Merge branch 'dev' into dev_config_init

libs/chatchat-server/chatchat/configs/_kb_config.py

@@ -1,11 +1,13 @@
 import os
 from pathlib import Path
 
-# chatchat 项目根目录
-CHATCHAT_ROOT = str(Path(__file__).absolute().parent.parent)
+import sys
+sys.path.append(str(Path(__file__).parent))
+
+from _basic_config import config_workspace
 
 # 用户数据根目录
-DATA_PATH = os.path.join(CHATCHAT_ROOT, "data")
+DATA_PATH = config_workspace.get_config().DATA_PATH
 
 # 默认使用的知识库
 DEFAULT_KNOWLEDGE_BASE = "samples"

libs/chatchat-server/chatchat/configs/_model_config.py

@@ -149,7 +149,7 @@ TOOL_CONFIG = {
     "search_local_knowledgebase": {
         "use": False,
         "top_k": 3,
-        "score_threshold": 1,
+        "score_threshold": 1.0,
         "conclude_prompt": {
             "with_result":
                 '<指令>根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 "根据已知信息无法回答该问题"，'

libs/chatchat-server/chatchat/server/agent/tools_factory/search_local_knowledgebase.py

@@ -1,13 +1,14 @@
 from urllib.parse import urlencode
 from chatchat.server.utils import get_tool_config
 from chatchat.server.pydantic_v1 import Field
-from .tools_registry import regist_tool, BaseToolOutput
+from chatchat.server.agent.tools_factory.tools_registry import regist_tool, BaseToolOutput
 from chatchat.server.knowledge_base.kb_api import list_kbs
 from chatchat.server.knowledge_base.kb_doc_api import search_docs, DocumentWithVSId
 from chatchat.configs import KB_INFO
 
 
-template = "Use local knowledgebase from one or more of these:\n{KB_info}\n to get information，Only local data on this knowledge use this tool. The 'database' should be one of the above [{key}]."
+template = ("Use local knowledgebase from one or more of these:\n{KB_info}\n to get information，Only local data on "
+            "this knowledge use this tool. The 'database' should be one of the above [{key}].")
 KB_info_str = '\n'.join([f"{key}: {value}" for key, value in KB_INFO.items()])
 template_knowledge = template.format(KB_info=KB_info_str, key="samples")
 
@@ -49,7 +50,7 @@ def search_local_knowledgebase(
     database: str = Field(description="Database for Knowledge Search", choices=[kb.kb_name for kb in list_kbs().data]),
     query: str = Field(description="Query for Knowledge Search"),
 ):
-    ''''''
+    """"""
     tool_config = get_tool_config("search_local_knowledgebase")
     ret = search_knowledgebase(query=query, database=database, config=tool_config)
     return KBToolOutput(ret, database=database)

libs/chatchat-server/chatchat/server/chat/file_chat.py

@@ -63,10 +63,10 @@ def upload_temp_docs(
         chunk_overlap: int = Form(OVERLAP_SIZE, description="知识库中相邻文本重合长度"),
         zh_title_enhance: bool = Form(ZH_TITLE_ENHANCE, description="是否开启中文标题加强"),
 ) -> BaseResponse:
-    '''
+    """
     将文件保存到临时目录，并进行向量化。
     返回临时目录名称作为ID，同时也是临时向量库的ID。
-    '''
+    """
     if prev_id is not None:
         memo_faiss_pool.pop(prev_id)
 
@@ -134,7 +134,7 @@ async def file_chat(query: str = Body(..., description="用户输入", examples=
             docs = [x[0] for x in docs]
 
         context = "\n".join([doc.page_content for doc in docs])
-        if len(docs) == 0: ## 如果没有找到相关文档，使用Empty模板
+        if len(docs) == 0:  # 如果没有找到相关文档，使用Empty模板
             prompt_template = get_prompt_template("knowledge_base_chat", "empty")
         else:
             prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)

libs/chatchat-server/chatchat/server/file_rag/document_loaders/myimgloader.py

@@ -1,6 +1,6 @@
 from typing import List
 from langchain_community.document_loaders.unstructured import UnstructuredFileLoader
-from chatchat.server.document_loaders.ocr import get_ocr
+from chatchat.server.file_rag.document_loaders.ocr import get_ocr
 
 
 class RapidOCRLoader(UnstructuredFileLoader):

libs/chatchat-server/chatchat/server/file_rag/document_loaders/mypdfloader.py

@@ -4,7 +4,7 @@ import cv2
 from PIL import Image
 import numpy as np
 from chatchat.configs import PDF_OCR_THRESHOLD
-from chatchat.server.document_loaders.ocr import get_ocr
+from chatchat.server.file_rag.document_loaders.ocr import get_ocr
 import tqdm
 
 

libs/chatchat-server/chatchat/server/file_rag/retrievers/__init__.py

@@ -0,0 +1,3 @@
+from chatchat.server.file_rag.retrievers.base import BaseRetrieverService
+from chatchat.server.file_rag.retrievers.vectorstore import VectorstoreRetrieverService
+from chatchat.server.file_rag.retrievers.ensemble import EnsembleRetrieverService

libs/chatchat-server/chatchat/server/file_rag/retrievers/base.py

@@ -0,0 +1,24 @@
+from langchain.vectorstores import VectorStore
+from abc import ABCMeta, abstractmethod
+
+
+class BaseRetrieverService(metaclass=ABCMeta):
+    def __init__(self, **kwargs):
+        self.do_init(**kwargs)
+
+    @abstractmethod
+    def do_init(self, **kwargs):
+        pass
+
+
+    @abstractmethod
+    def from_vectorstore(
+            vectorstore: VectorStore,
+            top_k: int,
+            score_threshold: int or float,
+    ):
+        pass
+
+    @abstractmethod
+    def get_relevant_documents(self, query: str):
+        pass

libs/chatchat-server/chatchat/server/file_rag/retrievers/ensemble.py

@@ -0,0 +1,48 @@
+from chatchat.server.file_rag.retrievers.base import BaseRetrieverService
+from langchain.vectorstores import VectorStore
+from langchain_core.retrievers import BaseRetriever
+from langchain_community.retrievers import BM25Retriever
+from langchain.retrievers import EnsembleRetriever
+
+
+class EnsembleRetrieverService(BaseRetrieverService):
+    def do_init(
+            self,
+            retriever: BaseRetriever = None,
+            top_k: int = 5,
+    ):
+        self.vs = None
+        self.top_k = top_k
+        self.retriever = retriever
+
+
+    @staticmethod
+    def from_vectorstore(
+            vectorstore: VectorStore,
+            top_k: int,
+            score_threshold: int or float,
+    ):
+        faiss_retriever = vectorstore.as_retriever(
+            search_type="similarity_score_threshold",
+            search_kwargs={
+                "score_threshold": score_threshold,
+                "k": top_k
+            }
+        )
+        # TODO: 换个不用torch的实现方式
+        # from cutword.cutword import Cutter
+        import jieba
+        # cutter = Cutter()
+        docs = list(vectorstore.docstore._dict.values())
+        bm25_retriever = BM25Retriever.from_documents(
+            docs,
+            preprocess_func=jieba.lcut_for_search,
+        )
+        bm25_retriever.k = top_k
+        ensemble_retriever = EnsembleRetriever(
+            retrievers=[bm25_retriever, faiss_retriever], weights=[0.5, 0.5]
+        )
+        return EnsembleRetrieverService(retriever=ensemble_retriever)
+
+    def get_relevant_documents(self, query: str):
+        return self.retriever.get_relevant_documents(query)[:self.top_k]

libs/chatchat-server/chatchat/server/file_rag/retrievers/vectorstore.py

@@ -0,0 +1,33 @@
+from chatchat.server.file_rag.retrievers.base import BaseRetrieverService
+from langchain.vectorstores import VectorStore
+from langchain_core.retrievers import BaseRetriever
+
+
+class VectorstoreRetrieverService(BaseRetrieverService):
+    def do_init(
+            self,
+            retriever: BaseRetriever = None,
+            top_k: int = 5,
+    ):
+        self.vs = None
+        self.top_k = top_k
+        self.retriever = retriever
+
+
+    @staticmethod
+    def from_vectorstore(
+            vectorstore: VectorStore,
+            top_k: int,
+            score_threshold: int or float,
+    ):
+        retriever = vectorstore.as_retriever(
+            search_type="similarity_score_threshold",
+            search_kwargs={
+                "score_threshold": score_threshold,
+                "k": top_k
+            }
+        )
+        return VectorstoreRetrieverService(retriever=retriever)
+
+    def get_relevant_documents(self, query: str):
+        return self.retriever.get_relevant_documents(query)[:self.top_k]

libs/chatchat-server/chatchat/server/file_rag/utils.py

@@ -0,0 +1,13 @@
+from chatchat.server.file_rag.retrievers import (
+    BaseRetrieverService,
+    VectorstoreRetrieverService,
+    EnsembleRetrieverService,
+)
+
+Retrivals = {
+    "vectorstore": VectorstoreRetrieverService,
+    "ensemble": EnsembleRetrieverService,
+}
+
+def get_Retriever(type: str = "vectorstore") -> BaseRetrieverService:
+    return Retrivals[type]

libs/chatchat-server/chatchat/server/knowledge_base/kb_cache/faiss_cache.py

@@ -110,7 +110,8 @@ class KBFaissPool(_FaissPool):
 
                     if os.path.isfile(os.path.join(vs_path, "index.faiss")):
                         embeddings = get_Embeddings(embed_model=embed_model)
-                        vector_store = FAISS.load_local(vs_path, embeddings, normalize_L2=True)
+                        vector_store = FAISS.load_local(vs_path, embeddings, normalize_L2=True,
+                                                        allow_dangerous_deserialization=True)
                     elif create:
                         # create an empty vector store
                         if not os.path.exists(vs_path):

libs/chatchat-server/chatchat/server/knowledge_base/kb_doc_api.py

@@ -28,7 +28,7 @@ def search_docs(
                                       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),
+                                      ge=0.0, le=1.0),
         file_name: str = Body("", description="文件名称，支持 sql 通配符"),
         metadata: dict = Body({}, description="根据 metadata 进行过滤，仅支持一级键"),
 ) -> List[Dict]:
@@ -37,7 +37,8 @@ def search_docs(
     if kb is not None:
         if query:
             docs = kb.search_docs(query, top_k, score_threshold)
-            data = [DocumentWithVSId(**x[0].dict(), score=x[1], id=x[0].metadata.get("id")) for x in docs]
+            # data = [DocumentWithVSId(**x[0].dict(), score=x[1], id=x[0].metadata.get("id")) for x in docs]
+            data = [DocumentWithVSId(**x.dict(), id=x.metadata.get("id")) for x in docs]
         elif file_name or metadata:
             data = kb.list_docs(file_name=file_name, metadata=metadata)
             for d in data:

libs/chatchat-server/chatchat/server/knowledge_base/kb_service/chromadb_kb_service.py

@@ -9,6 +9,7 @@ from chatchat.configs import SCORE_THRESHOLD
 from chatchat.server.knowledge_base.kb_service.base import KBService, SupportedVSType
 from chatchat.server.knowledge_base.utils import KnowledgeFile, get_kb_path, get_vs_path
 from chatchat.server.utils import get_Embeddings
+from chatchat.server.file_rag.utils import get_Retriever
 
 
 def _get_result_to_documents(get_result: GetResult) -> List[Document]:
@@ -75,10 +76,13 @@ class ChromaKBService(KBService):
 
     def do_search(self, query: str, top_k: int, score_threshold: float = SCORE_THRESHOLD) -> List[
         Tuple[Document, float]]:
-        embed_func = get_Embeddings(self.embed_model)
-        embeddings = embed_func.embed_query(query)
-        query_result: QueryResult = self.collection.query(query_embeddings=embeddings, n_results=top_k)
-        return _results_to_docs_and_scores(query_result)
+        retriever = get_Retriever("vectorstore").from_vectorstore(
+             self.collection,
+             top_k=top_k,
+             score_threshold=score_threshold,
+         )
+        docs = retriever.get_relevant_documents(query)
+        return docs
 
     def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
         doc_infos = []

libs/chatchat-server/chatchat/server/knowledge_base/kb_service/es_kb_service.py

@@ -8,6 +8,7 @@ from chatchat.server.knowledge_base.utils import KnowledgeFile
 from chatchat.server.utils import get_Embeddings
 from elasticsearch import Elasticsearch, BadRequestError
 from chatchat.configs import kbs_config, KB_ROOT_PATH
+from chatchat.server.file_rag.utils import get_Retriever
 
 import logging
 
@@ -107,8 +108,12 @@ class ESKBService(KBService):
 
     def do_search(self, query:str, top_k: int, score_threshold: float):
         # 文本相似性检索
-        docs = self.db.similarity_search_with_score(query=query,
-                                         k=top_k)
+        retriever = get_Retriever("vectorstore").from_vectorstore(
+            self.db,
+            top_k=top_k,
+            score_threshold=score_threshold,
+        )
+        docs = retriever.get_relevant_documents(query)
         return docs
 
     def get_doc_by_ids(self, ids: List[str]) -> List[Document]:

libs/chatchat-server/chatchat/server/knowledge_base/kb_service/faiss_kb_service.py

@@ -5,9 +5,9 @@ from chatchat.configs import SCORE_THRESHOLD
 from chatchat.server.knowledge_base.kb_service.base import KBService, SupportedVSType
 from chatchat.server.knowledge_base.kb_cache.faiss_cache import kb_faiss_pool, ThreadSafeFaiss
 from chatchat.server.knowledge_base.utils import KnowledgeFile, get_kb_path, get_vs_path
-from chatchat.server.utils import get_Embeddings
 from langchain.docstore.document import Document
-from typing import List, Dict, Optional, Tuple
+from typing import List, Dict, Tuple
+from chatchat.server.file_rag.utils import get_Retriever
 
 
 class FaissKBService(KBService):
@@ -62,10 +62,13 @@ class FaissKBService(KBService):
                   top_k: int,
                   score_threshold: float = SCORE_THRESHOLD,
                   ) -> List[Tuple[Document, float]]:
-        embed_func = get_Embeddings(self.embed_model)
-        embeddings = embed_func.embed_query(query)
         with self.load_vector_store().acquire() as vs:
-            docs = vs.similarity_search_with_score_by_vector(embeddings, k=top_k, score_threshold=score_threshold)
+            retriever = get_Retriever("ensemble").from_vectorstore(
+                 vs,
+                 top_k=top_k,
+                 score_threshold=score_threshold,
+             )
+            docs = retriever.get_relevant_documents(query)
         return docs
 
     def do_add_doc(self,

libs/chatchat-server/chatchat/server/knowledge_base/kb_service/milvus_kb_service.py

@@ -10,7 +10,7 @@ from chatchat.server.db.repository import list_file_num_docs_id_by_kb_name_and_f
 from chatchat.server.knowledge_base.kb_service.base import KBService, SupportedVSType, \
     score_threshold_process
 from chatchat.server.knowledge_base.utils import KnowledgeFile
-from chatchat.server.utils import get_Embeddings
+from chatchat.server.file_rag.utils import get_Retriever
 
 
 class MilvusKBService(KBService):
@@ -67,10 +67,16 @@ class MilvusKBService(KBService):
 
     def do_search(self, query: str, top_k: int, score_threshold: float):
         self._load_milvus()
-        embed_func = get_Embeddings(self.embed_model)
-        embeddings = embed_func.embed_query(query)
-        docs = self.milvus.similarity_search_with_score_by_vector(embeddings, top_k)
-        return score_threshold_process(score_threshold, top_k, docs)
+        # embed_func = get_Embeddings(self.embed_model)
+        # embeddings = embed_func.embed_query(query)
+        # docs = self.milvus.similarity_search_with_score_by_vector(embeddings, top_k)
+        retriever = get_Retriever("vectorstore").from_vectorstore(
+            self.milvus,
+            top_k=top_k,
+            score_threshold=score_threshold,
+        )
+        docs = retriever.get_relevant_documents(query)
+        return docs
 
     def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
         for doc in docs:

libs/chatchat-server/chatchat/server/knowledge_base/kb_service/pg_kb_service.py

@@ -15,6 +15,7 @@ import shutil
 import sqlalchemy
 from sqlalchemy.engine.base import Engine
 from sqlalchemy.orm import Session
+from chatchat.server.file_rag.utils import get_Retriever
 
 
 class PGKBService(KBService):
@@ -60,10 +61,13 @@ class PGKBService(KBService):
             shutil.rmtree(self.kb_path)
 
     def do_search(self, query: str, top_k: int, score_threshold: float):
-        embed_func = get_Embeddings(self.embed_model)
-        embeddings = embed_func.embed_query(query)
-        docs = self.pg_vector.similarity_search_with_score_by_vector(embeddings, top_k)
-        return score_threshold_process(score_threshold, top_k, docs)
+        retriever = get_Retriever("vectorstore").from_vectorstore(
+            self.pg_vector,
+            top_k=top_k,
+            score_threshold=score_threshold,
+        )
+        docs = retriever.get_relevant_documents(query)
+        return docs
 
     def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
         ids = self.pg_vector.add_documents(docs)

libs/chatchat-server/chatchat/server/knowledge_base/kb_service/zilliz_kb_service.py

@@ -1,5 +1,4 @@
-from typing import List, Dict, Optional
-from langchain.embeddings.base import Embeddings
+from typing import List, Dict
 from langchain.schema import Document
 from langchain.vectorstores import Zilliz
 from chatchat.configs import kbs_config
@@ -7,6 +6,7 @@ from chatchat.server.knowledge_base.kb_service.base import KBService, SupportedV
     score_threshold_process
 from chatchat.server.knowledge_base.utils import KnowledgeFile
 from chatchat.server.utils import get_Embeddings
+from chatchat.server.file_rag.utils import get_Retriever
 
 
 class ZillizKBService(KBService):
@@ -60,10 +60,13 @@ class ZillizKBService(KBService):
 
     def do_search(self, query: str, top_k: int, score_threshold: float):
         self._load_zilliz()
-        embed_func = get_Embeddings(self.embed_model)
-        embeddings = embed_func.embed_query(query)
-        docs = self.zilliz.similarity_search_with_score_by_vector(embeddings, top_k)
-        return score_threshold_process(score_threshold, top_k, docs)
+        retriever = get_Retriever("vectorstore").from_vectorstore(
+            self.zilliz,
+            top_k=top_k,
+            score_threshold=score_threshold,
+        )
+        docs = retriever.get_relevant_documents(query)
+        return docs
 
     def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
         for doc in docs:

libs/chatchat-server/chatchat/server/knowledge_base/utils.py

@@ -10,7 +10,7 @@ from chatchat.configs import (
     TEXT_SPLITTER_NAME,
 )
 import importlib
-from chatchat.server.text_splitter import zh_title_enhance as func_zh_title_enhance
+from chatchat.server.file_rag.text_splitter import zh_title_enhance as func_zh_title_enhance
 import langchain_community.document_loaders
 from langchain.docstore.document import Document
 from langchain.text_splitter import TextSplitter, MarkdownHeaderTextSplitter

libs/chatchat-server/chatchat/webui_pages/dialogue/dialogue.py

@@ -24,28 +24,28 @@ chat_box = ChatBox(
 
 
 def save_session():
-    '''save session state to chat context'''
+    """save session state to chat context"""
     chat_box.context_from_session(exclude=["selected_page", "prompt", "cur_conv_name"])
 
 
 def restore_session():
-    '''restore sesstion state from chat context'''
+    """restore sesstion state from chat context"""
     chat_box.context_to_session(exclude=["selected_page", "prompt", "cur_conv_name"])
 
 
 def rerun():
-    '''
+    """
     save chat context before rerun
-    '''
+    """
     save_session()
     st.rerun()
 
 
 def get_messages_history(history_len: int, content_in_expander: bool = False) -> List[Dict]:
-    '''
+    """
     返回消息历史。
     content_in_expander控制是否返回expander元素中的内容，一般导出的时候可以选上，传入LLM的history不需要
-    '''
+    """
 
     def filter(msg):
         content = [x for x in msg["elements"] if x._output_method in ["markdown", "text"]]
@@ -66,10 +66,10 @@ def get_messages_history(history_len: int, content_in_expander: bool = False) ->
 
 @st.cache_data
 def upload_temp_docs(files, _api: ApiRequest) -> str:
-    '''
+    """
     将文件上传到临时目录，用于文件对话
     返回临时向量库ID
-    '''
+    """
     return _api.upload_temp_docs(files).get("data", {}).get("id")
 
 
@@ -157,11 +157,13 @@ def dialogue_page(
             tools = list_tools(api)
             tool_names = ["None"] + list(tools)
             if use_agent:
-                # selected_tools = sac.checkbox(list(tools), format_func=lambda x: tools[x]["title"], label="选择工具", check_all=True, key="selected_tools")
+                # selected_tools = sac.checkbox(list(tools), format_func=lambda x: tools[x]["title"], label="选择工具",
+                # check_all=True, key="selected_tools")
                 selected_tools = st.multiselect("选择工具", list(tools), format_func=lambda x: tools[x]["title"],
                                                 key="selected_tools")
             else:
-                # selected_tool = sac.buttons(list(tools), format_func=lambda x: tools[x]["title"], label="选择工具", key="selected_tool")
+                # selected_tool = sac.buttons(list(tools), format_func=lambda x: tools[x]["title"], label="选择工具",
+                # key="selected_tool")
                 selected_tool = st.selectbox("选择工具", tool_names,
                                              format_func=lambda x: tools.get(x, {"title": "None"})["title"],
                                              key="selected_tool")
@@ -338,7 +340,7 @@ def dialogue_page(
             elif d.status == AgentStatus.agent_finish:
                 text = d.choices[0].delta.content or ""
                 chat_box.update_msg(text.replace("\n", "\n\n"))
-            elif d.status == None:  # not agent chat
+            elif d.status is None:  # not agent chat
                 if getattr(d, "is_ref", False):
                     chat_box.insert_msg(Markdown(d.choices[0].delta.content or "", in_expander=True, state="complete",
                                                  title="参考资料"))

libs/chatchat-server/poetry.toml

@@ -1,9 +1,6 @@
 [virtualenvs]
 in-project = true
 
-[installer]
-modern-installation = false
-
 [plugins]
 [plugins.pypi_mirror]
 url = "https://pypi.tuna.tsinghua.edu.cn/simple"

libs/chatchat-server/pyproject.toml

@@ -29,6 +29,9 @@ unstructured = "~0.11.0"
 python-magic-bin = {version = "*", platform = "win32"}
 SQLAlchemy = "~2.0.25"
 faiss-cpu = "~1.7.4"
+#cutword = "0.1.0"
+jieba = "0.42.1"
+rank_bm25 = "0.2.2"
 # accelerate = "~0.24.1"
 # spacy = "~3.7.2"
 PyMuPDF = "~1.23.16"
@@ -264,4 +267,4 @@ dotenv = "dotenv:plugin"
 [[tool.poetry.source]]
 name = "tsinghua"
 url = "https://pypi.tuna.tsinghua.edu.cn/simple/"
-priority = "default"
+priority = "primary"

libs/model-providers/poetry.toml

@@ -1,9 +1,6 @@
 [virtualenvs]
 in-project = true
 
-[installer]
-modern-installation = false
-
 [plugins]
 [plugins.pypi_mirror]
 url = "https://pypi.tuna.tsinghua.edu.cn/simple"

poetry.toml

@@ -1,9 +1,5 @@
 [virtualenvs]
 in-project = true
 
-[installer]
-modern-installation = false
-
-[plugins]
 [plugins.pypi_mirror]
 url = "https://pypi.tuna.tsinghua.edu.cn/simple"

tools/model_loaders/xinference_manager.py

@@ -137,7 +137,7 @@ model_format = None
 model_quant = None
 
 if model_type == "LLM":
-    cur_family = xf_llm.LLMFamilyV1.model_validate(cur_reg)
+    cur_family = xf_llm.LLMFamilyV1.parse_obj(cur_reg)
     cur_spec = None
     model_formats = []
     for spec in cur_reg["model_specs"]: