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bugfix: 使用向量计算方式METRIC_INNER_PRODUCT时启用normalize_L2会导致向量化失败

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hxb 2024-04-28 16:12:23 +08:00
parent 2c7feae7bb
commit 0078cdc724
1 changed files with 2 additions and 2 deletions
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4
server/knowledge_base/kb_cache/faiss_cache.py
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@ -57,7 +57,7 @@ class _FaissPool(CachePool):
) -> FAISS:
embeddings = EmbeddingsFunAdapter(embed_model)
doc = Document(page_content="init", metadata={})
vector_store = FAISS.from_documents([doc], embeddings, normalize_L2=True,distance_strategy="METRIC_INNER_PRODUCT")
vector_store = FAISS.from_documents([doc], embeddings, distance_strategy="METRIC_INNER_PRODUCT")
ids = list(vector_store.docstore._dict.keys())
vector_store.delete(ids)
return vector_store
@ -94,7 +94,7 @@ 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,distance_strategy="METRIC_INNER_PRODUCT")
vector_store = FAISS.load_local(vs_path, embeddings, distance_strategy="METRIC_INNER_PRODUCT")
elif create:
# create an empty vector store
if not os.path.exists(vs_path):