fixed file-based RAG

Author: GaiZhenbiao

modules/index_func.py

@@ -1,13 +1,17 @@
-import os
+import hashlib
 import logging
+import os
 
-import hashlib
 import PyPDF2
+from langchain_community.chat_models import ChatOpenAI
+from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_openai import OpenAIEmbeddings
 from tqdm import tqdm
 
+from modules.config import local_embedding
 from modules.presets import *
 from modules.utils import *
-from modules.config import local_embedding
 
 
 def get_documents(file_src):
@@ -28,8 +32,8 @@ def get_documents(file_src):
             if file_type == ".pdf":
                 logging.debug("Loading PDF...")
                 try:
-                    from modules.pdf_func import parse_pdf
                     from modules.config import advance_docs
+                    from modules.pdf_func import parse_pdf
 
                     two_column = advance_docs["pdf"].get("two_column", False)
                     pdftext = parse_pdf(filepath, two_column).text
@@ -43,12 +47,14 @@ def get_documents(file_src):
                                   metadata={"source": filepath})]
             elif file_type == ".docx":
                 logging.debug("Loading Word...")
-                from langchain.document_loaders import UnstructuredWordDocumentLoader
+                from langchain.document_loaders import \
+                    UnstructuredWordDocumentLoader
                 loader = UnstructuredWordDocumentLoader(filepath)
                 texts = loader.load()
             elif file_type == ".pptx":
                 logging.debug("Loading PowerPoint...")
-                from langchain.document_loaders import UnstructuredPowerPointLoader
+                from langchain.document_loaders import \
+                    UnstructuredPowerPointLoader
                 loader = UnstructuredPowerPointLoader(filepath)
                 texts = loader.load()
             elif file_type == ".epub":
@@ -93,9 +99,6 @@ def construct_index(
     separator=" ",
     load_from_cache_if_possible=True,
 ):
-    from langchain.chat_models import ChatOpenAI
-    from langchain.vectorstores import FAISS
-
     if api_key:
         os.environ["OPENAI_API_KEY"] = api_key
     else:
@@ -109,11 +112,9 @@ def construct_index(
     index_name = get_file_hash(file_src)
     index_path = f"./index/{index_name}"
     if local_embedding:
-        from langchain.embeddings.huggingface import HuggingFaceEmbeddings
         embeddings = HuggingFaceEmbeddings(
             model_name="sentence-transformers/distiluse-base-multilingual-cased-v2")
     else:
-        from langchain.embeddings import OpenAIEmbeddings
         if os.environ.get("OPENAI_API_TYPE", "openai") == "openai":
             embeddings = OpenAIEmbeddings(openai_api_base=os.environ.get(
                 "OPENAI_API_BASE", None), openai_api_key=os.environ.get("OPENAI_EMBEDDING_API_KEY", api_key))
@@ -122,7 +123,7 @@ def construct_index(
                                           model=os.environ["AZURE_EMBEDDING_MODEL_NAME"], openai_api_base=os.environ["AZURE_OPENAI_API_BASE_URL"], openai_api_type="azure")
     if os.path.exists(index_path) and load_from_cache_if_possible:
         logging.info(i18n("找到了缓存的索引文件，加载中……"))
-        return FAISS.load_local(index_path, embeddings)
+        return FAISS.load_local(index_path, embeddings, allow_dangerous_deserialization=True)
     else:
         documents = get_documents(file_src)
         logging.debug(i18n("构建索引中……"))