Update pages/bot.py
Browse files- pages/bot.py +4 -2
pages/bot.py
CHANGED
@@ -19,6 +19,8 @@ from langchain.llms import HuggingFaceHub
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#pip install InstructorEmbedding
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###############
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# PDF in String umwandeln
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def get_pdf_text(folder_path):
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text = ""
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@@ -49,7 +51,7 @@ def get_text_chunks(text):
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# nur zum Anlegen des lokalen Verzeichnisses "Store" und speichern der Vektor-Datenbank
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def create_vectorstore_and_store(text_chunks):
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embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-base")
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# Initiate Faiss DB
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vectorstoreDB = FAISS.from_texts(texts=text_chunks,embedding=embeddings)#texts=text_chunks,
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###
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@@ -65,7 +67,7 @@ def create_vectorstore_and_store(text_chunks):
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########
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def get_vectorstore():
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embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-base")
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#Abruf lokaler Vektordatenbank
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save_directory = "Store"
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vectorstoreDB = FAISS.load_local(save_directory, embeddings)
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#pip install InstructorEmbedding
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###############
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os.environ['SENTENCE_TRANSFORMERS_HOME'] = './.cache'
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# PDF in String umwandeln
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def get_pdf_text(folder_path):
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text = ""
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# nur zum Anlegen des lokalen Verzeichnisses "Store" und speichern der Vektor-Datenbank
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def create_vectorstore_and_store(text_chunks):
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embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-base", cache_folder = "./.cache")
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# Initiate Faiss DB
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vectorstoreDB = FAISS.from_texts(texts=text_chunks,embedding=embeddings)#texts=text_chunks,
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###
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########
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def get_vectorstore():
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embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-base", cache_folder = "./.cache")
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#Abruf lokaler Vektordatenbank
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save_directory = "Store"
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vectorstoreDB = FAISS.load_local(save_directory, embeddings)
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