MikeMann commited on
Commit
a90bf1d
·
1 Parent(s): 37d2ac9

removing Thread

Browse files
Files changed (1) hide show
  1. app.py +7 -4
app.py CHANGED
@@ -32,12 +32,14 @@ from huggingface_hub import login
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  login(token=HF_KEY)
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  class BSIChatbot:
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  def __init__(self, model_paths: Dict[str, str], docs_path: str):
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  self.embedding_model = None
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  self.llmpipeline = None
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  self.llmtokenizer = None
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- self.vectorstore = None
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  self.reranking_model = None
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  self.streamer = None
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  self.images = [None]
@@ -50,7 +52,7 @@ class BSIChatbot:
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  @spaces.GPU
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  def initialize_embedding_model(self, rebuild_embeddings: bool):
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  raw_knowledge_base = []
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-
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  # Initialize embedding model
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  self.embedding_model = HuggingFaceEmbeddings(
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  model_name=self.word_and_embed_model_path,
@@ -91,18 +93,19 @@ class BSIChatbot:
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  self.vectorstore.save_local(os.path.join(self.docs, "_embeddings"))
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  else:
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  # Load existing vector store
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- self.vectorstore = FAISS.load_local(os.path.join(self.docs, "_embeddings"), self.embedding_model, allow_dangerous_deserialization=True)
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  print("DBG: Vectorstore Status Initialization:", self.vectorstore)
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  @spaces.GPU
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  def retrieve_similar_embedding(self, query: str):
 
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  #lazy load
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  #if (self.vectorstore == None):
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  # self.vectorstore = FAISS.load_local(os.path.join(self.docs, "_embeddings"), self.embedding_model,
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  # allow_dangerous_deserialization=True)
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  print("DBG: Vectorstore Status retriever:", self.vectorstore)
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  query = f"Instruct: Given a search query, retrieve the relevant passages that answer the query\nQuery:{query}"
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- return self.vectorstore.similarity_search(query=query, k=20)
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  @spaces.GPU
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  def initialize_llm(self):
 
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  login(token=HF_KEY)
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+ vectorstore=None
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+
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  class BSIChatbot:
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  def __init__(self, model_paths: Dict[str, str], docs_path: str):
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  self.embedding_model = None
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  self.llmpipeline = None
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  self.llmtokenizer = None
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+ #self.vectorstore = None
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  self.reranking_model = None
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  self.streamer = None
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  self.images = [None]
 
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  @spaces.GPU
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  def initialize_embedding_model(self, rebuild_embeddings: bool):
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  raw_knowledge_base = []
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+ global vectorstore
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  # Initialize embedding model
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  self.embedding_model = HuggingFaceEmbeddings(
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  model_name=self.word_and_embed_model_path,
 
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  self.vectorstore.save_local(os.path.join(self.docs, "_embeddings"))
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  else:
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  # Load existing vector store
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+ vectorstore = FAISS.load_local(os.path.join(self.docs, "_embeddings"), self.embedding_model, allow_dangerous_deserialization=True)
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  print("DBG: Vectorstore Status Initialization:", self.vectorstore)
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  @spaces.GPU
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  def retrieve_similar_embedding(self, query: str):
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+ global vectorstore
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  #lazy load
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  #if (self.vectorstore == None):
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  # self.vectorstore = FAISS.load_local(os.path.join(self.docs, "_embeddings"), self.embedding_model,
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  # allow_dangerous_deserialization=True)
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  print("DBG: Vectorstore Status retriever:", self.vectorstore)
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  query = f"Instruct: Given a search query, retrieve the relevant passages that answer the query\nQuery:{query}"
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+ return vectorstore.similarity_search(query=query, k=20)
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  @spaces.GPU
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  def initialize_llm(self):