support for bge-large-zh-noinstruct and openai text-embedding-ada-002 (#1119)

* support for bge-large-zh-noinstruct and openai text-embedding-ada-002

* 完善了readme,修改了BGE模型的载入方式

* 补充了readme中bge-large-zh-noinstruct的信息

* Update faiss_kb_service.py

* Update utils.py

---------

Co-authored-by: zR <zRzRzRzRzRzRzR>
Co-authored-by: imClumsyPanda <littlepanda0716@gmail.com>

README.md

@@ -126,6 +126,7 @@ docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/ch
 - [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh)
 - [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh)
 - [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh)
+- [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct)
 - [text2vec-base-chinese-sentence](https://huggingface.co/shibing624/text2vec-base-chinese-sentence)
 - [text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase)
 - [text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual)
@@ -133,6 +134,7 @@ docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/ch
 - [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese)
 - [nghuyong/ernie-3.0-nano-zh](https://huggingface.co/nghuyong/ernie-3.0-nano-zh)
 - [nghuyong/ernie-3.0-base-zh](https://huggingface.co/nghuyong/ernie-3.0-base-zh)
+- [OpenAI/text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings)
 
 ---
 
@@ -206,6 +208,7 @@ embedding_model_dict = {
                         "m3e-base": "/Users/xxx/Downloads/m3e-base",
                        }
 ```
+如果你选择使用OpenAI的Embedding模型，请将模型的```key```写入`embedding_model_dict`中。使用该模型，你需要鞥能够访问OpenAI官的API，或设置代理。
 
 ### 4. 知识库初始化与迁移
 

configs/model_config.py.example

@@ -24,7 +24,9 @@ embedding_model_dict = {
     "m3e-large": "moka-ai/m3e-large",
     "bge-small-zh": "BAAI/bge-small-zh",
     "bge-base-zh": "BAAI/bge-base-zh",
-    "bge-large-zh": "BAAI/bge-large-zh"
+    "bge-large-zh": "BAAI/bge-large-zh",
+    "bge-large-zh-noinstruct": "BAAI/bge-large-zh-noinstruct",
+    "text-embedding-ada-002": os.environ.get("OPENAI_API_KEY")
 }
 
 # 选用的 Embedding 名称

server/knowledge_base/kb_service/faiss_kb_service.py

@@ -13,7 +13,8 @@ from functools import lru_cache
 from server.knowledge_base.utils import get_vs_path, load_embeddings, KnowledgeFile
 from langchain.vectorstores import FAISS
 from langchain.embeddings.base import Embeddings
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings,HuggingFaceBgeEmbeddings
+from langchain.embeddings.openai import OpenAIEmbeddings
 from typing import List
 from langchain.docstore.document import Document
 from server.utils import torch_gc
@@ -21,10 +22,19 @@ from server.utils import torch_gc
 
 # make HuggingFaceEmbeddings hashable
 def _embeddings_hash(self):
+    if isinstance(self, HuggingFaceEmbeddings):
         return hash(self.model_name)
-
+    elif isinstance(self, HuggingFaceBgeEmbeddings):
+        return hash(self.model_name)
+    elif isinstance(self, OpenAIEmbeddings):
+        return hash(self.model)
 
 HuggingFaceEmbeddings.__hash__ = _embeddings_hash
+OpenAIEmbeddings.__hash__ = _embeddings_hash
+HuggingFaceBgeEmbeddings.__hash__ = _embeddings_hash
+
+_VECTOR_STORE_TICKS = {}
+
 
 _VECTOR_STORE_TICKS = {}
 

server/knowledge_base/utils.py

@@ -1,5 +1,7 @@
 import os
 from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.embeddings import HuggingFaceBgeEmbeddings
 from configs.model_config import (
     embedding_model_dict,
     KB_ROOT_PATH,
@@ -41,11 +43,20 @@ def list_docs_from_folder(kb_name: str):
 
 @lru_cache(1)
 def load_embeddings(model: str, device: str):
-    embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[model],
+    if model == "text-embedding-ada-002":  # openai text-embedding-ada-002
-                                       model_kwargs={'device': device})
+        embeddings = OpenAIEmbeddings(openai_api_key=embedding_model_dict[model], chunk_size=CHUNK_SIZE)
+    elif 'bge-' in model:
+        embeddings = HuggingFaceBgeEmbeddings(model_name=embedding_model_dict[model],
+                                              model_kwargs={'device': device},
+                                              query_instruction="为这个句子生成表示以用于检索相关文章：")
+        if model == "bge-large-zh-noinstruct":  # bge large -noinstruct embedding
+            embeddings.query_instruction = ""
+    else:
+        embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[model], model_kwargs={'device': device})
     return embeddings
 
 
+
 LOADER_DICT = {"UnstructuredFileLoader": ['.eml', '.html', '.json', '.md', '.msg', '.rst',
                                           '.rtf', '.txt', '.xml',
                                           '.doc', '.docx', '.epub', '.odt', '.pdf',