
|
|
|
|||
|
|
@ -54,11 +54,6 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
|
|||
text = f"{text} 当前知识库： `{cur_kb}`。"
|
||||
st.toast(text)
|
||||
|
||||
if is_lite:
|
||||
dialogue_modes = ["LLM 对话",
|
||||
"搜索引擎问答",
|
||||
]
|
||||
else:
|
||||
dialogue_modes = ["LLM 对话",
|
||||
"知识库问答",
|
||||
"搜索引擎问答",
|
||||
|
|
@ -116,12 +111,6 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
|
|||
st.success(msg)
|
||||
st.session_state["prev_llm_model"] = llm_model
|
||||
|
||||
if is_lite:
|
||||
index_prompt = {
|
||||
"LLM 对话": "llm_chat",
|
||||
"搜索引擎问答": "search_engine_chat",
|
||||
}
|
||||
else:
|
||||
index_prompt = {
|
||||
"LLM 对话": "llm_chat",
|
||||
"自定义Agent问答": "agent_chat",
|
||||
|
|
@ -167,7 +156,7 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
|
|||
kb_top_k = st.number_input("匹配知识条数：", 1, 20, VECTOR_SEARCH_TOP_K)
|
||||
|
||||
## Bge 模型会超过1
|
||||
score_threshold = st.slider("知识匹配分数阈值：", 0.0, 1.0, float(SCORE_THRESHOLD), 0.01)
|
||||
score_threshold = st.slider("知识匹配分数阈值：", 0.0, 2.0, float(SCORE_THRESHOLD), 0.01)
|
||||
|
||||
elif dialogue_mode == "搜索引擎问答":
|
||||
search_engine_list = api.list_search_engines()
|
||||
|
|
|
|||
|
|
@ -103,6 +103,9 @@ def knowledge_base_page(api: ApiRequest, is_lite: bool = None):
|
|||
key="vs_type",
|
||||
)
|
||||
|
||||
if is_lite:
|
||||
embed_models = list_online_embed_models()
|
||||
else:
|
||||
embed_models = list_embed_models() + list_online_embed_models()
|
||||
|
||||
embed_model = cols[1].selectbox(
|
||||
|
|
|
|||
Loading…
Reference in New Issue