temporarily save faiss_cache

requirements.txt

@@ -18,7 +18,7 @@ starlette~=0.27.0
 unstructured[all-docs]==0.11.0
 python-magic-bin; sys_platform == 'win32'
 SQLAlchemy==2.0.19
-faiss-cpu
+faiss-cpu # `conda install faiss-gpu -c conda-forge` if you want to accelerate with gpus
 accelerate>=0.24.1
 spacy>=3.7.2
 PyMuPDF
@@ -64,7 +64,7 @@ metaphor-python>=0.1.23
 
 # WebUI requirements
 
-streamlit>=1.29.0
+streamlit>=1.29.0 # do remember to add streamlit to environment variables if you use windows
 streamlit-option-menu>=0.3.6
 streamlit-antd-components>=0.2.3
 streamlit-chatbox>=1.1.11

server/knowledge_base/kb_cache/faiss_cache.py

@@ -45,7 +45,7 @@ class _FaissPool(CachePool):
         # create an empty vector store
         embeddings = EmbeddingsFunAdapter(embed_model)
         doc = Document(page_content="init", metadata={})
-        vector_store = FAISS.from_documents([doc], embeddings, normalize_L2=True)
+        vector_store = FAISS.from_documents([doc], embeddings, normalize_L2=True,distance_strategy="METRIC_INNER_PRODUCT")
         ids = list(vector_store.docstore._dict.keys())
         vector_store.delete(ids)
         return vector_store
@@ -82,7 +82,7 @@ class KBFaissPool(_FaissPool):
 
                 if os.path.isfile(os.path.join(vs_path, "index.faiss")):
                     embeddings = self.load_kb_embeddings(kb_name=kb_name, embed_device=embed_device, default_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,distance_strategy="METRIC_INNER_PRODUCT")
                 elif create:
                     # create an empty vector store
                     if not os.path.exists(vs_path):